Smart community monitoring method, system and device based on Internet of Things and medium

Abstract

The invention relates to a smart community monitoring method, system and device based on the Internet of Things, and a medium. The method comprises the following steps: acquiring sensor data, monitoring data and service data of a smart community; performing calculation processing on the sensor data to obtain initial sensing data, and performing optimization processing on the initial sensing data to obtain environment data; performing association analysis on the environment data according to the service data to obtain corresponding integrated association data; performing deep analysis on the integrated associated data according to the monitoring data to obtain a corresponding intelligent analysis result, and performing comprehensive processing on the intelligent analysis result based on the service data to obtain a corresponding monitoring analysis report; and performing security policy analysis according to the monitoring analysis report and the intelligent analysis result to obtain initial scheduling policies, and performing comprehensive analysis on all the initial scheduling policies to obtain a comprehensive scheduling policy. According to the invention, the overall safety and management efficiency of the community can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart communities, and in particular to a smart community monitoring method, system, device and medium based on the Internet of Things. Background Art

[0002] With the development of Internet of Things technology, the construction of smart communities has become an important part of smart cities. Traditional community monitoring systems often rely on manual inspections and video surveillance, and have problems such as slow response speed and unreasonable resource allocation. In recent years, through the application of Internet of Things technology, various data in the community can be collected in real time, such as environmental monitoring data, human flow statistics, etc., and these data can be processed using big data analysis technology to achieve intelligent management of the community. However, in the actual application process, there are still some challenges, such as insufficient data processing capabilities leading to information processing delays and difficulty in responding in time; the correlation analysis between data is not in-depth enough, making the results of data analysis not accurate enough; at the same time, due to the lack of effective integration methods, it is difficult to form a synergistic effect between the various subsystems, affecting the overall safety management level. Therefore, how to efficiently process and analyze data from various sensors and combine them with monitoring and service data to improve the overall safety and management efficiency of the community has become an urgent problem to be solved. Summary of the invention

[0003] The main purpose of the present invention is to provide a smart community monitoring method, system, device and medium based on the Internet of Things, which can improve the overall safety and management efficiency of the community.

[0004] To achieve the above object, the present invention provides a smart community monitoring method based on the Internet of Things, comprising: Obtain sensor data, monitoring data, and service data for smart communities; Performing calculation processing on the sensor data to obtain initial sensor data, and performing optimization processing on the initial sensor data to obtain environmental data; Performing correlation analysis on the environment data according to the service data to obtain corresponding integrated correlation data; Performing in-depth analysis on the integrated related data according to the monitoring data to obtain corresponding intelligent analysis results, and performing comprehensive processing on the intelligent analysis results based on the service data to obtain corresponding monitoring analysis reports; A security strategy analysis is performed based on the monitoring analysis report and the intelligent analysis results to obtain an initial scheduling strategy, and a comprehensive analysis is performed on all the initial scheduling strategies to obtain a comprehensive scheduling strategy.

[0005] Furthermore, the acquisition of sensor data, monitoring data and service data of the smart community includes: Collecting data from multi-source heterogeneous sensors in the smart community to obtain raw sensor data, cleaning and preprocessing the raw sensor data to obtain preprocessed data, and fusing multi-source data on the preprocessed data to obtain the sensor data; Performing real-time data collection on the video surveillance equipment of the smart community to obtain an original surveillance video stream, and performing video encoding and compression processing on the original surveillance video stream to obtain compressed data; Performing target detection and tracking on the compressed data according to a preset video analysis algorithm to obtain structured monitoring data; Acquire original service data from the community system of the smart community, and perform data standardization processing on the original service data to obtain standard data; The standard data is classified and clustered to obtain the service data.

[0006] Further, the calculating and processing the sensor data to obtain initial sensor data, and optimizing and processing the initial sensor data to obtain environmental data, include: Performing feature extraction processing on the sensor data to obtain initial feature data, performing data dimensionality reduction processing on the initial feature data to obtain reduced-dimensional feature data, and performing data fusion processing on all reduced-dimensional feature data to obtain fused feature data; Performing anomaly detection processing on the fused feature data to obtain an anomaly detection result, and performing data correction processing on the fused feature data according to the anomaly detection result to obtain the initial sensing data; Performing data standardization processing on the initial sensing data to obtain standard sensing data; The standard sensor data is optimized and analyzed to obtain an optimization result, and the standard sensor data is optimized according to the optimization result to obtain the environmental data.

[0007] Furthermore, the performing correlation analysis on the environment data according to the service data to obtain corresponding integrated correlation data includes: Performing subclass splitting processing on the environment data to obtain multiple environment data subsets, and performing semantic analysis processing on the service data to obtain a service feature vector; Calculate the similarity of the environment data subset according to the service feature vector to obtain the environment service similarity matrix, perform threshold filtering on the environment service similarity matrix to obtain correlation data pairs; According to the correlation data, rules are constructed to obtain an environmental service association rule set, and matching analysis is performed on the environmental service association rule set to obtain corresponding service data prediction results; Performing time series correlation analysis on the service prediction results to obtain the integrated correlation data.

[0008] Further, the integrated related data is deeply analyzed according to the monitoring data to obtain corresponding intelligent analysis results, and the intelligent analysis results are comprehensively processed based on the service data to obtain a corresponding monitoring analysis report, including: Performing trend forecasting analysis on the integrated correlation data to obtain dynamic change characteristics, performing pattern recognition on the dynamic change characteristics according to the monitoring data to obtain classification results, performing deviation detection and deviation processing on the classification results to obtain a prediction rule set, and performing forward-looking analysis on the prediction rule set to obtain an estimated result; Performing data mining based on the estimation results and the service data to obtain a set of associated patterns, and graphically presenting the set of associated patterns to obtain a data visualization chart; Generate a deep insight report based on the data visualization chart; perform text analysis on the deep insight report based on the service data to obtain semantic features; Performing intelligent text processing on the deep insight report according to semantic features to obtain structured content; performing sentiment evaluation on the structured content to obtain sentiment trend data; Comprehensively analyze the structured content and sentiment trend data to obtain the monitoring analysis report.

[0009] Furthermore, the security strategy analysis is performed based on the monitoring analysis report and the intelligent analysis result to obtain an initial scheduling strategy, and all the initial scheduling strategies are comprehensively analyzed to obtain a comprehensive scheduling strategy, including: Performing data mining on the monitoring and analysis report to obtain key safety factors, and performing weight calculation on the key safety factors according to the intelligent analysis results to obtain weighted safety factors; Perform cluster analysis on weighted security factors to obtain security risk categories, obtain corresponding historical security event data based on security risk categories, and perform correlation analysis and time series prediction processing on historical security event data based on security risk categories to obtain risk warning indicators; Obtain the existing security strategy based on the risk warning indicators, and conduct a feasibility analysis on the existing security strategy based on the risk warning indicators to obtain the initial scheduling strategy; Comprehensively evaluate and screen all initial scheduling strategies to obtain candidate scheduling solutions, perform simulations based on the candidate scheduling solutions, and obtain simulation results; The simulation results are performance evaluated to obtain evaluation indicators, candidate scheduling schemes are ranked according to the evaluation indicators to obtain a target scheduling scheme, resource allocation is processed for the target scheduling scheme to obtain a resource scheduling plan, and the target scheduling scheme and the resource scheduling plan are comprehensively processed to obtain a comprehensive scheduling strategy.

[0010] The present invention also provides a smart community monitoring system based on the Internet of Things, which is applied to any one of the smart community monitoring methods based on the Internet of Things, including: A collection module, which is used to obtain sensor data, monitoring data and service data of the smart community; An analysis module, the analysis module is used to perform calculation processing on the sensor data to obtain initial sensor data, and to optimize the initial sensor data to obtain environmental data; A correlation module, the correlation module is used to perform correlation analysis on the environment data according to the service data to obtain corresponding integrated correlation data; A processing module, the processing module is used to perform in-depth analysis on the integrated related data according to the monitoring data to obtain corresponding intelligent analysis results, and perform comprehensive processing on the intelligent analysis results based on the service data to obtain corresponding monitoring analysis reports; A control module, wherein the control module is used to perform a security strategy analysis based on the monitoring analysis report and the intelligent analysis results to obtain an initial scheduling strategy, and to perform a comprehensive analysis on all the initial scheduling strategies to obtain a comprehensive scheduling strategy.

[0011] The present invention also provides a smart community monitoring device based on the Internet of Things, comprising: Memory, used to store programs; The processor is used to execute the program to implement each step of any one of the above-mentioned smart community monitoring methods based on the Internet of Things.

[0012] The present invention also provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0013] The present invention provides the following beneficial effects: By calculating and optimizing the sensor data, environmental data can be obtained quickly and accurately, improving the speed and accuracy of data processing. By correlating service data with environmental data, the inherent connection between data can be understood more deeply, improving the accuracy and effectiveness of data analysis. By deeply analyzing the monitoring data and integrated related data, and combining service data for comprehensive processing, a more comprehensive and accurate monitoring and analysis report can be obtained, enhancing the synergy between various subsystems. By performing security strategy analysis on the monitoring and analysis reports and intelligent analysis results, a more reasonable initial scheduling strategy can be formulated, and a comprehensive scheduling strategy can be obtained through comprehensive analysis, improving the level of security management and response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1This is a flow chart of a smart community monitoring method based on the Internet of Things provided by the present invention; Figure 2 This is a structural diagram of a smart community monitoring system based on the Internet of Things provided by the present invention; Figure 3 It is a structural diagram of a smart community monitoring structure based on the Internet of Things provided by the present invention.

[0015] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0018] Reference Figure 1 As shown, the present invention provides a smart community monitoring method based on the Internet of Things, comprising: Step S1: Obtain sensor data, monitoring data and service data of the smart community; Step S2: Calculate and process the sensor data to obtain initial sensor data, and optimize the initial sensor data to obtain environmental data; Step S3: performing correlation analysis on the environment data according to the service data to obtain corresponding integrated correlation data; Step S4: Perform in-depth analysis on the integrated related data according to the monitoring data to obtain corresponding intelligent analysis results, and perform comprehensive processing on the intelligent analysis results based on the service data to obtain corresponding monitoring analysis reports; Step S5: Perform security strategy analysis based on the monitoring analysis report and intelligent analysis results to obtain an initial scheduling strategy, and perform comprehensive analysis on all initial scheduling strategies to obtain a comprehensive scheduling strategy.

[0019] Based on the above steps, the detailed process is as follows: Step S1: Collect real-time environmental and safety data through various sensors deployed in the smart community (such as temperature and humidity sensors, smoke detectors, cameras, etc.). Sensors can be wireless or wired connected and transmit data to the data processing center through IoT protocols (such as LoRaWAN, NB-IoT, etc.).

[0020] Video image data within the community is collected through a video surveillance system, which includes fixed cameras and mobile surveillance equipment such as drones.

[0021] The smart community management system can collect users’ service requests, complaints, feedback and other information. Service data includes users’ behavioral habits, preferences, activity participation, etc.

[0022] Step S2: Perform preliminary processing on the collected sensor data, such as data cleaning and denoising, to obtain initial sensor data, and merge the data from different sensors by using data fusion technology to improve data quality.

[0023] The initial sensor data is further optimized, such as outlier detection and data standardization, to improve the reliability and availability of the data, and feature extraction and dimensionality reduction are performed on the data to obtain environmental data.

[0024] Step S3: Associate service data (such as user service requests, activity participation, etc.) with environmental data (such as temperature, humidity, air quality, etc.), and use data mining technology to find out the potential connection between service data and environmental data. Perform in-depth analysis on the associated data to explore the interaction between different services and environmental factors, and identify patterns and trends between data through statistical analysis, machine learning and other methods.

[0025] According to the results of the association analysis, the service data and the environmental data are integrated to obtain the integrated association data. The integrated association data provides a more comprehensive information view and helps to better understand various phenomena in the community.

[0026] Step S4: Combining monitoring data (such as video images, crowd statistics, etc.) with integrated related data can link monitoring data with environmental and service data to obtain deeper insights.

[0027] In-depth analysis of the combined data can be performed through methods such as artificial intelligence and big data analysis, with the aim of discovering potential patterns, trends and anomalies, thereby obtaining intelligent analysis results.

[0028] The intelligent analysis results are comprehensively processed based on service data, combined with service needs, and can be presented through data visualization technology to make them easy to understand and act on.

[0029] Based on the results of the comprehensive processing, a monitoring and analysis report is prepared, which includes the main findings, recommended measures, etc. to support decision-making.

[0030] Step S5: Based on the monitoring analysis report and intelligent analysis results, conduct a comprehensive assessment of the community's security status and analyze possible security risks, weak links and potential threats.

[0031] Based on the results of security policy analysis, formulate initial scheduling strategies for different scenarios. The initial scheduling strategies include emergency response plans, resource scheduling plans, etc.

[0032] Conduct a comprehensive analysis of all initial scheduling strategies, taking into account the mutual influence and synergy between various strategies. On the basis of comprehensive analysis, integrate the advantages of each initial scheduling strategy to form a set of comprehensive scheduling strategies.

[0033] Comprehensive dispatch strategies can cover all aspects within the community, including security prevention, emergency response, resource allocation, etc.

[0034] The present invention provides a smart community monitoring method based on the Internet of Things. By calculating and processing sensor data and optimizing the processing, environmental data can be obtained quickly and accurately, thereby improving the speed and accuracy of data processing. By performing correlation analysis on service data and environmental data, the intrinsic connection between data can be understood more deeply, thereby improving the accuracy and effectiveness of data analysis. By performing in-depth analysis on monitoring data and integrated related data, and combining service data for comprehensive processing, a more comprehensive and accurate monitoring analysis report can be obtained, thereby enhancing the synergy between various subsystems. By performing security strategy analysis on monitoring analysis reports and intelligent analysis results, a more reasonable initial scheduling strategy can be formulated, and a comprehensive scheduling strategy can be obtained through comprehensive analysis, thereby improving the level of security management and response speed.

[0035] In one embodiment, obtaining sensor data, monitoring data, and service data of a smart community includes: Raw sensor data is collected through multi-source heterogeneous sensors (such as temperature and humidity sensors, smoke detectors, etc.) deployed in the smart community.

[0036] The raw sensor data is cleaned to remove invalid data and outliers, and the cleaned data is preprocessed, such as missing value filling and data standardization, to obtain preprocessed data. Multi-source data fusion is performed on the preprocessed data to integrate data from different sensors to obtain sensor data.

[0037] Real-time data collection is performed on the video surveillance equipment of the smart community to obtain the original surveillance video stream, and the original surveillance video stream is encoded and compressed to obtain compressed data.

[0038] Use the preset video analysis algorithm to perform target detection and tracking on the compressed data to obtain structured monitoring data.

[0039] Obtain original service data from the community system of the smart community, such as user activity records, service requests, etc., and perform data standardization on the original service data to obtain standard data.

[0040] Classify and cluster the standard data to obtain the service data.

[0041] This embodiment can remove invalid data and outliers and improve the accuracy and reliability of the data by cleaning and preprocessing the raw sensor data. Video encoding and compression processing reduces the pressure of data transmission and storage while maintaining the quality of video data. Multi-source data fusion processing can integrate data from different sensors to provide a more comprehensive information view. Through data standardization processing and classification and clustering of service data, the needs and patterns of community services can be better understood. Using video analysis algorithms for target detection and tracking can automatically identify important events in the video and reduce the workload of manual monitoring. Structured monitoring data is easier to analyze and process, which helps to respond quickly to emergencies.

[0042] In one embodiment, the sensor data is processed by calculation to obtain initial sensor data, and the initial sensor data is optimized to obtain environmental data, including: The sensor data is subjected to feature extraction processing to obtain initial feature data, the initial feature data is subjected to data dimensionality reduction processing to obtain reduced-dimensional feature data, and all reduced-dimensional feature data are subjected to data fusion processing to obtain fused feature data.

[0043] Anomaly detection processing is performed on the fused feature data to obtain anomaly detection results, and data correction processing is performed on the fused feature data based on the anomaly detection results to obtain initial sensing data.

[0044] The initial sensing data is subjected to data standardization processing to obtain standard sensing data.

[0045] The standard sensor data is optimized and analyzed to obtain optimization results, and the standard sensor data is optimized based on the optimization results to obtain environmental data.

[0046] This embodiment reduces redundant information and improves the interpretability and processing efficiency of data through feature extraction and data dimension reduction. Data fusion processing can integrate data from different sensors to provide a more comprehensive information view. Anomaly detection processing can promptly detect anomalies in sensor data, which helps to quickly respond to potential problems. Data correction processing can correct abnormal data and improve the accuracy and reliability of data. Data standardization processing can unify data formats and units and simplify subsequent data processing steps. Optimization analysis and data optimization can improve the utilization efficiency of data and reduce unnecessary computing burdens.

[0047] In one embodiment, the environment data is analyzed for correlation based on the service data to obtain corresponding integrated correlation data, including: The environmental data is split into subcategories to obtain multiple environmental data subsets, and the service data is semantically analyzed to obtain a service feature vector.

[0048] The similarity of the environment data subset is calculated according to the service feature vector to obtain the environment service similarity matrix, and the environment service similarity matrix is ​​filtered by threshold to obtain the correlation data pair.

[0049] According to the correlation data, rules are constructed to obtain an environmental service association rule set, and matching analysis is performed on the environmental service association rule set to obtain corresponding service data prediction results.

[0050] Perform time series correlation analysis on the service prediction results to obtain integrated correlation data.

[0051] This embodiment analyzes different types of environmental data in more detail and improves the accuracy of data processing by performing subclass splitting processing on environmental data. Semantic analysis processing can better understand the meaning and context of service data, which helps to improve the accuracy of analysis. Similarity calculation and threshold filtering processing effectively identify the correlation between environmental data and service data, which helps to discover potential correlation patterns. The construction of association rule sets can help understand the relationship between different data and provide a basis for subsequent decision-making. The generation of service data prediction results predicts future service needs and service responses, which helps to prepare in advance. Time series correlation analysis better understands the changing trend of data over time and improves the accuracy of predictions.

[0052] In one embodiment, the integrated related data is deeply analyzed according to the monitoring data to obtain the corresponding intelligent analysis results, and the intelligent analysis results are comprehensively processed based on the service data to obtain the corresponding monitoring analysis report, including: Perform trend forecasting analysis on the integrated related data to obtain dynamic change characteristics, perform pattern recognition on the dynamic change characteristics based on the monitoring data to obtain classification results, perform deviation detection and deviation processing on the classification results to obtain a prediction rule set, perform forward-looking analysis on the prediction rule set to obtain an estimated result; Perform data mining based on the estimation results and service data to obtain a set of associated patterns, and present the associated pattern sets graphically to obtain a data visualization chart; Generate deep insight reports based on data visualization charts; perform text analysis on deep insight reports based on service data to obtain semantic features; Intelligent text processing is performed on the deep insight report based on semantic features to obtain structured content; emotional evaluation is performed on the structured content to obtain emotional trend data; Comprehensively analyze structured content and sentiment trend data to obtain a monitoring and analysis report.

[0053] This embodiment can more accurately predict the dynamic change characteristics of data by performing trend forecasting analysis on integrated related data. Pattern recognition and deviation detection processing help improve the accuracy of predictions and reduce errors. Prediction rule sets and forward-looking analysis provide a scientific basis for decision-making and help make more reasonable decisions. Deep insight reports provide important information about community activities and service needs, which helps to formulate effective strategies. The graphical presentation of related pattern sets can help users understand data more intuitively and improve the readability and usability of data. Data visualization charts and deep insight reports can better convey information and improve user experience. By conducting a comprehensive analysis of service data, In one embodiment, a security policy analysis is performed based on the monitoring analysis report and the intelligent analysis results to obtain an initial scheduling policy, and a comprehensive analysis is performed on all the initial scheduling policies to obtain a comprehensive scheduling policy, including: Perform data mining on monitoring and analysis reports to obtain key safety factors, and calculate the weights of key safety factors based on the intelligent analysis results to obtain weighted safety factors; Perform cluster analysis on weighted security factors to obtain security risk categories, obtain corresponding historical security event data based on security risk categories, and perform correlation analysis and time series prediction processing on historical security event data based on security risk categories to obtain risk warning indicators; Obtain the existing security strategy based on the risk warning indicators, and conduct a feasibility analysis on the existing security strategy based on the risk warning indicators to obtain the initial scheduling strategy; Comprehensively evaluate and screen all initial scheduling strategies to obtain candidate scheduling solutions, perform simulations based on the candidate scheduling solutions, and obtain simulation results; The simulation results are performance evaluated to obtain evaluation indicators, candidate scheduling schemes are ranked according to the evaluation indicators to obtain a target scheduling scheme, resource allocation is processed for the target scheduling scheme to obtain a resource scheduling plan, and the target scheduling scheme and the resource scheduling plan are comprehensively processed to obtain a comprehensive scheduling strategy.

[0054] This embodiment can accurately identify key safety factors by performing data mining on the monitoring and analysis report. The calculation of weighted safety factors can highlight the impact of important factors and make the safety strategy more targeted. Different safety risk categories can be identified through cluster analysis, which helps to better understand the distribution and characteristics of risks. Correlation analysis and time series prediction processing can predict future risk trends and improve the accuracy of early warning. The formulation of the initial scheduling strategy is based on risk warning indicators to ensure the feasibility and effectiveness of the strategy. Comprehensive evaluation and screening can eliminate inappropriate scheduling strategies and retain the optimal strategy. The simulation results obtained through simulation can help evaluate the performance of different scheduling schemes. Performance evaluation and sorting ensure that resources are reasonably allocated to the most valuable places.

[0055] Reference Figure 2 As shown, the present invention also provides a smart community monitoring system based on the Internet of Things, which is applied to any of the above-mentioned smart community monitoring methods based on the Internet of Things, including: Collection module, which is used to obtain sensor data, monitoring data and service data of the smart community; An analysis module is used to perform calculation processing on sensor data to obtain initial sensor data, and to optimize the initial sensor data to obtain environmental data; The association module is used to perform association analysis on the environment data based on the service data to obtain corresponding integrated association data; The processing module is used to perform in-depth analysis on the integrated related data according to the monitoring data to obtain the corresponding intelligent analysis results, and to perform comprehensive processing on the intelligent analysis results based on the service data to obtain the corresponding monitoring analysis report; The control module is used to perform security strategy analysis based on the monitoring analysis report and intelligent analysis results to obtain the initial scheduling strategy, and to perform comprehensive analysis on all initial scheduling strategies to obtain the comprehensive scheduling strategy.

[0056] The present invention provides a smart community monitoring system based on the Internet of Things. By calculating and processing sensor data and optimizing the processing, environmental data can be obtained quickly and accurately, thereby improving the speed and accuracy of data processing. By performing correlation analysis on service data and environmental data, the intrinsic connection between data can be understood more deeply, thereby improving the accuracy and effectiveness of data analysis. By performing in-depth analysis on monitoring data and integrated related data, and combining service data for comprehensive processing, a more comprehensive and accurate monitoring analysis report can be obtained, thereby enhancing the synergy between various subsystems. By performing security strategy analysis on monitoring analysis reports and intelligent analysis results, a more reasonable initial scheduling strategy can be formulated, and a comprehensive scheduling strategy can be obtained through comprehensive analysis, thereby improving the level of security management and response speed.

[0057] Reference Figure 3 As shown, the present invention also provides a smart community monitoring device based on the Internet of Things, comprising: Memory, used to store programs; The processor is used to execute the program to implement each step of any one of the above-mentioned smart community monitoring methods based on the Internet of Things.

[0058] In this embodiment, the processor and the memory may be connected via a bus or other means. The memory may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk, or a solid-state drive. The processor may be a general-purpose processor, such as a central processing unit, a digital signal processor, an application-specific integrated circuit, or one or more integrated circuits configured to implement the embodiments of the present invention.

[0059] The present invention also provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0060] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0061] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A smart community monitoring method based on the Internet of Things, characterized in that: include: Obtain sensor data, monitoring data, and service data for smart communities; Performing calculation processing on the sensor data to obtain initial sensor data, and performing optimization processing on the initial sensor data to obtain environmental data; Performing correlation analysis on the environment data according to the service data to obtain corresponding integrated correlation data; Performing in-depth analysis on the integrated related data according to the monitoring data to obtain corresponding intelligent analysis results, and performing comprehensive processing on the intelligent analysis results based on the service data to obtain corresponding monitoring analysis reports; A security strategy analysis is performed based on the monitoring analysis report and the intelligent analysis results to obtain an initial scheduling strategy, and a comprehensive analysis is performed on all the initial scheduling strategies to obtain a comprehensive scheduling strategy.

2. The method for monitoring a smart community based on the Internet of Things according to claim 1, characterized in that: The acquisition of sensor data, monitoring data and service data of the smart community includes: Collecting data from multi-source heterogeneous sensors in the smart community to obtain raw sensor data, cleaning and preprocessing the raw sensor data to obtain preprocessed data, and fusing multi-source data on the preprocessed data to obtain the sensor data; Performing real-time data collection on the video surveillance equipment of the smart community to obtain an original surveillance video stream, and performing video encoding and compression processing on the original surveillance video stream to obtain compressed data; Performing target detection and tracking on the compressed data according to a preset video analysis algorithm to obtain structured monitoring data; Acquire original service data from the community system of the smart community, and perform data standardization processing on the original service data to obtain standard data; The standard data is classified and clustered to obtain the service data.

3. The method for monitoring a smart community based on the Internet of Things according to claim 1, characterized in that: The step of calculating and processing the sensor data to obtain initial sensor data, and optimizing and processing the initial sensor data to obtain environmental data includes: Performing feature extraction processing on the sensor data to obtain initial feature data, performing data dimensionality reduction processing on the initial feature data to obtain reduced-dimensional feature data, and performing data fusion processing on all reduced-dimensional feature data to obtain fused feature data; Performing anomaly detection processing on the fused feature data to obtain an anomaly detection result, and performing data correction processing on the fused feature data according to the anomaly detection result to obtain the initial sensing data; Performing data standardization processing on the initial sensing data to obtain standard sensing data; The standard sensor data is optimized and analyzed to obtain an optimization result, and the standard sensor data is optimized according to the optimization result to obtain the environmental data.

4. The method for monitoring a smart community based on the Internet of Things according to claim 1, characterized in that: The performing correlation analysis on the environment data according to the service data to obtain corresponding integrated correlation data includes: Performing subclass splitting processing on the environment data to obtain multiple environment data subsets, and performing semantic analysis processing on the service data to obtain a service feature vector; Calculate the similarity of the environment data subset according to the service feature vector to obtain the environment service similarity matrix, perform threshold filtering on the environment service similarity matrix to obtain correlation data pairs; According to the correlation data, rules are constructed to obtain an environmental service association rule set, and matching analysis is performed on the environmental service association rule set to obtain corresponding service data prediction results; Performing time series correlation analysis on the service prediction results to obtain the integrated correlation data.

5. The method for monitoring a smart community based on the Internet of Things according to claim 1, characterized in that: The in-depth analysis of the integrated related data according to the monitoring data to obtain a corresponding intelligent analysis result, and the comprehensive processing of the intelligent analysis result based on the service data to obtain a corresponding monitoring analysis report includes: Performing trend forecasting analysis on the integrated correlation data to obtain dynamic change characteristics, performing pattern recognition on the dynamic change characteristics according to the monitoring data to obtain classification results, performing deviation detection and deviation processing on the classification results to obtain a prediction rule set, and performing forward-looking analysis on the prediction rule set to obtain an estimated result; Performing data mining based on the estimation results and the service data to obtain a set of associated patterns, and graphically presenting the set of associated patterns to obtain a data visualization chart; Generate a deep insight report based on the data visualization chart; perform text analysis on the deep insight report based on the service data to obtain semantic features; Performing intelligent text processing on the deep insight report according to semantic features to obtain structured content; performing sentiment evaluation on the structured content to obtain sentiment trend data; Comprehensively analyze the structured content and sentiment trend data to obtain the monitoring analysis report.

6. The method for monitoring a smart community based on the Internet of Things according to claim 1, characterized in that: The security strategy analysis is performed based on the monitoring analysis report and the intelligent analysis result to obtain an initial scheduling strategy, and all the initial scheduling strategies are comprehensively analyzed to obtain a comprehensive scheduling strategy, including: Performing data mining on the monitoring and analysis report to obtain key safety factors, and performing weight calculation on the key safety factors according to the intelligent analysis results to obtain weighted safety factors; Perform cluster analysis on weighted security factors to obtain security risk categories, obtain corresponding historical security event data based on security risk categories, and perform correlation analysis and time series prediction processing on historical security event data based on security risk categories to obtain risk warning indicators; Obtain the existing security strategy based on the risk warning indicators, and conduct a feasibility analysis on the existing security strategy based on the risk warning indicators to obtain the initial scheduling strategy; Comprehensively evaluate and screen all initial scheduling strategies to obtain candidate scheduling solutions, perform simulations based on the candidate scheduling solutions, and obtain simulation results; The simulation results are performance evaluated to obtain evaluation indicators, candidate scheduling schemes are ranked according to the evaluation indicators to obtain a target scheduling scheme, resource allocation is processed for the target scheduling scheme to obtain a resource scheduling plan, and the target scheduling scheme and the resource scheduling plan are comprehensively processed to obtain a comprehensive scheduling strategy.

7. A smart community monitoring system based on the Internet of Things, characterized in that: The smart community monitoring method based on the Internet of Things applied to any one of claims 1 to 6 above comprises: A collection module, which is used to obtain sensor data, monitoring data and service data of the smart community; An analysis module, the analysis module is used to perform calculation processing on the sensor data to obtain initial sensor data, and to optimize the initial sensor data to obtain environmental data; A correlation module, the correlation module is used to perform correlation analysis on the environment data according to the service data to obtain corresponding integrated correlation data; A processing module, the processing module is used to perform in-depth analysis on the integrated related data according to the monitoring data to obtain corresponding intelligent analysis results, and perform comprehensive processing on the intelligent analysis results based on the service data to obtain corresponding monitoring analysis reports; A control module, wherein the control module is used to perform a security strategy analysis based on the monitoring analysis report and the intelligent analysis results to obtain an initial scheduling strategy, and to perform a comprehensive analysis on all the initial scheduling strategies to obtain a comprehensive scheduling strategy.

8. A smart community monitoring device based on the Internet of Things, characterized in that: include: Memory, used to store programs; A processor is used to execute the program to implement each step of a smart community monitoring method based on the Internet of Things as described in any one of claims 1-6.

9. A storage medium, characterized in that: Computer instructions are stored, and the computer instructions are used to make a computer execute the method according to any one of claims 1 to 6.

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