Intelligent risk assessment system for industrial cluster complex pollution based on data analysis

Abstract

The present invention discloses an intelligent risk assessment system for industrial agglomeration complex pollution based on data analysis, and relates to the technical field of pollution risk assessment. It includes: an acquisition module: dividing the industrial area into multiple areas, and detecting the main pollutant types in each area to obtain a pollutant type set, and detecting the soil conditions and atmospheric conditions around the pollution source to obtain soil characteristic items and atmospheric characteristic items; a prediction module: based on the pollutant type set, predicting the contact time between pollutants to obtain a contact time prediction set. The present invention first divides the industrial area into multiple areas, detects the main pollutant types in each area, and predicts the time when the pollutants merge into the sewage treatment pipeline and the time when the pollutants merge into the atmosphere, and simulates and analyzes the mixing of pollutants, so as to obtain a more accurate assessment result based on the analysis result.

Description

Technical Field

[0001] The present invention relates to the technical field of pollution risk assessment, and in particular to an intelligent risk assessment system for industrial agglomeration composite pollution based on data analysis. Background Art

[0002] Industrial agglomeration complex pollution refers to the phenomenon of interaction, superposition and common pollution of multiple pollutants caused by the emissions and production processes of multiple industrial enterprises in industrial agglomeration areas. In industrial agglomeration areas, due to the concentrated emissions or joint production of multiple enterprises, complex pollution effects may be generated. Different pollutants influence each other, resulting in a higher degree of pollution than that caused by emissions from a single enterprise. Industrial agglomeration complex pollution usually causes problems such as aggravated environmental pollution, deteriorated air quality, soil and water pollution, and poses potential health risks to the surrounding ecological environment and residents. Therefore, monitoring, evaluating and managing complex pollution in industrial agglomeration areas is of great significance to protecting the environment and human health.

[0003] The patent publication number is CN114155129A, which is an atmospheric environment tracing and evaluation method and system based on industrial parks. The system includes: an abnormal event monitoring unit, an abnormal event retrieval unit, an abnormal event tracing unit, a pollution prediction unit, a management and control task dispatching unit, a management and control task tracking unit, an event evaluation unit, and a task flow form unit; the method includes: seven steps: detection, tracing, prediction, management, governance and evaluation. The atmospheric pollution tracing model, the pollution diffusion model and the task flow form are used to realize the full-process closed-loop management of the park's atmospheric environment management work from early warning to timely response to accurate tracing and finally to scientific governance, reduce the delay and blindness of conventional human offline judgment, and realize the reception of early warning information of daily pollution events, pollution tracing, situation prediction, and the subsequent proposal of corporate governance measures, the supervision and management of corporate governance work, and the evaluation of governance effect after rectification.

[0004] During the use of the above-mentioned and similar technical solutions, due to the pollution sources of industrial clustered complex pollution and the pollutants are related to enterprises, different enterprises emit different pollutants, which will have different impacts on the environment, and when different pollutants are mixed together, the pollutants may trigger subsequent chemical reactions with each other, thereby causing more serious or less serious impacts on the environment. It is difficult to predict the consequences, and in different regions, different land materials and climatic environments have different pollution carrying effects. At this time, when the same amount of pollutants are emitted, the degree of damage to the environment is different, which will cause certain interference to the evaluation results. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent risk assessment system for industrial agglomeration complex pollution based on data analysis to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: an intelligent risk assessment system for industrial agglomeration complex pollution based on data analysis, comprising:

[0007] Collection module: Divide the industrial area into multiple areas, and detect the main pollutant types in each area to obtain a pollutant type set. At the same time, detect the soil and atmospheric conditions around the pollution source to obtain soil characteristic items and atmospheric characteristic items;

[0008] Prediction module: Based on the pollutant type set, the contact time between pollutants is predicted to obtain a contact time prediction set;

[0009] Analysis module: Based on the pollutant type set and contact time prediction set, the subsequent changes of pollutants are analyzed through analytical methods to obtain risk assessment data;

[0010] Optimization module: Based on soil characteristics and atmospheric characteristics, the risk assessment data results are optimized to improve the accuracy of risk assessment data;

[0011] The analysis method comprises:

[0012] Step 1: Attribute analysis: based on the pollutant type set, obtain the chemical properties of different types of pollutants, and obtain their harmfulness to the environment respectively to obtain the pollutant attribute set;

[0013] Step 2: attribute combination, based on the contact time prediction set, obtain the combination results of different types of pollutants in the pollutant type set, obtain at least one pollutant combination item, and analyze the harmfulness of the pollutant combination item to the environment based on the pollutant attribute set to obtain at least one pollutant hazard data;

[0014] Step 3: Data output, output risk assessment data based on pollutant hazard data;

[0015] Step 4: Data combination. Based on the pollutant binding items and contact time prediction set, determine whether the pollutant binding items will combine again. When the pollutant binding items will combine again, repeat steps 2 to 4 and make additional changes to the risk assessment data. When the pollutant binding items will not combine again, repeat step 3 to output the risk assessment data.

[0016] Furthermore, the soil characteristic item includes land material, and the soil condition detection method includes:

[0017] S1: Range division: taking the coordinate position of the pollutant as the origin, the three ranges are divided outwards, which are set as the first pollution range, the second pollution range and the third pollution range;

[0018] S2: Sample detection: based on the first pollution range, the second pollution range and the third pollution range, at least one sample is collected and analyzed to obtain land material items;

[0019] S3: Result analysis, based on the land material items and range division results, obtain the feedback results of the soil conditions, and then obtain the soil characteristic items.

[0020] Furthermore, the soil characteristic item includes land structure, and the soil condition detection method further includes:

[0021] S4: Classification: Based on the land material items, it is divided into three levels, namely the first level, the second level and the third level, and three groups of land structure items are obtained, corresponding to different levels of pollutant absorption capacity;

[0022] S5: Effect combination, based on the feedback results of soil conditions and the classification results, nine different levels of pollutant absorption capacity are obtained, and then the feedback results of soil conditions are divided and analyzed in more detail.

[0023] Furthermore, the atmospheric characteristic items include atmospheric temperature and atmospheric wind speed, and the atmospheric condition detection method includes:

[0024] M1: Taking the coordinate position of the pollutant as the detection point, set up at least one detection station to detect the temperature and wind speed at the location of the pollution source, and obtain the atmospheric temperature item and the atmospheric wind speed item;

[0025] M2: Use the timed detection method, set the detection interval, obtain multiple groups of atmospheric temperature items and atmospheric wind speed items, and then obtain the changing trends of atmospheric temperature and atmospheric wind speed. The changing trends of atmospheric temperature and atmospheric wind speed are combined into atmospheric characteristic items.

[0026] Furthermore, the atmospheric condition detection method also includes predictive detection: based on the changing trends of the atmospheric temperature and the atmospheric wind speed, the atmospheric temperature and the atmospheric wind speed are predicted to obtain the predicted items of the atmospheric temperature and the atmospheric wind speed respectively.

[0027] Furthermore, the method for predicting the contact time between the pollutants includes:

[0028] N1: Distance acquisition, taking the coordinate position of the pollutant discharge point as the starting feature point and the coordinate position of the sewage pipe as the end feature point, obtaining the distance between the starting feature point and the end feature point, and obtaining multiple groups of distance feature items;

[0029] N2: Flow velocity acquisition, respectively detecting the flow velocity of pollutants when they pass through the starting point feature points, and obtaining multiple groups of starting point flow velocity feature items, which correspond to the distance feature items respectively;

[0030] N3: Time prediction, based on the distance characteristic item corresponding to the starting point flow velocity characteristic item, the time when the pollutant reaches the end point characteristic point is obtained respectively, and multiple groups of time items are obtained, and then the contact time prediction set is obtained.

[0031] Furthermore, the method for predicting the contact time between pollutants also includes:

[0032] N4: Obtain the distances between adjacent endpoint feature points respectively to obtain adjacent distance feature items;

[0033] N5: Detect the flow velocity of pollutants when they pass through the end point characteristic points respectively, and obtain multiple groups of end point flow velocity characteristic items, and the end point flow velocity characteristic items correspond to the start point flow velocity characteristic items respectively;

[0034] N6: Based on the time term, the endpoint velocity characteristic term and the adjacent distance characteristic term, the contact time between pollutants is obtained, and then the contact time prediction set is obtained.

[0035] Furthermore, the optimization method includes: based on land material items and land structure items, combining nine different levels of pollutant absorption capacity with risk assessment data to obtain an order of severity of pollutant damage to the land from light to heavy, and then optimizing the risk assessment data into nine sub-assessment data.

[0036] Furthermore, the optimization method includes: dividing the atmospheric temperature and atmospheric wind speed into three levels respectively, and the atmospheric temperature and atmospheric wind speed at different levels correspond to different pollutant purification capabilities, obtaining nine different levels of pollutant purification capabilities, combining the nine different levels of pollutant purification capabilities with risk assessment data, and then optimizing the risk assessment data.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The intelligent risk assessment system for industrial cluster complex pollution based on data analysis first divides the industrial area into multiple areas, detects the main types of pollutants in each area, and predicts the time when pollutants enter the sewage treatment pipeline and the time when pollutants enter the atmosphere. Based on the detection results of pollutant types and the predicted results of the time when pollutants enter the sewage pipeline, the system simulates and analyzes the mixing of pollutants, so as to obtain more accurate assessment results based on the analysis results.

[0039] At the same time, the soil and atmospheric conditions are monitored and divided into multiple levels. Different levels of soil conditions have different absorption and decomposition intensities of pollutants, and different atmospheric conditions have different diffusion rates of pollutants. The monitoring results are combined with the results of simulation analysis of the mixing conditions of pollutants to further improve the pollution assessment results.

[0040] Moreover, taking the emission location of pollutants as the origin, since pollutants will diffuse outward from the emission location to pollute the surrounding environment, different distances will be affected differently, and different land materials have different absorption capacities for the same dose of pollutants. Therefore, by dividing the area into multiple ranges and simultaneously testing and analyzing the land materials in multiple ranges, the land's absorption capacity for pollutants can be more accurately determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the relationship between the collection and analysis optimization module processes of the present invention;

[0042] Figure 2 It is a schematic diagram of the analytical method of the present invention;

[0043] Figure 3 It is a schematic diagram of the soil quality detection method of the present invention;

[0044] Figure 4 This is a schematic diagram of the structure corresponding to the land material items and range divisions of the present invention;

[0045] Figure 5 It is a schematic diagram of the atmospheric condition detection method of the present invention;

[0046] Figure 6 It is a schematic diagram of the prediction detection method of the present invention;

[0047] Figure 7 A schematic diagram of a method for predicting the contact time between pollutants of the present invention;

[0048] Figure 8 It is a schematic diagram of the method for obtaining adjacent distance feature items of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0050] When actually assessing the risk, the pollutants are judged based on the type and nature of the pollution source or pollutants, and their subsequent impact is judged. Due to the diversity of pollutants in industrial agglomeration complex pollution, different pollutants may react differently, such as aggravated reactions, which are more harmful, or neutralized reactions, which are less harmful. All of these will affect the results of the assessment. It is necessary to carefully classify the types and properties of the pollution sources and identify the possible contact situations of the pollutants. The technical solution provided in this application first divides the industrial area into multiple areas, detects the main types of pollutants in each area, and collects the pollutants into the sewage treatment pipe. The time it takes for pollutants to enter the sewage pipe, the time it takes for pollutants to enter the atmosphere, and the time it takes for pollutants to spread in the soil are predicted. Based on the detection results of pollutant types and the predicted results of the time it takes for pollutants to enter the sewage pipe, a simulation analysis of the mixing of pollutants is performed, so as to obtain more accurate assessment results based on the analysis results. The soil and atmospheric conditions are monitored and divided into multiple levels. The absorption and decomposition intensity of pollutants are different in soil conditions of different levels, and the diffusion speed of pollutants is different in different atmospheric conditions. The monitoring results are combined with the results of the simulation analysis of the mixing of pollutants to further improve the pollution assessment results.

[0051] like Figure 1-Figure 2As shown, the present invention provides a technical solution: an intelligent risk assessment system for industrial agglomeration complex pollution based on data analysis, comprising: an acquisition module: dividing the industrial area into multiple areas, and detecting the main pollutant types in each area to obtain a pollutant type set, and at the same time detecting the soil conditions and atmospheric conditions around the pollution source to obtain soil characteristic items and atmospheric characteristic items; a prediction module: based on the pollutant type set, predicting the contact time between pollutants to obtain a contact time prediction set; an analysis module: based on the pollutant type set and the contact time prediction set, analyzing the subsequent changes of pollutants through an analysis method to obtain risk assessment data; an optimization module: based on the soil characteristic items and the atmospheric characteristic items, optimizing the risk assessment data results to improve the accuracy of the risk assessment data; the analysis method comprises: step one: attribute analysis, based on the pollution type set The first step is to obtain the chemical properties of different types of pollutants from the species set, and obtain their harmfulness to the environment respectively, to obtain the pollutant attribute set; the second step is to combine the attributes, based on the contact time prediction set, obtain the combination results of different types of pollutants in the pollutant species set, obtain at least one pollutant combination item, and analyze the harmfulness of the pollutant combination item to the environment based on the pollutant attribute set, to obtain at least one pollutant hazard data; the third step is to output the data, based on the pollutant hazard data, output the risk assessment data; the fourth step is to combine the data, based on the pollutant combination item and the contact time prediction set, determine whether the pollutant combination items will combine again. When the pollutant combination items will combine again, repeat steps two to four, and supplement and change the risk assessment data. When the pollutant combination items will not combine again, repeat step three to output the risk assessment data.

[0052] It should be noted that the industrial area is first divided into multiple areas. Since different pollutants are generated in industrial areas during production operations, the main types of pollutants in each area are detected, and the properties of different pollutants are analyzed. According to the results of the analysis, the harmfulness of different pollutants to the environment is judged, and according to the properties of the pollutants and the reactions that will occur when the pollutants come into contact with each other, when the pollutants are generated, they will first be discharged into the sewage treatment pipe after preliminary purification by the enterprise, and flow to the sewage treatment plant through a unified sewage treatment pipe, or the enterprise will directly discharge the less polluting pollutants into the land after purification, and the land will decompose and purify the pollutants by itself. When the second method is adopted, the pollutants penetrate into the land and may also come into contact and fuse with each other. At this time, according to the properties of different pollutants, based on the contact time prediction set, the combination results of the pollutants are judged to re-evaluate the harmfulness of the combined pollutants to the environment, and this is used to generate risk assessment data. Since the pollutants may come into contact and fuse with other pollutants again after contact and fusion, it is necessary to judge the property changes after the secondary or even tertiary fusion of multiple pollutants based on the contact time prediction set and the properties of the pollutants, so as to generate more accurate assessment data.

[0053] like Figure 3 As shown, the soil characteristic items include land material, and the soil condition detection method includes: S1: range division, taking the coordinate position of the pollutant as the origin, dividing the range outward into three ranges, which are respectively set as the first pollution range, the second pollution range and the third pollution range; S2: sample detection, based on the first pollution range, the second pollution range and the third pollution range, at least one sample is collected and analyzed to obtain the land material item; S3: result analysis, based on the land material item and the range division result, the feedback result of the soil condition is obtained, and then the soil characteristic item is obtained.

[0054] It should be noted that with the emission location of the pollutants as the origin, since the pollutants will diffuse outward from the emission location to pollute the surrounding environment, different distances will be affected differently, and different land materials have different absorption capacities for the same dose of pollutants. Therefore, by dividing the area into multiple ranges and simultaneously testing and analyzing the land materials in multiple ranges, the land's absorption capacity for pollutants can be more accurately determined.

[0055] like Figure 4As shown, the soil characteristic items include land structure, and the soil condition detection method also includes S4: grade division, which is based on the land material item and is divided into three levels, namely the first level, the second level and the third level, to obtain three groups of land structure items, which correspond to different levels of pollutant absorption capacity; S5: effect combination, which is based on the feedback results of the soil condition and the grade division results, to obtain nine different levels of pollutant absorption capacity, and then to make a more detailed division and analysis of the feedback results of the soil condition.

[0056] It should be noted that different soil structures have different absorption capacities for pollutants. The land materials are divided into three levels according to the structural range, namely: compact structure, soil structure with small porosity and high bulk density, which has certain obstacles to the diffusion of pollutants; loose structure, soil structure with large porosity, good permeability and air permeability, which is more conducive to the diffusion and decomposition of pollutants in the soil; fluffy structure, soil structure with appropriate porosity and bulk density, which is more conducive to the activity of microorganisms in the soil and the degradation of pollutants. Based on these three levels and the divided first pollution range, second pollution range and third pollution range, we can get the impact assessment of nine different levels of pollutants with the pollution source as the center.

[0057] like Figure 5 As shown, the atmospheric characteristic items include atmospheric temperature and atmospheric wind speed, and the atmospheric condition detection method includes: M1: taking the coordinate position of the pollutant as the detection point, setting at least one detection station, and detecting the temperature and wind speed at the location of the pollution source respectively to obtain the atmospheric temperature item and the atmospheric wind speed item; M2: adopting the timed detection method, setting the detection interval time, obtaining multiple groups of atmospheric temperature items and atmospheric wind speed items, and then obtaining the change trend of atmospheric temperature and atmospheric wind speed, and the change trend of atmospheric temperature and atmospheric wind speed is combined into the atmospheric characteristic item.

[0058] like Figure 6 As shown, the atmospheric condition detection method also includes predictive detection: based on the changing trends of the atmospheric temperature and the atmospheric wind speed, the atmospheric temperature and the atmospheric wind speed are predicted to obtain the predicted items of the atmospheric temperature and the atmospheric wind speed respectively.

[0059] It should be noted that under different atmospheric temperatures and wind speeds, the processing capacity for pollutants evaporated into the air is different. For example, as the temperature gradually rises, the volatilization rate of pollutants will become faster. Conversely, the lower the temperature, the slower the volatilization rate of pollutants. At the same time, when the wind speed is higher, the diffusion rate of pollutants in the atmosphere will increase, which will help dilute and diffuse pollutants and reduce the degree of pollution in local areas. Conversely, when the wind speed is lower, pollutants will accumulate locally at the location of the pollution source, and the impact on the environment will be greater.

[0060] like Figure 7 As shown, the method for predicting the contact time between pollutants includes: N1: distance acquisition, taking the coordinate position of the pollutant discharge point as the starting feature point, taking the coordinate position of the sewage pipe as the end feature point, acquiring the distance between the starting feature point and the end feature point, and obtaining multiple groups of distance feature items; N2: flow velocity acquisition, respectively detecting the flow velocity of the pollutant when passing through the starting feature point, and obtaining multiple groups of starting flow velocity feature items, and the starting flow velocity feature items correspond to the distance feature items respectively; N3: time prediction, based on the distance feature items corresponding to the starting flow velocity feature items, respectively obtaining the time when the pollutant reaches the end feature point, obtaining multiple groups of time items, and then obtaining the contact time prediction set.

[0061] It should be noted that when pollutants spread mainly by ground infiltration, the pollutant emission coordinate position is used as the starting feature point. The pollutants may enter the sewage pipe through the transmission pipe. At this time, the pollutants will contact and react in the sewage pipe. At this time, the junction of the transmission pipe and the sewage pipe is used as the key feature point, and the distance between the starting feature point and the end feature point is obtained. The time when the pollutants enter the sewage pipe is judged by the flow rate of the pollutants, thereby obtaining the contact time of the pollutants.

[0062] like Figure 8 As shown, the method for predicting the contact time between pollutants also includes: N4: respectively obtaining the distances between adjacent terminal feature points to obtain adjacent distance feature items; N5: respectively detecting the flow velocity of pollutants when passing through the terminal feature points to obtain multiple groups of terminal flow velocity feature items, and the terminal flow velocity feature items respectively correspond to the starting point flow velocity feature items; N6: based on the time item, the terminal flow velocity feature item and the adjacent distance feature item, obtaining the contact time between pollutants, and then obtaining a contact time prediction set.

[0063] It should be noted that when pollutants flow into the sewage pipe, they do not flow into the sewage pipe through a unified entry point, but flow into the sewage pipe in sections at different locations. At this time, it is also necessary to consider the flow time of the pollutants flowing into the sewage pipe between two adjacent terminal feature points, so as to obtain a more accurate contact time of the pollutants.

[0064] like Figure 4 As shown, the optimization method includes: based on the land material item and the land structure item, nine different levels of pollutant absorption capacity are combined with the risk assessment data to obtain an order of severity of pollutant damage to the land from light to heavy, and then the risk assessment data is optimized into nine sub-assessment data.

[0065] It should be noted that when generating risk assessment data based on the properties of pollutants and the combined properties of pollutants, the impact of pollutants on the environment is closely related to the land material item and the land structure item. That is, the same dose of pollutants or pollutants with the same properties will have very different impacts when they are in different land materials and land structures. At this time, according to the three levels of land material items and land structure items and the division of the first pollution range, the second pollution range and the third pollution range, the risk assessment data can be optimized into nine sub-assessment data.

[0066] The optimization method includes: dividing the atmospheric temperature and atmospheric wind speed into three levels respectively. The atmospheric temperature and atmospheric wind speed at different levels correspond to different pollutant purification capabilities, and nine different levels of pollutant purification capabilities are obtained. The nine different levels of pollutant purification capabilities are combined with risk assessment data, and then the risk assessment data is optimized.

[0067] It should be noted that the atmospheric temperature and atmospheric wind speed are divided into three levels respectively. For example, the atmospheric temperature is divided into low temperature, medium temperature and high temperature, and the atmospheric wind speed is divided into low speed, medium speed and high speed. The temperature ranges corresponding to low temperature, medium temperature and high temperature are below 15 degrees, 15-25 degrees and above 25 degrees respectively. The wind speed ranges corresponding to low speed, medium speed and high speed are 0.3-1.5 meters per second, 1.6-3.3 meters per second and greater than 3.3 meters per second respectively. At this time, nine different combination results can also be obtained, corresponding to different decomposition and purification capabilities of pollutants.

[0068] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the attached embodiments and their equivalents.

Claims

1. An intelligent risk assessment system for industrial clustered complex pollution based on data analysis, characterized by: include: Collection module: Divide the industrial area into multiple areas, and detect the main pollutant types in each area to obtain a pollutant type set. At the same time, detect the soil and atmospheric conditions around the pollution source to obtain soil characteristic items and atmospheric characteristic items; Prediction module: Based on the pollutant type set, the contact time between pollutants is predicted to obtain a contact time prediction set; Analysis module: Based on the pollutant type set and contact time prediction set, the subsequent changes of pollutants are analyzed through analytical methods to obtain risk assessment data; Optimization module: Based on soil characteristics and atmospheric characteristics, the risk assessment data results are optimized to improve the accuracy of risk assessment data; The analysis method comprises: Step 1: Attribute analysis: based on the pollutant type set, obtain the chemical properties of different types of pollutants, and obtain their harmfulness to the environment respectively to obtain the pollutant attribute set; Step 2: attribute combination, based on the contact time prediction set, obtain the combination results of different types of pollutants in the pollutant type set, obtain at least one pollutant combination item, and analyze the harmfulness of the pollutant combination item to the environment based on the pollutant attribute set to obtain at least one pollutant hazard data; Step 3: Data output, output risk assessment data based on pollutant hazard data; Step 4: Data combination: Based on the pollutant combination items and the contact time prediction set, determine whether the pollutant combination items will combine again. When the pollutant combination items will combine again, repeat steps 2 to 4 and make additional changes to the risk assessment data. When the pollutant combination items will not combine again, repeat step 3 to output the risk assessment data. The method for predicting the contact time between the pollutants includes: N1: Distance acquisition, taking the coordinate position of the pollutant discharge point as the starting feature point and the coordinate position of the sewage pipe as the end feature point, obtaining the distance between the starting feature point and the end feature point, and obtaining multiple groups of distance feature items; N2: Flow velocity acquisition, respectively detecting the flow velocity of pollutants when they pass through the starting point feature points, and obtaining multiple groups of starting point flow velocity feature items, which correspond to the distance feature items respectively; N3: Time prediction, based on the distance characteristic item corresponding to the starting point flow velocity characteristic item, the time when the pollutant reaches the end point characteristic point is obtained respectively, and multiple groups of time items are obtained, and then the contact time prediction set is obtained.

2. The intelligent risk assessment system for industrial agglomeration complex pollution based on data analysis according to claim 1 is characterized by: The soil characteristic items include land material, and the soil condition detection method includes: S1: Range division, taking the coordinate position of the pollutant as the origin, divide at least two ranges outward; S2: Sample detection: based on the divided range, at least one sample is collected and analyzed to obtain land material items; S3: Result analysis, based on the land material items and range division results, obtain the feedback results of the soil conditions, and then obtain the soil characteristic items.

3. The intelligent risk assessment system for industrial agglomeration complex pollution based on data analysis according to claim 2 is characterized by: The soil characteristic item includes land structure, and the soil condition detection method also includes: S4: Classification: Based on the land material item, it is divided into at least two levels to obtain at least two groups of land structure items, corresponding to different levels of pollutant absorption capacity; S5: Effect combination, based on the feedback results of soil conditions and the classification results, nine different levels of pollutant absorption capacity are obtained, and then the feedback results of soil conditions are divided and analyzed in more detail.

4. The intelligent risk assessment system for industrial agglomeration complex pollution based on data analysis according to claim 1 is characterized by: The atmospheric characteristic items include atmospheric temperature and atmospheric wind speed, and the atmospheric condition detection method includes: M1: Taking the coordinate position of the pollutant as the detection point, set up at least one detection station to detect the temperature and wind speed at the location of the pollution source, and obtain the atmospheric temperature item and the atmospheric wind speed item; M2: Use the timed detection method, set the detection interval, obtain multiple groups of atmospheric temperature items and atmospheric wind speed items, and then obtain the changing trends of atmospheric temperature and atmospheric wind speed. The changing trends of atmospheric temperature and atmospheric wind speed are combined into atmospheric characteristic items.

5. The intelligent risk assessment system for industrial agglomeration complex pollution based on data analysis according to claim 4 is characterized by: The atmospheric condition detection method also includes predictive detection: based on the change trends of the atmospheric temperature and the atmospheric wind speed, the atmospheric temperature and the atmospheric wind speed are predicted to obtain prediction items of the atmospheric temperature and the atmospheric wind speed respectively.

6. The intelligent risk assessment system for industrial agglomeration complex pollution based on data analysis according to claim 1 is characterized by: The method for predicting the contact time between the pollutants also includes: N4: Obtain the distances between adjacent endpoint feature points respectively to obtain adjacent distance feature items; N5: Detect the flow velocity of pollutants when they pass through the end point characteristic points respectively, and obtain multiple groups of end point flow velocity characteristic items, and the end point flow velocity characteristic items correspond to the start point flow velocity characteristic items respectively; N6: Based on the time term, the endpoint velocity characteristic term and the adjacent distance characteristic term, the contact time between pollutants is obtained, and then the contact time prediction set is obtained.

7. The intelligent risk assessment system for industrial agglomeration complex pollution based on data analysis according to claim 3 is characterized by: The optimization method includes: based on land material items and land structure items, combining at least two different levels of pollutant absorption capacity with risk assessment data to obtain an order of severity of pollutant damage to the land from light to heavy, and then optimizing the risk assessment data into at least four sub-assessment data.

8. The intelligent risk assessment system for industrial agglomeration complex pollution based on data analysis according to claim 4 is characterized by: The optimization method includes: dividing the atmospheric temperature and the atmospheric wind speed into at least two levels respectively, the atmospheric temperature and the atmospheric wind speed at different levels correspond to different pollutant purification capabilities, and at least four different levels of pollutant purification capabilities are obtained, and the at least four different levels of pollutant purification capabilities are combined with risk assessment data, and then the risk assessment data is optimized.

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