Stadium intelligent security and protection system based on digital twinborn technology

Abstract

The invention provides a stadium intelligent security and protection system based on a digital twin technology, and aims to improve the intelligent level and response speed of stadium safety monitoring. The system collects audience behavior data through the data acquisition unit, and creates a virtual model of the venue by using the digital twin mapping unit. The personalized safety early warning module analyzes audience behaviors and predicts potential risks. And the emergency evacuation simulation and optimization module improves the evacuation efficiency and safety. And the environmental factor analysis module evaluates the influence of the environment on the security performance. And the special event response strategy module simulates and optimizes emergency response. The system further comprises a regulation compliance analysis module, an intelligent inspection assistance module, a self-adaptive learning module and the like, operation compliance is ensured, and continuous optimization is performed according to real-time data. According to the system, comprehensive monitoring, early warning, analysis and optimization of the safety condition of the stadium are realized, and the safety management capability of the stadium is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent security systems, and in particular to an intelligent security system for sports venues based on digital twin technology. Background Art

[0002] Existing stadium security systems mostly use traditional monitoring methods, such as video surveillance and access control systems. Although these methods can ensure safety to a certain extent, they have problems such as blind spots, poor real-time performance, and passive response. In addition, during emergency evacuation, the existing indication system often cannot provide fast and effective evacuation guidance based on real-time crowd flow and environmental changes. In special events such as violent conflicts and medical emergencies, the existing plans may not be perfect, resulting in insufficient timely and effective handling. Therefore, it is necessary to develop a more intelligent, proactive, and faster-responding security system. Summary of the invention

[0003] In order to solve the above problems, the present invention provides an intelligent security system based on digital twin technology, which has a personalized safety warning system: personalized safety warning is achieved by analyzing audience behavior data. Emergency evacuation simulation is performed using a digital twin model to optimize the evacuation plan. The impact of environmental factors on the performance of the security system is evaluated, and adjustment suggestions are made. Response strategies for special events are simulated to improve the emergency handling capabilities of the venue. In-depth analysis of audience behavior is performed to predict and warn of potential safety risks. Digital twin technology is used to assist intelligent inspection robots in efficient inspections. The system is enabled to perform adaptive learning and optimization based on real-time data and historical events. In order to achieve comprehensive monitoring, early warning, analysis and optimization of the safety status of the venue.

[0004] To achieve the above objectives, this application adopts the following technical solutions:

[0005] Smart security system includes:

[0006] The data acquisition unit uses an array of at least 5 high-definition cameras, each with at least 4K resolution and 30fps frame rate, and uses time synchronization technology to ensure consistency in data acquisition;

[0007] The digital twin mapping unit uses a laser scanner for 3D modeling with an accuracy of ±2mm and combines GIS technology to achieve mapping with the actual geographic location;

[0008] The individual behavior analysis unit uses a deep learning algorithm, including but not limited to a convolutional neural network (CNN), to extract behavioral features through at least 3 convolutional layers and a fully connected layer.

[0009] The real-time monitoring unit includes: an edge computing component, which uses a processor with a processing frequency of at least 2 GHz to achieve real-time analysis of data with a delay of no more than 100 ms; a behavioral risk assessment component, which uses a fuzzy logic assessment component and contains at least 15 fuzzy rules for quantifying the behavioral risk level.

[0010] The early warning decision unit includes: a rule engine component, which contains at least 100 predefined rules for generating early warning information; a risk response automation component, which uses a genetic algorithm to optimize the evacuation path to ensure the shortest evacuation time.

[0011] The information presentation and notification unit includes: an augmented reality (AR) visualization component, which uses AR markers with at least 80% transparency to ensure the clarity of information presentation; a customized notification component, which uses at least 3 different color codes to push warning information according to the responsibility area of ​​the security personnel.

[0012] The adaptive optimization unit includes: a reinforcement learning algorithm component, which adopts a deep Q network (DQN) and contains at least 2 hidden layers, each with at least 128 neurons; a digital twin simulation environment component, which simulates at least 3 different emergency situations for testing early warning algorithms and safety strategies.

[0013] The user feedback unit includes: a multi-channel user feedback collection component that uses natural language processing technology to analyze user feedback in at least 3 languages; an agile development iteration framework component that uses the Scrum framework to iteratively update system functions every two weeks.

[0014] The security situation analysis unit includes: a data fusion technology component that uses the principal component analysis (PCA) algorithm to reduce the dimension of CCTV, Wi-Fi tracking and access control system data; a comprehensive risk assessment tool component that uses the fault tree analysis (FTA) method to assess at least 5 security risk levels.

[0015] The legal and regulatory compliance analysis unit includes: a regulatory database component that stores at least 1,000 legal and regulatory provisions related to security systems; an automatic compliance checking tool component that uses at least 5 rule verification algorithms to ensure compliance of operations.

[0016] The emergency evacuation simulation and optimization subsystem includes: a three-dimensional environmental modeling unit, which uses BIM technology to create a digital twin model of the venue from at least 10 different perspectives; a crowd dynamic simulation unit, which simulates the movement of at least 5,000 individuals to evaluate evacuation efficiency.

[0017] The subsystem further includes: an evacuation strategy optimization unit, which uses an ant colony algorithm to generate at least 10 evacuation strategies; a real-time evacuation guidance unit, which uses at least 3 evacuation guidance modes according to real-time data to provide an optimal evacuation path.

[0018] Through the above technical solution, the digital twin technology-based intelligent security system for sports venues of the present invention can comprehensively monitor, warn, analyze and optimize the security status of the venues. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to make the embodiments, technical solutions and technical advantages of the present application clearer, the following drawings provide non-limiting views of the present application:

[0020] Figure 1 The overall architecture diagram of the intelligent security system provided in this application.

[0021] Figure 2 A flow chart showing the personalized safety early warning system.

[0022] Figure 3 The flow chart of the emergency evacuation simulation and optimization system is shown.

[0023] Figure 4 A flow chart showing the analysis system of the impact of environmental factors on security.

[0024] Figure 5 A flow chart showing the simulation of a special incident response strategy.

[0025] Figure 6 A flow chart of the audience behavior analysis and prediction system is shown.

[0026] Figure 7 The flow chart of the digital twin-assisted intelligent inspection system is shown.

[0027] Figure 8 The adaptive learning flow chart of the intelligent security system is shown. DETAILED DESCRIPTION

[0028] In order to elaborate on the embodiments, technical solutions and technical advantages of the present application, a detailed and complete description will be provided below in conjunction with the accompanying drawings. It should be clear that the described embodiments only represent some application scenarios of the present application, not all possibilities. According to the embodiments of the present disclosure, those skilled in the art can obviously obtain other possible embodiments without performing creative work, and these embodiments also belong to the scope of protection of the present application.

[0029] 1. Personalized safety warning

[0030] The personalized safety warning system of the present invention is a highly advanced module designed to achieve early identification and warning of potential safety threats through in-depth analysis of individual behavior patterns. The following is a detailed description and expansion of the system:

[0031] The data collection and preprocessing module uses digital twin technology to achieve a full range of digital mapping of the physical venue, including seating layout, audience flow patterns, and security facility distribution. The specific techniques for data collection are described in detail, such as using POI (Point of Interest) tracking technology to monitor audience flow and capturing mobile application interaction data through integrated APIs. The advanced techniques for data preprocessing are described, such as using deep convolutional neural networks for image denoising and using time series analysis for pattern recognition and anomaly detection of data.

[0032] The module on individual behavior pattern learning describes the process of using a digital twin environment to simulate different situations, such as behavior patterns in different activities such as sports events and concerts. It also explains the training process of machine learning algorithms, including data annotation, feature selection, model selection and cross-validation, as well as the application of deep learning models such as LSTM and CNN in behavior pattern recognition.

[0033] In this unit, we will use deep learning techniques to extract and classify behavioral features. In particular, we will use a convolutional neural network (CNN), whose loss function is defined as follows:

[0034]

[0035] in, is the probability of category c predicted by the model, y o,c is the one-hot encoding of the actual category.

[0036] The Behavioral Feature Extraction and Analysis module describes the use of advanced image processing techniques in a digital twin environment, such as background subtraction, shape recognition, and motion vector analysis, to extract behavioral features. It discusses the use of behavioral science theories to guide feature extraction and analysis, such as personal space, group dynamics, etc.

[0037] The Real-time Behavior Monitoring and Assessment module describes the architecture of the real-time behavior monitoring system, including the integration of edge computing to reduce latency and increase data processing speed. The decision support system of the assessment module is discussed, such as using fuzzy logic or neural networks to perform real-time assessment of behavioral risks.

[0038] The fuzzy logic assessment component uses fuzzy set theory to quantify the uncertainty of behavioral risk assessment by defining language variables and fuzzy rules. The specific implementation includes: fuzzification of data, converting precise data into fuzzy values ​​through membership functions; construction of fuzzy rule base, storing predefined assessment rules; fuzzy reasoning mechanism, reasoning based on input fuzzy values ​​and rule base to obtain the assessment results of behavioral risks; defuzzification process, converting the reasoning results into specific safety levels.

[0039] The neural network assessment component uses deep learning technology to automatically learn the mapping relationship between behavior patterns and risk levels by training a large amount of behavior data. This component includes an input layer, a hidden layer, and an output layer, and continuously adjusts the network weights through a back-propagation algorithm to improve the accuracy of the assessment.

[0040] The early warning decision and response module describes the decision tree or rule engine of the early warning decision engine, and how to combine the digital twin analysis results for risk assessment. It also describes the automated process of response measures, such as using the digital twin model to simulate the optimal path planning and evacuation guidance of security personnel.

[0041] In this unit, we will use genetic algorithms to optimize evacuation routes. The fitness function of the genetic algorithm is defined as follows:

[0042] F=ω1f1+ω2f2+…+ω n f n

[0043] Among them, f i is the value of the i-th fitness function, ω i is the corresponding weight.

[0044] The warning information presentation and notification module describes the use of augmented reality (AR) technology to visualize warning information in a digital twin environment, and to conduct safety drills through virtual reality (VR). The personalized strategies of the notification module are discussed, such as customizing notification content according to the responsibility areas of different security personnel.

[0045] The customized notification system filters relevant information from the early warning information database based on the responsibility area and role of the security personnel through preset logic and algorithms. The system adopts message queue and event-driven architecture to ensure the real-time and accuracy of information transmission. In addition, the system supports multiple notification methods, including SMS, email, application push, etc., to meet the needs of different scenarios.

[0046] The system adaptation and optimization module describes the process of using digital twin models for simulation training and model optimization, such as testing early warning algorithms by simulating different behavior patterns. It also describes the implementation of adaptive learning algorithms, such as using reinforcement learning to allow the system to improve itself based on feedback.

[0047] The User Feedback and System Iteration module describes the multi-channel collection mechanism of user feedback, including social media monitoring, online surveys, and real-time feedback systems. It discusses agile development methods for system iteration, such as using the Scrum framework to quickly respond to user feedback and iterate the system.

[0048] The multi-dimensional security situation analysis module describes how to use the digital twin environment to integrate data from multiple sources such as CCTV, Wi-Fi tracking, access control systems, etc. It discusses comprehensive assessment methods for security situation analysis, such as using a comprehensive risk index to assess the security status of a venue.

[0049] In the safety situation analysis unit, we will use the fault tree analysis (FTA) method to evaluate the safety risk level of the venue. The top event probability calculation formula of FTA is as follows:

[0050]

[0051] Among them, P(Top) is the probability of the top event occurring, E i is a basic event, P(E i ) is the probability of the basic event occurring, B i It is the impact of basic events on top events.

[0052] The Regulatory Compliance Analysis module describes how to use digital twin models to simulate regulatory compliance, such as simulating the restrictions of privacy regulations on data collection and processing. It also discusses the specific implementation of data protection measures, such as using blockchain technology to ensure data integrity and traceability.

[0053] The regulatory database is built using a relational database management system with high data consistency and security. The database stores legal and regulatory provisions related to security systems, including but not limited to privacy protection, data security, etc. Each regulatory provision has a unique identifier and is attached with a keyword tag for easy retrieval and matching.

[0054] The automatic compliance checking tool monitors system operations in real time and matches them with the provisions in the regulatory database to ensure that all operations comply with legal and regulatory requirements. The tool uses rule engine technology to dynamically update the inspection rules according to regulatory changes, realizing the automation and intelligence of compliance checking.

[0055] In the Legal and Regulatory Compliance Analysis Unit, we will use rule engine technology to ensure the compliance of system operations. The rule engine will dynamically update the inspection rules based on the following logic:

[0056]

[0057] Among them, Rule i is the i-th regulation rule, and m is the total number of rules.

[0058] 2. Emergency Evacuation Simulation and Optimization

[0059] The emergency evacuation simulation and optimization system of the present invention is a comprehensive solution that focuses on improving the efficiency and safety of personnel evacuation in sports stadiums in emergency situations. The following is a detailed description and expansion of the system:

[0060] The 3D environment modeling module uses digital twin technology, combined with high-precision 3D scanning, to create a virtual model that completely corresponds to the physical stadium, including audience areas, passages, exits, etc. Model details include seat arrangements, emergency exit locations, signage, emergency evacuation routes, etc., ensuring that every detail is consistent with the actual situation.

[0061] The crowd dynamics simulation module develops crowd dynamics simulation algorithms in the digital twin environment based on historical event data and crowd behavior research to simulate crowd movement in emergency situations. The simulation includes factors such as crowd density, flow speed, and psychological reactions to predict crowd behavior under different evacuation strategies.

[0062] The evacuation strategy generation module uses digital twin technology and combines the results of dynamic crowd flow simulation to generate multiple evacuation strategies and test them in a virtual environment. Advanced optimization algorithms, such as genetic algorithms and particle swarm optimization, are used to evaluate the efficiency and safety of each strategy in the digital twin model.

[0063] In the Emergency Evacuation Simulation and Optimization subsystem, we will use agent-based modeling methods to simulate individual behaviors and group dynamics. The evaluation of the evacuation strategy will be based on the following formula:

[0064]

[0065] Among them, evacuation efficiency is defined as the number of people evacuated within the total evacuation time.

[0066] The strategy evaluation and analysis module conducts a detailed evaluation of the evacuation strategy in the digital twin environment, including key indicators such as evacuation time, congestion points, and crowd distribution. Using data visualization technology, the simulation results are presented in the form of graphics and animations to help analyze and understand the dynamics of human flow.

[0067] In an emergency, the real-time evacuation guidance module uses the optimal evacuation strategy in the digital twin model to guide the evacuation of people in real time through the on-site indication system and broadcasting system. Considering the needs of special groups, the digital twin model can identify the location of special groups and provide customized evacuation guidance.

[0068] The environmental factor adaptation module monitors the environmental factors in the physical venue in real time and feeds the data back to the digital twin model to simulate the impact of environmental changes on evacuation. According to the changes in environmental factors, the digital twin model can dynamically adjust the evacuation strategy to ensure the real-time and adaptability of the evacuation plan.

[0069] The evacuation path optimization module uses the digital twin model, combined with graph theory and network flow theory, to conduct detailed analysis and optimization of the evacuation path. The optimization algorithm takes into account multiple exits, multiple paths, and dynamic changes on the path, such as congestion and obstacles.

[0070] The safety facility layout optimization module evaluates the effectiveness of the existing safety facility layout in the digital twin model and proposes improvement suggestions. It simulates the adjustment of safety facilities, such as the addition of emergency exits and the rearrangement of fire-fighting facilities, to improve evacuation efficiency.

[0071] In an emergency, the real-time evacuation guidance unit uses the real-time data of the crowd flow dynamic simulation unit to quickly generate an evacuation strategy. The unit uses a multi-objective optimization algorithm to comprehensively consider factors such as evacuation time and path safety to determine the optimal evacuation route. The generated evacuation guidance information is conveyed to the on-site indication system and broadcasting system in real time through the virtual model of the digital twin mapping unit, achieving effective guidance for the audience.

[0072] The evacuation drill and training module uses digital twin technology to design and implement evacuation drill plans, provide virtual emergency simulations, and train venue managers and spectators. Drill data is collected to evaluate evacuation efficiency and provide a basis for further optimizing evacuation strategies.

[0073] The regulatory compliance check module integrates the regulatory database in the digital twin model to ensure that all evacuation strategies and facility layouts comply with local regulatory requirements. The regulatory database is updated regularly to ensure the compliance of evacuation strategies and adjust evacuation plans according to regulatory changes.

[0074] The multi-scenario adaptability analysis module simulates different emergency scenarios in the digital twin environment, such as fire, earthquake, terrorist attack, etc., to evaluate the adaptability of the evacuation strategy. The evacuation strategy is customized for each scenario to ensure that the system can flexibly respond to various emergency situations.

[0075] The system self-assessment and feedback module designs a self-assessment mechanism, which enables the digital twin model to self-assess the evacuation efficiency and safety based on evacuation drills and actual evacuation situations. It collects feedback from managers and audiences, combines the self-assessment results, and continuously optimizes the evacuation strategy and system performance.

[0076] 3. Analysis of the impact of environmental factors on security

[0077] The environmental factors impact analysis system of the present invention is an innovative module that focuses on evaluating and quantifying the impact of environmental factors on the performance of stadium security systems. The following is a detailed description and expansion of the system:

[0078] The environmental monitoring data acquisition module deploys a sensor network to collect environmental parameters such as temperature, humidity, and light intensity, and transmits this data to the digital twin model in real time. The digital twin model is synchronously updated to reflect the current environmental status of the physical venue, providing an accurate data source for further analysis.

[0079] The environmental factor impact assessment module evaluates the specific impact of environmental parameter changes on the performance of security equipment in the digital twin environment, such as simulating the coverage of surveillance cameras under different lighting conditions. The digital twin model is used to predict the potential impact of environmental changes on human behavior and evaluate the need for security measures.

[0080] The security system performance simulation module uses digital twin technology to build a virtual copy of the security system and simulate the performance of the equipment under various environmental conditions. It can perform multi-scenario simulations, such as simulating the reaction time and accuracy of the security system in severe weather.

[0081] The Data Analysis and Pattern Recognition module applies machine learning algorithms to analyze historical and real-time data in a digital twin environment to identify correlations between environmental factors and security system performance. Deep learning models, such as convolutional neural networks (CNNs), are used to perform pattern recognition on video data to identify abnormal behaviors or events.

[0082] The environment adaptability strategy generation module formulates security strategies that adapt to different environmental conditions based on the analysis results in the digital twin model, such as adjusting camera sensitivity or changing patrol routes. Optimization algorithms such as linear programming or dynamic programming are used to generate the most cost-effective environment adaptability strategy.

[0083] The real-time environmental adaptability module monitors environmental parameters in real time and automatically adjusts security system settings, such as adjusting the operation of the ventilation system according to indoor temperature changes to prevent equipment overheating. Using adaptive control algorithms, the security system response is dynamically adjusted to maintain optimal performance.

[0084] The risk assessment and early warning module uses the digital twin model to model risks and evaluate the probability and potential consequences of environmental factors on the failure of the security system. The risk threshold is set, and when the monitored risk exceeds the threshold, an early warning is triggered and preventive measures are recommended.

[0085] The environmental factor and event correlation analysis module analyzes historical event data to identify the correlation between specific environmental conditions and safety events, such as the impact of humidity on electrical fires. The digital twin model is used to conduct hypothesis testing to simulate the impact of changes in different environmental factors on event outcomes.

[0086] The system performance feedback learning module collects system performance data under different environmental conditions and uses this data to conduct feedback learning on the digital twin model. Using the reinforcement learning algorithm, the system can autonomously optimize security strategies according to environmental changes.

[0087] In the adaptive optimization unit, we will use reinforcement learning algorithms, especially Q-learning, to continuously optimize the behavior recognition model. The update formula of Q-learning is as follows:

[0088] Q(s t ,a t )←Q(s t ,a t )+α[r t+1 +γmax α Q(s t+1 ,a)-Q(s t ,a t )]

[0089] Where Q is the value function of the state-action pair, α is the learning rate, and γ is the discount factor.

[0090] The user-customized report module generates a customized environmental impact analysis report based on user needs and the analysis results of the digital twin model. The report should include detailed environmental impact assessment, optimization suggestions, risk prediction, etc.

[0091] The Regulatory Compliance and Standards Compliance Check module integrates regulatory and industry standard databases to ensure that all environmental adaptability strategies and adjustments are in compliance with regulations. Regularly review and update the regulatory database to ensure the continuous compliance of the system.

[0092] The multi-scenario environmental adaptability test module simulates various extreme environmental scenarios in the digital twin environment to test the adaptability and robustness of the security system. Based on the test results, the system design is optimized to improve its performance and reliability under extreme conditions.

[0093] The Technology Integration and Innovation module continuously explores new technologies, such as the Internet of Things (IoT), big data, and cloud computing, and integrates them with digital twin technology to improve system performance. It also promotes interdisciplinary cooperation and applies the latest scientific research results to the analysis of the impact of environmental factors on security.

[0094] 4. Special incident response strategy simulation

[0095] The special event response strategy simulation system of the present invention is an advanced module that aims to improve the emergency response capabilities of sports venues when facing special events through simulation and optimization. The following is a detailed description and expansion of the system:

[0096] The special event definition and classification module clarifies the definition of special events, including but not limited to fires, earthquakes, terrorist attacks, violent conflicts, medical emergencies, etc. Digital twin technology is used to define the characteristics and classification of special events and create virtual templates of events for subsequent simulation and analysis.

[0097] The event impact simulation module uses digital twin technology to build an accurate virtual replica of the stadium and simulate the specific impact of special events on the stadium, such as fire spread, earthquake damage, etc. The impact of simulated events on personnel behavior, facility integrity and venue operations provides a basis for emergency strategy formulation.

[0098] In the digital twin environment, the emergency response strategy generation module generates and tests a variety of emergency response strategies based on event simulation results, including evacuation path planning, rescue point setting, etc. Multi-objective optimization algorithms, such as genetic algorithms, are applied to balance the efficiency and safety of different strategies.

[0099] The strategy simulation and evaluation module performs strategy simulation in the digital twin environment to evaluate evacuation efficiency, rescue resource allocation, and potential risks. Visualization tools are used to display the strategy execution process to help decision makers understand the strategy effect.

[0100] The real-time response command module integrates the digital twin model with the real-time command and control system to achieve rapid response when special events occur. The simulation results in the digital twin environment are used to guide on-site personnel to evacuate and rescue.

[0101] The resource optimization and configuration module analyzes resource requirements in the digital twin model, simulates the resource allocation process, and optimizes resource allocation strategies. It applies resource optimization algorithms to ensure that key resources can be deployed quickly at critical moments.

[0102] The personnel behavior simulation and guidance module simulates the behavioral responses of personnel in special events, such as panic and following, and tests different guidance strategies in the digital twin environment. Based on the simulation results, an effective personnel behavior guidance plan is designed to improve evacuation efficiency and safety.

[0103] The multi-department collaborative response module simulates the collaborative response of different departments in a digital twin environment to ensure information sharing and coordinated actions. A virtual multi-department command platform is established to simulate cross-department communication and decision-making processes.

[0104] The plan update and optimization module combines the simulation results of the digital twin model with actual response experience to regularly update the emergency plan. Using machine learning technology, the plan can adapt to new event types and environmental changes.

[0105] The training and drill module uses digital twin technology and virtual reality technology to design immersive training and drill programs, simulate real event scenarios, and improve the emergency response capabilities of managers and rescuers.

[0106] The regulatory compliance check module integrates the regulatory database in the digital twin environment to ensure that all emergency response strategies comply with legal and regulatory requirements. Regularly review the regulatory database and update the plan in a timely manner to maintain compliance.

[0107] The effect evaluation and feedback module evaluates the implementation effect of the emergency response strategy and collects feedback from managers, rescuers and spectators. The digital twin model is used to analyze the effect and adjust and optimize the strategy based on the feedback.

[0108] The Technology Integration and Innovation module explores the integration of technologies such as the Internet of Things (IoT), big data analysis, and cloud computing with digital twin technology to enhance the intelligence level of the system and promote technological innovation, such as using blockchain technology to ensure data security and integrity.

[0109] The intelligent decision support module develops an intelligent decision support system, using digital twin models to provide real-time data analysis and decision suggestions. Combined with artificial intelligence technologies such as natural language processing (NLP), it improves decision-making efficiency and accuracy.

[0110] In the User Feedback Unit, we will use Natural Language Processing (NLP) technology to analyze the user's textual feedback. Sentiment analysis will use the following formula to determine the sentiment tendency of the text:

[0111]

[0112] Among them, Sentiment i ( i ) is a word i The sentiment score is the number of words in the text.

[0113] 5. Audience Behavior Analysis and Prediction

[0114] The audience behavior analysis and prediction system of the present invention is an intelligent module that focuses on in-depth analysis and prediction of audience behavior patterns in sports venues in order to identify and respond to potential safety risks in advance. The following is a detailed description and expansion of the system:

[0115] The data collection and integration module uses digital twin technology to integrate data from high-definition cameras, Wi-Fi tracking, mobile applications, sensors and other devices in the physical venue into a unified digital model. Through the digital twin platform, real-time data synchronization and update are achieved to ensure that the virtual model is consistent with the physical venue status.

[0116] The behavior feature extraction module uses advanced image processing and pattern recognition technology in the digital twin environment to automatically extract the audience's behavior features from video streams and tracking data. It uses machine learning algorithms to reduce the dimension and filter the features to extract the most critical data for behavior analysis.

[0117] The behavior pattern recognition module uses the digital twin model to simulate and classify different types of behavior patterns and build a rich behavior pattern database. It applies deep learning techniques, such as convolutional neural networks (CNN), to conduct in-depth analysis of behavior characteristics and automatically identify and classify normal and abnormal behavior patterns.

[0118] The behavior trend prediction module combines digital twin models and time series prediction techniques, such as long short-term memory networks (LSTM), to predict short-term and long-term trends in audience behavior. The prediction model considers historical data, real-time data, and environmental factors to improve prediction accuracy.

[0119] The safety risk assessment module evaluates the safety risk level of different areas in the digital twin environment based on the behavior prediction results and the three-dimensional layout of the venue. Using risk assessment algorithms such as fault tree analysis (FTA), potential safety risks are identified and their likelihood and severity are determined.

[0120] The behavior influencing factor analysis module simulates and analyzes the impact of internal and external factors on audience behavior in the digital twin model, such as event type, venue facility changes, social events, etc. Combined with the expert system and knowledge graph, it evaluates the potential impact of these factors on audience behavior patterns and safety risks.

[0121] The real-time behavior monitoring and early warning module uses digital twin technology to monitor audience behavior in real time and compare it with the behavior pattern database to quickly identify abnormal behavior. The integrated real-time early warning system triggers an early warning and notifies security managers through the digital twin platform when potential risks are detected.

[0122] The audience behavior guidance strategy module simulates different audience behavior guidance strategies in the digital twin environment, such as information release and crowd control, based on the safety risk assessment results. It evaluates the effects of different strategies and selects the best solution for implementation in the physical venue.

[0123] The multi-scenario adaptive analysis module simulates different events and activity scenarios in the digital twin model, evaluates their impact on audience behavior, and optimizes the behavior prediction model. It considers the special impact of special days and large-scale events on audience behavior and formulates corresponding response strategies.

[0124] The system self-learning and optimization module uses data and feedback in the digital twin environment to enable the system to have self-learning capabilities and continuously optimize the accuracy of behavioral feature extraction and behavioral pattern recognition. The application of reinforcement learning algorithms enables the system to autonomously adjust the prediction model based on real-time data and warning effects.

[0125] The user interaction and feedback module provides a user interaction interface, allowing security managers to input additional information and feedback in the digital twin environment. Based on user feedback, the parameters and strategies of behavior analysis and prediction are adjusted to improve the adaptability and accuracy of the system.

[0126] The Regulatory Compliance and Privacy Protection module ensures that data collection and analysis activities in the digital twin environment comply with relevant laws, regulations and privacy protection requirements. Design data desensitization and anonymization processes to protect audience personal information while ensuring data security and reliability.

[0127] The Technology Integration and Innovation module explores the integration of technologies such as the Internet of Things (IoT), big data analysis, and cloud computing with digital twin technology to enhance the intelligence level of the system and promote technological innovation, such as using blockchain technology to ensure data security and integrity.

[0128] The integrated decision support module develops an integrated decision support system that uses digital twin models to provide real-time data analysis and decision suggestions. Combined with artificial intelligence technologies such as natural language processing (NLP), it improves decision-making efficiency and accuracy.

[0129] 6. Intelligent Inspection Assisted by Digital Twins

[0130] The digital twin-assisted intelligent inspection system of the present invention is a solution that integrates advanced technologies to improve the efficiency and accuracy of sports stadium safety inspections. The following is a detailed description and expansion of the system:

[0131] The digital twin environment building module uses high-precision 3D scanning technology, combined with geographic information system (GIS) and building information model (BIM) technology, to create a detailed digital twin model of the stadium. The digital twin model not only includes the physical structure, but also integrates the venue's operational data, historical event records and maintenance logs to provide comprehensive background information for inspection tasks.

[0132] In the process of creating the digital twin model, we will use high-precision LiDAR scanning technology, which can capture the three-dimensional structure of the venue at a rate of millions of points per second. To ensure the accuracy of the model, we will use the following formula to calculate the model's error:

[0133]

[0134] Among them, dist model,iand dist actual,i represent the distance between the model and the actual measurement, and n is the number of measurement points.

[0135] The inspection task planning module applies artificial intelligence algorithms, such as genetic algorithms or ant colony optimization algorithms, to automatically plan the optimal inspection path based on the digital twin model and safety requirements. Taking into account the venue's operating hours, event schedules, and audience flow, the inspection tasks are dynamically adjusted to achieve the highest efficiency and maximum coverage.

[0136] The intelligent inspection robot integration module integrates advanced intelligent inspection robots, equipped with sensors such as high-definition cameras, infrared thermal imagers, gas detectors, and laser radar (LiDAR) and GPS systems for autonomous navigation. The robot is capable of performing autonomous navigation, obstacle avoidance and path planning, while exchanging real-time data with the digital twin model.

[0137] The real-time data synchronization and analysis module realizes real-time data synchronization, synchronously updating the sensor data and video streams collected by the robot with the digital twin model. Apply big data analysis and machine learning technologies, such as anomaly detection algorithms, to conduct in-depth analysis of real-time data and quickly identify potential safety issues.

[0138] In the real-time monitoring unit, we will use edge computing technology to reduce the latency of data processing. Data compression is a key technology to reduce the amount of transmitted data. We use the following formula to calculate the amount of compressed data:

[0139]

[0140] Among them, C is the amount of compressed data, I is the amount of original data, and r is the compression ratio.

[0141] The intelligent inspection robot in the task execution and adjustment module automatically performs inspections according to the planned tasks, and uses edge computing technology to perform preliminary data processing and analysis locally. The system can dynamically adjust inspection tasks and paths to respond to emergencies based on real-time data analysis results and feedback from the digital twin model.

[0142] The environmental adaptability analysis module analyzes the impact of different environmental conditions, such as light changes, weather conditions, and changes in human traffic, on the inspection effect. Based on the results of the environmental adaptability analysis, the robot's sensor parameters and inspection strategy are automatically adjusted to ensure the best inspection effect in various environments.

[0143] The security incident identification and response module uses digital twin models and pattern recognition technology to automatically identify and classify security incidents, such as fires, fights, abnormal intrusions, etc. Once a security incident is identified, the system immediately activates the emergency plan, notifies relevant personnel through the digital twin platform, and guides the robot to respond quickly.

[0144] The inspection effect evaluation module designs inspection effect evaluation standards and methods, including key performance indicators such as inspection coverage, event recognition accuracy, and response time. The digital twin model is used for simulation evaluation, and the inspection strategy is continuously optimized by comparing the actual inspection results with the simulation expectations.

[0145] The system has adaptive learning capabilities and can continuously learn and optimize inspection strategies based on historical inspection data and event processing results. It applies machine learning algorithms such as random forest or gradient boosting machine (GBM) to improve the accuracy of event recognition and the rationality of inspection paths.

[0146] The user interaction and control module provides an intuitive user interaction interface, allowing security managers to monitor the inspection status in real time, adjust inspection tasks, and manually control the intelligent inspection robot when necessary. The interface integrates map visualization, event tracking and task editing functions, providing a comprehensive operation platform.

[0147] The data storage and historical analysis module stores all data collected during the inspection process, including videos, sensor data, event records, etc., and ensures the security and traceability of the data. It provides historical data analysis tools, such as trend analysis and correlation analysis, to help managers deeply understand safety trends and formulate improvement measures.

[0148] The Regulatory Compliance and Safety Standards module ensures that the design and operation of the inspection system complies with national and international safety regulations and standards. Regularly review regulatory changes, update system configuration and inspection strategies in a timely manner to maintain compliance and ensure continuous improvement of the system.

[0149] The Technology Integration and Innovation module explores the integration of technologies such as the Internet of Things (IoT), cloud computing, and artificial intelligence with digital twin technology to enhance the intelligence level of the system. It also promotes technological innovation, such as using blockchain technology to ensure data security or using virtual reality (VR) for inspection simulation and training.

[0150] The Risk Management and Decision Support module develops risk management tools that combine digital twin models and historical data analysis to assess and manage potential safety risks. It also provides a decision support system that uses artificial intelligence technologies such as natural language processing (NLP) to provide managers with real-time decision recommendations and action plans.

[0151] 7. Adaptive Learning of Intelligent Security Systems

[0152] The adaptive learning module of the intelligent security system of the present invention is a core component that enables the system to continuously learn from actual operation and optimize its performance. The following is a detailed description and expansion of the module:

[0153] The data collection and management module refines the data collection process, including using high-resolution cameras to capture behavioral patterns, using Wi-Fi and Bluetooth technology to track audience movement, and collecting user feedback and behavioral data through integrated mobile applications. Strengthen the functions of the data management platform to achieve real-time data processing, storage and analysis, while ensuring data security and privacy.

[0154] In this module, we will deploy a set of high-resolution cameras. Specific examples include using 8K resolution cameras to capture images at 30 frames per second (fps). The cameras will be equipped with advanced image processing algorithms, including but not limited to denoising and feature extraction algorithms. For example, we use a non-local mean filter for image denoising, with the following formula:

[0155]

[0156] Among them, I filtered is the filtered image, I is the original image, (,) is the neighborhood of the pixel (x, y), and is the weight calculated based on the spatial distance and intensity difference between pixels.

[0157] The feature extraction and analysis module uses digital twin technology, combined with image recognition and machine learning algorithms, to automatically extract key behavioral features from video streams, such as crowd density, abnormal behavior, etc. Through deep learning models, such as convolutional neural networks (CNN), the features are reduced in dimension and classified to improve the accuracy of behavior recognition.

[0158] The behavior pattern recognition and classification module uses historical data in the digital twin environment to train a variety of behavior recognition models, including supervised learning, unsupervised learning, and reinforcement learning models. Through model fusion technology, the advantages of different models are combined to improve the overall performance of behavior pattern recognition.

[0159] The adaptive algorithm development module develops adaptive learning algorithms, enabling the system to dynamically adjust behavior pattern recognition and prediction models based on real-time feedback and historical data. Digital twin technology is used to simulate the effects of different learning strategies and select the optimal algorithm for practical application.

[0160] The performance evaluation and optimization module designs a comprehensive evaluation system, including quantitative indicators (such as detection rate and false alarm rate) and qualitative indicators (such as system responsiveness and user satisfaction). The digital twin model is used for simulation testing to evaluate the impact of different optimization strategies on system performance.

[0161] The prediction model building module combines time series analysis and machine learning techniques to build a prediction model to predict future behavior trends and potential security incidents. The accuracy of the prediction model is tested in the digital twin environment, and the model parameters are adjusted to improve the reliability of the prediction.

[0162] The feedback mechanism implementation module uses digital twin technology to implement a fast feedback mechanism, which transmits performance evaluation results and user feedback to the adaptive learning algorithm in real time. Through closed-loop control, the system parameters are continuously adjusted to optimize system performance.

[0163] The multi-scenario adaptive strategy module uses digital twin models to simulate multiple scenarios and develop adaptive strategies to adapt to different events, activities and environmental conditions. Through simulation and testing, the effectiveness of the adaptive strategy is evaluated and necessary adjustments are made.

[0164] The intelligent decision support module uses digital twin technology and adaptive learning results to provide security managers with decision support, including risk assessment, resource allocation and emergency response plans. Combined with artificial intelligence technologies such as natural language processing (NLP), it provides intuitive decision suggestions and action plans.

[0165] The user interaction and customization module provides a user interaction interface, allowing users to customize system behaviors and parameters according to specific needs, such as adjusting monitoring sensitivity, setting key focus areas, etc. Collect user feedback, simulate feedback results through the digital twin model, and optimize system performance.

[0166] The continuous learning and knowledge update module designs a continuous learning mechanism, which enables the system to continuously learn from new data and events, update the knowledge base and behavior patterns. Using digital twin technology, simulate the effects of different learning strategies and select the optimal strategy for knowledge update.

[0167] The system robustness and security assurance module uses digital twin technology to test the robustness of the system under different pressures and attacks to ensure the stability and security of the system. Security measures such as data encryption, access control, and intrusion detection are implemented to prevent data leakage and system attacks.

[0168] The Regulatory Compliance and Ethical Considerations module ensures that all operations in the digital twin model comply with laws, regulations and ethical standards, especially data protection and privacy regulations. Regularly review regulatory changes and adjust system design and operating procedures in a timely manner to ensure compliance.

[0169] The Technology Integration and Innovation module integrates the latest artificial intelligence technologies and industry best practices, such as the Internet of Things (IoT), big data analysis, and cloud computing, to improve the performance of the adaptive learning module. It also explores emerging technologies, such as federated learning and reinforcement learning, to further enhance the intelligence level of the system.

[0170] The digital twin integration and synchronization module ensures real-time synchronization between the digital twin model and the physical venue, collects data using IoT technology, and processes and analyzes it in the digital twin environment. Using digital twin technology, comprehensive monitoring and simulation of the physical venue can be achieved, providing a rich data source for adaptive learning.

[0171] The virtual testing and scenario simulation module conducts virtual testing and scenario simulation in a digital twin environment to evaluate the effectiveness of different security strategies and optimize emergency response plans. Using simulation results, it trains and tests adaptive learning algorithms to improve the system's predictive capabilities and response speed.

[0172] The predictive maintenance and resource scheduling module uses digital twin technology to perform predictive maintenance, predict equipment failures and perform maintenance in advance to reduce system downtime. The digital twin model is used to optimize resource scheduling strategies, improve resource utilization efficiency, and reduce operating costs.

Claims

1. An intelligent security system for sports venues based on digital twin technology, It is characterized in that include: -The data acquisition unit uses an array of high-definition cameras and uses time synchronization technology to ensure the consistency of data acquisition; -Digital twin mapping unit, which uses laser scanners for 3D modeling and combines GIS technology to achieve mapping with actual geographic locations; -Individual behavior analysis unit, using deep learning algorithms, including but not limited to convolutional neural networks (CNN), to extract behavioral features through convolutional layers and fully connected layers.

2. The system according to claim 1, It is characterized in that The real-time monitoring unit comprises: -The edge computing component uses a processor with a processing frequency of at least 2 GHz to achieve real-time analysis of data; -The behavior risk assessment component adopts a fuzzy logic assessment component to quantify the behavior risk level.

3. The system according to claim 1, It is characterized in that The early warning decision unit comprises: -The rule engine component includes predefined rules for generating warning information; -The risk response automation component uses a genetic algorithm to optimize the evacuation path to ensure the shortest evacuation time.

4. The system according to claim 1, It is characterized in that The information presentation and notification unit comprises: - The augmented reality (AR) visualization component uses AR markers to ensure the clarity of information presentation; -The customized notification component uses different color codes to push warning information according to the responsibility area of ​​the security personnel.

5. The system according to claim 1, It is characterized in that The adaptive optimization unit comprises: -The reinforcement learning algorithm component adopts a deep Q network (DQN); -The digital twin simulation environment components simulate different emergency situations for testing early warning algorithms and safety strategies.

6. The system according to claim 1, It is characterized in that The user feedback unit comprises: -The multi-channel user feedback collection component uses natural language processing technology to analyze user feedback; -The agile development iterative framework component adopts the Scrum framework and iteratively updates system functions every two weeks.

7. The system according to claim 1, It is characterized in that The security situation analysis unit comprises: -The data fusion technology component uses the principal component analysis (PCA) algorithm to reduce the dimension of CCTV, Wi-Fi tracking and access control system data; -The comprehensive risk assessment tool component uses the fault tree analysis (FTA) method to assess the safety risk level.

8. The system according to claim 1, It is characterized in that The legal and regulatory compliance analysis unit includes: -The regulatory database component stores laws and regulations related to the security system; -The automatic compliance checking tool component uses a rule verification algorithm to ensure compliance of operations.

9. The system according to claim 1, It is characterized in that The emergency evacuation simulation and optimization subsystem includes: -The three-dimensional environment modeling unit uses BIM technology to create digital twin models of the venue from different perspectives; -The crowd flow dynamic simulation unit simulates the movement of individuals and evaluates the evacuation efficiency.

10. The subsystem according to claim 9, It is characterized in that Further including: -The evacuation strategy optimization unit uses an ant colony algorithm to generate an evacuation strategy; -The real-time evacuation guidance unit adopts an evacuation guidance mode according to real-time data to provide an optimal evacuation route.

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