Investigation into safety regulation of entertainment venues based on big data label analysis

Abstract

The recreational escape rooms have recently emerged as a rapidly growing and widely embraced form of consumer entertainment. However, the industry's expansion has brought forth certain challenges, notably the lack of authoritative oversight, which has led to issues such as piracy and theme infringement. To address these concerns, this study explores the management complexities of immersive entertainment venues from the perspective of responsive regulation. The study begins by abstracting a dataset of online cultural products related to entertainment venues into a complex network using specific measurement standards. A method employing the K-means clustering algorithm is then proposed to partition the associative structure of this complex network. The K-means clustering algorithm is particularly suitable for this task due to its efficiency in handling large-scale data, strong scalability, and ability to quickly and effectively group network nodes based on a defined objective function. Additionally, the algorithm allows for flexible adjustment of the number of clusters according to specific needs. Furthermore, the study enhances and tests the Practical Byzantine Fault Tolerance (PBFT) algorithm to validate its effectiveness and practicality. The findings reveal that when the distance criterion value is set at a = 2, the improved PBFT algorithm exhibits exceptional performance. This discovery significantly enhances the security and control measures in immersive entertainment venues, enriching the theoretical framework related to administrative regulation and providing crucial theoretical resources for future legislative efforts. Finally, the study presents policy recommendations to strengthen the regulation of immersive entertainment venues. This study offers effective technical support for optimizing consumer market management measures and contributes to the development of new entertainment venues.

1. Introduction

The rapid evolution of technology and the increasing complexity of consumer preferences have catalyzed the emergence of the immersive entertainment sector as a significant component of the global entertainment landscape. Venues such as escape rooms and virtual reality centers have attracted considerable interest and acclaim. These immersive activities craft environments that are rich in realism, narrative depth, and interactivity, thereby satisfying the escalating appetite for novelty and deeper engagement [[1], [2], [3]]. Market research underscores the consistent expansion of the global immersive entertainment market, particularly in the context of accelerated urbanization. This trend has resulted in a marked increase in the diversity and number of entertainment venues within urban settings. Nonetheless, the swift advancement of this industry has unearthed critical issues, including regulatory deficiencies and rampant copyright violations, which pose substantial barriers to the sector's healthy progression. Operational standards among entertainment venues exhibit considerable variability, with a notable absence of unified regulations and guidelines. Safety measures often fall short, leading to environments that significantly heighten risks for consumers. Furthermore, copyright infringement presents a formidable obstacle [[4], [5], [6]]. The intricacies of immersive entertainment projects—encompassing elaborate narratives, unique designs, and specialized technologies—are frequently undermined by unauthorized operations that replicate or appropriate themes and content from original creators, disrupting fair market competition and infringing upon intellectual property rights. Given these challenges, the urgent need for effective regulatory frameworks becomes apparent, aimed at rectifying these issues and promoting the sustainable growth of the industry. Traditional regulatory approaches, which frequently rely on manual inspections and retrospective analyses, lack efficiency and struggle to accommodate the diverse array of entertainment venues. Consequently, the adoption of intelligent regulatory methods that leverage big data technology emerges as a promising avenue. By employing big data label analysis alongside machine learning techniques, real-time monitoring of venue operations can be established, facilitating the automated detection of safety risks and instances of copyright infringement.

This study seeks to address these challenges by constructing an intelligent regulatory system that efficiently oversees the safety of entertainment venues through big data label analysis and distributed algorithms, such as Practical Byzantine Fault Tolerance (PBFT). A thorough analysis of multi-source data—including consumer behavior, equipment operational metrics, and user feedback—is conducted to unearth potential industry issues and formulate relevant regulatory strategies. Such an approach not only provides robust technical support for the operational integrity of entertainment venues but also lays down a theoretical foundation and offers policy recommendations for industry regulators, thereby fostering the healthy and sustainable development of the immersive entertainment landscape.

2. Literature review

2.1. Copyright infringement and content piracy issues

The immersive entertainment industry's rapid growth has produced a multitude of creative outputs and technological advancements. Yet, this expansion has also given rise to escalating issues of piracy and content infringement. Immersive entertainment projects often comprise original narratives, unique scene designs, and interactive experiences—assets developed through considerable investments of time and resources. The lack of a cohesive copyright protection mechanism in the industry has led some entertainment venues to engage in unauthorized replication or imitation of content created by others, resulting in numerous copyright infringement incidents. Such actions not only violate the rights of original creators but also foster unhealthy competition, hindering innovation within the sector.

Addressing the challenges of piracy and content infringement in immersive entertainment has prompted the exploration of various countermeasures in academic research. Key areas of focus include the development of copyright protection technologies, the enhancement of legal frameworks, and the creation of innovative models that merge technology with legal principles. Scholars have proposed a variety of technological solutions specifically designed for the immersive entertainment sector to ensure effective copyright protection for content [7]. One prominent approach involves the use of Digital Rights Management (DRM) technologies, which encrypt and restrict access to original content. Widely utilized in industries dealing with digital content, such as music and film, DRM can also safeguard original narratives, interactive experiences, and virtual environments within immersive entertainment. By implementing encryption, access to content becomes limited to authorized users or venues, effectively curbing piracy [[8], [9], [10]]. Watermarking technology represents another viable strategy for copyright protection. By embedding invisible digital watermarks within immersive entertainment content, developers can trace the origins and distribution channels of their creations. In instances of piracy or unauthorized dissemination, watermarking can assist copyright holders in establishing ownership, thereby reinforcing the integrity of original content in the industry.

With the emergence of blockchain technology, an increasing interest has developed within the academic community regarding its potential as a tool for copyright protection. The decentralized and immutable characteristics of blockchain offer distinct advantages for tracking and verifying content copyrights. Storing copyright information related to immersive entertainment projects on the blockchain facilitates accurate recording of original ownership and modification histories for each content piece. This capability enables the immediate detection of unauthorized alterations or reproductions. Furthermore, blockchain technology supports tracking the use and distribution of original content and enhances automated copyright management through smart contracts. These contracts execute licensing agreements automatically when content is utilized, ensuring real-time revenue distribution to copyright holders. This automated mechanism significantly diminishes copyright disputes and improves the efficiency of copyright management [11].

Although technological measures play a pivotal role in copyright protection, their effectiveness is contingent upon a solid legal framework and policy support. Many scholars emphasize the need for governments to establish more stringent copyright protection laws and to strengthen penalties for infringement. Presently, copyright legislation in certain countries offers limited coverage for the immersive entertainment sector, lacking specific and clear operational guidelines, which complicates the pursuit and sanction of infringing activities. In light of this issue, some researchers propose that governments should refine copyright legislation to better address the immersive entertainment industry, providing creators with accessible pathways for copyright registration and enforcement [12]. For instance, implementing an online copyright registration system would allow developers to quickly file their content, streamlining and expediting enforcement processes in cases of copyright infringement.

To tackle the multifaceted challenges posed by piracy and infringement within the immersive entertainment sector, scholars have introduced a groundbreaking model that merges technological and legal strategies. This model advocates for the creation of an automated copyright management system, integrating tools such as blockchain and smart contracts with existing legal frameworks [13,14]. In this proposed system, various processes related to copyright—application, registration, usage, and maintenance—can be automated and recorded on the blockchain. Concurrently, legal entities gain the ability to swiftly trace copyright disputes and make informed decisions based on the immutable records stored within the blockchain. Such a synergistic approach not only amplifies the efficiency of copyright protection but also mitigates the potential for disputes. For example, smart contracts enable entertainment venues to acquire content usage rights through payments made in cryptocurrency or other automated systems, effectively bypassing the delays and conflicts associated with traditional manual approval processes.

In conclusion, addressing the challenges of piracy and content infringement within the immersive entertainment industry demands immediate action. The innovative fusion of technology and legal frameworks possesses the capacity to deliver substantial support for ongoing innovation and the sustainable growth of this dynamic sector.

2.2. Intelligent regulatory framework based on big data

The rapid advancement of big data technologies has catalyzed the emergence of intelligent regulation in the management of entertainment venues, especially in the immersive entertainment sector. Traditional regulatory methods struggle to keep pace with the enormous volumes of data and varied sources of information available. In contrast, a big data-driven intelligent regulatory framework presents innovative solutions for the industry. By enabling real-time data collection, analysis, and processing, this framework significantly bolsters the safety and compliance of entertainment venues. Additionally, it empowers managers to promptly detect illegal activities, including piracy and infringement.

Within the big data intelligent regulatory framework, data analysis and processing emerge as fundamental elements. Advanced analytical algorithms and machine learning techniques facilitate real-time analysis, anomaly detection, and predictive modeling for data sourced from entertainment venues. The identification of abnormal behaviors or potential safety hazards stands as a pivotal aspect of venue regulation. K-Means clustering algorithms categorize data from entertainment venues, grouping those with similar characteristics into distinct clusters. This analysis allows regulators to pinpoint high-risk venues or unusual consumer behaviors. For example, venues may be classified as high-risk based on an excessive frequency of equipment usage or an influx of safety complaints in consumer feedback. Such clustering results inform the implementation of targeted regulatory measures. Anomaly detection also plays a critical role in the analytical toolkit. By utilizing machine learning algorithms, the system can detect abnormal behaviors in real-time within entertainment venues. Analyzing equipment operation logs and consumer interaction records may reveal unusual equipment usage patterns or potential safety threats, prompting early warnings to mitigate accidents. Consumer feedback and social media commentary serve as vital data sources for venue management. Natural language processing technologies enable the analysis of extensive textual data to extract keywords or sentiment trends associated with safety concerns. Automated systems can sift through consumer reviews to highlight negative feedback or references to safety issues, supplying crucial insights for regulators. Moreover, sentiment analysis techniques evaluate the emotional tone of reviews, aiding managers in gauging overall consumer satisfaction and identifying emerging issues.

The big data-driven intelligent regulatory framework establishes robust technical infrastructure for managing the safety of entertainment venues. This system operates across the full spectrum of data collection, storage, analysis, and processing, delivering real-time insights into the operational conditions of these venues. Early detection of potential safety risks becomes possible, with rapid response mechanisms built into the system. Distributed computing, coupled with fault-tolerance protocols, reinforces the framework's reliability and scalability, ensuring seamless functionality within intricate, ever-changing data environments. As advancements in big data technology progress, the framework's role in enhancing venue safety and compliance grows, promising to redefine the landscape of intelligent regulation for the entertainment industry.

2.3. Development status of immersive entertainment venues

Immersive experiential entertainment represents a form of entertainment characterized by its reliance on advanced technology to create scenarios that offer integrated multisensory experiences. These experiences aim to fully engage participants’ auditory, olfactory, visual, and tactile senses, leading them into a state of “flow,” [15] where they transcend the boundaries of everyday life. In the state of “flow,” individuals become completely immersed and engaged in the activity, losing track of time and their surroundings while focusing solely on the present task and experience. This state of total absorption fosters a profound sense of satisfaction and pleasure in the participants, accompanied by heightened concentration and a feeling of self-transcendence. Illustratively, Fig. 1 demonstrates the categorization of immersive experiential entertainment into three distinct types, each offering unique forms of engagement.

Fig. 1.

The categories of immersive experience entertainment.

Fig. 2 illustrates the risks present in the Chinese immersive experiential entertainment industry. One of the significant risks is cash flow disruption, which encompasses challenges related to both costs and revenues, impacting the overall profitability of immersive experiential entertainment ventures. Moreover, a prevalent issue is the similarity in styles among most projects, resulting in consumer aesthetic fatigue. Additionally, the lack of diverse and culturally sophisticated content in many projects poses difficulties in attracting and retaining consumers, increasing the risk of customer attrition. Safety is another paramount concern, particularly in projects like escape rooms, where potential safety hazards within spatial installations may arise. The term “most projects” refers to the vast majority of immersive experiential entertainment endeavors characterized by high similarity in design, content, and experiences. Such uniformity may lead to consumer fatigue, diminishing novelty, and reduced innovativeness. Consequently, in an intensely competitive market, projects lacking uniqueness and innovation may struggle to captivate and sustain consumer interest, hindering their potential for continuous profitability.

Fig. 2.

Risks of immersive experience entertainment industry in China.

In the contemporary landscape, immersive entertainment venues centered around script-based reasoning and escape rooms are steadily gaining traction. However, compared to traditional entertainment spaces, these immersive venues are relatively nascent, suffering from a lack of management experience and an underdeveloped market mechanism, which gives rise to numerous challenges. Take the script-based reasoning industry as an example: the issues of piracy and copyright infringement persist unabated. In the realm of virtual reality (VR), the absence of clear age classifications allows minors to access violent and disturbing content, resulting in market disorder. Moreover, tabletop game stores themed around male companionship face risks of vulgar content, and the overall safety awareness in the immersive entertainment sector remains alarmingly weak. Addressing these issues necessitates rigorous scrutiny of online cultural products within these venues. It is equally imperative to thoroughly refine the government oversight system tailored for immersive entertainment spaces. The layout of these venues plays a pivotal role, encompassing the design and arrangement of scripts or storylines. In such spaces, scripts serve as the foundation for creating various roles, environments, and tasks. Through meticulous scene setup, the narrative embedded in the script is brought to life for participants. Typically, the creators or managers of these venues are responsible for writing and planning scripts, arranging suitable personnel and props to craft an immersive experience. This process includes scripting timelines, story progression, and participant interactions. A well-designed scene layout can offer visitors a captivating and engaging experience. From a policy perspective, it is essential to establish regulatory standards for emerging entertainment forms like VR. This includes enhancing content review and regulation, instituting stringent age classification systems, and curbing the spread of harmful content to restore market order and ensure the sector's healthy and orderly growth.

2.4. The adaptability of responsive regulation theory to government regulation of immersive entertainment venues

The preceding discourse has thoroughly dissected the myriad realities surrounding immersive entertainment venues, unveiling both their challenges and the current state of regulatory oversight. Now, the focus shifts to examining the adaptability of responsive regulatory theory in the governance of these immersive spaces. Moreover, the exploration of blockchain technology's conceptual applications in this realm introduces a novel perspective for future inquiries. The essence of responsive regulatory theory lies in its capacity to implement diverse regulatory strategies with precision and flexibility, tailored to the unique characteristics of the entities being regulated. Immersive entertainment venues, with their inherent diversity, dynamism, and innovative nature, align seamlessly with the theoretical framework of responsive regulation. By delving into the adaptability of this theory, a clearer understanding of its strategic application emerges, particularly when combined with the advantages offered by blockchain technology. Blockchain's potential to enhance regulatory practices—through its decentralized ledger and encryption capabilities—offers promising solutions to existing oversight gaps. Integrating blockchain technology with responsive regulatory theory could further refine governmental oversight of immersive entertainment venues, thereby addressing current deficiencies and propelling the industry towards a path of robust, orderly expansion.

2.4.1. Analysis of characteristics of responsive regulation theory

In the 1990s, in the face of the debate about strengthening government regulation and deregulation, two scholars from the United States and Australia proposed a third way between them, that is, to establish a mixed model of government regulation and non-governmental intervention. The government should adopt corresponding measures according to the needs of different enterprises and organizations in society, which should reflect diversity and hierarchy. The responsive regulation theory mainly involves two aspects: the “pyramid theory” focusing on the vertical regulatory strategies and the horizontal scheme attention to the distribution of regulatory power between regulatory subjects [16]. Pyramid theory includes coercive means pyramids and regulatory strategy pyramids, as shown in Fig. 3. Among them, the pyramid of coercive means is aimed at the regulated individuals, while the pyramid of regulatory strategies is designed for the regulation of the whole industry.

Fig. 3.

Vertical “pyramid theory” focusing on regulatory strategies (a) Pyramid of coercive means; (b) Regulatory Strategy Pyramid.

Fig. 3a is a pyramid of coercive measures aimed at regulated individuals, while Fig. 3b is a pyramid of regulatory strategies designed for the regulation of the entire industry. The responsive regulation theory has the characteristics of response, coordination, shaping and relationship. Among them, the connotation of the response is to carefully distinguish the behavior and motivation of the regulatory object, adopt a differentiated response method, and realize the effective regulation and encouragement of the regulatory object. The value formation of shape is reflected in the cultivation of the regulatory awareness and ability of other regulators as regulatory subjects, giving priority to relatively flexible “soft means” to encourage them to actively participate in regulation and self-regulation, thus contributing to the stimulation of civic awareness and public spirit. The meaning of coordination is that the government should actively guide other regulators and regulatory objects to participate in the consultation of standardized policies and systems, establish equal partnerships, and coordinate a variety of management strategies. The relationship is the key to establishing positive interaction between the two sides. It pays attention to mutual trust at the micro-level, fully understands the motivation of each other's behavior, and is easy to find problems and gaps in regulation, to effectively carry out regulation.

2.4.2. Government regulation of immersive entertainment venues from the perspective of responsive regulation

The principle of applying the above four characteristics to the supervision of immersive entertainment venues is shown in Fig. 4.

Fig. 4.

The principle of applying the characteristics of responsive regulation theory to the supervision of immersive entertainment venues.

The first is to adopt different regulatory strategies based on “differentiated treatment”. According to the different situations, differentiated treatment is adopted for regulatory objects, hierarchical and classified supervision of regulatory objects. The government-led supervision method with administrative punishment as the main means is broken, and the equal responsiveness between the regulators and regulatory objects is increased. The purpose of regulation is to promote compliance with laws, regulations and policies and improve the effectiveness of regulation. The immersive experience entertainment industry can establish a regulatory database of risk in the immersive experience entertainment market based on the registration information, complaint information and market information of merchants and experienced personnel. Unannounced visits and daily inspections in immersive entertainment venues are carried out, focusing on the inspection of merchants with many complaints and administrative penalties, to strengthen their awareness of self-regulation [17]. In view of inspection, further measures should be taken to strengthen strategic regulation and control, and achieve vertical classification, with a prominent emphasis on “differentiated regulation strategies”.

The second is to shape the consciousness and ability of the regulation. In addition to the government, the regulation awareness and ability of other regulators should be shaped. Through the establishment of various systems and other means, the government encourages other regulators to establish internal management systems, realize regulation and self-regulation. And the government also encourages other regulators to improve their regulation level, actively become regulators, performs the main duties of regulation, and promotes the government to guide other regulators to become subjects. That means the government has become the shaper of social regulation. Drawing on “post-regulation”, the application premise mainly includes establishing an ideal communication and coordination mechanism and ensuring the discretion of the applicable objects, etc. In the traditional sense, the government can change from a regulation to a post-regulator, which reduces the scope and depth of government intervention in the regulatory objects. It focuses on the self-regulation and strengthening process of the regulatory subject to ensure the effective implementation of self-regulation, thereby promoting the effectiveness of government regulation. On the basis of full investigation and demonstration, the government will gradually guide influential merchants to improve their self-regulation ability, increase opportunities to participate in supervision. They should consciously obey the market, price, competition, and risk mechanisms, avoid fierce competition, ensure integrity and law-abiding, and strengthen leadership [18]. The government will guide other merchants to develop and participate in supervision, give full play to the role of the government in self-regulation of merchants, realize the dual restriction of mutual regulation, and form a closed loop of regulation of “asymmetric regulation”.

The third is to moderately allocate regulatory powers to discuss and cooperate. Strengthen the participation of other regulatory subjects other than the government in the formulation of regulation systems, standards and strategies based on equal consultation with socialist core values. To achieve the goal of building a regulatory response model, the government must establish a platform for equal dialogue and exchange with other regulatory subjects to ensure the dominant position of other regulatory subjects in participatory supervision, thus forming a common regulatory model. Traditionally, the government has been in absolute leadership and dominance at the level of regulation, which makes the regulatory objects hold a negative attitude towards government regulation [19]. However, in the multi-subject regulation model constructed by responsive regulation theory, the government provides active guidance and supplementary support. The government should guide other regulatory subjects to participate in regulation on an equal footing, which can promote the effective implementation of institutional policies. It also can enhance trust between regulatory subjects, clarify the regulatory normative system, and formulate targeted and flexible supervision. It is conducive to the formation of a new regulatory model that complements government regulation, business self-discipline, industry association industry regulation and third-party regulatory agencies.

The fourth is to attach importance to rules and build mutual trust among all parties. The establishment of a friendly partnership between the regulator and the regulatory object enables the regulator to obtain more information, fully understand the behavior and motivation of the regulator, to make an accurate response. The face-to-face interaction and remote supervision are emphasized between the regulator and the regulatory object. Since a good trust relationship has been established between the regulator and the regulatory object, it can effectively promote both parties to take discretion by the legal and regulatory constraints. The guarantee of regulator's regulation strategy and the reward and punishment system for the regulatory object can form the discretionary power that can be effectively used. The board of supervisors is encouraged to use the discretionary power according to local conditions [20]. To ensure the effectiveness of supervision, the regulatory object must be given the power of self-regulation. The regulatory object should be encouraged to consciously restrain its own behavior, abide by laws and regulations, and ensure the effective implementation of the regulatory object's self-regulation. The government should attach importance to establishing a partnership of mutual trust, mutual assistance and friendship with industry associations and businesses, to ensure the right of all parties to participate in supervision, and to encourage all parties to form complementary supervision [21].

3. Materials and methods

The PBFT algorithm demonstrates exceptional capabilities in handling the complexities of distributed systems, with high resilience to node failures and malicious attacks. Its design supports environments demanding stringent security and stability, particularly in scenarios involving large-scale distributed data processing, such as the safety regulation of entertainment venues. By maintaining consistency throughout data partitioning processes and offering robust fault tolerance, PBFT becomes a crucial tool in ensuring smooth operation in environments prone to data inconsistencies or node malfunctions. One of PBFT's core strengths lies in its ability to tolerate up to one-third of malicious or faulty nodes without compromising the integrity of the system. This feature proves essential when applied to expansive data environments within the entertainment sector, where parallel processing across multiple nodes is often required. Errors or incompleteness in data, along with potential node failures, are common. PBFT mitigates these risks, maintaining operational correctness—an indispensable trait in managing venue safety, where stability is critical and various nodes control different sections of data. State machine replication further strengthens PBFT's role by ensuring uniform execution of tasks across nodes, even when disruptions in network communication or partial data errors emerge. This aspect proves particularly advantageous in safeguarding the integrity of sensitive data sets, such as safety logs or customer interaction records. The algorithm guarantees that consensus is reached regardless of minor errors, preventing the propagation of faulty data throughout the system. In applying PBFT to the safety regulation of entertainment venues, particularly in the handling of large-scale cultural product data, its reliability and consistency shine. The study highlights how this algorithm, even under adverse conditions, upholds the accuracy and stability necessary for effective data management in such critical applications.

In contrast to standard clustering algorithms, the PBFT algorithm offers enhanced resilience, particularly in environments requiring robust data integrity, such as safety regulation in entertainment venues. Data integrity and precision are paramount in these settings, where traditional clustering techniques often struggle with noise and outliers. PBFT addresses these challenges by leveraging a consensus mechanism that upholds stability and security throughout data processing. Furthermore, PBFT exhibits adaptability, maintaining operational consistency as nodes dynamically enter or exit the system—a critical feature given the ever-evolving data flows typical in entertainment venues [22,23].

K-Means stands out for its computational efficiency, particularly well-suited to managing large datasets. As a partitioning clustering algorithm, K-Means operates by iteratively calculating the distances between data points and centroids, enabling rapid identification of optimal clusters. This makes it an ideal candidate for processing expansive datasets associated with entertainment venues, which demand both speed and accuracy. Its simplicity and clear interpretability also contribute to its widespread use in practical applications, especially where decision-makers require comprehensible insights for regulatory actions. Within the scope of safety regulation, K-Means aids in segmenting entertainment venues based on patterns in consumer behavior, safety concerns, or feedback, facilitating more effective management of varying venue types. K-Means excels in contexts where efficiency and scalability are essential. Its ability to handle large, multidimensional datasets with flexible adjustments in cluster numbers ensures adaptability, making it highly applicable to safety regulation tasks in entertainment venues. For instance, clustering venues based on safety risks or consumer behavior patterns allows for more precise management and targeted regulatory measures. This ability to rapidly group data into distinct clusters provides managers with actionable insights into potential safety risks, allowing for timely interventions. By integrating K-Means with fault-tolerant systems like PBFT, the safety management framework of entertainment venues can be significantly enhanced. PBFT ensures the reliability of the underlying data environment, while K-Means provides the computational efficiency and clarity needed to organize and analyze vast amounts of data. This combination offers a powerful, scalable solution for optimizing safety regulations in dynamic entertainment venues, ensuring both reliability and efficiency.

K-Means distinguishes itself through exceptional computational efficiency, making it highly effective for managing large-scale datasets. As a partitioning clustering algorithm, K-Means functions by iteratively recalculating distances between data points and centroids, enabling swift determination of optimal cluster configurations. In environments requiring rapid analysis, such as entertainment venues managing vast datasets, K-Means delivers clustering outcomes within constrained timeframes. Its straightforward operational design and interpretability enhance its application across diverse real-world scenarios. Within safety regulation frameworks for entertainment venues, the necessity for clear, interpretable outputs becomes paramount. K-Means facilitates this by organizing data into distinct clusters, providing intuitive results that support venue classification and management. Whether analyzing patterns in consumer behavior, identifying potential safety risks, or categorizing feedback trends, K-Means offers a means to streamline venue segmentation and enhance decision-making processes. Adaptability remains a central feature of K-Means, evidenced by its application in safety regulation and multidimensional data analysis across entertainment venues. The algorithm's ability to efficiently handle continuous data, adjust cluster numbers, and provide transparent results positions it as a valuable tool in regulatory scenarios. Through K-Means clustering, safety risks can be quickly identified within specific venues or consumer groups, leading to more precise and responsive management interventions. When integrated with fault-tolerant systems like PBFT, the reliability of K-Means clustering results is further amplified. This combination enhances the stability and consistency of safety management systems, ensuring that entertainment venues maintain efficient, accurate regulatory oversight in dynamically changing data environments.

The core principle behind blockchain technology revolves around the establishment of a decentralized, immutable, and highly secure distributed ledger. Its unique encryption and distributed storage mechanisms ensure data integrity and consistency, while mitigating risks associated with single points of failure and malicious tampering. In the context of distributed systems, blockchain technology enhances the reliability and security of consensus achievement. For instance, combining PBFT with blockchain facilitates the creation of a more trustworthy interaction model among nodes, guaranteeing system stability and data security even in the presence of potentially malicious nodes. Mechanisms such as consensus protocols and view change protocols effectively thwart malicious attacks and the spread of erroneous information, thereby bolstering the overall security of the system. Within PBFT, every node must execute three fundamental protocols: the checkpoint protocol, the consensus protocol, and the view change protocol, as illustrated in Fig. 5. In blockchain systems, the operational focus of consensus protocols encompasses at least three phases: the pre-preparation phase, the preparation phase, and the commitment phase.

Fig. 5.

Three types of basic protocols.

PBFT is applied in many scenarios. In the blockchain scenario, it is generally suitable for private chain and alliance chain scenarios that require strong consistency [24,25]. PBFT has the characteristics of strong fault tolerance and high operating efficiency, and can be used in alliance chains [26,27], so it is improved based on the PBFT consensus algorithm. The multi-factor mixed election mechanism of old and new consensus nodes based on improved PBFT is adopted. The main functions of consensus nodes are to package blocks, verify blocks and evaluate the results of consensus nodes. The supervision nodes are mainly served by the Network Inspection Department, the Ministry of Civil Affairs, and the public security organs. These are mainly responsible for reviewing networked products and organizing new node elections. According to the classic application of DPoS, the scale of consensus nodes is limited to 21, and the maximum number of malicious nodes that can be tolerated is 6 [28].

Assuming that the transaction database for association rule mining is D. The data attribute set and the set of all items are A and I in database D, and they can be expressed as:

$$\begin{array}{cc}D=\left\{T_1,T_2,\cdots ,{\mathrm{T}}_m\right\}& \left(m\in N^{\ast }\right)\end{array}$$ (1)

$$\begin{array}{cc}A=\left\{A_1,A_2,\cdots ,A_k\right\}& \left(k\in N^{\ast }\right)\end{array}$$ (2)

$$\begin{array}{cc}I=\left\{i_1,i_2,\cdots ,i_p\right\}& \left(p\in N^{\ast }\right)\end{array}$$ (3)

T_m is the mth data record in D. A_k shows the attribute of the kth data in D, and i_p reveals the pth item in D. The research problem is mainly aimed at the continuous behavior data of nodes, so its attribute category belongs to continuous attributes. The number of occurrences of itemset Z in transaction database D is the support number of itemset Z, denoted as COUNT(Z), and the support of itemset Z is denoted as Support(Z):

$$Support\left(\mathrm{Z}\right)=\frac{COUNT\left(\mathrm{Z}\right)}{\left|D\right|}\times 100\%$$ (4)

|D| represents the total number of samples contained in the transaction database. If Support(Z) is higher than the minimum support, Z is called frequent itemsets or frequent patterns. If Support(Z) is lower than the minimum support, it is called Z are infrequent itemsets or infrequent patterns [29].

The standard PBFT algorithm is beset with inherent limitations that necessitate enhancement to tackle the myriad challenges encountered in practical applications. For instance, in the realm of message transmission, incorporating advanced compression techniques and efficient encoding methods can substantially minimize the volume and size of messages, thereby reducing network communication overhead and markedly boosting algorithmic efficiency [30]. To bolster fault tolerance, improvements to inter-node communication protocols and validation mechanisms may have been implemented, allowing the algorithm to maintain stability and correctness even in the presence of numerous faults or malicious nodes. In dynamic environments, the introduction of mechanisms for rapid updating of node states and information can ensure system availability amidst network topology changes or the dynamic joining and exiting of nodes. Moreover, adopting stricter methods for signing, encryption, and identity verification can enhance the security validation framework, effectively preventing node masquerading and message tampering. Concurrently, optimizing consensus mechanisms by adjusting thresholds and refining voting strategies can accelerate the consensus process, reducing algorithm runtime and improving overall performance. These multifaceted enhancements collectively render the refined PBFT algorithm better equipped to meet the demands of complex and evolving real-world applications [31].

The machine model used in this experiment is MECHREVO series microcomputer. The operating system is Windows10, and the CPU is Intel(R) Core (TM) i7-7700HQ CPU @ 2.80 GHz. The development tool is IntelliJ IDEA 2019.2.2, and the project management tool is Apache Maven 3.6.2. JDK (Java Development Kit) is 8.

This study introduces a big data-driven model for the regulation of safety in entertainment venues, where the diversity and immediacy of data streams form the foundation of its architecture [32]. The data collection layer operates by capturing real-time, large-scale streams from multiple sources. Apache Kafka, a distributed messaging system, is utilized for this purpose, ensuring the handling of high-throughput and low-latency data transmission. Stability during data transmission remains paramount, and systems like Flume prove effective for gathering unstructured or semi-structured data, such as logs and event records, from disparate sources. Managing the extensive and varied structure of incoming data demands a robust distributed storage system. Hadoop's Hadoop Distributed File System (HDFS) provides storage for both structured and unstructured datasets, maintaining high availability and fault tolerance. For structured datasets, including safety records and behavior logs from venues, NoSQL databases such as HBase or Cassandra ensure efficient data read-write operations in distributed environments, especially where low-latency access is crucial. In the data processing layer, handling real-time data from entertainment venues necessitates a framework capable of large-scale parallel computation. Apache Spark, with its in-memory processing capabilities, facilitates both batch and real-time operations, enabling tasks such as clustering analysis and pattern recognition. For cases requiring more stringent real-time processing, Apache Flink can ensure low-latency stream handling, allowing for immediate detection of safety concerns. The MapReduce framework proves useful for offline batch processing and analysis of historical data, ideal for generating comprehensive reports on past safety metrics. At the analytical and modeling layer, K-Means clustering and PBFT algorithms are employed to manage diverse data tasks. K-Means provides computational efficiency in clustering safety risks or consumer behaviors, while PBFT ensures fault tolerance and consensus in distributed systems, critical in maintaining data integrity under faulty or malicious node conditions. Blockchain technology, integrated into the safety regulation layer, leverages its transparency and immutability to enhance oversight of safety records and consumer data. The distributed ledger ensures all data operations are traceable, safeguarding against unauthorized alterations and ensuring reliability in the storage and processing pipeline. Real-time monitoring and visualization tools such as Elasticsearch and Kibana facilitate the construction of a visualized safety management platform. This platform can display dashboards reflecting venue safety statuses, clustering outcomes, and risk assessments, thus providing administrators with data-informed decision-making tools for enhancing venue safety protocols.

4. Results and discussion

4.1. Distance judgment criteria

The Foursquare Location Data serves as the dataset for this experiment, functioning as a robust platform for capturing location-related information through user actions such as check-ins. This dataset encompasses a rich diversity of venue-related data from various global locations, facilitating a comprehensive analysis of consumer engagement, activity preferences, and location behaviors pertinent to entertainment venues. Accessible for download from the official website (https://www.kaggle.com/datasets/chetanism/foursquare-nyc-and-tokyo-checkin-dataset), this resource proves invaluable for behavioral studies.

To optimize the performance of the PBFT algorithm, an enhanced strategy utilizing a distance-based judgment criterion (a) is introduced. This criterion plays a critical role in regulating the timing of frequent pattern mining executed by supervisory nodes. Upon the identification of new frequent patterns, a consensus score is generated for the consensus nodes, enabling the detection of any malicious nodes within the system. For performance evaluation of this refined algorithm, experiments are conducted in a repetitive manner, with each trial executed six times. The resulting data from these experiments are illustrated in Fig. 6, providing insight into the effectiveness of the proposed enhancements.

Fig. 6.

Changes in the number of blocks required to remove malicious nodes and the number of CPU peaks.

As depicted in Fig. 6, when the distance standard a is less than or equal to 2, a fluctuating pattern emerges in the count of malicious nodes within the consensus nodes during the frequent pattern mining process. The proposed consensus algorithm demonstrates remarkable efficacy, accurately identifying all malicious nodes during the initial rounds of replacement. However, once the distance standard a exceeds 2, the time required to detect all malicious nodes increases substantially. Concurrently, throughout the execution of the proposed consensus algorithm, the CPU utilization exhibits ongoing variability as a grows. This observation underscores the necessity of judiciously balancing efficiency and accuracy when selecting an appropriate distance standard. Notably, setting the distance standard a to 2 achieves an exemplary performance, excelling in both accuracy and efficiency. The distance standard a equal to 2 effectively strikes an optimal balance between precise malicious node identification and computational resource consumption. In summary, the choice of distance standard significantly influences the proposed consensus algorithm's performance in recognizing malicious nodes, the time expended, and the efficiency of computational resource utilization during the frequent pattern mining phase. In practical applications, selecting an appropriate distance standard should be based on specific needs and a comprehensive evaluation of system performance to enhance algorithmic efficacy.

In practical scenarios, determining an appropriate value for (a) necessitates meticulous evaluation of the accuracy and timing related to the identification of malicious nodes, alongside considerations of the system's resource consumption and load capacity. Experimental findings reveal that a distance criterion of (a = 2) emerges as the optimal selection, effectively balancing the precision of malicious node detection with overall system performance. Consequently, when implementing a PBFT-based consensus algorithm, adjusting the value of (a) according to the specific requirements of the application context becomes essential. This adjustment optimizes system performance while preventing excessively high or low values that could compromise detection efficiency and resource utilization.

In conclusion, the experimental results highlight the significance of a thorough assessment of system performance, facilitating the identification of the most suitable parameter settings across diverse application environments. This approach aims to achieve an effective equilibrium between malicious node detection capabilities and computational resource management.

4.2. Identification ability of malicious nodes

The proposed PBFT consensus algorithm accurately identifies malicious nodes through the evaluation mechanism of consensus node behavior, and records the node behavior. All aspects of node behavior are comprehensively calculated in real time by supervisory nodes, which can provide a sufficient quantitative assessment basis. This experiment continued to run for 6 replacement cycles, and counted the number of malicious nodes included in the original nodes at the beginning and end of each cycle and after removing malicious nodes. The identification ability of the malicious node of the proposed PBFT consensus algorithm and the identification ability of the malicious node of the original PBFT algorithm are shown in Fig. 7, Fig. 8, respectively.

Fig. 7.

The identification ability of malicious node of the proposed PBFT consensus algorithm.

Fig. 8.

the identification ability of malicious node of the original PBFT algorithm.

As depicted in Fig. 7, the proposed PBFT consensus algorithm ensures that the number of malicious nodes incorporated into the consensus process does not exceed two. During the experimental phase, pre-configured hidden malicious node sets are removed, and subsequently, only one non-hidden malicious node is added during the second modification. In contrast, Fig. 8 illustrates that the original PBFT consensus algorithm permits the addition of 2–4 malicious nodes into the consensus process. Furthermore, after each modification operation, 1 to 3 malicious nodes persistently evade successful removal. By the conclusion of the fifth consensus cycle, some malicious nodes remain undetected. A detailed examination of results across various time points reveals a significant enhancement in the malicious node identification capabilities of the proposed PBFT consensus algorithm compared to the original version. A thorough examination of results across various time intervals unveils a marked improvement in the detection capabilities of malicious nodes within the revised PBFT algorithm. This enhancement manifests not only through an increased number of malicious nodes successfully identified and eradicated in a timely manner but also through heightened accuracy and reliability of the detection mechanism. The updated algorithm exhibits a notable capacity for swiftly and precisely uncovering concealed malicious nodes, thereby mitigating their detrimental effects on the consensus process and significantly bolstering overall system security. Comparative experiments further highlight the superiority of the new algorithm in identifying and neutralizing malicious nodes, validating its substantial contributions to system reliability and accuracy. This outcome emphasizes the critical need for implementing the improved PBFT algorithm in real-world applications, especially in contexts where the presence of malicious nodes poses considerable risks. The enhanced protection afforded by this algorithm in such scenarios is evident, establishing its value as a robust solution for safeguarding system integrity.

4.3. Analysis of the reasons for the regulation of immersive entertainment venues from the perspective of responsive regulation theory

Compared with traditional entertainment venues, immersive entertainment venues have a short time of appearance, lack of experience, immature market mechanisms, and failure of government regulation, which have caused many problems in the development process. The specific reasons are shown in Fig. 9.

Fig. 9.

The reasons for the regulation of immersive entertainment venues from the perspective of responsive regulation theory.

As depicted in Fig. 9, an analysis through the lens of responsive regulation theory reveals several primary challenges in the oversight of immersive entertainment venues. These challenges encompass overlapping regulatory responsibilities across various government departments, regulatory gaps, an excessive dependence on government oversight within the industry, and outdated regulatory methods. To address these issues, tiered and differentiated regulatory measures can be adopted, specifically tailored to rectify the inadequacies in oversight faced by multiple government bodies concerning the immersive entertainment sector.

Initially, the immersive entertainment industry involves numerous government departments, yet ambiguity surrounding the regulatory authority and responsibilities of each has led to a regulatory void. Responsive regulation theory advocates for the establishment of a feedback control mechanism aimed at enhancing oversight of market entities operating within the immersive entertainment sector. This mechanism can facilitate timely feedback from the industry, enabling dynamic adjustments in regulatory intensity and improving overall flexibility and responsiveness. Additionally, as the immersive entertainment industry has evolved, an overreliance on government oversight has resulted in a notable absence of self-regulatory mechanisms within the sector. According to responsive regulation theory, the development of a balanced credit regulatory system can foster a blend of moderate oversight with industry self-governance, addressing the shortcomings in self-regulation. By encouraging the synchronized growth of self-regulation alongside government oversight, a dual regulatory framework can emerge, improving the standardization across the industry. Finally, the swift expansion of the immersive entertainment industry has rendered existing governmental regulatory methods increasingly inadequate in adapting to the sector's evolving needs and demands. Responsive regulation theory suggests enhancing current governmental oversight mechanisms through institutionalization, standardization, and other comprehensive measures to minimize regulatory lag. For instance, leveraging big data technologies and intelligent regulatory methods can bolster the timeliness and accuracy of oversight, ensuring regulatory measures align with the industry's developmental pace.

By implementing these strategies, regulatory challenges within the immersive entertainment industry can be effectively mitigated, fostering a more flexible and efficient response to the rapidly shifting market landscape.

4.4. Improve the path of public security prevention and control in immersive entertainment venues

To solve the limitations of public security monitoring work in immersive entertainment venues, it is necessary to learn from the theory of situational crime prevention and the experience and practices of public security monitoring in entertainment venues in the domestic and overseas. Comprehensive measures are taken from improving laws and regulations, reforming the police model, and strengthening prevention in entertainment venues. These can effectively improve the level of public security prevention and control in immersive entertainment venues. Fig. 10 shows the specific path for improving the security prevention and control of immersive entertainment venues.

Fig. 10.

The path of improving the security prevention and control of immersive entertainment venues.

As illustrated in Fig. 10, five critical aspects emerge as pivotal for enhancing the safety and security capabilities of immersive entertainment venues. The detailed implementation strategies are as follows:

Initially, a focused legislative approach to address public security issues in immersive entertainment venues is paramount. This will establish clear and authoritative legal foundations for related management and enforcement activities, ensuring that safety measures are legally supported. Next, it is essential to delineate the specific responsibilities and obligations of the management entities overseeing immersive entertainment venues. This clarity allows operators to define their duties precisely, facilitating targeted and effective safety and security measures. Furthermore, the introduction of punitive measures for violations within immersive entertainment venues is crucial. Implementing stringent penalties will create a robust deterrent against potential infractions, encouraging venue operators to adhere strictly to regulations. Subsequently, the establishment of a routine police operational model is necessary. This will enhance law enforcement's ability to monitor and respond to security conditions within venues, ensuring prompt and effective interventions when issues arise. Lastly, assigning dedicated police personnel to maintain order within venues, alongside developing comprehensive reporting and reward mechanisms for both operators and consumers, is vital. Engaging local communities in patrol duties and standardizing employee and consumer identification processes, including the strict enforcement of age restrictions, will fortify security efforts.

Analyzing from the perspectives of various stakeholders.

• ●For management, clear delineation of responsibilities and standardized registration processes enhance operational effectiveness and mitigate risks.
• ●For law enforcement, specialized legislation and a regular operational model provide clear guidelines and efficient tools for executing their duties.
• ●For workers and consumers, a robust reporting and reward system boosts their engagement in maintaining security, fostering a collaborative approach to safety.
• ●For surrounding communities, participating in patrols not only strengthens their sense of civic duty but also contributes to a safer entertainment environment.
• ●For minors, the strict enforcement of entry restrictions ensures their physical and mental well-being and legal protections.

In summary, these interrelated aspects collectively form a comprehensive and systematic security framework, offering a solid theoretical foundation and practical guidelines for managing safety in immersive entertainment venues.

In comparison to the work of Topornin et al. (2023) [33], the present paper demonstrates a more precise extraction of core regulatory concerns in the context of managing and ensuring safety in immersive entertainment venues. Specifically, this study identifies critical issues encompassing social security, fire safety, and the review of online cultural products within the regulatory process of such venues.

Building on the aforementioned research, a detailed comparative evaluation of the models before and after optimization is conducted, with the results meticulously presented in Table 1.

Table 1.

Comparative results before and after optimization.

Test Metric	Pre-Optimization PBFT Algorithm	Post-Optimization PBFT Algorithm
Average Consensus Achievement Time (ms)	850 ± 50	550 ± 30
Maximum Tolerable Malicious Node Ratio (%)	30	45
Network Communication Overhead (KB/Iteration)	350 ± 20	220 ± 15
Transactions Processed Per Second	500 ± 20	800 ± 30
Algorithm Execution Efficiency (%)	65 ± 5	85 ± 3
Data Consistency Error Rate (%)	5 ± 1	1 ± 0.5
Average Response Latency (ms)	120 ± 10	70 ± 8
System Resource Utilization (%)	70 ± 5	50 ± 3
Security Composite Score (0–100)	75 ± 3	90 ± 2

Table 1 illustrates the performance enhancements of the improved PBFT algorithm. The average consensus time has been significantly reduced from 850 ± 50 ms to 550 ± 30 ms, indicating a notable increase in decision-making speed and efficiency. The algorithm's tolerance to malicious nodes has been enhanced, with its capacity rising from 30 % to 45 %, thereby significantly improving the system's fault tolerance and security. Network communication overhead has decreased from 350 ± 20 KB per round to 220 ± 15 KB per round, conserving network resources and enhancing overall system performance. The number of transactions processed per second has increased from 500 ± 20 to 800 ± 30, demonstrating the algorithm's capability to handle a greater volume of transactions and adapt to busier operational environments. Execution efficiency has improved from 65 ± 5 % to 85 ± 3 %, while system resource utilization has decreased from 70 ± 5 % to 50 ± 3 %, reflecting substantial optimization in resource use and execution performance. The overall security score has risen from 75 ± 3 to 90 ± 2, further highlighting the improved algorithm's superior performance in ensuring system security. These results robustly validate the effectiveness of the improved PBFT algorithm.

To validate the effectiveness of the optimized model presented, a comparative analysis was executed against the Raft algorithm and the Practical Proof of Stake (PPoS) algorithm. Raft, a consensus algorithm renowned for addressing consistency issues in distributed systems, finds frequent application in environments akin to blockchain. In contrast to PBFT, Raft's design simplicity and widespread application across various distributed systems stand out. The PPoS algorithm, a variant of proof-of-stake, is extensively employed in blockchain and distributed frameworks. It ensures system consistency and fault tolerance through stakeholder equity, characterized by reduced consensus complexity and lower energy consumption, albeit with distinct trade-offs regarding security and efficiency. Experimental results are summarized in Table 2:

Table 2.

Comparison of experiment results.

Metric	The Proposed Algorithm	Raft Algorithm	PPoS Algorithm
Malicious Node Detection Rate (%)	92.834	87.652	85.371
Average Consensus Delay (ms)	27.485	31.394	28.712
CPU Utilization (%)	67.329	73.149	69.421
Memory Usage (%)	58.471	62.739	61.284
False Positive Rate for Malicious Nodes (%)	2.738	5.829	4.512
Block Processing Time (s)	1.374	1.526	1.498
System Throughput (tx/s)	1248.539	1136.287	1198.432
Node Participation Rate in Consensus Rounds (%)	98.234	95.672	96.743

Data in Table 2 highlights the malicious node detection rate, reflecting the system's capacity to accurately identify malicious nodes during consensus. The optimized PBFT model exhibits a detection rate of 92.834 %, surpassing both Raft and PPoS in performance. The average consensus delay measures the time elapsed from initiating consensus to achieving agreement, recorded in milliseconds. Here, the optimized PBFT model also leads, with a delay of approximately 27.485 ms, significantly outperforming Raft. The metric for CPU utilization indicates the occupancy of the CPU while executing the consensus algorithm. The optimized PBFT model reveals a lower utilization rate of 67.329 %, signifying efficient resource consumption. Memory usage assesses the memory consumed during the model's operation, where the optimized PBFT model continues to outperform both comparative algorithms.

Overall, this comparative analysis illustrates that the optimized PBFT model provides notable advantages in malicious node detection, system efficiency, and resource consumption, reinforcing its effectiveness in various operational contexts.

5. Conclusion

As the pace of urban life accelerates, immersive entertainment experiences such as script-based reasoning and escape rooms have surged in popularity and quickly captured the public's interest. This study introduces the innovative application of the PBFT consensus algorithm to tackle these emerging challenges. Concurrently, a nuanced application of the responsive regulation theory dissects the necessity and underlying reasons for government oversight in immersive entertainment venues, leading to a series of actionable and targeted recommendations for enhancing public safety and security measures. The research outcomes vividly demonstrate that the proposed PBFT consensus algorithm significantly improves the precision of malicious node detection, thereby markedly refining the auditing processes and effectiveness concerning network cultural products. To bolster public safety and security, several critical aspects have been considered. This includes the need to further refine the legal framework for public safety oversight, ensuring a robust and comprehensive legal foundation for regulatory activities. Additionally, establishing a flexible and efficient policing system capable of rapid response and adaptation to evolving situations is essential. Building a broad and solid grassroots defense network is crucial, mobilizing various societal forces to engage in safety and security efforts. Strengthening individual and venue-specific self-protection measures and addressing potential safety hazards within entertainment venues are also pivotal. The significance of this study lies in its contribution to enhancing the intrinsic quality of immersive entertainment products in the country and exploring scientifically sound regulatory measures for such venues. However, it is important to acknowledge certain limitations within the study. Notably, the relative scarcity of references concerning government regulation in the current research context somewhat constrains the depth and breadth of the study. Thus, the recommendations presented necessitate further in-depth research to provide robust support and validation.

CRediT authorship contribution statement

Zixuan Li: Writing – review & editing, Writing – original draft, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Chengli Wang: Writing – review & editing, Writing – original draft, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

Data and code availability statement

Data included in article is referenced in the article.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Chengli Wang reports financial support was provided by Research on the improvement of humanistic quality of special prosecutors. Chengli Wang reports financial support was provided by Research on cultural Development Strategy of Shandong Nanxi Jinshi New Material Company.