Method, device, equipment and medium for predicting urban traffic atmospheric particulate matter concentration

Abstract

The present application relates to a method, device, equipment and medium for predicting the concentration of particulate matter in urban traffic atmosphere. The method comprises: obtaining multiple urban street scene images and their corresponding geographical location information, PM2.5 concentration of the traffic section, and PM2.5 concentration of the traffic section. 2.5 Concentration; based on PM 2.5 Concentration calculation determines the PM corresponding to the traffic section of the city street view image 2.5 Fluctuation rates in pollution and PM 2.5 Recovery ratio after pollution, based on PM 2.5 Fluctuation rates in pollution and PM 2.5 The recovery rate after pollution is calculated to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 Road section elasticity coefficient; based on the geographical location information, urban environment characteristic factors, fluctuation ratio, recovery ratio, PM 2.5 Road section elasticity coefficient and its corresponding PM 2.5 The concentration of the sample data set is constructed, and the atmospheric particulate matter concentration prediction model trained with the sample data set is used to predict the PM2.5 concentration of the target city’s corresponding traffic section. 2.5 This application can significantly improve the prediction accuracy of atmospheric particulate matter concentration.

Description

Technical Field

[0001] The present application relates to the field of atmospheric pollution detection, and in particular to a method for predicting the concentration of particulate matter in urban traffic atmosphere, a corresponding device, an electronic device, and a computer-readable storage medium. Background Art

[0002] With the rapid development of society and the acceleration of urbanization, air pollution control has become a major issue facing urban development. Among the numerous air pollutants in cities, atmospheric particulate matter is considered one of the main air pollutants, which seriously threatens the sustainable development of the ecological environment and human health.

[0003] Atmospheric particulate matter concentration monitoring is the data basis for conducting atmospheric particulate matter concentration predictions. Currently, atmospheric particulate matter concentration monitoring mainly relies on fixed monitoring stations. Due to the small number of fixed monitoring stations, limited monitoring range, and the existence of monitoring blind spots, in recent years, the integration of mobile monitoring technology into atmospheric particulate matter concentration monitoring has become a new direction. The full coverage of mobile monitoring technology can not only provide more accurate and high-resolution atmospheric particulate matter concentration data, but also reveal the dynamic distribution of atmospheric particulate matter in different urban built environments.

[0004] Atmospheric particulate matter concentration is closely related to urbanization and urban activities, including industrial production, transportation, and energy consumption. Furthermore, the distribution pattern of atmospheric particulate matter concentration is influenced by multiple factors within the urban built environment. For example, the clustering of buildings facing the street significantly impacts the dispersion of atmospheric particulate matter; the layout and height of buildings, among other factors, influence airflow, leading to increased accumulation of atmospheric particulate matter. Therefore, the urban built environment plays a key role in regulating the distribution pattern and concentration of atmospheric particulate matter. Therefore, in-depth research on the impact of the urban built environment on atmospheric particulate matter concentration is of great significance for understanding and addressing urban air pollution.

[0005] Domestic and foreign scholars have carried out a lot of research on atmospheric particulate matter pollution and concentration prediction. In terms of research methods, early studies used simple linear or nonlinear regression models to estimate PM 2.5 However, these methods often fail to fully capture the complexity of the urban built environment and the nonlinear relationship of atmospheric particulate matter concentrations.

[0006] In recent years, many studies have begun to use advanced statistical models to predict PM 2.5 Concentration, such as generalized linear model (GLM), generalized additive model (GAM) and land use regression (LUR), these traditional statistical models are based on data modeling and generate estimates with fixed structure. However, traditional fixed structure statistical models may simplify the PM 2.5Modeling of complex relationships between concentrations and explanatory variables, for example, ignoring interactions between explanatory variables or relationships between explanatory variables and PM 2.5 The nonlinear relationship between concentrations.

[0007] Although domestic and foreign scholars have conducted multi-dimensional and multi-angle discussions and research in the field of atmospheric particulate matter pollution and concentration prediction, and proposed many relatively effective atmospheric particulate matter prediction models, these studies have focused on using land use models or meteorological factors for prediction, and mainly focus on horizontal factors such as road networks, building layouts, and land use types. However, there are still deficiencies and shortcomings:

[0008] First, in PM 2.5 In terms of concentration data collection methods, most studies use traditional fixed monitoring stations for collection. However, fixed monitoring stations have problems such as a small number of stations, limited monitoring range, and blind spots, which make it impossible to provide full-area PM 2.5 Concentration data can be obtained from mobile monitoring in real time at multiple locations. However, existing mobile monitoring mainly relies on handheld mobile sensors, which require high labor costs, or professional atmospheric cruise monitoring systems placed in taxis, which have insufficient coverage in remote areas.

[0009] Second, in predicting PM 2.5 Concentration and characterization of PM 2.5 In terms of diffusion paths, computational fluid dynamics (CFD) simulation methods are mainly used for exploration. While this method simplifies the urban built environment, it provides approximate rather than realistic and accurate prediction results. Although CFD models can provide vertical dimension analysis, they have high hardware requirements, long simulation times, and high prediction costs.

[0010] Third, urban street view images are increasingly being used to measure the vertical dimension of urban street environments. However, urban street view images are mostly used in urban planning, and are rarely used in PM. 2.5 Tracing and predicting research, detailed description of urban built environment, in-depth exploration of the relationship between urban built environment and PM 2.5 The coupling relationship between pollution.

[0011] To sum up, in order to adapt to the problems in the existing technology where mobile monitoring mainly relies on handheld mobile sensors, which have high labor costs and insufficient coverage, and the CFD model has high hardware requirements, long simulation time, and high prediction costs, the applicant has made corresponding explorations to solve these problems. Summary of the Invention

[0012] The purpose of this application is to solve the above problems and provide a method for predicting the concentration of particulate matter in urban traffic atmosphere, a corresponding device, an electronic device and a computer-readable storage medium.

[0013] In order to meet the various objectives of this application, this application adopts the following technical solutions:

[0014] A method for predicting urban traffic atmospheric particulate matter concentration, which is proposed to meet one of the purposes of this application, includes:

[0015] In response to the urban traffic atmospheric particulate matter concentration prediction instruction, obtain multiple city street scene images taken continuously by the vehicle-mounted camera equipment and their corresponding geographical location information, PM2.5 concentration of the traffic section, and the corresponding location information of the traffic section. 2.5 Concentration, wherein the geographic location information includes one or more of POI information, normalized vegetation index, and building density;

[0016] Performing image segmentation on the urban street scene image using an image segmentation model that has been trained to a convergent state to extract mask data corresponding to the outline of target regions in each target region image in the urban street scene image, wherein the target regions include green plant regions, motor vehicle regions, sky regions, building regions, fence regions, pillar regions, and wall regions;

[0017] Calculating and determining the number of pixels corresponding to the target area outline in each target area image in the urban street view image based on the mask data, and calculating and determining urban environment characteristic factors based on the number of pixels corresponding to each target area outline, wherein the urban environment characteristic factors include a green view rate, a vehicle interference index, a sky visibility index, and a space enclosure index;

[0018] Based on the PM 2.5 The concentration calculation determines the PM corresponding to the traffic section where the urban street view image is located. 2.5 Fluctuation rates in pollution and PM 2.5 Recovery ratio after pollution, based on the PM 2.5 Fluctuations in pollution and the PM 2.5 The recovery rate after pollution is calculated to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 Section elasticity coefficient;

[0019] Based on the geographical location information, urban environment characteristic factors, fluctuation ratio, recovery ratio, PM 2.5 Road section elasticity coefficient and its corresponding PM 2.5 The concentration of the sample data set is constructed, and the atmospheric particulate matter concentration prediction model trained with the sample data set is used to predict the PM2.5 concentration of the target city’s corresponding traffic section. 2.5 concentration to complete the prediction of urban traffic atmospheric particulate matter concentration.

[0020] Optionally, the step of performing image segmentation on the city street view image using an image segmentation model that has been trained to a convergent state to extract mask data corresponding to the target area contour in each target area image in the city street view image includes:

[0021] Performing multi-level encoding on the target area image and generating corresponding scale intermediate feature information, wherein the intermediate feature information is a feature representation of the information to be processed in the target area image;

[0022] Multi-level decoding is performed accordingly on the decoding path, and the first image feature information is produced with the intermediate feature information of the smallest scale. In addition, the image feature information of the previous level and the intermediate feature information produced by the encoding of the same level are used as references to decode the image feature information of higher scales accordingly. The image feature information is used to represent the contour features of the information to be processed in the target area image in the form of a mask;

[0023] All image feature information is fused to generate mask data of the target contour area in the target area image.

[0024] Optionally, the step of calculating and determining the number of pixels corresponding to the target area outline in each target area image in the urban street view image based on the mask data, and calculating and determining the urban environment characteristic factor based on the number of pixels corresponding to each target area outline includes:

[0025] Calculating and determining the number of pixels corresponding to the green plant area outline, the motor vehicle area outline, the sky area outline, the building area outline, the fence area outline, the pillar area outline, and the wall area outline in the urban street view image based on mask data corresponding to the green plant area outline, the motor vehicle area outline, the sky area outline, the building area outline, the fence area outline, the pillar area outline, and the wall area outline in the urban street view image;

[0026] calculating and determining a first ratio between the number of pixels corresponding to the green plant area outline and the total number of pixels in the urban street view image, and using the first ratio as the green viewing rate to determine the green viewing rate;

[0027] calculating and determining a second ratio between the number of pixels corresponding to the motor vehicle area outline and the total number of pixels in the urban street view image, and using the second ratio as a vehicle interference index to determine the vehicle interference index;

[0028] calculating and determining a third ratio between the number of pixels corresponding to the sky area outline and the total number of pixels in the urban street view image, and using the third ratio as a sky visibility index to determine the sky visibility index;

[0029] The spatial enclosure index is determined by calculating the sum of the numbers of pixels corresponding to the building area outline, the green plant area outline, the fence area outline, the pillar area outline, and the wall area outline. Based on a fourth ratio between the sum and the total number of pixels in the urban street view image, the fourth ratio is used as the spatial enclosure index.

[0030] Optional, based on the PM 2.5 The concentration calculation determines the PM corresponding to the traffic section where the urban street view image is located. 2.5 The steps for the fluctuation ratio in pollution include:

[0031] Get the PM of the traffic section where the city street view image is located 2.5 The maximum concentration and PM at a certain time 2.5 concentration;

[0032] Calculate and determine the PM of the traffic section where the city street view image is located 2.5 The highest concentration value and the PM at a certain time in the traffic section where the urban street view image is located 2.5 a first difference between the concentrations;

[0033] Based on the first difference and the PM at a certain time of the traffic section where the urban street view image is located 2.5 The fifth ratio between the concentrations is used as the PM 2.5 The fluctuation ratio of pollution is used to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 Fluctuation ratio in pollution.

[0034] Optional, based on the PM 2.5 The concentration calculation determines the PM corresponding to the traffic section where the urban street view image is located. 2.5 The steps for post-contamination recovery include:

[0035] The traffic section where the city street view image is located is designated as PM 2.5 The PM corresponding to the previous day of pollution 2.5 Concentration, PM removal 2.5 PM corresponding to the day after pollution 2.5 Concentration and PM2.5 concentration of traffic sections in urban street view images 2.5 Maximum concentration;

[0036] Calculate and determine the PM of the traffic section where the city street view image is located 2.5 The highest concentration value is consistent with the PM 2.5 PM corresponding to the day after pollution 2.5 a second difference between the concentrations;

[0037] Based on the second difference and the PM 2.5 The PM corresponding to the previous day of pollution 2.5 The sixth ratio between the concentrations is used as the PM 2.5 The recovery ratio after pollution is used to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 Recovery ratio after contamination.

[0038] Optional, based on the PM 2.5 Fluctuations in pollution and the PM 2.5 The recovery rate after pollution is calculated to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 The steps for calculating the elastic coefficient of a road section include:

[0039] Determine the PM corresponding to the traffic section where the city street view image is located 2.5 The fluctuation ratio in pollution and the PM corresponding to the traffic section where the urban street view image is located 2.5 Recovery rate after contamination;

[0040] Calculate and determine the PM 2.5 The recovery ratio after pollution is related to the PM 2.5 The seventh ratio between the fluctuation ratios in pollution is used as the PM 2.5 The road section elasticity coefficient is used to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 Elastic coefficient of road section.

[0041] Optionally, the vehicle-mounted camera device includes one or more of a driving recorder, a roof camera, a front-facing camera, a rear-facing camera or a side-view camera; the basic network architecture of the image segmentation model is U 2 net model; the basic network architecture of the atmospheric particulate matter concentration prediction model is a machine learning model, and the machine learning model includes a random forest model.

[0042] A device for predicting urban traffic atmospheric particulate matter concentration, provided for another purpose of the present application, comprises:

[0043] The data acquisition module is configured to respond to the urban traffic atmospheric particulate matter concentration prediction instruction and obtain multiple city street scene images continuously shot by the vehicle-mounted camera device and their corresponding geographical location information, PM2.5 concentration of the traffic section, and the corresponding location information of the traffic section. 2.5 Concentration, wherein the geographic location information includes one or more of POI information, normalized vegetation index, and building density;

[0044] a mask data extraction module configured to perform image segmentation on the urban street view image using an image segmentation model that has been trained to a convergent state, so as to extract mask data corresponding to the outline of target areas in each target area image in the urban street view image, wherein the target areas include green plant areas, motor vehicle areas, sky areas, building areas, fence areas, pillar areas, and wall areas;

[0045] an environmental feature determination module configured to calculate and determine, based on the mask data, the number of pixels corresponding to the target area outline in each target area image in the urban street view image, and to calculate and determine an urban environmental feature factor based on the number of pixels corresponding to each target area outline, the urban environmental feature factor including a green view rate, a vehicle interference index, a sky visibility index, and a space enclosure index;

[0046] The elastic coefficient determination module is configured to be based on the PM 2.5 The concentration calculation determines the PM corresponding to the traffic section where the urban street view image is located. 2.5 Fluctuation rates in pollution and PM 2.5 Recovery ratio after pollution, based on the PM 2.5 Fluctuations in pollution and the PM 2.5 The recovery rate after pollution is calculated to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 Section elasticity coefficient;

[0047] PM 2.5 The concentration prediction module is configured to be based on the geographical location information, urban environment characteristic factors, fluctuation ratio, recovery ratio, PM 2.5 Road section elasticity coefficient and its corresponding PM 2.5 The concentration of the sample data set is constructed, and the atmospheric particulate matter concentration prediction model trained with the sample data set is used to predict the PM2.5 concentration of the target city’s corresponding traffic section. 2.5 concentration to complete the prediction of urban traffic atmospheric particulate matter concentration.

[0048] An electronic device provided to meet another purpose of the present application includes a central processing unit and a memory, wherein the central processing unit is used to call and run a computer program stored in the memory to execute the steps of the urban traffic atmospheric particulate matter concentration prediction method described in the present application.

[0049] A computer-readable storage medium is provided to meet another purpose of the present application, which stores a computer program implemented according to the urban traffic atmospheric particulate matter concentration prediction method in the form of computer-readable instructions. When the computer program is called and executed by a computer, the steps included in the corresponding method are executed.

[0050] Compared with the existing technology, this application addresses the problems of existing mobile monitoring technology that mainly relies on handheld mobile sensors, which have high labor costs and insufficient coverage, as well as the high hardware requirements of CFD models, long simulation time, and high prediction costs. This application includes but is not limited to the following beneficial effects:

[0051] First, this application can greatly expand the monitoring range and temporal resolution. By using image analysis and deep learning technology, it breaks through the spatial limitations of traditional fixed monitoring stations and covers a wider urban area. At the same time, through continuous image data collection, it greatly improves the temporal resolution and can capture PM2.5 in a timely manner. 2.5 The dynamic changes of PM concentration, this comprehensive analysis not only enhances the 2.5 The accuracy of pollution source tracking also improves the detection of PM 2.5 Real-time monitoring capability of concentration change trends.

[0052] Secondly, this application uses the geographical location information corresponding to the traffic section in the urban street view image, urban environment characteristic factors, PM 2.5 Fluctuation rate in pollution, PM 2.5 Recovery rate after pollution, PM 2.5 Road section elasticity coefficient and its corresponding PM 2.5 The concentration of the sample data set is constructed, and the atmospheric particulate matter concentration prediction model trained with the sample data set is used to predict the PM2.5 concentration of the target city’s corresponding traffic section. 2.5 concentration, can significantly increase PM 2.5 The prediction accuracy of PM concentration was improved by using comparative selection strategy to build a prediction model and optimizing parameters. 2.5 The accuracy of concentration prediction is improved by comprehensively analyzing historical data and real-time data. The model can adapt to various environmental scenarios, enhancing the stability and reliability of the prediction.

[0053] Third, this application can be applied to vehicle-mounted terminal equipment to achieve PM 2.5 The real-time response and decision support capabilities of pollution enable the urban traffic atmospheric particulate matter concentration prediction system to quickly process real-time updated data and immediately provide PM 2.5 Concentration predictions provide real-time information to city managers, environmental protection workers, and the public. Information can be transmitted efficiently and conveniently through in-vehicle terminals, mobile applications, or web platforms, facilitating prompt action to improve air quality.

[0054] Fourth, the PM of this application 2.5 Fluctuation rate in pollution, PM 2.5 Recovery rate after pollution and PM 2.5 The elasticity coefficient of the road section can be used to quantify the resilience of air quality.2.5 During the concentration process, PM 2.5 Fluctuation rate in pollution, PM 2.5 Recovery rate after pollution and PM 2.5 The road section elasticity coefficient can measure the self-repair ability of air quality under specific conditions, as well as the speed and ability to recover to a better state. 2.5 Fluctuation rate in pollution, PM 2.5 Recovery rate after pollution and PM 2.5 By continuously revising the elastic coefficient of the road section, the prediction model can better adapt to the changes in environmental factors and ensure the stability and reliability of the prediction results.

[0055] Furthermore, the present application proposes an innovative method for tracing and predicting the concentration of atmospheric particulate matter, in response to the lag and roughness problems existing in the traditional prediction process of atmospheric particulate matter concentration. This method is based on a deep learning framework, which transforms and fuses high-resolution urban street view images with satellite remote sensing images, thereby extracting the characteristics of the urban built environment in the image. Compared with the traditional model built based on social panel statistical data, the prediction model architecture of the present application has a shorter time step and a stronger ability to capture spatial details. Specifically, the present application first uses deep learning technology to fuse high-resolution urban street view images and satellite remote sensing images, uses a deep learning image segmentation model to identify street view pictures, extracts places or objects in street view pictures, calculates the pixel coverage of different places or objects, constructs urban environment characteristic factors, extracts urban environment characteristic factors that significantly affect the concentration of atmospheric particulate matter, and then constructs an atmospheric particulate pollution event analysis framework based on urban environment characteristic factors. Finally, PM 2.5 Fluctuation rate in pollution, PM 2.5 Recovery rate after pollution and PM 2.5 Parameters such as road section elasticity coefficient can be used to accurately analyze the changing characteristics of atmospheric particulate matter pollution in time and space.

[0056] Furthermore, the urban traffic atmospheric particulate matter concentration prediction method proposed in this application can effectively solve the lag and roughness problems existing in traditional prediction methods; the urban traffic atmospheric particulate matter concentration prediction method proposed in this application has broad application prospects in the field of intelligent management of atmospheric particulate matter pollution in different scenarios, and is expected to provide effective technical support for improving urban air quality and pollution prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0058] Figure 1This is a flow chart of a method for predicting urban traffic atmospheric particulate matter concentration in an embodiment of the present application;

[0059] Figure 2 This is an interface display diagram of the urban traffic atmospheric particulate matter concentration prediction system in the embodiment of the present application;

[0060] Figure 3 In the embodiment of this application, 2 net model's exemplary network architecture;

[0061] Figure 4 This is a functional block diagram of the device for predicting the concentration of particulate matter in urban traffic atmosphere according to an embodiment of the present application;

[0062] Figure 5 Schematic diagram of the structure of the computer device in the embodiment of the present application. DETAILED DESCRIPTION

[0063] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and are not to be construed as limiting the present application.

[0064] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0065] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0066] It will be understood by those skilled in the art that the terms "client," "terminal," and "terminal device" as used herein include both devices that are wireless signal receivers, i.e., devices that only have wireless signal receivers without transmission capabilities, and devices that have receiving and transmitting hardware capable of two-way communication over a two-way communication link. Such devices may include: cellular or other communication devices such as personal computers and tablet computers, which have single-line displays, multi-line displays, or cellular or other communication devices without multi-line displays; PCS (Personal Communications Service), which may combine voice, data processing, fax, and / or data communication capabilities; PDA (Personal Digital Assistant), which may include a radio frequency receiver, a pager, Internet / Intranet access, a web browser, a notepad, a calendar, and / or a GPS (Global Positioning System) receiver; and conventional laptop and / or palmtop computers or other devices, which have and / or include a radio frequency receiver. As used herein, the terms "client," "terminal," or "terminal device" may be portable, transportable, or installed in a vehicle (air, sea, and / or land), or may be adapted and / or configured to operate locally and / or in a distributed manner at any other location on Earth and / or in space. As used herein, the terms "client," "terminal," or "terminal device" may also refer to a communication terminal, an Internet terminal, or a music / video playback terminal, such as a PDA, an MID (Mobile Internet Device), and / or a mobile phone with music / video playback capabilities, or may include a smart TV, a set-top box, or other device.

[0067] The hardware referred to by names such as "server", "client", and "service node" in this application is essentially an electronic device with capabilities equivalent to those of a personal computer. It is a hardware device that has the necessary components revealed by the von Neumann principle, such as a central processing unit (including an arithmetic unit and a controller), a memory, an input device, and an output device. Computer programs are stored in its memory, and the central processing unit loads the program stored in the external memory into the internal memory for execution, executes the instructions in the program, and interacts with the input and output devices to complete specific functions.

[0068] It should be noted that the concept of "server" referred to in this application can also be extended to server clusters. Based on the network deployment principles understood by those skilled in the art, the servers described should be logically divided. In physical space, these servers can be independent of each other but callable through interfaces, or integrated into a single physical computer or a computer cluster. Those skilled in the art should understand this flexibility and should not use it to constrain the implementation of the network deployment method of this application.

[0069] Unless expressly specified, one or more technical features of the present application can be deployed on a server for implementation and accessed by a client through a remote call to obtain an online service interface provided by the server, or can be directly deployed and run on a client for implementation.

[0070] Unless expressly specified otherwise, the neural network models referenced or may be referenced in this application may be deployed on a remote server and remotely called on the client, or may be deployed and directly called on a client with sufficient device capabilities. In some embodiments, when it runs on the client, its corresponding intelligence may be obtained through transfer learning to reduce the requirements for the client's hardware operating resources and avoid excessive occupation of the client's hardware operating resources.

[0071] Unless explicitly specified, the various data involved in this application can be stored remotely on a server or on a local terminal device, as long as they are suitable for being called by the technical solution of this application.

[0072] Those skilled in the art should be aware that although the various methods of this application are described based on the same concept and thus exhibit commonality, unless otherwise specified, these methods can be independently executed. Similarly, the various embodiments disclosed in this application are all based on the same inventive concept. Therefore, concepts with the same expression, as well as concepts that are appropriately transformed for convenience despite different expression, should be understood as equivalent.

[0073] Unless expressly stated to be mutually exclusive, the various embodiments disclosed in this application may be cross-combined with the relevant technical features of the various embodiments to flexibly construct new embodiments, as long as such combination does not deviate from the creative spirit of this application and can meet the needs of the prior art or resolve certain deficiencies in the prior art. Those skilled in the art should be aware of such flexibility.

[0074] Based on the above example scenarios, please refer to Figure 1 as well as Figure 2 In one embodiment, the method for predicting urban traffic atmospheric particulate matter concentration of the present application includes:

[0075] Step S10: In response to the urban traffic atmospheric particulate matter concentration prediction instruction, obtain multiple city street scene images taken continuously by the vehicle-mounted camera device and their corresponding geographical location information, PM concentration of the traffic section, and the like. 2.5 Concentration, wherein the geographic location information includes one or more of POI information, normalized vegetation index, and building density;

[0076] The urban traffic atmospheric particulate matter concentration prediction system in the vehicle terminal device, mobile terminal device or web platform can respond to the urban traffic atmospheric particulate matter concentration prediction instruction, obtain multiple city street scene images continuously shot by the vehicle camera device and their corresponding geographical location information, PM2.5 of the traffic section, and the corresponding location information of the traffic section. 2.5 Concentration, wherein the geographic location information includes one or any multiple of POI information, normalized vegetation index, and building density; the vehicle-mounted camera equipment includes one or any multiple of a driving recorder, a roof camera, a front-facing camera, a rear-facing camera, or a side-view camera;

[0077] Specifically, the type and source of the urban street view image depend on the actual application scenario. For example, in the application scenario of predicting the concentration of atmospheric particulate matter in urban traffic, the urban street view image can be a static picture specified by the user, or it can be an urban street view image frame in the urban street view video stream submitted by the camera equipment in a moving vehicle, unmanned vehicle or autonomous driving vehicle (such as a driving recorder, roof camera, front-facing camera, rear camera or side-view camera, etc.) to the on-board terminal device. Depending on the specific application scenario, the urban street view image can be determined as needed.

[0078] In some embodiments, the POI information provides the function and purpose of the traffic section where the urban street view image is located, such as business, education, entertainment, etc. This information helps to analyze the economic and social activities of the region to predict PM. 2.5 The normalized vegetation index (NDVI) is an indicator of vegetation coverage and health status, which can reflect the ecological environment of the region. A high NDVI value usually indicates lush vegetation and good environmental quality, which is consistent with the PM2.5 concentration in the traffic section of the urban street view image. 2.5 The building density reflects the utilization degree of urban space in the traffic section of the urban street view image, affecting the population density and traffic flow of the area. High-density areas are often accompanied by PM 2.5 Higher concentration.

[0079] In some embodiments, the real-time positioning capability of the vehicle can be used to send a data request to the map service provider. The request should include specific spatial scope and attribute requirements. After accessing the map service provider, the geographic location information returned by the map service provider is received, which includes POI information, NVDI, building density, etc., to ensure that the collected urban street view images are strictly synchronized with the geographic location information. The urban traffic atmospheric particulate matter concentration prediction system can automatically record the capture time and location of the image, so that the image analysis results are closely coupled with the actual environmental conditions. Through time series analysis, the changes in environmental characteristics on the driving path are combined with the time dimension to analyze PM 2.5 Correlation between concentration and urban environmental characteristic factors.

[0080] Step S20: performing image segmentation on the urban street scene image using the image segmentation model that has been trained to a convergent state to extract mask data corresponding to the target area contours in each target area image in the urban street scene image, wherein the target areas include green plant areas, motor vehicle areas, sky areas, building areas, fence areas, pillar areas, and wall areas;

[0081] Obtain multiple city street scene images taken continuously by the vehicle-mounted camera equipment and their corresponding geographical location information, PM information of the traffic section where they are located, 2.5 After the concentration, the image segmentation model that has been trained to a convergent state is used to segment the urban street scene image to extract mask data corresponding to the target area contour in each target area image in the urban street scene image, wherein the target area includes a green plant area, a motor vehicle area, a sky area, a building area, a fence area, a pillar area and a wall area; the basic network architecture of the image segmentation model can be U 2 net model.

[0082] Furthermore, the step of using the image segmentation model that has been trained to a convergent state to segment the urban street scene image to extract mask data corresponding to the target area contour in each target area image in the urban street scene image includes:

[0083] Step S201: performing multi-level encoding on the target area image and generating corresponding scale intermediate feature information, wherein the intermediate feature information is a feature representation of the information to be processed in the target area image;

[0084] See also Figure 3 , according to the six encoders (En_1 to En_6) of the side branch path, the specification original image corresponding to the urban street scene image, that is, the image adapted to U 2NET model's input image specifications require it to crop the urban street scene image to a specified size, and then encode the original image of this size step by step. The first-level encoder at the top level extracts intermediate feature information corresponding to the first scale from the original image, then passes it to the encoder at the next level to extract intermediate feature information corresponding to the second scale. This continues in this order, with the six-level encoder extracting corresponding intermediate feature information. Thus, after encoding the original image step by step through the encoding path, six intermediate feature information of corresponding spatial resolutions are obtained.

[0085] It can be understood that the intermediate feature information of each scale is the representation obtained after deep semantic understanding of the standard image at the corresponding scale, and is the information extracted from the contour features of the human body in the urban street scene image. 2 This capability of the net model is well known to those skilled in the art. As long as it is trained to convergence with a sufficient number of training samples, its encoding path can have the deep semantic understanding ability to capture the information to be processed from the target area image.

[0086] Step S202: Perform multi-level decoding on the decoding path accordingly, generate first image feature information with the smallest-scale intermediate feature information, and then decode higher-scale image feature information with reference to the image feature information of the previous level and the intermediate feature information generated by the encoding of the same level. The image feature information is used to represent the contour features of the information to be processed in the target area image in the form of a mask;

[0087] See also Figure 3 In the decoding path, among the five decoders (De_1 to De_5) of the right branch path, starting from the bottom layer, each decoder stage takes the upsampled feature map from the previous stage and the cascade of the feature map from its symmetric encoder stage as input, decodes and outputs feature information. The output results of the last encoder stage (En_6) and each decoder stage are subjected to 1*1 convolution kernel transformation, Sigmoid activation function and upsampling operation to extract six image feature information with the same specifications as the original image. The image feature information can represent the green plant area contour information, motor vehicle area contour information, sky area contour information, building area contour information, fence area contour information, pillar area contour information or wall area in the urban street scene image, and the contour features are represented in the form of masks, which are essentially mask image data (Mask1 to Mask6).

[0088] Step S203: Fusing all image feature information to generate mask data of the target contour area in the target area image.

[0089] At this point, based on the disclosure of the structure and principles of the image segmentation model herein, it can be understood that the target area image of the present application, after being encoded and decoded step by step by the image segmentation model, obtains multiple image feature information (mask image data: Mask1 to Mask6), and all image feature information is fused to generate mask data of the target contour area in the target area image.

[0090] Step S30: calculating and determining the number of pixels corresponding to the target area outline in each target area image in the urban street view image based on the mask data, and calculating and determining urban environment characteristic factors based on the number of pixels corresponding to each target area outline, wherein the urban environment characteristic factors include a green view rate, a vehicle interference index, a sky visibility index, and a space enclosure index;

[0091] The urban street view image is segmented using an image segmentation model that has been trained to a convergent state to extract mask data corresponding to the target area contour in each target area image in the urban street view image. The number of pixels corresponding to the target area contour in each target area image in the urban street view image is then calculated and determined based on the mask data. The urban environment characteristic factors are calculated and determined based on the number of pixels corresponding to each target area contour. The urban environment characteristic factors include a green viewing rate, a vehicle interference index, a sky visibility index, and a space enclosure index. It is not difficult to understand that when the mask data corresponding to the target area contour in each target area image in the urban street view image is determined, the number of pixels corresponding to the target area contour in each target area image in the urban street view image is also determined, which will not be elaborated here.

[0092] In some embodiments, the steps of calculating and determining the number of pixels corresponding to the target area outline in each target area image in the urban street view image based on the mask data, and calculating and determining the urban environment characteristic factor based on the number of pixels corresponding to each target area outline include:

[0093] Step S301: calculating and determining the number of pixels corresponding to the green plant area outline, the motor vehicle area outline, the sky area outline, the building area outline, the fence area outline, the pillar area outline, and the wall area outline in the urban street view image based on mask data corresponding to the green plant area outline, the motor vehicle area outline, the sky area outline, the building area outline, the fence area outline, the pillar area outline, and the wall area outline in the urban street view image;

[0094] Step S302: Calculate and determine a first ratio between the number of pixels corresponding to the green plant area outline and the total number of pixels in the urban street view image, and use the first ratio as the green viewing rate to determine the green viewing rate;

[0095] Specifically, the Green View Index (GVI) is used to quantify three-dimensional street greening, evaluate the visibility of urban greening, and reflect people's visual perception of surrounding green spaces in three dimensions. It is defined as the number of green pixels in an urban street view image divided by the total number of pixels. The calculation formula for the Green View Index is as follows:

[0096]

[0097] Where, Area g Area is the number of pixels corresponding to the outline of the green area in the urban street view image. i is the total number of pixels in the urban street view image, and GVI represents the green view rate in the urban street view image.

[0098] Step S303: Calculate and determine a second ratio between the number of pixels corresponding to the motor vehicle area outline and the total number of pixels in the urban street view image, and use the second ratio as a vehicle interference index to determine the vehicle interference index;

[0099] Specifically, the vehicle interference index (VII) is an indicator that measures the concentration of motor vehicles on urban roads. VII reflects the amount of travel by urban residents and greatly affects PM2.5. 2.5 The emission and concentration of the vehicle interference index are calculated as follows:

[0100]

[0101] Where VII represents the vehicle interference index, C n is the number of pixels corresponding to the outline of the motor vehicle area in the nth city street view image, A n is the total number of pixels in the street view image of the nth city.

[0102] Step S304: Calculate and determine a third ratio between the number of pixels corresponding to the sky area outline and the total number of pixels in the city street view image, and use the third ratio as a sky visibility index to determine the sky visibility index;

[0103] Specifically, the sky visibility index (SVI) refers to the degree of streetscape permeability, which has a significant impact on PM 2.5 The spatial circulation has an important impact on the spatial circulation, and its calculation formula is as follows:

[0104]

[0105] Where SVI is the sky visibility index, V n is the number of pixels corresponding to the sky area outline in the nth city street view image, A n is the total number of pixels in the street view image of the nth city.

[0106] Step S305: Calculate and determine the sum of the numbers of pixels corresponding to the building area outline, the green plant area outline, the fence area outline, the pillar area outline, and the wall area outline; and based on a fourth ratio between the sum and the total number of pixels in the urban street view image, use the fourth ratio as a spatial enclosure index to determine the spatial enclosure index.

[0107] Specifically, the spatial enclosure index (SEI) refers to the degree of enclosure of the overall space on both sides of the street. The degree of maintenance determines the PM 2.5 The diffusion efficiency of PM 2.5 It is easier to be trapped in the street space. SEI is determined by the proportion of facade elements (such as buildings, plants and walls), and its calculation formula is as follows:

[0108]

[0109] Where, SEI represents the space enclosure index, B n Indicates the number of pixels corresponding to the building area outline, G n Indicates the number of pixels corresponding to the outline of the green plant area, Q n Indicates the number of pixels corresponding to the outline of the fence area, P n Indicates the number of pixels corresponding to the outline of the column area, F n Indicates the number of pixels corresponding to the outline of the wall area.

[0110] Step S40: Based on the PM 2.5 The concentration calculation determines the PM corresponding to the traffic section where the urban street view image is located. 2.5 Fluctuation rates in pollution and PM 2.5 Recovery ratio after pollution, based on the PM 2.5 Fluctuations in pollution and the PM 2.5 The recovery rate after pollution is calculated to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 Section elasticity coefficient;

[0111] The number of pixels corresponding to the target area outline in each target area image in the urban street view image is calculated based on the mask data, and the urban environment characteristic factor is calculated based on the number of pixels corresponding to the target area outline. 2.5 The concentration calculation determines the PM corresponding to the traffic section where the urban street view image is located. 2.5 Fluctuation rates in pollution and PM 2.5 Recovery ratio after pollution, based on the PM 2.5 Fluctuations in pollution and the PM2.5 The recovery rate after pollution is calculated to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 Section elasticity coefficient;

[0112] Furthermore, based on the PM 2.5 The concentration calculation determines the PM corresponding to the traffic section where the urban street view image is located. 2.5 The steps for the fluctuation ratio in pollution include:

[0113] Step S401: Obtain the PM of the traffic section where the city street view image is located. 2.5 The maximum concentration and PM at a certain time 2.5 concentration;

[0114] Step S402: Calculate and determine the PM of the traffic section where the city street view image is located. 2.5 The highest concentration value and the PM at a certain time in the traffic section where the urban street view image is located 2.5 a first difference between the concentrations;

[0115] Step S403: based on the first difference and the PM value of the traffic section where the city street view image is located at a certain time 2.5 The fifth ratio between the concentrations is used as the PM 2.5 The fluctuation ratio of pollution is used to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 Fluctuation ratio in pollution.

[0116] Specifically, the PM corresponding to the traffic section where the city street view image is located 2.5 The fluctuation ratio in pollution is calculated as follows:

[0117]

[0118] Among them, FR i Indicates the PM value of the i-th traffic section in the city street view image 2.5 Fluctuation ratios in pollution; Indicates the i-th traffic section in the city street view image at t m PM of time 2.5 The highest concentration, represents the PM of the i-th traffic section at time t in the city street view image 2.5 Concentration, t m Indicates PM 2.5 The time when pollution reaches its peak.

[0119] Furthermore, based on the PM 2.5 The concentration calculation determines the PM corresponding to the traffic section where the urban street view image is located.2.5 The steps for post-contamination recovery include:

[0120] Step S4001: Obtain the traffic section where the city street view image is located as PM 2.5 The PM corresponding to the previous day of pollution 2.5 Concentration, PM removal 2.5 PM corresponding to the day after pollution 2.5 Concentration and PM2.5 concentration of traffic sections in urban street view images 2.5 Maximum concentration;

[0121] Step S4002: Calculate and determine the PM of the traffic section where the city street view image is located. 2.5 The highest concentration value is consistent with the PM 2.5 PM corresponding to the day after pollution 2.5 a second difference between the concentrations;

[0122] Step S4003: Based on the second difference and the PM 2.5 The PM corresponding to the previous day of pollution 2.5 The sixth ratio between the concentrations is used as the PM 2.5 The recovery ratio after pollution is used to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 Recovery ratio after contamination.

[0123] Specifically, the PM corresponding to the traffic section where the city street view image is located 2.5 The recovery rate after pollution is calculated as follows:

[0124]

[0125] Among them, RR i Indicates the PM value of the i-th traffic section in the city street view image 2.5 Recovery rate after contamination; Indicates the i-th traffic section in the city street view image at t m PM of time 2.5 Maximum concentration; represents the PM corresponding to the i-th traffic section at time t1 in the city street view image 2.5 concentration; represents the PM corresponding to the i-th traffic section at time t2 in the city street view image 2.5 concentration; t1 represents the demarcation of PM 2.5 The day before the pollution; t2 means the removal of PM 2.5 The day after the pollution; m Indicates PM 2.5 The time when pollution reaches its peak.

[0126] Furthermore, based on the PM 2.5 Fluctuations in pollution and the PM 2.5 The recovery rate after pollution is calculated to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 The steps for calculating the elastic coefficient of a road section include:

[0127] Step S1100: Determine the PM corresponding to the traffic section where the city street view image is located. 2.5 The fluctuation ratio in pollution and the PM corresponding to the traffic section where the urban street view image is located 2.5 Recovery rate after contamination;

[0128] Step S1200: Calculate and determine the PM 2.5 The recovery ratio after pollution is related to the PM 2.5 The seventh ratio between the fluctuation ratios in pollution is used as the PM 2.5 The road section elasticity coefficient is used to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 Elastic coefficient of road section.

[0129] Specifically, the PM corresponding to the traffic section where the city street view image is located 2.5 The calculation formula of the road section elastic coefficient is expressed as:

[0130]

[0131] Among them, RR i Indicates the PM value of the i-th traffic section in the city street view image 2.5 Recovery ratio after contamination; FR i Indicates the PM value of the i-th traffic section in the city street view image 2.5 Fluctuation ratio in pollution; CRE i，t represents the PM corresponding to the i-th traffic section at time t in the city street view image 2.5 Elastic coefficient of road section.

[0132] Step S50: Based on the geographical location information, urban environment characteristic factors, fluctuation ratio, recovery ratio, PM 2.5 Road section elasticity coefficient and its corresponding PM 2.5 The concentration of the sample data set is constructed, and the atmospheric particulate matter concentration prediction model trained with the sample data set is used to predict the PM2.5 concentration of the target city’s corresponding traffic section. 2.5 concentration to complete the prediction of urban traffic atmospheric particulate matter concentration.

[0133] Calculate and determine the PM corresponding to the traffic section where the city street view image is located 2.5After the road section elasticity coefficient, based on the geographical location information, urban environment characteristic factors, fluctuation ratio, recovery ratio, PM 2.5 Road section elasticity coefficient and its corresponding PM 2.5 The concentration of the sample data set is constructed, and the atmospheric particulate matter concentration prediction model trained with the sample data set is used to predict the PM2.5 concentration of the target city’s corresponding traffic section. 2.5 concentration, in order to complete the prediction of urban traffic atmospheric particulate matter concentration, wherein the basic network architecture of the atmospheric particulate matter concentration prediction model is a machine learning model, and the machine learning model includes a random forest model, a generalized additive model (GAM) or a support vector machine (SVM), etc. The atmospheric particulate matter concentration prediction model of the present application can be selected from any of the above models.

[0134] In some embodiments, the present application adopts a comparative selection strategy to construct an atmospheric particulate matter concentration prediction model. This strategy involves pairwise comparison of all potential influencing factors, including the above-mentioned geographical location information, urban environment characteristic factors, fluctuation ratio, recovery ratio, PM 2.5 Road section elasticity coefficient and its corresponding PM 2.5 The most effective feature combination is gradually screened out by combining the concentration. Through iterative optimization, the simplest and most efficient feature set is determined to improve the explanatory power and prediction accuracy of the model. Then, multiple machine learning models are constructed and the filtered feature sets are put into the machine learning models. The coefficient of determination (R 2 ), mean absolute percentage error (MAPE), root mean square error (RMSE), mean absolute error (MAE), complete the performance evaluation of the machine learning algorithm and find the machine learning algorithm with the best prediction performance. RMSE is used to evaluate the average difference between the predicted value and the actual value. The smaller its value, the smaller the difference between the model's predicted value and the actual value, and the better the model performance; MAE is used to evaluate the average difference between the predicted value and the actual value. It can better handle outliers. The smaller its value, the smaller the difference between the model's predicted value and the actual value, and the better the model performance; R 2 The value range of R is from 0 to 1. The closer it is to 1, the better the model explains the variability of the dependent variable and the better the model performance. When R is equal to 1, it means that the model can perfectly explain the variability of the dependent variable and the predicted value is completely consistent with the actual value. The performance of the machine learning method is measured by the coefficient of determination (R 2 ), mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) are calculated according to the following formulas:

[0135]

[0136] Where n is the number of samples, y i is the true value of each sample, is the mean of the sample data, is the predicted value of the sample. The smaller the RMSE value, MAE value and MAPE value, the higher the accuracy of the model in describing the data. 2 The value of R is between [0,1]. 2 The closer it is to 1, the stronger the explanatory power of the predictor variable for the predicted object, and the better the model fits the data.

[0137] In some embodiments, the basic network architecture of the atmospheric particulate matter concentration prediction model of the present application is a random forest model, which does not constitute a limitation to the present application, and the geographical location information, urban environment characteristic factors, fluctuation ratio, recovery ratio, PM 2.5 Road section elasticity coefficient and its corresponding PM 2.5 The sample data set is constructed by the random forest model, and the sample data set is input into the preset random forest model for training until the model training reaches a convergence state or reaches a certain number of iterations. After the atmospheric particulate matter concentration prediction model is trained to a convergence state, it can be put into production and used to predict the PM2.5 concentration corresponding to the traffic section where the target city street view image is located. 2.5 concentration.

[0138] As can be seen from the above embodiments, compared with the existing technology, this application addresses the problems of existing technology in which mobile monitoring mainly relies on handheld mobile sensors, which have high labor costs and insufficient coverage, and the high hardware requirements of CFD models, long simulation time, and high prediction costs. This application includes but is not limited to the following beneficial effects:

[0139] First, this application can greatly expand the monitoring range and temporal resolution. By using image analysis and deep learning technology, it breaks through the spatial limitations of traditional fixed monitoring stations and covers a wider urban area. At the same time, through continuous image data collection, it greatly improves the temporal resolution and can capture PM2.5 in a timely manner. 2.5 The dynamic changes of PM concentration, this comprehensive analysis not only enhances the 2.5 The accuracy of pollution source tracking also improves the detection of PM 2.5 Real-time monitoring capability of concentration change trends.

[0140] Secondly, this application uses the geographical location information corresponding to the traffic section in the urban street view image, urban environment characteristic factors, PM 2.5 Fluctuation rate in pollution, PM 2.5 Recovery rate after pollution, PM 2.5 Road section elasticity coefficient and its corresponding PM2.5 The concentration of the sample data set is constructed, and the atmospheric particulate matter concentration prediction model trained with the sample data set is used to predict the PM2.5 concentration of the target city’s corresponding traffic section. 2.5 concentration, can significantly increase PM 2.5 The prediction accuracy of PM concentration was improved by using comparative selection strategy to build a prediction model and optimizing parameters. 2.5 The accuracy of concentration prediction is improved by comprehensively analyzing historical data and real-time data. The model can adapt to various environmental scenarios, enhancing the stability and reliability of the prediction.

[0141] Third, this application can be applied to vehicle-mounted terminal equipment to achieve PM 2.5 The real-time response and decision support capabilities of pollution enable the urban traffic atmospheric particulate matter concentration prediction system to quickly process real-time updated data and immediately provide PM 2.5 Concentration predictions provide real-time information to city managers, environmental protection workers, and the public. Information can be transmitted efficiently and conveniently through in-vehicle terminals, mobile applications, or web platforms, facilitating prompt action to improve air quality.

[0142] Fourth, the PM of this application 2.5 Fluctuation rate in pollution, PM 2.5 Recovery rate after pollution and PM 2.5 The elasticity coefficient of the road section can be used to quantify the resilience of air quality. 2.5 During the concentration process, PM 2.5 Fluctuation rate in pollution, PM 2.5 Recovery rate after pollution and PM 2.5 The road section elasticity coefficient can measure the self-repair ability of air quality under specific conditions, as well as the speed and ability to recover to a better state. 2.5 Fluctuation rate in pollution, PM 2.5 Recovery rate after pollution and PM 2.5 By continuously revising the elastic coefficient of the road section, the prediction model can better adapt to the changes in environmental factors and ensure the stability and reliability of the prediction results.

[0143] Furthermore, the present application proposes an innovative method for tracing and predicting the concentration of atmospheric particulate matter, in response to the lag and roughness problems existing in the traditional prediction process of atmospheric particulate matter concentration. This method is based on a deep learning framework, which transforms and fuses high-resolution urban street view images with satellite remote sensing images, thereby extracting the characteristics of the urban built environment in the image. Compared with the traditional model built based on social panel statistical data, the prediction model architecture of the present application has a shorter time step and a stronger ability to capture spatial details. Specifically, the present application first uses deep learning technology to fuse high-resolution urban street view images and satellite remote sensing images, uses a deep learning image segmentation model to identify street view pictures, extracts places or objects in street view pictures, calculates the pixel coverage of different places or objects, constructs urban environment characteristic factors, extracts urban environment characteristic factors that significantly affect the concentration of atmospheric particulate matter, and then constructs an atmospheric particulate pollution event analysis framework based on urban environment characteristic factors. Finally, PM 2.5 Fluctuation rate in pollution, PM 2.5 Recovery rate after pollution and PM 2.5 Parameters such as road section elasticity coefficient can be used to accurately analyze the changing characteristics of atmospheric particulate matter pollution in time and space.

[0144] Furthermore, the urban traffic atmospheric particulate matter concentration prediction method proposed in this application can effectively solve the lag and roughness problems existing in traditional prediction methods; the urban traffic atmospheric particulate matter concentration prediction method proposed in this application has broad application prospects in the field of intelligent management of atmospheric particulate matter pollution in different scenarios, and is expected to provide effective technical support for improving urban air quality and pollution prevention and control.

[0145] See also Figure 4 , adapted to one of the purposes of this application, provides an urban traffic atmospheric particulate matter concentration prediction device, including a data acquisition module 1100, a mask data extraction module 1200, an environmental feature determination module 1300, an elastic coefficient determination module 1400 and PM 2.5 Concentration prediction module 1500. Among them, data acquisition module 1100 is configured to respond to the urban traffic atmospheric particulate matter concentration prediction instruction, obtain multiple city street scene images taken continuously by the vehicle-mounted camera device and their corresponding geographical location information, PM2.5 information of the traffic section, and the PM2.5 information of the traffic section. 2.5concentration, wherein the geographic location information includes one or any multiple of POI information, normalized vegetation index, and building density; a mask data extraction module 1200 is configured to use an image segmentation model that has been trained to a convergent state to perform image segmentation on the urban street view image to extract mask data corresponding to the target area contour in each target area image in the urban street view image, wherein the target area includes a green plant area, a motor vehicle area, a sky area, a building area, a fence area, a pillar area, and a wall area; an environmental feature determination module 1300 is configured to calculate and determine the number of pixels corresponding to the target area contour in each target area image in the urban street view image according to the mask data, and calculate and determine the urban environment characteristic factor according to the number of pixels corresponding to the contour of each target area, wherein the urban environment characteristic factor includes a green view rate, a vehicle interference index, a sky visibility index, and a space enclosure index; an elasticity coefficient determination module 1400 is configured to calculate and determine the urban environment characteristic factor based on the PM 2.5 The concentration calculation determines the PM corresponding to the traffic section where the urban street view image is located. 2.5 Fluctuation rates in pollution and PM 2.5 Recovery ratio after pollution, based on the PM 2.5 Fluctuations in pollution and the PM 2.5 The recovery rate after pollution is calculated to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 Section elastic coefficient; PM 2.5 The concentration prediction module 1500 is configured to be based on the geographical location information, urban environment characteristic factors, fluctuation ratio, recovery ratio, PM 2.5 Road section elasticity coefficient and its corresponding PM 2.5 The concentration of the sample data set is constructed, and the atmospheric particulate matter concentration prediction model trained with the sample data set is used to predict the PM2.5 concentration of the target city’s corresponding traffic section. 2.5 concentration to complete the prediction of urban traffic atmospheric particulate matter concentration.

[0146] Based on any embodiment of this application, please refer to Figure 5 Another embodiment of the present application further provides an electronic device, which can be implemented by a computer device, such as Figure 5As shown, a schematic diagram of the internal structure of a computer device. The computer device includes a processor, a computer-readable storage medium, a memory, and a network interface connected via a system bus. The computer-readable storage medium of the computer device stores an operating system, a database, and computer-readable instructions. The database may store a control information sequence. When the computer-readable instructions are executed by the processor, the processor may implement a method for predicting the concentration of particulate matter in the atmosphere of urban traffic. The processor of the computer device is used to provide computing and control capabilities to support the operation of the entire computer device. The memory of the computer device may store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor may execute the method for predicting the concentration of particulate matter in the atmosphere of urban traffic of the present application. The network interface of the computer device is used to connect and communicate with a terminal. Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0147] In this embodiment, the processor is used to execute Figure 4 The memory stores the program code and various data required to execute the specific functions of each module and its submodule. The network interface is used to transmit data between user terminals or servers. The memory in this embodiment stores the program code and data required to execute all modules / submodules in the urban traffic atmospheric particulate matter concentration prediction device of this application. The server can call the server's program code and data to execute the functions of all submodules.

[0148] The present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the urban traffic atmospheric particulate matter concentration prediction method described in any embodiment of the present application.

[0149] The present application also provides a computer program product, including a computer program / instruction, which, when executed by one or more processors, implements the steps of the urban traffic atmospheric particulate matter concentration prediction method described in any embodiment of the present application.

[0150] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments of the present application can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments of the method. The aforementioned storage medium can be a computer-readable storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0151] The above description is only part of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

[0152] In summary, the urban traffic atmospheric particulate matter concentration prediction method proposed in this application can effectively solve the lag and roughness problems existing in traditional prediction methods; the urban traffic atmospheric particulate matter concentration prediction method proposed in this application has broad application prospects in the field of intelligent management of atmospheric particulate matter pollution in different scenarios, and is expected to provide effective technical support for improving urban air quality and pollution prevention and control.

Claims

1. A method for predicting urban traffic atmospheric particulate matter concentration, characterized in that: include: In response to the urban traffic atmospheric particulate matter concentration prediction instruction, obtain multiple city street scene images taken continuously by the vehicle-mounted camera equipment and their corresponding geographical location information, PM2.5 concentration of the traffic section, and the corresponding location information of the traffic section. 2.5 Concentration, wherein the geographic location information includes one or more of POI information, normalized vegetation index, and building density; Performing image segmentation on the urban street scene image using an image segmentation model that has been trained to a convergent state to extract mask data corresponding to the outline of target regions in each target region image in the urban street scene image, wherein the target regions include green plant regions, motor vehicle regions, sky regions, building regions, fence regions, pillar regions, and wall regions; Calculating and determining the number of pixels corresponding to the target area outline in each target area image in the urban street view image based on the mask data, and calculating and determining urban environment characteristic factors based on the number of pixels corresponding to each target area outline, wherein the urban environment characteristic factors include a green view rate, a vehicle interference index, a sky visibility index, and a space enclosure index; Based on the PM 2.5 The concentration calculation determines the PM corresponding to the traffic section where the urban street view image is located. 2.5 Fluctuation rates in pollution and PM 2.5 Recovery ratio after pollution, based on the PM 2.5 Fluctuations in pollution and the PM 2.5 The recovery rate after pollution is calculated to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 Section elasticity coefficient; Based on the geographical location information, urban environment characteristic factors, fluctuation ratio, recovery ratio, PM 2.5 Road section elasticity coefficient and its corresponding PM 2.5 The concentration of the sample data set is constructed, and the atmospheric particulate matter concentration prediction model trained with the sample data set is used to predict the PM2.5 concentration of the target city’s corresponding traffic section. 2.5 concentration to complete the prediction of urban traffic atmospheric particulate matter concentration.

2. The method for predicting urban traffic atmospheric particulate matter concentration according to claim 1, characterized in that: The step of using an image segmentation model that has been trained to a convergent state to segment the urban street scene image to extract mask data corresponding to the target area contour in each target area image in the urban street scene image includes: Performing multi-level encoding on the target area image and generating corresponding scale intermediate feature information, wherein the intermediate feature information is a feature representation of the information to be processed in the target area image; Multi-level decoding is performed accordingly on the decoding path, and the first image feature information is produced with the intermediate feature information of the smallest scale. In addition, the image feature information of the previous level and the intermediate feature information produced by the encoding of the same level are used as references to decode the image feature information of higher scales accordingly. The image feature information is used to represent the contour features of the information to be processed in the target area image in the form of a mask; All image feature information is fused to generate mask data of the target contour area in the target area image.

3. The method for predicting urban traffic atmospheric particulate matter concentration according to claim 1, characterized in that: The steps of calculating and determining the number of pixels corresponding to the target area outline in each target area image in the urban street view image according to the mask data, and calculating and determining the urban environment characteristic factor according to the number of pixels corresponding to the target area outline include: Calculating and determining the number of pixels corresponding to the green plant area outline, the motor vehicle area outline, the sky area outline, the building area outline, the fence area outline, the pillar area outline, and the wall area outline in the urban street view image based on mask data corresponding to the green plant area outline, the motor vehicle area outline, the sky area outline, the building area outline, the fence area outline, the pillar area outline, and the wall area outline in the urban street view image; calculating and determining a first ratio between the number of pixels corresponding to the green plant area outline and the total number of pixels in the urban street view image, and using the first ratio as the green viewing rate to determine the green viewing rate; calculating and determining a second ratio between the number of pixels corresponding to the motor vehicle area outline and the total number of pixels in the urban street view image, and using the second ratio as a vehicle interference index to determine the vehicle interference index; calculating and determining a third ratio between the number of pixels corresponding to the sky area outline and the total number of pixels in the urban street view image, and using the third ratio as a sky visibility index to determine the sky visibility index; The spatial enclosure index is determined by calculating the sum of the numbers of pixels corresponding to the building area outline, the green plant area outline, the fence area outline, the pillar area outline, and the wall area outline. Based on a fourth ratio between the sum and the total number of pixels in the urban street view image, the fourth ratio is used as the spatial enclosure index.

4. The method for predicting urban traffic atmospheric particulate matter concentration according to claim 1, characterized in that: Based on the PM 2.5 The concentration calculation determines the PM corresponding to the traffic section where the urban street view image is located. 2.5 The steps for the fluctuation ratio in pollution include: Get the PM of the traffic section where the city street view image is located 2.5 The maximum concentration and PM at a certain time 2.5 concentration; Calculate and determine the PM of the traffic section where the city street view image is located 2.5 The highest concentration value and the PM at a certain time in the traffic section where the urban street view image is located 2.5 a first difference between the concentrations; Based on the first difference and the PM at a certain time of the traffic section where the urban street view image is located 2.5 The fifth ratio between the concentrations is used as the PM 2.5 The fluctuation ratio of pollution is used to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 Fluctuation ratio in pollution.

5. The method for predicting urban traffic atmospheric particulate matter concentration according to claim 1, characterized in that: Based on the PM 2.5 The concentration calculation determines the PM corresponding to the traffic section where the urban street view image is located. 2.5 The steps for post-contamination recovery include: The traffic section where the city street view image is located is designated as PM 2.5 The PM corresponding to the previous day of pollution 2.5 Concentration, PM removal 2.5 PM corresponding to the day after pollution 2.5 Concentration and PM2.5 concentration of traffic sections in urban street view images 2.5 Maximum concentration; Calculate and determine the PM of the traffic section where the city street view image is located 2.5 The highest concentration value is consistent with the PM 2.5 PM corresponding to the day after pollution 2.5 a second difference between the concentrations; Based on the second difference and the PM 2.5 The PM corresponding to the previous day of pollution 2.5 The sixth ratio between the concentrations is used as the PM 2.5 The recovery ratio after pollution is used to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 Recovery ratio after contamination.

6. The method for predicting urban traffic atmospheric particulate matter concentration according to claim 1, characterized in that: Based on the PM 2.5 Fluctuations in pollution and the PM 2.5 The recovery rate after pollution is calculated to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 The steps for calculating the elastic coefficient of a road section include: Determine the PM corresponding to the traffic section where the city street view image is located 2.5 The fluctuation ratio in pollution and the PM corresponding to the traffic section where the urban street view image is located 2.5 Recovery rate after contamination; Calculate and determine the PM 2.5 The recovery ratio after pollution is related to the PM 2.5 The seventh ratio between the fluctuation ratios in pollution is used as the PM 2.5 The road section elasticity coefficient is used to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 Elastic coefficient of road section.

7. The method for predicting urban traffic atmospheric particulate matter concentration according to any one of claims 1 to 6, characterized in that: The vehicle-mounted camera device includes one or more of a driving recorder, a roof camera, a front-facing camera, a rear-facing camera or a side-view camera; the basic network architecture of the image segmentation model is U 2 net model; the basic network architecture of the atmospheric particulate matter concentration prediction model is a machine learning model, and the machine learning model includes a random forest model.

8. A device for predicting urban traffic atmospheric particulate matter concentration, characterized in that: include: The data acquisition module is configured to respond to the urban traffic atmospheric particulate matter concentration prediction instruction and obtain multiple city street scene images continuously shot by the vehicle-mounted camera device and their corresponding geographical location information, PM2.5 concentration of the traffic section, and the corresponding location information of the traffic section. 2.5 Concentration, wherein the geographic location information includes one or more of POI information, normalized vegetation index, and building density; a mask data extraction module configured to perform image segmentation on the urban street view image using an image segmentation model that has been trained to a convergent state, so as to extract mask data corresponding to the outline of target areas in each target area image in the urban street view image, wherein the target areas include green plant areas, motor vehicle areas, sky areas, building areas, fence areas, pillar areas, and wall areas; an environmental feature determination module configured to calculate and determine, based on the mask data, the number of pixels corresponding to the target area outline in each target area image in the urban street view image, and to calculate and determine an urban environmental feature factor based on the number of pixels corresponding to each target area outline, the urban environmental feature factor including a green view rate, a vehicle interference index, a sky visibility index, and a space enclosure index; The elastic coefficient determination module is configured to be based on the PM 2.5 The concentration calculation determines the PM corresponding to the traffic section where the urban street view image is located. 2.5 Fluctuation rates in pollution and PM 2.5 Recovery ratio after pollution, based on the PM 2.5 Fluctuations in pollution and the PM 2.5 The recovery rate after pollution is calculated to determine the PM corresponding to the traffic section where the urban street view image is located. 2.5 Section elasticity coefficient; PM 2.5 The concentration prediction module is configured to be based on the geographical location information, urban environment characteristic factors, fluctuation ratio, recovery ratio, PM 2.5 Road section elasticity coefficient and its corresponding PM 2.5 The concentration of the sample data set is constructed, and the atmospheric particulate matter concentration prediction model trained with the sample data set is used to predict the PM2.5 concentration of the target city’s corresponding traffic section. 2.5 concentration to complete the prediction of urban traffic atmospheric particulate matter concentration.

9. An electronic device comprising a central processing unit and a memory, characterized in that: The central processing unit is configured to call and run a computer program stored in the memory to execute the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that It stores a computer program implemented according to the method described in any one of claims 1 to 7 in the form of computer-readable instructions, and when the computer program is called and executed by a computer, the steps included in the corresponding method are executed.

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