Intelligent community management system integrating digital and intelligence

Abstract

The present invention relates to the field of community management technology, and specifically to a digital-intelligence integrated intelligent community management system, including a data acquisition module, a data processing module, an intelligent prediction and maintenance module, an intelligent analysis module, a user interaction module, and an automatic control module, wherein: the data acquisition module acquires management data; the data processing module processes the acquired management data; the intelligent prediction and maintenance module predicts equipment failures; the intelligent analysis module combines the structure of equipment failure prediction to intelligently analyze the generated management information; the user interaction module provides information display and interactive interfaces to community managers and residents; and the automatic control module automatically performs corresponding control operations according to the results of the intelligent analysis. The present invention provides accurate preventive maintenance suggestions for community management, and enhances the capabilities of intelligent community management and the efficiency of equipment maintenance.

Description

Technical Field

[0001] The present invention relates to the field of community management technology, and in particular to a digital-intelligence integrated intelligent community management system. Background Art

[0002] With the acceleration of urbanization, the application of intelligent community management systems in modern cities is becoming more and more extensive. Intelligent community management systems integrate information technology, the Internet of Things, artificial intelligence and other advanced technologies to achieve effective management and intelligent control of various resources in the community. They aim to improve the efficiency of community management and improve the quality of life and safety of residents. They can monitor various activities and environmental conditions in the community in real time, and provide comprehensive support and services for community management.

[0003] However, the existing intelligent community management system still has some shortcomings in data collection, processing and analysis. First, the scope of data collection is limited, and it is impossible to fully cover the various data sources in the community, resulting in incomplete and inaccurate management information. Secondly, the data processing and analysis capabilities are insufficient, and it is impossible to effectively integrate and utilize multi-source data for in-depth analysis. There is a lack of real-time monitoring and early warning mechanisms for equipment operating status. Furthermore, the existing system has a low level of intelligence in fault prediction and maintenance, and it is difficult to provide efficient preventive maintenance recommendations, resulting in a high equipment failure rate and increased maintenance costs. In addition, the response speed and ease of operation of the existing system in user interaction and automated control need to be improved, and it is difficult to meet the increasingly complex community management needs.

[0004] In view of the shortcomings of the above-mentioned prior art, the present invention proposes a smart community management system that integrates digital and intelligent data, aiming to achieve comprehensive monitoring and processing of multiple data sources within the community, provide accurate equipment failure prediction and preventive maintenance recommendations, and enhance the decision-making support capabilities of community management through intelligent analysis. Summary of the invention

[0005] Based on the above objectives, the present invention provides an intelligent community management system that integrates digital and intelligence.

[0006] The intelligent community management system integrating digital and intelligent technologies includes data collection module, data processing module, intelligent prediction and maintenance module, intelligent analysis module, user interaction module and automation control module, among which;

[0007] The data collection module collects management data from various data sources within the community, including resident information, security monitoring data, and environmental monitoring data;

[0008] The data processing module processes the collected management data, including data cleaning, data fusion and data analysis, to generate management information;

[0009] The intelligent predictive maintenance module monitors and analyzes the operating status of various devices in the community in real time, predicts equipment failures, and provides preventive maintenance suggestions, including:

[0010] Operation data collection: Through sensors installed on various devices in the community, the operation status data of the equipment is collected in real time, and the collected operation status data is pre-processed;

[0011] Fault prediction: Based on the extraction of operating parameters from the pre-processed operating status data, a fault prediction model is constructed, and fault prediction is performed on the real-time collected operating status data to identify fault modes;

[0012] Maintenance plan generation: Automatically generate preventive maintenance plans based on prediction results;

[0013] Early warning notification: When equipment failure is predicted, a fault early warning notification will be issued;

[0014] The intelligent analysis module combines the structure of equipment failure prediction to perform intelligent analysis on the generated management information, including anomaly detection, pattern recognition and predictive analysis;

[0015] The user interaction module provides information display and interactive interfaces to community managers and residents, including mobile applications, web pages and smart terminal devices;

[0016] The automation control module automatically performs corresponding control operations according to the results of intelligent analysis, including security equipment control, environmental regulation and equipment maintenance.

[0017] Furthermore, the data acquisition module includes:

[0018] Resident information collection: Through the registration and identity verification devices in the community management platform, residents’ personal information and behavior data are collected and updated in real time, including resident registration, visitor records, and identity verification data for entering and leaving the community;

[0019] Security monitoring data collection: Through the video surveillance cameras, access control units and alarm mechanisms deployed in the community, security monitoring data is collected, including video images, access control card swiping records and alarm triggering information;

[0020] Environmental monitoring data collection: Real-time monitoring and collection of air quality, temperature, humidity and noise levels through environmental sensors installed in the community.

[0021] Furthermore, the data processing module includes:

[0022] Data cleaning: The collected management data is filtered for noise and cleaned using the mean filter algorithm. The calculation formula is:

[0023]

[0024] Among them, x(t) represents the data at time t, N represents the sampling window size, and the data is smoothed and noise is removed by taking the mean of the data in the window;

[0025] Data fusion: The management data from different data sources are fused using the weighted average method. The calculation formula is:

[0026]

[0027] Among them, x fused represents the fused data, w i is the weight of the i-th sensor data, x i is the data of the i-th sensor;

[0028] Data analysis: The cluster analysis algorithm is used to analyze the cleaned and fused management data. The calculation formula is:

[0029]

[0030] Where k represents the number of clusters, C i represents the i-th cluster, μ i is the centroid of the ith cluster.

[0031] Furthermore, the operation data collection includes:

[0032] Sensor installation: Install various types of sensors, including temperature sensors, vibration sensors, and current sensors, on various devices in the community to monitor the operating status of the devices;

[0033] Real-time data collection: The operating status data of the equipment is collected in real time through sensors, including temperature data of the equipment monitored by temperature sensors, vibration data of the equipment monitored by vibration sensors, and current data of the equipment monitored by current sensors;

[0034] Data preprocessing: Preprocess the operating status data collected in real time, including noise filtering and outlier detection. Noise filtering uses the mean filtering algorithm, and outlier detection uses the standard deviation method to identify and process data points that deviate from the normal range.

[0035] Furthermore, the fault prediction includes:

[0036] Extracting operating parameters: Extracting operating parameters from the preprocessed operating status data, including temperature changes, vibration frequency, and current fluctuations;

[0037] Model construction: construct a fault prediction model based on the extracted operating parameters;

[0038] Predict faults: Input the real-time collected operating status data into the fault prediction model to perform fault prediction, identify fault modes, and output fault prediction results.

[0039] Furthermore, the fault prediction model adopts a graph neural network (GNN), and the graph neural network (GNN) includes:

[0040] Node feature representation and normalization: The feature vector h of each device node v (temperature change, vibration frequency and current fluctuation), and the calculation formula is:

[0041]

[0042] Where, ΔT v 、FFT(v) v and ΔI v are temperature variation, vibration frequency and current fluctuation, μ and σ are mean and standard deviation, respectively;

[0043] Message passing mechanism: Consider the weight W between devices uv and the relationship strength α uv , update the node representation through the message passing mechanism, and the calculation formula is:

[0044]

[0045] Among them, α uv is the attention weight between nodes u and v, W uv is the connection weight, is the representation of node u at layer t;

[0046] Multi-head attention mechanism: Use a multi-head attention mechanism to capture the importance of different relationships and features. The calculation formula is:

[0047]

[0048] Among them, W Q and W K is the weight matrix of query and key, d k is the dimension of the key;

[0049] Node feature aggregation: Aggregate the neighbor information of the node and update the node representation. The calculation formula is:

[0050]

[0051] in, is the set of neighbor nodes of node v, W0 is the self-connection weight, and σ is the ReLU activation function;

[0052] Fault prediction output layer: outputs the fault prediction results of the device based on the node characteristics and aggregated information. The calculation formula is:

[0053]

[0054] Among them, W o is the output layer weight matrix, b is the bias, is the predicted probability of failure of node v.

[0055] Furthermore, the maintenance plan generation includes:

[0056] Fault prediction result analysis: Analyze the generated fault prediction results, including equipment identification, fault type and fault probability

[0057] Maintenance priority determination: based on failure probability The maintenance priority score is generated based on the severity of the fault type and the maintenance task priority. The calculation formula is:

[0058]

[0059] Among them, α and β are weight coefficients used to adjust the impact of fault probability and fault severity on priority score. is the predicted probability of failure of device v, and Fault_Severity is the severity of the fault type;

[0060] Maintenance task allocation: According to the equipment type and fault type, select the corresponding maintenance task from the predefined maintenance task library and assign it to the maintenance personnel or team;

[0061] Maintenance plan generation: Combine the equipment's operating status, fault history, and maintenance records to automatically generate a preventive maintenance plan, including maintenance steps, required tools and spare parts, estimated time, and maintenance personnel.

[0062] Furthermore, the warning notification includes:

[0063] Fault identification: When the fault prediction model identifies the failure mode of the equipment and calculates the failure probability When the preset threshold is exceeded, the device is judged to be at risk of failure;

[0064] Early warning information generation: Generate early warning notifications containing equipment identification, fault type, fault probability, recommended operations and maintenance plans;

[0065] Notification delivery: Send early warning notifications to community managers and maintenance teams through multiple channels (such as SMS, email, and community management platform notifications);

[0066] Notification confirmation and response: After receiving the early warning notification, the community managers and maintenance team will confirm receipt and make corresponding maintenance responses.

[0067] Furthermore, the intelligent analysis module includes:

[0068] Anomaly detection: Detect anomalies in equipment operation data using statistical methods. By calculating the degree of deviation of each data point relative to the mean and standard deviation, anomalies above the preset threshold are identified. The calculation formula is:

[0069]

[0070] Among them, x i is the data point, μ is the mean of the data, σ is the standard deviation of the data, and θ is the preset threshold;

[0071] Pattern recognition: using clustering algorithms to identify patterns in equipment operation data;

[0072] Predictive analysis: Use time series analysis and regression models to perform predictive analysis. The calculation formula is:

[0073]

[0074] Among them, y t is the predicted value at time t, c is a constant, and θ j are the autoregressive and moving average coefficients, ∈ t is the error term.

[0075] Furthermore, the automation control module includes:

[0076] Security equipment control: Automatically adjust the status of security equipment based on the results of anomaly detection and pattern recognition, including automatically adjusting the direction of the camera and enabling the alarm mechanism;

[0077] Environmental control: Automatically adjust the environmental parameters within the community, including temperature control and air quality control, based on environmental monitoring data and intelligent analysis results;

[0078] Equipment maintenance: Automatically perform preventive maintenance operations based on equipment failure prediction results, including automatic restart of equipment and remote diagnosis.

[0079] Beneficial effects of the present invention:

[0080] The present invention, through comprehensive monitoring and processing of various data sources in the community, the system can accurately collect and process resident information, security monitoring data and environmental monitoring data, and generate high-quality management information, providing a solid data foundation for intelligent predictive maintenance and intelligent analysis. The system effectively improves the operating reliability and maintenance efficiency of equipment through automated fault prediction and preventive maintenance measures, reduces equipment failure rate and maintenance costs, and significantly improves the intelligence level and decision-making ability of community management.

[0081] The present invention uses graph neural networks to perform equipment failure prediction, which significantly improves the accuracy and robustness of the prediction. Through the dynamic update of node features and the multi-head attention mechanism, the system can capture the temporal changes in the equipment's operating status and the importance of different relationships, and then perform high-precision fault prediction through the graph neural network model, providing accurate preventive maintenance recommendations for community management, further enhancing the capabilities of intelligent community management and the efficiency of equipment maintenance.

[0082] The present invention uses a simple threshold detection method of mean and standard deviation, and the system can quickly and effectively identify abnormal conditions in equipment operation, and use clustering algorithms to identify patterns in equipment operation data, revealing operating rules and potential problems. At the same time, the application of time series analysis and regression models enables the system to accurately predict the future operating status of the equipment, provide community managers with timely fault warnings and maintenance suggestions, and ensure the stable operation of the equipment and the safety of the community. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0084] Figure 1 A schematic diagram of system function modules according to an embodiment of the present invention;

[0085] Figure 2 Schematic diagram of an intelligent predictive maintenance module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0086] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0087] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0088] like Figure 1-Figure 2 As shown, the intelligent community management system integrating digital intelligence includes a data collection module, a data processing module, an intelligent prediction and maintenance module, an intelligent analysis module, a user interaction module and an automatic control module, among which;

[0089] The data collection module collects management data from various data sources within the community, including resident information, security monitoring data, and environmental monitoring data;

[0090] The data processing module processes the collected management data, including data cleaning, data fusion and data analysis, to generate management information;

[0091] The intelligent predictive maintenance module monitors and analyzes the operating status of various devices in the community in real time, predicts equipment failures, and provides preventive maintenance suggestions, including:

[0092] Operation data collection: Through sensors installed on various devices in the community, the operation status data of the equipment is collected in real time, and the collected operation status data is pre-processed;

[0093] Fault prediction: Based on the extraction of operating parameters from the pre-processed operating status data, a fault prediction model is constructed, and fault prediction is performed on the real-time collected operating status data to identify fault modes;

[0094] Maintenance plan generation: Automatically generate preventive maintenance plans based on prediction results;

[0095] Early warning notification: When equipment failure is predicted, a fault early warning notification will be issued;

[0096] The intelligent analysis module combines the structure of equipment failure prediction to perform intelligent analysis on the generated management information, including anomaly detection, pattern recognition and predictive analysis, to provide decision support for community management;

[0097] The user interaction module provides information display and interactive interfaces to community managers and residents, including mobile applications, web pages, and smart terminal devices;

[0098] The automation control module automatically performs corresponding control operations based on the results of intelligent analysis, including security equipment control, environmental regulation and equipment maintenance;

[0099] Through the above content, comprehensive monitoring and processing of various data sources within the community are achieved, accurate equipment failure prediction and preventive maintenance are provided, and at the same time, the decision-making support capability of community management is improved through intelligent analysis, providing convenient information display and interactive interfaces for community managers and residents, effectively improving the intelligence level and operational efficiency of community management, and enhancing the safety and reliability of community services.

[0100] The data acquisition module includes:

[0101] Resident information collection: Through the registration and identity verification devices in the community management platform, residents’ personal information and behavior data are collected and updated in real time, including resident registration, visitor records, and identity verification data for entering and leaving the community;

[0102] Security monitoring data collection: Through the video surveillance cameras, access control units and alarm mechanisms deployed in the community, security monitoring data is collected, including video images, access control card swiping records and alarm triggering information;

[0103] Environmental monitoring data collection: Through the environmental sensors installed in the community, air quality, temperature, humidity and noise level are monitored and collected in real time to ensure the comprehensiveness and accuracy of environmental monitoring data;

[0104] Through the above content, the comprehensiveness and accuracy of the data are ensured, providing a reliable data foundation for subsequent data processing and intelligent analysis, thereby improving the safety of community services, the effectiveness of environmental monitoring and overall management efficiency.

[0105] The data processing module includes:

[0106] Data cleaning: The collected management data is filtered for noise and cleaned using the mean filter algorithm. The calculation formula is:

[0107]

[0108] Among them, x(t) represents the data at time t, N represents the sampling window size, and the data is smoothed and noise is removed by taking the mean of the data in the window;

[0109] Data fusion: The management data from different data sources are fused using the weighted average method. The calculation formula is:

[0110]

[0111] Among them, x fused represents the fused data, w i is the weight of the i-th sensor data, x i is the data of the i-th sensor, weight w i Can be set according to the accuracy and reliability of the sensor;

[0112] Data analysis: The cluster analysis algorithm is used to analyze the cleaned and fused management data. The calculation formula is:

[0113]

[0114] Where k represents the number of clusters, C i represents the i-th cluster, μ i is the centroid of the i-th cluster;

[0115] Through the above steps, the high quality and reliability of data are ensured, accurate management information is generated, and a solid data foundation is provided for intelligent predictive maintenance and intelligent analysis, thereby improving the intelligence level and decision-making ability of community management and effectively improving the efficiency and safety of community services.

[0116] Operational data collection includes:

[0117] Sensor installation: Install various types of sensors, including temperature sensors, vibration sensors, and current sensors, on various devices in the community to monitor the operating status of the devices;

[0118] Real-time data collection: The operating status data of the equipment is collected in real time through sensors, including temperature data of the equipment monitored by temperature sensors, vibration data of the equipment monitored by vibration sensors, and current data of the equipment monitored by current sensors;

[0119] Data preprocessing: preprocess the running status data collected in real time, including noise filtering and outlier detection. Noise filtering uses the mean filter algorithm, and outlier detection uses the standard deviation method to identify and process data points that deviate from the normal range.

[0120] Through the above content, the operating status of the equipment is monitored in real time, and the collected data is preprocessed to ensure the accuracy and reliability of the data, providing high-quality data support for fault prediction and intelligent analysis, thereby improving the intelligence of community management and the predictability of equipment maintenance, and reducing equipment failure rate and maintenance costs.

[0121] Failure prediction includes:

[0122] Extracting operating parameters: Extracting operating parameters from the preprocessed operating status data, including temperature changes, vibration frequency, and current fluctuations, specifically including:

[0123] Temperature change: Extract the rate of change of the device temperature sensor data. The calculation formula is:

[0124] ΔT = T(t) - T(t-1);

[0125] Where ΔT represents the temperature change rate, T(t) is the temperature value at time t;

[0126] Vibration frequency: Extract the frequency component of the device vibration sensor data and use fast Fourier transform (FFT) to calculate the vibration frequency. The calculation formula is:

[0127]

[0128] Where X(f) represents the vibration amplitude at frequency f, and x(t) is the vibration data at time t;

[0129] Current fluctuation: Extract the fluctuation rate of the device current sensor data. The calculation formula is:

[0130] ΔI=I(t)-I avg ;

[0131] Where ΔI represents the current fluctuation rate, I(t) is the current value at time t, and I avg is the average current value within the predetermined time window;

[0132] Model construction: construct a fault prediction model based on the extracted operating parameters;

[0133] Predicting faults: Inputting the real-time collected operating status data into the fault prediction model to predict faults, identify fault modes, and output the fault prediction results to prompt community managers to perform preventive maintenance;

[0134] Through the above content, based on real-time data, high-precision fault prediction and early warning are provided, which enhances the robustness and adaptability of the system, helps to prevent equipment failures in advance and optimize maintenance plans, thereby greatly improving the intelligence level of community management and the reliability of equipment operation.

[0135] The fault prediction model uses a graph neural network (GNN), which includes:

[0136] Node feature representation and normalization: The feature vector h of each device node v (temperature change, vibration frequency and current fluctuation), and the calculation formula is:

[0137]

[0138] Where, ΔT v 、FFT(v) v and ΔI v are temperature variation, vibration frequency and current fluctuation, μ and σ are mean and standard deviation, respectively;

[0139] Message passing mechanism: Consider the weight W between devices uv and the relationship strength α uv , update the node representation through the message passing mechanism, and the calculation formula is:

[0140]

[0141] Among them, α uv is the attention weight between nodes u and v, W uv is the connection weight, is the representation of node u at layer t;

[0142] Multi-head attention mechanism: Use a multi-head attention mechanism to capture the importance of different relationships and features. The calculation formula is:

[0143]

[0144] Among them, W Q and W K is the weight matrix of query and key, d k is the dimension of the key;

[0145] Node feature aggregation: Aggregate the neighbor information of the node and update the node representation. The calculation formula is:

[0146]

[0147] in, is the set of neighbor nodes of node v, W0 is the self-connection weight, and σ is the ReLU activation function;

[0148] Fault prediction output layer: outputs the fault prediction results of the device based on the node characteristics and aggregated information. The calculation formula is:

[0149]

[0150] Among them, W o is the output layer weight matrix, b is the bias, is the predicted probability of failure of node v;

[0151] Through the above content, graph neural network (GNN) is more suitable for fault prediction in intelligent community management systems, which improves the prediction accuracy and robustness of the model, and effectively improves the intelligence level of community management and the efficiency of equipment maintenance.

[0152] Maintenance plan generation includes:

[0153] Fault prediction result analysis: Analyze the generated fault prediction results, including equipment identification, fault type and fault probability

[0154] Maintenance priority determination: based on failure probability The maintenance priority score is generated based on the severity of the fault type and the maintenance task priority. The calculation formula is:

[0155]

[0156] Among them, α and β are weight coefficients used to adjust the impact of fault probability and fault severity on priority score. is the predicted probability of failure of device v, and Fault_Severity is the severity of the fault type;

[0157] Maintenance task allocation: According to the equipment type and fault type, select the corresponding maintenance task from the predefined maintenance task library and assign it to the maintenance personnel or team;

[0158] Maintenance plan generation: Combine the equipment's operating status, fault history and maintenance records to automatically generate a preventive maintenance plan, including maintenance steps, required tools and spare parts, estimated time and maintenance personnel;

[0159] Through the above steps, timely and priority processing of maintenance tasks is ensured, the frequency of equipment failures and repair time are reduced, maintenance efficiency and equipment reliability are improved, thereby greatly improving the overall operational efficiency and service quality of community management.

[0160] The predefined maintenance task library includes:

[0161] Equipment type: covers all types of equipment in the community, including security equipment (cameras, access control systems, etc.), environmental monitoring equipment (air quality sensors, temperature and humidity sensors, etc.), energy equipment (power systems, water supply systems, etc.) and communication equipment (network routers, signal amplifiers, etc.);

[0162] Fault type: For each device type, common fault types are listed, including connection failure, image blur, system crash of security equipment, data deviation, sensor failure, power supply problem of environmental monitoring equipment, voltage anomaly, equipment overheating, power supply interruption of energy equipment, signal loss, network delay, hardware failure of communication equipment;

[0163] Maintenance steps: Detailed maintenance steps for each fault type, including checking device connection status, restarting the device or system, replacing or repairing damaged parts, and recalibrating sensors;

[0164] Required tools and spare parts: A list of specific tools and spare parts required for the maintenance task, including screwdrivers, multimeters, soldering tools, spare batteries, sensor modules, and connecting cables;

[0165] Estimated time: The estimated time required to complete each maintenance task, for example: simple fault: 30 minutes, complex fault: 2 hours;

[0166] Maintenance personnel or team: The skill requirements and allocation strategies of personnel or teams suitable for performing the maintenance tasks, including general maintenance personnel, professional technicians, and special equipment suppliers.

[0167] Warning notifications include:

[0168] Fault identification: When the fault prediction model identifies the failure mode of the equipment and calculates the failure probability When the preset threshold is exceeded, the device is judged to be at risk of failure;

[0169] Early warning information generation: Generate early warning notifications containing equipment identification, fault type, fault probability, recommended operations and maintenance plans;

[0170] Notification delivery: Send early warning notifications to community managers and maintenance teams through multiple channels (such as SMS, email, and community management platform notifications);

[0171] Notification confirmation and response: After receiving the warning notification, the community managers and maintenance team will confirm receipt and make corresponding maintenance responses;

[0172] Through the above content, detailed early warning notifications can be quickly generated and delivered to relevant personnel in a timely manner through multiple channels. The efficient notification mechanism ensures that community managers can take preventive measures before failures occur, thereby reducing equipment downtime and maintenance costs, improving the response speed and overall operating efficiency of community management, and ensuring the safe and stable operation of community facilities.

[0173] Intelligent analysis modules include:

[0174] Anomaly detection: Detect anomalies in equipment operation data using statistical methods. By calculating the degree of deviation of each data point relative to the mean and standard deviation, anomalies above the preset threshold are identified. The calculation formula is:

[0175]

[0176] Among them, x iis the data point, μ is the mean of the data, σ is the standard deviation of the data, and θ is the preset threshold. is greater than the threshold θ, then x i It is considered an outlier;

[0177] Pattern recognition: Use clustering algorithms to identify patterns in equipment operation data. The calculation formula is:

[0178]

[0179] Among them, C i is the i-th cluster, μ i is the center of the i-th cluster;

[0180] Predictive analysis: Use time series analysis and regression models to perform predictive analysis. The calculation formula is:

[0181]

[0182] Among them, y t is the predicted value at time t, c is a constant, and θ j are the autoregressive and moving average coefficients, ∈ t is the error term;

[0183] Through the above content, the intelligent community management system can comprehensively and accurately analyze equipment operation data, provide scientific management decision support, and effectively improve the intelligence level of community management and the reliability of equipment operation.

[0184] The automation control module includes:

[0185] Security equipment control: Automatically adjust the status of security equipment based on the results of anomaly detection and pattern recognition, including automatically adjusting the direction of the camera and enabling the alarm mechanism to ensure the safety of the community;

[0186] Environmental control: Based on environmental monitoring data and intelligent analysis results, automatically adjust environmental parameters within the community, including temperature control and air quality control, to ensure the comfort and health of residents;

[0187] Equipment maintenance: Automatically perform preventive maintenance operations based on equipment failure prediction results, including automatic restart of equipment and remote diagnosis, to extend equipment life and reduce failure rates;

[0188] Through the above, the safety of the community and the comfort of residents are ensured, the need for manual intervention is reduced, the reliability and life of equipment operation are improved, and the intelligent level of community management and overall operational efficiency are significantly improved. Through automated control, community management becomes more efficient and reliable, providing residents with a safer, more comfortable and convenient living environment.

[0189] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present invention is limited to these examples. Under the concept of the present invention, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity.

Claims

1. The intelligent community management system integrating digital and intelligence is characterized by: It includes data acquisition module, data processing module, intelligent predictive maintenance module, intelligent analysis module, user interaction module and automatic control module, among which; The data collection module collects management data from various data sources within the community, including resident information, security monitoring data, and environmental monitoring data; The data processing module processes the collected management data, including data cleaning, data fusion and data analysis, to generate management information; The intelligent predictive maintenance module monitors and analyzes the operating status of various devices in the community in real time, predicts equipment failures, and provides preventive maintenance suggestions, including: Operation data collection: Through sensors installed on various devices in the community, the operation status data of the equipment is collected in real time, and the collected operation status data is pre-processed; Fault prediction: Based on the extraction of operating parameters from the pre-processed operating status data, a fault prediction model is constructed, and fault prediction is performed on the real-time collected operating status data to identify fault modes; Maintenance plan generation: Automatically generate preventive maintenance plans based on prediction results; Early warning notification: When equipment failure is predicted, a fault early warning notification is issued; The intelligent analysis module combines the structure of equipment failure prediction to perform intelligent analysis on the generated management information, including anomaly detection, pattern recognition and predictive analysis; The user interaction module provides information display and interactive interfaces to community managers and residents, including mobile applications, web pages and smart terminal devices; The automation control module automatically performs corresponding control operations according to the results of intelligent analysis, including security equipment control, environmental regulation and equipment maintenance; The fault prediction model adopts a graph neural network, and the graph neural network includes: Node feature representation and normalization: The feature vector hv of each device node is standardized and the calculation formula is: Where, ΔT v 、FFT(v) v and ΔI v are temperature variation, vibration frequency and current fluctuation, μ and σ are mean and standard deviation, respectively; Message passing mechanism: Consider the weight W between devices uv and the tie strength α uv , update the node representation through the message passing mechanism, and the calculation formula is: Among them, α uv is the attention weight between nodes u and v, W uv is the connection weight, is the representation of node u at layer t; Multi-head attention mechanism: Use a multi-head attention mechanism to capture the importance of different relationships and features. The calculation formula is: Among them, W Q and W K is the weight matrix of query and key, dk is the dimension of key; Node feature aggregation: Aggregate the neighbor information of the node and update the node representation. The calculation formula is: in, is the set of neighbor nodes of node v, W0 is the self-connection weight, and σ is the ReLU activation function; Fault prediction output layer: outputs the fault prediction results of the device based on the node characteristics and aggregated information. The calculation formula is: Among them, W o is the output layer weight matrix, b is the bias, is the predicted probability of failure of node v; The maintenance plan generation includes: Fault prediction result analysis: Analyze the generated fault prediction results, including equipment identification, fault type and fault probability Maintenance priority determination: based on failure probability The maintenance priority score is generated based on the severity of the fault type and the maintenance task priority. The calculation formula is: Among them, α and β are weight coefficients used to adjust the impact of fault probability and fault severity on priority score. is the predicted probability of failure of device v, and Fault_Severity is the severity of the fault type; Maintenance task allocation: According to the equipment type and fault type, select the corresponding maintenance task from the predefined maintenance task library and assign it to the maintenance personnel or team; Maintenance plan generation: Combine the equipment's operating status, fault history and maintenance records to automatically generate a preventive maintenance plan, including maintenance steps, required tools and spare parts, estimated time and maintenance personnel; The warning notice includes: Fault identification: When the fault prediction model identifies the failure mode of the equipment and calculates the failure probability When the preset threshold is exceeded, the device is judged to be at risk of failure; Early warning information generation: Generate early warning notifications containing equipment identification, fault type, fault probability, recommended operations and maintenance plans; Notification delivery: Send early warning notifications to community managers and maintenance teams through multiple channels; Notification confirmation and response: After receiving the warning notification, the community managers and maintenance team will confirm receipt and make corresponding maintenance responses; The intelligent analysis module includes: Anomaly detection: Detect anomalies in equipment operation data using statistical methods. By calculating the degree of deviation of each data point relative to the mean and standard deviation, anomalies above the preset threshold are identified. The calculation formula is: Among them, x i is the data point, μ is the mean of the data, σ is the standard deviation of the data, and θ is the preset threshold; Pattern recognition: using clustering algorithms to identify patterns in equipment operation data; Predictive analysis: Use time series analysis and regression models to perform predictive analysis. The calculation formula is: Among them, y t is the predicted value at time t, c is a constant, and θ j are the autoregressive and moving average coefficients, ∈ t is the error term.

2. The intelligent community management system integrating digital and intelligence according to claim 1 is characterized in that: The data acquisition module comprises: Resident information collection: Through the registration and identity verification devices in the community management platform, residents’ personal information and behavior data are collected and updated in real time, including resident registration, visitor records, and identity verification data for entering and leaving the community; Security monitoring data collection: Through the video surveillance cameras, access control units and alarm mechanisms deployed in the community, security monitoring data is collected, including video images, access control card swiping records and alarm triggering information; Environmental monitoring data collection: Real-time monitoring and collection of air quality, temperature, humidity and noise levels through environmental sensors installed in the community.

3. The intelligent community management system integrating digital and intelligence according to claim 2 is characterized in that: The data processing module comprises: Data cleaning: The collected management data is filtered for noise and cleaned using the mean filter algorithm. The calculation formula is: Among them, x(t) represents the data at time t, N represents the sampling window size, and the data is smoothed and noise is removed by taking the mean of the data in the window; Data fusion: The management data from different data sources are fused using the weighted average method. The calculation formula is: Among them, x fused represents the fused data, w i is the weight of the i-th sensor data, x i is the data of the i-th sensor; Data analysis: The cluster analysis algorithm is used to analyze the cleaned and fused management data. The calculation formula is: Where k represents the number of clusters, Ci represents the i-th cluster, μ i is the centroid of the ith cluster.

4. The intelligent community management system integrating digital and intelligence according to claim 1 is characterized in that: The operation data collection includes: Sensor installation: Install various types of sensors, including temperature sensors, vibration sensors, and current sensors, on various devices in the community to monitor the operating status of the devices; Real-time data collection: The operating status data of the equipment is collected in real time through sensors, including temperature data of the equipment monitored by temperature sensors, vibration data of the equipment monitored by vibration sensors, and current data of the equipment monitored by current sensors; Data preprocessing: Preprocess the operating status data collected in real time, including noise filtering and outlier detection. Noise filtering uses the mean filtering algorithm, and outlier detection uses the standard deviation method to identify and process data points that deviate from the normal range.

5. The intelligent community management system integrating digital and intelligence according to claim 4 is characterized in that: The fault prediction includes: Extracting operating parameters: Extracting operating parameters from the preprocessed operating status data, including temperature changes, vibration frequency, and current fluctuations; Model construction: construct a fault prediction model based on the extracted operating parameters; Predict faults: Input the real-time collected operating status data into the fault prediction model to perform fault prediction, identify fault modes, and output fault prediction results.

6. The intelligent community management system integrating digital intelligence according to claim 1 is characterized in that: The automation control module comprises: Security equipment control: Automatically adjust the status of security equipment based on the results of anomaly detection and pattern recognition, including automatically adjusting the direction of the camera and enabling the alarm mechanism; Environmental control: Automatically adjust the environmental parameters within the community, including temperature control and air quality control, based on environmental monitoring data and intelligent analysis results; Equipment maintenance: Automatically perform preventive maintenance operations based on equipment failure prediction results, including automatic restart of equipment and remote diagnosis.

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