Atmospheric pollution channel transmission data analysis system

Abstract

The invention belongs to the technical field of atmospheric environment monitoring, and particularly relates to an atmospheric pollution channel transmission data analysis system which comprises an acquisition and analysis end and a visual display end which are in communication connection. The acquisition and analysis end comprises a multi-source heterogeneous data acquisition and integration unit, a data preprocessing unit, a pollution channel transmission identification unit, a transmission process analysis unit, a pollution source tracking unit and a dynamic prediction unit; according to the invention, a full-chain analysis framework from multi-source data acquisition and processing, pollution channel dynamic identification to source analysis and prediction and early warning is constructed, the identification precision and timeliness of a pollution transmission path are significantly improved, and a key supervision park in a monitoring area is determined through pollution management and control decision analysis. The key supervision parks are continuously monitored, and the atmospheric pollution emission management performance of the corresponding key supervision parks is judged, so that the targeted industrial park management is realized, and the pollution of each industrial park to the atmospheric environment is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of atmospheric environment monitoring, and specifically to an atmospheric pollution channel transmission data analysis system. Background Technique

[0002] Atmospheric pollution refers to the phenomenon that due to human activities or natural processes, the concentration of pollutants discharged into the atmosphere exceeds the environmental capacity or self-purification ability, resulting in the deterioration of the atmospheric environmental quality and causing harm to the ecosystem, human health and the climate system. With the acceleration of the industrialization process, regional atmospheric pollution problems have become increasingly prominent, and the cross-regional transmission of pollutants has become an important factor affecting air quality. It is necessary to monitor and manage atmospheric pollution;

[0003] Existing atmospheric pollution monitoring systems are usually limited to single-point data collection or static model analysis, lacking the capabilities of dynamic identification of pollution transmission channels, multi-dimensional data fusion analysis and accurate positioning of pollution sources, and unable to reasonably evaluate the pollution hazard status of each industrial park and accurately judge the performance of atmospheric pollution emission management for each key supervised park, making it difficult to provide key technical support for the precise prevention and control of atmospheric pollution;

[0004] In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide an atmospheric pollution channel transmission data analysis system, which solves the problems in the prior art that lack the capabilities of dynamic identification of pollution transmission channels, multi-dimensional data fusion analysis and accurate positioning of pollution sources, and are unable to reasonably evaluate the pollution hazard status of each industrial park and accurately judge the performance of atmospheric pollution emission management for each key supervised park, and is not conducive to the precise prevention and control of atmospheric pollution.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] An atmospheric pollution channel transmission data analysis system includes a collection and analysis terminal and a visualization display terminal. The collection and analysis terminal is communicatively connected to the visualization display terminal, and the collection and analysis terminal includes a multi-source heterogeneous data collection and integration unit, a data preprocessing unit, a pollution channel transmission identification unit, a transmission process analysis unit, a pollution source tracking unit and a dynamic prediction unit; the multi-source heterogeneous data collection and integration unit obtains data in real time through an API interface or a sensor, performs timestamp alignment and spatial grid processing on the data, and preprocesses the collected data through the data preprocessing unit;

[0008] The pollution channel transmission identification unit identifies the main transmission paths based on the correlation between the dynamic wind field and pollutant concentration. The transmission process analysis module simulates the diffusion process of pollutants in the channel. The pollution source tracking unit reversely analyzes the pollution transmission path, locates potential pollution sources, and the dynamic prediction module predicts the pollution transmission trend based on a machine learning model. The visualization display terminal overlays and displays the pollution channels, monitoring point data, and prediction results on an electronic map, shows the transmission process of pollutants in the channel in the form of an animation, and automatically generates an analysis report containing channel characteristics, source analysis, and prediction conclusions.

[0009] Furthermore, the data collected by the multi-source heterogeneous data acquisition and integration unit includes meteorological data, pollution monitoring data, geographical information data, and social activity data. Among them, the meteorological data comes from meteorological satellites and ground stations, including wind speed, wind direction, temperature, humidity, and air pressure. The pollution monitoring data comes from environmental protection monitoring stations and mobile devices, including PM2.5, and NOx concentrations. The geographical information data includes terrain elevation, surface cover type, and urban building distribution. The social activity data includes traffic flow, industrial emission inventory, and energy consumption data.

[0010] Furthermore, the preprocessing of the data preprocessing unit includes outlier processing, missing value filling, data fusion, and feature extraction. Among them, outlier processing eliminates abnormal data based on statistical thresholds. Missing value filling uses spatio-temporal interpolation methods and combines adjacent grid data and historical data to supplement missing values. Data fusion is used to overlay meteorological data and geographical information data to generate a wind field model including terrain blocking effects. Feature extraction is used to extract spatio-temporal distribution characteristics of pollutant concentrations.

[0011] Furthermore, the specific operation process of the pollution channel transmission identification unit is as follows:

[0012] Wind field-pollution coupling analysis: According to wind direction and wind speed data, combined with the spatial distribution of pollutant concentration, calculate the potential diffusion direction of pollutants. Channel clustering: Use the density clustering algorithm to identify continuous areas with high pollution concentration, and determine the channel boundary in combination with the wind field model. Channel grading: Sort the channels according to the transmission flux and label them as main channels or secondary channels.

[0013] Furthermore, the specific operation process of the transmission process analysis unit is as follows:

[0014] Diffusion simulation: Based on the Lagrangian particle diffusion model, simulate the migration path of pollutants in the channel. Retention effect analysis: Identify pollutant retention areas in combination with terrain data. Time evolution analysis: Statistically analyze the changing trend of pollutant concentration in the channel at hourly / daily granularity.

[0015] Furthermore, the specific operation process of the pollution source tracking unit is as follows:

[0016] Backward trajectory simulation: Starting from a highly polluted area, combined with historical wind field data to trace back the potential sources of pollutants; Source-receptor association: Matching the pollutant emission inventory of pollution sources and calculating the contribution rate; Uncertainty analysis: Evaluating the confidence interval of the tracking results through the Monte Carlo method;

[0017] The specific operation process of the dynamic prediction unit is as follows:

[0018] Model training: Using historical data to train the LSTM neural network, the input variables include meteorological data, current pollution concentration, and channel status; Real-time prediction: Outputting the changes in pollutant concentration and transmission direction within the channel in the next 6 - 72 hours; Early warning generation: Triggering an early warning signal if the predicted concentration exceeds the threshold.

[0019] Furthermore, the visualization display terminal is communicatively connected to the industrial park control terminal. The industrial park control terminal obtains all industrial parks within the monitoring area, marks the corresponding industrial park as i, and i is a natural number greater than or equal to 1; Judging whether to mark industrial park i as a key supervision park through pollution control decision-making analysis, and strengthening the subsequent supervision of its air pollution emissions when marking industrial park i as a key supervision park.

[0020] Furthermore, the specific analysis process of pollution control decision-making analysis is as follows:

[0021] Set a monitoring period of L1 days. When the number of days reaches L1, obtain the number of times industrial park i is marked as the source of air pollution during the monitoring period and define it as the pollution source matching value. Compare the pollution source matching value with the preset pollution source matching threshold. If the pollution source matching value exceeds the preset pollution source matching threshold, mark industrial park i as a key supervision park;

[0022] If the pollution source matching value does not exceed the preset pollution source matching threshold, when the industrial park is marked as the source of air pollution, analyze to determine whether to assign a pollution judgment symbol ZP-1 to the corresponding air pollution process; Obtain the number of times the pollution judgment symbol ZP-1 corresponding to industrial park i is assigned during the monitoring period and mark it as the high-hazard assignment frequency value, and calculate the average value of all pollution comprehensive evaluation values corresponding to industrial park i during the monitoring period to obtain the pollution hazard performance value;

[0023] Calculate the pollution control decision value by weighted summing the pollution source matching value, high-hazard assignment frequency value, and pollution hazard performance value, and compare the pollution control decision value with the preset pollution control decision threshold. If the pollution control decision value exceeds the preset pollution control decision threshold, mark industrial park i as a key supervision park.

[0024] Further, the specific analysis process for determining whether to assign the pollution judgment symbol ZP-1 to the corresponding air pollution process is as follows:

[0025] Collect the size of the area involved in the corresponding air pollution process and label it as the pollution coverage detection value, and collect the duration of the corresponding air pollution process and label it as the pollution duration condition value; and obtain the types of air pollutants involved in the corresponding air pollution process, label the average concentration of the corresponding type of air pollutant in the corresponding air pollution process as the pollution concentration detection value, and preset a set of preset hazard weight values for each type of air pollutant respectively. Multiply the pollution concentration detection value of the corresponding type of air pollutant by the corresponding preset hazard weight value to obtain the pollution analysis value, and sum up the pollution analysis values of all types of air pollutants to obtain the pollution hazard assessment value;

[0026] Calculate the pollution comprehensive assessment value by performing a weighted sum calculation on the pollution coverage detection value, pollution duration condition value, and pollution hazard assessment value of the corresponding air pollution process, and compare the pollution comprehensive assessment value with the preset pollution comprehensive assessment threshold. If the pollution comprehensive assessment value exceeds the preset pollution comprehensive assessment threshold, assign the pollution judgment symbol ZP-1 to the corresponding air pollution process.

[0027] Further, after marking the industrial park i as a key supervision park, the industrial park control end takes the current date as the start date and sets the number of days as L2 for the management period, and continuously monitors the key supervision park during the management period. When the number of days reaches L2, analyze the air pollution emission supervision performance of the corresponding key supervision park during the management period, and accordingly determine whether to generate an emission supervision alarm signal; the specific analysis process is as follows:

[0028] Real-time collect the real-time concentrations of various types of air pollutants in the corresponding key supervision park, compare the real-time concentrations of the corresponding types of air pollutants with the corresponding preset concentration thresholds. If the real-time concentration of the corresponding type of air pollutant exceeds the corresponding preset concentration threshold, label the corresponding type of air pollutant as an object with excessive emissions; if there are objects with excessive emissions in the corresponding key supervision park, determine that the corresponding key supervision park is in a state of pollution to be treated;

[0029] Start timing when it is determined that the key supervision park is in a state of pollution to be treated until there are no objects with excessive emissions in the key supervision park, and accordingly obtain the treatment duration value; compare the treatment duration value with the preset treatment duration threshold. If the treatment duration value exceeds the preset treatment duration threshold, label the corresponding treatment duration value as an abnormal treatment duration value;

[0030] The number of persistent outliers to be treated corresponding to the corresponding key supervision park during the management period is obtained and marked as the risk value to be treated, and all persistent values to be treated corresponding to the corresponding key supervision park during the management period are obtained and summed up to obtain the statistical value to be treated, and the risk value to be treated and the statistical value to be treated are numerically compared with the preset risk threshold to be treated and the preset statistical threshold to be treated; if the risk value to be treated or the statistical value to be treated exceeds the corresponding preset threshold, an emission supervision alarm signal for the corresponding key supervision park is generated.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. In the present invention, data is acquired in real time through a multi-source heterogeneous data acquisition and integration unit, the data preprocessing unit preprocesses the collected data, the pollution channel transmission identification unit identifies the main transmission path based on the correlation between the dynamic wind field and the pollutant concentration, the transmission process analysis module simulates the diffusion process of pollutants in the channel, the pollution source tracking unit reversely analyzes the pollution transmission path to locate potential pollution sources, the dynamic prediction module predicts the pollution transmission trend based on the machine learning model, and the visual display terminal performs multi-dimensional data interactive display and decision support, which significantly improves the recognition accuracy and timeliness of atmospheric pollution transmission paths;

[0033] 2. In the present invention, pollution control decision analysis is performed on the industrial park management and control end to determine the key supervision parks in the monitoring area, and subsequent atmospheric pollution emissions from the key supervision parks are paid special attention to. The key supervision parks are continuously monitored and the atmospheric pollution emission management performance of the corresponding key supervision parks is judged. When the emission supervision alarm signal is generated, the governance investment of the corresponding key supervision parks is increased, which is conducive to achieving targeted industrial park management and reducing the pollution caused by each industrial park to the atmospheric environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0035] Figure 1 This is an overall system block diagram of Embodiment 1 of the present invention;

[0036] Figure 2 This is a system block diagram of Embodiment 2 and Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0037] 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.

[0038] Embodiment 1: As Figure 1 shown, an air pollution channel transmission data analysis system proposed by the present invention includes an acquisition and analysis terminal and a visualization display terminal. The acquisition and analysis terminal is communicatively connected to the visualization display terminal, and the acquisition and analysis terminal includes a multi-source heterogeneous data acquisition and integration unit, a data preprocessing unit, a pollution channel transmission identification unit, a transmission process analysis unit, a pollution source tracking unit, and a dynamic prediction unit;

[0039] The multi-source heterogeneous data acquisition and integration unit obtains data in real time through an API interface or sensors, mainly including meteorological data, pollution monitoring data, geographic information data, and social activity data; among them, the meteorological data comes from meteorological satellites and ground stations, including wind speed, wind direction, temperature, humidity, and air pressure, etc.; the pollution monitoring data comes from environmental protection monitoring stations and mobile devices, including PM2.5, and NOx concentrations, etc.; the geographic information data includes terrain elevation, surface cover type, and urban building distribution, etc.; the social activity data includes traffic flow, industrial emission inventory, and energy consumption data, etc.; and the data is subjected to timestamp alignment and spatial grid processing (such as dividing into 1km×1km grids), and the data is classified and stored in a distributed database.

[0040] The data preprocessing unit preprocesses the collected data, including outlier processing, missing value filling, data fusion, and feature extraction; among them, outlier processing eliminates abnormal data based on statistical thresholds (such as the 3σ principle); missing value filling uses spatio-temporal interpolation method and combines adjacent grid data and historical data to supplement missing values; data fusion is used to overlay meteorological data and geographic information data to generate a wind field model including terrain blocking effects; feature extraction is used to extract spatio-temporal distribution features of pollutant concentrations (such as daily variation trends, spatial gradients).

[0041] The pollution channel transmission identification unit identifies the main transmission paths based on the correlation between the dynamic wind field and pollutant concentrations. Specifically: Wind field-pollution coupling analysis: According to wind direction and wind speed data, combined with the spatial distribution of pollutant concentrations, calculate the potential diffusion direction of pollutants; Channel clustering: Use density clustering algorithms (such as DBSCAN) to identify continuous regions with high pollution concentrations, and combine with the wind field model to determine the channel boundaries; Channel grading: Sort the channels according to the transmission flux (concentration×wind speed) to mark the main channels or secondary channels.

[0042] The transmission process analysis module simulates the diffusion process of pollutants in the channel. Specifically: Diffusion simulation: Based on the Lagrangian particle diffusion model, simulate the migration path of pollutants in the channel; Retention effect analysis: Combine terrain data (such as mountains, urban agglomerations) to identify pollutant retention areas; Time evolution analysis: Statistically analyze the hourly / daily variation trends of pollutant concentrations in the channel and correlate with changes in meteorological conditions.

[0043] The pollution source tracking unit reversely analyzes the pollution transmission path and locates potential pollution sources, specifically including: reverse trajectory simulation: starting from high-pollution areas and backtracking the potential sources of pollutants in combination with historical wind field data; source-receptor correlation: matching the pollution source emission inventory (such as the location and emission intensity of industrial parks) and calculating the contribution rate; uncertainty analysis: evaluating the confidence interval of the tracking results through the Monte Carlo method.

[0044] The dynamic prediction module predicts the pollution transmission trend based on a machine learning model, specifically including: model training: training an LSTM neural network using historical data, with input variables including meteorological data, current pollution concentration, and channel status; real-time prediction: outputting the changes in pollutant concentration and transmission direction within the channel in the next 6 - 72 hours; early warning generation: triggering an early warning signal if the predicted concentration exceeds the threshold.

[0045] The visualization display terminal overlays and displays pollution channels, monitoring point data, and prediction results on an electronic map, shows the transmission process of pollutants in the channel in the form of an animation, and automatically generates an analysis report containing channel characteristics, source analysis, and prediction conclusions, transforming complex data analysis conclusions into an intuitive visualization interface, providing intuitive decision-making support, and reducing the professional threshold.

[0046] The technical solution of the present invention effectively solves the problems of insufficient dynamic tracking ability, fuzzy cross-regional transmission analysis, and prediction lag existing in traditional pollution monitoring technologies through deep integration of multi-source data and modular collaborative analysis, and integrates multi-dimensional data such as meteorology, geography, pollution monitoring, and social activities, constructing a full-chain analysis framework from dynamic identification of pollution channels to source analysis, prediction, and early warning, significantly improving the recognition accuracy and timeliness of pollution transmission paths.

[0047] For example, the channel identification module based on wind field-pollution coupling analysis can capture the diffusion direction and main path of pollutants in real time, and the diffusion model corrected by terrain data can accurately simulate the retention and migration process of pollutants, providing reliable data support for cross-regional pollution joint prevention and control.

[0048] In terms of pollution source tracing and decision-making support, through reverse trajectory simulation and source-receptor correlation analysis, potential pollution sources with high contribution rates can be quickly locked, and combined with the machine learning model of the dynamic prediction module, the pollution transmission trend can be predicted in advance, providing a scientific basis for environmental management departments to formulate targeted control measures; the popularization and application of this system can not only serve the joint prevention and control of urban agglomeration air pollution, but also be extended to scenarios such as the emission supervision of industrial parks and the air quality guarantee of major events, with significant environmental benefits and social and economic values.

[0049] Example 2: As Figure 2As shown, the difference between this embodiment and the first embodiment is that the visual display terminal is communicatively connected to the industrial park control terminal. The main source of air environmental pollution is the industrial parks within the corresponding area. The industrial park control terminal obtains all the industrial parks within the monitoring area, marks the corresponding industrial park as i, and i is a natural number greater than or equal to 1;

[0050] Through pollution control decision analysis, it is judged whether to mark Industrial Park i as a key supervision park, and key attention is paid to the subsequent air pollution emissions of the key supervision park, which is conducive to realizing targeted management of industrial parks, thereby reducing the pollution brought by each industrial park to the air environment; the specific analysis process of pollution control decision analysis is as follows:

[0051] Set a monitoring period of L1 days. Preferably, L1 is 60 days; when the number of days reaches L1, obtain the number of times Industrial Park i is marked as the source of air pollution during the monitoring period and define it as the pollution source matching value. Compare the pollution source matching value with the preset pollution source matching threshold. If the pollution source matching value exceeds the preset pollution source matching threshold, it indicates that the potential risk of air pollution emission supervision in Industrial Park i is relatively high, then mark Industrial Park i as a key supervision park;

[0052] If the pollution source matching value does not exceed the preset pollution source matching threshold, when the industrial park is marked as the source of air pollution, collect the area size involved in the corresponding air pollution process and mark it as the pollution coverage detection value, and collect the duration of the corresponding air pollution process and mark it as the pollution duration condition value;

[0053] And obtain the types of air pollutants involved in the corresponding air pollution process, mark the average concentration of the corresponding type of air pollutant in the corresponding air pollution process as the pollution concentration detection value. Preset a set of preset hazard weight values greater than zero for each type of air pollutant respectively. Moreover, the higher the harm caused by the corresponding type of air pollutant, the greater the value of the preset hazard weight value matched with it; multiply the pollution concentration detection value of the corresponding type of air pollutant by the corresponding preset hazard weight value, and thus obtain the pollution analysis value. Sum up the pollution analysis values of all types of air pollutants involved to obtain the pollution hazard assessment value;

[0054] The comprehensive pollution assessment value is obtained by calculating the weighted sum of the pollution coverage detection value, the pollution duration condition value, and the pollution hazard assessment value of the corresponding air pollution process, that is, corresponding preset weight coefficients are assigned to the pollution coverage detection value, the pollution duration condition value, and the pollution hazard assessment value, and the pollution coverage detection value, the pollution duration condition value, and the pollution hazard assessment value are respectively multiplied by the corresponding preset weight coefficients, and the sum of the three sets of product results is marked as the comprehensive pollution assessment value; moreover, the larger the value of the comprehensive pollution assessment value, the higher the overall harm caused by the corresponding air pollution process;

[0055] The comprehensive pollution assessment value is numerically compared with the preset comprehensive pollution assessment threshold. If the comprehensive pollution assessment value exceeds the preset comprehensive pollution assessment threshold, indicating that the overall harm caused by the corresponding air pollution process is relatively high, then the corresponding air pollution process is assigned the pollution judgment symbol ZP-1; the number of times the pollution judgment symbol ZP-1 is assigned to Industrial Park i within the monitoring period is obtained and marked as the high-hazard frequency value, and the average value of all the comprehensive pollution assessment values corresponding to Industrial Park i during the monitoring period is calculated to obtain the pollution hazard performance value;

[0056] The pollution control decision value is obtained by calculating the weighted sum of the pollution source matching value, the high-hazard frequency value, and the pollution hazard performance value, that is, corresponding preset weight coefficients are assigned to the pollution source matching value, the high-hazard frequency value, and the pollution hazard performance value, and the pollution source matching value, the high-hazard frequency value, and the pollution hazard performance value are respectively multiplied by the corresponding preset weight coefficients, and the sum of the three sets of product results is marked as the pollution control decision value; moreover, the larger the value of the pollution control decision value, the higher the overall hidden danger of air pollution emission supervision in Industrial Park i;

[0057] The pollution control decision value is numerically compared with the preset pollution control decision threshold. If the pollution control decision value exceeds the preset pollution control decision threshold, indicating that the overall hidden danger of air pollution emission supervision in Industrial Park i is relatively high, then Industrial Park i is marked as a key supervision park.

[0058] Embodiment 3: As Figure 2 shown, the difference between this embodiment and Embodiment 1 and Embodiment 2 is that after marking Industrial Park i as a key supervision park, the management period with the current date as the start date and the number of days set to L2 is set by the industrial park control end. Preferably, L2 is ten days;

[0059] And during the management period, continuous monitoring is carried out on the key supervised parks. When the number of days reaches L2, analyze the performance of air pollution emission management for the corresponding key supervised parks during the management period, and accordingly judge whether to generate an emission supervision alarm signal. When an emission supervision alarm signal is generated, adjust the supervision plan for the corresponding key supervised park, and increase the investment in the treatment of air pollution in the corresponding key supervised park, including human and financial investment, to further reduce the air pollution brought by each industrial park. The specific analysis process is as follows:

[0060] The real-time concentrations of various types of air pollutants in the corresponding key supervised park are collected in real time. The real-time concentrations of the corresponding types of air pollutants are numerically compared with the corresponding preset concentration thresholds. If the real-time concentration of a corresponding type of air pollutant exceeds the corresponding preset concentration threshold, the corresponding type of air pollutant is marked as an object with excessive emissions; if there are objects with excessive emissions in the corresponding key supervised park, it is judged that the corresponding key supervised park is in a state of pollution to be treated;

[0061] When it is judged that the key supervised park is in a state of pollution to be treated, start timing until there are no objects with excessive emissions in the key supervised park, and accordingly obtain the continuous value to be treated; numerically compare the continuous value to be treated with the preset continuous treatment threshold. If the continuous value to be treated exceeds the preset continuous treatment threshold, the corresponding continuous value to be treated is marked as an abnormal value to be treated;

[0062] Obtain the number of abnormal values to be treated corresponding to the corresponding key supervised park during the management period and mark it as the risk value to be treated, and obtain all the continuous values to be treated corresponding to the corresponding key supervised park during the management period and calculate their sum to obtain the statistical value to be treated. Numerically compare the risk value to be treated and the statistical value to be treated with the preset risk threshold to be treated and the preset statistical threshold to be treated respectively;

[0063] If the risk value to be treated or the statistical value to be treated exceeds the corresponding preset threshold, indicating that the overall air pollution management status of the corresponding key supervised park during the management period is poor, then generate an emission supervision alarm signal for the corresponding key supervised park.

[0064] Working principle of the present invention: In use, by integrating multi-dimensional data such as meteorology, geography, pollution monitoring, and social activities, a full-chain analysis framework from dynamic identification of pollution channels to source analysis, prediction, and early warning is constructed, significantly improving the identification accuracy and timeliness of pollution transmission paths, which is beneficial to solving the problems of insufficient dynamic tracking ability, fuzzy cross-regional transmission analysis, and prediction lag existing in traditional pollution monitoring technologies, facilitating the precise prevention and control of air pollution. And through the pollution control decision-making analysis at the industrial park control end to determine the key supervised parks in the monitoring area, continuously monitor the key supervised parks and analyze the air pollution emission management performance of the corresponding key supervised parks during the management period, and increase the governance investment in the air pollution of the corresponding key supervised parks when generating emission supervision alarm signals, which is beneficial to realizing targeted industrial park management, thereby reducing the pollution brought by each industrial park to the atmospheric environment, with a high degree of intelligence.

[0065] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific implementation manners. Obviously, many modifications and variations can be made according to the content of this specification. This specification selects and specifically describes these embodiments to better explain the principle and practical application of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An air pollution channel transmission data analysis system, characterized in that, It includes a collection and analysis terminal and a visualization display terminal. The collection and analysis terminal is communicatively connected to the visualization display terminal. The collection and analysis terminal includes a multi-source heterogeneous data collection and integration unit, a data preprocessing unit, a pollution channel transmission identification unit, a transmission process analysis unit, a pollution source tracking unit, and a dynamic prediction unit. The multi-source heterogeneous data collection and integration unit obtains data in real time through an API interface or sensors, performs timestamp alignment and spatial grid processing on the data, and preprocesses the collected data through the data preprocessing unit. The pollution channel transmission identification unit identifies the main transmission paths based on the correlation between the dynamic wind field and pollutant concentration. The transmission process analysis module simulates the diffusion process of pollutants in the channel. The pollution source tracking unit reversely analyzes the pollution transmission path and locates potential pollution sources. The dynamic prediction module predicts the pollution transmission trend based on a machine learning model. The visualization display terminal overlays and displays the pollution channels, monitoring point data, and prediction results on an electronic map, and shows the transmission process of pollutants in the channel in the form of an animation, and automatically generates an analysis report including channel characteristics, source analysis, and prediction conclusions.

2. The air pollution channel transmission data analysis system according to claim 1, wherein The data collected by the multi-source heterogeneous data collection and integration unit includes meteorological data, pollution monitoring data, geographic information data, and social activity data.

3. The air pollution channel transmission data analysis system according to claim 1, characterized in that, The preprocessing of the data preprocessing unit includes outlier processing, missing value filling, data fusion, and feature extraction.

4. The air pollution channel transmission data analysis system according to claim 1, wherein The specific operation process of the pollution channel transmission identification unit is: wind field-pollution coupling analysis, channel clustering, and channel grading.

5. The air pollution channel transmission data analysis system according to claim 1, characterized in that The specific operation process of the transmission process analysis unit is: diffusion simulation, residence effect analysis, and time evolution analysis.

6. The air pollution channel transmission data analysis system according to claim 1, wherein The specific operation process of the pollution source tracking unit is: backward trajectory simulation, source-receptor association, and uncertainty analysis. The specific operation process of the dynamic prediction unit is: model training, real-time prediction, and early warning generation.

7. The air pollution channel transmission data analysis system according to claim 1, wherein The visualization display terminal is communicatively connected to the industrial park management and control terminal. The industrial park management and control terminal obtains all industrial parks within the monitoring area, marks the corresponding industrial park as i, and i is a natural number greater than or equal to 1. It determines whether to mark industrial park i as a key supervision park through pollution control decision analysis.

8. An air pollution channel transmission data analysis system according to claim 7, characterized in that, The specific analysis process of the pollution control decision analysis is as follows: Obtain the number of times industrial park i is marked as an air pollution source during the monitoring period and define it as the pollution source matching value. If the pollution source matching value exceeds the preset pollution source matching threshold, mark industrial park i as a key supervision park. If the pollution source matching value does not exceed the preset pollution source matching threshold, when the industrial park is marked as an air pollution source, analyze to determine whether to assign the pollution judgment symbol ZP-1 to the corresponding air pollution process. Obtain the number of times the pollution judgment symbol ZP-1 corresponding to industrial park i is assigned during the monitoring period and mark it as the high-hazard frequency value, and calculate the average value of all pollution comprehensive evaluation values corresponding to industrial park i during the monitoring period to obtain the pollution hazard performance value. Calculate the pollution control decision value by weighted summation of the pollution source matching value, the high-hazard frequency value, and the pollution hazard performance value. If the pollution control decision value exceeds the preset pollution control decision threshold, mark industrial park i as a key supervision park.

9. An air pollution channel transmission data analysis system according to claim 8, characterized in that, The specific analysis process for determining whether to assign the pollution judgment symbol ZP-1 to the corresponding air pollution process is as follows: The pollution comprehensive evaluation value is obtained by calculating the weighted sum of the pollution coverage detection value, the pollution duration condition value, and the pollution hazard assessment value of the corresponding air pollution process. If the pollution comprehensive evaluation value exceeds the preset pollution comprehensive evaluation threshold, the corresponding air pollution process is assigned the pollution judgment symbol ZP-1.

10. An air pollution channel transmission data analysis system according to claim 7, characterized in that, After marking industrial park i as a key supervision park, the industrial park control terminal analyzes the air pollution emission management performance of the corresponding key supervision park during the management period, and accordingly determines whether to generate an emission supervision alarm signal; the specific analysis process is as follows: The number of special values to be treated corresponding to the corresponding key supervision park during the management period is obtained and marked as the risk value to be treated, and the sum of all the continuous values to be treated corresponding to the corresponding key supervision park during the management period is obtained and calculated to obtain the statistical value to be treated. If the risk value to be treated or the statistical value to be treated exceeds the corresponding preset threshold, an emission supervision alarm signal for the corresponding key supervision park is generated.

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