Intelligent service system and device for urban community micro-grid management

Abstract

The invention provides an intelligent service system and device for urban community micro-grid governance, and belongs to the technical field of community intelligent services, the system comprises a community data acquisition module, a community data management module, an intelligent algorithm module, a service function module, a micro-grid task module and an application execution module; deeply mining multi-source data of grid management basic data and community monitoring data; according to the community intelligent management system, community data overall management is realized by combining a database, a computing cluster device and a deep learning framework, multiple functions of resident community life circle, community position space management, community event intelligent processing and personnel service management are realized by utilizing an artificial intelligence model algorithm, and community intelligent management efficiency and service level are improved; the problems that an existing micro-grid intelligent governance framework is not clear, data collection and processing are difficult, task distribution responsibility is not clear, service quality is not high, and the intelligent degree of event processing is low are solved.

Description

Technical Field

[0001] The present invention belongs to the field of community intelligent service technology, and in particular, relates to an intelligent service system and device for urban community micro-grid management. Background Art

[0002] With the advancement of urbanization, community grid management has gradually become an important part of urban governance. In response to the management needs of megacities, the microgrid governance system is a refined governance tool that extends grid management to every household, promotes the minimization of management units and maximizes service efficiency. Its basic content is to divide microgrids into units of 30-100 households or businesses, and to complete household organization and mobilization within 3 hours as the standard. Microgrid management needs to integrate diverse community data, provide a platform for various social entities to participate in grassroots governance, meet the needs of multiple user groups, and provide fixed and normalized convenience service functions, and quickly resolve residents' demands. Intelligent decision-making center. The current smart community management tools mainly have problems such as difficulty in data collection and processing, unclear task allocation responsibilities and powers, low service quality, and low intelligence level of event processing. Therefore, it is urgent to propose a clear and clear microgrid intelligent governance framework to divide and manage information, personnel and assets in the community, so as to achieve effective control of urban management. Summary of the invention

[0003] In response to the above-mentioned deficiencies in the prior art, the present invention provides an intelligent service system and device for urban community micro-grid governance, which solves the problems of unclear existing micro-grid intelligent governance framework, difficulty in data collection and processing, unclear task allocation responsibilities and rights, low service quality and low level of intelligent event processing.

[0004] In order to achieve the above objectives, the technical solution adopted by the present invention is as follows: On the one hand, the present invention provides an intelligent service system for urban community micro-grid governance, including: Community data collection module: Based on intelligent collection equipment and recognition technology, it is used to collect various real-time and static data in the community and the usage status of community public facilities; Community data management module: connects different hardware devices through interface programs, is used to transmit various types of collected data into the system, stores, classifies and maintains the incoming data, and processes micro-grid task requests; Intelligent algorithm module: Based on the intelligent algorithm integrating spatial perception, behavior analysis and task decision-making, it is used to analyze and process the stored data and generate task decisions; Service function module: used to provide various services to community residents and managers and perform task scheduling based on the generated task decisions; Micro-grid task module: used to divide the community into several micro-grids, and according to task scheduling, allocate and manage tasks in each micro-grid as needed; Application execution module: used to convert the instructions and tasks of each module into actual execution operations.

[0005] The beneficial effects of the present invention are as follows: The present invention establishes a micro-grid governance system, and through intelligent terminals and mobile terminals, realizes the comprehensive management of basic data, network data, and health control data, provides refined community services for multiple subjects such as managers, residents, and merchants, solves the problems of grass-roots management in current community governance, and breaks through the cumbersome data collection, information islands, and technical integration barriers.

[0006] Furthermore, the community data collection module is applied to the data layer of the intelligent service system, including: The first collection sub-module: used to collect real-time environmental data by installing sensors; The second collection sub-module: used to obtain image and video data within the community by using cameras, and monitor the personnel flow and conduct security monitoring; The third collection sub-module: used to collect the basic information, needs, and feedback of residents; The fourth collection sub-module: based on social media data, used to collect community-related event data and community resident demand data.

[0007] The beneficial effects of the above further solution are as follows: The present invention establishes a unified data standard and interface specification in data integration, integrates the data resources of various departments and systems within the community, and breaks the information islands.

[0008] Still further, the community data management module is applied to the management layer of the intelligent service system, including: The first processing sub-module: used to clean, transform, and integrate different types of data sources, and convert the data into a unified format; The first fusion sub-module: used to analyze multi-source data by applying data fusion algorithms; The first database sub-module: used to establish a database to store user information, community environment monitoring data, micro-grid data, and deep learning model data; The first cluster sub-module: used to adopt distributed computing cluster devices to provide computing resources, train deep learning models, and process big data and algorithm computing tasks; The first server sub-module: used to perform parallel processing by using multiple servers or GPU devices; The first engine sub-module: used to manage and schedule computing resources by using container orchestration engine tools; The first backend framework submodule: Based on the lightweight Flask backend framework, it is used to process frontend requests and provide an API interface to receive, process and return data, interact with user management, service request processing and database, and coordinate the database submodule, distributed cluster device submodule and deep learning model; The first interface submodule: It is used to display the collected data by accessing the front-end display data and service interface, provide a user interaction interface, and link with the API interface in the Flask back-end framework submodule.

[0009] The beneficial effects of the above further scheme are as follows: the present invention connects different hardware devices through an interface program, thereby improving the judgment ability and response speed of the intelligent service system in emergencies; utilizing distributed computing cluster equipment to provide high-performance computing resources; utilizing multiple servers to improve the response speed of the intelligent service system, and utilizing the Flask backend framework to make the backend support more flexible; and improving the timeliness and accuracy of collected data by linking the front-end display and service interface with the API interface.

[0010] Furthermore, the intelligent algorithm module is applied to the technical layer of the intelligent service system, including: The first learning submodule is used to analyze the data collected by the community grid using data mining and machine learning, and to detect trends in community population flow, resource demand, and environmental changes; The first recognition submodule: by calling the second acquisition submodule in the community data acquisition module, it is used to perform behavior recognition on the image and video data collected in the second acquisition submodule; The first analysis submodule: by calling the fourth collection submodule in the community data collection module, it is used to analyze the collected community event data and community residents' demand data using the semantic analysis model; First Vision Submodule: By integrating machine vision algorithm models, it identifies abnormal behaviors in videos or images and provides feedback on security information. First language submodule: used to process residents’ voice and text needs, and provide corresponding service responses and task allocation; The first calling submodule: based on the event trigger mechanism of artificial intelligence, it calls the spatial perception, behavior analysis and task decision algorithms corresponding to the event to optimize the micro-grid task allocation and generate task decisions.

[0011] The beneficial effects of the above further solution are as follows: By integrating intelligent algorithms for spatial perception, behavior analysis, and task decision-making, the present invention solves the problems of insufficient intelligent analysis and decision-making capabilities in existing smart community systems, difficulty in quickly judging, and dynamically responding to various event scenarios; through data mining and machine learning technologies, potential laws and trends in data are discovered to provide intelligent decision-making support for task allocation and community governance; by establishing a powerful data analysis platform and an artificial intelligence model library, various types of data within the community are deeply analyzed; by invoking natural language processing for voice and text requirements, the accuracy of service responses and task allocation is improved, and the event trigger mechanism is used to ensure the real-time responsiveness of the intelligent service system.

[0012] Furthermore, the service function module is applied to the function layer of the intelligent service system and includes: The first function sub-module: used to construct a community micro-network system and build a first service system covering the entire community, providing a management platform for learning communities and community integration; The second function sub-module: used to provide community services to community residents and give early warnings about the community environment and safety; The third function sub-module: used to provide functions such as intelligent access control, personnel identification, identity verification, pedestrian flow statistics, and query and reservation for micro-grid management, and to be interconnected with the terminal application program to provide space usage services for community managers, grid administrators, residents, and property management; The fourth function sub-module: used to access data from sensors, cameras, robots, and communication devices; The fifth function sub-module: used to upload the processed community environment, equipment status, and resident demand information to the central management platform in real time; The sixth function sub-module: used to provide a self-learning function for the micro-grid governance system and train, manage, optimize, and update the model.

[0013] Furthermore, the second function sub-module includes: The first service unit: used to collect and update community data on the terminal platform and rationally manage the community data based on the unified scheduling advantages of the system platform; The second service unit: facing all community residents, used to manage and handle service contents such as community event response, service handling, life services, and community assistance; The first management unit: used to detect and give early warnings about the community environment and safety by combining a disaster prevention simulation system, provide emergency services, and intelligently manage community resources.

[0014] The beneficial effects of the above further solution are as follows: By constructing a community micro-network system and building a community service system covering the entire community, the present invention improves the management efficiency and convenience of learning communities and community integration; by utilizing the function of the residents' living circle, the intelligence and comprehensiveness of community management are improved; through the function of managing and enhancing the capabilities of artificial intelligence, the intelligent service system is adaptable to the changing community environment and the ability of continuous improvement of artificial intelligence is enhanced.

[0015] Furthermore, the micro-grid task module is applied to the task layer of the intelligent service system and includes: The first sub-module for division: used to divide the community into several micro-grids; The first connection sub-module: used to connect the functions of the micro-grid task module with the micro-grid tasks and supervise the execution progress and effects of the micro-grid tasks; The second analysis sub-module: used to record and analyze the task execution situations of each micro-grid, evaluate the governance effects of the micro-grids, and optimize the task allocation according to the evaluation results; The first optimization sub-module: used to collect the feedback from the residents within the micro-grid and adjust and optimize the governance strategies according to the residents' feedback.

[0016] The beneficial effects of the above further solution are as follows: By monitoring the execution progress and effects of each micro-grid task in real time, the present invention realizes the efficient governance of the community, ensuring that the community governance tasks are completed on time and efficiently; by comprehensively evaluating the data analysis results, providing multiple decision-making schemes and analyzing their advantages and disadvantages, the feedback ability of the intelligent service system is improved.

[0017] Furthermore, the application execution module is applied to the application layer of the intelligent service system, and its specific functions are as follows: The application execution module arranges service personnel or intelligent devices to complete various tasks according to the analysis results of the intelligent algorithm module, and conveys the instructions of the management and scheduling system to the execution terminals or staff within the micro-grid; Utilizing the linkage among monitoring, service robots, and sensor devices, the tasks are automatically executed, the feedback of the tasks is monitored, and the sudden situations during the task execution are adjusted and optimized.

[0018] To achieve the above object, according to the second aspect of the present invention, there is provided an intelligent service device for urban community micro-grid governance, characterized in that it includes a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the above-mentioned intelligent service system for urban community micro-grid governance is realized.

[0019] The beneficial effects of the above further solution are as follows: By applying the execution module to connect each module in the intelligent service system, the present invention solves the problem of insufficient coverage of intelligent services. A functional terminal for multi-agent use by integrated managers, community workers, residents, and merchants is set up, realizing the interconnection and refined services among various departments and systems within the community. By ensuring the linkage among monitoring, service robots, and sensor devices, the automation of task execution is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a system structure diagram of the present invention.

[0021] Figure 2 It is a topology diagram of the "micro-grid and real grid" intelligent management integrated platform in the community in the embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following describes the specific embodiments of the present invention to facilitate those skilled in the art of the present technology to understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art of the present technology, as long as various changes are within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions made using the concept of the present invention are within the scope of protection.

[0023] Before describing this embodiment, the following terms are first explained: Kubernetes: A container orchestration platform for automating the deployment, scaling, and management of containerized applications; Docker Swarm: A native container orchestration tool provided by Docker; Flask: A lightweight web application framework written in Python.

[0024] Embodiment 1 In this embodiment, the intelligent service system obtains real-time and static data within the community through the data collection module, and transmits it to the data management module for storage, cleaning, and classification; the data management module provides the cleaned data to the intelligent algorithm module for analysis and prediction, and the generated task decisions are assigned to the micro-grid task module; the service function module uses the cleaned data and analysis results to provide residents with life, health monitoring, and emergency services, and through the feedback mechanism, re-transmits the user needs and execution situations to the micro-grid task module; the application execution module completes the actual operations, realizing the efficient coordination and optimization of the entire process.

[0025] As Figure 1 shown, the present invention provides an intelligent service system for urban community micro-grid governance, including: Community Data Collection Module: Based on intelligent collection devices and recognition technologies, it is used to collect various real-time and static data within the community and the usage status of community public facilities; the data collection module is applied to the data layer of the intelligent service system, including: The first collection sub-module: By installing sensors, it is used to collect real-time environmental data; The second collection sub-module: It is used to obtain image and video data within the community by using cameras, and monitor the personnel flow and conduct security monitoring; The third collection sub-module: It is used to collect the basic information, needs, and feedback of residents; The fourth collection sub-module: Based on social media data, it is used to collect community-related event data and community resident demand data.

[0026] In this embodiment, the data collection module mainly collects various real-time and static data within the community based on intelligent collection devices and recognition technologies, such as population, vehicles, equipment status, environmental data (temperature, humidity, air quality, etc.), and the usage status of community public facilities, to achieve multi-source data collection in the community, mainly including: sensor data collection sub-module, image and video collection sub-module, micro-grid basic data collection sub-module, social media data collection sub-module; Sensor data collection sub-module: Collect real-time environmental data, such as air quality, noise level, parking volume, and visitor number, through sensors installed in the community; Image and video collection sub-module: Obtain image and video data within the community by using cameras, and call the relevant behavior recognition sub-module in the intelligent algorithm module to achieve personnel flow monitoring and security monitoring; Micro-grid basic data collection sub-module: Combine the micro-grid operator information collection form, household registration form, intelligent devices, and network platform to collect the basic information, needs, and feedback of residents; Social media data collection sub-module: Based on social media data, call the first analysis sub-module in the intelligent algorithm model to collect community-related event data and community resident demand data.

[0027] As Figure 1 shown, the community data management module: Connect different hardware devices through an interface program, used to transfer various collected data into the system, store, classify, and maintain the transferred data, and process micro-grid task requests; the community data management module is applied to the management layer of the intelligent service system, including: The first processing sub-module: Used to clean, transform, and integrate different types of data sources, and convert the data into a unified format; The first fusion sub-module: Used to apply data fusion algorithms to analyze multi-source data; The first database sub-module: used to establish a database to store user information, community environment monitoring data, micro-grid data, and deep learning model data; The first cluster sub-module: used to adopt distributed computing cluster devices to provide computing resources, train deep learning models, and process big data and algorithm computing tasks; The first server sub-module: used to perform parallel processing using multiple servers or GPU devices; The first engine sub-module: used to manage and schedule computing resources using container orchestration engine tools; The first backend framework sub-module: based on the lightweight Flask backend framework, used to process front-end requests, provide API interfaces, receive, process, and return data, interact with user management, service request processing, and the database, and coordinate with the database sub-module, distributed cluster device sub-module, and deep learning model; The first interface sub-module: by accessing the front-end display data and service interfaces, used to display the collected data, provide a user interaction interface, and link with the API interfaces in the Flask backend framework sub-module.

[0028] In this embodiment, the community data management module is mainly responsible for storing, classifying, and maintaining data to ensure the integrity, availability, and security of the data; connecting different hardware devices (such as sensors, cameras, intelligent terminals) through an interface program, and transmitting the collected data into the system to achieve integrated management of multi-modal data; mainly including: The multi-source adaptation processing sub-module, using multi-source adaptation processing, to collect, clean, transform, and integrate different types of data sources, ensure the consistency and standardization of the data, and convert the data into a unified format for storage and analysis; The data fusion sub-module, applying data fusion algorithms, comprehensively analyzing multi-source data, and improving the system's judgment ability and response speed in emergencies; The database sub-module, establishing a database to store user information, community environment monitoring data, micro-grid data, deep learning model data, etc.; The distributed cluster device sub-module, adopting distributed computing cluster devices, providing high-performance computing resources, supporting the training of deep learning models and big data processing, and processing complex algorithm computing tasks, such as real-time crowd evacuation simulation, emergency warning push, image recognition, etc.; The multi-server sub-module: using multiple servers or GPU devices for parallel processing to improve the system's response speed; The container orchestration engine sub-module, using Kubernetes and Docker Swarm tools to manage and schedule computing resources to ensure the efficient allocation of tasks; Flask backend framework submodule, using the lightweight Flask backend framework, Flask processes frontend requests, provides API interfaces, supports data reception, processing and return, implements user management, service request processing and database interaction, provides flexible backend support for the system, and collaborates with the database submodule, distributed cluster device submodule and deep learning model to process microgrid task requests and data analysis requirements; The front-end display and service interface submodule is connected to the front-end display data and service interface, which displays crowd density, environmental monitoring, equipment status, etc. It provides a user interactive interface, realizes dynamic data updates, and links with the API interface of the Flask backend to ensure the timeliness and accuracy of the data.

[0029] like Figure 1 As shown, the intelligent algorithm module: based on the intelligent algorithm of integrated space perception, behavior analysis and task decision, is used to analyze and process the stored data and generate task decisions; the intelligent algorithm module is applied to the technical layer of the intelligent service system, including: The first learning submodule is used to analyze the data collected by the community grid using data mining and machine learning, and to detect trends in community population flow, resource demand, and environmental changes; The first recognition submodule: by calling the second acquisition submodule in the community data acquisition module, it is used to perform behavior recognition on the image and video data collected in the second acquisition submodule; The first analysis submodule: by calling the fourth collection submodule in the community data collection module, it is used to analyze the collected community event data and community residents' demand data using the semantic analysis model; First Vision Submodule: By integrating machine vision algorithm models, it identifies abnormal behaviors in videos or images and provides feedback on security information. First language submodule: used to process residents’ voice and text needs, and provide corresponding service responses and task allocation; The first calling submodule: based on the event trigger mechanism of artificial intelligence, it calls the spatial perception, behavior analysis and task decision algorithms corresponding to the event to optimize the micro-grid task allocation and generate task decisions.

[0030] In this embodiment, the intelligent algorithm module integrates intelligent algorithms such as space perception, behavior analysis, and task decision-making to analyze and process the collected data, provide intelligent decision support, and implement AI algorithm support; The machine learning and data mining submodule uses machine learning and data mining techniques to analyze community grid-collected data and monitor community population flow, resource demand, and environmental change trends; Behavior recognition sub-module: It is called through the image and video acquisition sub-module in the community data acquisition module, and is used to recognize the behaviors of the image and video data collected by the video acquisition sub-module; Data analysis sub-module: It is called through the social media data acquisition sub-module in the community data acquisition module, and is used to analyze the collected community event data and community resident demand data by using the semantic analysis model; Machine vision sub-module: Incorporating machine vision algorithm models, it can call to identify abnormal behaviors (such as illegal entry, equipment damage, etc.) in videos or images and provide real-time security information feedback; Natural language processing sub-module: It is used to process the voice and text needs of residents and provide accurate service responses or task assignments; Mechanism mobilization sub-module: Using the event-triggered mechanism mobilization algorithm based on AI to optimize the micro-grid task assignment. For example, when it is detected that the community's pedestrian flow exceeds the standard, it calls the real-time pedestrian flow evacuation algorithm; when an abnormal object is detected, it starts the image recognition algorithm to ensure the real-time responsiveness of the system.

[0031] Such as Figure 1 As shown, the service function module: It is used to provide various services to community residents and managers according to the generated task decisions and perform task scheduling; the service function module is applied to the function layer of the intelligent service system, including: The first function sub-module: It is used to build a community micro-network system and construct a first service system covering the entire community, providing a management platform for learning communities and community integration; The second function sub-module: It is used to provide community services to community residents and give early warnings about the community environment and safety, including: The first service unit: It is used to collect and update community data on the terminal platform and rationally manage the community data based on the unified scheduling advantages of the system platform; The second service unit: It is for all community residents and is used to manage and handle the service contents of community event response, service handling, life services, and community assistance; The first management unit: By combining with the disaster prevention simulation system, it is used to detect and give early warnings about the community environment and safety, provide emergency services, and intelligently manage community resources; The third function sub-module: It is used to provide functions such as intelligent access control, personnel identification, identity verification, pedestrian flow statistics, and query and reservation for micro-grid management, and is interconnected with the terminal application program to provide space use services for community managers, grid administrators, residents, and property management; The fourth function sub-module: It is used to access the data of sensors, cameras, robots, and communication devices; The fifth functional sub-module: used to upload the processed community environment, equipment status, and resident demand information to the central management platform in real time; The sixth functional sub-module: used to provide a self-learning function for the micro-grid governance system, and train, manage, optimize, and update the model.

[0032] In this embodiment, the service function module provides various service functions for community residents and managers, including community management functions, resident living circle functions, community position space management functions, offline hardware support functions, information reporting management functions, AI capability management and improvement functions, etc., to realize the comprehensive community service function; The community management function sub-module consists of a community culture construction unit, a community organization and community personnel information management unit, a personnel learning unit, and a mass service unit, mainly used to construct a community micro-grid system, build a personnel service system covering the entire community, and provide a management platform for learning communities and community integration; The resident living circle function sub-module consists of a data collection and update service unit, a public service unit, a community resource management unit, etc.; The data collection and update service unit provides data collection and update services to enable micro-grid operators to collect and update community data on the terminal platform, and can complete the rational management of data based on the unified scheduling advantages of the system platform; The public service unit is oriented to all community residents and can manage and handle service contents such as community event response, service handling, life services, and community assistance; The community resource management unit combines with the disaster prevention simulation system to monitor and warn the community environment and safety, provide emergency services, and intelligently manage the community resources; The community position space management function sub-module provides many functions such as intelligent access control, personnel identification, identity verification, passenger flow statistics, query, and reservation for micro-grid management, and interconnects the terminal application program with this module to provide intelligent space usage services for community managers, grid operators, residents, and property management; The offline hardware support function sub-module provides offline hardware support functions, which is the infrastructure layer of the intelligent service system, supporting the data access of various sensors, cameras, robots, and communication devices, and providing a solid foundation for data collection, real-time monitoring, and task execution; The information reporting management function sub-module provides an information reporting management function, and uploads the processed community environment, equipment status, and resident demand information to the central management platform in real time to provide real-time data support for AI analysis and task scheduling; The artificial intelligence capability management and enhancement functional sub-module provides AI capability management and enhancement functions. It is the self-learning module of the micro-grid governance system, with model training, management, optimization, and update to ensure the system's adaptability and continuous improvement ability in a changing community environment.

[0033] As Figure 1 shown, the micro-grid task module is used to divide the community into several micro-grids, and according to task scheduling, allocate and manage tasks in each micro-grid as needed; the micro-grid task module is applied to the task layer of the intelligent service system.

[0034] In this embodiment, the micro-grid task module divides the entire community into several micro-grids, allocates and manages tasks as needed, and realizes efficient governance; The first division sub-module divides the entire community into several micro-grids; The first connection sub-module links all functions of the micro-grid task module with the micro-grid tasks, and monitors the execution progress and effects of each micro-grid task in real time to ensure the timely and efficient completion of community governance tasks; The second analysis sub-module records and analyzes the task execution situations of each micro-grid, evaluates the effects of micro-grid governance, and optimizes future task allocation; The first optimization sub-module collects the feedback from residents within the micro-grid and adjusts and optimizes the governance strategy according to the residents' feedback.

[0035] The application execution module is used to convert the instructions and tasks of each module into actual execution operations; the application execution module is applied to the application layer of the intelligent service system.

[0036] In this embodiment, the application execution module is responsible for converting the instructions and tasks of each module into actual execution operations, arranging service personnel or intelligent devices to complete various tasks according to the analysis results of the intelligent algorithm module; conveying the instructions of the management and scheduling system to the execution terminals or staff within the micro-grid; ensuring the linkage between devices such as monitoring, service robots, and sensors to achieve automated task execution; and having a real-time feedback and adjustment function to monitor the real-time feedback of tasks, adjust and optimize for unexpected situations during task execution, ensure the flexibility and response speed of the system, and realize the execution of micro-grid real-grid management tasks.

[0037] As Figure 2 shown, in this embodiment, by using the intelligent service system for urban community micro-grid governance, a smart management integrated service platform for community "micro-grid real-grid" is built. Among them, the data layer is equipped with a community data collection module to realize in-depth mining of various community data, including basic grid management data, community resident behavior data, social media data, community monitoring data, and community POI (point of interest) data; The management layer is equipped with a community data management module to achieve overall management of the data obtained through in-depth mining, including the Flask backend framework, web front-end components, database, deep learning framework, and distributed computer cluster devices, capable of realizing multi-source data adaptation processing and hardware interface and data parsing; The technology layer is equipped with an intelligent algorithm module to achieve AI technology support, analyze and process the stored data, and generate task decisions. The supported functions include: multi-modal embedding, neural network, feature extraction, sentiment analysis, behavior understanding, perception simulation, and edge-cloud collaboration; the function layer is equipped with a service function module to achieve the technological empowerment of the functions of the residents' living circle, the 5G access control function of the community site space, the offline hardware support function, the clue reporting management function, and the community service function; The task layer is equipped with a micro-grid task module to achieve the division of the community into several micro-grids, obtain specific tasks, and perform on-demand allocation and management of the tasks; the application layer is equipped with an application execution module to achieve the requirements of the departments. The requirements of the departments include: political and comprehensive governance, public services, public management, public safety, and cultural publicity; according to the requirements of the departments, the specific tasks of the task layer are as follows. Political and comprehensive governance includes: people's livelihood demands, equipment information, social conditions and public opinions, and assistance in execution; public services include: care and assistance, service agency, and public interest litigation; public management includes: environmental protection, education supervision, traffic management, conflict resolution, and labor security; public safety includes fire safety, food safety, drug safety, and traffic safety; cultural publicity includes: community publicity, information management, community learning, and cultural activities.

[0038] Embodiment 2 In this embodiment, an intelligent service device for urban community micro-grid governance is provided, which is characterized in that it includes a memory, a processor, and a computer program stored in the memory and running on the processor. The processor, when executing the computer program, realizes the intelligent service system for urban community micro-grid governance as described in Embodiment 1.

Claims

1. An intelligent service system for urban community microgrid management, characterized in that: include: Community data collection module: Based on intelligent collection equipment and recognition technology, it is used to collect various real-time and static data in the community and the usage status of community public facilities; Community data management module: connects different hardware devices through interface programs, is used to transmit various types of collected data into the system, stores, classifies and maintains the incoming data, and processes micro-grid task requests; Intelligent algorithm module: Based on intelligent algorithms that integrate spatial perception, behavior analysis, and task decision-making, it is used to analyze and process stored data and generate task decisions; Service function module: used to provide various services to community residents and managers and perform task scheduling based on the generated task decisions; Microgrid task module: used to divide the community into several microgrids, and to allocate and manage tasks in each microgrid on demand according to task scheduling; Application execution module: used to convert the instructions and tasks of each module into actual execution operations.

2. The intelligent service system for urban community microgrid management according to claim 1 is characterized in that: The community data collection module is applied to the data layer of the intelligent service system, including: The first acquisition submodule: is used to collect real-time environmental data by installing sensors; The second acquisition submodule is used to use cameras to obtain image and video data in the community, and monitor personnel flow and security; The third collection submodule: used to collect basic information, needs and feedback from residents; The fourth collection sub-module: based on social media data, is used to collect community-related event data and community residents' demand data.

3. The intelligent service system for urban community microgrid management according to claim 2 is characterized in that: The community data management module is applied to the management layer of the intelligent service system, including: The first processing submodule is used to clean, convert and integrate different types of data sources and convert the data into a unified format; The first fusion submodule: used to apply data fusion algorithm to analyze multi-source data; The first database submodule: used to establish a database to store user information, community environment monitoring data, microgrid data, and deep learning model data; The first cluster submodule: is used to use distributed computing cluster equipment to provide computing resources, train deep learning models, and process big data and algorithm computing tasks; The first server submodule is used for performing parallel processing using multiple servers or GPU devices; The first engine submodule is used to manage and schedule computing resources using the container orchestration engine tool; The first backend framework submodule: Based on the lightweight Flask backend framework, it is used to process frontend requests and provide an API interface to receive, process and return data, interact with user management, service request processing and database, and coordinate the database submodule, distributed cluster device submodule and deep learning model; The first interface submodule: It is used to display the collected data by accessing the front-end display data and service interface, provide a user interaction interface, and link with the API interface in the Flask back-end framework submodule.

4. The intelligent service system for urban community microgrid management according to claim 3 is characterized in that: The intelligent algorithm module is applied to the technical layer of the intelligent service system, including: The first learning submodule is used to analyze the data collected by the community grid using data mining and machine learning, and to detect trends in community population flow, resource demand, and environmental changes; The first recognition submodule: by calling the second acquisition submodule in the community data acquisition module, it is used to perform behavior recognition on the image and video data collected in the second acquisition submodule; The first analysis submodule: by calling the fourth collection submodule in the community data collection module, it is used to analyze the collected community event data and community residents' demand data using the semantic analysis model; First Vision Submodule: By integrating machine vision algorithm models, it identifies abnormal behaviors in videos or images and provides feedback on security information. First language submodule: used to process residents’ voice and text needs, and provide corresponding service responses and task allocation; The first calling submodule: based on the event trigger mechanism of artificial intelligence, it calls the spatial perception, behavior analysis and task decision algorithms corresponding to the event to optimize the micro-grid task allocation and generate task decisions.

5. The intelligent service system for urban community microgrid management according to claim 4 is characterized in that: The service function module is applied to the functional layer of the intelligent service system, including: The first functional submodule: used to build a community microgrid system and the first service system covering the entire community, providing a management platform for learning communities and community integration; The second functional submodule: used to provide community services to community residents and to issue early warnings on community environment and safety; The third functional submodule is used to provide intelligent access control, personnel identification, identity verification, crowd flow statistics, and query and reservation functions for micro-grid management, and to interconnect with terminal applications to provide space use services for community managers, grid members, residents, and properties; The fourth functional submodule: used to access sensor, camera, robot and communication equipment data; The fifth functional submodule: used to upload the processed community environment, equipment status and residents' demand information to the central management platform in real time; The sixth functional sub-module is used to provide self-learning functions for the micro-grid governance system and to train, manage, optimize and update the model.

6. The intelligent service system for urban community microgrid management according to claim 5 is characterized in that: The second functional submodule includes: The first service unit is used to collect and update community data on the terminal platform, and to rationally manage community data based on the unified scheduling advantage of the system platform; The second service unit: for all residents of the community, used to manage and handle community event response, service handling, life services and community assistance services; The first management unit: through the combination of disaster prevention simulation system, it is used to detect and warn the community environment and safety, provide emergency services, and intelligently manage community resources.

7. The intelligent service system for urban community microgrid management according to claim 5 is characterized in that: The microgrid task module is applied to the task layer of the intelligent service system, including: The first division submodule: used to divide the community into several microgrids; The first connection submodule is used to connect the function of the micro-grid task module with the micro-grid task and monitor the execution progress and effect of the micro-grid task; The second analysis submodule is used to record and analyze the task execution of each microgrid, evaluate the microgrid governance effect, and optimize the task allocation according to the evaluation results; The first optimization submodule is used to collect feedback from residents within the microgrid and adjust and optimize the governance strategy based on the feedback from residents.

8. The intelligent service system for urban community microgrid management according to claim 7 is characterized in that: The application execution module is applied to the application layer of the intelligent service system, and its specific functions are as follows: The application execution module arranges service personnel or intelligent devices to complete various tasks according to the analysis results of the intelligent algorithm module, and transmits the instructions of the management and scheduling system to the execution terminals or staff in the microgrid; By utilizing the linkage between monitoring, service robots and sensor equipment, tasks can be automatically executed, task feedback can be monitored, and unexpected situations during task execution can be adjusted and optimized.

9. An intelligent service device for urban community micro-grid management, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that when the processor executes the computer program, it implements the intelligent service system for urban community micro-grid governance as described in any one of claims 1-8.

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