Air pollution monitoring and tracing system based on multi-source data perception

Abstract

This application provides an atmospheric pollution monitoring and tracing system under multi-source data perception, which involves the field of pollution monitoring and tracing technology, including: a sensor distribution network of interactive preset monitoring areas; collecting and transmitting device source data, performing multi-source data fitting and pollution level assessment, and generating dynamic contour maps; performing big data retrieval and mining smoke flow influence functions; performing sample-driven training and building a pollution tracing model; performing pollution element tracing analysis and determining pollution trend distribution; identifying pollution sources and marking pollution exceeding limits, and performing terminal equipment visualization and early warning. This application can solve the technical problem that the existing atmospheric pollution monitoring and tracing technology is difficult to accurately analyze the pollution diffusion process, resulting in greater uncertainty in the tracing results, achieve the technical goal of accurate tracing, and achieve the technical effect of improving the accuracy of pollution source positioning, enhancing the ability to predict pollution diffusion, and improving the efficiency of pollution control response.

Description

Technical Field

[0001] The present application relates to the technical field of pollution monitoring and tracing, and in particular to an atmospheric pollution monitoring and tracing system under multi-source data perception. Background Art

[0002] In the field of air pollution control, existing monitoring and tracing technologies have made significant progress, but there are still many shortcomings. Traditional pollution monitoring systems mainly rely on data collection from fixed monitoring sites. Through the real-time data from these sites, environmental managers can roughly understand the changes in pollutant concentrations in a certain area. However, the monitoring range of this method is limited, and it is difficult to fully cover the entire atmospheric environment, especially in areas with wide areas or complex terrain. The monitoring results may not accurately reflect the actual pollution situation. In addition, the monitoring data from fixed sites are usually point-like and cannot provide a full picture of the spread of pollutants, resulting in limited data for speculation during the tracing process, and the accuracy and reliability of the tracing results are insufficient.

[0003] In summary, the existing air pollution monitoring and tracing technologies have defects such as limited coverage, which makes it difficult to accurately analyze the pollution diffusion process, and further affects the technical problem of large uncertainty in the tracing results. Summary of the invention

[0004] The purpose of this application is to provide an air pollution monitoring and tracing system under multi-source data perception, in order to solve the technical problem that the existing air pollution monitoring and tracing technologies have defects such as limited coverage, which makes it difficult to accurately analyze the pollution diffusion process, and further affects the tracing results with great uncertainty.

[0005] In view of the above problems, the present application provides an atmospheric pollution monitoring and tracing system under multi-source data perception, including: a sensor distribution network interaction module, the sensor distribution network interaction module is used to interact with the sensor distribution network of the preset monitoring area, wherein the sensor equipment includes pollution sensor class and environmental sensor class; a dynamic contour map generation module, the dynamic contour map generation module is used to collect and return the device source data collected by the sensor distribution network, perform multi-source data fitting and pollution level evaluation based on spatial position, and generate a dynamic contour map, wherein the dynamic contour map is a four-dimensional map, including spatial three-dimensional and temporal dimensions, and the dynamic contour map is updated in real time; a smoke flow influence function mining module, the smoke flow influence function mining module is used to collect and return the device source data collected by the sensor distribution network, perform multi-source data fitting and pollution level evaluation based on spatial position, and generate a dynamic contour map, wherein the dynamic contour map is a four-dimensional map, including spatial three-dimensional and temporal dimensions, and the dynamic contour map is updated in real time; a smoke flow influence function mining module is used to collect and return the device source data collected by the sensor distribution network, perform multi-source data fitting and pollution level evaluation based on spatial position, and generate a dynamic contour map, wherein the dynamic contour map is a four-dimensional map, including spatial three-dimensional and temporal dimensions, and the dynamic contour map is updated in real time; a smoke flow influence function mining module is used to collect and return the device source data collected by the sensor distribution network, and ... the dynamic contour map generation module is used to collect and return the device source data collected by the sensor distribution network, and the dynamic contour map generation module is used to collect and return the device source data collected by the sensor distribution network, and the dynamic contour map generation module is used to collect and return the device source data collected by the sensor distribution network, and the dynamic contour map generation module is used to Based on the standardized environment as the baseline, big data retrieval and mining of smoke flow influence function are carried out, and the smoke flow influence function represents the unit influence relationship of a single environmental element or a collaborative environmental element; a pollution source tracing model construction module, the pollution source tracing model construction module is used to combine the smoke flow influence function, perform sample driven training, and construct a pollution source tracing model; a pollution trend distribution determination module, the pollution trend distribution determination module is used to traverse the dynamic contour map, perform pollution element source tracing analysis based on the pollution source tracing model, and determine the pollution trend distribution; a pollution limit exceeding identification module, the pollution limit exceeding identification module is used to identify the pollution source and perform pollution limit exceeding identification based on the pollution trend distribution, and perform terminal equipment visualization and early warning.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] Through the sensor distribution network interaction module, the sensor distribution network interaction module is used to interact with the sensor distribution network of the preset monitoring area, wherein the sensor equipment includes pollution sensors and environmental sensors; a dynamic contour map generation module, the dynamic contour map generation module is used to collect and return the device source data collected by the sensor distribution network, perform multi-source data fitting and pollution level assessment based on spatial position, and generate a dynamic contour map, wherein the dynamic contour map is a four-dimensional map, including spatial three-dimensional and temporal dimensions, and the dynamic contour map is updated in real time; a smoke flow influence function mining module, the smoke flow influence function mining module is used to perform big data retrieval and mining of smoke flow influence functions based on a standardized environment as a baseline, and the smoke flow influence function represents a single environmental element or The unit influence relationship of collaborative environmental elements; a pollution source tracing model construction module, which is used to combine the smoke flow influence function, perform sample-driven training, and construct a pollution source tracing model; a pollution trend distribution determination module, which is used to traverse the dynamic contour map, perform pollution element source tracing analysis based on the pollution source tracing model, and determine the pollution trend distribution; a pollution over-limit identification module, which is used to identify pollution sources and perform pollution over-limit identification based on the pollution trend distribution, perform terminal equipment visualization and early warning, and achieve the technical goal of accurate tracing, so as to achieve the technical effect of improving the accuracy of pollution source positioning, enhancing the pollution diffusion prediction capability, and improving the efficiency of pollution control response.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0010] Figure 1 This is a structural diagram of the air pollution monitoring and tracing system under multi-source data perception in this application.

[0011] Figure 2 This is a flow chart of generating and mining smoke flow influence functions in the atmospheric pollution monitoring and tracing system under multi-source data perception in this application.

[0012] Description of reference numerals:

[0013] Sensor distribution network interaction module 11, dynamic contour map generation module 12, smoke flow influence function mining module 13, pollution source tracing model construction module 14, pollution trend distribution determination module 15, pollution limit-exceeding identification module 16. DETAILED DESCRIPTION

[0014] This application solves the technical problem that the existing atmospheric pollution monitoring and tracing technologies have limited coverage, which makes it difficult to accurately analyze the pollution diffusion process and further affects the uncertainty of the tracing results by providing an atmospheric pollution monitoring and tracing system under multi-source data perception. The technical goal of achieving accurate tracing is to improve the accuracy of pollution source positioning, enhance the ability to predict pollution diffusion, and improve the efficiency of pollution control response.

[0015] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0016] Please see attached Figure 1 , this application provides an air pollution monitoring and tracing system under multi-source data perception, specifically including:

[0017] The sensor distribution network interaction module 11 is used to interact with the sensor distribution network of the preset monitoring area, wherein the sensor equipment includes pollution sensors and environmental sensors.

[0018] Specifically, since it is impossible to deploy in the entire area, a preset monitoring area can be obtained, such as a preset monitoring area as a standardized measuring point. The sensor distribution network constructed in the preset monitoring area is obtained in an interactive manner. In the sensor distribution network, the sensor equipment is divided into sensor equipment for monitoring pollutants and sensor equipment for sensing environmental parameters. Through the collaborative work of sensor equipment, the source and diffusion path of pollutants can be obtained more accurately, thereby providing reliable data support for the traceability analysis of air pollution. In addition, multi-source data perception makes it possible to comprehensively consider multiple environmental factors and improve the accuracy and effectiveness of monitoring.

[0019] A dynamic contour map generation module 12 is used to collect and transmit back the device source data collected by the sensor distribution network, perform multi-source data fitting and pollution level assessment based on spatial position, and generate a dynamic contour map, wherein the dynamic contour map is a four-dimensional map, including three spatial dimensions and a temporal dimension, and the dynamic contour map is updated in real time.

[0020] Specifically, after collecting and transmitting the data from the source of the equipment in the sensor distribution network, the multi-source data is fitted and the pollution level is evaluated based on the spatial position of the equipment source data, that is, the source data of the equipment at the adjacent positions are spliced ​​according to the specific spatial position, and a dynamic contour map is generated. The dynamic contour map is a four-dimensional image, which contains three-dimensional spatial information and time dimension, so that the dynamic contour map can reflect the distribution of pollutants in space and their changes over time. At the same time, the dynamic contour map will be updated in real time, thereby providing accurate visual support for the continuous monitoring and evaluation of the pollution status, thereby better showing the changing trend of pollutants and helping to take timely response measures.

[0021] The smoke flow influence function mining module 13 is used to perform big data retrieval and mining of smoke flow influence functions based on the standardized environment as a baseline. The smoke flow influence function represents the unit influence relationship of a single environmental element or a coordinated environmental element.

[0022] Specifically, in a standardized environment, the diffusion characteristics of the smoke flow are determined as a reference baseline for subsequent analysis. Among them, through big data retrieval technology, the patterns and laws of smoke flow diffusion are extracted and analyzed from a large amount of relevant data, and finally the smoke flow influence function is obtained, which is used to characterize the influence of single environmental factors or coordinated environmental factors on smoke flow diffusion. Among them, a single environmental factor refers to an independent environmental factor, such as temperature, humidity or wind speed, while a coordinated environmental factor involves the joint action of multiple factors. The smoke flow influence function helps understand the changes in smoke flow under different environmental conditions by quantifying the degree of influence of different environmental factors, provides a clear explanation of the impact of a single environmental factor, and reveals the complex influence relationship when multiple environmental factors work together, thereby providing a scientific basis and data support for predicting and controlling the diffusion of atmospheric pollution.

[0023] The pollution source tracing model construction module 14 is used to combine the smoke flow influence function, perform sample driven training, and construct a pollution source tracing model.

[0024] Specifically, through sample-driven training, using the smoke flow influence function, we continuously learn and optimize from a large amount of actual pollution data to build a pollution source tracing model. Sample-driven training means extracting data samples from a large number of known pollution events. The samples contain key information such as the specific path of pollutant diffusion, influencing factors, and results. Through the analysis of samples, the pollution source tracing model can identify the complex relationship between pollution sources and environmental conditions, and gradually optimize its judgment and prediction capabilities. As the training progresses, the pollution source tracing model will more and more accurately grasp the specific impact of different environmental factors on pollutant diffusion, and will be able to quickly make accurate tracing judgments when faced with new pollution events. Through sample-driven methods, the pollution source tracing model can not only adapt to the diffusion of pollution under different environmental conditions, but also continuously improve its accuracy and reliability based on new data, thereby providing strong technical support for environmental monitoring and governance.

[0025] The pollution trend distribution determination module 15 is used to traverse the dynamic contour map, perform pollution element source tracing analysis based on the pollution source tracing model, and determine the pollution trend distribution.

[0026] Specifically, a comprehensive scan and analysis of the dynamic contour map shows the dynamic changes of environmental factors and pollutants in time and space, and obtains detailed information about the diffusion of pollutants, such as changes in pollutant concentrations, diffusion directions, and diffusion speeds. The pollution source tracing model is called, and through the training of a large amount of historical data and smoke flow influence functions, the data in the dynamic contour map is compared with historical data to identify the specific location and diffusion path of the pollution source, determine the trend distribution of pollutants in the future, including the possible diffusion areas and changes in pollution intensity, and reduce harm to the environment and public health.

[0027] The pollution exceeding limit identification module 16 is used to identify the pollution source and perform pollution exceeding limit identification based on the pollution trend distribution, and perform terminal equipment visualization and early warning.

[0028] Specifically, the pollution trend distribution is used to identify potential pollution sources. The pollution trend distribution provides detailed information about how pollutants spread in the environment, including their sources, diffusion directions and diffusion speeds, and then identifies the location of the pollution sources, which helps to determine the initial release points of pollutants and which areas are most likely to be affected. Next, over-limit marking is carried out based on the identified pollution sources. Over-limit marking refers to identifying areas where the concentration of pollutants exceeds a certain safety threshold, which may pose a significant threat to the environment or human health. Through over-limit marking, places where pollutant concentrations have reached or exceeded dangerous levels can be highlighted, providing important reference for subsequent emergency responses. Finally, it is transmitted to the terminal device, displayed through a visual interface, and an early warning signal is issued at the same time. Visualization can help decision makers intuitively understand the location of pollution sources, diffusion paths, and changes in pollution concentrations, while the early warning function ensures that relevant personnel can obtain information in a timely manner and take necessary countermeasures.

[0029] The air pollution monitoring and tracing system under the multi-source data perception can achieve the technical goal of accurate tracing, and achieve the technical effects of improving the accuracy of pollution source positioning, enhancing the ability to predict pollution spread, and improving the efficiency of pollution control response.

[0030] Further, if Figure 2 As shown, this application also includes:

[0031] Perform big data retrieval to determine the atmospheric monitoring data; determine the standardized smoke dispersion characteristics based on the standardized environment; divide the atmospheric monitoring data based on variable environmental elements based on the standardized environment to determine multiple groups of monitoring data, wherein the variable environmental elements include single variable elements or collaborative variable elements; traverse the multiple groups of monitoring data, explore the influence trend of smoke under the element variables, and integrate to generate the smoke influence function.

[0032] Specifically, by conducting big data retrieval, information related to air monitoring can be screened out from massive historical and real-time data. Atmospheric monitoring data may include data from various environmental monitoring stations, satellite remote sensing data, and other source information related to air pollution.

[0033] Next, we determine the standardized smoke dispersion characteristics based on the standardized environment, that is, we study the diffusion pattern and behavior of smoke under a set standard environmental condition. The standardized environment refers to the environmental conditions where parameters such as temperature, humidity, and wind speed are controlled at fixed levels. By studying the smoke characteristics under a standardized environment, we can understand the performance of smoke under ideal conditions, thus providing a reference for subsequent variable environment analysis.

[0034] Then, taking the standardized environment as the baseline, based on the variable environmental elements, the atmospheric monitoring data is divided to determine multiple groups of monitoring data, that is, various variables in the actual environment, such as different temperature, humidity and wind speed, are compared with the standardized environment, and the atmospheric monitoring data are classified according to the standardized environment, which can form multiple groups of monitoring data under different environmental conditions, making the subsequent analysis more detailed and targeted. Among them, the variable environmental elements include single variable elements or synergistic variable elements, that is, when analyzing atmospheric monitoring data, environmental elements can be a single variable or a combination of multiple variables. Single variable elements refer to an independent environmental factor, such as temperature, humidity or wind speed, which can affect the diffusion of smoke alone. Synergistic variable elements are a combination of multiple environmental factors. When synergistic variable elements act together, the impact on smoke is more complicated. By considering synergistic variable elements, the analysis can be more comprehensive and accurate, because the diffusion of atmospheric pollution in the actual environment is often the result of the joint action of multiple factors. Distinguishing between single variables and synergistic variables helps to more accurately predict and explain the behavior of smoke, and provide stronger support for environmental management and pollution control.

[0035] Finally, by traversing multiple sets of monitoring data, we explored the influence trends of smoke flow under factor variables and integrated them to generate smoke flow influence functions. By analyzing multiple sets of monitoring data in turn and exploring the specific effects of different environmental variables on smoke flow diffusion, we can obtain the changing trends of smoke flow under different conditions and integrate the changing trends into functions to predict and control the diffusion behavior of smoke flow in actual environments.

[0036] By ensuring the scientific and systematic nature of data analysis, it also provides operational models and methods for practical applications.

[0037] Furthermore, the present application also includes:

[0038] The dynamic contour map includes an environment contour map and a pollution contour map, wherein the dynamic contour map can be decomposed and split.

[0039] Specifically, dynamic contour maps include environmental contour maps and pollution contour maps. Environmental contour maps are contour maps that reflect environmental factors, while pollution contour maps are contour maps that reflect the distribution of pollutant concentrations. Dynamic contour maps can fully display the relationship between environmental conditions and pollutant diffusion in different spatial and temporal dimensions.

[0040] The dynamic contour map can be decomposed and split, which means that the dynamic contour map can separate the environmental contour map from the pollution contour map as needed so that they can be analyzed independently. This allows researchers to analyze and process data more flexibly, and they can study the changes in environmental factors and the distribution of pollutants separately, which helps to more deeply understand and solve the problem of air pollution.

[0041] By decomposing the dynamic contour map, we can more clearly analyze the impact of each independent factor on the overall system, providing an accurate scientific basis for formulating effective environmental governance strategies.

[0042] Furthermore, the present application also includes:

[0043] Traversing the device source data, determining the environmental sensor data; performing four-dimensional dynamic distribution conversion based on the environmental sensor data, and determining the environmental contour map; based on the environmental contour map, combined with the smoke flow influence function, determining the influence characteristic value, wherein the influence characteristic at least includes diffusion, spatial flow direction, and flow velocity.

[0044] Specifically, all device source data collected from the sensor distribution network are accessed in turn to filter out environment-related data to ensure the comprehensiveness and accuracy of the environmental sensor data obtained, including data on environmental factors such as temperature, humidity, and wind speed, thereby providing a solid data foundation for subsequent analysis.

[0045] Next, the acquired environmental sensor data is transformed through four-dimensional dynamic distribution conversion technology to generate an environmental contour map that can show changes in time and space. The four-dimensional dynamic distribution conversion can show the dynamic changes of environmental factors in different time and space, providing a more three-dimensional and real-time view of environmental changes.

[0046] Then, the generated environmental contour map is combined with the smoke flow influence function to obtain the characteristic value of the environment's influence on smoke flow diffusion through analysis. The influence characteristics at least include key parameters such as diffusion degree, spatial flow direction, and flow velocity, which can quantify and describe the diffusion behavior of smoke flow in the environment.

[0047] By fully understanding the impact of environmental factors on smoke diffusion, we can provide important support for predicting and controlling pollution diffusion.

[0048] Furthermore, the present application also includes:

[0049] The pollution contour map is traversed to locate abnormal trend changes and determine the pollution exchange area; the exchange diffusion impact analysis is performed on the pollution exchange area to determine the first distribution area; the preset monitoring area is traversed to take the area outside the first distribution area as the second distribution area.

[0050] Specifically, by scanning and analyzing the generated pollution contour maps in sequence, we can find out the areas with abnormal trend changes, indicating that the concentration of pollutants has fluctuated significantly or the diffusion is abnormal. The abnormal trend changes are marked as pollution exchange areas, and the pollution exchange areas are determined. The areas where pollutants may be interacting or diffusing are located, laying the foundation for the next step of analysis.

[0051] Next, by analyzing the pollution exchange areas one by one and studying the diffusion and interaction patterns of pollutants in the pollution exchange areas, we can determine the main diffusion range of pollutants, that is, the first distribution area. Pollutants in the first distribution area may have a strong diffusion trend or interaction, which has a significant impact on the overall pollution situation.

[0052] Then, the preset monitoring area is compared with the first distribution area in turn, and the part of the monitoring area that does not belong to the first distribution area is identified and defined as the second distribution area. The second distribution area may be an area with less pollution diffusion or has not been significantly affected by pollution, but still needs to be monitored and analyzed.

[0053] By determining the first distribution area and the second distribution area, it is helpful to analyze the spread of pollution and formulate targeted monitoring and response measures.

[0054] Furthermore, the present application also includes:

[0055] The pollution source tracing model includes a contour tracing branch and a smoke flow tracing branch; based on the contour tracing branch, the pollution contour map is identified, and based on the trend change state of the contour map, a first tracing result is determined, wherein the trend change characteristics are the type and concentration of pollutants; based on the first distribution area and the second distribution area, the first tracing result is segmented and marked as one result and two results; the one result is transferred to the smoke flow tracing branch, and the pollution trend distribution is determined by analysis and fitting.

[0056] Specifically, the pollution source tracing model includes the contour tracing branch and the smoke flow tracing branch. The contour tracing branch mainly focuses on the spatial distribution of pollutants, while the smoke flow tracing branch focuses on analyzing the diffusion path and trend of pollutants.

[0057] Next, the contour tracing branch is used to analyze the pollution contour map. By identifying the changing trend in the pollution contour map, i.e., the trend characteristics, the preliminary location and characteristics of the pollution source are determined. The trend characteristics include the type of pollutants and the distribution of their concentrations, and then the possible source of the pollution is inferred, i.e., the first tracing result.

[0058] Then, the first tracing result is further refined and segmented. By comparing the first distribution area and the second distribution area, the parts of the first tracing result corresponding to the first distribution area and the second distribution area are segmented and marked as one result and two results respectively, so as to distinguish the influence range of the pollution source in different areas for subsequent analysis.

[0059] Finally, one of the segmented results is handed over to the smoke source tracing branch for further analysis. The smoke source tracing branch uses analytical fitting methods to determine the diffusion trend and path of pollutants in the environment, and ultimately generates a pollution trend distribution model to predict the future development direction of pollution and potential impact areas.

[0060] Accurately locating the pollution source and predicting its spread trend can help to take effective response measures in a timely manner.

[0061] Furthermore, the present application also includes:

[0062] Based on the smoke tracing branch and in combination with the influencing characteristic value, a smoke tracing impact analysis based on environmental factors is performed on the one result to determine a second tracing result; the one result and the second tracing result are fitted as the tracing result of the first distribution area; the tracing result of the first distribution area and the two results are spliced ​​as the pollution trend distribution.

[0063] Specifically, the smoke plume tracing branch combines influencing characteristic values, such as diffusion, spatial flow direction and flow velocity, to conduct an in-depth analysis of a result, taking into account the specific impact of environmental factors on the smoke plume, thereby obtaining a more accurate location of the pollution source, that is, the second tracing result, which can provide a more comprehensive understanding of the behavior of pollution sources under different environmental conditions.

[0064] Next, one result is fitted with the second source tracing result obtained through smoke flow impact analysis, that is, they are integrated together to form a more complete and accurate source tracing result, which represents the final location of the pollution source in the first distribution area and lays the foundation for subsequent pollution trend analysis.

[0065] Then, the tracing results of the first distribution area are spliced ​​with the binomial results to form a complete pollution trend distribution map, which integrates the analysis results of different regions to provide a global perspective, showing the overall diffusion trend of pollutants and possible affected areas in the future.

[0066] By providing a comprehensive and accurate pollution source location and diffusion trend forecast, it provides a scientific basis for pollution control.

[0067] Furthermore, the present application also includes:

[0068] Based on the pollution trend distribution, the emission trajectory based on each pollution source is determined; a pollution control strategy is generated, and pollution control response tracking is performed based on the emission trajectory.

[0069] Specifically, based on the distribution of pollution trends, the diffusion path or escape trajectory of each pollution source is identified, that is, the process and direction of the spread of pollutants from the source to the outside, including the process and direction that changes with time and space, so as to have a clearer understanding of how pollutants spread in the environment and find out the areas that may be affected, thereby providing direction for the next step of governance.

[0070] Next, formulate corresponding pollution control strategies based on the trajectory of pollutant escape. Control strategies may include controlling pollution sources, preventing pollutant spread, and treating affected areas. At the same time, based on pollution control strategies, track the effects of pollution control in real time to ensure that the implementation of measures can effectively mitigate or eliminate the impact of pollution. Through response tracking, managers can adjust strategies in a timely manner to ensure that the effects of pollution control meet expectations.

[0071] By locating the diffusion path of pollution sources, we can also ensure the effectiveness of control measures, thereby better responding to and controlling environmental pollution problems.

[0072] In summary, the air pollution monitoring and tracing system under multi-source data perception provided by this application has the following technical effects:

[0073] Through the sensor distribution network interaction module, the sensor distribution network interaction module is used to interact with the sensor distribution network of the preset monitoring area, wherein the sensor equipment includes pollution sensors and environmental sensors; a dynamic contour map generation module, the dynamic contour map generation module is used to collect and return the device source data collected by the sensor distribution network, perform multi-source data fitting and pollution level assessment based on spatial position, and generate a dynamic contour map, wherein the dynamic contour map is a four-dimensional map, including spatial three-dimensional and temporal dimensions, and the dynamic contour map is updated in real time; a smoke flow influence function mining module, the smoke flow influence function mining module is used to perform big data retrieval and mining of smoke flow influence functions based on a standardized environment as a baseline, and the smoke flow influence function represents a single environmental element or The unit influence relationship of collaborative environmental elements; a pollution source tracing model construction module, which is used to combine the smoke flow influence function, perform sample-driven training, and construct a pollution source tracing model; a pollution trend distribution determination module, which is used to traverse the dynamic contour map, perform pollution element source tracing analysis based on the pollution source tracing model, and determine the pollution trend distribution; a pollution over-limit identification module, which is used to identify pollution sources and perform pollution over-limit identification based on the pollution trend distribution, perform terminal equipment visualization and early warning, and achieve the technical goal of accurate tracing, so as to achieve the technical effect of improving the accuracy of pollution source positioning, enhancing the pollution diffusion prediction capability, and improving the efficiency of pollution control response.

[0074] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0075] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. The air pollution monitoring and tracing system based on multi-source data perception is characterized by: include: A sensor distribution network interaction module, wherein the sensor distribution network interaction module is used to interact with the sensor distribution network of the preset monitoring area, wherein the sensor devices include pollution sensors and environmental sensors; A dynamic contour map generation module, which is used to collect and return the source data of the equipment collected by the sensor distribution network, perform multi-source data fitting and pollution level assessment based on spatial position, and generate a dynamic contour map, wherein the dynamic contour map is a four-dimensional map, including three spatial dimensions and a temporal dimension, and the dynamic contour map is updated in real time; A smoke flow influence function mining module, which is used to perform big data retrieval and mining of smoke flow influence functions based on a standardized environment as a baseline, wherein the smoke flow influence function represents a unit influence relationship of a single environmental element or a coordinated environmental element; A pollution source tracing model construction module, wherein the pollution source tracing model construction module is used to perform sample-driven training in combination with the smoke flow influence function to construct a pollution source tracing model; A pollution trend distribution determination module, the pollution trend distribution determination module is used to traverse the dynamic contour map, perform pollution element source tracing analysis based on the pollution source tracing model, and determine the pollution trend distribution; A pollution over-limit identification module, which is used to identify pollution sources and perform pollution over-limit identification based on the pollution trend distribution, and to perform terminal equipment visualization and early warning; The mining of smoke flow influence function includes: Conduct big data retrieval to determine atmospheric monitoring data; Determine standardized smoke flow dispersion characteristics based on the standardized environment; Taking the standardized environment as a baseline and based on variable environmental elements, the atmospheric monitoring data is divided to determine multiple groups of monitoring data, wherein the variable environmental elements include single variable elements or collaborative variable elements; The plurality of monitoring data sets are traversed to mine the influence trend of smoke flow under the element variables, and the smoke flow influence function is generated by integration.

2. The air pollution monitoring and tracing system under multi-source data perception as claimed in claim 1, characterized in that: The dynamic contour map includes an environment contour map and a pollution contour map, wherein the dynamic contour map can be decomposed and split.

3. The air pollution monitoring and tracing system under multi-source data perception as claimed in claim 2 is characterized in that: Also includes: Traversing the device source data to determine the environmental sensor data; Based on the environmental sensing data, four-dimensional dynamic distribution conversion is performed to determine an environmental contour map; Based on the environmental contour map and in combination with the smoke flow influence function, an influence characteristic value is determined, wherein the influence characteristic at least includes diffusion, spatial flow direction, and flow velocity.

4. The air pollution monitoring and tracing system under multi-source data perception as claimed in claim 3 is characterized in that: Before conducting the pollution source analysis, the following steps are required: Traversing the pollution contour map, locating abnormal trend changes, and determining the pollution exchange area; Performing an exchange diffusion impact analysis on the polluted exchange area to determine a first distribution area; The preset monitoring area is traversed, and the area outside the first distribution area is used as the second distribution area.

5. The air pollution monitoring and tracing system under multi-source data perception as claimed in claim 4 is characterized in that: The determining of the pollution trend distribution comprises: The pollution source tracing model includes an iso-height tracing branch and a smoke flow tracing branch; Based on the contour tracing branch, the pollution contour map is identified, and based on the trend change state of the contour map, a first tracing result is determined, wherein the trend change feature is the type and concentration of the pollutant; Based on the first distribution area and the second distribution area, the first tracing result is divided and marked as one result and two results; The result is transferred to the smoke source tracing branch, and the pollution trend distribution is determined by analysis and fitting.

6. The air pollution monitoring and tracing system under multi-source data perception as claimed in claim 5, characterized in that: The analysis and fitting to determine the pollution trend distribution includes: Based on the smoke flow tracing branch and in combination with the influencing characteristic value, a smoke flow impact analysis based on environmental factors is performed on the one result to determine a second tracing result; Fitting the one result and the second tracing result as the tracing result of the first distribution area; The tracing result of the first distribution area and the two results are spliced ​​together to serve as the pollution trend distribution.

7. The air pollution monitoring and tracing system under multi-source data perception as claimed in claim 1, characterized in that: After terminal equipment visualization and early warning, including: Based on the pollution trend distribution, determining the escape trajectory based on each pollution source; A pollution control strategy is generated, and pollution control response tracking is performed based on the emission trajectory.

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