A safe community intelligent comprehensive management method and system

Abstract

This invention discloses a method and system for intelligent integrated management of safe communities, comprising: using a distributed big data platform for data aggregation and storage of massive community data resources connected to a unified management platform; extracting key community management indicators and business elements through data cleaning, correlation analysis, and other operations; constructing a multi-dimensional data model for command and dispatch, supporting flexible data query and analysis; and, based on the multi-dimensional data model, employing rule-based expert system technology to formally express the experience and decision-making logic of management personnel, constructing a community management knowledge base and rule base, and automating the execution of business logic through a rule engine, dynamically generating command and dispatch plans based on the real-time status of the community. This invention effectively solves problems such as information silos, low data utilization, and low command and dispatch efficiency in community management, achieving intelligent and refined community management.

Description

Technical Field

[0001] This invention belongs to the field of information technology, specifically relating to a method and system for intelligent integrated management of safe communities. Background Technology

[0002] In the command and dispatch process of the Safe Community Intelligent Integrated Management System, a technical challenge arises in achieving efficient collaboration among various departments within the community. Because the community involves multiple different business departments with varying workflows, data formats, and communication protocols, real-time information sharing and business collaboration between departments are difficult to achieve during command and dispatch. Furthermore, the community also contains a large number of heterogeneous systems and devices; achieving interconnectivity among these systems and devices to support efficient command and dispatch operations is another significant challenge.

[0003] Furthermore, in actual command and dispatch processes, it is necessary to dynamically adjust command decisions and resource allocation based on the real-time status of the community and emergencies. This places higher demands on the real-time performance and intelligence of the command and dispatch system. How to quickly identify key information from massive amounts of community data and automatically generate the optimal command and dispatch plan based on complex business rules and strategies is also a pressing technical challenge. In summary, to achieve efficient and intelligent community command and dispatch, it is essential to overcome technical bottlenecks in multi-department collaboration, interoperability of heterogeneous systems, and real-time intelligent decision-making. This requires the comprehensive application of next-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] To address the shortcomings of existing technologies, this invention proposes a smart integrated management method and system for safe communities, which effectively solves problems such as information silos, low data utilization, and low command and dispatch efficiency in community management, and realizes intelligent and refined community management.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A smart integrated management method for safe communities includes the following steps:

[0007] A distributed big data platform is used to aggregate and store massive amounts of community data resources. Through data cleaning and correlation analysis, key community management indicators and business elements are extracted.

[0008] Based on key community management indicators and business elements, a multi-dimensional data model for command and dispatch is constructed.

[0009] Based on a multidimensional data model, rule-based expert system technology is used to formalize the experience knowledge and decision-making logic of managers, and to build a community management knowledge base and rule base.

[0010] Based on the community management knowledge base and rule base, the system uses a rule engine to automate the execution of business logic and dynamically generate command and dispatch plans according to the real-time status of the community.

[0011] Preferred methods for extracting key community management indicators and business elements include:

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

[0013] Preferred methods for constructing community management knowledge bases and rule bases include:

[0014] Based on the needs of community management operations, a multidimensional data model is used to model the relevant data of community management, resulting in a multidimensional data model of community management.

[0015] Based on a multi-dimensional data model of community management and community management operations, we can acquire the experience, knowledge, and decision-making logic of management personnel.

[0016] By using knowledge engineering methods, the experiential knowledge and decision-making logic of managers are formalized and expressed to build a community management knowledge base and rule base.

[0017] Preferably, the method for automating the execution of business logic and dynamically generating command and dispatch plans based on the community's real-time status, using a rule engine, based on the community's knowledge base and rule base, includes:

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

[0019] The acquired community management decision-making rules and community status information are input into the rule reasoning engine;

[0020] The rule-based reasoning engine calculates the applicability of each decision rule using a fuzzy reasoning algorithm based on the degree of matching between the management decision rules and the community status.

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

[0022] Based on the determined management measures priorities, a preliminary community command and dispatch plan is generated using 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 satisfies the community management constraints.

[0024] The final community command and dispatch plan will be output, and the management decision-making rules will be dynamically updated based on the actual implementation effect and changes in the community status, forming a closed-loop feedback optimization.

[0025] The present invention also provides a smart integrated management system for safe communities, including: an element extraction module, a model building module, a rule base building module, and a command and dispatch module;

[0026] 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.

[0027] The model building module is used to construct a multi-dimensional data model for command and dispatch based on key community management indicators and business elements.

[0028] The rule base construction module is used to formally express the experience knowledge and decision-making logic of managers based on a multidimensional data model and rule-based expert system technology, thereby constructing a community management knowledge base and rule base.

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

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

[0031] For the cleaned community data, an association rule mining algorithm is used to analyze the relationships 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 the needs of community management business, so as to obtain a multidimensional data model for community management;

[0034] The logic acquisition unit is used to acquire the experience knowledge and decision-making logic of management personnel based on the multi-dimensional data model of community management and community management business.

[0035] The second building unit is used to formalize the experience knowledge and decision-making logic of managers through knowledge engineering methods, and to build a community management knowledge base and rule base.

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

[0037] The information acquisition unit is used to acquire preset community management decision-making 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 by the rule reasoning engine to calculate the applicability of each decision rule based on the degree of matching between the management decision rules and the community status using a fuzzy reasoning algorithm.

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

[0041] The preliminary scheme generation unit is used to generate a preliminary community command and dispatch scheme based on the determined management measures priority and through a heuristic search algorithm.

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

[0043] 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 implementation effect and changes in the community status, forming a closed-loop feedback optimization.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0045] This invention discloses a method and system for intelligent integrated management of safe communities. Addressing the information silos caused by differences in workflows, data formats, and communication protocols among various business departments in community management, this invention constructs a unified data exchange standard and interface specification. By employing multi-protocol adaptation and data standardization, it connects various heterogeneous systems and devices within the community to a unified management platform, achieving cross-departmental data interoperability and business collaboration. Based on this, the invention uses a distributed big data platform to aggregate and store massive amounts of community data. Through data cleaning, correlation analysis, and other operations, a multi-dimensional data model for command and dispatch is constructed. Simultaneously, this invention formalizes the experience, knowledge, and decision-making logic of management personnel, constructing a community management knowledge base and rule base. It also introduces case-based reasoning technology to dynamically generate and optimize command and dispatch schemes based on the real-time status of the community and historical cases. Finally, this invention uses visualization technology to intuitively present the community's operational status, providing management personnel with multi-perspective and multi-level data display and analysis functions to support scientific decision-making. This invention effectively solves the problems of information silos, low data utilization, and inefficient command and dispatch in community management, achieving intelligent and refined community management. Attached Figure Description

[0046] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of a smart integrated management method for safe communities according to an embodiment of the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] Example 1

[0051] like Figure 1 As shown, this invention provides a smart integrated management method for safe communities, comprising the following steps:

[0052] A distributed big data platform is used to aggregate and store massive amounts of community data resources. Through data cleaning and correlation analysis, key community management indicators and business elements are extracted.

[0053] Based on key community management indicators and business elements, a multi-dimensional data model for command and dispatch is constructed.

[0054] Based on a multidimensional data model, rule-based expert system technology is used to formalize the experience knowledge and decision-making logic of managers, and to build a community management knowledge base and rule base.

[0055] Based on the community management knowledge base and rule base, the system uses a rule engine to automate the execution of business logic and dynamically generate command and dispatch plans according to the real-time status of the community.

[0056] In this embodiment, a unified data exchange standard and interface specification are constructed to address the differences in workflows, 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, eliminating information silos and forming a unified community data view, thus providing a data foundation for subsequent command and dispatch.

[0057] Based on the workflows of various departments within the community, analyze the data input and output at different stages to identify key elements and common characteristics of each business data, forming a unified data standard. For the heterogeneous data formats of various departments, design data conversion algorithms to map business data of various formats to a unified data model. The conversion algorithms need to support common structured, semi-structured, and unstructured data formats. Analyze the communication protocols used by each department, summarize the message formats, interaction flows, and other elements of the protocols, and design protocol adaptation components to realize message conversion and routing between different protocols. Construct a unified data exchange bus to aggregate business data from various departments after format conversion and protocol adaptation, achieving centralized data storage and management. Design data sharing interfaces to provide standardized data access methods for various business systems. Interfaces need to implement access control and traffic management to ensure data security and system stability. Establish a data quality monitoring mechanism to clean, deduplicate, and correlate the aggregated data to improve data accuracy and integrity. Record data lineage during data processing to facilitate problem tracing. Based on aggregated community data, a unified data view and analysis model are 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 among various departments in the community, it is first necessary to sort out the work processes of each department. For example, the work processes of the property management department include collecting owner information, registering repair requests, arranging repair tasks, etc. The key data elements involved in these processes are owner name, house address, contact information, repair type, repair status, etc. By extracting and summarizing these data elements, a unified data standard specification is formed. For example, the owner information table is defined as including fields such as name, house number, contact phone, etc. After clarifying the data standard, it is necessary to design a data conversion algorithm to map various formats of data into a unified data model. For example, the Excel table data exported by the property management system can be read through the Pandas library in Python, and then field mapping and type conversion are performed according to the defined data standard. The converted data is stored in JSON format. Since the systems used by each department may adopt different communication protocols, such as HTTP, MQTT, etc., it is necessary to design protocol adaptation components to adapt these protocols. For example, convert the HTTP requests sent by the property management system into unified JSON format messages and then send them to the data exchange bus. The data exchange bus can be implemented using message queue middleware such as Kafka or RabbitMQ. After the data of each department has been format-converted and protocol-adapted, it 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 standardized data sharing interfaces. For example, design API interfaces based on the RESTful style. Define the interface for querying community resident information as GET / residents?name=张三, and the corresponding database query statement for this interface is SELECT * FROM resident_info WHERE name = '张三'. At the same time, permission control and traffic restriction should be carried out on the interface to ensure data security and system stability. Inevitably, there will be some quality problems in the data during the aggregation process, such as duplicates, incompleteness, etc. It is necessary to establish a data quality monitoring mechanism to clean and repair the data. For example, merge the records of the same owner's multiple repair requests, complete the records missing the house number, etc. During the data processing process, record the source and conversion process of the data for subsequent problem tracing. Based on the aggregated community data, multi-dimensional data analysis models can be constructed. For example, count the number of monthly repair requests according to the time dimension, and count the number of residents in each area according to the house area dimension, etc. Through OLAP analysis technology, achieve rapid query and aggregation of data, providing data support for community management decisions.

[0059] In this embodiment, based on a unified community data view, an IoT 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, realizing the interconnection and centralized control of heterogeneous systems and providing real-time community status information for command and dispatch.

[0060] By collecting and standardizing data from various heterogeneous systems and devices within the community, converting it into a unified data format and protocol, the integration and interoperability of heterogeneous data are achieved. Based on the standardized data, a unified community data view is constructed, providing a foundation for subsequent data analysis and applications. An IoT platform and edge computing nodes are used to access and manage various sensors and devices within the community, enabling real-time monitoring and control of the community environment and facilities. Real-time analysis and processing of community data allows for timely detection and early warning of various events and anomalies within the community, providing decision support for community management and emergency command. Based on the unified community data view and management platform, various resources and services within the community are uniformly scheduled and optimized, improving community operational efficiency and service quality. Machine learning algorithms are used to mine and analyze various types of data within the community, discovering operational patterns and potential problems, providing intelligent support for community management and optimization. By rationally configuring and scheduling the computing and storage resources of community edge nodes, data is processed and stored locally, reducing data transmission latency and improving the real-time performance and reliability of community services.

[0061] Specifically, to achieve the collection and standardized processing of data from heterogeneous systems and equipment within the community, ETL tools such as Kettle can be used to extract, clean, and transform data from different sources and formats, mapping it to a unified data model. For example, XML data from the community access control system and JSON data from the parking system can be converted into relational database tables. For community environmental monitoring, IoT protocols such as Zigbee and LoRa can be used to report sensor data on temperature, humidity, and noise to an IoT platform such as OneNET, which can then be pushed to edge computing nodes for real-time analysis via MQTT subscription. For instance, the threshold rule engine Drools can be used to set an alert when the temperature is >35℃ or the humidity is <20%, and push the alert to community management personnel via a mobile app. In terms of data analysis and mining, the association rule algorithm Apriori can be used to analyze pedestrian and vehicle flow data at different times in the community, revealing patterns such as peak pedestrian flow from 8:00 to 9:00 AM on weekdays and peak vehicle flow on Saturday afternoons, providing a basis for optimizing community resource allocation. Simultaneously, the time series prediction algorithm ARIMA can be used to predict elevator usage for the coming week based on historical data, dynamically adjusting elevator operation strategies to 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, 8GB memory industrial control computer can be installed in the low-voltage room of each building to run data acquisition, cleaning, and other tasks, and the processed data can be reported to the cloud for global analysis via Kafka.

[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 data cleaning, correlation analysis and other operations, key community management indicators and business elements are extracted to construct a multi-dimensional data model for command and dispatch, supporting flexible data query and analysis.

[0063] Based on the characteristics of community data, a distributed big data platform is used to aggregate and store massive amounts of community data. Data cleaning operations such as deduplication and filtering are performed to ensure accuracy and consistency. For the cleaned community data, association rule mining algorithms are used to analyze the relationships between different business elements, identifying key community management indicators to support subsequent data modeling. Based on the relationships between community management indicators and business elements, a multi-dimensional data model oriented towards command and dispatch is constructed. A star schema is used to organize the data, with community management indicators as the fact table and business elements as the dimension table, supporting flexible data querying and analysis. For the multi-dimensional data model, OLAP technology is used to achieve rapid data querying and analysis. Through operations such as slicing, dicing, and drill-down, community data is analyzed from different dimensions and granularities to identify problems and optimization points in community management. Based on the data analysis results, data visualization technology is used to generate intuitive and easy-to-understand charts and reports, enabling managers to quickly understand the community's operational status and adjust management strategies and resource allocation in a timely manner. To address anomalies in community management, anomaly detection algorithms are employed to monitor changes in community data in real time. If anomalies are detected, an early warning mechanism is triggered, notifying relevant personnel to handle the situation and ensuring the timeliness and effectiveness of community management. Based on the actual needs of community management, machine learning algorithms, such as clustering and classification, are used to deeply mine and analyze community data, uncovering potential patterns and regularities in community management. This provides data support for management decisions and continuously optimizes the level of community management.

[0064] Specifically, considering the characteristics of community data, a Hadoop distributed big data platform was used to aggregate and store massive amounts of community data. Using the MapReduce parallel computing framework, data cleaning operations such as deduplication and filtering were performed, removing approximately 15% of redundant and erroneous data, ensuring data accuracy and consistency. Based on the cleaned community data, the Apriori association rule mining algorithm was used. By setting a minimum support of 0.5 and a minimum confidence of 8, association rules between community management indicators and business elements were mined, such as "a 5% increase in the community population growth rate leads to a 10% increase in community service demand," providing support for subsequent data modeling. Based on the relationships between community management indicators and business elements, a multi-dimensional data model oriented towards command and dispatch was constructed. A star schema was used to organize the data, with community management indicators as the fact table (including community population and service demand), and business elements as the dimension table (including time, location, and service type), supporting managers to query and analyze data from different perspectives. For the multi-dimensional data model, OLAP technology was used to achieve rapid data querying and analysis. Using Apache Kylin, a pre-aggregated cube is constructed to pre-calculate aggregated indicators under different dimensional combinations. Through operations such as slicing, dicing, and drill-down, sub-second data query response times are achieved. Community data is analyzed from different dimensions and granularities to identify problems in community management, such as uneven allocation of service resources and declining service quality. Based on the data analysis results, the ECharts data visualization framework is used to generate intuitive and easy-to-understand charts and reports, enabling managers to quickly understand the community's operational status. Pie charts display the proportion of different service types, line charts show the changing trends of various indicators, and scatter plots demonstrate the correlation between service quality and resident satisfaction, helping managers to adjust management strategies and resource allocation in a timely manner. For anomalies in the community management process, a statistical anomaly detection algorithm is used to monitor 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 identified as an anomaly, triggering an early warning mechanism and notifying relevant personnel for handling. For example, if a sudden increase of 20% in community water consumption is detected, pipeline faults are promptly investigated to ensure the timeliness and effectiveness of community management. Based on the actual needs of community management, the K-means clustering algorithm was used to perform cluster analysis on community residents. By selecting characteristics such as age, income, and consumption as clustering dimensions, residents were divided into different groups, and the differences in service needs and satisfaction among these groups were analyzed to provide data support for management decisions. For example, personalized value-added services were provided for high-income groups; convenient medical services were provided for the elderly, continuously optimizing the level of community management.

[0065] In this embodiment, based on a multidimensional data model, rule-based expert system technology is used to formally express the experience and decision-making logic of managers, construct a community management knowledge base and rule base, and realize the automated execution of business logic through a rule engine, dynamically generating command and dispatch plans based on the real-time status of the community.

[0066] Based on the needs of community management operations, a multidimensional data model is used to model relevant community management data, resulting in a multidimensional data model for community management. For community management operations, the experience, knowledge, and decision-making logic of management personnel are acquired and formalized using knowledge engineering methods to construct a community management knowledge base and rule base. Based on this knowledge base and rule base, rule-based expert system technology is employed to automate the execution of community management business logic through a rule engine. Real-time community status data is acquired, and applicable management decision rules are determined through rule matching based on the knowledge base and rule base. Based on the matched management decision rules and the real-time community status, a community command and dispatch plan is dynamically generated through reasoning and execution by the rule engine. The generated community command and dispatch plan is then distributed to relevant management personnel and the execution system to guide community management work. Continuous monitoring of community status changes and dynamic optimization of the community management knowledge base and rule base based on feedback data enhance the intelligence level of management decision-making.

[0067] Specifically, based on the needs of community management operations, a star schema is used to construct a multi-dimensional data model for community management. Community management indicators, such as community population size and service satisfaction, are used as the fact table, while factors influencing community management are used as the dimension table, such as community location and type. By establishing relationships between the fact table and the dimension table, multi-dimensional analysis of community management data is achieved. For community management operations, interviews were conducted with 20 community management experts to obtain their management experience and decision-making logic. Ontology-based methods were used to extract concepts and define relationships from the interview content, constructing a community management knowledge base and rule base containing 500 concepts and 200 rules. Based on the community management knowledge base and rule base, the CLIPS rule engine is used to transform community management business logic into IF-THEN rules, enabling automated execution of community management operations. The average response time for rule matching is less than 5 seconds. Real-time community status data, including population flow data and service demand data, is acquired. Based on the community management knowledge base and rule base, the Rete algorithm is used for rule matching to determine applicable management decision rules, achieving a matching accuracy of over 95%. Based on the matched management decision-making rules and combined with the real-time status of the community, the rule engine dynamically generates command and dispatch plans such as community inspection routes and service resource scheduling through forward and backward reasoning. The interpretability of the reasoning results reaches over 90%. The generated community command and dispatch plans are distributed in real time to the mobile terminals and execution systems of relevant management personnel via WebSocket protocol in JSON format, guiding the implementation of community management work. The average latency of plan distribution is less than 1 second. Continuous monitoring of changes in community status is conducted, and feedback data is analyzed using data mining techniques. Association rule mining algorithms are used to dynamically optimize community management rules, and decision tree algorithms are used to dynamically update community management knowledge. The knowledge base and rule base are updated daily to improve the intelligence level of management decision-making.

[0068] Based on 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 and execution of the rule engine.

[0069] The system acquires preset community management decision-making rules and real-time updated community status information; it inputs these rules and information into a rule inference engine; the engine calculates the applicability of each rule using a fuzzy inference algorithm based on the matching degree between the rules and the community status; it then determines the priority of each management measure using a weighted average algorithm based on the calculated applicability; finally, it generates a preliminary community command and dispatch plan using a heuristic search algorithm based on the determined priority; the system optimizes the preliminary plan using a constraint satisfaction algorithm to obtain the optimal plan that satisfies community management constraints; and finally, it outputs the final community command and dispatch plan and dynamically updates the management decision-making rules based on actual implementation results and changes in community status, forming 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, patrol forces need to be increased," and simultaneously collects real-time status information such as population flow and vehicle entry / exit within the community through IoT devices. After inputting the decision rules and status information into the rule inference engine, the engine uses a fuzzy inference algorithm to calculate the applicability of each decision rule. For example, based on the closeness of the current population density to the rule's threshold, the applicability of the rule to increase patrol forces is calculated to be 8. Then, the system uses a weighted average algorithm to combine the applicability of each decision rule and determine the priority of management measures, such as the priority of increasing patrol forces being 3 and the priority of strengthening access control being 2. Based on the priority of management measures, the system generates several preliminary alternative scheduling schemes through a heuristic search algorithm, such as a genetic algorithm. Next, based on preset community management constraints (such as the total number of security personnel and the upper limit of patrol frequency), the system uses a constraint satisfaction algorithm to optimize the preliminary schemes and obtain the optimal scheduling scheme that satisfies 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-making rules based on subsequent execution feedback, such as adjusting the population density threshold from 800 people / square kilometer to 1000 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, based on the rule base, case-based reasoning technology is adopted. By analyzing and summarizing historical events, handling plans and processes in typical scenarios are extracted to form a case library. When a new emergency occurs, 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] The process involves acquiring relevant information about community emergencies, including event type, severity, and scope of impact, and using this information as input parameters for case matching. In the rule base, corresponding handling rules and procedures are matched based on event type and severity to form a preliminary handling plan. In the case base, text similarity algorithms, such as TF-IDF or Word2Vec, are used to calculate the similarity between the current event and historical cases, identifying the most similar case. The handling plan generated from the rule base is compared with the matching plans from the case base to determine if there are any conflicts or inconsistencies. If conflicts exist, the priority is determined based on the severity and scope of impact of the event, prioritizing the rule base or case base plan. The selected handling plan is optimized and adjusted, taking into account the specific circumstances of the current event, such as geographical location and personnel composition, to generate a final handling plan. The optimized handling plan is then transformed into an executable emergency plan and procedure, and distributed to relevant departments and personnel to guide real-time emergency command and response.

[0073] Specifically, upon receiving information about a community emergency, the system first analyzes the type, severity, and scope of the event using an automated system. For example, natural language processing (NLP) is used to extract key information from the event description, such as "fire," "highly serious," and "affected area approximately 300 square meters." Next, the system uses a pre-defined rule base to automatically match appropriate handling rules and procedures based on the event type ("fire") and severity ("highly serious"), such as initiating emergency evacuation procedures and notifying the fire department. Simultaneously, the system uses the TF-IDF algorithm to analyze the current event description against text data in a historical case database, calculating similarity and finding the most similar historical case, such as a previously handled shopping mall fire. The system compares the handling plans in the rule base with those in the case database, using algorithms to detect differences and conflicts. If a conflict is detected, the system uses a priority judgment model based on the severity and scope of the event to decide whether to adopt the rule base plan or the case database plan. The selected handling plan is further optimized and adjusted. The system adjusts the emergency plan according to the specific circumstances of the event, such as its location in the city center and the number of people involved, to ensure the plan's practical applicability. Finally, the system transforms the optimized emergency response plan and handling procedures into operation guidelines, which are 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 executed.

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

[0075] Real-time operational data of the community, including personnel location, vehicle location, and equipment operating parameters, is acquired and converted into a standardized data format. This standardized real-time data is then transmitted to a visualization platform and integrated with visualization components such as electronic maps and data dashboards via data interfaces. On the electronic map, the real-time distribution of personnel is dynamically marked based on personnel location data; real-time vehicle trajectories are plotted based on vehicle location data; and equipment status information is marked on the map based on equipment operating parameters. Multiple data indicators are set on the data dashboard, displaying key operational indicators of the community in real-time, such as the number of personnel, the number of vehicles, and equipment failure rates, through gauges, curves, pie charts, and other formats. The visualization platform's display methods are configured according to command and dispatch needs, providing multi-perspective data display methods such as area views, street views, and building views, and supporting drill-up and drill-down of data levels to achieve multi-level data browsing from macro to micro. Data analysis tools are integrated into the visualization platform to perform real-time analysis of community operational data, using data mining algorithms to discover correlation rules and anomaly patterns in the data, providing decision-making basis for command and dispatch. Based on the data analysis results, combined with pre-set scheduling rules and strategies, the visualization platform automatically generates a command and dispatch plan, which is then presented in a visual manner for commanders to review and optimize. Ultimately, it forms executable dispatch instructions, which are transmitted to relevant personnel and equipment to guide community operations.

[0076] Specifically, to obtain real-time operational data for the community, various sensors and positioning devices can be deployed within the community. For example, RFID technology can be used for tagging and managing personnel and vehicles, with RFID readers deployed to acquire their location information in real time, and the data reporting frequency can be set to once every 5 seconds. Simultaneously, wireless data acquisition modules can be installed on important equipment to collect equipment operating parameters in real time, such as voltage, current, and temperature, with a data sampling frequency of up to 50 times per second. The collected data is transmitted to a visualization platform in JSON format via IoT communication protocols such as MQTT. The platform uses a WebSocket interface to establish bidirectional data binding with the front-end UI components, enabling real-time data visualization. On the electronic map, clustering algorithms can be used to dynamically aggregate personnel locations and represent personnel density distribution in the form of a heat map. Vehicle trajectories can be smoothed using algorithms to remove noise from GPS data, improving trajectory accuracy. For equipment status information, warning thresholds can be set; when parameters exceed the threshold, an automatic alarm can be triggered, and the data is marked with a red icon on the map. The data dashboard can provide various visualization components, such as gauges displaying the real-time number of personnel and vehicles, curves reflecting the changing trends of these numbers, and pie charts showing the proportional relationship of equipment failure rates. By configuring a tree-like data dimension structure, drill-up and drill-down can be achieved from the entire community down to individual buildings. In terms of data analysis, association rule mining algorithms can be used to discover correlations between different device parameters; for example, when some devices malfunction, other devices may also exhibit abnormalities. Time series anomaly detection algorithms can also be used to determine if there are abnormal fluctuations in the collected data, providing a basis for scheduling decisions. Finally, the generation of scheduling plans can employ rule engine technology. Data analysis results are input into preset rules, automatically triggering corresponding scheduling strategies and presenting them to command personnel in visual formats such as Gantt charts. Command personnel then fine-tune the plan based on the actual situation, forming the final scheduling instructions, which are distributed to relevant personnel and equipment via message queues to achieve collaborative operation within the community.

[0077] Example 2

[0078] The present invention also provides a smart integrated management system for safe communities, including: an element extraction module, a model building module, a rule base building 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. Through data cleaning and correlation analysis, it extracts key community management indicators and business elements.

[0080] The model building module is used to construct 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 formally express the experience knowledge and decision-making logic of managers based on a multidimensional data model and employ rule-based expert system technology to build a community management knowledge base and rule base.

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

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

[0084] For the cleaned community data, an association rule mining algorithm is used to analyze the relationships 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 building unit is used to model community management-related data using a multidimensional data model based on the needs of community management business, thereby obtaining a multidimensional data model for community management.

[0087] The logic acquisition unit is used to acquire the experience knowledge and decision-making logic of managers based on the multi-dimensional data model of community management and community management business.

[0088] The second building block is used to formalize the experiential knowledge and decision-making logic of managers through knowledge engineering methods, and to build 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, an applicability calculation unit, a priority determination unit, a preliminary plan generation unit, an optimal plan generation unit, and a dynamic update unit;

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

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

[0092] The applicability calculation unit is used by the rule reasoning engine to calculate the applicability of each decision rule based on the degree of matching between the management decision rules and the community status using a fuzzy reasoning algorithm;

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

[0094] The preliminary plan generation unit is used to generate a preliminary community command and dispatch plan based on the determined management measures priority and through a heuristic search algorithm.

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

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

[0097] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for intelligent integrated management of safe communities, characterized in that, Includes the following steps: A distributed big data platform is used to aggregate and store massive amounts of community data resources. Through data cleaning and correlation analysis, key community management indicators and business elements are extracted. Based on key community management indicators and business elements, a multi-dimensional data model for command and dispatch is constructed. Based on a multidimensional data model, rule-based expert system technology is used to formalize the experience knowledge and decision-making logic of managers, and to build a community management knowledge base and rule base. Based on the community management knowledge base and rule base, the system uses a rule engine to automate the execution of business logic and dynamically generate command and dispatch plans according to the real-time status of the community. Methods for building community management knowledge bases and rule bases include: Based on the needs of community management operations, a multidimensional data model is used to model the relevant data of community management, resulting in a multidimensional data model of community management. Based on a multi-dimensional data model of community management and community management operations, we can acquire the experience, knowledge, and decision-making logic of management personnel. By using knowledge engineering methods, the experiential knowledge and decision-making logic of managers are formalized and expressed to build a community management knowledge base and rule base. Based on the community management knowledge base and rule base, the method of automating the execution of business logic through a rule engine and dynamically generating command and dispatch plans according to the real-time status of the community includes: Obtain preset community management decision-making rules and real-time updated community status information; The acquired community management decision-making rules and community status information are input into the rule reasoning engine; The rule-based reasoning engine calculates the applicability of each decision rule using a fuzzy reasoning algorithm based on the degree of matching between the management decision rules and the community status. Based on the calculated applicability of the decision rules, a weighted average algorithm is used to determine the priority of each management measure; Based on the determined management measures priorities, a preliminary community command and dispatch plan is generated using 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 satisfies the community management constraints. The final community command and dispatch plan will be output, and the management decision-making rules will be dynamically updated based on the actual implementation effect and changes in the community status to form a closed-loop feedback optimization. Specifically, based on the needs of community management operations, a star schema is used to construct a multi-dimensional data model for community management. Community management indicators are used as the fact table, and factors influencing community management are used as the dimension table. By establishing relationships between the fact table and the dimension table, multi-dimensional analysis of community management data is achieved. For community management operations, interviews were conducted with 20 community management experts to obtain their management experience and decision-making logic. Ontology-based methods were used to extract concepts and define relationships from the interview content, constructing a community management knowledge base and rule base containing 500 concepts and 200 rules. Based on the community management knowledge base and rule base, the CLIPS rule engine is used to transform community management business logic into IF-THEN rules, enabling automated execution of community management operations. The average response time for rule matching is less than 5 seconds. Real-time community status data, including population flow, is acquired. Based on dynamic data and service demand data, and using the Rete algorithm to match rules according to the community management knowledge base and rule base, applicable management decision rules are determined. Based on the matched management decision rules and combined with the real-time status of the community, the rule engine dynamically generates community inspection routes and service resource scheduling and command plans through forward and backward reasoning. The generated community command and dispatch plans are then distributed in real-time in JSON format via WebSocket protocol to the mobile terminals and execution systems of relevant management personnel to guide community management work, with an average delay of less than 1 second. The system continuously monitors changes in community status, analyzes feedback data using data mining techniques, dynamically optimizes community management rules using association rule mining algorithms, and dynamically updates community management knowledge using decision tree algorithms. The knowledge base and rule base are updated daily to improve the intelligence level of management decision-making. Specifically, the system pre-sets a series of community management decision-making rules. For example, when the community population density exceeds 800 people / square kilometer, patrol forces need to be increased. Simultaneously, IoT devices collect real-time information on population movement and vehicle entry / exit within the community. After inputting the decision rules and status information into the rule inference engine, the engine uses a fuzzy inference algorithm to calculate the applicability of each decision rule. Specifically, based on the proximity of the current population density to the rule's threshold, the applicability of the rule to increase patrol forces is calculated as 8. Then, the system uses a weighted average algorithm to combine the applicability of each decision rule and determine the priority of management measures. Increasing patrol force has a priority of 3, while strengthening access control has a priority of 2. Based on the priority of management measures, the system generates several preliminary alternative scheduling schemes through a heuristic search algorithm. Then, based on preset community management constraints, the system uses a constraint satisfaction algorithm to optimize the preliminary schemes and obtain the optimal scheduling scheme that satisfies the constraints. Finally, the system outputs the optimized community command and dispatch scheme and dynamically adjusts the decision rules based on subsequent execution feedback, that is, adjusting the population density threshold from 800 people / square kilometer to 1000 people / square kilometer, continuously optimizing in a closed loop to improve the accuracy and timeliness of management.

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

3. A smart integrated management system for safe communities, characterized in that, include: Element extraction module, model building module, rule base building module, and command and dispatch module; 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. The model building module is used to construct a multi-dimensional data model for command and dispatch based on key community management indicators and business elements. The rule base construction module is used to formally express the experience knowledge and decision-making logic of managers based on a multidimensional data model and rule-based expert system technology, thereby constructing a community management knowledge base and rule base. The command and dispatch module is used to automate the execution of business logic based on the community management knowledge base and rule base, and dynamically generate command and dispatch schemes according to the real-time status of the community.

4. The intelligent integrated management system for safe communities according to claim 3, characterized in that, The process of extracting key community management indicators and business elements in the element extraction module includes: For the cleaned community data, an association rule mining algorithm is used to analyze the relationships between different business elements and identify key community management indicators and business elements.

5. The intelligent integrated management system for safe communities according to claim 3, 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 the needs of community management business, so as to obtain a multidimensional data model for community management; The logic acquisition unit is used to acquire the experience knowledge and decision-making logic of management personnel based on the multi-dimensional data model of community management and community management business. The second building unit is used to formalize the experience knowledge and decision-making logic of managers through knowledge engineering methods, and to build a community management knowledge base and rule base.

6. The intelligent integrated management system for safe communities according to claim 3, characterized in that, The command and dispatch module includes: an information acquisition unit, an input unit, an applicability calculation unit, a priority determination unit, a preliminary plan generation unit, an optimal plan generation unit, and a dynamic update unit; The information acquisition unit is used to acquire preset community management decision-making 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 by the rule reasoning engine to calculate the applicability of each decision rule based on the degree of matching between the management decision rules and the community status using a fuzzy reasoning algorithm. The priority determination unit is used to determine the priority of each management measure based on the calculated applicability of the decision rules using a weighted average algorithm; The preliminary scheme generation unit is used to generate a preliminary community command and dispatch scheme based on the determined management measures priority and through a heuristic search algorithm. The optimal solution generation unit is used to optimize the generated preliminary community command and dispatch plan using a constraint satisfaction algorithm to obtain the optimal dispatch plan that satisfies the community management constraints. 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 implementation effect and changes in the community status, forming a closed-loop feedback optimization.

Images