Mobile source pollution data visualization platform construction method

Abstract

The invention discloses a mobile source pollution data visualization platform construction method, and relates to the technical field of environment monitoring, and the method comprises the steps: building an urban historical multi-source heterogeneous emission database; pollutant emission and diffusion trends in the urban historical multi-source heterogeneous emission data are analyzed, and emission trend indexes of different vehicle types of each road section of the city are obtained; analyzing the pollutant emission state of the mobile source, and evaluating the dynamic emission index of the mobile source of the urban traffic road network; identifying an execution working condition corresponding to an engineering machinery operation mode in the non-road mobile machinery data, and evaluating an urban non-road mobile source dynamic emission index; using the mobile source dynamic emission index of the urban traffic road network and the mobile source dynamic emission index of the urban non-road to correct the emission trend indexes of different vehicle types of each road section of the city to obtain an urban pollutant emission diffusion real-time state distribution map; the method has the advantages that the pollution prevention and control capability is effectively improved, and urban environment management is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and specifically relates to a method for constructing a visualization platform for mobile source pollution data. Background Technique

[0002] Visualization of mobile source pollution data intuitively displays the pollutant emissions of different types of transportation vehicles or non-road mobile sources in a specific area or time period through charts, maps, etc. Through visual elements such as colors and sizes, the distribution of pollution sources, emission intensity, and spatio-temporal change trends can be clearly presented, helping decision-makers identify pollution hotspots, evaluate policy effects, and formulate effective environmental management measures.

[0003] Existing mobile source pollution monitoring mainly collects pollutant data by deploying edge sensors on urban roads and conducts data fitting analysis to determine the urban pollution status. However, the main sources of urban pollution are mobile vehicles and engineering vehicles, and the urban regional pollution status is easily affected by external conditions. There are problems of poor accuracy and high implementation costs for monitoring the pollution status of fixed-point deployed edge sensors. Summary of the Invention

[0004] To solve the above technical problems, a method for constructing a visualization platform for mobile source pollution data is provided. This technical solution solves the problems of the existing mobile source pollution monitoring, which mainly collects pollutant data by deploying edge sensors on urban roads and conducts data fitting analysis to determine the urban pollution status. However, the main sources of urban pollution are mobile vehicles and engineering vehicles, and the urban regional pollution status is easily affected by external conditions. There are problems of poor accuracy and high implementation costs for monitoring the pollution status of fixed-point deployed edge sensors.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for constructing a visualization platform for mobile source pollution data, including:

[0007] Obtain historical vehicle emission test data, non-road mobile machinery data, road network data, road traffic flow data, oil product data, and meteorological data of several types, and establish a historical multi-source heterogeneous emission database for the city;

[0008] Based on the historical multi-source heterogeneous emission database of the city, analyze the pollutant emission and diffusion trends in the historical multi-source heterogeneous emission data of the city, obtain the emission trend indicators of different vehicle types for each section of the city, and construct a temporal distribution map of urban pollutant emission and diffusion;

[0009] Obtain the urban traffic road network, analyze the emission status of mobile source pollutants in the real-time traffic flow information of the traffic road network, and evaluate the dynamic emission indicators of mobile sources in the urban traffic road network;

[0010] Obtain the data of urban non-road mobile machinery, identify the operating conditions corresponding to the construction machinery operation modes in the data of non-road mobile machinery, and evaluate the dynamic emission indicators of mobile sources in urban non-road areas;

[0011] Use the dynamic emission indicators of mobile sources in the urban traffic road network and the dynamic emission indicators of mobile sources in urban non-road areas to correct the emission trend indicators of different vehicle types for each section of the city, and obtain the real-time state distribution map of urban pollutant emission diffusion;

[0012] Among them, using the dynamic emission indicators of mobile sources in the urban traffic road network and the dynamic emission indicators of mobile sources in urban non-road areas to correct the emission trend indicators of different vehicle types for each section of the city is specifically as follows:

[0013]

[0014] Among them, is the emission trend correction index of the i-th vehicle type in the k-th section of the city, is the total emission of the i-th vehicle type in the k-th section of the city, E′ i is the dynamic emission index of the i-th vehicle type of urban non-road mobile.

[0015] Preferably, based on the urban historical multi-source heterogeneous emission database, analyze the pollutant emission and diffusion trends in the urban historical multi-source heterogeneous emission data, and constructing the time-series distribution map of urban pollutant emission diffusion specifically includes:

[0016] Based on the urban historical multi-source heterogeneous emission database, divide the data according to several dimensions concerned by urban pollutants to obtain an array of urban historical multi-dimensional emission factor data; the multi-dimensional emission factors include: vehicle dimension, environmental dimension, oil product dimension and road dimension;

[0017] Based on the on-vehicle emission test data in the urban historical multi-dimensional emission factor data array, determine several types of emission factors for different vehicle types, and determine the basic emission factor list for different vehicle types;

[0018] Based on the meteorological data in the urban historical multi-dimensional emission factor data array, use the environmental factor comparison table affecting pollutant emission to determine the environmental comprehensive correction factor;

[0019] Based on the urban oil product data in the urban historical multi-dimensional emission factor data array, use the oil product factor comparison table affecting pollutant emission to determine the oil product comprehensive correction factor;

[0020] Based on the road network data and road traffic flow data in the city's historical multi-dimensional emission factor data array, the comprehensive road correction factor is determined using the road grade correction factor comparison table and speed correction factor comparison table that affect pollutant emissions;

[0021] Based on the basic emission factor list of different vehicle types, comprehensive environmental correction factors, comprehensive oil correction factors and comprehensive road correction factors, the time attributes are aligned to establish a time sliding window to obtain the time series data of multi-dimensional factors affecting historical pollutants in the city;

[0022] Based on the multi-dimensional time series data of historical pollutant impacts in the city, a M0BILE neural network model is established to generate emission trend indicators of different vehicle types in various sections of the city and construct a time series distribution map of urban pollutant emission diffusion;

[0023] The MOBILE neural network model is specifically:

[0024]

[0025] In the formula, is the total emission of the i-th vehicle type on the k-th road section in the city, BEF ij is the jth basic emission factor of the i-th vehicle type, γ is the comprehensive environmental correction factor, is the comprehensive correction factor of oil products, λ k is the comprehensive correction factor of the kth road section, P i is the number of vehicles of the i-th type in the city, VKT i is the average mileage of the i-th vehicle type, n is the total number of vehicle types, and m is the total number of road sections in the urban road network.

[0026] Preferably, obtaining the urban traffic road network, analyzing the mobile source pollutant emission status in the real-time traffic flow information of the traffic road network, and evaluating the dynamic emission indicators of the mobile sources of the urban traffic road network specifically include:

[0027] Based on the urban road network data and road traffic flow data, the historical urban road traffic flow data is obtained, and the historical road vehicle type composition data and historical road section traffic flow data are obtained by dividing and extracting the road section vehicle type composition information and road section traffic flow information;

[0028] Based on the historical road vehicle type composition data, the BP neural network is trained. The vehicle type composition data, road type and spatial characteristics in the historical road vehicle type composition data are used as input. The BP neural network is used to fit the complex function between the vehicle type ratio and the road section characteristics to predict the future road vehicle type composition data.

[0029] Based on the urban historical road traffic flow data, train the LSTM urban road network vehicle type flow prediction model. Use the traffic flow data of the sections with detectors in the historical road section flow data to drive the spatial topology flow. For the sections without detectors, combine the upstream and downstream traffic flows and the spatial feature influence coefficient as inputs to predict the future road section flow data;

[0030] Based on the predicted future road vehicle type composition data, the predicted future road section flow data, and the basic emission factor list of different vehicle types, perform dynamic prediction calculations on the emissions of each vehicle type on each section of the urban road network to obtain the mobile source dynamic emission indicators of the urban traffic road network;

[0031] Among them, the mobile source dynamic emission indicators of the urban traffic road network are specifically:

[0032]

[0033] In the formula, is the dynamic emission of the i-th vehicle type on the k-th section of the urban road network, is the traffic volume of the i-th vehicle type on the k-th section of the urban road network, is the passing time of the i-th vehicle type on the k-th section of the urban road network, is the total passing volume of the i-th vehicle type on the k-th section of the urban road network.

[0034] Preferably, obtain the urban non-road mobile machinery data, identify the operating conditions corresponding to the construction machinery operation modes in the non-road mobile machinery data, and evaluate the mobile source dynamic emission indicators of the urban non-road specifically including:

[0035] Based on the non-road mobile machinery data, establish the original data set of the construction machinery operation modes of non-road mobile vehicle types according to the construction machinery operation modes of different non-road mobile vehicle types;

[0036] Determine the operating condition parameters corresponding to the construction machinery operation modes of non-road mobile vehicle types;

[0037] Based on the original data set of the construction machinery operation modes of non-road mobile vehicle types, train the C-SVC vector machine, using the construction machinery operation modes corresponding to the original data of the construction machinery operation modes as inputs and the operating condition parameters of the construction machinery operation modes of non-road mobile vehicle types as outputs;

[0038] Use linear mapping to transform the operating condition parameters of the construction machinery operation modes of non-road mobile vehicle types to obtain the operating condition parameter vector of the construction machinery operation modes of non-road mobile vehicle types;

[0039] Calculate the linear regression coefficient between the operating condition parameter vector of the construction machinery operation mode of the non-road mobile vehicle type and the pollutant emission amount, and use it as the pollutant emission influence coefficient of the construction machinery operation mode of the non-road mobile vehicle type;

[0040] Based on the pollutant emission influence coefficient of the construction machinery operation mode of the non-road mobile vehicle type and the real-time urban non-road mobile machinery data, calculate the dynamic emission index of the urban non-road mobile vehicle type, and determine the dynamic emission index of the urban non-road mobile source;

[0041] Among them, the specific calculation of the dynamic emission index of the urban non-road mobile vehicle type is as follows:

[0042]

[0043] In the formula, E′ i is the dynamic emission index of the i-th vehicle type of urban non-road mobile, and E i is the emission index of the i-th vehicle type of urban non-road mobile, and δ iv is the pollutant emission influence coefficient of the v-th construction machinery operation mode of the i-th vehicle type of non-road mobile, and X iv is the operating condition parameter of the v-th construction machinery operation mode of the i-th vehicle type of non-road mobile.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0045] The present invention proposes a construction scheme for a mobile source pollution data visualization platform. By constructing a historical multi-source heterogeneous emission database for the city, combining various data sources such as traffic, non-road machinery, road network, and meteorology, it analyzes the pollutant emission and diffusion trends of urban mobile sources. By real-time monitoring the emission status of traffic flow and non-road machinery, dynamically evaluating the emission index of mobile source pollutants, and correcting the pollutant diffusion map, it finally realizes the accurate time-series prediction of urban pollutant emission and diffusion. This method can provide real-time, dynamic, and accurate decision-making support for urban pollution control, effectively improve the pollution prevention and control ability, and optimize urban environmental management. Description of the Drawings

[0046] Figure 1 is a flow chart of a method for constructing a mobile source pollution data visualization platform;

[0047] Figure 2 is a flow chart of a method for analyzing the emission trend index of different vehicle types in each section of the city;

[0048] Figure 3 is a flow chart of a method for evaluating the dynamic emission index of mobile sources of the urban traffic road network;

[0049] Figure 4 A flow chart of a method for evaluating dynamic emission indicators of mobile sources in urban non-road areas. Detailed implementation manners

[0050] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0051] Referring to Figure 1 As shown, a method for constructing a visualization platform for mobile source pollution data includes:

[0052] Obtain historical vehicle emission test data, non-road mobile machinery data, road network data, road traffic flow data, oil product data, and meteorological data of several types, and establish an urban historical multi-source heterogeneous emission database;

[0053] Based on the urban historical multi-source heterogeneous emission database, analyze the pollutant emission and diffusion trends in the urban historical multi-source heterogeneous emission data, obtain the emission trend indicators of different vehicle types for each road section in the city, and construct a time-sequence distribution map of urban pollutant emission and diffusion;

[0054] Obtain the urban traffic road network, analyze the mobile source pollutant emission status in the real-time traffic flow information of the traffic road network, and evaluate the dynamic emission indicators of mobile sources in the urban traffic road network;

[0055] Obtain urban non-road mobile machinery data, identify the execution working conditions corresponding to the construction machinery operation modes in the non-road mobile machinery data, and evaluate the dynamic emission indicators of mobile sources in urban non-road areas;

[0056] Use the dynamic emission indicators of mobile sources in the urban traffic road network and the dynamic emission indicators of mobile sources in urban non-road areas to correct the emission trend indicators of different vehicle types for each road section in the city, and obtain a real-time status distribution map of urban pollutant emission and diffusion;

[0057] Among them, using the dynamic emission indicators of mobile sources in the urban traffic road network and the dynamic emission indicators of mobile sources in urban non-road areas to correct the emission trend indicators of different vehicle types for each road section in the city is specifically:

[0058]

[0059] Among them, is the emission trend correction index of the i-th vehicle type for the k-th road section in the city, is the total emission of the i-th vehicle type for the k-th road section in the city, and E′ i is the dynamic emission index of the i-th vehicle type for urban non-road mobile.

[0060] This solution analyzes the emission and diffusion trends of urban mobile source pollutants by constructing a multi-source heterogeneous emission database for urban history and combining various data sources such as traffic, non-road machinery, road network, and meteorology. By real-time monitoring the emission status of traffic flow and non-road machinery, dynamically evaluating the emission indicators of mobile source pollutants, and correcting the pollutant diffusion map, it finally realizes the accurate time-series prediction of urban pollutant emission and diffusion. This method can provide real-time, dynamic, and accurate decision-making support for urban pollution control, effectively improve the pollution prevention and control ability, and optimize urban environmental management.

[0061] Refer to Figure 2 As shown, based on the multi-source heterogeneous emission database for urban history, analyzing the pollutant emission and diffusion trends in the multi-source heterogeneous emission data of urban history, and constructing the time-series distribution map of urban pollutant emission and diffusion specifically includes:

[0062] Based on the multi-source heterogeneous emission database for urban history, divide the data according to several dimensions concerned by urban pollutants to obtain an array of multi-dimensional emission factor data for urban history; the multi-dimensional emission factors include: vehicle dimension, environmental dimension, oil product dimension, and road dimension;

[0063] Based on the on-vehicle emission test data in the array of multi-dimensional emission factor data for urban history, determine several types of emission factors for different vehicle types and determine the basic emission factor list for different vehicle types;

[0064] Based on the meteorological data in the array of multi-dimensional emission factor data for urban history and with the environmental factor comparison table affecting pollutant emission, determine the comprehensive environmental correction factor;

[0065] Based on the urban oil product data in the array of multi-dimensional emission factor data for urban history and with the oil product factor comparison table affecting pollutant emission, determine the comprehensive oil product correction factor;

[0066] Based on the road network data and road traffic flow data in the array of multi-dimensional emission factor data for urban history and with the road grade correction factor comparison table and speed correction factor comparison table affecting pollutant emission, determine the comprehensive road correction factor;

[0067] Based on the basic emission factor list for different vehicle types, the comprehensive environmental correction factor, the comprehensive oil product correction factor, and the comprehensive road correction factor, align them according to the time attribute, establish a time sliding window, and obtain the time-series data of multi-dimensional factors affecting urban historical pollutants;

[0068] Based on the time-series data of multi-dimensional factors affecting urban historical pollutants, establish a M0BILE neural network model, generate the emission trend indicators of different vehicle types for each section of the city, and construct the time-series distribution map of urban pollutant emission and diffusion;

[0069] Among them, the MOBILE neural network model is specifically as follows:

[0070]

[0071] In the formula, is the total emission of the i-th vehicle type in the k-th section of the city, and BEF ij is the j-th basic emission factor of the i-th vehicle type, γ is the comprehensive environmental correction factor, is the comprehensive oil product correction factor, and λ k is the comprehensive correction factor of the k-th section, and P i is the vehicle ownership of the i-th vehicle type in the city, VKT i is the average driving mileage of the i-th vehicle type, n is the total number of vehicle types, and m is the total number of sections of the urban road network.

[0072] This solution is based on the data of the urban historical multi-source heterogeneous emission database, systematically analyzes the urban pollutant emission and diffusion trends, and covers multiple dimensions of emission factors, including vehicles, environment, oil products, and roads, etc. In the data processing process, combined with on-vehicle emission test data, meteorological data, oil product information, and traffic flow data, the basic emission factors and comprehensive correction factors of each dimension are calculated respectively. Through the time sliding window technology, these factors are aligned with the time attribute to form multi-dimensional pollutant impact factor time series data. Finally, based on these time series data, a MOBILE neural network model is established, which can predict the pollutant emission trends of each section of the city and construct a time series distribution map of pollutant diffusion. This method not only provides an accurate tool for real-time predicting the pollutant diffusion trend, but also helps urban managers optimize pollution control measures, improve air quality management efficiency, and reduce the negative impact of pollution on the urban environment.

[0073] Refer to Figure 3 As shown, obtain the urban traffic road network, analyze the mobile source pollutant emission status in the real-time traffic flow information of the traffic road network, and evaluate the mobile source dynamic emission indicators of the urban traffic road network, specifically including:

[0074] Based on the urban road network data and road traffic flow data, obtain the urban historical road traffic flow data, and divide and extract it according to the vehicle type composition information and road traffic flow information of the section to obtain the historical road vehicle type composition data and historical road section flow data;

[0075] Based on the historical road vehicle type composition data, train a BP neural network, use the vehicle type composition data, road type, and spatial characteristics in the historical road vehicle type composition data as inputs, and use the BP neural network to fit the complex function between the vehicle type ratio and section characteristics to predict the future road vehicle type composition data;

[0076] Based on the urban historical road traffic flow data, train the LSTM urban road network vehicle type flow prediction model. Use the flow data of the sections with detectors in the historical road section flow data to drive the spatial topology flow. For the sections without detectors, combine the upstream and downstream traffic flows and the spatial feature influence coefficient as inputs to predict the future road section flow data;

[0077] Based on the predicted future road vehicle type composition data, the predicted future road section flow data, and the basic emission factor list of different vehicle types, conduct dynamic prediction calculations on the emissions of vehicle types in each section of the urban road network to obtain the mobile source dynamic emission indicators of the urban traffic road network;

[0078] Among them, the mobile source dynamic emission indicators of the urban traffic road network are specifically:

[0079]

[0080] In the formula, is the dynamic emission of the i-th vehicle type in the k-th section of the urban road network, is the traffic volume of the i-th vehicle type in the k-th section of the urban road network, is the passing time of the i-th vehicle type in the k-th section of the urban road network, is the total passing volume of the i-th vehicle type in the k-th section of the urban road network.

[0081] This solution integrates urban road network data and road traffic flow data, uses historical traffic flow and vehicle type composition information, and adopts the BP neural network and LSTM model to predict road vehicle type composition and traffic flow respectively. In the specific implementation process, the complex relationship between vehicle type composition and section characteristics is fitted by the BP neural network, and the future traffic flow is predicted by combining the LSTM model, and then the future road section flow is dynamically predicted. Combining the emission factors of different vehicle types, the mobile source pollutant emissions of each section are finally calculated. This method can not only accurately predict future traffic flow and vehicle type composition, but also effectively evaluate the mobile source dynamic emissions of the road network, provide data support for environmental protection, and reduce pollutant emissions.

[0082] Refer to Figure 4 As shown, obtain the urban non-road mobile machinery data, identify the operating conditions corresponding to the construction machinery operation modes in the non-road mobile machinery data, and the evaluation of the mobile source dynamic emission indicators of urban non-road specifically includes:

[0083] Based on the non-road mobile machinery data, establish the original data set of the construction machinery operation modes of non-road mobile vehicle types according to the construction machinery operation modes of different non-road mobile vehicle types;

[0084] Determine the operating condition parameters corresponding to the construction machinery operation mode of non-road mobile vehicle types;

[0085] Based on the original dataset of the construction machinery operation mode of non-road mobile vehicle types, train a C-SVC vector machine, using the construction machinery operation mode corresponding to the original data of the construction machinery operation mode as the input and the operating condition parameters of the construction machinery operation mode of non-road mobile vehicle types as the output;

[0086] Use linear mapping to convert the operating condition parameters of the construction machinery operation mode of non-road mobile vehicle types to obtain the operating condition parameter vector of the construction machinery operation mode of non-road mobile vehicle types;

[0087] Calculate the linear regression coefficient between the operating condition parameter vector of the construction machinery operation mode of non-road mobile vehicle types and the pollutant emissions, and use it as the pollutant emissions impact coefficient of the construction machinery operation mode of non-road mobile vehicle types;

[0088] Based on the pollutant emissions impact coefficient of the construction machinery operation mode of non-road mobile vehicle types and the real-time urban non-road mobile machinery data, calculate the dynamic emission index of urban non-road mobile vehicle types and determine the dynamic emission index of urban non-road mobile sources;

[0089] Among them, the specific calculation of the dynamic emission index of urban non-road mobile vehicle types is as follows:

[0090]

[0091] In the formula, E′ i is the dynamic emission index of the i-th vehicle type of urban non-road mobile, E i is the emission index of the i-th vehicle type of urban non-road mobile, δ iv is the pollutant emissions impact coefficient of the v-th construction machinery operation mode of the i-th vehicle type of non-road mobile, X iv is the operating condition parameter of the v-th construction machinery operation mode of the i-th vehicle type of non-road mobile.

[0092] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for constructing a mobile source pollution data visualization platform, characterized in that: include: Obtain historical vehicle emission test data, non-road mobile machinery data, road network data, road traffic flow data, oil product data and meteorological data to establish a historical multi-source heterogeneous urban emission database; Based on the city's historical multi-source heterogeneous emission database, the pollutant emission and diffusion trends in the city's historical multi-source heterogeneous emission data are analyzed to obtain emission trend indicators for different vehicle types on each road section in the city, and to construct a time series distribution map of urban pollutant emission diffusion; Obtain the urban traffic road network, analyze the mobile source pollutant emission status in the real-time traffic flow information of the traffic road network, and evaluate the dynamic emission indicators of mobile sources in the urban traffic road network; Obtain urban non-road mobile machinery data, identify the execution conditions corresponding to the operation modes of the engineering machinery in the non-road mobile machinery data, and evaluate the dynamic emission indicators of urban non-road mobile sources; Using the dynamic emission indicators of mobile sources in the urban traffic road network and the dynamic emission indicators of mobile sources in the urban non-road, the emission trend indicators of different vehicle types in each section of the city are corrected to obtain the real-time distribution map of urban pollutant emission diffusion; Among them, the dynamic emission indicators of mobile sources in the urban traffic road network and the dynamic emission indicators of mobile sources in urban non-roads are used to correct the emission trend indicators of different vehicle types in various sections of the city. Specifically: in, is the emission trend correction index of the i-th vehicle type on the k-th road section in the city, is the total emission of the i-th vehicle type on the k-th road section in the city, E′ i is the dynamic emission index of the i-th vehicle type in urban non-road mobility.

2. The method for constructing a mobile source pollution data visualization platform according to claim 1, characterized in that: Based on the urban historical multi-source heterogeneous emission database, the pollutant emission and diffusion trends in the urban historical multi-source heterogeneous emission data are analyzed, and the urban pollutant emission diffusion time series distribution map is constructed. Specifically, it includes: Based on the urban historical multi-dimensional heterogeneous emission database, data is divided according to several dimensions of urban pollutants to obtain a data array of urban historical multi-dimensional emission factors; the multi-dimensional emission factors include: vehicle dimension, environmental dimension, oil dimension and road dimension; Based on the vehicle emission test data in the city's historical multi-dimensional emission factor data array, several types of emission factors for different vehicle types are determined, and a list of basic emission factors for different vehicle types is determined; Based on the meteorological data in the city's historical multi-dimensional emission factor data array, the comprehensive environmental correction factor is determined using a comparison table of environmental factors that affect pollutant emissions; Based on the city oil product data in the city's historical multi-dimensional emission factor data array, and the comparison table of oil product factors affecting pollutant emissions, determine the comprehensive oil product correction factor; Based on the road network data and road traffic flow data in the city's historical multi-dimensional emission factor data array, the comprehensive road correction factor is determined using the road grade correction factor comparison table and speed correction factor comparison table that affect pollutant emissions; Based on the basic emission factor list of different vehicle types, comprehensive environmental correction factors, comprehensive oil correction factors and comprehensive road correction factors, the time attributes are aligned to establish a time sliding window to obtain the time series data of multi-dimensional factors affecting historical pollutants in the city; Based on the time series data of multi-dimensional factors affecting historical pollutants in the city, a MOBILE neural network model is established to generate emission trend indicators of different vehicle types on various road sections in the city, and to construct a time series distribution map of urban pollutant emission diffusion.

3. The method for constructing a mobile source pollution data visualization platform according to claim 2, characterized in that: The MOBILE neural network model is specifically: In the formula, is the total emission of the i-th vehicle type on the k-th road section in the city, BEF ij is the jth basic emission factor of the i-th vehicle type, γ is the comprehensive environmental correction factor, is the comprehensive correction factor of oil products, λ k is the comprehensive correction factor of the kth road section, P i is the number of vehicles of the i-th type in the city, VKT i is the average mileage of the i-th vehicle type, n is the total number of vehicle types, and m is the total number of road sections in the urban road network.

4. The method for constructing a mobile source pollution data visualization platform according to claim 3, characterized in that: Obtain the urban traffic road network, analyze the mobile source pollutant emission status in the real-time traffic flow information of the traffic road network, and evaluate the dynamic emission indicators of mobile sources in the urban traffic road network, including: Based on the urban road network data and road traffic flow data, the historical urban road traffic flow data is obtained, and the historical road vehicle type composition data and historical road section traffic flow data are obtained by dividing and extracting the road section vehicle type composition information and road section traffic flow information; Based on the historical road vehicle type composition data, the BP neural network is trained. The vehicle type composition data, road type and spatial characteristics in the historical road vehicle type composition data are used as input. The BP neural network is used to fit the complex function between the vehicle type ratio and the road section characteristics to predict the future road vehicle type composition data. Based on the historical road traffic flow data of the city, the LSTM urban road network vehicle flow prediction model is trained to drive the spatial topological flow for the sections without detectors with the flow data of the sections with detectors in the historical road section flow data, and combine the upstream and downstream traffic flow and the spatial feature influence coefficient as input to predict the future road section flow data; Based on the predicted future road vehicle composition data, predicted future road section traffic data, and a list of basic emission factors for different vehicle types, dynamic prediction and calculation are performed on the emissions of vehicle types on each section of the urban road network to obtain dynamic emission indicators for mobile sources in the urban traffic road network.

5. The method for constructing a mobile source pollution data visualization platform according to claim 4, characterized in that: The dynamic emission indicators of mobile sources in the urban traffic road network are specifically: In the formula, is the dynamic emission of the i-th vehicle type on the k-th road section in the urban road network, is the traffic volume of the i-th type of vehicle on the k-th road section in the urban road network, is the travel time of the i-th vehicle type on the k-th road section in the urban road network, is the total traffic volume of the i-th type of vehicles on the k-th road section in the urban road network.

6. The method for constructing a mobile source pollution data visualization platform according to claim 5, characterized in that: Obtain urban non-road mobile machinery data, identify the execution conditions corresponding to the operation modes of the engineering machinery in the non-road mobile machinery data, and evaluate the dynamic emission indicators of urban non-road mobile sources, including: Based on the non-road mobile machinery data, according to the operation modes of the engineering machinery of different non-road mobile vehicle types, an original data set of the operation modes of the engineering machinery of the non-road mobile vehicle type is established; Determine the operating condition parameters corresponding to the operation mode of the construction machinery of the non-road mobile vehicle type; Based on the original data set of the operation mode of the construction machinery of the non-road mobile vehicle type, the C-SVC vector machine is trained, with the operation mode of the construction machinery corresponding to the original data of the operation mode of the construction machinery as input and the operating condition parameters of the operation mode of the construction machinery of the non-road mobile vehicle type as output; The operating condition parameters of the non-road mobile vehicle type engineering machinery operation mode are converted by linear mapping to obtain the operating condition parameter vector of the non-road mobile vehicle type engineering machinery operation mode; Calculate the linear regression coefficient between the operating condition parameter vector of the operation mode of the non-road mobile vehicle type engineering machinery and the pollutant emission as the pollutant emission influence coefficient of the operation mode of the non-road mobile vehicle type engineering machinery; Based on the pollutant emission impact coefficient of the engineering machinery operation mode of non-road mobile vehicle types and real-time urban non-road mobile machinery data, the dynamic emission indicators of urban non-road mobile vehicle types are calculated, and the dynamic emission indicators of urban non-road mobile sources are determined.

7. The method for constructing a mobile source pollution data visualization platform according to claim 6, characterized in that: The dynamic emission index for calculating the urban non-road mobile vehicle type is specifically: In the formula, E′ i is the dynamic emission index of the i-th vehicle type in urban non-road mobility, E i is the emission index of the i-th vehicle type in urban non-road mobility, δ iv is the pollutant emission impact coefficient of the vth construction machinery operation mode of the i-th non-road mobile vehicle type, X iv The operating condition parameters of the vth construction machinery operation mode of the i-th non-road mobile vehicle type.

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