Smart village intelligent safety management system

Abstract

The invention discloses an intelligent village intelligent safety management system, relates to the technical field of village management, and solves the problems that grid management is lacked, association of water body pollution, soil pollution and air pollution is difficult to consider, soil pollution and air pollution areas influenced by water bodies are difficult to find accurately, and the management efficiency is improved. And subsequent management is not convenient. The resource utilization efficiency is improved by formulating hierarchical and differentiated treatment modes according to different levels of pollution degrees. The areas with the water area pollution degree coefficients larger than the second early warning threshold value are marked, and sampling points are additionally arranged in key areas of farmland grids and residence grids adjacent to the marked polluted water areas, so that farmland areas and residence areas possibly affected by the polluted water areas can be detected in time; pollutions possibly existing in farmland areas and residential areas can be detected more quickly, and it is ensured that limited resources are used in places where attention is most needed.

Description

Technical Field

[0001] The present invention belongs to the field of village management, and specifically is an intelligent safety management system for a smart village. Background Art

[0002] In recent years, the rapid development of information technology has provided new possibilities for the intelligent management of rural areas. By building an intelligent security management system, real-time monitoring, data analysis and decision support for the rural environment can be achieved, thereby improving the efficiency of rural governance and ensuring the quality of life of residents. The Smart Village Intelligent Security Management System is a platform based on advanced information technology to provide comprehensive, safe and efficient management services for rural areas.

[0003] Most rural safety management plans, when conducting rural environmental safety supervision, only detect soil and water data through random sampling, lack grid management, and find it difficult to consider the relationship between water pollution, soil pollution and air pollution. It is difficult to accurately find soil pollution and air pollution areas affected by water bodies, which is not convenient for subsequent management. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes an intelligent safety management system for a smart village, which is used to solve the technical problems of lack of grid management, difficulty in considering the relationship between water pollution, soil pollution and air pollution, difficulty in accurately finding soil pollution and air pollution areas affected by water bodies, and inconvenience for subsequent management.

[0005] To solve the above problems, the first aspect of the present invention provides a smart village intelligent security management system, including:

[0006] Rural plot division module: used to obtain authorized rural maps and divide the water plots, farmland plots, residential plots and road plots on the rural maps;

[0007] Rural water area data collection module: used to mark rural water areas, set water quality detection sensors and water area video monitoring equipment for water areas with an area larger than a preset threshold, and collect water quality data and monitoring data of the water areas;

[0008] Rural soil and air data collection module: used to mark farmland and residential plots in rural areas, divide farmland and residential plots into grids, randomly select several grids in farmland plots to set up soil sampling points, collect soil pollution data, and randomly select several grids in residential plots to set up air sampling points, collect air pollution data;

[0009] Data analysis module: used to analyze the water pollution coefficient based on the data collected by the rural water data collection module, generate warning signals for water areas with a water pollution coefficient greater than the first warning threshold, mark areas where the water pollution coefficient is greater than the second warning threshold, add sampling points to the farmland grids and residential grids adjacent to the marked polluted waters, and analyze the rural pollution index through the soil pollution data and air pollution data collected at the sampling points.

[0010] As a further solution of the present invention: also include:

[0011] Pollution marking module: based on the collected soil pollution data and air pollution data, the polluted areas are marked on the map, the main pollution sources in the villages are analyzed, and the analysis results are sent to the backend management end;

[0012] Rural road data monitoring module: used to mark road plots, set up surveillance cameras on roads with a width greater than a threshold, collect road monitoring videos, and extract the driving trajectories of foreign vehicles entering the village. Based on the driving trajectories of foreign vehicles, analyze whether foreign vehicles stay in the surrounding areas of marked polluted areas, and intercept video segments of foreign vehicles staying in marked polluted areas and send them to the background management end.

[0013] As a further solution of the present invention: the pollution marking module comprises:

[0014] Pollution analysis unit: Mark the grids whose soil pollution data and air pollution data exceed the threshold, and sample the adjacent grids again until the soil pollution data and air pollution data of the sampled grids are less than or equal to the threshold;

[0015] Area marking unit: The grids where the detected soil pollution data exceeds the threshold are regarded as soil pollution area grids, and the grids where the air pollution data exceeds the threshold are regarded as air pollution area grids, and the pollution area grids are marked on the map;

[0016] Pollution source analysis unit: Analyze the main pollution sources in rural areas based on the soil pollution area grid, air pollution area grid, rural pollution index and water pollution degree coefficient.

[0017] As a further solution of the present invention: the data analysis module analyzes the water pollution degree coefficient according to the data collected by the rural water data collection module, including the following steps:

[0018] According to the water quality data of the water area collected by the rural water area data collection module, including: water turbidity, pH value, chemical oxygen demand, ammonia nitrogen content and water phosphorus content, the five water quality data of water turbidity, pH value, chemical oxygen demand, ammonia nitrogen content and water phosphorus content are sequentially combined into a set {A 1 , A 2 , …, A5}, setting the water areas that exceed the preset safety threshold in the water quality data as polluted water areas and marking them as polluted water areas on the map;

[0019] Obtain monitoring images of polluted waters and monitoring images of unpolluted waters from historical data, mark the polluted areas in the monitoring images of polluted waters, and train the deep learning model with the marked historical data;

[0020] Extract one image frame every 50 frames from the collected monitoring data and send it to the trained deep learning model to mark the polluted areas in the water area image frames;

[0021] The water pollution degree coefficient is analyzed by the following formula:

[0022]

[0023] Among them, α is the water pollution degree coefficient, A i Asta is the collected value of the i-th water quality data of the collected water area, i is the safety value of the i-th water quality data collected in the water area, S 1 is the area of ​​the contaminated area marked in the image frame, S 0 is the total area of ​​the water area marked in the image frame, C 1 is the average gray value of the contaminated area marked in the image frame, C 0 It is the average gray value of the water area marked in the image frame without the pollution area.

[0024] As a further solution of the present invention: the data analysis module marks the area where the water pollution degree coefficient is greater than the second warning threshold, and adds sampling points in the farmland grid and residential grid adjacent to the marked polluted water area, including the following steps:

[0025] Areas with a pollution degree coefficient greater than the second warning threshold are set as polluted waters and marked as polluted waters on the map;

[0026] Screen the farmland grids and residential grids adjacent to the marked polluted waters, and add 1 to 5 sampling points in the farmland grids and residential grids adjacent to the marked polluted waters.

[0027] As a further solution of the present invention: the data analysis module analyzes the rural pollution index through the soil pollution data and air pollution data collected at the sampling points, including the following steps:

[0028] Obtain soil pollution data and air pollution data collected at sampling points, where soil pollution data collected at sampling points include: soil pH value, soil heavy metal content and soil organic pollutant content, and air pollution data collected include: PM2.5 value, sulfur dioxide content, carbon monoxide content and nitrogen oxide content;

[0029] The detection values ​​of soil pollution data are sequentially combined into sets {B 1 , B 2 , …, B n}, and the detection values ​​in the air pollution data form a set {D 1 , D 2 , …, D 5};

[0030] The rural pollution index is analyzed by the following formula:

[0031]

[0032] Among them, β is the rural pollution index, B i is the mean value of the j-th soil pollution data detection value collected in the farmland grid, Bsta i is the safety value of the jth soil pollution data collected in the farmland grid, j∈(1,2,…,n), D i is the mean value of the k-th soil pollution data detected in the residential grid, Dsta i is the safety value of the kth soil pollution data collected in the residential grid, X i is the variance of the detection value of the kth type of soil pollution data collected in the residential grid within a week, k∈(1,2,…,m).

[0033] As a further solution of the present invention: the rural road data monitoring module analyzes whether the foreign vehicle stays in the surrounding area of ​​the marked pollution area according to the driving trajectory of the foreign vehicle, and intercepts the video segment of the foreign vehicle staying in the marked pollution area, including the following steps:

[0034] Based on the driving trajectories of out-of-town vehicles, the locations where out-of-town vehicles have stayed for more than 20 minutes are marked;

[0035] Detect whether there is a marked pollution area within a set radius around the marked parking position of the foreign vehicle. If so, the foreign vehicle will stop in the surrounding area of ​​the marked pollution area, where the set radius is a multiple of the longest diagonal distance of the farmland grid;

[0036] Capture the video segments of the time intervals during which foreign vehicles stay in the marked pollution areas.

[0037] As a further solution of the present invention: the pollution source analysis unit analyzes the main pollution sources in the countryside according to the soil pollution area grid, the air pollution area grid, the rural pollution index and the water pollution degree coefficient, including the following steps:

[0038] S1: Obtain the soil pollution area grid and air pollution area grid data marked on the map, as well as the rural pollution index and water pollution degree coefficient;

[0039] S2: If the rural pollution index and the water pollution degree coefficient do not exceed the threshold, the village is pollution-free and the analysis of the main pollution sources in the village is stopped. Otherwise, proceed to step S3;

[0040] S3: If the difference between the water pollution degree coefficient and the rural pollution index is greater than a preset threshold, it is determined that the main source of rural pollution is water pollution, otherwise, proceed to step S4;

[0041] S4: If the difference between the water pollution degree coefficient and the rural pollution index is less than the preset threshold, proceed to step S5; otherwise, there is pollution in the village, but no major pollution source;

[0042] S5: Detect the number of grids in the soil pollution area and the number of grids in the air pollution area, the number of grids in the soil pollution area minus the number of grids in the air pollution area;

[0043] S6: If the obtained difference is greater than the maximum value of the preset threshold range, it is judged that the main pollution source of the village is soil pollution. If the obtained difference is less than the minimum value of the preset threshold range, it is judged that the main pollution source of the village is air pollution. Otherwise, there is pollution in the village, but there is no main pollution source.

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

[0045] The present invention marks the farmland and residential plots in the village through the rural soil and air data collection module, divides the farmland and residential plots into grids respectively, regularly randomly selects several grids in the farmland to set soil sampling points, collects soil pollution data, and regularly randomly selects several grids in the residential plot to set air sampling points, collects air pollution data; by dividing the land into grids, it is convenient to systematically manage and monitor the pollution conditions in different areas, and ensure the comprehensiveness of data collection. The random selection method can reduce deviations, make the obtained data more representative, and thus improve the credibility of the research results; by monitoring the soil and air quality of farmland and residential areas respectively, it is convenient to timely discover potential pollution sources and evaluate their impact on residents' health and agricultural production.

[0046] The present invention analyzes the water pollution degree coefficient according to the data collected by the rural water data collection module through the data analysis module, generates a warning signal for the water area with the water pollution degree coefficient greater than the first warning threshold, marks the area with the water pollution degree coefficient greater than the second warning threshold, adds sampling points in the farmland grid and residential grid adjacent to the marked polluted water area, analyzes the rural pollution index through the soil pollution data and air pollution data collected by the sampling points; formulates hierarchical and differentiated processing methods according to different levels of pollution, and improves resource utilization efficiency. Mark the area with the water pollution degree coefficient greater than the second warning threshold, add sampling points in the key areas of the farmland grid and residential grid adjacent to the marked polluted water area, facilitate timely detection of farmland areas and residential areas that may be affected by the polluted water area, facilitate faster detection of possible pollution in farmland areas and residential areas, and ensure that limited resources are used in the areas that need the most attention. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 It is a schematic diagram of the system framework of the present invention;

[0049] Figure 2 This is a schematic diagram of the pollution marking module framework of the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] See also Figure 1-Figure 2 The first aspect of the present invention provides a smart village intelligent security management system, including:

[0052] Rural plot division module: used to obtain authorized rural maps and divide the water plots, farmland plots, residential plots and road plots on the rural maps;

[0053] Rural water area data collection module: used to mark rural water areas, set water quality detection sensors and water area video monitoring equipment for water areas with an area larger than a preset threshold, and collect water quality data and monitoring data of the water areas;

[0054] Rural soil and air data collection module: used to mark farmland and residential plots in rural areas, divide farmland and residential plots into grids, randomly select several grids in farmland plots to set up soil sampling points, collect soil pollution data, and randomly select several grids in residential plots to set up air sampling points, collect air pollution data;

[0055] Data analysis module: used to analyze the water pollution coefficient based on the data collected by the rural water data collection module, generate warning signals for water areas with a water pollution coefficient greater than the first warning threshold, mark areas where the water pollution coefficient is greater than the second warning threshold, add sampling points to the farmland grids and residential grids adjacent to the marked polluted waters, and analyze the rural pollution index through the soil pollution data and air pollution data collected at the sampling points.

[0056] Specifically, in this embodiment, by contacting the local government or land management department to apply for official rural map data, or searching the national or local open data platform, authorized rural maps can be obtained. These data may include land use status maps, remote sensing images, etc.

[0057] Convert map data into usable formats such as Shapefile, GeoJSON, and KML for subsequent processing.

[0058] Load the map data through GIS software, load the basic layer in the GIS software, and import the obtained village map into the GIS software as the basic layer.

[0059] Define the classification standards and define the characteristics of each type of land according to the actual situation:

[0060] Water areas: usually appear as blue areas and may include rivers, lakes, ponds, etc.

[0061] Farmland plots: mostly green areas, may appear as cultivated areas, orchards, etc.

[0062] Residential plots: generally appear as densely built-up areas and may appear in gray or other colors.

[0063] Road plot: Appears as a linear structure, generally marked in black or other traffic sign colors.

[0064] Manually draw division lines on the base layer to divide different types of plots. Use the "Edit" function to create new graphic objects in the corresponding areas and set their properties to the corresponding categories.

[0065] Add attribute information for each newly divided area, including category and area. Categories include: water area, farmland, residential area, and road.

[0066] The rural soil and air data collection module is used to mark the farmland and residential plots in the village, and the farmland and residential plots are divided into grids. Several grids in the farmland are randomly selected to set up soil sampling points to collect soil pollution data, and several grids in the residential plots are randomly selected to set up air sampling points to collect air pollution data; by dividing the land into grids, it is convenient to systematically manage and monitor the pollution in different areas, ensuring the comprehensiveness of data collection. The random selection method can reduce bias, make the data obtained more representative, and thus improve the credibility of the research results.

[0067] By monitoring soil and air quality in farmland and residential areas respectively, it is easy to detect potential pollution sources in a timely manner and assess their impact on residents' health and agricultural production.

[0068] It is convenient to formulate corresponding environmental governance measures based on the collected data to effectively reduce pollution risks.

[0069] The data analysis module analyzes the water pollution coefficient based on the data collected by the rural water data collection module, generates a warning signal for the water area with a water pollution coefficient greater than the first warning threshold, marks the area with a water pollution coefficient greater than the second warning threshold, adds sampling points to the farmland grid and residential grid adjacent to the marked polluted water area, and analyzes the rural pollution index through the soil pollution data and air pollution data collected at the sampling points;

[0070] By setting different warning thresholds, it is easy to issue early warning signals when more serious pollution is detected, reducing potential environmental and health risks. Early identification of highly polluted areas will help take measures to prevent pollution from spreading to surrounding areas.

[0071] According to the different levels of pollution, a hierarchical and differentiated treatment method is formulated to improve resource utilization efficiency. Areas where the water pollution coefficient is greater than the second warning threshold will be marked, and sampling points will be added in key areas of farmland grids and residential grids adjacent to the marked polluted waters to facilitate timely detection of farmland areas and residential areas that may be affected by polluted waters, facilitate faster detection of possible pollution in farmland areas and residential areas, and ensure that limited resources are used in areas that need the most attention.

[0072] In one embodiment of the present invention, it also includes:

[0073] Pollution marking module: based on the collected soil pollution data and air pollution data, the polluted areas are marked on the map, the main pollution sources in the villages are analyzed, and the analysis results are sent to the backend management end;

[0074] Rural road data monitoring module: used to mark road plots, set up surveillance cameras on roads with a width greater than a threshold, collect road monitoring videos, and extract the driving trajectories of foreign vehicles entering the village. Based on the driving trajectories of foreign vehicles, analyze whether foreign vehicles stay in the surrounding areas of marked polluted areas, and intercept video segments of foreign vehicles staying in marked polluted areas and send them to the background management end.

[0075] In one embodiment of the present invention, the pollution marking module includes:

[0076] Pollution analysis unit: Mark the grids whose soil pollution data and air pollution data exceed the threshold, and sample the adjacent grids again until the soil pollution data and air pollution data of the sampled grids are less than or equal to the threshold;

[0077] Area marking unit: The grids where the detected soil pollution data exceeds the threshold are regarded as soil pollution area grids, and the grids where the air pollution data exceeds the threshold are regarded as air pollution area grids, and the pollution area grids are marked on the map;

[0078] Pollution source analysis unit: Analyze the main pollution sources in rural areas based on the soil pollution area grid, air pollution area grid, rural pollution index and water pollution degree coefficient.

[0079] Specifically, in this embodiment, by establishing a grid, the research area is divided into a number of grids, such as 10m*10m, and each grid represents a spatial unit.

[0080] Obtain soil and air pollution data for each grid, and determine the thresholds for specific detection data of soil and air pollution based on national or regional environmental or policy standards.

[0081] All grids are traversed, and soil and air pollution data exceeding the set threshold are marked as polluted area grids.

[0082] For each grid marked as "exceeding the standard", check its adjacent grids, usually in the four directions of up, down, left and right; if the adjacent grid has not been detected, perform a new sample on it and repeat the above steps until all adjacent undetected grids are processed.

[0083] In one embodiment of the present invention, the data analysis module analyzes the water pollution degree coefficient based on the data collected by the rural water data collection module, including the following steps:

[0084] According to the water quality data of the water area collected by the rural water area data collection module, including: water turbidity, pH value, chemical oxygen demand, ammonia nitrogen content and water phosphorus content, the five water quality data of water turbidity, pH value, chemical oxygen demand, ammonia nitrogen content and water phosphorus content are sequentially combined into a set {A 1 , A 2 , …, A 5}, set the water areas that exceed the preset safety threshold in the water quality data as polluted water areas, if not, and mark them as polluted water areas on the map;

[0085] Obtain monitoring images of polluted waters and monitoring images of unpolluted waters from historical data, mark the polluted areas in the monitoring images of polluted waters, and train the deep learning model with the marked historical data;

[0086] Extract one image frame every 50 frames from the collected monitoring data and send it to the trained deep learning model to mark the polluted areas in the water area image frames;

[0087] The water pollution degree coefficient is analyzed by the following formula:

[0088]

[0089] Among them, α is the water pollution degree coefficient, A i Asta is the collected value of the i-th water quality data of the collected water area, i is the safety value of the i-th water quality data collected in the water area, S 1 is the area of ​​the contaminated area marked in the image frame, S 0 is the total area of ​​the water area marked in the image frame, C 1 is the average gray value of the contaminated area marked in the image frame, C 0 It is the average gray value of the water area marked in the image frame without the pollution area.

[0090] Specifically, in this embodiment, the safety thresholds of water turbidity, pH value, chemical oxygen demand, ammonia nitrogen content and water phosphorus content are set according to national policy standards, for example, GB 3838-2002 "Surface Water Environmental Quality Standards".

[0091] The analysis of the water pollution coefficient is to generate a warning signal for the water area with a pollution coefficient greater than the first warning threshold, mark the area with a pollution coefficient greater than the second warning threshold, and add sampling points in the farmland grid and residential grid adjacent to the marked polluted water area. The first warning threshold is set to 0.6 and the second warning threshold is set to 0.3.

[0092] In one embodiment of the present invention, the data analysis module marks the area where the water pollution degree coefficient is greater than the second warning threshold, and adds sampling points in the farmland grid and the residential grid adjacent to the marked polluted water area, including the following steps:

[0093] Areas with a pollution degree coefficient greater than the second warning threshold are set as polluted waters and marked as polluted waters on the map;

[0094] Screen the farmland grids and residential grids adjacent to the marked polluted waters, and add 1 to 5 sampling points in the farmland grids and residential grids adjacent to the marked polluted waters.

[0095] In one embodiment of the present invention, the data analysis module analyzes the rural pollution index through the soil pollution data and air pollution data collected at the sampling points, including the following steps:

[0096] Obtain soil pollution data and air pollution data collected at sampling points, where soil pollution data collected at sampling points include: soil pH value, soil heavy metal content and soil organic pollutant content, and air pollution data collected include: PM2.5 value, sulfur dioxide content, carbon monoxide content and nitrogen oxide content;

[0097] The detection values ​​of soil pollution data are sequentially combined into sets {B 1 , B 2 , …, B n}, and the detection values ​​in the air pollution data form a set {D 1 , D 2 , …, D 5};

[0098] The rural pollution index is analyzed by the following formula:

[0099]

[0100] Among them, β is the rural pollution index, B i is the mean value of the j-th soil pollution data detection value collected in the farmland grid, Bsta i is the safety value of the jth soil pollution data collected in the farmland grid, j∈(1,2,…,n), D i is the mean value of the k-th soil pollution data detected in the residential grid, Dstai is the safety value of the kth soil pollution data collected in the residential grid, X i is the variance of the detection value of the kth type of soil pollution data collected in the residential grid within a week, k∈(1,2,…,m).

[0101] In one embodiment of the present invention, the rural road data monitoring module analyzes whether the foreign vehicle stays in the surrounding area of ​​the marked pollution area according to the driving trajectory of the foreign vehicle, and intercepts the video segment of the foreign vehicle staying in the marked pollution area, including the following steps:

[0102] Based on the driving trajectories of out-of-town vehicles, the locations where out-of-town vehicles have stayed for more than 20 minutes are marked;

[0103] Detect whether there is a marked pollution area within a set radius around the marked parking position of the foreign vehicle. If so, the foreign vehicle will stop in the surrounding area of ​​the marked pollution area, where the set radius is a multiple of the longest diagonal distance of the farmland grid;

[0104] Capture the video segments of the time intervals during which foreign vehicles stay in the marked pollution areas.

[0105] In one embodiment of the present invention, the pollution source analysis unit analyzes the main pollution sources in the village according to the soil pollution area grid, the air pollution area grid, the village pollution index and the water pollution degree coefficient, including the following steps:

[0106] S1: Obtain the soil pollution area grid and air pollution area grid data marked on the map, as well as the rural pollution index and water pollution degree coefficient;

[0107] S2: If the rural pollution index and the water pollution degree coefficient do not exceed the threshold, the village is pollution-free and the analysis of the main pollution sources in the village is stopped. Otherwise, step S3 is performed. In this embodiment, the thresholds of the rural pollution index and the water pollution degree coefficient are both set to 0.3.

[0108] S3: If the difference between the water pollution degree coefficient and the rural pollution index is greater than a preset threshold, it is determined that the main source of rural pollution is water pollution, otherwise, proceed to step S4; in this embodiment, the threshold of the difference between the water pollution degree coefficient and the rural pollution index is set to 0.3;

[0109] S4: If the difference between the water pollution degree coefficient and the rural pollution index is less than the preset threshold, proceed to step S5; otherwise, there is pollution in the village, but no major pollution source;

[0110] S5: Detect the number of grids in the soil pollution area and the number of grids in the air pollution area, the number of grids in the soil pollution area minus the number of grids in the air pollution area;

[0111] S6: If the obtained difference is greater than the maximum value of the preset threshold range, it is judged that the main pollution source of the village is soil pollution. If the obtained difference is less than the minimum value of the preset threshold range, it is judged that the main pollution source of the village is air pollution. Otherwise, there is pollution in the village, but there is no main pollution source. In this embodiment, the preset threshold range of the difference obtained by subtracting the number of grids in the air pollution area from the number of grids in the soil pollution area is set to [0,10].

[0112] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A smart village intelligent security management system, characterized in that: include: Rural plot division module: used to obtain authorized rural maps and divide the water plots, farmland plots, residential plots and road plots on the rural maps; Rural water area data collection module: used to mark rural water areas, set water quality detection sensors and water area video monitoring equipment for water areas with an area larger than a preset threshold, and collect water quality data and monitoring data of the water areas; Rural soil and air data collection module: used to mark farmland and residential plots in rural areas, divide farmland and residential plots into grids, randomly select several grids in farmland plots to set up soil sampling points, collect soil pollution data, and randomly select several grids in residential plots to set up air sampling points, collect air pollution data; Data analysis module: used to analyze the water pollution coefficient based on the data collected by the rural water data collection module, generate warning signals for water areas with a water pollution coefficient greater than the first warning threshold, mark areas where the water pollution coefficient is greater than the second warning threshold, add sampling points to the farmland grids and residential grids adjacent to the marked polluted waters, and analyze the rural pollution index through the soil pollution data and air pollution data collected at the sampling points.

2. According to claim 1, the intelligent safety management system for smart villages is characterized in that: Also includes: Pollution marking module: based on the collected soil pollution data and air pollution data, the polluted areas are marked on the map, the main pollution sources in the villages are analyzed, and the analysis results are sent to the backend management end; Rural road data monitoring module: used to mark road plots, set up surveillance cameras on roads with a width greater than a threshold, collect road monitoring videos, and extract the driving trajectories of foreign vehicles entering the village. Based on the driving trajectories of foreign vehicles, analyze whether foreign vehicles stay in the surrounding areas of marked polluted areas, and intercept video segments of foreign vehicles staying in marked polluted areas and send them to the background management end.

3. The smart village intelligent safety management system according to claim 2 is characterized in that: The pollution marking module comprises: Pollution analysis unit: Mark the grids whose soil pollution data and air pollution data exceed the threshold, and sample the adjacent grids again until the soil pollution data and air pollution data of the sampled grids are less than or equal to the threshold; Area marking unit: The grids where the detected soil pollution data exceeds the threshold are regarded as soil pollution area grids, and the grids where the air pollution data exceeds the threshold are regarded as air pollution area grids, and the pollution area grids are marked on the map; Pollution source analysis unit: Analyze the main pollution sources in rural areas based on the soil pollution area grid, air pollution area grid, rural pollution index and water pollution degree coefficient.

4. The smart village intelligent safety management system according to claim 1 is characterized in that: The data analysis module analyzes the water pollution degree coefficient according to the data collected by the rural water data collection module, including the following steps: According to the water quality data of the water area collected by the rural water area data collection module, including: water turbidity, pH value, chemical oxygen demand, ammonia nitrogen content and water phosphorus content, the five water quality data of water turbidity, pH value, chemical oxygen demand, ammonia nitrogen content and water phosphorus content are sequentially combined into a set {A1, A2, ..., A5}, and the water area that exceeds the preset safety threshold in the water quality data is set as a polluted water area and marked as a polluted water area in the map; Obtain monitoring images of polluted waters and monitoring images of unpolluted waters from historical data, mark the polluted areas in the monitoring images of polluted waters, and train the deep learning model with the marked historical data; Extract one image frame every 50 frames from the collected monitoring data and send it to the trained deep learning model to mark the polluted areas in the water area image frames; The water pollution degree coefficient is analyzed by the following formula: Among them, α is the water pollution degree coefficient, A i Asta is the collected value of the i-th water quality data of the collected water area, i is the safety value of the i-th water quality data of the collected water area, S1 is the area of ​​the polluted area marked in the image frame, S0 is the total area of ​​the water area marked in the image frame, C1 is the average grayscale value of the polluted area marked in the image frame, and C0 is the average grayscale value of the water area image marked in the image frame except for the polluted area.

5. The smart village intelligent security management system according to claim 1 is characterized in that: The data analysis module marks the area where the water pollution degree coefficient is greater than the second warning threshold, and adds sampling points in the farmland grid and residential grid adjacent to the marked polluted water area, including the following steps: Areas with a pollution degree coefficient greater than the second warning threshold are set as polluted waters and marked as polluted waters on the map; Screen the farmland grids and residential grids adjacent to the marked polluted waters, and add 1 to 5 sampling points in the farmland grids and residential grids adjacent to the marked polluted waters.

6. The smart village intelligent security management system according to claim 1 is characterized in that: The data analysis module analyzes the rural pollution index through the soil pollution data and air pollution data collected at the sampling points, including the following steps: Obtain soil pollution data and air pollution data collected at sampling points, where soil pollution data collected at sampling points include: soil pH value, soil heavy metal content and soil organic pollutant content, and air pollution data collected include: PM2.5 value, sulfur dioxide content, carbon monoxide content and nitrogen oxide content; The detection values ​​of soil pollution data are sequentially combined into a set {B1, B2, ..., B n }, and the detection values ​​in the air pollution data form a set {D1, D2, ..., D5}; The rural pollution index is analyzed by the following formula: Among them, β is the rural pollution index, B i is the mean value of the j-th soil pollution data detection value collected in the farmland grid, Bsta i is the safety value of the jth soil pollution data collected in the farmland grid, j∈(1,2,…,n), D i is the mean value of the k-th soil pollution data detected in the residential grid, Dsta i is the safety value of the kth soil pollution data collected in the residential grid, X i is the variance of the detection value of the kth type of soil pollution data collected in the residential grid within a week, k∈(1,2,…,m).

7. The smart village intelligent security management system according to claim 2 is characterized in that: The rural road data monitoring module analyzes whether the foreign vehicle stays in the surrounding area of ​​the marked pollution area according to the driving trajectory of the foreign vehicle, and intercepts the video segment of the foreign vehicle staying in the marked pollution area, including the following steps: Based on the driving trajectories of out-of-town vehicles, the locations where out-of-town vehicles have stayed for more than 20 minutes are marked; Detect whether there is a marked pollution area within a set radius around the marked parking position of the foreign vehicle. If so, the foreign vehicle will stop in the surrounding area of ​​the marked pollution area, where the set radius is a multiple of the longest diagonal distance of the farmland grid; Capture the video segments of the time intervals during which foreign vehicles stay in the marked pollution areas.

8. The smart village intelligent safety management system according to claim 3 is characterized in that: The pollution source analysis unit analyzes the main pollution sources in the countryside according to the soil pollution area grid, the air pollution area grid, the rural pollution index and the water pollution degree coefficient, including the following steps: S1: Obtain the soil pollution area grid and air pollution area grid data marked on the map, as well as the rural pollution index and water pollution degree coefficient; S2: If the rural pollution index and the water pollution degree coefficient do not exceed the threshold, the village is pollution-free and the analysis of the main pollution sources in the village is stopped. Otherwise, proceed to step S3; S3: If the difference between the water pollution degree coefficient and the rural pollution index is greater than a preset threshold, it is determined that the main source of rural pollution is water pollution, otherwise, proceed to step S4; S4: If the difference between the water pollution degree coefficient and the rural pollution index is less than the preset threshold, proceed to step S5; otherwise, there is pollution in the village, but no major pollution source; S5: Detect the number of grids in the soil pollution area and the number of grids in the air pollution area, the number of grids in the soil pollution area minus the number of grids in the air pollution area; S6: If the obtained difference is greater than the maximum value of the preset threshold range, it is judged that the main pollution source of the village is soil pollution. If the obtained difference is less than the minimum value of the preset threshold range, it is judged that the main pollution source of the village is air pollution. Otherwise, there is pollution in the village, but there is no main pollution source.

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