An accurate traceability analysis method and system for air pollutants based on artificial intelligence

Abstract

The embodiments of the present application disclose an accurate traceability analysis method and system for atmospheric pollutants based on artificial intelligence. The technical solutions provided by the embodiments of the present application can accurately detect target atmospheric pollutants through an atmospheric pollutant characteristic detection model, and then construct a traceability path in combination with the real-time wind field to accurately trace the target atmospheric pollutants, which can improve the traceability efficiency and accuracy of atmospheric pollutants, timely carry out atmospheric pollution control, and improve the reliability and stability of atmospheric pollution control.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technology, and in particular, to an accurate traceability analysis method and system for atmospheric pollutants based on artificial intelligence. Background Art

[0002] At present, with the rapid development of economy and industry, the emission rate of air pollution is also increasing. Air environmental pollution has become the most serious environmental safety problem, which has a great impact on people's quality of life. In order to detect and control air pollution in a timely manner, it is necessary to accurately and efficiently trace the sources of atmospheric pollutants. For this purpose, detection stations are usually arranged at various positions in the detection area to monitor the atmospheric component data in real time. Then, through the observation of the changes in the atmospheric component data by the background analysts, it is judged whether atmospheric pollutants appear. When atmospheric pollutants appear, they will go to the relevant stations in time to search for the pollution sources.

[0003] However, since atmospheric pollutants will drift continuously with the environmental wind direction, often at the downwind position of the pollution source, the characteristics of atmospheric pollutants characterized by the atmospheric component data are more obvious than those at the actual pollution source position. The method of simply locating the emission position of atmospheric pollutants based on the atmospheric component data and then tracing the sources of atmospheric pollutants has a large error, which easily leads to the situation of incorrect pollution source positioning and affects the tracing efficiency and accuracy of atmospheric pollutants. Summary of the Invention

[0004] The embodiments of the present application provide an accurate traceability analysis method and system for atmospheric pollutants based on artificial intelligence, which can improve the traceability accuracy and efficiency of atmospheric pollutants and solve the traceability error problem of atmospheric pollutants.

[0005] In a first aspect, the embodiments of the present application provide an accurate traceability analysis method for atmospheric pollutants based on artificial intelligence, including:

[0006] Obtain the atmospheric component data information of the target detection area, input the atmospheric component data information into a pre-constructed atmospheric pollutant feature detection model, and based on the output of the atmospheric pollutant feature detection model, obtain the target atmospheric pollutants and the corresponding target associated industry information of the target detection area. The atmospheric pollutant feature detection model is pre-constructed with training samples based on the atmospheric component feature data of different atmospheric pollutants and the corresponding associated industry information, and the model is trained based on the training samples;

[0007] Determine the target index data to be detected based on the target atmospheric pollutants in the target detection area, and collect the target index data detected by the collection nodes at each specified detection position in the target detection area;

[0008] Obtain the real-time wind field map of the target detection area, mark the target index data on the real-time wind field map based on the specified detection position, and determine the distribution information of the target index data on the real-time wind field map;

[0009] Construct a traceability path for the target detection area based on the distribution information, query for target location points that match the target-related industry information within the set range of the traceability path, and perform source location warning for the target air pollutants based on the target location points.

[0010] Further, the constructing the traceability path for the target detection area based on the distribution information includes:

[0011] Construct a distribution path for the target air pollutants according to the distribution information, and determine the section on the distribution path that matches the wind direction of the real-time wind field map as the traceability path of the target detection area.

[0012] Further, the constructing the traceability path for the target detection area based on the distribution information includes:

[0013] Construct a pollution concentration gradient change path for the target air pollutants according to the distribution information, and when the pollution concentration gradient change path matches the wind direction of the real-time wind field map, determine the pollution concentration gradient change path as the traceability path of the target detection area.

[0014] Further, after performing the source location warning for the target air pollutants based on the target location points, it further includes:

[0015] Construct a detection path according to the target location points and the wind direction data of the real-time wind field map, drive an unmanned device to collect the pollution concentration change information of the target air pollutants corresponding to the detection path, and perform traceability location verification on the target location points based on the pollution concentration change information.

[0016] Further, after driving the unmanned device to collect the pollution concentration change information of the target air pollutants corresponding to the detection path, it further includes:

[0017] When it is detected based on the pollution concentration change information that the pollution concentration drop at the specified position point of the detection path reaches the set threshold, perform source location warning for the target air pollutants based on the specified position point.

[0018] Further, after determining the distribution information of the target index data on the real-time wind field map, it further includes:

[0019] Query the historical database of atmospheric pollutant traceability records, determine the historical atmospheric pollutant traceability records that match the real-time wind field map and the distribution information, and extract the historical pollution sources of the historical atmospheric pollutant traceability records as the traceability reference position points of the current target atmospheric pollutant.

[0020] Further, after collecting the target index data detected by the collection nodes at each specified detection position in the target detection area, it further includes:

[0021] Construct an enhanced learning sample based on the target index data and the target atmospheric pollutant, and train the atmospheric pollutant feature detection model based on the enhanced learning sample.

[0022] In a second aspect, an embodiment of the present application provides an accurate traceability analysis system for atmospheric pollutants based on artificial intelligence, including:

[0023] A model detection module, configured to obtain the atmospheric component data information of the target detection area, input the atmospheric component data information into a pre-constructed atmospheric pollutant feature detection model, and output the target atmospheric pollutant and the corresponding target associated industry information of the target detection area based on the atmospheric pollutant feature detection model. The atmospheric pollutant feature detection model is pre-constructed with training samples based on the atmospheric component feature data of different atmospheric pollutants and the corresponding associated industry information, and the model is trained based on the training samples;

[0024] An index collection module, configured to determine the target index data to be detected based on the target atmospheric pollutant in the target detection area, and collect the target index data detected by the collection nodes at each specified detection position in the target detection area;

[0025] A distribution analysis module, configured to obtain the real-time wind field map of the target detection area, label the target index data on the real-time wind field map based on the specified detection position, and determine the distribution information of the target index data on the real-time wind field map;

[0026] A traceability module, configured to construct a traceability path of the target detection area based on the distribution information, query the target position points that match the target associated industry information within the set range of the traceability path, and perform pollution source location warning of the target atmospheric pollutant based on the target position points.

[0027] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0028] A memory and one or more processors;

[0029] The memory is used to store one or more programs;

[0030] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for precise traceability analysis of atmospheric pollutants based on artificial intelligence as described in the first aspect.

[0031] In a fourth aspect, an embodiment of the present application provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute the method for precise traceability analysis of atmospheric pollutants based on artificial intelligence as described in the first aspect when executed by a computer processor.

[0032] In the embodiment of the present application, by obtaining the atmospheric component data information of the target detection area, inputting the atmospheric component data information into a pre-constructed atmospheric pollutant feature detection model, and based on the atmospheric pollutant feature detection model, outputting the target atmospheric pollutants and corresponding target associated industry information of the target detection area. The atmospheric pollutant feature detection model pre-constructs training samples based on the atmospheric component feature data of different atmospheric pollutants and the corresponding associated industry information, and performs model training based on the training samples; determining the target index data to be detected based on the target atmospheric pollutants in the target detection area, and collecting the target index data detected by the acquisition nodes at each specified detection position in the target detection area; obtaining the real-time wind field map of the target detection area, marking the target index data on the real-time wind field map based on the specified detection position, and determining the distribution information of the target index data on the real-time wind field map; constructing a traceability path of the target detection area based on the distribution information, querying target position points that match the target associated industry information within the set range of the traceability path, and performing source location warning of the target atmospheric pollutants based on the target position points. By adopting the above technical means, the target atmospheric pollutants can be accurately detected through the atmospheric pollutant feature detection model, and then the precise traceability of the target atmospheric pollutants can be carried out in combination with the real-time wind field, which can improve the traceability efficiency and traceability accuracy of atmospheric pollutants, timely carry out atmospheric pollution control, and improve the reliability and stability of atmospheric pollution control. Description of the Drawings

[0033] Figure 1 is a flowchart of a method for precise traceability analysis of atmospheric pollutants based on artificial intelligence provided in Embodiment 1 of the present application;

[0034] Figure 2 is an interaction schematic diagram between an acquisition node and a traceability server in Embodiment 1 of the present application;

[0035] Figure 3 is a flowchart for determining a traceability reference position point in Embodiment 1 of the present application;

[0036] Figure 4 is a schematic diagram of the wind field direction and the distribution position of the target atmospheric pollutants in Embodiment 1 of the present application;

[0037] Figure 5 It is a schematic structural diagram of an accurate source tracing and analysis system for air pollutants based on artificial intelligence provided in the second embodiment of the present application;

[0038] Figure 6 It is a schematic structural diagram of an electronic device provided in the third embodiment of the present application. Detailed implementation manners

[0039] In order to make the purpose, technical solutions and advantages of the present application clearer, the following further describes the specific embodiments of the present application in detail with reference to the accompanying drawings. It can be understood that the specific embodiments described herein are only used to explain the present application, rather than limiting the present application. Additionally, it should be noted that for the convenience of description, only the parts related to the present application are shown in the drawings, rather than all the content. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. When the operations are completed, the process can be terminated, but there can also be additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0040] Embodiment 1:

[0041] Figure 1 It gives a flowchart of an accurate source tracing and analysis method for air pollutants based on artificial intelligence provided in the first embodiment of the present application. The accurate source tracing and analysis method for air pollutants based on artificial intelligence provided in this embodiment can be executed by an accurate source tracing and analysis device for air pollutants based on artificial intelligence. The accurate source tracing and analysis device for air pollutants based on artificial intelligence can be implemented in software and / or hardware. The accurate source tracing and analysis device for air pollutants based on artificial intelligence can be composed of two or more physical entities, or can be composed of one physical entity. Generally speaking, the accurate source tracing and analysis device for air pollutants based on artificial intelligence can be a computing device such as an air pollution source tracing server or a computer.

[0042] The following takes the source tracing and analysis device as the main body for executing the accurate source tracing and analysis method for air pollutants based on artificial intelligence as an example for description. Refer to Figure 1 , the accurate source tracing and analysis method for air pollutants based on artificial intelligence specifically includes:

[0043] S110. Obtain the atmospheric component data information of the target detection area, input the atmospheric component data information into a pre-constructed atmospheric pollutant feature detection model, and based on the output of the atmospheric pollutant feature detection model, obtain the target atmospheric pollutants in the target detection area and the corresponding target associated industry information. The atmospheric pollutant feature detection model is pre-trained based on the atmospheric component feature data of different atmospheric pollutants and the corresponding associated industry information, and the model is trained based on the training samples.

[0044] When tracing the source of atmospheric pollutants in this application, by obtaining the atmospheric component data information of the area to be traced, the area to be traced is defined as the target detection area. The detection of atmospheric pollutants is carried out through a pre-constructed atmospheric pollutant feature detection model to determine the atmospheric pollutants present in the atmospheric environment of the target detection area.

[0045] Among them, by deploying atmospheric component monitoring devices at corresponding positions in the target detection area, these devices can measure and record various component data in the atmosphere, such as PM2.5, PM10, SO2, NOx, VOCs, etc. By collecting the atmospheric component data from these monitoring devices in real time and transmitting it to the source tracing analysis device in real time. The source tracing analysis device first cleans and preprocesses the collected atmospheric component data, including removing outliers, filling in missing values, correcting measurement errors, etc., to ensure the quality and accuracy of the data. Then, the detection of atmospheric pollutants is carried out through a pre-constructed atmospheric pollutant feature detection model.

[0046] Before that, a pre-designed atmospheric pollutant feature detection model based on deep learning or machine learning is used. This model can receive atmospheric component data as input and output the types of identified atmospheric pollutants and possible associated industry information.

[0047] When training the atmospheric pollutant feature detection model, select the atmospheric component feature data containing different atmospheric pollutants from the historical atmospheric pollutant source tracing database as input features. For each input sample, the corresponding atmospheric pollutant type and associated industry information need to be marked. The associated industry information can be determined through expert knowledge, industry surveys or historical pollution event records. Then, the processed atmospheric component feature data and associated industry information are combined into a training data set, where each sample contains a set of atmospheric component feature data and its corresponding associated industry information.

[0048] Design a suitable detection model for the characteristics of air pollutants according to the complexity of the problem and the characteristics of the data. This model can be machine learning-based (such as decision trees, random forests, gradient boosting machines, etc.) or deep learning-based (such as convolutional neural networks, recurrent neural networks, etc.). This application does not impose a fixed limit on the selection of the specific model architecture and will not elaborate here. Through model training, enable it to capture the correlation between air component data and the types of air pollutants and related industries. Then use the training dataset to train the model. During the training process, the model will learn how to map the input air component feature data to the output air pollutant types and related industry information. By continuously adjusting the parameters and structure of the model, minimize the prediction error and improve the generalization ability of the model.

[0049] After the model is implemented, when conducting air pollutant detection, input the real-time or newly collected air component data information into the trained air pollutant characteristic detection model. The model outputs the types of target air pollutants and the corresponding target related industry information in the target detection area according to the input air component feature data. This information can be used as the basis for subsequent traceability analysis.

[0050] Optionally, with the emergence of new types of air pollutants and related industries, and the continuous development of monitoring equipment and technologies, it is necessary to regularly update and maintain the air pollutant characteristic detection model to ensure its accuracy and effectiveness. By continuously collecting new air component data and using these data to verify and calibrate the prediction results of the model, improve the stability and reliability of the model. In some embodiments, the air component data can also be fused with other relevant data (such as meteorological data, traffic data, industrial emission data, etc.) to further improve the accuracy and comprehensiveness of air pollutant traceability analysis.

[0051] S120. Determine the target index data to be detected based on the target air pollutants in the target detection area, and collect the target index data detected by the acquisition nodes at each specified detection position in the target detection area.

[0052] After determining the target air pollutants in the target detection area, it is necessary to clarify the target index data to be detected according to the target air pollutants and collect this data. These target index data are usually closely related to specific air pollutants and are important bases for evaluating pollutant concentration, distribution, and change trends.

[0053] First, conduct an in-depth analysis of the identified target air pollutants to understand their physical and chemical properties and their impacts on the environment and human health. Based on the analysis results, determine the key indicators closely related to the target air pollutants. For example, for PM2.5, the key indicators may include its concentration, particle size distribution, chemical composition, etc.; for SO2, the key indicators may be its concentration and emission rate. In addition, the target indicator data can also be some associated pollutant components, such as the related sulfides that SO2 may convert to in the atmosphere. According to relevant laws, regulations, environmental quality standards or scientific research needs, set the detection standards and thresholds for the target indicator data accordingly. Then, in the target detection area, send a collection instruction to a large number of pre-set collection nodes in the target detection area to instruct the collection nodes to collect the target indicator data. The system will deploy appropriate monitoring devices on each collection node. The monitoring devices integrate various types of sensors for real-time collection of the target indicator data. These sensors can include gas analyzers, particulate samplers, weather stations, etc. By collecting the target indicator data at the corresponding specified detection positions in the target detection area through a large number of collection nodes, the distribution of the target air pollutants can be comprehensively and accurately reflected. Then, transmit the collected data to the traceability analysis device in real time to ensure the security and accessibility of the data for subsequent data analysis and processing.

[0054] Exemplarily, referring to Figure 2 , in the target detection area A, by pre-setting collection nodes 12 at each specified detection position, the collection nodes 12 communicate with the traceability analysis device 11. When receiving the collection instruction from the traceability analysis device 11, it can collect the target indicator data at the corresponding target detection position and summarize it to the traceability analysis device 11 in real time.

[0055] Optionally, during the data collection process, adopt strict data quality control measures, specifically including equipment calibration, data verification, and outlier processing, etc., to ensure the accuracy and reliability of the data. In addition, ensure that the collected data has a complete time series and spatial distribution for subsequent spatio-temporal analysis and pollution source tracking. For the key target indicator data, a real-time monitoring and early warning system should be established so as to issue an alarm in a timely manner when the data exceeds the preset threshold and take corresponding countermeasures. Through the above steps, the key indicator data of the target air pollutants in the target detection area can be efficiently collected, providing strong data support for subsequent air pollutant traceability analysis.

[0056] Optionally, after collecting the target indicator data detected by the collection nodes at each specified detection position in the target detection area, it further includes:

[0057] Construct an enhanced learning sample based on the target indicator data and the target air pollutants, and train an air pollutant feature detection model based on the enhanced learning sample.

[0058] After collecting the target index data detected by the acquisition nodes at each specified detection position in the target detection area, these data can be further used to optimize or update the atmospheric pollutant characteristic detection model. Among them, according to the collected target index data and the known information of target atmospheric pollutants, corresponding labels are generated for each sample. These labels may include the types of atmospheric pollutants, concentration levels, pollution source types, etc. To improve the generalization ability of the model, the original data can be enhanced. The methods of data enhancement include but are not limited to noise addition, data transformation (such as rotation, scaling, translation), synthesizing new samples (generating new samples based on known samples while maintaining key features), etc. These enhanced data will together with the original data constitute the reinforcement learning sample set. Then, the reinforcement learning sample set is screened to remove invalid or low-quality samples to ensure the accuracy and representativeness of the training data.

[0059] After that, the model parameters are fine-tuned or optimized based on supervised learning (such as neural networks, decision trees, etc.) and reinforcement learning. The atmospheric pollutant characteristic detection model is trained by using the reinforcement learning sample set. During the training process, the model will learn how to extract features from the input target index data and predict corresponding information such as the types and concentrations of atmospheric pollutants. By continuously adjusting the parameters and structure of the model, the prediction error is minimized and the accuracy of the model is improved. During or after the training process, an independent validation set and test set can also be used to validate and test the model to evaluate the performance of the model and discover possible problems or deficiencies. According to the results of validation and testing, the model is further optimized, such as adjusting model parameters, improving model structure, using more complex feature extraction methods, etc.

[0060] By constructing reinforcement learning samples based on target index data and target atmospheric pollutants and training a more accurate and adaptable atmospheric pollutant characteristic detection model, the tracing efficiency and accuracy of atmospheric pollutants can be improved.

[0061] S130. Obtain the real-time wind field map of the target detection area, label the target index data on the real-time wind field map based on the specified detection positions, and determine the distribution information of the target index data on the real-time wind field map.

[0062] Furthermore, based on the determined target index data, by labeling it on the real-time wind field map of the target detection area, the correlation between the target index data and the real-time wind field can be observed. To obtain the real-time wind field map of the target detection area and label the target index data on the map to determine the data distribution information, first, the real-time wind field map of the current target detection area needs to be obtained from data sources such as meteorological platforms through interface calls and other means.

[0063] Optionally, when obtaining the real-time wind field map of the target detection area, the real-time wind field map can also be drawn using data such as the real-time wind speed and wind direction of the target detection area provided by reliable meteorological data sources. These data can come from weather stations, meteorological satellites, radar systems, or professional meteorological service providers, etc. Receive real-time wind field data through API interfaces, data transfer protocols (such as FTP, HTTP, etc.), or directly from meteorological devices. Process the received wind field data, including the conversion of wind speed and wind direction (such as converting from vector form to the format required for graphical representation), interpolation processing (to fill data gaps or improve data resolution), etc. Then use GIS (Geographic Information System), meteorological software, or programming tools (such as libraries like Python's Matplotlib, Plotly, etc.) to draw the real-time wind field map. The generated real-time wind field map can clearly display the distribution of wind speed and wind direction, and the wind speed and wind direction can be represented by the size and direction of arrows.

[0064] Based on the real-time wind field map of the target detection area, display the target index data on the real-time wind field map using appropriate annotation methods. Among them, it is achieved by adding color-coded points, circles, icons, or other graphical elements on the wind field map. The color, size, or shape of each element can represent different target index values (such as pollutant concentration, particulate matter quantity, etc.). Place the annotation elements at the corresponding detection positions to ensure that the elements are coordinated with the wind speed and wind direction information on the real-time wind field map. After completing the data annotation, analysts can directly observe the annotation elements on the real-time wind field map to understand the distribution of the target index data in the detection area. For example, information such as high-concentration pollutant areas and pollutant diffusion paths can be observed. Optionally, based on the real-time wind field map with this annotation data, GIS tools or data analysis software can also be used to further analyze the annotation data and calculate quantitative indicators such as pollutant diffusion speed and influence range. Through the above steps, the real-time wind field map of the target detection area can be combined with the target index data to intuitively display the distribution of atmospheric pollutants, providing strong support for subsequent pollution source tracking and pollution control.

[0065] Optionally, referring to Figure 3 , after determining the distribution information of the target index data on the real-time wind field map, it further includes:

[0066] S1301. Query the historical atmospheric pollutant traceability record database to determine the historical atmospheric pollutant traceability records that match the real-time wind field map and the distribution information;

[0067] S1302. Extract the historical pollution sources of the historical atmospheric pollutant traceability records to serve as the traceability reference position points for the current target atmospheric pollutants.

[0068] After determining the distribution information of the target index data on the real-time wind field map, in order to further narrow down the scope of the pollution source and respond quickly, this application queries the historical atmospheric pollutant traceability record database and extracts relevant information as the traceability reference location points for the current target atmospheric pollutants. Among them, by accessing the database for storing historical atmospheric pollutant traceability records, this database contains various data such as detailed pollution source information, pollutant types, occurrence times, influence ranges, wind field conditions, etc. When querying the database, according to the characteristics of the real-time wind field map (such as wind speed, wind direction, pollutant distribution pattern) and the distribution information of the target index data (such as the location of the pollutant concentration peak, diffusion path, etc.), conditions for querying the historical database are constructed. These conditions can filter out historical records similar or matching the current situation.

[0069] Furthermore, the constructed query conditions are submitted to the database management system to execute the query operation. The historical atmospheric pollutant traceability record that best matches the current situation is filtered out from the query results. The filtering process can be achieved based on the evaluation and comparison of similarity, matching degree, or probability. Finally, key information is extracted from the filtered historical records, especially the location, type, emission characteristics, etc. of the historical pollution sources. These information will be used as the traceability reference location for the current target atmospheric pollutants. The extracted historical pollution source information is combined with the real-time wind field map and the distribution information of the target index data for data integration and comparison to form a more comprehensive basis for pollution source traceability analysis. In this way, the information in the historical atmospheric pollutant traceability record database can be effectively utilized to provide strong reference and support for the traceability of the current target atmospheric pollutants.

[0070] S140. Construct a traceability path for the target detection area based on the distribution information, query the target location points that match the target associated industry information within the set range of the traceability path, and perform early warning of the pollution source location of the target atmospheric pollutants based on the target location points.

[0071] Finally, based on the distribution information of the target index data on the real-time wind field map, by constructing a traceability path for the target detection area and further querying the target location points that match the target associated industry information, the early warning of the pollution source location of the target atmospheric pollutants is realized.

[0072] Among them, optionally constructing a traceability path for the target detection area based on the distribution information includes:

[0073] Construct a distribution path for the target atmospheric pollutants according to the distribution information, and determine the section on the distribution path that matches the wind direction of the real-time wind field map as the traceability path of the target detection area.

[0074] By analyzing the distribution information of the target indicator data on the real-time wind field map, especially the key parameters such as the peak value of pollutant concentration, diffusion direction, and diffusion speed, it is determined whether the distribution direction of the distribution information matches a certain wind direction path on the real-time wind field map based on the wind field data and pollutant distribution information. If so, this matching section is used as the traceability path of the target detection area.

[0075] Reference Figure 4 In the target detection area A, the wind direction a of the corresponding area is obtained according to the real-time wind field map. Similarly, the position b of the distribution information on the wind direction a path is determined according to the distribution information on the real-time wind field map, that is, the distribution information position b matches the wind direction a. Then the section where the distribution information b matches the wind direction a is used as the tracing path of the target detection area. It should be noted that if the number of matches between the distribution information b and the wind direction a is small, the determination of the path can be ignored. The tracing path is determined only when the matching amount between the distribution information b and the wind direction a reaches the set threshold. By drawing possible tracing paths on the real-time wind field map, these paths may cover all directions from which pollutants may come, and take into account changes in wind speed and wind direction as well as the influence of topography.

[0076] Then, within the set range of the traceability path, query the target location points that match the target-related industry information corresponding to the target air pollutant. These location points should be the sources of pollutants such as enterprises or factories. According to the query results, the possible traceability location points are prioritized. The sorting basis may include the distance from the pollution source, the contribution rate of the pollutant concentration, the historical pollution record, etc. The location points with the highest ranking are defined as the target location points, so as to carry out the pollution source location warning of the target air pollutant based on the target location points. Based on the target location point, the analyst can conduct on-site verification to confirm whether there is pollutant emission behavior and whether the emission intensity is consistent with the real-time monitoring data. If it is confirmed that there is a pollution source and its emission behavior is consistent with the monitored pollutant distribution information, the warning information will be issued immediately. The warning information should include the location of the pollution source, the type of pollutant, the concentration range, the possible affected area and the recommended protective measures. After the warning is issued, continue to monitor the target detection area in real time, and pay attention to the changes in the pollutant concentration and the diffusion trend. If the pollutant concentration continues to rise or the diffusion range expands, the emergency plan can be immediately activated and necessary measures can be taken to reduce the emission and diffusion of pollutants. In addition, analytical equipment can share monitoring data and warning information with relevant departments, enterprises and the public in a timely manner so that everyone can jointly address air pollution issues, thereby achieving accurate tracing of air pollutants and early warning prompts.

[0077] Optionally, a traceability path of the target detection area is constructed based on the distribution information, including:

[0078] Construct a pollution concentration gradient change path for the target air pollutant according to the distribution information. When the direction of the pollution concentration gradient change path matches the wind direction in the real-time wind field map, determine the pollution concentration gradient change path as the traceability path of the target detection area.

[0079] In the process of constructing the traceability path of the target detection area based on the distribution information above, by collecting real-time concentration data on the target air pollutant, and then using GIS (Geographic Information System) or other mapping tools, plot the collected concentration data into a distribution map to visually display the spatial distribution of the pollutant. On the distribution map, identify the gradient changes of the pollutant concentration from high to low, and these gradient changes indicate the diffusion direction of the pollutant. Connect the points with gradient changes in the concentration data on the distribution map from high to low to form one or more possible distribution paths. Then compare the constructed distribution paths with the wind direction on the real-time wind field map to find the sections that conform to the wind direction diffusion law. By screening out the sections that match the wind direction from the comparison results, use these sections as the traceability path of the pollutant diffusion. It can be understood that the diffusion characteristics of the pollutant may change in a gradient along the wind direction. Therefore, by constructing the gradient change distribution path of the pollutant and then constructing an accurate traceability path by matching the real-time wind direction, it can provide strong support for the rapid positioning and effective prevention and control of the pollution source.

[0080] In addition, after the pollution source location warning of the target air pollutant based on the target location point, it further includes:

[0081] Construct a detection path according to the target location point and the wind direction data of the real-time wind field map, drive the unmanned device to collect the pollution concentration change information of the target air pollutant corresponding to the detection path, and conduct traceability location verification on the target location point based on the pollution concentration change information.

[0082] After the pollution source location warning of the target air pollutant based on the target location point, in order to further confirm and verify the accuracy of the pollution source, the present application constructs a detection path according to the target location point and the wind direction data of the real-time wind field map, and uses the unmanned device to collect relevant data for traceability location verification.

[0083] Among them, by extracting the wind direction data around the target location point from the real-time wind field map, special attention is paid to the dominant wind direction and wind speed information. According to the wind direction data and the target location point, one or more detection paths are designed. These paths should extend as much as possible along the direction in which pollutants may spread and cover the possible pollution source areas. At the same time, considering the influence of factors such as terrain and obstacles on the wind direction and wind speed, the paths are reasonably adjusted. According to the requirements of the detection task, appropriate unmanned devices are selected, such as drones, unmanned vehicles or robots, etc. These devices should have environmental monitoring functions and be able to collect the pollution concentration change information of the target atmospheric pollutants. Corresponding sensors, such as gas detectors and particulate matter detectors, are configured on the unmanned devices to ensure that the concentration of the target atmospheric pollutants can be accurately measured. Task parameters are set for the unmanned devices, including flight / travel routes, sampling frequencies, data transmission methods, etc., to ensure that they can collect data according to the preset detection paths. Then, according to the preset detection paths, the unmanned devices are driven to collect data. The unmanned devices will move along the paths and measure the pollution concentration at the preset sampling points. While collecting data, the unmanned devices transmit the data to the traceability analysis device through wireless communication. In this way, the pollution concentration change situation at each point on the detection path can be understood in real time.

[0084] According to the pollution concentration change information collected by the unmanned devices, analyze the diffusion law and concentration distribution of the pollutants on the detection path. Then, compare the analysis results with the target location point to verify whether the target location point is indeed the location of the pollution source. If the pollution concentration change trend on the detection path is consistent with the target location point and an obvious concentration peak appears near this location, it can be considered that the target location point is the location of the pollution source. In addition, in addition to the pollution concentration change information, other evidence (such as on-site investigation results, enterprise emission records, etc.) can be combined for comprehensive evaluation to further improve the accuracy and reliability of the traceability positioning.

[0085] Furthermore, after driving the unmanned devices to collect the pollution concentration change information of the target atmospheric pollutants corresponding to the detection path, it also includes:

[0086] Based on the fact that the pollution concentration drop at the designated location point of the detection path is detected to reach the set threshold based on the pollution concentration change information, a warning for the source location of the target atmospheric pollutants is issued based on the designated location point.

[0087] After driving an unmanned device to collect information on the change in the pollution concentration of the target air pollutant corresponding to the detection path, through a detailed analysis of the pollution concentration change information collected by the unmanned device, especially paying attention to the concentration data at each point on the detection path. On the detection path, calculate the pollution concentration drop between adjacent sampling points or specific sections. According to historical data, environmental standards or expert opinions, set a reasonable pollution concentration drop threshold. This threshold is used to judge whether the change in pollution concentration is significant, thus indicating the existence of a pollution source.

[0088] By screening out those sampling points or sections on the detection path where the pollution concentration drop reaches or exceeds the set threshold. From these screened points or sections, further determine one or more designated location points that are most likely to indicate the existence of a pollution source. These location points usually have significant concentration change characteristics, such as sharply rising or falling concentration values. Once the designated location points are determined and it is confirmed that their pollution concentration drop reaches the set threshold, a pollution source location warning can be issued. The warning information can include the specific location of the designated location points, the change in pollution concentration, the possible type of pollution source, and recommended countermeasures, etc.

[0089] It can be understood that affected by the real-time wind field, the target air pollutant will diffuse along the wind field direction. Therefore, at the starting point of the diffusion, there must be a certain pollution concentration drop situation of the target air pollutant. By detecting this situation, the pollution source of the target air pollutant can be located and warned in a timely and accurate manner, providing strong support for subsequent treatment and prevention work.

[0090] As described above, by obtaining the atmospheric component data information of the target detection area, inputting the atmospheric component data information into a pre-constructed atmospheric pollutant feature detection model, and based on the output of the atmospheric pollutant feature detection model, the target atmospheric pollutants and corresponding target associated industry information of the target detection area are obtained. The atmospheric pollutant feature detection model pre-constructs training samples based on the atmospheric component feature data of different atmospheric pollutants and the corresponding associated industry information, and conducts model training based on the training samples; based on the target atmospheric pollutants in the target detection area, the target index data to be detected is determined, and the target index data detected by the acquisition nodes at each specified detection position in the target detection area is collected; the real-time wind field map of the target detection area is obtained, and based on the specified detection position, the target index data is marked on the real-time wind field map to determine the distribution information of the target index data on the real-time wind field map; based on the distribution information, the traceability path of the target detection area is constructed, and within the set range of the traceability path, the target position points matching the target associated industry information are queried, and based on the target position points, the pollution source positioning warning of the target atmospheric pollutants is carried out. By adopting the above technical means, the target atmospheric pollutants can be accurately detected through the atmospheric pollutant feature detection model, and then the accurate traceability of the target atmospheric pollutants can be carried out in combination with the real-time wind field, which can improve the traceability efficiency and accuracy of atmospheric pollutants, timely carry out atmospheric pollution control, and improve the reliability and stability of atmospheric pollution control.

[0091] Embodiment 2:

[0092] Based on the above embodiment, Figure 5 FIG. is a schematic structural diagram of an accurate traceability analysis system for atmospheric pollutants based on artificial intelligence provided in Embodiment 2 of the present application. Refer to Figure 5 , the accurate traceability analysis system for atmospheric pollutants based on artificial intelligence provided in this embodiment specifically includes: a model detection module 21, an index acquisition module 22, a distribution analysis module 23, and a traceability module 24.

[0093] Among them, the model detection module 21 is used to obtain the atmospheric component data information of the target detection area, input the atmospheric component data information into a pre-constructed atmospheric pollutant feature detection model, and based on the output of the atmospheric pollutant feature detection model, output the target atmospheric pollutants and corresponding target associated industry information of the target detection area. The atmospheric pollutant feature detection model pre-constructs training samples based on the atmospheric component feature data of different atmospheric pollutants and the corresponding associated industry information, and conducts model training based on the training samples;

[0094] The index acquisition module 22 is used to determine the target index data to be detected based on the target air pollutants in the target detection area, and acquire the target index data detected by the acquisition nodes at each specified detection position in the target detection area;

[0095] The distribution analysis module 23 is used to obtain the real-time wind field map of the target detection area, mark the target index data on the real-time wind field map based on the specified detection position, and determine the distribution information of the target index data on the real-time wind field map;

[0096] The traceability module 24 constructs the traceability path of the target detection area based on the distribution information, queries the target position points that match the target associated industry information within the set range of the traceability path, and performs the pollution source location warning of the target air pollutants based on the target position points.

[0097] Specifically, after acquiring the target index data detected by the acquisition nodes at each specified detection position in the target detection area, it further includes:

[0098] Construct an enhanced learning sample based on the target index data and the target air pollutants, and train the air pollutant feature detection model based on the enhanced learning sample. Specifically,

[0099] Specifically, after determining the distribution information of the target index data on the real-time wind field map, it further includes:

[0100] Query the historical air pollutant traceability record database, determine the historical air pollutant traceability record that matches the real-time wind field map and the distribution information, and extract the historical pollution source of the historical air pollutant traceability record as the traceability reference position point of the current target air pollutant.

[0101] Specifically, constructing the traceability path of the target detection area based on the distribution information includes:

[0102] Construct the distribution path of the target air pollutants according to the distribution information, and determine the section on the distribution path that matches the wind direction of the real-time wind field map as the traceability path of the target detection area.

[0103] Specifically, constructing the traceability path of the target detection area based on the distribution information includes:

[0104] Construct the pollution concentration gradient change path of the target air pollutants according to the distribution information. When the pollution concentration gradient change path matches the wind direction of the real-time wind field map, determine the pollution concentration gradient change path as the traceability path of the target detection area.

[0105] Specifically, after performing the pollution source location warning of the target air pollutants based on the target position points, it further includes:

[0106] Construct a detection path based on the target position point and the wind direction data in the real-time wind field map, drive the unmanned device to collect the pollution concentration change information of the target air pollutant corresponding to the detection path, and verify the source location of the target position point based on the pollution concentration change information.

[0107] Specifically, after driving the unmanned device to collect the pollution concentration change information of the target air pollutant corresponding to the detection path, it further includes:

[0108] Based on the pollution concentration change information, when it is detected that the pollution concentration drop at the specified position point of the detection path reaches the set threshold, a source location warning for the target air pollutant is carried out based on the specified position point.

[0109] As described above, by obtaining the atmospheric component data information of the target detection area, inputting the atmospheric component data information into a pre-constructed atmospheric pollutant characteristic detection model, and outputting the target air pollutant and the corresponding target associated industry information of the target detection area based on the atmospheric pollutant characteristic detection model. The atmospheric pollutant characteristic detection model pre-constructs training samples based on the atmospheric component characteristic data of different air pollutants and the corresponding associated industry information, and conducts model training based on the training samples; determines the target index data to be detected based on the target air pollutant in the target detection area, and collects the target index data detected by the acquisition nodes at each specified detection position in the target detection area; obtains the real-time wind field map of the target detection area, marks the target index data on the real-time wind field map based on the specified detection position, and determines the distribution information of the target index data on the real-time wind field map; constructs a traceability path of the target detection area based on the distribution information, queries for target position points that match the target associated industry information within the set range of the traceability path, and conducts a source location warning for the target air pollutant based on the target position points. By adopting the above technical means, the target air pollutant can be accurately detected through the atmospheric pollutant characteristic detection model, and then the accurate traceability of the target air pollutant can be carried out in combination with the real-time wind field, which can improve the traceability efficiency and traceability accuracy of air pollutants, timely carry out air pollution control, and improve the reliability and stability of air pollution control.

[0110] The accurate traceability analysis system for air pollutants based on artificial intelligence provided in the second embodiment of the present application can be used to execute the method for accurate traceability analysis of air pollutants based on artificial intelligence provided in the first embodiment, and has the corresponding functions and beneficial effects.

[0111] Embodiment Three:

[0112] The third embodiment of the present application provides an electronic device, referring to Figure 6, the electronic device includes: a processor 31, a memory 32, a communication module 33, an input device 34, and an output device 35. The number of processors in the electronic device may be one or more, and the number of memories in the electronic device may be one or more. The processor, memory, communication module, input device, and output device of the electronic device may be connected through a bus or other means.

[0113] As a computer-readable storage medium, the memory can be used to store software programs, computer-executable programs, and modules, such as program instructions / modules corresponding to the artificial intelligence-based precise traceability analysis method of air pollutants described in any embodiment of the present application (for example, the model detection module, index collection module, distribution analysis module, and traceability module in the artificial intelligence-based precise traceability analysis system of air pollutants). The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the device. In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some instances, the memory may further include a memory remotely set relative to the processor, and these remote memories can be connected to the device through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0114] The communication module is used for data transmission.

[0115] The processor executes various functional applications and data processing of the device by running software programs, instructions, and modules stored in the memory, that is, implements the above-mentioned artificial intelligence-based precise traceability analysis method of air pollutants.

[0116] The input device can be used to receive input digital or character information, and generate key signal inputs related to the user settings and function control of the device. The output device may include a display device such as a display screen.

[0117] The above-provided electronic device can be used to execute the artificial intelligence-based precise traceability analysis method of air pollutants provided in the first embodiment, and has corresponding functions and beneficial effects.

[0118] Embodiment 4:

[0119] The embodiment of the present application further provides a storage medium containing computer-executable instructions, which are used to execute an accurate traceability analysis method of air pollutants based on artificial intelligence when executed by a computer processor. The accurate traceability analysis method of air pollutants based on artificial intelligence includes: obtaining the air component data information of the target detection area, inputting the air component data information into a pre-constructed air pollutant feature detection model, and outputting the target air pollutants and corresponding target associated industry information of the target detection area based on the air pollutant feature detection model. The air pollutant feature detection model is pre-constructed with training samples based on the air component feature data of different air pollutants and the corresponding associated industry information, and the model is trained based on the training samples; determining the target index data to be detected based on the target air pollutants in the target detection area, and collecting the target index data detected by the acquisition nodes at each specified detection position in the target detection area; obtaining the real-time wind field map of the target detection area, marking the target index data on the real-time wind field map based on the specified detection position, and determining the distribution information of the target index data on the real-time wind field map; constructing a traceability path of the target detection area based on the distribution information, querying for target location points that match the target associated industry information within the set range of the traceability path, and performing source location warning of the target air pollutants based on the target location points.

[0120] Storage medium - Any of various types of memory devices or storage devices. The term "storage medium" is intended to include: installation media such as CD-ROMs, floppy disks or magnetic tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory such as flash memory, magnetic media (such as hard disks or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. Additionally, the storage medium may be located in a first computer system in which the program is executed, or may be located in a different second computer system that is connected to the first computer system via a network (such as the Internet). The second computer system may provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media residing at different locations (such as in different computer systems connected via a network). The storage medium may store program instructions (such as specifically implemented as a computer program) executable by one or more processors.

[0121] Of course, for a storage medium containing computer-executable instructions provided in an embodiment of the present application, the computer-executable instructions are not limited to the above-mentioned artificial intelligence-based precise traceability analysis method for atmospheric pollutants, and can also execute relevant operations in the artificial intelligence-based precise traceability analysis method for atmospheric pollutants provided in any embodiment of the present application.

[0122] The artificial intelligence-based precise traceability analysis system, storage medium, and electronic device provided in the above embodiments can execute the artificial intelligence-based precise traceability analysis method provided in any embodiment of the present application. For technical details not described in detail in the above embodiments, reference can be made to the artificial intelligence-based precise traceability analysis method provided in any embodiment of the present application.

[0123] The above is only a preferred embodiment of the present application and the technical principles applied. The present application is not limited to the specific embodiments described herein. Various obvious changes, re-adjustments, and substitutions that can be made by those skilled in the art will not depart from the protection scope of the present application. Therefore, although the present application has been described in detail through the above embodiments, the present application is not limited to the above embodiments. Without departing from the concept of the present application, it can also include more other equivalent embodiments, and the scope of the present application is determined by the scope of the claims.

Claims

1. An accurate source tracing analysis method for atmospheric pollutants based on artificial intelligence, characterized in that include: Acquire atmospheric component data information of a target detection area, input the atmospheric component data information into a pre-constructed atmospheric pollutant characteristic detection model, and output target atmospheric pollutants and corresponding target associated industry information of the target detection area based on the atmospheric pollutant characteristic detection model, wherein the atmospheric pollutant characteristic detection model constructs training samples based on atmospheric component characteristic data of different atmospheric pollutants and corresponding associated industry information in advance, and performs model training based on the training samples, wherein the associated industry information is determined through expert knowledge, industry surveys, or historical pollution event records; Determine the target index data to be detected based on the target air pollutants in the target detection area, and collect the target index data detected by the collection nodes at each designated detection position in the target detection area; Obtain a real-time wind field map of the target detection area, mark the target indicator data on the real-time wind field map based on the designated detection position, and determine the distribution information of the target indicator data on the real-time wind field map, wherein color-coded points, circles, icons or other graphic elements are added to the wind field map, and the color, size or shape of each element represents the value of different target indicator data; query the historical atmospheric pollutant source tracing record database to determine the historical atmospheric pollutant source tracing records that match the real-time wind field map and the distribution information, and extract the historical pollution sources of the historical atmospheric pollutant source tracing records as the tracing reference location points of the current target atmospheric pollutants; Based on the distribution information, a traceability path for the target detection area is constructed, and within the set range of the traceability path, a target location point matching the target-related industry information is searched, and a pollution source location warning for the target air pollutant is performed based on the target location point, wherein the traceability location points found are sorted according to the distance to the pollution source, the contribution rate of the pollutant concentration, and the historical pollution records, and the traceability location points with the highest sorting are determined as the target location points; The constructing the tracing path of the target detection area based on the distribution information includes: The distribution path of the target atmospheric pollutant is constructed according to the distribution information, and the road section on the distribution path that matches the wind direction of the real-time wind field map is determined as the tracing path of the target detection area; wherein, according to the distribution information on the real-time wind field map, when the distribution information exists on the wind direction path of the real-time wind field map, it is determined that the distribution information matches the wind direction path, and when the number of matches between the distribution information and the wind direction path reaches a set threshold, the road section that matches the distribution information and the wind direction path is used as the tracing path of the target detection area; or, The constructing the tracing path of the target detection area based on the distribution information includes: Constructing a pollution concentration gradient change path of the target atmospheric pollutant according to the distribution information, and determining the pollution concentration gradient change path as a tracing path of the target detection area when the pollution concentration gradient change path matches the wind direction of the real-time wind field map; After performing the source location warning of the target air pollutant based on the target location point, the following steps are further included: Construct a detection path according to the target location point and the wind direction data of the real-time wind field map, drive an unmanned device to collect the pollution concentration change information of the target air pollutant corresponding to the detection path, and perform traceability location verification on the target location point based on the pollution concentration change information.

2. The method for accurate tracing and analysis of air pollutants based on artificial intelligence according to claim 1, characterized in that, After driving the unmanned device to collect the pollution concentration change information of the target air pollutant corresponding to the detection path, the following steps are further included: Based on the pollution concentration change information, it is detected that the pollution concentration drop at a specified location point of the detection path reaches a set threshold, and a source location warning of the target air pollutant is performed based on the specified location point.

3. The method for accurate traceability analysis of atmospheric pollutants based on artificial intelligence according to claim 1, characterized in that, After collecting the target index data detected by the collection nodes at each specified detection location in the target detection area, the following steps are further included: Construct an enhanced learning sample based on the target index data and the target air pollutant, and train the air pollutant feature detection model based on the enhanced learning sample.

4. An accurate traceability analysis system for atmospheric pollutants based on artificial intelligence, characterized in that, It includes: A model detection module, which is used to obtain the atmospheric component data information of the target detection area, input the atmospheric component data information into a pre-constructed air pollutant feature detection model, and output the target air pollutant and the corresponding target associated industry information of the target detection area based on the air pollutant feature detection model. The air pollutant feature detection model is pre-constructed with training samples based on the atmospheric component feature data of different air pollutants and the corresponding associated industry information, and the model is trained based on the training samples. The associated industry information is determined through expert knowledge, industry surveys or historical pollution event records; An index collection module, which is used to determine the target index data to be detected based on the target air pollutant in the target detection area, and collect the target index data detected by the collection nodes at each specified detection location in the target detection area; A distribution analysis module, which is used to obtain the real-time wind field map of the target detection area, mark the target index data on the real-time wind field map based on the specified detection location, and determine the distribution information of the target index data on the real-time wind field map. Among them, color-coded points, circles, icons or other graphic elements are added to the wind field map, and the color, size or shape of each element represents the value of different target index data; query the historical air pollutant traceability record database, determine the historical air pollutant traceability record matching the real-time wind field map and the distribution information, and extract the historical pollution source of the historical air pollutant traceability record as the traceability reference location point of the current target air pollutant; The traceability module constructs a traceability path for the target detection area based on the distribution information, queries for target location points that match the target-related industry information within the set range of the traceability path, and performs source location warning for the target air pollutants based on the target location points. Among them, for the traceability location points queried and sorted according to the source distance, pollutant concentration contribution rate, and historical pollution records, the traceability location points ranked higher are determined as the target location points. The constructing the traceability path for the target detection area based on the distribution information includes: Constructing a distribution path for the target air pollutants according to the distribution information, and determining the section that matches the wind direction of the real-time wind field map on the distribution path as the traceability path for the target detection area. Among them, according to the distribution information on the real-time wind field map, when the distribution information exists on the wind direction path of the real-time wind field map, it is determined that the distribution information matches the wind direction path. When the number of matches between the distribution information and the wind direction path reaches the set threshold, the section where the distribution information matches the wind direction path is used as the traceability path for the target detection area; or, The constructing the traceability path for the target detection area based on the distribution information includes: Constructing a pollution concentration gradient change path for the target air pollutants according to the distribution information, and determining the pollution concentration gradient change path as the traceability path for the target detection area when the pollution concentration gradient change path matches the wind direction of the real-time wind field map. After performing the source location warning for the target air pollutants based on the target location points, it further includes: Constructing a detection path according to the target location points and the wind direction data of the real-time wind field map, driving an unmanned device to collect the pollution concentration change information of the target air pollutants corresponding to the detection path, and performing traceability location verification on the target location points based on the pollution concentration change information.

5. An electronic device, characterized in that, It includes: A memory and one or more processors; The memory is used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the accurate traceability analysis method for air pollutants based on artificial intelligence as described in any one of claims 1-3.

6. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions are used to execute the accurate traceability analysis method for air pollutants based on artificial intelligence as described in any one of claims 1-3 when executed by a computer processor.

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