Method, device and equipment for generating underway monitoring task and medium

Abstract

The invention relates to a method, a device, equipment and a medium for generating underway monitoring tasks, and belongs to the technical field of environment monitoring. The method comprises the following steps: screening high-value hot spots in a monitoring station according to pollutant concentration data; determining a voyage target area in the monitoring area, wherein the step of determining the voyage target area comprises the substeps of screening associated enterprises matched with high-value pollution factors in the high-value hotspot monitoring range according to the enterprise information data; screening core enterprises in the associated enterprises by using a clustering algorithm, and determining an enterprise dense area in each high-value hotspot monitoring range; identifying a voyage target area in each enterprise dense area; and generating underway monitoring tasks of the underway target areas according to the underway vehicle information data. According to the method, high-value hot spots are positioned through actual data, accuracy and reliability are achieved, a scientific algorithm is used for dividing the underway target area, and underway tasks are efficiently generated.

Description

Technical Field

[0001] This invention belongs to the field of environmental monitoring technology, and specifically relates to a method, apparatus, equipment and medium for generating mobile monitoring tasks. Background Technology

[0002] Mobile monitoring is a flexible and efficient method of atmospheric environmental monitoring. It utilizes a vehicle-mounted mobile platform equipped with various high-precision sensors to conduct patrol-style monitoring in a specific area, thereby acquiring large-scale, high-resolution real-time environmental data. The accurate positioning of the target area of ​​mobile monitoring is crucial to its effectiveness, as it can avoid blind patrols and concentrate limited resources on the areas that need monitoring the most.

[0003] In existing technologies, air pollution mobile monitoring mainly relies on the traditional "human experience-driven" model. This involves manually reviewing real-time concentration data from monitoring stations, identifying high-pollution areas through "manual threshold comparison + subjective judgment," manually delineating the mobile monitoring range based on the location of high-value points and a list of local enterprises (mostly static tables), and manually compiling information on the location of mobile monitoring vehicles and equipment configurations within the jurisdiction. Tasks are then assigned based on the "proximity principle." Traditional methods suffer from technical problems such as inaccurate high-value location, blind delineation of target areas, and inefficient generation of mobile monitoring tasks. Summary of the Invention

[0004] Based on the above analysis, the embodiments of the present invention aim to provide a method, apparatus, equipment and medium for generating mobile monitoring tasks, so as to solve the technical problems of inaccurate high-value positioning, blind delineation of target areas and inefficient generation of mobile monitoring tasks in the prior art.

[0005] The objective of this invention is achieved as follows: A first aspect of the present invention provides a method for generating mobile monitoring tasks, comprising: Acquire mobile monitoring vehicle information data, enterprise information data within the monitoring area, and pollutant concentration data from various monitoring stations; High-value hotspots were selected from the monitoring stations based on the pollutant concentration data. Determining mobile target areas within the monitoring area includes: screening related enterprises that match high-value pollution factors within the high-value hotspot monitoring range based on the enterprise information data; using clustering algorithms to screen core enterprises among the related enterprises to determine enterprise-dense areas within each of the high-value hotspot monitoring ranges; and identifying mobile target areas within each of the enterprise-dense areas. Based on the mobile vehicle information data, mobile monitoring tasks are generated for each of the mobile target areas.

[0006] Furthermore, the step of screening high-value hotspots among the monitoring stations based on the pollutant concentration data includes: constructing a hierarchical evaluation model with a target layer, a criterion layer, and a scheme layer. The target layer is for screening high-value hotspots, the criterion layer includes the number of alarms, the duration of high values, the rate of increase of pollutant concentration, and the peak value of pollutant concentration, and the scheme layer includes each monitoring station. Based on the hierarchical evaluation model, the analytic hierarchy process (AHP) is used to calculate the comprehensive score of each monitoring station, and the high-value hotspots are screened based on the comprehensive score.

[0007] Furthermore, the number of alarms, duration of high values, rate of increase of pollutant concentration, and peak pollutant concentration are obtained by analyzing the pollutant concentration data.

[0008] Further, the step of calculating the comprehensive score of each monitoring station using the analytic hierarchy process (AHP) based on the hierarchical evaluation model includes: comparing the importance of the indicators in the criterion layer to obtain a judgment matrix; calculating the weight of each indicator based on the judgment matrix to obtain a weight vector; and calculating the comprehensive score based on the value and weight of each indicator, expressed as: in, Indicates site i Total score The weights for the number of alarms, duration of high values, rate of increase of pollutant concentration, and peak pollutant concentration are respectively determined. They represent the sites respectively. i The values ​​of alarm frequency, duration of high values, pollutant concentration growth rate, and peak pollutant concentration are as follows: These represent the number of alarms, the duration of the maximum high value, and the rate of increase of the maximum pollutant concentration, respectively. Standard limits that indicate the peak concentration of pollutants.

[0009] Furthermore, the step of using clustering algorithms to screen core enterprises among associated enterprises and determine enterprise-dense areas within the monitoring range of each high-value hotspot includes: based on the density clustering algorithm DBSCAN, a density threshold and a neighborhood radius are pre-set. For each high-value hotspot, each associated enterprise within the monitoring range is traversed, and the number of enterprises within the neighborhood radius of each associated enterprise is counted. If the number of enterprises is greater than the density threshold, it is set as a core enterprise. All core enterprises belonging to the same high-value hotspot monitoring range are connected in pairs to form the enterprise-dense area.

[0010] Furthermore, the identification of mobile target areas within each of the enterprise-dense areas includes: acquiring the prevailing wind direction from meteorological data at the time of high-value hotspot occurrence; filtering enterprise-dense areas located in the upwind region based on the prevailing wind direction and setting them as candidate target areas; for candidate target areas with more than 2 associated enterprises, using the midpoint of the line connecting the two candidate target areas with the largest number of core enterprises as the center and a range with a preset radius as the mobile target area; for candidate target areas with only one associated enterprise, using the center coordinates of the current candidate target area as the center and a range with a second preset radius as the mobile target area.

[0011] Furthermore, the step of generating mobile monitoring tasks for each of the mobile monitoring target areas based on the mobile vehicle information data includes: screening candidate mobile vehicles whose monitoring equipment matches the high-value pollution factors in the current mobile monitoring target area; calculating the comprehensive matching degree between the candidate mobile vehicles and the mobile monitoring target area, wherein the comprehensive matching degree is calculated by weighting the distance between the mobile vehicle and the mobile monitoring target area, the location matching relationship, the load of the candidate mobile vehicle and the load of the mobile monitoring target area; selecting the mobile vehicle with the highest comprehensive matching degree as the target mobile vehicle; and automatically generating mobile monitoring tasks for each of the target mobile vehicles, wherein the mobile monitoring tasks include the monitoring area, the monitoring route, the monitoring indicators and the monitoring duration.

[0012] A second aspect of the present invention provides an apparatus for generating mobile monitoring tasks, comprising: The data acquisition module is used to acquire mobile monitoring vehicle information data, enterprise information data within the monitoring area, and pollutant concentration data from various monitoring stations; The high-value hotspot screening module is used to screen high-value hotspots in the monitoring stations based on the pollutant concentration data. The mobile target area determination module is used to determine the mobile target area within the monitoring area, including: filtering related enterprises that match high-value pollution factors within the high-value hotspot monitoring range based on the enterprise information data; using a clustering algorithm to filter core enterprises among the related enterprises to determine the enterprise-dense areas within each of the high-value hotspot monitoring ranges; and identifying the mobile target area within each of the enterprise-dense areas. The monitoring task generation module is used to generate mobile monitoring tasks for each of the mobile target areas based on the mobile vehicle information data.

[0013] A third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the method for generating mobile monitoring tasks as described in any embodiment.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for generating mobile monitoring tasks as described in any embodiment.

[0015] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: The method for generating mobile monitoring tasks provided by this invention constructs a hierarchical analysis model, combines multi-dimensional quantitative indicators to screen high-value hotspots, combines enterprise information data and high-value hotspots to determine the mobile monitoring target area, and then combines mobile vehicle information data to generate monitoring tasks for the mobile monitoring target area. The entire process requires no human intervention, locates high-value hotspots through actual data, is accurate and reliable, and uses scientific algorithms to divide the mobile monitoring target area, efficiently generating mobile monitoring tasks. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings.

[0017] Figure 1 This is a flowchart of the method for generating mobile monitoring tasks provided in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the device for generating mobile monitoring tasks provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the electronic device architecture provided in Embodiment 3 of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be noted that, unless otherwise specified, the implementation methods and features in the implementation methods in this disclosure can be combined, separated, interchanged, and / or rearranged. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1 A specific embodiment of the present invention, such as Figure 1 As shown, a method for generating mobile monitoring tasks is disclosed, including the following steps: S1. Obtain mobile monitoring vehicle information data, enterprise information data within the monitoring area, and pollutant concentration data from each monitoring station.

[0020] Specifically, pollutant concentration data are collected in real time from PM2.5, PM10, SO2, NO2, CO, O3, NO, and NO3 through a standardized API interface provided by the national / local ecological and environmental monitoring network platform. x Pollutant concentration data is collected; missing data is filled using the "average of adjacent time points" method, and abnormal data (such as concentration values ​​exceeding reasonable ranges, e.g., PM2.5 > 1000 μg / m3) are identified and removed using the "3σ principle" to ensure data validity; mobile monitoring vehicle information data is uploaded to the system in real time according to the "HJ212 Environmental Communication Protocol" based on the GPS positioning module and data transmission terminal installed on the mobile monitoring vehicle terminal; vehicle equipment configuration information is manually entered into the system and updated regularly; drift points in the location data (such as sudden changes in coordinates exceeding 5km in a short period of time) are corrected using a "trajectory smoothing algorithm" to ensure location accuracy; a "uniqueness check" is established for equipment configuration information to avoid duplicate entry or information errors; enterprise information data is obtained by connecting to the local ecological and environmental departments' "discharge permit management platform" and "key pollution source monitoring platform" to obtain basic enterprise information and emission information; for enterprises lacking emission factors, the average emission factor of enterprises of the same industry and scale is used as a reference to ensure data integrity.

[0021] S2. Based on the pollutant concentration data, high-value hotspots are selected from the monitoring stations.

[0022] In this embodiment, step S2 includes: S201. Construct a hierarchical evaluation model consisting of a target layer, a criterion layer, and a scheme layer. The target layer is for screening high-value hotspots. The criterion layer includes the number of alarms, the duration of high values, the rate of increase of pollutant concentration, and the peak value of pollutant concentration. The scheme layer includes various monitoring stations. S202. Based on the hierarchical evaluation model, the analytic hierarchy process (AHP) is used to calculate the comprehensive score of each monitoring station, and the high-value hotspots are selected according to the comprehensive score.

[0023] Specifically, the implementation process of S202 is as follows: (1) Compare the importance of the indicators in the criterion layer to obtain the judgment matrix; For example, by having experts score the indicators or by having relevant personnel compare each indicator in the criteria layer in pairs based on the actual situation, such as comparing the relative importance of alarm frequency and high value duration in determining high value hotspot areas, and assigning values ​​according to the 1-9 scale (1 indicates that the two indicators are equally important, 3 indicates that the former is slightly more important than the latter, 5 indicates that the former is significantly more important than the latter, 7 indicates that the former is strongly more important than the latter, 9 indicates that the former is extremely more important than the latter, and 2, 4, 6, and 8 are the median values ​​of the above adjacent judgments), a judgment matrix is ​​obtained.

[0024] (2) Calculate the weight of each indicator based on the judgment matrix to obtain the weight vector; For example, common methods for calculating weight vectors include the sum-product method and the square root method. Taking the sum-product method as an example, the weight vector is obtained by normalizing each column of the judgment matrix, adding the normalized matrix row by row, and normalizing the added vector. Then, a consistency check is performed. When the consistency ratio is less than 0.1, the consistency of the judgment matrix is ​​considered acceptable, and the weight vector obtained at this time is reliable; otherwise, the judgment matrix needs to be readjusted until the consistency check is passed.

[0025] (3) Calculate the comprehensive score based on the values ​​and weights of each indicator, as follows: in, Indicates site i Total score The weights for the number of alarms, duration of high values, rate of increase of pollutant concentration, and peak pollutant concentration are respectively determined. They represent the sites respectively. i The values ​​of alarm frequency, duration of high values, pollutant concentration growth rate, and peak pollutant concentration are as follows: These represent the number of alarms, the duration of the maximum high value, and the rate of increase of the maximum pollutant concentration, respectively. Standard limits that indicate the peak concentration of pollutants.

[0026] In this embodiment, the number of alarms, duration of high values, pollutant concentration growth rate, and pollutant concentration peak are obtained by analyzing the pollutant concentration data.

[0027] Specifically, the number of alarms refers to the number of times a monitoring station exceeds the pollutant concentration standard within a time window T (configurable, such as 24 hours), the duration of high value is the duration of continuous exceedance of pollutant concentration, the pollutant concentration growth rate is the hourly month-on-month growth rate of pollutant concentration, and the pollutant concentration peak value is the maximum pollutant concentration within the time window T.

[0028] (4) Sort the monitoring stations according to the comprehensive score and select the high-value hotspots.

[0029] S3. Determine the mobile target area within the monitoring area.

[0030] In this embodiment, step S3 includes: S301. Based on the enterprise information data, filter the associated enterprises within the high-value hotspot monitoring range that match the high-value pollution factors; Specifically, the high-value pollutants of each high-value hotspot are first identified, and then enterprises that emit the same gas as the high-value pollutants of their respective high-value hotspots within the radiation radius of each high-value hotspot are selected as related enterprises.

[0031] S302. Use clustering algorithms to screen core enterprises among related enterprises and determine the densely populated areas of enterprises within the monitoring range of each high-value hotspot; Specifically, the implementation process of S302 is as follows: Based on the density clustering algorithm DBSCAN, a density threshold and neighborhood radius are preset. For each high-value hotspot, each associated enterprise within the monitoring range is traversed, and the number of enterprises within the neighborhood radius of each associated enterprise is counted. If the number of enterprises is greater than the density threshold, it is set as a core enterprise. All core enterprises belonging to the same high-value hotspot within the monitoring range are connected in pairs to form the enterprise density area.

[0032] S303. Identify the target areas for mobile navigation within each of the aforementioned densely populated enterprise areas.

[0033] Specifically, the implementation process of S303 is as follows: (1) Obtain the prevailing wind direction from the meteorological data at the moment when the high-value hotspot occurs; (2) Based on the prevailing wind direction, select the densely populated areas of enterprises located in the upwind region and set them as candidate target areas; (3) For candidate target areas with more than 2 related enterprises, the first preset radius is used as the target area for navigation, with the midpoint of the line connecting the two candidate target areas with the largest number of core enterprises as the center; for candidate target areas with only one related enterprise, the second preset radius is used as the target area for navigation, with the center coordinates of the current candidate target area as the center.

[0034] In some embodiments, when there is no prevailing wind direction or no densely populated enterprise area in the upwind area, the target area for navigation is defined by the midpoint of the line connecting the two densely populated enterprise areas with the largest number of core enterprises.

[0035] In some embodiments, the pollution contribution of all associated enterprises in each mobile target area is calculated, and the top n associated enterprises in terms of pollution contribution are selected as key mobile enterprises. The pollution contribution is calculated based on the emission factor and annual output that match the high-value factor.

[0036] S4. Generate mobile monitoring tasks for each of the mobile target areas based on the mobile vehicle information data.

[0037] In this embodiment, step S4 includes: S401. Select candidate mobile monitoring vehicles whose monitoring equipment matches the high-value pollutants in the current mobile monitoring target area; For example, if the high-value pollutant is PM2.5, and assuming that monitoring PM2.5 requires two types of equipment, mobile monitoring vehicles equipped with these two types of equipment are initially selected as candidate mobile monitoring vehicles.

[0038] S402. Calculate the comprehensive matching degree between the candidate mobile monitoring vehicle and the mobile monitoring target area respectively. The comprehensive matching degree is calculated based on the distance between the mobile monitoring vehicle and the mobile monitoring target area, the local matching relationship, the load of the candidate mobile monitoring vehicle and the load of the mobile monitoring target area. Specifically, after initially screening out mobile monitoring vehicles that match high-value pollutants, several priority levels need to be set to determine the most suitable mobile monitoring vehicle. The priority levels are set as follows: the vehicle with the smallest standard deviation of distance from the center point of the mobile monitoring target area, the vehicle whose jurisdiction is the same as that of the mobile monitoring target area, the vehicle with the least load (number of tasks accepted on the day) of the candidate mobile monitoring vehicle, and the area with the least load (area that has been mobile monitored on the day).

[0039] S403. Select the mobile monitoring vehicle with the highest comprehensive matching degree as the target mobile monitoring vehicle; automatically generate mobile monitoring tasks for each target mobile monitoring vehicle, wherein the mobile monitoring task includes monitoring area, monitoring route, monitoring indicators and monitoring duration.

[0040] Specifically, after S401 and S402, the system automatically generates a mobile monitoring task file containing the monitoring area, monitoring route, monitoring indicators, and monitoring duration. After the business personnel confirm the task on the system, they can send the task to the vehicle through the "one-click dispatch" function. The mobile monitoring personnel on the vehicle can then sign for the mobile monitoring task on the APP and carry out the mobile monitoring work according to the task arrangement.

[0041] Compared with existing technologies, the method for generating mobile monitoring tasks provided in this embodiment constructs a hierarchical analysis model, combines multi-dimensional quantitative indicators to screen high-value hotspots, combines enterprise information data and high-value hotspots to determine the mobile monitoring target area, and then combines mobile vehicle information data to generate monitoring tasks for the mobile monitoring target area. The entire process requires no human intervention, locates high-value hotspots through actual data, scientifically divides the mobile monitoring target area, and efficiently generates mobile monitoring tasks.

[0042] Example 2 This embodiment provides a device for generating mobile monitoring tasks, such as... Figure 2 As shown, it includes: The data acquisition module is used to acquire mobile monitoring vehicle information data, enterprise information data within the monitoring area, and pollutant concentration data from various monitoring stations; The high-value hotspot screening module is used to screen high-value hotspots in the monitoring stations based on the pollutant concentration data. The mobile target area determination module is used to determine the mobile target area within the monitoring area, including: filtering related enterprises that match high-value pollution factors within the high-value hotspot monitoring range based on the enterprise information data; using a clustering algorithm to filter core enterprises among the related enterprises to determine the enterprise-dense areas within each of the high-value hotspot monitoring ranges; and identifying the mobile target area within each of the enterprise-dense areas. The monitoring task generation module is used to generate mobile monitoring tasks for each of the mobile target areas based on the mobile vehicle information data.

[0043] Example 3 This embodiment provides an electronic device, such as... Figure 3 As shown, it includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, implements the method for generating mobile monitoring tasks as described in any of the above embodiments.

[0044] Example 4 This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, it implements the method for generating mobile monitoring tasks as described in any of the above embodiments.

[0045] Computer-readable storage media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0046] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0047] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0048] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for generating mobile monitoring tasks, characterized in that, include: Acquire mobile monitoring vehicle information data, enterprise information data within the monitoring area, and pollutant concentration data from various monitoring stations; High-value hotspots were selected from the monitoring stations based on the pollutant concentration data. Determining mobile target areas within the monitoring area includes: screening related enterprises that match high-value pollution factors within the high-value hotspot monitoring range based on the enterprise information data; using clustering algorithms to screen core enterprises among the related enterprises to determine enterprise-dense areas within each of the high-value hotspot monitoring ranges; and identifying mobile target areas within each of the enterprise-dense areas. Based on the mobile vehicle information data, mobile monitoring tasks are generated for each of the mobile target areas.

2. The method for generating mobile monitoring tasks according to claim 1, characterized in that, The step of screening high-value hotspots from the monitoring stations based on the pollutant concentration data includes: The hierarchical evaluation model is constructed with a target layer, a criterion layer, and a scheme layer. The target layer is for screening high-value hotspots, the criterion layer includes the number of alarms, the duration of high values, the rate of increase of pollutant concentration, and the peak value of pollutant concentration, and the scheme layer includes each monitoring station. Based on the hierarchical evaluation model, the analytic hierarchy process (AHP) is used to calculate the comprehensive score of each monitoring station, and the high-value hotspots are selected based on the comprehensive score.

3. The method for generating mobile monitoring tasks according to claim 2, characterized in that, The number of alarms, duration of high values, rate of increase of pollutant concentration, and peak pollutant concentration are obtained by analyzing the pollutant concentration data.

4. The method for generating mobile monitoring tasks according to claim 2, characterized in that, The calculation of the comprehensive score for each monitoring station based on the hierarchical evaluation model using the analytic hierarchy process includes: The importance of the indicators in the criterion layer is compared to obtain the judgment matrix; The weights of each indicator are calculated based on the judgment matrix to obtain the weight vector; The comprehensive score is calculated based on the values ​​and weights of each indicator, and is expressed as follows: in, Indicates site i Total score The weights for the number of alarms, duration of high values, rate of increase of pollutant concentration, and peak pollutant concentration are respectively determined. They represent the sites respectively. i The values ​​of alarm frequency, duration of high values, pollutant concentration growth rate, and peak pollutant concentration are as follows: These represent the number of alarms, the duration of the maximum high value, and the rate of increase of the maximum pollutant concentration, respectively. Standard limits that indicate the peak concentration of pollutants.

5. The method for generating mobile monitoring tasks according to claim 1, characterized in that, The process of using clustering algorithms to screen core enterprises among related enterprises and determining densely populated areas of enterprises within each high-value hotspot monitoring range includes: Based on the density clustering algorithm DBSCAN, a density threshold and neighborhood radius are preset. For each high-value hotspot, each associated enterprise within the monitoring range is traversed, and the number of enterprises within the neighborhood radius of each associated enterprise is counted. If the number of enterprises is greater than the density threshold, it is set as a core enterprise. All core enterprises belonging to the same high-value hotspot within the monitoring range are connected in pairs to form the enterprise density area.

6. The method for generating mobile monitoring tasks according to claim 5, characterized in that, The identification of the target areas within each of the densely populated enterprise areas includes: Obtain the prevailing wind direction from meteorological data at the moment when high-value hotspots occur; Based on the prevailing wind direction, densely populated areas of enterprises located in the upwind region are selected as candidate target areas; For candidate target areas with more than 2 related enterprises, the target area is defined as the range of the line connecting the two candidate target areas with the largest number of core enterprises, with the center as the center and a preset radius. For candidate target areas with only one related enterprise, the target area is defined as the range of the center coordinates of the current candidate target area, with a second preset radius as the center.

7. The method for generating mobile monitoring tasks according to any one of claims 1-6, characterized in that, The step of generating mobile monitoring tasks for each of the mobile target areas based on the mobile vehicle information data includes: Candidate mobile monitoring vehicles whose monitoring equipment matches the high-value pollutants in the current mobile monitoring target area are selected; The overall matching degree between the candidate mobile monitoring vehicle and the mobile monitoring target area is calculated separately. The overall matching degree is calculated by weighting the distance between the mobile monitoring vehicle and the mobile monitoring target area, the local matching relationship, the load of the candidate mobile monitoring vehicle and the load of the mobile monitoring target area. Select the mobile monitoring vehicle with the highest overall matching degree as the target mobile monitoring vehicle; automatically generate mobile monitoring tasks for each target mobile monitoring vehicle, the mobile monitoring tasks including monitoring area, monitoring route, monitoring indicators and monitoring duration.

8. A device for generating mobile monitoring tasks, characterized in that, The device includes: The data acquisition module is used to acquire mobile monitoring vehicle information data, enterprise information data within the monitoring area, and pollutant concentration data from various monitoring stations; The high-value hotspot screening module is used to screen high-value hotspots in the monitoring stations based on the pollutant concentration data. The mobile target area determination module is used to determine the mobile target area within the monitoring area, including: filtering related enterprises that match high-value pollution factors within the high-value hotspot monitoring range based on the enterprise information data; using a clustering algorithm to filter core enterprises among the related enterprises to determine the enterprise-dense areas within each of the high-value hotspot monitoring ranges; and identifying the mobile target area within each of the enterprise-dense areas. The monitoring task generation module is used to generate mobile monitoring tasks for each of the mobile target areas based on the mobile vehicle information data.

9. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, implements the method for generating mobile monitoring tasks as described in any one of claims 1-7.

10. A storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the method for generating mobile monitoring tasks as described in any one of claims 1-7.

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