Community management system based on VR scene
By introducing technologies such as VR scenarios, Internet of Things and artificial intelligence into the community management system, the problems of untimely data collection, lack of virtual scenarios and insufficient interaction in the traditional community management model are solved, and efficient and intelligent community management and security monitoring are achieved.
Patent Information
- Application Number
- CN202411858794.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-12
- Publication Date
- 2025-04-11
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The traditional community management model has problems such as untimely data collection, lagging information, and lack of basis for management decisions, lack of virtual scenes and three-dimensional modeling, insufficient interactivity, insufficient community security and monitoring, unintelligent property management, and weak ability to respond to emergencies.
It adopts a community management system based on VR scenarios, combined with cutting-edge technologies such as virtual reality technology, the Internet of Things, artificial intelligence, big data analysis, etc., and through data acquisition and storage modules, VR scenario construction modules, intelligent monitoring modules and virtual property management modules, efficient collection, storage and analysis of community data is achieved, and a three-dimensional virtual community scenario is generated to provide real-time interactive experience and intelligent monitoring.
It improves the intelligence level and efficiency of community management, improves the real-time and accuracy of data, enhances user experience and community security, and optimizes property services and emergency response capabilities.
Smart Images
Figure CN119806318A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to a community management system based on VR scenes, and belongs to the field of information technology and community management. Specifically, the present invention combines a number of cutting-edge technologies such as virtual reality technology, Internet of Things technology, big data analysis, and artificial intelligence algorithms to construct an intelligent, visual, and highly interactive community management system. The system constructs an accurate three-dimensional virtual community scene by collecting, storing, and processing various types of data in the community, and provides real-time interactive experience and management functions for community residents, property managers, and related staff. Its application scope is not limited to residential community management, but can also be extended to various community forms such as commercial areas, office buildings, and smart cities. Background Art
[0002] With the growing development of community construction and the increase in residents' service needs, the traditional community management model has shown many shortcomings. Traditional community management relies on manual operations and infrastructure, and lacks efficient information technology, resulting in large efficiency bottlenecks in property management, event response, environmental monitoring, etc. For example, in many communities, information collection relies on manual records and traditional equipment, resulting in untimely data collection, delayed and incomplete information, and lack of basis for management decisions; at the same time, traditional property management and incident emergency response are mostly based on manual judgment and routine operations, which are prone to response delays or errors, affecting the safety and quality of life of community residents.
[0003] In addition, with the development of science and technology, although some communities have begun to introduce intelligent means, such as IoT devices and video surveillance systems, existing technologies still have obvious limitations in many aspects; specific problems include:
[0004] Data collection and storage issues:
[0005] Existing community management systems usually rely on infrastructure and manual methods to collect information, which makes it difficult to ensure the real-time and accuracy of data. For example, the collection of resident identity information, equipment operating status and environmental parameters mostly relies on manual input or traditional equipment. The data quality is difficult to be effectively guaranteed, and there is a lack of the ability to intelligently clean and store data. This makes it difficult to integrate and analyze information, further affecting the accuracy of decision-making and service efficiency.
[0006] Lack of virtual scenes and difficulties in 3D modeling:
[0007] Traditional community management cannot provide intuitive and interactive community scenes. Managers and residents cannot intuitively perceive the layout, facilities and environmental status of the community in a virtual environment. In community management, there is a lack of virtual scenes based on three-dimensional modeling to display and manage various areas of the community. Even if existing technologies monitor the community environment through video surveillance, it is difficult to conduct a unified virtual display and it is difficult to help managers control the dynamic changes of the community from a global perspective.
[0008] Insufficient interactive user experience:
[0009] Under the traditional management model, the interaction between residents and community managers is usually limited to telephone or offline communication, and there is a lack of convenient, real-time and intelligent communication channels. When residents need to report repairs, consult or obtain services, they often have to wait a long time to get a response, and there is often uneven service quality. These problems have greatly affected the residents' experience, especially in a busy community management environment, where communication between managers and residents is often one-way or information asymmetric.
[0010] Inadequate community safety and surveillance:
[0011] Although many communities have installed video surveillance systems, existing surveillance systems still have the problem of being unable to detect abnormal behavior and emergencies in a timely manner. Existing systems usually simply record and store image or video data, and cannot perform real-time analysis of video streams. They lack intelligent abnormal behavior recognition and alarm mechanisms. In addition, in terms of environmental monitoring, the data fusion and processing capabilities of sensors are insufficient, and they are unable to effectively monitor environmental changes within the community, such as air quality, water and electricity failures, etc.
[0012] The property management service is not intelligent:
[0013] Traditional property management usually relies on manual scheduling and service response, which is inefficient and prone to errors. In most cases, property companies need to manually handle residents' repair requests, service complaints, etc., and there is a lack of intelligent means in task allocation, execution monitoring and other links, and the service quality is uneven. In addition, there is a lack of intelligent payment and service feedback mechanisms, and it is impossible to collect residents' evaluation of property services in real time, which affects the continuous improvement of service quality.
[0014] Weak ability to respond to emergencies:
[0015] The existing community management system lacks an effective emergency simulation and response mechanism, which leads to managers often lacking effective plans and training when faced with emergencies. Traditional emergency response training often relies on on-site drills and lacks the ability to simulate different emergencies in a virtual environment, resulting in weak emergency handling capabilities.
[0016] In response to the above problems, the traditional community management model urgently needs to achieve intelligent, virtualized and data-based transformation through technological innovation. The community management system based on VR scenarios proposed in the present invention effectively solves the pain points in the above traditional management models by introducing cutting-edge technologies such as virtual reality technology, the Internet of Things, artificial intelligence, and big data analysis. Specifically, various data in the community are efficiently collected and processed through the data acquisition and storage module, and a three-dimensional virtual community environment is generated through the VR scene construction module. The intelligent monitoring and virtual property management modules are combined to realize intelligent monitoring of the environment in the community and optimization of property services, thereby improving the efficiency, intelligence level and service quality of community management. Summary of the invention
[0017] The present invention relates to a community management system based on VR scenarios, which mainly combines VR virtual reality, Internet of Things technology, big data analysis, artificial intelligence and other technologies, aiming to improve the intelligence level and convenience of community management. The system improves community service efficiency, management accuracy and residents' experience by comprehensively integrating various functions of community management.
[0018] The present invention provides a community management system based on VR scenes, which includes the following main modules:
[0019] Data acquisition and storage module
[0020] The main function of the data collection and storage module is to collect various basic data in the community through various data collection methods, and store and clean the collected data to provide data support for subsequent analysis and decision-making; the implementation process includes:
[0021] Data collection unit: This unit collects data including resident identity, access control status, community environment (temperature, humidity, gas concentration) and equipment status (elevators, public facilities, etc.) through Internet of Things (IoT) devices. Specific sensors include temperature and humidity sensors, access control sensors, video surveillance cameras, smoke detectors, etc.
[0022] Data cleaning unit: After data collection, a data cleaning algorithm (Z-Score standardization) is used to remove noise data and abnormal data. The cleaned data will be stored in a standardized format to ensure data accuracy and consistency. It is stored in a distributed database and partitioned storage is used to improve retrieval efficiency.
[0023] Data storage unit: Data is stored in a highly available cloud platform database, and distributed storage and backup are used to ensure data security and availability. The database of this module supports high-concurrency queries and can provide real-time data access services.
[0024] VR scene building blocks
[0025] The VR scene construction module is responsible for generating a highly restored three-dimensional virtual community scene based on the collected community data to enhance the user's immersion and interactive experience. The specific implementation steps include:
[0026] 3D modeling unit: It uses multi-view reconstruction algorithms and deep learning-based automated modeling methods to perform 3D modeling from 2D images (building plans, street views) collected from the community environment, and uses algorithms such as multi-view stereo vision (MVS) and structured light sensors to generate a 3D model of the community area.
[0027] Texture mapping unit: In order to increase the realism of the virtual environment, the actual image data (outdoor, building facades, public space photos) are texture mapped, the realism of the scene is improved through ambient lighting simulation, and the BRDF (bidirectional reflectance distribution function) algorithm is used to simulate the reflection effects of different materials under light.
[0028] Scene optimization unit: Dynamically adjust the level of detail of the model through LOD (Level of Detail) technology to save computing resources and increase rendering speed. Low-resolution models are used for distant objects, while high-resolution models are used for close objects. This optimization method minimizes resource consumption while ensuring the quality of the virtual scene.
[0029] User interaction module
[0030] The user interaction module enables users to interact with the virtual community scene through a variety of interaction methods. The modules include:
[0031] Virtual navigation unit: Through VR devices (VR helmets, handheld controllers), users can freely navigate in virtual community scenes. The system supports both autonomous navigation and automatic navigation. Users can use head tracking and gesture control to browse scenes, and the system automatically generates navigation paths based on user behavior.
[0032] Interactive tagging unit: Users can select specific objects (buildings, equipment, areas) in the scene through gestures or clicks, and obtain corresponding detailed information, including facility usage status, maintenance records, occupancy status, etc.
[0033] Speech recognition and command unit: This unit integrates advanced speech recognition systems (Google Speech API, Microsoft Azure Speech, etc.), which can receive and analyze user voice commands in real time. The system first collects the user's voice signal through a microphone, uses natural language processing technology (NLP) to convert speech to text, and understands the user's intention. After speech to text, the system matches and analyzes the user's command according to the preset voice command library and performs corresponding operations; for example, when the user says "view community building information", the system will automatically display detailed information of the corresponding building; during the speech recognition process, the system will also use semantic analysis technology (GPT model) to enhance the understanding accuracy and response speed of voice commands.
[0034] Gesture operation unit: Through computer vision technology, the system can recognize gestures and convert them into operation instructions. Common gestures include "grabbing" to select objects and "sliding" to browse scenes. The recognized gestures will be interpreted as specific operation instructions; for example, the user selects a virtual object or information through the gesture of "clicking", and the system will perform the corresponding action, such as opening the information box of the object or displaying data. Gesture instructions can also be used for scene navigation control. For example, "swiping up" can scroll up to view the scene, and "swiping left" means moving the view to the left.
[0035] Scene customization unit: Users can adjust various elements in the scene through gestures or voice commands, such as object selection, perspective rotation, function triggering and other interactive operations.
[0036] Intelligent monitoring module
[0037] This module monitors various activities in the community in real time, including environmental data and personnel activities, and responds promptly. The core functions are as follows:
[0038] Video surveillance unit: collects image data through video surveillance and uses deep learning target detection technology (YOLO) to identify and alarm abnormal behaviors in the community in real time, such as illegal intrusion, fire, accidental falls, etc.
[0039] Environmental monitoring unit: Use sensor data to monitor environmental changes, adopt data fusion technology (Kalman filtering) to improve data accuracy, conduct real-time analysis of gas concentrations (such as carbon monoxide, ammonia) and temperature and humidity changes, and ensure timely response to sudden environmental events.
[0040] Automatic alarm and response unit: When the system detects an abnormal situation, it automatically displays the alarm information through the VR scene and sends notifications to relevant personnel (property personnel, security personnel). The alarm system supports real-time audio and video transmission and storage.
[0041] Virtual Property Management Module
[0042] The virtual property management module provides a virtual management platform for property managers, allowing them to manage community affairs and handle residents' requests through virtual scenes. The module functions include:
[0043] Service Request Unit: Residents can submit various service requests (such as equipment maintenance, facility usage issues, complaints, etc.) through a virtual interface;
[0044] Task allocation unit: The request is allocated to the appropriate property personnel for processing through the task allocation algorithm. The allocation strategy adopts the following optimization model:
[0045]
[0046] Among them, C i is the cost of the ith service task, T i For the completion time of the service task, the goal is to minimize the product of total cost and time;
[0047] Online payment unit: Residents can complete online payment of property fees through a virtual interface. The system integrates a payment gateway to support multiple payment methods such as bank card payment, Alipay, WeChat, etc., and ensures data encryption and security during the payment process;
[0048] Service Feedback Unit: Property staff can view the processing status and progress of tasks through virtual scenes to ensure that tasks are completed on time and provide real-time feedback to residents.
[0049] Data analysis and prediction module
[0050] The data analysis and prediction module is based on big data analysis technology to perform trend prediction and risk assessment of community operations. It uses a time series prediction algorithm based on LSTM (Long Short-Term Memory Network) to predict the community's energy consumption, garbage classification efficiency, etc., and generate improvement plans. The prediction process of the LSTM network includes the following formula:
[0051] Let the input sequence be X = {x1, x2, ..., x t}, the predicted value is y t+1 , the prediction formula is:
[0052] y t+1 =σ(W y ·h t +b y )
[0053] Among them, W y is the weight matrix, g t is the hidden layer state, b y is the bias term, σ is the activation function;
[0054] Hidden layer state h t The update formula is:
[0055] h t =o t ·tanh(c t )
[0056] Among them, c t is the memory unit state, o t is the output gate, and the calculation formula is:
[0057] o t =σ(W0·[h t-1 ,x t ]+b0)
[0058] Among them, σ is the activation function, W0 is the weight matrix, [h t-1 ,x t ] represents the concatenated vector of the hidden layer state and the current input at the same time step, and b0 is the bias term.
[0059] Community Event Simulation Module
[0060] This module allows managers to simulate various emergencies that may occur in the community and conduct emergency response drills; implementation methods include:
[0061] The event state transition model based on Markov chain is used to simulate the occurrence of various emergencies, such as fire and equipment failure. The system can simulate the development process of events under different scenarios by adjusting the state transition probability. The event state transition probability of Markov chain is expressed by the following formula:
[0062] P(S t+1 =j|S t =i)=p ij
[0063] Among them, S t is the state at time t, p ij represents the probability of transitioning from state i to state j, satisfying:
[0064]
[0065] The expected number of occurrences of the simulated event E(X) can be expressed as:
[0066]
[0067] System security module
[0068] This module ensures the security of the community management system and the privacy protection of data. The module includes the following functions:
[0069] All user data and communication data are encrypted. AES is used to encrypt storage data, and RSA is used to encrypt communication data to ensure data confidentiality and security.
[0070] Multi-factor authentication (fingerprint recognition, verification code, password, etc.) ensures that only authorized users can access sensitive information and system management functions.
[0071] Beneficial Effects
[0072] A community management system based on VR scenes has the following beneficial effects:
[0073] Comprehensive community management and information integration
[0074] The system of the present invention can realize the efficient collection, classified storage and visual management of community data. Through the virtual scene construction module, the system integrates the static data (buildings, greenery) and dynamic data (vehicles, pedestrian flow) of community management into a unified virtual scene. Users can intuitively understand the community situation, thereby improving management efficiency.
[0075] Efficient user interaction
[0076] The system's user interaction module provides a variety of interaction methods through voice recognition and command units, gesture operation units and scene customization units. Voice recognition technology combined with natural language processing (NLP) allows users to quickly access community information or perform control operations through voice commands. The gesture operation unit provides users with a more natural control method, enhancing immersion and operational convenience. The scene customization unit supports users to personalize the layout of virtual scenes and interactive behaviors, greatly improving the flexibility of user experience.
[0077] Real-time dynamic management and monitoring
[0078] With the help of real-time data processing modules and scene rendering modules, the system can feed back dynamic data (environmental anomalies, equipment failures, personnel flows, etc.) to the virtual scene in real time, thus realizing dynamic monitoring of the community status.
[0079] Efficient equipment and resource management
[0080] The data acquisition and storage module integrates sensor data to achieve accurate monitoring and intelligent management of community equipment. Combined with environmental analysis results, the module can provide fault warnings, equipment operation optimization and regular maintenance recommendations, thereby reducing equipment wear and extending equipment life. In addition, the system's intelligent control function supports remote startup or shutdown of specific equipment, saving manpower and resources for community management.
[0081] Immersive community experience and convenience
[0082] The system uses virtual reality (VR) technology to present complex community management information in the form of intuitive 3D scenes. Users can immersively view the community layout, equipment distribution and dynamic conditions. Combined with the voice and gesture control functions of the user interaction module, users can easily operate the system without mastering complex operating procedures. This design not only improves the user experience, but also brings great operational convenience to managers.
[0083] Modular design enhances system scalability and adaptability
[0084] The functional modules of the present invention are designed to be closely integrated and can run independently and can be flexibly expanded. For example, the user interaction module can support newer interaction technologies (such as AR devices). The system as a whole has strong scalability and adaptability, and can quickly respond to the actual needs of different communities and achieve personalized customization.
[0085] Reduce costs and improve management efficiency
[0086] Compared with traditional community management methods, the present invention replaces a large number of manual operations through virtual scene construction and intelligent algorithms, reduces human resource consumption and management costs, and especially through the collaboration of the equipment status monitoring module and the real-time data processing module, can realize automatic resource allocation and problem handling, greatly improving community management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS
[0087] Figure 1 It is the flow chart of data acquisition and storage module;
[0088] Figure 2 It is a flow chart of VR scene building modules;
[0089] Figure 3 It is a flow chart of the user interaction module;
[0090] Figure 4 It is the flow chart of intelligent monitoring module;
[0091] Figure 5 It is a flow chart of the virtual property management module;
[0092] Figure 6 It is the flow chart of data analysis and prediction module;
[0093] Figure 7 It is a flow chart of the community event simulation module;
[0094] Figure 8 It is the flow chart of the system security module;
[0095] Fig. 9 It is a system module summary flow chart. DETAILED DESCRIPTION
[0096] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention:
[0097] Data acquisition and storage module
[0098] The main function of the data collection and storage module is to collect various basic data in the community through various data collection methods, and store and clean the collected data to provide data support for subsequent analysis and decision-making. The implementation process includes:
[0099] Data collection unit: This unit collects data including resident identity, access control status, community environment (temperature, humidity, gas concentration) and equipment status (elevators, public facilities, etc.) through Internet of Things (IoT) devices. Specific sensors include temperature and humidity sensors, access control sensors, video surveillance cameras, smoke detectors, etc.
[0100] Data cleaning unit: After data collection, a data cleaning algorithm (Z-Score standardization) is used to remove noise data and abnormal data. The cleaned data will be stored in a standardized format to ensure data accuracy and consistency. It is stored in a distributed database and partitioned storage is used to improve retrieval efficiency.
[0101] Data storage unit: Data is stored in a highly available cloud platform database. Distributed storage and backup are used to ensure data security and availability. The database of this module supports high-concurrency queries and can provide real-time data access services.
[0102] VR scene building blocks
[0103] The VR scene construction module is responsible for generating a highly restored three-dimensional virtual community scene based on the collected community data to enhance the user's immersion and interactive experience; the specific implementation steps include:
[0104] 3D modeling unit: It uses multi-view reconstruction algorithms and deep learning-based automated modeling methods to perform 3D modeling from 2D images (building plans, street views) collected from the community environment, and uses algorithms such as multi-view stereo vision (MVS) and structured light sensors to generate a 3D model of the community area.
[0105] Texture mapping unit: In order to increase the realism of the virtual environment, the actual image data (outdoor, building facades, public space photos) are texture mapped, the realism of the scene is improved through ambient lighting simulation, and the BRDF (bidirectional reflectance distribution function) algorithm is used to simulate the reflection effects of different materials under light.
[0106] Scene optimization unit: Dynamically adjust the level of detail of the model through LOD (Level of Detail) technology to save computing resources and increase rendering speed. Low-resolution models are used for distant objects, while high-resolution models are used for close objects. This optimization method minimizes resource consumption while ensuring the quality of the virtual scene.
[0107] User interaction module
[0108] The user interaction module enables users to interact with the virtual community scene through a variety of interaction methods; the module includes:
[0109] Virtual navigation unit: Through VR devices (VR helmets, handheld controllers), users can freely navigate in virtual community scenes. The system supports both autonomous navigation and automatic navigation. Users can use head tracking and gesture control to browse scenes, and the system automatically generates navigation paths based on user behavior.
[0110] Interactive tagging unit: Users can select specific objects (buildings, equipment, areas) in the scene through gestures or clicks, and obtain corresponding detailed information, including facility usage status, maintenance records, occupancy status, etc.
[0111] Speech recognition and command unit: This unit integrates advanced speech recognition systems (Google Speech API, Microsoft Azure Speech, etc.), which can receive and analyze user voice commands in real time. The system first collects the user's voice signal through a microphone, uses natural language processing technology (NLP) to convert speech to text, and understands the user's intention. After speech to text, the system matches and analyzes the user's command according to the preset voice command library and performs corresponding operations; for example, if the user says "view community building information", the system will automatically display detailed information of the corresponding building. During the speech recognition process, the system will also use semantic analysis technology (GPT model) to enhance the understanding accuracy and response speed of voice commands.
[0112] Gesture operation unit: Through computer vision technology, the system can recognize gestures and convert them into operation instructions. Common gestures include "grabbing" to select objects and "sliding" to browse scenes. The recognized gestures will be interpreted as specific operation instructions. For example, the user selects a virtual object or information through the gesture of "clicking", and the system will perform the corresponding action, such as opening the information box of the object or displaying data. Gesture instructions can also be used for scene navigation control. For example, "swipe up" can scroll up to view the scene, and "swipe left" means moving the view to the left.
[0113] Scene customization unit: Users can adjust various elements in the scene through gestures or voice commands, such as object selection, perspective rotation, function triggering and other interactive operations.
[0114] Intelligent monitoring module
[0115] This module monitors various activities in the community in real time, including environmental data and personnel activities, and responds promptly; the core functions are as follows:
[0116] Video surveillance unit: collects image data through video surveillance and uses deep learning target detection technology (YOLO) to identify and alarm abnormal behaviors in the community in real time, such as illegal intrusion, fire, accidental falls, etc.
[0117] Environmental monitoring unit: Use sensor data to monitor environmental changes, adopt data fusion technology (Kalman filtering) to improve data accuracy, conduct real-time analysis of gas concentrations (such as carbon monoxide, ammonia) and temperature and humidity changes, and ensure timely response to sudden environmental events.
[0118] Automatic alarm and response unit: When the system detects an abnormal situation, it automatically displays the alarm information through the VR scene and sends notifications to relevant personnel (property personnel, security personnel). The alarm system supports real-time audio and video transmission and storage.
[0119] Virtual Property Management Module
[0120] The virtual property management module provides a virtual management platform for property managers, allowing them to manage community affairs and handle residents' requests through virtual scenes. The module functions include:
[0121] Service Request Unit: Residents can submit various service requests (such as equipment maintenance, facility usage issues, complaints, etc.) through a virtual interface;
[0122] Task allocation unit: The request is allocated to the appropriate property personnel for processing through the task allocation algorithm. The allocation strategy adopts the following optimization model:
[0123]
[0124] Among them, C i is the cost of the ith service task, T i For the completion time of the service task, the goal is to minimize the product of total cost and time;
[0125] Online payment unit: Residents can complete online payment of property fees through a virtual interface. The system integrates a payment gateway to support multiple payment methods such as bank card payment, Alipay, WeChat, etc., and ensures data encryption and security during the payment process;
[0126] Service Feedback Unit: Property staff can view the processing status and progress of tasks through virtual scenes to ensure that tasks are completed on time and provide real-time feedback to residents.
[0127] Data analysis and prediction module
[0128] The data analysis and prediction module is based on big data analysis technology to perform trend prediction and risk assessment of community operations. It uses a time series prediction algorithm based on LSTM (Long Short-Term Memory Network) to predict the community's energy consumption, garbage classification efficiency, etc., and generate improvement plans. The prediction process of the LSTM network includes the following formula:
[0129] Let the input sequence be X = {x1, x2, ..., x t}, the predicted value is y t+1 , the prediction formula is:
[0130] y t+1 =σ(W y ·h t +b y )
[0131] Among them, W y is the weight matrix, h t is the hidden layer state, b y is the bias term, σ is the activation function;
[0132] Hidden layer state h t The update formula is:
[0133] h t =o t tanh(c t )
[0134] Among them, c t is the memory unit state, o t is the output gate, and the calculation formula is:
[0135] o t =σ(W0·[h t-1 ,x t ]+b0)
[0136] Among them, σ is the activation function, W0 is the weight matrix, [h t-1 ,x t ] represents the concatenated vector of the hidden layer state and the current input at the same time step, and b0 is the bias term.
[0137] Community Event Simulation Module
[0138] This module allows managers to simulate various emergencies that may occur in the community and conduct emergency response drills. The implementation methods include:
[0139] The event state transition model based on Markov chain is used to simulate the occurrence of various emergencies, such as fire and equipment failure. The system can simulate the development process of events under different scenarios by adjusting the state transition probability. The event state transition probability of Markov chain is expressed by the following formula:
[0140] P(S t+1 =j|S t =i)=p ij
[0141] Among them, S t is the state at time t, p ij represents the probability of transitioning from state i to state j, satisfying:
[0142]
[0143] The expected number of occurrences of the simulated event E(X) can be expressed as:
[0144]
[0145] System security module
[0146] This module ensures the security of the community management system and the privacy protection of data. The module includes the following functions:
[0147] All user data and communication data are encrypted. AES is used to encrypt storage data, and RSA is used to encrypt communication data to ensure data confidentiality and security.
[0148] Multi-factor authentication (fingerprint recognition, verification code, password, etc.) ensures that only authorized users can access sensitive information and system management functions.
Claims
1. A community management system based on VR scene, characterized in that: Includes the following modules: Data collection and storage module, used to collect and store basic data of residents and equipment in the community; VR scene construction module, used to generate a three-dimensional virtual scene of the community based on the collected data; User interaction module, used to realize the user's interactive function in the VR scene; Intelligent monitoring module, used to monitor community environment and residents' activities in real time, identify abnormal situations and issue alarms; Virtual property management module, used to provide virtual operation of property services, including online payment, problem reporting and service records; Data analysis and prediction module, used to analyze community data and generate optimization suggestions; Community event simulation module, used to simulate emergencies in virtual scenarios for plan verification and training; The system security module is used to protect user data privacy and ensure the security of system operation.
2. The system according to claim 1, characterized in that The data acquisition and storage module includes: Data collection unit, used to collect resident identity information, community equipment operating status and environmental parameters through IoT devices; The data cleaning and storage unit is used to remove noise and normalize the collected data using a data cleaning algorithm, and store it in a distributed database.
3. The system according to claim 1, characterized in that The VR scene construction module includes: A three-dimensional modeling unit, used to generate a three-dimensional model of the community using a three-dimensional reconstruction algorithm (such as Poisson reconstruction); A texture mapping unit, used to map the captured image to a three-dimensional model to improve the realism of the scene; The scene optimization unit is used to dynamically adjust model details based on the LOD (Level of Detail) algorithm to optimize rendering performance.
4. The system according to claim 1, characterized in that The user interaction module comprises: The virtual navigation unit is used to support users to freely navigate in the community virtual scene through VR devices. It can choose automatic path navigation or manual control mode to provide real-time location guidance; Interactive marking unit, which allows users to mark areas or facilities of interest in the virtual scene and add notes, and the marked content is automatically synchronized to the system database; The voice recognition and command unit, based on natural language processing (NLP) technology, supports users to issue voice commands to implement function calls within the scene, including navigation, information query and property repair; The gesture operation unit recognizes the user's gestures through the motion capture device to achieve interactive operations such as object selection, view rotation and function triggering; The scene customization unit allows users to customize scene display settings, such as adjusting ambient lighting, turning on or off specific virtual devices, and switching between different community scene themes.
5. The system according to claim 1, characterized in that The intelligent monitoring module comprises: Video surveillance unit, used for object detection and behavior recognition based on the YOLO algorithm for images captured by community cameras; Environmental monitoring unit, which combines data from multiple sensors to identify abnormal gas concentrations, temperature and humidity changes, or water and electricity failures; Automatic alarm and response unit: When the system detects an abnormal situation, it automatically displays the alarm information through the VR scene and sends notifications to relevant personnel.
6. The system according to claim 1, characterized in that The virtual property management module includes: The service request unit is used to receive property service requests from users in the VR scene, including repairs, complaints, suggestions and other needs; The task allocation unit, combined with the community's resource scheduling strategy, automatically allocates tasks to the best executors based on task priority, resource availability, and service execution efficiency. The allocation strategy adopts the following optimization model: Among them, C i is the cost of the ith service task, T i For the completion time of the service task, the goal is to minimize the product of total cost and time; Online payment unit, used to support users to complete online payment of property fees, including management fees, water and electricity fees and other service fees, to ensure the security and convenience of payment channels; The service feedback unit supports users to conduct real-time evaluation of completed property services in VR scenes, and generates service scores and user suggestions to provide reference for service optimization.
7. The system according to claim 1, characterized in that The data analysis and prediction module includes: using a time series prediction algorithm based on LSTM (Long Short-Term Memory Network) to predict the community's energy consumption, garbage classification efficiency, etc., and generate improvement plans, wherein the prediction process of the LSTM network includes the following formula: Let the input sequence be X = {x1, x2, ..., x t }, the predicted value is y t+1 , the prediction formula is: y t+1 =σ(W y ·h t +b y ) Among them, W y is the weight matrix, h t is the hidden layer state, b y is the bias term, σ is the activation function; Hidden layer state h t The update formula is: h t =o t ·tanh(c t ) Among them, c t is the memory unit state, o t is the output gate, and the calculation formula is: about t =σ(W0 [h t-1 ,x t ]+b0) Among them, σ is the activation function, W0 is the weight matrix, [h t-1 ,x t ] represents the concatenated vector of the hidden layer state and the current input at the same time step, and b0 is the bias term.
8. The system according to claim 1, characterized in that The community event simulation module includes using an event state transition model based on a Markov chain to dynamically simulate community emergencies, wherein the event state transition probability of the Markov chain is expressed by the following formula: P(S t+1 =j│S t =i)=p ij Among them, S t is the state at time t, p ij represents the probability of transitioning from state i to state j, satisfying: The expected number of occurrences of the simulated event E(X) can be expressed as:
9. The system according to claim 1, characterized in that The system security module includes a hybrid encryption algorithm (combination of AES and RSA) to perform hierarchical encryption on user data to prevent unauthorized access.