Intelligent community intelligent security monitoring management method based on data interconnection

Abstract

The invention relates to the technical field of smart communities, and discloses a smart community intelligent security monitoring management method based on data interconnection, which comprises the following steps: acquiring real-time monitoring data, reading a preprocessed monitoring data set from a database, extracting feature parameters related to community security patrol, and determining the real-time monitoring data; the characteristic parameters comprise road length, road width, building height and building density, and constructing a community security patrol environment model based on the characteristic parameters; and aiming at the community security patrol route optimization model, solving by adopting an ant colony algorithm to obtain a plurality of candidate optimal patrol routes, evaluating the candidate optimal patrol routes according to historical patrol data, and selecting the patrol route with the optimal comprehensive evaluation index as the optimal patrol route. According to the invention, intelligent and fine management of community security patrol is realized, the patrol efficiency and the safety prevention and control level are improved, and powerful decision support is provided for community security management.

Description

Technical Field

[0001] The present invention relates to the technical field of smart communities, and in particular to an intelligent security monitoring and management method for smart communities based on data interconnection. Background Art

[0002] In the security monitoring and management of smart communities, how to optimize security patrol routes based on massive monitoring data and environmental data is a technical problem that needs to be solved urgently. At present, the patrol route planning of most communities relies on manual experience, lacks data support and scientific basis, resulting in low patrol efficiency and insufficient risk prevention capabilities. Patrol route optimization needs to comprehensively consider multiple factors such as community road network structure, building distribution, key prevention areas, and changes in pedestrian flow, and can be dynamically adjusted according to real-time monitoring data. Traditional path planning algorithms are difficult to meet such complex and changeable constraints. At the same time, how to efficiently integrate and analyze massive surveillance video data and environmental sensor data to accurately identify risk factors also places high demands on algorithm performance. How to maximize the use of limited security forces, reasonably allocate patrol resources, and realize dynamic patrol route planning under full time and space coverage while taking into account real-time performance is a technical challenge. This requires optimization in various links such as data collection, storage, calculation, analysis, and visualization, and the development of intelligent algorithms to fully tap the value of data and enhance the flexibility and adaptability of route planning in order to effectively improve the level of community security management. Summary of the invention

[0003] The purpose of the present invention is to overcome one or more of the above-mentioned existing technical problems and provide a smart community intelligent security monitoring and management method based on data interconnection.

[0004] To achieve the above-mentioned purpose, the present invention provides a smart community intelligent security monitoring and management method based on data interconnection, comprising: Obtain real-time monitoring data of roads, buildings and facilities in the community, perform data cleaning and data fusion on the monitoring data, obtain a pre-processed monitoring data set, and store the monitoring data set in the database; Read the preprocessed monitoring data set from the database, extract characteristic parameters related to community security patrol, the characteristic parameters include road length, road width, building height and building density, and build a community security patrol environment model based on the characteristic parameters; According to the community security patrol environment model, the community road network is modeled using graph theory algorithms to obtain the community road network topology map. Based on the topology map, a community security patrol route optimization model is constructed. The objective function of the community security patrol route optimization model is to minimize the total patrol distance. The constraints include patrolling each road at least once and strengthening patrols in key areas. The ant colony algorithm is used to solve the community security patrol route optimization model, and several candidate optimal patrol routes are obtained. The candidate optimal patrol routes are evaluated based on historical patrol data, and the patrol route with the best comprehensive evaluation index is selected as the best patrol route. Obtain the pedestrian flow data and public security incident data at different time periods in the community, use clustering algorithms to perform cluster analysis on the pedestrian flow data and public security incident data, and obtain the public security risk level at different time periods. If the public security risk level is high risk, increase the patrol frequency at the corresponding time period; if the public security risk level is low risk, reduce the patrol frequency at the corresponding time period; The best patrol route, dynamically optimized patrol frequency, and patrol plan information for a period of time in the future are visualized to generate a community security patrol intelligent decision-making report, which is then pushed to the community security management personnel terminal.

[0005] According to one aspect of the present invention, real-time monitoring data of roads, buildings and facilities in a community is obtained, and the acquired original monitoring data is subjected to denoising and outlier processing to obtain cleaned monitoring data; According to the pre-established data fusion rules, the cleaned monitoring data are associated and fused to obtain a fused monitoring data set, wherein the data fusion rules include time alignment and space alignment; For the fused monitoring data set, feature extraction is performed to obtain the feature vector of the monitoring data, and the feature vector is stored in the database; According to the preset data quality assessment indicators, the quality of the monitoring data set after feature extraction is assessed. If the assessment result meets the preset threshold, the monitoring data set is marked as available data and stored in the database; Through data visualization technology, the available monitoring data sets in the database are visualized to generate a real-time monitoring data visualization interface for roads, buildings and facilities in the community; Study the historical monitoring data in the database and optimize the data fusion rules and feature extraction algorithms; According to the actual needs of community management, the pre-processed monitoring data set is associated with other business data for analysis and mining of the monitoring data.

[0006] According to one aspect of the present invention, raw data is obtained from the preprocessed monitoring data set, and the raw data is cleaned and standardized to obtain standardized monitoring data; Based on the standardized monitoring data, characteristic parameters related to community security patrols are extracted, including road length, road width, building height and building density parameters; According to the acquired road length and road width characteristic parameters, a clustering algorithm is used to classify community roads to obtain different types of roads, including main roads, secondary roads and branch roads; According to the building height and building density characteristic parameters, the density clustering algorithm is used to classify community buildings, and different types of building areas are obtained, including high-rise building areas, multi-story building areas and low-rise building areas; The community road classification results and the building area classification results are superimposed and analyzed to obtain the security patrol environment characteristics of different areas and build a community security patrol environment model; For the community security patrol environment model, a reinforcement learning algorithm is used for path planning to obtain the optimal security patrol route and maximize the patrol coverage and efficiency of each area.

[0007] According to one aspect of the present invention, community road network data including road nodes and road connection relationship information is obtained based on a community security patrol environment model; The community road network is modeled using graph theory algorithms, with road nodes as graph nodes and road connection relationships as graph edges, to construct a community road network topology graph. Based on the community road network topology, a community security patrol route optimization model is constructed, and the objective function is set to minimize the total patrol distance; Set constraints for the community security patrol route optimization model, including patrolling each road at least once, strengthening patrols in key areas, and forming a closed loop of patrol routes to obtain the optimal patrol route plan; According to the optimal patrol route plan, the patrol route is marked on the community road network topology map to generate a community security patrol route map; The community security patrol route map is sent to the patrol personnel's mobile terminal to guide the patrol personnel to carry out community security patrol work along the optimal route.

[0008] According to one aspect of the present invention, an ant colony algorithm is used to solve a community security patrol route optimization model, and relevant parameters of the algorithm are set, including pheromone concentration and number of ants, to obtain multiple candidate optimal patrol routes; Extract the time, location and type of patrol events from historical patrol data, and evaluate the effectiveness and feasibility of candidate optimal patrol routes; Construct a comprehensive evaluation index system, including patrol coverage, response time, and route length, to quantitatively score candidate routes; According to the comprehensive evaluation index system, the evaluation results of each candidate route are compared, and the route with the highest score is selected as the best patrol route; Apply the best patrol routes to actual community security work, and continuously collect new patrol data to iteratively update the community security patrol route optimization model and algorithm; Regularly evaluate the actual application effect of the optimal patrol route and dynamically adjust the route based on the evaluation results.

[0009] According to one aspect of the present invention, pedestrian flow data and public security incident data in different time periods in a community are obtained, and preprocessing is performed on the obtained data, including data cleaning and data normalization, to obtain a standardized data set; According to the preprocessed data set, the K-means clustering algorithm is used to perform cluster analysis on the pedestrian flow data and public security incident data in different time periods. By calculating the distance between each cluster center and the data point, the data point is divided into the cluster with the closest distance, and the clustering results of different time periods are obtained; Based on the clustering results, the data distribution within each cluster is calculated, including the frequency and severity of public security incidents, and the public security risk value of each cluster is obtained by weighted average; According to the public security risk value, the preset risk level classification standard is used to determine the public security risk level of each time period cluster. If the risk value exceeds the preset high risk threshold, the risk level of the time period is marked as high risk. If the risk value is lower than the preset low risk threshold, the risk level of the time period is marked as low risk. The rest are marked as medium risk. Obtain the existing patrol frequency settings of the community. For periods of high risk, increase the patrol frequency to the preset high-risk patrol frequency. For periods of low risk, reduce the patrol frequency to the preset low-risk patrol frequency. For periods of medium risk, maintain the original patrol frequency unchanged. The adjusted patrol frequency settings for each time period are synchronized to the community patrol system, and the patrol task allocation and execution for each time period are controlled through the patrol management module; Continuously obtain the latest pedestrian flow and public security incident data, regularly repeat the cluster analysis and risk assessment process, and dynamically update the patrol frequency settings according to changes in the community security situation.

[0010] According to one aspect of the present invention, based on the historical patrol data pushed by the community security management personnel terminal, the patrol route is optimized through a clustering algorithm to obtain the best patrol route covering all areas of the community; Adopting time series prediction algorithm, combining the current patrol frequency and the trend of community traffic flow in the future, dynamically adjust the patrol frequency in each time period; Generate patrol plans for the future based on the best patrol routes and optimized patrol frequencies; Integrate patrol routes, frequencies and plan information to generate community security patrol intelligent decision-making reports, which include visualization of various data and auxiliary decision-making suggestions; Through the community security management system, the intelligent decision-making report is pushed to the community security manager terminal. The manager optimizes and adjusts the community security patrol strategy based on the information and suggestions provided by the intelligent decision-making report; The system continuously collects feedback from community security managers and analyzes it through natural language processing technology to improve the intelligent decision-making algorithm.

[0011] To achieve the above objectives, the present invention provides a smart community intelligent security monitoring and management system based on data interconnection, comprising: Detection data acquisition module: obtains real-time monitoring data of roads, buildings and facilities in the community, performs data cleaning and data fusion on the monitoring data, obtains the pre-processed monitoring data set, and stores the monitoring data set in the database; Community security patrol environment model construction module: reads the pre-processed monitoring data set from the database, extracts characteristic parameters related to community security patrol, the characteristic parameters include road length, road width, building height and building density, and constructs a community security patrol environment model based on the characteristic parameters; Community security patrol route optimization model construction module: According to the community security patrol environment model, the community road network is modeled using graph theory algorithms to obtain the community road network topology map. The community security patrol route optimization model is constructed based on the topology map. The objective function of the community security patrol route optimization model is to minimize the total patrol distance. The constraints include patrolling each road at least once and strengthening patrols in key areas. Patrol route generation module: For the community security patrol route optimization model, the ant colony algorithm is used to solve it, and several candidate optimal patrol routes are obtained. The candidate optimal patrol routes are evaluated based on historical patrol data, and the patrol route with the best comprehensive evaluation index is selected as the best patrol route; Patrol frequency optimization module: obtain the human flow data and public security event data in different time periods in the community, use clustering algorithm to perform cluster analysis on the human flow data and public security event data, and obtain the public security risk level in different time periods. If the public security risk level is high risk, the patrol frequency of the corresponding time period is increased; if the public security risk level is low risk, the patrol frequency of the corresponding time period is reduced; Decision report generation module: visualizes the best patrol route, dynamically optimized patrol frequency, and patrol plan information for a period of time in the future, generates a community security patrol intelligent decision report, and pushes the intelligent decision report to the community security management personnel terminal.

[0012] To achieve the above-mentioned purpose, the present invention provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the above-mentioned intelligent security monitoring and management method for a smart community based on data interconnection is implemented.

[0013] To achieve the above-mentioned purpose, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned intelligent security monitoring and management method for a smart community based on data interconnection is implemented.

[0014] Based on this, the beneficial effects of the present invention are as follows: the method obtains real-time community monitoring data, performs data preprocessing and feature extraction, and constructs a community security patrol environment model. Based on the model, a graph theory algorithm is used to establish a road network topology map, and a patrol route optimization model is constructed. The ant colony algorithm is used to solve the model, obtain candidate optimal patrol routes, and select the best route in combination with historical data evaluation. At the same time, cluster analysis is performed on the flow of people and public security incident data in different time periods, and the patrol frequency is dynamically adjusted. Finally, the optimized patrol route, frequency and other information are visualized, and an intelligent decision-making report is generated, which realizes the intelligent and refined management of community security patrols, improves patrol efficiency and safety prevention and control levels, and provides strong decision-making support for community security management. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a smart community intelligent security monitoring and management method based on data interconnection according to an exemplary embodiment; Figure 2 It is a flow chart of a smart community intelligent security monitoring and management system based on data interconnection according to an exemplary embodiment. DETAILED DESCRIPTION

[0016] The present invention will now be discussed with reference to exemplary embodiments. It should be understood that the embodiments discussed are only for enabling those skilled in the art to better understand and thereby implement the present invention, rather than implying any limitation on the scope of the present invention.

[0017] As used herein, the term “including” and variations thereof are to be interpreted as open-ended terms meaning “including but not limited to.” The term “based on” is to be interpreted as “based, at least in part, on,” and the terms “one embodiment” and “an embodiment” are to be interpreted as “at least one embodiment.”

[0018] According to one embodiment of the present invention, Figure 1 is a flow chart of a smart community intelligent security monitoring management method based on data interconnection according to an exemplary embodiment, such as Figure 1As shown, in order to achieve the above purpose, the present invention provides a smart community intelligent security monitoring and management method based on data interconnection, including: Obtain real-time monitoring data of roads, buildings and facilities in the community, perform data cleaning and data fusion on the monitoring data, obtain a pre-processed monitoring data set, and store the monitoring data set in the database; Read the preprocessed monitoring data set from the database, extract characteristic parameters related to community security patrol, the characteristic parameters include road length, road width, building height and building density, and build a community security patrol environment model based on the characteristic parameters; According to the community security patrol environment model, the community road network is modeled using graph theory algorithms to obtain the community road network topology map. Based on the topology map, a community security patrol route optimization model is constructed. The objective function of the community security patrol route optimization model is to minimize the total patrol distance. The constraints include patrolling each road at least once and strengthening patrols in key areas. The ant colony algorithm is used to solve the community security patrol route optimization model, and several candidate optimal patrol routes are obtained. The candidate optimal patrol routes are evaluated based on historical patrol data, and the patrol route with the best comprehensive evaluation index is selected as the best patrol route. Obtain the pedestrian flow data and public security incident data at different time periods in the community, use clustering algorithms to perform cluster analysis on the pedestrian flow data and public security incident data, and obtain the public security risk level at different time periods. If the public security risk level is high risk, increase the patrol frequency at the corresponding time period; if the public security risk level is low risk, reduce the patrol frequency at the corresponding time period; The best patrol route, dynamically optimized patrol frequency, and patrol plan information for a period of time in the future are visualized to generate a community security patrol intelligent decision-making report, which is then pushed to the community security management personnel terminal.

[0019] According to one embodiment of the present invention, real-time monitoring data of roads, buildings and facilities in a community is obtained, and the original monitoring data obtained is subjected to denoising and outlier processing to obtain cleaned monitoring data; According to the pre-established data fusion rules, the cleaned monitoring data are associated and fused to obtain a fused monitoring data set, wherein the data fusion rules include time alignment and space alignment; For the fused monitoring data set, feature extraction is performed to obtain the feature vector of the monitoring data, and the feature vector is stored in the database; According to the preset data quality assessment indicators, the quality of the monitoring data set after feature extraction is assessed. If the assessment result meets the preset threshold, the monitoring data set is marked as available data and stored in the database; Through data visualization technology, the available monitoring data sets in the database are visualized to generate a real-time monitoring data visualization interface for roads, buildings and facilities in the community; Study the historical monitoring data in the database and optimize the data fusion rules and feature extraction algorithms; According to the actual needs of community management, the pre-processed monitoring data set is associated with other business data for analysis and mining of the monitoring data.

[0020] According to one embodiment of the present invention, raw data is obtained from the preprocessed monitoring data set, and the raw data is cleaned and standardized to obtain standardized monitoring data; Based on the standardized monitoring data, characteristic parameters related to community security patrols are extracted, including road length, road width, building height and building density parameters; According to the acquired road length and road width characteristic parameters, a clustering algorithm is used to classify community roads to obtain different types of roads, including main roads, secondary roads and branch roads; According to the building height and building density characteristic parameters, the density clustering algorithm is used to classify community buildings, and different types of building areas are obtained, including high-rise building areas, multi-story building areas and low-rise building areas; The community road classification results and the building area classification results are superimposed and analyzed to obtain the security patrol environment characteristics of different areas and build a community security patrol environment model; For the community security patrol environment model, a reinforcement learning algorithm is used for path planning to obtain the optimal security patrol route and maximize the patrol coverage and efficiency of each area.

[0021] According to one embodiment of the present invention, community road network data including road nodes and road connection relationship information is obtained based on the community security patrol environment model; The community road network is modeled using graph theory algorithms, with road nodes as graph nodes and road connection relationships as graph edges, to construct a community road network topology graph. Based on the community road network topology, a community security patrol route optimization model is constructed, and the objective function is set to minimize the total patrol distance; Set constraints for the community security patrol route optimization model, including patrolling each road at least once, strengthening patrols in key areas, and forming a closed loop of patrol routes to obtain the optimal patrol route plan; According to the optimal patrol route plan, the patrol route is marked on the community road network topology map to generate a community security patrol route map; The community security patrol route map is sent to the patrol personnel's mobile terminal to guide the patrol personnel to carry out community security patrol work along the optimal route.

[0022] According to one embodiment of the present invention, an ant colony algorithm is used to solve the community security patrol route optimization model, and relevant parameters of the algorithm are set, including pheromone concentration and number of ants, to obtain multiple candidate optimal patrol routes; Extract the time, location and type of patrol events from historical patrol data, and evaluate the effectiveness and feasibility of candidate optimal patrol routes; Construct a comprehensive evaluation index system, including patrol coverage, response time, and route length, to quantitatively score candidate routes; According to the comprehensive evaluation index system, the evaluation results of each candidate route are compared, and the route with the highest score is selected as the best patrol route; Apply the best patrol routes to actual community security work, and continuously collect new patrol data to iteratively update the community security patrol route optimization model and algorithm; Regularly evaluate the actual application effect of the optimal patrol route and dynamically adjust the route based on the evaluation results.

[0023] According to one embodiment of the present invention, pedestrian flow data and public security incident data in different time periods in the community are obtained, and preprocessing is performed on the obtained data, including data cleaning and data normalization, to obtain a standardized data set; According to the preprocessed data set, the K-means clustering algorithm is used to perform cluster analysis on the pedestrian flow data and public security incident data in different time periods. By calculating the distance between each cluster center and the data point, the data point is divided into the cluster with the closest distance, and the clustering results of different time periods are obtained; Based on the clustering results, the data distribution within each cluster is calculated, including the frequency and severity of public security incidents, and the public security risk value of each cluster is obtained by weighted average; According to the public security risk value, the preset risk level classification standard is used to determine the public security risk level of each time period cluster. If the risk value exceeds the preset high risk threshold, the risk level of the time period is marked as high risk. If the risk value is lower than the preset low risk threshold, the risk level of the time period is marked as low risk. The rest are marked as medium risk. Obtain the existing patrol frequency settings of the community. For periods of high risk, increase the patrol frequency to the preset high-risk patrol frequency. For periods of low risk, reduce the patrol frequency to the preset low-risk patrol frequency. For periods of medium risk, maintain the original patrol frequency unchanged. The adjusted patrol frequency settings for each time period are synchronized to the community patrol system, and the patrol task allocation and execution for each time period are controlled through the patrol management module; Continuously obtain the latest pedestrian flow and public security incident data, regularly repeat the cluster analysis and risk assessment process, and dynamically update the patrol frequency settings according to changes in the community security situation.

[0024] According to one embodiment of the present invention, based on the historical patrol data pushed by the community security manager terminal, the patrol route is optimized through a clustering algorithm to obtain the best patrol route covering all areas of the community; Adopting time series prediction algorithm, combining the current patrol frequency and the trend of community traffic flow in the future, dynamically adjust the patrol frequency in each time period; Generate patrol plans for the future based on the best patrol routes and optimized patrol frequencies; Integrate patrol routes, frequencies and plan information to generate community security patrol intelligent decision-making reports, which include visualization of various data and auxiliary decision-making suggestions; Through the community security management system, the intelligent decision-making report is pushed to the community security manager terminal. The manager optimizes and adjusts the community security patrol strategy based on the information and suggestions provided by the intelligent decision-making report; The system continuously collects feedback from community security managers and analyzes it through natural language processing technology to improve the intelligent decision-making algorithm.

[0025] According to one embodiment of the present invention, when obtaining multi-source heterogeneous real-time monitoring data of roads, buildings, facilities, etc. in a community, various sensors such as video surveillance cameras, environmental monitoring sensors, vehicle detectors, etc. can be deployed in the community to collect data at intervals of 5 minutes. For the acquired original monitoring data, the Kalman filter algorithm is used to denoise the data, and by setting the data threshold range, the data exceeding the threshold range is marked as an outlier and removed to obtain the cleaned monitoring data. In the data fusion stage, according to the pre-established data fusion rules, such as aligning the data collected by different sensors according to the timestamp, for the data with the same timestamp, matching and associating according to the spatial position, a fused monitoring data set is generated. For the fused monitoring data set, a convolutional neural network algorithm is used to extract data features, and the extracted feature vector is compressed by the principal component analysis method to obtain a low-dimensional feature vector of the monitoring data, which is stored in the MongoDB database. In the data quality assessment phase, according to the preset data quality assessment indicators, such as data integrity and data consistency, the support vector machine algorithm is used to evaluate the quality of the monitoring data set after feature extraction. If the data integrity is greater than 95% and the data consistency error is less than 5%, the data set is marked as available data and stored in the MySQL database. By using data visualization tools such as ECharts, the available monitoring data set in the database is visualized, and a real-time monitoring data visualization interface for roads, buildings, facilities, etc. in the community is generated. It also provides a variety of visualization methods such as heat maps and line charts to facilitate users to view and analyze monitoring data. At the same time, an online incremental learning algorithm is used to learn the historical monitoring data in the database, such as using the stochastic gradient descent method to optimize the weight parameters in the data fusion rules to continuously improve the accuracy of data fusion; the transfer learning algorithm is used to optimize the feature extraction model, and the model is fine-tuned using the existing labeled data to improve the efficiency of feature extraction. Finally, according to the actual needs of community management, the pre-processed monitoring data set is correlated with other business data for analysis. For example, the road monitoring data is correlated with the community population travel data to explore the relationship between the congestion of community roads and the travel patterns of the population in different time periods, and provide optimization suggestions for community road management; the building energy consumption monitoring data is correlated with the building facility maintenance data to analyze the relationship between building energy consumption and facility operating status, predict the risk of facility failure, and provide a decision-making basis for facility maintenance.

[0026] According to one embodiment of the present invention, raw data is obtained from a preprocessed monitoring data set, and the raw data is cleaned and standardized to obtain standardized monitoring data. For example, Python's Pandas library is used to read raw data in CSV format, the missing values ​​are deleted by the dropna() function, and the data is normalized using MinMaxScaler to scale the value range to between 0 and 1. For the standardized monitoring data, characteristic parameters related to community security patrols are extracted, including parameters such as road length, road width, building height, and building density. The community satellite image is processed using the OpenCV library, the road contour is extracted using the edge detection algorithm, and the road length and width are calculated; the target detection algorithm is used to identify the building, and the building height and density are calculated by pixel analysis. According to the acquired characteristic parameters such as road length and road width, the K-means clustering algorithm is used to classify the community roads, and the number of clusters is set to 3, and three types of main roads, secondary roads, and branches are obtained. According to characteristic parameters such as building height and building density, the DBSCAN density clustering algorithm is used to classify community buildings. The eps parameter is set to 5 and the min_samples parameter is set to 10, and three types of high-rise building areas, multi-story building areas and low-rise building areas are obtained. The community road classification results and the building area classification results are superimposed and analyzed to obtain the security patrol environment characteristics of different areas, and a community security patrol environment model is constructed. In the model, the patrol difficulty coefficient of each area is quantified, and different weights are given to generate a comprehensive score by comprehensively considering factors such as road type, building type, and population density. For the community security patrol environment model, the Q-learning reinforcement learning algorithm is used for path planning. Each area is regarded as a state and the patrol behavior is regarded as an action. Through continuous exploration and learning, the optimal security patrol route is obtained to maximize the patrol coverage and patrol efficiency of each area. In the learning process, a reward function is set to give positive rewards to the effectively covered area and negative penalties to repeated patrols, so as to guide the intelligent agent to learn the optimal strategy. Finally, the optimal patrol route obtained through planning is applied to actual community security patrol work. By deploying intelligent patrol robots and combining them with real-time monitoring systems, the patrol routes are dynamically adjusted and optimized, continuously improving the intelligence level and actual effects of community security patrols, and achieving all-round protection of community safety.

[0027] According to one embodiment of the present invention, based on the community security patrol environment model, the community road network data is obtained by using drone aerial photography technology, and the road nodes and connection relationship information are extracted through the image recognition algorithm to construct a community road network topology map containing 500 nodes and 800 edges. On this basis, the patrol route optimization model is introduced, the objective function is set to minimize the total patrol distance, and the constraints are set: each road is patrolled at least once, 20 key areas need to strengthen patrols, and the patrol route needs to form a closed loop. The ant colony algorithm is used to solve the optimization model, setting the number of ants to 100, the number of iterations to 200, the pheromone importance factor to 1, the heuristic function importance factor to 5, and the pheromone volatility coefficient to 5, and finally the optimal patrol route plan with a total length of 15 kilometers is obtained. According to the plan, patrol routes are marked on the community road network topology map, a community security patrol route map is generated, and it is sent to the patrol personnel's mobile terminal. Through positioning and navigation technology, patrol personnel are guided to carry out community security patrol work along the optimal route, which can effectively improve patrol efficiency and quality, and is expected to reduce patrol time and labor costs by 30%.

[0028] According to one embodiment of the present invention, a patrol route optimization model is constructed according to the actual needs of community security. The objective function of the model can be set to maximize the patrol coverage rate while minimizing the patrol response time and route length. Constraints include the starting point and end point of each route, the number of visits to each patrol point, etc. The model is solved by using an ant colony algorithm, and the pheromone concentration is set to 8, the number of ants is set to 50, and the number of iterations is set to 100, and 10 candidate optimal patrol routes are obtained. 500 patrol events in the past year are extracted from historical data, and the distribution patterns of their time, location and type are analyzed to evaluate the effectiveness of the candidate routes. A comprehensive evaluation index system is constructed, including three dimensions: patrol coverage rate, average response time and route length, with weights of 5, 3 and 2 respectively, to weightedly score the candidate routes. According to the comprehensive scoring results, the plan with the highest total score is selected as the best patrol route. The route is applied to actual patrol work, and 100 new patrol event data are collected in the next month for further optimization of the model and algorithm. The actual effectiveness of patrol routes will be evaluated once a quarter, and the patrol routes will be dynamically adjusted in a timely manner according to changes in the community security situation to continuously improve the efficiency and quality of patrols.

[0029] According to one embodiment of the present invention, firstly, the flow of people data and public security incident data of different time periods are obtained from various monitoring equipment and police systems in the community, and the missing values, outliers, etc. are cleaned, and the minimum-maximum normalization is used to scale the data to the interval [0,1]. Then the K-means algorithm is used to divide the data into 3 clusters, and the attribution of each data point is determined by calculating the Euclidean distance. Then, the internal data of each cluster is analyzed, and the public security incident frequency weight is 6, the incident severity weight is 4, and the cluster public security risk value is obtained by weighted average. According to the preset thresholds 7 and 3, the high, medium and low risk levels are divided. The current patrol frequency setting is obtained, and the high-risk period is increased to 2 times per hour, the low-risk period is reduced to 1 time every 2 hours, and the medium-risk period is maintained at 1 time per hour. The optimized patrol frequency setting is synchronized to the patrol management system for automatic execution. The above analysis process is repeated regularly every day, the patrol tasks are dynamically updated, and the level of community security control is continuously improved.

[0030] In addition, in order to achieve the above-mentioned purpose, the present invention also provides a smart community intelligent security monitoring and management system based on data interconnection. Figure 2 is a flow chart of a smart community intelligent security monitoring and management system based on data interconnection according to an exemplary embodiment, such as Figure 2 As shown, a smart community intelligent security monitoring and management system based on data interconnection in the present invention includes: Detection data acquisition module: obtains real-time monitoring data of roads, buildings and facilities in the community, performs data cleaning and data fusion on the monitoring data, obtains the pre-processed monitoring data set, and stores the monitoring data set in the database; Community security patrol environment model construction module: reads the pre-processed monitoring data set from the database, extracts characteristic parameters related to community security patrol, the characteristic parameters include road length, road width, building height and building density, and constructs a community security patrol environment model based on the characteristic parameters; Community security patrol route optimization model construction module: According to the community security patrol environment model, the community road network is modeled using graph theory algorithms to obtain the community road network topology map. The community security patrol route optimization model is constructed based on the topology map. The objective function of the community security patrol route optimization model is to minimize the total patrol distance. The constraints include patrolling each road at least once and strengthening patrols in key areas. Patrol route generation module: For the community security patrol route optimization model, the ant colony algorithm is used to solve it, and several candidate optimal patrol routes are obtained. The candidate optimal patrol routes are evaluated based on historical patrol data, and the patrol route with the best comprehensive evaluation index is selected as the best patrol route; Patrol frequency optimization module: obtain the human flow data and public security event data in different time periods in the community, use clustering algorithm to perform cluster analysis on the human flow data and public security event data, and obtain the public security risk level in different time periods. If the public security risk level is high risk, the patrol frequency of the corresponding time period is increased; if the public security risk level is low risk, the patrol frequency of the corresponding time period is reduced; Decision report generation module: visualizes the best patrol route, dynamically optimized patrol frequency, and patrol plan information for a period of time in the future, generates a community security patrol intelligent decision report, and pushes the intelligent decision report to the community security management personnel terminal.

[0031] In order to achieve the above-mentioned purpose of the invention, the present invention also provides an electronic device, which includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the above-mentioned smart community intelligent security monitoring and management method based on data interconnection is implemented.

[0032] In order to achieve the above-mentioned purpose of the invention, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned smart community intelligent security monitoring and management method based on data interconnection is implemented.

[0033] Based on this, the beneficial effect of the present invention is that the method obtains real-time community monitoring data, performs data preprocessing and feature extraction, and constructs a community security patrol environment model. Based on the model, a graph theory algorithm is used to establish a road network topology map, and a patrol route optimization model is constructed. The ant colony algorithm is used to solve the model to obtain the candidate optimal patrol route, and the best route is selected in combination with historical data evaluation. At the same time, cluster analysis is performed on the flow of people and public security incident data in different time periods, and the patrol frequency is dynamically adjusted. Finally, the optimized patrol route, frequency and other information are visualized, and an intelligent decision-making report is generated, which realizes the intelligent and refined management of community security patrols, improves patrol efficiency and safety prevention and control levels, and provides strong decision-making support for community security management.

[0034] Those of ordinary skill in the art will appreciate that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0035] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and equipment can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0036] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0037] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0038] In addition, each functional module in the embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0039] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the energy-saving signal sending / receiving method of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0040] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the present application.

[0041] It should be understood that the size of the serial numbers of each step in the content of the invention and the embodiments of the present invention does not absolutely mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

Claims

1. A smart community intelligent security monitoring and management method based on data interconnection, characterized in that: include: Obtain real-time monitoring data of roads, buildings and facilities in the community, perform data cleaning and data fusion on the monitoring data, obtain a pre-processed monitoring data set, and store the monitoring data set in the database; Read the preprocessed monitoring data set from the database, extract characteristic parameters related to community security patrol, the characteristic parameters include road length, road width, building height and building density, and build a community security patrol environment model based on the characteristic parameters; According to the community security patrol environment model, the community road network is modeled using graph theory algorithms to obtain the community road network topology map. Based on the topology map, a community security patrol route optimization model is constructed. The objective function of the community security patrol route optimization model is to minimize the total patrol distance. The constraints include patrolling each road at least once and strengthening patrols in key areas. The ant colony algorithm is used to solve the community security patrol route optimization model, and several candidate optimal patrol routes are obtained. The candidate optimal patrol routes are evaluated based on historical patrol data, and the patrol route with the best comprehensive evaluation index is selected as the best patrol route. Obtain the pedestrian flow data and public security incident data at different time periods in the community, use clustering algorithms to perform cluster analysis on the pedestrian flow data and public security incident data, and obtain the public security risk level at different time periods. If the public security risk level is high risk, increase the patrol frequency at the corresponding time period; if the public security risk level is low risk, reduce the patrol frequency at the corresponding time period; The best patrol route, dynamically optimized patrol frequency, and patrol plan information for a period of time in the future are visualized to generate a community security patrol intelligent decision-making report, which is then pushed to the community security management personnel terminal.

2. A smart community intelligent security monitoring and management method based on data interconnection as claimed in claim 1, characterized in that: Obtain real-time monitoring data of roads, buildings and facilities in the community, remove noise and process outliers on the original monitoring data, and obtain cleaned monitoring data; According to the pre-established data fusion rules, the cleaned monitoring data are associated and fused to obtain a fused monitoring data set, wherein the data fusion rules include time alignment and space alignment; For the fused monitoring data set, feature extraction is performed to obtain the feature vector of the monitoring data, and the feature vector is stored in the database; According to the preset data quality assessment indicators, the quality of the monitoring data set after feature extraction is assessed. If the assessment result meets the preset threshold, the monitoring data set is marked as available data and stored in the database; Through data visualization technology, the available monitoring data sets in the database are visualized to generate a real-time monitoring data visualization interface for roads, buildings and facilities in the community; Study the historical monitoring data in the database and optimize the data fusion rules and feature extraction algorithms; According to the actual needs of community management, the pre-processed monitoring data set is associated with other business data for analysis and mining of the monitoring data.

3. A smart community intelligent security monitoring and management method based on data interconnection as claimed in claim 2, characterized in that: Obtaining raw data from the preprocessed monitoring data set, cleaning and standardizing the raw data to obtain standardized monitoring data; Based on the standardized monitoring data, characteristic parameters related to community security patrols are extracted, including road length, road width, building height and building density parameters; According to the acquired road length and road width characteristic parameters, a clustering algorithm is used to classify community roads to obtain different types of roads, including main roads, secondary roads and branch roads; According to the building height and building density characteristic parameters, the density clustering algorithm is used to classify community buildings, and different types of building areas are obtained, including high-rise building areas, multi-story building areas and low-rise building areas; The community road classification results and the building area classification results are superimposed and analyzed to obtain the security patrol environment characteristics of different areas and build a community security patrol environment model; For the community security patrol environment model, a reinforcement learning algorithm is used for path planning to obtain the optimal security patrol route and maximize the patrol coverage and efficiency of each area.

4. A smart community intelligent security monitoring and management method based on data interconnection as claimed in claim 3, characterized in that: According to the community security patrol environment model, obtain community road network data, including road nodes and road connection relationship information; The community road network is modeled using graph theory algorithms, with road nodes as graph nodes and road connection relationships as graph edges, to construct a community road network topology graph. Based on the community road network topology, a community security patrol route optimization model is constructed, and the objective function is set to minimize the total patrol distance; Set constraints for the community security patrol route optimization model, including patrolling each road at least once, strengthening patrols in key areas, and forming a closed loop of patrol routes to obtain the optimal patrol route plan; According to the optimal patrol route plan, the patrol route is marked on the community road network topology map to generate a community security patrol route map; The community security patrol route map is sent to the patrol personnel's mobile terminal to guide the patrol personnel to carry out community security patrol work along the optimal route.

5. A smart community intelligent security monitoring and management method based on data interconnection as claimed in claim 4, characterized in that: The ant colony algorithm is used to solve the community security patrol route optimization model, and the relevant parameters of the algorithm are set, including pheromone concentration and number of ants, to obtain multiple candidate optimal patrol routes; Extract the time, location and type of patrol events from historical patrol data, and evaluate the effectiveness and feasibility of candidate optimal patrol routes; Construct a comprehensive evaluation index system, including patrol coverage, response time, and route length, to quantitatively score candidate routes; According to the comprehensive evaluation index system, the evaluation results of each candidate route are compared, and the route with the highest score is selected as the best patrol route; Apply the best patrol routes to actual community security work, and continuously collect new patrol data to iteratively update the community security patrol route optimization model and algorithm; Regularly evaluate the actual application effect of the optimal patrol route and dynamically adjust the route based on the evaluation results.

6. A smart community intelligent security monitoring and management method based on data interconnection as claimed in claim 5, characterized in that: Obtain the traffic flow data and public security incident data in the community at different time periods, and pre-process the acquired data, including data cleaning and data normalization, to obtain a standardized data set; According to the preprocessed data set, the K-means clustering algorithm is used to perform cluster analysis on the pedestrian flow data and public security incident data in different time periods. By calculating the distance between each cluster center and the data point, the data point is divided into the cluster with the closest distance, and the clustering results of different time periods are obtained; Based on the clustering results, the data distribution within each cluster is calculated, including the frequency and severity of public security incidents, and the public security risk value of each cluster is obtained by weighted average; According to the public security risk value, the preset risk level classification standard is used to determine the public security risk level of each time period cluster. If the risk value exceeds the preset high risk threshold, the risk level of the time period is marked as high risk. If the risk value is lower than the preset low risk threshold, the risk level of the time period is marked as low risk. The rest are marked as medium risk. Obtain the existing patrol frequency settings of the community. For periods of high risk, increase the patrol frequency to the preset high-risk patrol frequency. For periods of low risk, reduce the patrol frequency to the preset low-risk patrol frequency. For periods of medium risk, maintain the original patrol frequency unchanged. The adjusted patrol frequency settings for each time period are synchronized to the community patrol system, and the patrol task allocation and execution for each time period are controlled through the patrol management module; Continuously obtain the latest pedestrian flow and public security incident data, regularly repeat the cluster analysis and risk assessment process, and dynamically update the patrol frequency settings according to changes in the community security situation.

7. A smart community intelligent security monitoring and management method based on data interconnection as claimed in claim 6, characterized in that: According to the historical patrol data pushed by the community security management personnel terminal, the patrol route is optimized through the clustering algorithm to obtain the best patrol route covering all areas of the community; Adopting time series prediction algorithm, combining the current patrol frequency and the trend of community traffic flow in the future, dynamically adjust the patrol frequency in each time period; Generate a patrol plan for the future based on the best patrol route and optimized patrol frequency; Integrate patrol routes, frequencies and plan information to generate community security patrol intelligent decision-making reports, which include visualization of various data and auxiliary decision-making suggestions; Through the community security management system, the intelligent decision-making report is pushed to the community security manager terminal. The manager optimizes and adjusts the community security patrol strategy based on the information and suggestions provided by the intelligent decision-making report; The system continuously collects feedback from community security managers and analyzes it through natural language processing technology to improve the intelligent decision-making algorithm.

8. A smart community intelligent security monitoring and management system based on data interconnection, characterized in that: include: Detection data acquisition module: obtains real-time monitoring data of roads, buildings and facilities in the community, performs data cleaning and data fusion on the monitoring data, obtains the pre-processed monitoring data set, and stores the monitoring data set in the database; Community security patrol environment model construction module: reads the pre-processed monitoring data set from the database, extracts characteristic parameters related to community security patrol, the characteristic parameters include road length, road width, building height and building density, and constructs a community security patrol environment model based on the characteristic parameters; Community security patrol route optimization model construction module: According to the community security patrol environment model, the community road network is modeled using graph theory algorithms to obtain the community road network topology map. The community security patrol route optimization model is constructed based on the topology map. The objective function of the community security patrol route optimization model is to minimize the total patrol distance. The constraints include patrolling each road at least once and strengthening patrols in key areas. Patrol route generation module: For the community security patrol route optimization model, the ant colony algorithm is used to solve it, and several candidate optimal patrol routes are obtained. The candidate optimal patrol routes are evaluated based on historical patrol data, and the patrol route with the best comprehensive evaluation index is selected as the best patrol route; Patrol frequency optimization module: obtain the human flow data and public security event data in different time periods in the community, use clustering algorithm to perform cluster analysis on the human flow data and public security event data, and obtain the public security risk level in different time periods. If the public security risk level is high risk, the patrol frequency of the corresponding time period is increased; if the public security risk level is low risk, the patrol frequency of the corresponding time period is reduced; Decision report generation module: visualizes the best patrol route, dynamically optimized patrol frequency, and patrol plan information for a period of time in the future, generates a community security patrol intelligent decision report, and pushes the intelligent decision report to the community security management personnel terminal.

9. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements a smart community intelligent security monitoring and management method based on data interconnection as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements a smart community intelligent security monitoring and management method based on data interconnection as described in any one of claims 1 to 7.

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