Intelligent comprehensive management method and system for safe community

Abstract

The invention discloses an intelligent comprehensive management method and system for a safe community, and the method comprises the steps: carrying out the data aggregation and storage through a distributed big data platform for massive community data resources accessed to a unified management platform, extracting key community management indexes and business elements through the operation of data cleaning, correlation analysis and the like, and carrying out the data collection and storage. A command and dispatch-oriented multi-dimensional data model is constructed, and flexible data query and analysis are supported; on the basis of a multi-dimensional data model, a rule-based expert system technology is adopted, experience knowledge and decision logic of managers are subjected to formalized expression, a community management knowledge base and a rule base are constructed, automatic execution of business logic is achieved through a rule engine, and a command and dispatch scheme is dynamically generated according to the real-time state of a community. The problems of information isolated island, low data utilization rate, low commanding and dispatching efficiency and the like in community management are effectively solved, and intelligence and refinement of community management are realized.

Description

Technical Field

[0001] The present invention belongs to the field of information technology, and in particular relates to a safe community intelligent comprehensive management method and system. Background Art

[0002] In the process of command and dispatch of the Ping An Community Intelligent Integrated Management System, we are faced with the technical challenge of how to achieve efficient collaboration among various departments in the community. Since the community involves multiple different business departments, the work processes, data formats and communication protocols of each department are different, which makes it difficult to achieve real-time information sharing and business collaboration among departments during command and dispatch. At the same time, there are a large number of heterogeneous systems and devices in the community. How to achieve the interconnection and interoperability of these systems and devices to support efficient command and dispatch work is also a major challenge.

[0003] In addition, in the actual command and dispatch process, it is also necessary to dynamically adjust command decisions and resource allocation according to the real-time status and emergencies of the community, which puts higher requirements on the real-time and intelligent nature of the command and dispatch system. How to quickly identify key information in massive community data and automatically generate the optimal command and dispatch plan based on complex business rules and strategies is also a technical problem that needs to be solved urgently. In summary, to achieve efficient and intelligent community command and dispatch, it is necessary to break through the technical bottlenecks of multi-department collaboration, heterogeneous system intercommunication, and real-time intelligent decision-making. This requires the comprehensive use of new-generation information technologies such as big data, artificial intelligence, and the Internet of Things to build a highly integrated community command and dispatch technology system. Summary of the invention

[0004] In view of the shortcomings of the existing technology, the present invention proposes a safe community intelligent comprehensive management method and system, which effectively solves the problems of information islands, low data utilization, low command and dispatch efficiency, etc. in community management, and realizes the intelligent and refined community management.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A safe community intelligent integrated management method comprises the following steps:

[0007] Use a distributed big data platform to aggregate and store massive community data resources, and extract key community management indicators and business elements through data cleaning and correlation analysis operations;

[0008] Build a multi-dimensional data model for command and dispatch based on key community management indicators and business elements;

[0009] On the basis of multidimensional data model, rule-based expert system technology is used to formalize the management staff's experience knowledge and decision logic, and to build a community management knowledge base and rule base;

[0010] Based on the community management knowledge base and rule base, the rule engine is used to realize the automatic execution of business logic and dynamically generate command and dispatch plans according to the real-time status of the community.

[0011] Preferably, the method of extracting key community management indicators and business elements includes:

[0012] For the cleaned community data, the association rule mining algorithm is used to analyze the correlation between different business elements and identify key community management indicators and business elements.

[0013] Preferably, the method for constructing a community management knowledge base and rule base includes:

[0014] According to the business needs of community management, a multidimensional data model is used to model the community management related data to obtain a multidimensional data model of community management;

[0015] Based on the multidimensional data model of community management and community management business, obtain the experience knowledge and decision-making logic of managers;

[0016] Through knowledge engineering methods, managers' experiential knowledge and decision-making logic are formalized to build a community management knowledge base and rule base.

[0017] Preferably, based on the community management knowledge base and rule base, the method of realizing the automatic execution of business logic through the rule engine and dynamically generating the command and dispatch plan according to the real-time status of the community includes:

[0018] Obtain preset community management decision rules and real-time updated community status information;

[0019] Inputting the obtained community management decision rules and community status information into a rule reasoning engine;

[0020] The rule reasoning engine uses fuzzy reasoning algorithm to calculate the applicability of each decision rule according to the matching degree between the management decision rule and the community status;

[0021] According to the calculated applicability of decision rules, a weighted average algorithm is used to determine the priority of each management measure;

[0022] According to the determined priority of management measures, a preliminary community command and dispatch plan is generated through a heuristic search algorithm;

[0023] The generated preliminary community command and dispatch plan is optimized using a constraint satisfaction algorithm to obtain the optimal dispatch plan that meets the community management constraints;

[0024] The final community command and dispatch plan is output, and the management decision rules are dynamically updated according to the actual implementation results and changes in community status to form a closed-loop feedback optimization.

[0025] The present invention also provides a safe community intelligent integrated management system, including: an element extraction module, a model construction module, a rule base construction module and a command and dispatch module;

[0026] The element extraction module is used to use a distributed big data platform to aggregate and store massive community data resources, and extract key community management indicators and business elements through data cleaning and association analysis operations;

[0027] The model building module is used to build a multidimensional data model for command and dispatch based on key community management indicators and business elements;

[0028] The rule base construction module is used to formalize the management staff's experience knowledge and decision logic based on the multidimensional data model and construct a community management knowledge base and rule base by using rule-based expert system technology;

[0029] The command and dispatch module is used to realize the automatic execution of business logic through the rule engine based on the community management knowledge base and rule base, and dynamically generate the command and dispatch plan according to the real-time status of the community.

[0030] Preferably, in the element extraction module, the process of extracting key community management indicators and business elements includes:

[0031] For the cleaned community data, the association rule mining algorithm is used to analyze the correlation between different business elements and identify key community management indicators and business elements.

[0032] Preferably, the rule base construction module includes: a first construction unit, a logic acquisition unit and a second construction unit;

[0033] The first construction unit is used to model community management related data using a multidimensional data model according to community management business requirements to obtain a community management multidimensional data model;

[0034] The logic acquisition unit is used to acquire the management staff's experience knowledge and decision logic based on the community management multidimensional data model and the community management business;

[0035] The second construction unit is used to formalize the management personnel's experiential knowledge and decision-making logic through knowledge engineering methods to construct a community management knowledge base and rule base.

[0036] Preferably, the command and dispatch module includes: an information acquisition unit, an input unit, a suitability calculation unit, a priority determination unit, a preliminary solution generation unit, an optimal solution generation unit and a dynamic update unit;

[0037] The information acquisition unit is used to acquire preset community management decision rules and real-time updated community status information;

[0038] The input unit is used to input the acquired community management decision rules and community status information into the rule reasoning engine;

[0039] The applicability calculation unit is used for the rule reasoning engine to calculate the applicability of each decision rule using a fuzzy reasoning algorithm according to the matching degree between the management decision rule and the community status;

[0040] The priority determination unit is used to determine the priority of each management measure by using a weighted average algorithm according to the calculated applicability of the decision rule;

[0041] The preliminary plan generating unit is used to generate a preliminary community command and dispatch plan through a heuristic search algorithm according to the determined management measure priority;

[0042] The optimal solution generation unit is used to optimize the generated preliminary community command and dispatch solution by using a constraint satisfaction algorithm to obtain an optimal dispatch solution that satisfies the community management constraint conditions;

[0043] The dynamic updating unit is used to output the finally generated community command and dispatch plan, and dynamically update the management decision rules according to the actual execution effect and community status changes, so as to form a closed-loop feedback optimization.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention discloses a method and system for intelligent comprehensive management of safe communities. Aiming at the problem of information islands caused by differences in work processes, data formats and communication protocols of various business departments in community management, the present invention connects various heterogeneous systems and devices within the community to a unified management platform by constructing a unified data exchange standard and interface specification, and adopts a multi-protocol adaptation and data standardization method to achieve cross-departmental data intercommunication and business collaboration. On this basis, the present invention uses a distributed big data platform to aggregate and store massive community data, and constructs a multidimensional data model for command and dispatch through operations such as data cleaning and association analysis. At the same time, the present invention formalizes the experience knowledge and decision-making logic of managers, constructs a community management knowledge base and rule base, and introduces case-based reasoning technology to dynamically generate and optimize command and dispatch plans based on the real-time status of the community and historical cases. Finally, the present invention adopts visualization technology to intuitively present the operation status of the community, provide managers with multi-perspective, multi-level data display and analysis functions, and support scientific decision-making. The present invention effectively solves the problems of information islands, low data utilization, and low command and dispatch efficiency in community management, and realizes the intelligence and refinement of community management. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0047] Figure 1 A schematic diagram of a safe community intelligent integrated management method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] Embodiment 1

[0051] like Figure 1 As shown, the present invention provides a safe community intelligent comprehensive management method, comprising the following steps:

[0052] Use a distributed big data platform to aggregate and store massive community data resources, and extract key community management indicators and business elements through data cleaning and correlation analysis operations;

[0053] Build a multi-dimensional data model for command and dispatch based on key community management indicators and business elements;

[0054] On the basis of multidimensional data model, rule-based expert system technology is used to formalize the management staff's experience knowledge and decision logic, and to build a community management knowledge base and rule base;

[0055] Based on the community management knowledge base and rule base, the rule engine is used to realize the automatic execution of business logic and dynamically generate command and dispatch plans according to the real-time status of the community.

[0056] In this embodiment, unified data exchange standards and interface specifications are constructed in view of the differences in work processes, data formats, and communication protocols among various business departments in the community. Through data format conversion and protocol adaptation, cross-departmental data interoperability and business collaboration are achieved, information islands are eliminated, and a unified community data view is formed, providing a data basis for subsequent command and dispatch.

[0057] According to the workflow of each department in the community, analyze the data input and output of different links, determine the key elements and common characteristics of each business data, and form a unified data standard specification. Design data conversion algorithms for the heterogeneous data formats of each department to map business data of various formats into a unified data model. The conversion algorithm needs to support common structured, semi-structured and unstructured data formats. Analyze the communication protocols used by each department, summarize the message format, interaction process and other elements of the protocol, design protocol adapter components, and realize message conversion and routing between different protocols. Build a unified data exchange bus, aggregate the business data of each department to the bus after format conversion, protocol adaptation and other processing, and realize centralized storage and management of data. Design a data sharing interface to provide standardized data access methods for each business system. The interface needs to perform permission control and traffic management to ensure data security and system stability. Establish a data quality monitoring mechanism to clean, deduplicate, and associate the aggregated data to improve the accuracy and integrity of the data. During the data processing process, the data lineage should be recorded to facilitate problem tracing. Based on the aggregated community data, a unified data view and analysis model is built to support multi-dimensional and multi-granular data query and aggregation analysis, providing data insights for community management and emergency command.

[0058] Specifically, in order to achieve the unification and sharing of data from various departments in the community, it is necessary to first sort out the work processes of various departments. For example, the work processes of the property management department include collecting owner information, registering maintenance requests, arranging maintenance tasks, etc. The key data elements involved in these processes include owner name, house address, contact information, maintenance type, maintenance status, etc. By extracting and summarizing these data elements, a unified data standard specification is formed, such as defining the owner information table as containing fields such as name, house number, and contact number. After clarifying the data standard, it is necessary to design a data conversion algorithm to map data in various formats to a unified data model. For example, the Excel table data exported by the property management system can be read through Python's Pandas library, and then field mapping and type conversion are performed according to the defined data standards. The converted data is stored in JSON format. Since the systems used by various departments may use different communication protocols, such as HTTP, MQTT, etc., it is necessary to design protocol adapter components to adapt these protocols, such as converting the HTTP request sent by the property management system into a unified JSON format message, and then sending it to the data exchange bus. The data exchange bus can be implemented using message queue middleware such as Kafka or RabbitMQ. After format conversion and protocol adaptation, the data of each department is sent to the specified topic or queue in a unified JSON format to achieve data aggregation and storage. In order to facilitate the access of each business system to the aggregated data, it is necessary to provide a standardized data sharing interface. For example, the API interface is designed based on the RESTful style, and the interface for querying community resident information is defined as GET / residents? name=Zhang San. The database query statement corresponding to this interface is SELECT*FROM resident_info WHERE name='Zhang San'. At the same time, the interface must be subject to permission control and traffic restrictions to ensure data security and system stability. In the process of data aggregation, there will inevitably be some quality problems, such as duplication and incompleteness. It is necessary to establish a data quality monitoring mechanism to clean and repair the data, such as merging the records of multiple repair reports by the same owner, and completing the records of missing house numbers. In the process of data processing, the source and conversion process of the data must be recorded for subsequent problem tracing. Based on the aggregated community data, a multi-dimensional data analysis model can be constructed. For example, the number of monthly maintenance requests can be counted according to the time dimension, and the number of residents in each area can be counted according to the housing area dimension. OLAP analysis technology can be used to achieve rapid data query and aggregation, providing data support for community management decisions.

[0059] In this embodiment, based on a unified community data view, an Internet of Things platform and edge computing nodes are deployed. Through multi-protocol adaptation and data standardization, various heterogeneous systems and devices within the community are connected to a unified management platform to achieve interconnection and centralized control of heterogeneous systems, providing real-time community status information for command and dispatch.

[0060] By collecting and standardizing the data of various heterogeneous systems and devices within the community, the data is converted into a unified data format and protocol to achieve the integration and interoperability of heterogeneous data. Based on the standardized data, a unified community data view is constructed to provide a basis for subsequent data analysis and application. The Internet of Things platform and edge computing nodes are used to access and manage various sensors and devices within the community to achieve real-time monitoring and control of the community environment and facilities. By analyzing and processing community data in real time, various events and abnormal situations within the community are discovered and warned in a timely manner, providing decision support for community management and emergency command. Based on a unified community data view and management platform, various resources and services within the community are uniformly scheduled and optimized to improve community operation efficiency and service quality. Using machine learning algorithms, various data within the community are mined and analyzed to discover community operation rules and potential problems, providing intelligent support for community management and optimization. By reasonably configuring and scheduling the computing and storage resources of the community edge nodes, data can be processed and stored nearby, data transmission delays can be reduced, and the real-time and reliability of community services can be improved.

[0061] Specifically, in order to realize the collection and standardized processing of heterogeneous systems and equipment data within the community, ETL tools such as Kettle can be used to map data from different sources and formats to a unified data model through extraction, cleaning, conversion and other steps, such as converting XML data of the community access control system and JSON data of the parking system into relational database tables. For community environmental monitoring, sensor data such as temperature, humidity, and noise can be reported to IoT platforms such as OneNET through IoT protocols such as Zigbee and LoRa, and then pushed to edge computing nodes for real-time analysis through MQTT subscription. For example, the threshold rule engine Drools is used to set the temperature>35℃ or humidity<20% to trigger an early warning, which is pushed to community managers through a mobile phone APP. In terms of data analysis and mining, the association rule algorithm Apriori can be used to analyze the flow of people and vehicles in the community at different times, and draw rules such as 8 to 9 am on weekdays is the peak of flow of people and Saturday afternoon is the peak of flow of vehicles, etc., to provide a basis for optimizing community resource allocation. At the same time, the time series prediction algorithm ARIMA is used to predict the elevator usage in the next week based on historical data, dynamically adjust the elevator operation strategy, and improve the quality of community services. To improve the real-time performance of data processing, edge computing nodes can be deployed within the community. For example, a 4-core 8G memory industrial computer can be installed in the weak current room of each building to run data collection, cleaning and other tasks, and the processed data can be reported to the cloud through Kafka for global analysis.

[0062] In this embodiment, a distributed big data platform is used to aggregate and store the massive community data resources connected to the unified management platform. Through operations such as data cleaning and correlation analysis, key community management indicators and business elements are extracted, and a multidimensional data model for command and dispatch is constructed to support flexible data query and analysis.

[0063] According to the characteristics of community data, a distributed big data platform is used to aggregate and store massive community data. The accuracy and consistency of the data are ensured by performing cleaning operations such as deduplication and filtering on the data. For the cleaned community data, an association rule mining algorithm is used to analyze the association relationship between different business elements, identify key community management indicators, and provide support for subsequent data modeling. According to the association relationship between community management indicators and business elements, a multidimensional data model for command and dispatch is constructed. The star schema is used to organize data, community management indicators are used as fact tables, and business elements are used as dimension tables to support flexible data query and analysis. For the multidimensional data model, OLAP technology is used to achieve fast data query and analysis. Through operations such as slicing, dicing, and drilling, community data is analyzed from different dimensions and granularities to find problems and optimization points in community management. According to the data analysis results, data visualization technology is used to generate intuitive and easy-to-understand charts and reports to support managers to quickly understand the community operation situation and adjust management strategies and resource allocation in a timely manner. In response to abnormal situations in the community management process, anomaly detection algorithms are used to monitor changes in community data in real time. If abnormal data is found, the early warning mechanism is triggered to notify relevant personnel to handle it, ensuring the timeliness and effectiveness of community management. According to the actual needs of community management, machine learning algorithms such as clustering and classification are used to conduct in-depth mining and analysis of community data, discover potential patterns and laws in community management, provide data support for management decisions, and continuously optimize the level of community management.

[0064] Specifically, in view of the characteristics of community data, the Hadoop distributed big data platform is used to aggregate and store massive community data. By using the MapReduce parallel computing framework, the data is cleaned by deduplication, filtering and other cleaning operations, removing about 15% of redundant and erroneous data, ensuring the accuracy and consistency of the data. Based on the cleaned community data, the Apriori association rule mining algorithm is used to mine the association rules between community management indicators and business elements by setting the minimum support to 05 and the minimum confidence to 8, such as "the community population growth rate increases by 5%, and the community service demand increases by 10%", which provides support for subsequent data modeling. According to the association between community management indicators and business elements, a multidimensional data model for command and dispatch is constructed. The star schema is used to organize data, with community management indicators as fact tables, including community population, service demand, etc., and business elements as dimension tables, including time, location, service type, etc., to support managers to query and analyze data from different angles. For the multidimensional data model, OLAP technology is used to achieve fast query and analysis of data. Use Apache Kylin to build a pre-aggregated cube, pre-calculate the aggregate indicators under different dimensional combinations, and achieve sub-second data query response through operations such as slicing, dicing, and drilling. Analyze community data from different dimensions and granularity to find problems in community management, such as unbalanced service resource allocation and declining service quality. According to the data analysis results, the ECharts data visualization framework is used to generate intuitive and easy-to-understand charts and reports to support managers to quickly understand the community operation situation. The proportion of different types of services is displayed through pie charts, the trend of various indicators is displayed through line charts, and the correlation between service quality and resident satisfaction is displayed through scatter plots, which helps managers to adjust management strategies and resource allocation in time. For abnormal situations in the community management process, a statistical anomaly detection algorithm is used to monitor the changes in community data in real time. By calculating the mean and standard deviation of the data, if the data deviates from the mean by more than 3 standard deviations, it is judged as abnormal data, triggering the early warning mechanism, and notifying relevant personnel to handle it. If the water consumption in the community suddenly increases by 20%, the pipe network failure is promptly checked to ensure the timeliness and effectiveness of community management. According to the actual needs of community management, the K-means clustering algorithm is used to conduct cluster analysis on community residents. By selecting the age, income, consumption and other characteristics of residents as clustering dimensions, residents are divided into different groups, and the differences in service needs and satisfaction of different groups are analyzed to provide data support for management decisions. For example, personalized value-added services are provided for high-income people; convenient medical services are provided for the elderly, and the level of community management is continuously optimized.

[0065] In this embodiment, on the basis of the multidimensional data model, rule-based expert system technology is adopted to formally express the management personnel's experiential knowledge and decision-making logic, build a community management knowledge base and rule base, realize the automatic execution of business logic through the rule engine, and dynamically generate command and dispatch plans according to the real-time status of the community.

[0066] According to the business needs of community management, a multidimensional data model is used to model community management related data to obtain a multidimensional data model for community management; for community management business, the management staff's experience knowledge and decision-making logic are obtained, formalized through knowledge engineering methods, and a community management knowledge base and rule base are constructed; based on the community management knowledge base and rule base, rule-based expert system technology is used to realize the automated execution of community management business logic through a rule engine; the real-time status data of the community is obtained, and the applicable management decision rules are determined through rule matching based on the community management knowledge base and rule base; according to the matched management decision rules, combined with the real-time status of the community, a community command and dispatch plan is dynamically generated through the reasoning execution of the rule engine; the generated community command and dispatch plan is sent to relevant management staff and execution systems to guide the implementation of community management work; the changes in community status are continuously monitored, and the community management knowledge base and rule base are dynamically optimized based on feedback data to improve the intelligence level of management decision-making.

[0067] Specifically, according to the needs of community management business, a star-shaped model is used to build a multidimensional data model for community management. Community management indicators are used as fact tables, such as community population and community service satisfaction, and factors affecting community management are used as dimension tables, such as community location and community type. By establishing the relationship between the fact table and the dimension table, multidimensional analysis of community management data is achieved. For community management business, 20 community management experts are interviewed to obtain their management experience and decision-making logic. The ontology method is used to extract concepts and define relationships from the interview content, and a community management knowledge base and rule base containing 500 concepts and 200 rules are constructed. Based on the community management knowledge base and rule base, the CLIPS rule engine is used to convert the community management business logic into IF-THEN rules to realize the automated execution of community management business, and the average response time of rule matching is less than 5 seconds. Real-time community status data, including population flow data and service demand data, are obtained. According to the community management knowledge base and rule base, the Rete algorithm is used for rule matching to determine the applicable management decision rules, and the matching accuracy rate reaches more than 95%. According to the matching management decision rules, combined with the real-time status of the community, through the forward and reverse reasoning of the rule engine, the command and dispatch schemes such as community inspection routes and service resource dispatch are dynamically generated, and the interpretability of the reasoning results reaches more than 90%. The generated community command and dispatch scheme is sent to the mobile terminals and execution systems of relevant managers in real time through the WebSocket protocol in JSON format data to guide the development of community management work. The average delay in the issuance of the scheme is less than 1 second. Continuously monitor changes in community status, analyze feedback data through data mining technology, use association rule mining algorithms to dynamically optimize community management rules, use decision tree algorithms to dynamically update community management knowledge, and regularly update the knowledge base and rule base every day to improve the intelligence level of management decisions.

[0068] According to the matching management decision rules and combined with the real-time status of the community, the community command and dispatch plan is dynamically generated through the reasoning execution of the rule engine.

[0069] Obtain preset community management decision rules and real-time updated community status information; input the obtained community management decision rules and community status information into the rule reasoning engine; the rule reasoning engine uses a fuzzy reasoning algorithm to calculate the applicability of each decision rule based on the matching degree between the management decision rule and the community status; based on the calculated applicability of the decision rule, a weighted average algorithm is used to determine the priority of each management measure; based on the determined priority of the management measure, a preliminary community command and dispatch plan is generated through a heuristic search algorithm; the generated preliminary community command and dispatch plan is optimized using a constraint satisfaction algorithm to obtain the optimal dispatch plan that meets the community management constraints; the final generated community command and dispatch plan is output, and the management decision rules are dynamically updated based on the actual execution effect and changes in the community status to form a closed-loop feedback optimization.

[0070] Specifically, the system pre-sets a series of community management decision rules, such as "when the community population density exceeds 800 people / square kilometer, it is necessary to increase patrol forces", etc. At the same time, the Internet of Things devices collect real-time status information such as the flow of people and vehicle access in the community. After the decision rules and status information are input into the rule reasoning engine, the engine uses a fuzzy reasoning algorithm to calculate the applicability of each decision rule. For example, based on the proximity between the current population density and the threshold set by the rule, the applicability of the rule to increase patrol forces is calculated to be 8. Then, the system uses a weighted average algorithm to comprehensively consider the applicability of each decision rule and determine the priority of management measures, such as the priority of increasing patrol forces is 3, and the priority of strengthening access management is 2. According to the priority of management measures, the system generates several alternative preliminary scheduling plans through heuristic search algorithms, such as genetic algorithms. Then, based on the preset community management constraints (such as the total number of security forces, the upper limit of patrol frequency, etc.), the system uses a constraint satisfaction algorithm to optimize and solve the preliminary plan to obtain the optimal scheduling plan that meets the constraints, such as increasing the patrol force by 3 people and patrolling 5 times a day. Finally, the system outputs an optimized community command and dispatch plan, and dynamically adjusts the decision rules based on subsequent execution feedback, such as adjusting the population density threshold from 800 people / square kilometer to 1,000 people / square kilometer, continuously optimizing in a closed loop to improve the accuracy and timeliness of management.

[0071] In this embodiment, for the handling of community emergencies, case-based reasoning technology is used on the basis of the rule base. Through the analysis and summary of historical events, the handling plans and processes in typical scenarios are extracted to form a case library. When a new emergency occurs, the relevant cases are quickly located through case matching and similarity calculation, and the plan is optimized and adjusted according to the current situation to guide real-time emergency command.

[0072] Obtain relevant information about community emergencies, including event type, severity, and scope of impact, and use it as input parameters for case matching. In the rule base, match the corresponding handling rules and processes according to the event type and severity to form a preliminary handling plan. In the case base, use text similarity algorithms, such as TF-IDF or Word2Vec, to calculate the similarity between the current event and historical cases and obtain the most similar cases. Compare the handling plan generated by the rule base with the handling plan matched by the case base to determine whether there are conflicts or inconsistencies. If there is a conflict, determine whether to give priority to the plan in the rule base or the case base based on the severity and scope of impact of the event. Optimize and adjust the selected handling plan, and generate the final handling plan based on the specific circumstances of the current event, such as geographical location, personnel composition, etc. Convert the optimized handling plan into an executable emergency plan and process, and send it to relevant departments and personnel to guide real-time emergency command and handling work.

[0073] Specifically, after receiving information about community emergencies, the system first analyzes the type, severity, and scope of impact of the event through an automated system. For example, it uses natural language processing technology to extract key information from the event description, such as "fire", "highly serious", and "the impact area is about 300 square meters". Then, the system automatically matches the corresponding handling rules and processes, such as starting the emergency evacuation procedure and notifying the fire department, based on the event type "fire" and severity "highly serious" through the preset rule library. At the same time, the system uses the TF-IDF algorithm to analyze the current event description and the text data in the historical case library, calculates the similarity, and finds the most similar historical case to the current event, such as a shopping mall fire incident that was previously handled. The system compares the handling solutions in the rule library with those in the case library, and detects differences and conflicts between the two through algorithms. If a conflict is detected, the system decides whether to adopt the rule library solution or the case library solution based on the severity and scope of impact of the event through a priority judgment model. The selected handling solution will be further optimized and adjusted. The system adjusts the emergency plan according to the specific circumstances of the event, such as the geographical location in the city center and the large number of people involved, to ensure the actual applicability of the solution. Finally, the system converts the optimized emergency plan and handling process into an operation guide, which is automatically distributed to the mobile devices or computers of relevant departments and personnel to ensure that real-time emergency command and handling work can be effectively carried out.

[0074] In this embodiment, in the process of generating and optimizing the command and dispatch plan, visualization technology is used to intuitively present the real-time operating status of the community through electronic maps, data dashboards, etc., including personnel distribution, vehicle trajectory, equipment status, etc., and according to the needs of command and dispatch, multi-perspective, multi-level data display and analysis functions are provided to support scientific decision-making.

[0075] Obtain the real-time operation data of the community, including personnel location, vehicle location, equipment working parameters, etc., and convert them into a standardized data format. Transmit the standardized real-time data to the visualization platform and integrate it with visualization components such as electronic maps and data dashboards through data interfaces. Dynamically mark the real-time distribution of personnel on the electronic map based on personnel location data; draw the real-time trajectory of the vehicle based on vehicle location data; and mark the status information of the equipment on the map based on the equipment working parameters. Set multiple data indicator items on the data dashboard to display key indicator data of community operation in real time in the form of instruments, curves, pie charts, etc., such as the number of personnel, the number of vehicles, and the equipment failure rate. Configure the display mode of the visualization platform according to the needs of command and dispatch, provide multi-perspective data display methods such as regional view, street view, and building view, and support up-drilling and down-drilling of data levels to achieve multi-level data browsing from macro to micro. Integrate data analysis tools in the visualization platform to analyze community operation data in real time, discover association rules and abnormal patterns in the data through data mining algorithms, and provide decision-making basis for command and dispatch. Based on the data analysis results and combined with the pre-set dispatch rules and strategies, the visualization platform automatically generates a command and dispatch plan, and presents the plan in a visual way for the commander to review and optimize, ultimately forming executable dispatch instructions that are transmitted to relevant personnel and equipment to guide community operations.

[0076] Specifically, in order to obtain the real-time operation data of the community, various sensors and positioning devices can be deployed in the community. For example, RFID technology can be used to label and manage personnel and vehicles, and their location information can be obtained in real time by deploying RFID readers. The data reporting frequency can be set to once every 5 seconds. At the same time, wireless acquisition modules are installed on important equipment to collect the working parameters of the equipment in real time, such as voltage, current, temperature, etc. The data sampling frequency can reach 50 times per second. The collected data is transmitted to the visualization platform in JSON format through IoT communication protocols such as MQTT. The platform performs two-way data binding with the front-end UI component through the WebSocket interface to achieve real-time visualization of data. On the electronic map, clustering algorithms can be used to dynamically aggregate the positions of personnel and show the density distribution of personnel in the form of heat maps. The vehicle trajectory can remove noise points in GPS data through a smoothing algorithm to improve the accuracy of the trajectory. For equipment status information, an early warning threshold can be set. When the parameter exceeds the threshold, an alarm will be automatically triggered and marked with a red icon on the map. The data dashboard can provide a variety of visualization components, such as instruments that can represent the real-time number of personnel and vehicles, curves that can reflect the changing trend of these numbers, and pie charts that can represent the proportional relationship of equipment failure rates. By configuring a tree-like data dimension structure, it is possible to drill up and down data from the entire community to a single building. In terms of data analysis, association rule mining algorithms can be used to discover the correlation between different equipment parameters. For example, when some equipment is abnormal, other equipment will also be abnormal. You can also use time series anomaly detection algorithms to determine whether there are abnormal fluctuations in the collected data to provide a basis for scheduling decisions. Finally, the generation of scheduling plans can use rule engine technology to input data analysis results into preset rules, automatically trigger corresponding scheduling strategies, and present them to commanders in visual ways such as Gantt charts. The commander then fine-tunes the plan according to the actual situation to form the final scheduling instructions, which are distributed to relevant personnel and equipment through message queues to achieve collaborative operation of the community.

[0077] Embodiment 2

[0078] The present invention also provides a safe community intelligent integrated management system, including: an element extraction module, a model construction module, a rule base construction module and a command and dispatch module;

[0079] The element extraction module is used to aggregate and store massive community data resources using a distributed big data platform, and to extract key community management indicators and business elements through data cleaning and correlation analysis operations;

[0080] The model building module is used to build a multi-dimensional data model for command and dispatch based on key community management indicators and business elements;

[0081] The rule base construction module is used to formalize the management staff's experience knowledge and decision logic based on the multidimensional data model and construct the community management knowledge base and rule base by using the rule-based expert system technology.

[0082] The command and dispatch module is used to realize the automatic execution of business logic based on the community management knowledge base and rule base through the rule engine, and dynamically generate command and dispatch plans according to the real-time status of the community.

[0083] In this embodiment, in the element extraction module, the process of extracting key community management indicators and business elements includes:

[0084] For the cleaned community data, the association rule mining algorithm is used to analyze the correlation between different business elements and identify key community management indicators and business elements.

[0085] In this embodiment, the rule base construction module includes: a first construction unit, a logic acquisition unit, and a second construction unit;

[0086] The first construction unit is used to model community management related data using a multidimensional data model according to community management business needs, so as to obtain a community management multidimensional data model;

[0087] The logic acquisition unit is used to acquire the management personnel's experience knowledge and decision logic based on the community management multidimensional data model and community management business;

[0088] The second construction unit is used to formalize the management staff's experiential knowledge and decision-making logic through knowledge engineering methods, and to construct a community management knowledge base and rule base.

[0089] In this embodiment, the command and dispatch module includes: an information acquisition unit, an input unit, a suitability calculation unit, a priority determination unit, a preliminary solution generation unit, an optimal solution generation unit and a dynamic update unit;

[0090] The information acquisition unit is used to obtain preset community management decision rules and real-time updated community status information;

[0091] The input unit is used to input the acquired community management decision rules and community status information into the rule reasoning engine;

[0092] The suitability calculation unit is used in the rule reasoning engine to calculate the suitability of each decision rule using a fuzzy reasoning algorithm according to the matching degree between the management decision rule and the community status;

[0093] The priority determination unit is used to determine the priority of each management measure by using a weighted average algorithm according to the calculated applicability of the decision rule;

[0094] The preliminary plan generation unit is used to generate a preliminary community command and dispatch plan through a heuristic search algorithm according to the determined management measure priorities;

[0095] The optimal solution generation unit is used to optimize the generated preliminary community command and dispatch solution by using a constraint satisfaction algorithm to obtain an optimal dispatch solution that satisfies the community management constraint conditions;

[0096] The dynamic update unit is used to output the final generated community command and dispatch plan, and dynamically update the management decision rules according to the actual execution effect and changes in community status, forming a closed-loop feedback optimization.

[0097] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A safe community intelligent integrated management method, characterized in that: The following steps are involved: Use a distributed big data platform to aggregate and store massive community data resources, and extract key community management indicators and business elements through data cleaning and correlation analysis operations; Build a multi-dimensional data model for command and dispatch based on key community management indicators and business elements; On the basis of multidimensional data model, rule-based expert system technology is used to formalize the management staff's experience knowledge and decision logic, and to build a community management knowledge base and rule base; Based on the community management knowledge base and rule base, the rule engine is used to realize the automatic execution of business logic and dynamically generate command and dispatch plans according to the real-time status of the community.

2. The safe community intelligent integrated management method according to claim 1 is characterized in that: Methods for extracting key community management indicators and business elements include: For the cleaned community data, the association rule mining algorithm is used to analyze the correlation between different business elements and identify key community management indicators and business elements.

3. The safe community intelligent integrated management method according to claim 1 is characterized in that: Methods for building community management knowledge base and rule base include: According to the business needs of community management, a multidimensional data model is used to model the community management related data to obtain a multidimensional data model of community management; Based on the multidimensional data model of community management and community management business, obtain the experience knowledge and decision-making logic of managers; Through knowledge engineering methods, managers' experiential knowledge and decision-making logic are formalized to build a community management knowledge base and rule base.

4. The safe community intelligent integrated management method according to claim 1 is characterized in that: Based on the community management knowledge base and rule base, the method of realizing the automatic execution of business logic through the rule engine and dynamically generating command and dispatch plans according to the real-time status of the community includes: Obtain preset community management decision rules and real-time updated community status information; Inputting the obtained community management decision rules and community status information into a rule reasoning engine; The rule reasoning engine uses fuzzy reasoning algorithm to calculate the applicability of each decision rule according to the matching degree between the management decision rule and the community status; According to the calculated applicability of decision rules, a weighted average algorithm is used to determine the priority of each management measure; According to the determined priority of management measures, a preliminary community command and dispatch plan is generated through a heuristic search algorithm; The generated preliminary community command and dispatch plan is optimized using a constraint satisfaction algorithm to obtain the optimal dispatch plan that meets the community management constraints; The final community command and dispatch plan is output, and the management decision rules are dynamically updated according to the actual implementation results and changes in community status to form a closed-loop feedback optimization.

5. A safe community intelligent integrated management system, characterized in that: include: Factor extraction module, model building module, rule base building module and command and dispatch module; The element extraction module is used to use a distributed big data platform to aggregate and store massive community data resources, and extract key community management indicators and business elements through data cleaning and association analysis operations; The model building module is used to build a multidimensional data model for command and dispatch based on key community management indicators and business elements; The rule base construction module is used to formalize the management staff's experience knowledge and decision logic based on the multidimensional data model and construct a community management knowledge base and rule base by using rule-based expert system technology; The command and dispatch module is used to realize the automatic execution of business logic through the rule engine based on the community management knowledge base and rule base, and dynamically generate the command and dispatch plan according to the real-time status of the community.

6. The safe community intelligent integrated management system according to claim 5 is characterized in that: In the element extraction module, the process of extracting key community management indicators and business elements includes: For the cleaned community data, the association rule mining algorithm is used to analyze the correlation between different business elements and identify key community management indicators and business elements.

7. The safe community intelligent integrated management system according to claim 5 is characterized in that: The rule base construction module includes: a first construction unit, a logic acquisition unit and a second construction unit; The first construction unit is used to model community management related data using a multidimensional data model according to community management business requirements to obtain a community management multidimensional data model; The logic acquisition unit is used to acquire the management staff's experience knowledge and decision logic based on the community management multidimensional data model and the community management business; The second construction unit is used to formalize the management personnel's experiential knowledge and decision-making logic through knowledge engineering methods to construct a community management knowledge base and rule base.

8. The safe community intelligent integrated management system according to claim 5 is characterized in that: The command and dispatch module includes: an information acquisition unit, an input unit, a suitability calculation unit, a priority determination unit, a preliminary solution generation unit, an optimal solution generation unit and a dynamic update unit; The information acquisition unit is used to acquire preset community management decision rules and real-time updated community status information; The input unit is used to input the acquired community management decision rules and community status information into the rule reasoning engine; The applicability calculation unit is used for the rule reasoning engine to calculate the applicability of each decision rule using a fuzzy reasoning algorithm according to the matching degree between the management decision rule and the community status; The priority determination unit is used to determine the priority of each management measure by using a weighted average algorithm according to the calculated applicability of the decision rule; The preliminary plan generating unit is used to generate a preliminary community command and dispatch plan through a heuristic search algorithm according to the determined management measure priority; The optimal solution generation unit is used to optimize the generated preliminary community command and dispatch solution by using a constraint satisfaction algorithm to obtain an optimal dispatch solution that satisfies the community management constraint conditions; The dynamic updating unit is used to output the finally generated community command and dispatch plan, and dynamically update the management decision rules according to the actual execution effect and community status changes, so as to form a closed-loop feedback optimization.

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