Quantitative analysis method, equipment and medium for urban greenhouse gas source

Abstract

The invention discloses a quantitative analysis method and device for urban greenhouse gas sources and a medium, and relates to the technical field of greenhouse gas traceability, and the method comprises the steps: determining the average concentration and trajectory of greenhouse gas corresponding to a target time point based on greenhouse gas concentration data and meteorological field data, and further carrying out the concentration distribution and clustering, obtaining a plurality of trajectory clusters and an average concentration corresponding to each trajectory cluster, calculating a first contribution ratio of each passing province to the greenhouse gas of the target city based on the number of trajectories included in all the trajectory clusters and the average concentration corresponding to all the trajectory clusters, and calculating a second contribution ratio of each passing province to the greenhouse gas of the target city based on the total carbon emission of all the passing province; calculating to obtain a second contribution ratio of each passing province to the greenhouse gas of the target city, and further calculating to obtain a relative contribution ratio of each passing province to the greenhouse gas of the target city. According to the method, quantitative tracing of the urban greenhouse gas can be simply, quickly and accurately realized at low cost, and the method can be widely popularized and used.

Description

Technical Field

[0001] This application relates to the technical field of greenhouse gas traceability, and particularly to a method, device and medium for quantitatively analyzing the sources of urban greenhouse gases. Background Art

[0002] Greenhouse gases refer to gases that can absorb and re-radiate the heat of the Earth's surface, which are the main cause of the greenhouse effect and thus trigger global climate change. Carbon dioxide (CO2) and methane (CH4), as key regulatory factors in the global climate system, contribute 65% and 17% respectively to the total radiation of long-lived greenhouse gases. At present, the source-sink mechanism of greenhouse gases in the atmosphere is not yet clear. CO2 mainly comes from fossil fuel combustion (accounting for 85% of anthropogenic emissions) and is removed through photosynthesis, ocean absorption and other channels. CH4 comes from wetlands, agricultural activities and energy extraction and is mainly removed by hydroxyl radical oxidation. Soil is also an important sink. Cities account for 2% of the world's land area but have more than half of the world's population, and the emissions of air pollutants account for 70% of the total human emissions, of which the carbon emissions are as high as 85%. Therefore, it is crucial to quantitatively trace the greenhouse gases in cities and complete the quantitative analysis of the sources of urban greenhouse gases.

[0003] At present, the greenhouse gas observation network has covered 120 high-precision stations (which can also be called high-precision observation stations), realizing all-round and high-precision monitoring of greenhouse gases, providing strong support for global climate change research and governance. However, the application of greenhouse gas quantitative traceability technology at the urban scale still faces multiple technical bottlenecks. From the perspective of the coverage of the greenhouse gas observation network, the density of high-precision stations in cities is insufficient (the coverage rate is only 30%), making it difficult to effectively distinguish the contributions of local emissions and regional transport, and even more difficult to further quantify the contribution of regional transport.

[0004] Traditional methods for quantitatively tracing greenhouse gases include: (1) Tracing in the gas isotope ratio region: Isotopes have unique fingerprint characteristics. Gases from different sources often have unique isotope compositions. By measuring the isotope ratios of gases, their sources and migration paths can be traced. However, on the one hand, this method requires expensive equipment such as accelerator mass spectrometers (the cost of a single carbon-14 detection exceeds 10,000 yuan), and on the other hand, it requires complex pretreatment, which takes up to 48 hours. Moreover, the isotope fractionation effect during the treatment process may lead to inaccurate final quantitative tracing results. (2) Model simulation method: Global atmospheric chemical transport models (such as GEOS-Chem (Goddard Earth Observing System Chemistry Model), FLEXPART (Lagrangian Flexible Particle Dispersion Model)) and regional models (such as WRF-Chem (Weather Research and Forecasting coupled with Chemistry Model)) are used to reverse the emission sources by simulating the gas transport paths. However, on the one hand, the model data volume is very large, requiring the computing power of supercomputers, and the model operation has high requirements for professional knowledge and technical skills. There is a shortage of talents in urban sites in underdeveloped areas. On the other hand, the model highly depends on the quality of meteorological data. If the quality of meteorological data is poor, it will introduce large errors and requires a large amount of verification data support. The regional scale error can reach 20%. Summary of the Invention

[0005] The purpose of this application is to provide a method, device and medium for quantitatively analyzing the sources of urban greenhouse gases, which can realize the quantitative tracing of urban greenhouse gases at low cost, simply, quickly and accurately, and can be widely promoted and used.

[0006] To achieve the above purpose, the present application provides the following solutions.

[0007] In the first aspect, the present application provides a method for quantitatively analyzing the sources of urban greenhouse gases. The method for quantitatively analyzing the sources of urban greenhouse gases includes: Obtain the greenhouse gas concentration data of the target city in the target time period and the meteorological field data of the tracing area in the tracing time period; the tracing area includes the target city, and the tracing time period includes the target time period and the historical time period before the target time period; For each target time point in the target time period, based on the meteorological field data of the traceability region in the traceability time period, the air mass movement path is calculated backward to obtain the trajectory corresponding to the target time point. Based on the greenhouse gas concentration data of the target city in the target time period, the average greenhouse gas concentration corresponding to the target time point is determined, and based on the average greenhouse gas concentration corresponding to the target time point, the concentration distribution is performed on the trajectory corresponding to the target time point to obtain the trajectory concentration vector corresponding to the target time point; the trajectory concentration vector includes the greenhouse gas concentration corresponding to each trajectory time point in the trajectory corresponding to the target time point. Based on the trajectory concentration vectors corresponding to all the target time points, clustering is performed on the trajectories corresponding to all the target time points to obtain a plurality of trajectory clusters and the average concentration corresponding to each of the trajectory clusters. Based on the number of trajectories included in all the trajectory clusters and the average concentrations corresponding to all the trajectory clusters, the first contribution ratio of each passing province to the greenhouse gases of the target city is calculated. Based on the total carbon emissions of all the passing provinces, the second contribution ratio of each passing province to the greenhouse gases of the target city is calculated, and based on the first contribution ratio and the second contribution ratio, the relative contribution ratio of each passing province to the greenhouse gases of the target city is calculated; the passing province is any province passed by a trajectory cluster.

[0008] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the above-mentioned quantitative analysis method for the sources of urban greenhouse gases.

[0009] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned quantitative analysis method for the sources of urban greenhouse gases is implemented.

[0010] According to the specific embodiments provided by the present application, the present application has the following technical effects.

[0011] The present application provides a method, device and medium for quantitatively analyzing the sources of urban greenhouse gases. Based on the greenhouse gas concentration data of a target city in a target time period and the meteorological field data of a tracing area in a tracing time period, the average concentration and trajectory of greenhouse gases corresponding to a target time point are determined. Based on the average concentration of greenhouse gases corresponding to the target time point, concentration distribution is performed on the trajectory corresponding to the target time point to obtain a trajectory concentration vector corresponding to the target time point. Based on the trajectory concentration vectors corresponding to all target time points, clustering is performed on the trajectories corresponding to all target time points to obtain multiple trajectory clusters and the average concentration corresponding to each trajectory cluster. Based on the number of trajectories included in all trajectory clusters and the average concentration corresponding to all trajectory clusters, the first contribution ratio of each passing province to the greenhouse gases of the target city is calculated. Based on the total carbon emissions of all passing provinces, the second contribution ratio of each passing province to the greenhouse gases of the target city is calculated, and based on the first contribution ratio and the second contribution ratio, the relative contribution ratio of each passing province to the greenhouse gases of the target city is calculated. Compared with the gas isotope ratio regional tracing method, since this application does not require expensive equipment such as accelerator mass spectrometers and only needs to obtain greenhouse gas concentration data and meteorological field data, it has the advantage of low cost. Since it does not require complex pretreatment and only needs to complete the reverse calculation of the air mass movement path, concentration distribution, clustering and relative contribution ratio calculation, it has the advantages of simplicity and speed. Since the processing process is not affected by the isotope fractionation effect, it has the advantage of accuracy. Compared with the model simulation method, since this application does not require the use of global atmospheric chemistry transport models and regional models, there is no large amount of data volume, no complex model operations, does not highly depend on the quality of meteorological data, does not require a large amount of verification data support, and only needs to complete the reverse calculation of the air mass movement path, concentration distribution, clustering and relative contribution ratio calculation, it has the advantages of simplicity, speed and accuracy. Thus, it can realize the quantitative tracing of urban greenhouse gases at low cost, simply, quickly and accurately, and can be widely promoted and used. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0013] Figure 1 It is an application environment diagram of a method for quantitatively analyzing the sources of urban greenhouse gases provided in Embodiment 1 of the present application.

[0014] Figure 2 It is a schematic flowchart of a method for quantitatively analyzing the sources of urban greenhouse gases provided in Embodiment 1 of the present application.

[0015] Figure 3 A structural schematic diagram of a computer device provided in Embodiment 2 of this application. Detailed implementation manners

[0016] Next, the technical solutions in the embodiments of this application will be clearly and completely described with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0017] Embodiment 1.

[0018] The quantitative analysis method for the sources of urban greenhouse gases provided in the embodiments of this application can be applied to the application environment as Figure 1 shown. Among them, the terminal communicates with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be set up separately, integrated on the server, placed on the cloud or other servers. The terminal can send the quantitative analysis request to be processed to the server. After receiving the quantitative analysis request to be processed, for the quantitative analysis request to be processed, the server obtains the greenhouse gas concentration data of the target city in the target time period and the meteorological field data of the traceability area in the traceability time period; for each target time point in the target time period, based on the meteorological field data of the traceability area in the traceability time period, the air mass movement path is calculated backward to obtain the trajectory corresponding to the target time point, the average greenhouse gas concentration corresponding to the target time point is determined based on the greenhouse gas concentration data of the target city in the target time period, and the concentration of the trajectory corresponding to the target time point is allocated based on the average greenhouse gas concentration corresponding to the target time point to obtain the trajectory concentration vector corresponding to the target time point; based on the trajectory concentration vectors corresponding to all target time points, the trajectories corresponding to all target time points are clustered to obtain multiple trajectory clusters and the average concentration corresponding to each trajectory cluster; based on the number of trajectories included in all trajectory clusters and the average concentration corresponding to all trajectory clusters, the first contribution ratio of each passing province to the greenhouse gases of the target city is calculated, based on the total carbon emissions of all passing provinces, the second contribution ratio of each passing province to the greenhouse gases of the target city is calculated, and based on the first contribution ratio and the second contribution ratio, the relative contribution ratio of each passing province to the greenhouse gases of the target city is calculated. The server can feedback to the terminal the quantitative analysis result of the relative contribution ratio of each passing province to the greenhouse gases of the target city for the quantitative analysis request.

[0019] In addition, in some embodiments, the quantitative analysis method for urban greenhouse gas sources can also be implemented by the server or the terminal alone. For example, the terminal can directly process the quantitative analysis request to be processed, or the server can obtain the quantitative analysis request to be processed from the data storage system and process the quantitative analysis request to be processed.

[0020] Among them, the terminal can be, but is not limited to, various desktop computers, laptop computers, smartphones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc., and the portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0021] In an exemplary embodiment, as Figure 2 shown, a quantitative analysis method for urban greenhouse gas sources is provided. This method is executed by a computer device, and can be specifically executed by a computer device such as a terminal or a server alone, or can be jointly executed by the terminal and the server. In the embodiments of the present application, taking this method applied to Figure 1 the server in as an example for illustration, it includes the following steps.

[0022] Step S1, obtain the greenhouse gas concentration data of the target city in the target time period and the meteorological field data of the traceability area in the traceability time period; the traceability area includes the target city, and the traceability time period includes the target time period and the historical time period before the target time period.

[0023] Step S2, for each target time point in the target time period, reversely calculate the air mass movement path based on the meteorological field data of the traceability area in the traceability time period to obtain the trajectory corresponding to the target time point, determine the average greenhouse gas concentration corresponding to the target time point based on the greenhouse gas concentration data of the target city in the target time period, and perform concentration allocation on the trajectory corresponding to the target time point based on the average greenhouse gas concentration corresponding to the target time point to obtain the trajectory concentration vector corresponding to the target time point; the trajectory concentration vector includes the greenhouse gas concentration corresponding to each trajectory time point in the trajectory corresponding to the target time point.

[0024] Step S3, cluster the trajectories corresponding to all the target time points based on the trajectory concentration vectors corresponding to all the target time points to obtain a plurality of trajectory clusters and the average concentration corresponding to each trajectory cluster.

[0025] Step S4: Based on the number of trajectories included in all the trajectory clusters and the average concentration corresponding to all the trajectory clusters, calculate the first contribution ratio of each passing province to the greenhouse gases in the target city. Based on the total carbon emissions of all the passing provinces, calculate the second contribution ratio of each passing province to the greenhouse gases in the target city. And based on the first contribution ratio and the second contribution ratio, calculate the relative contribution ratio of each passing province to the greenhouse gases in the target city; the passing province is any province through which a trajectory cluster passes.

[0026] Implementing the above Step S1 to Step S4, in this embodiment, the air mass movement path is calculated backward to obtain multiple trajectories, the trajectories are subjected to concentration allocation and clustering to obtain multiple trajectory clusters, and further the first contribution ratio of each passing province to the greenhouse gases in the target city is calculated. At the same time, by introducing the total carbon emissions of all the passing provinces, the second contribution ratio of each passing province to the greenhouse gases in the target city is calculated. Combining the first contribution ratio and the second contribution ratio, the relative contribution ratio of each passing province to the greenhouse gases in the target city is calculated, thereby completing the quantitative tracing of the greenhouse gases in the target city. Compared with the gas isotope ratio regional tracing method, since it does not require expensive equipment such as accelerator mass spectrometry and only needs to obtain greenhouse gas concentration data and meteorological field data, it has the advantage of low cost. Since it does not require complex pretreatment and only needs to complete the backward calculation of the air mass movement path, concentration allocation, clustering, and relative contribution ratio calculation, it has the advantages of simplicity and speed. Since the processing process is not affected by the isotope fractionation effect, it has the advantage of accuracy. Compared with the model simulation method, since it does not require the use of global atmospheric chemical transport models and regional models, there is no large amount of data, no complex model operations, no high dependence on the quality of meteorological data, and no need for a large amount of verification data support, and only needs to complete the backward calculation of the air mass movement path, concentration allocation, clustering, and relative contribution ratio calculation, it has the advantages of simplicity, speed, and accuracy. In summary, this embodiment can realize the quantitative tracing of urban greenhouse gases at low cost, simply, quickly, and accurately, and can be widely promoted and used.

[0027] Next, the quantitative analysis method for the sources of urban greenhouse gases used in this embodiment will be introduced in detail, including the following steps.

[0028] (1) Data collection.

[0029] In this embodiment, high-precision greenhouse gas concentration data around the greenhouse gas monitoring stations in the target city during the target time period is first obtained. The greenhouse gas concentration data includes CO2 concentration data and CH4 concentration data. Specifically, the CO2 concentration data and CH4 concentration data are collected by the greenhouse gas detectors in the greenhouse gas monitoring stations. Subsequently, quantitative tracing of CO2 in the target city can be completed based on the CO2 concentration data, and quantitative tracing of CH4 in the target city can be completed based on the CH4 concentration data. Of course, it can also be extended to the quantitative tracing of other types of greenhouse gases. In addition, meteorological field data of the tracing area during the tracing time period is obtained. Specifically, the meteorological field data of the tracing area during the tracing time period can be obtained on the website of the National Environmental Prediction Center. It should be noted that both the target time period and the tracing time period are determined based on user requirements. The tracing time period includes the target time period and the historical time period before the target time period. Both the target city and the tracing area are determined based on user requirements. The tracing area includes the target city. Generally, the tracing area can be defined as the whole world or the whole country.

[0030] At this time, in this embodiment, the greenhouse gas concentration data of the target city during the target time period and the meteorological field data of the tracing area during the tracing time period are obtained.

[0031] This embodiment further conducts statistical analysis on the greenhouse gas concentration data. Specifically, statistical analysis is respectively conducted on the CO2 concentration data and CH4 concentration data during the target time period to obtain the maximum value, minimum value, and average value. Subsequently, further analysis is conducted based on the collected greenhouse gas concentration data and meteorological field data.

[0032] (2) Trajectory calculation.

[0033] The core of trajectory calculation is to solve the equation of the movement of the air mass (an important concept in meteorology, which is an air mass that occupies a large space and has relatively uniform physical properties in the horizontal direction) over time. It is mainly realized by integrating the meteorological field data based on the obtained meteorological field data. Assuming that the air mass is a passive tracer particle, its core calculation principle is based on the Lagrangian integration method. For each target time point in the target time period, the target time point can include the start time point and end time point of the target time period, and the time interval between adjacent target time points can be the same. For example, if the target time period is 1 day, the target time points can be 24 hours in 1 day. In order to reflect the near-surface transport characteristics, the height of the end position of the reverse calculation is selected to be 100 meters above the ground where the greenhouse gas monitoring station is located. Therefore, starting from the longitude and latitude where the greenhouse gas monitoring station in the target city is located and the position 100 meters above the ground, the movement path of the air mass within a preset time period (such as 72 hours, and the specific duration can be determined according to user requirements) before the target time point is calculated in reverse. The last time point of the preset time period is the target time point, and the trajectory corresponding to the target time point is obtained.

[0034] The calculation formula for the reverse calculation of the air mass movement path is as follows: (1); In formula (1), is the position of the air mass at moment, and this position is represented by a three-dimensional position vector. The three-dimensional position vector includes longitude , latitude and altitude . is the calculation step, which is determined according to the user's needs and can be 5s; is the position of the air mass at moment; is the meteorological field data, representing the change of the wind field with position and time . The wind speed in the meteorological field data is represented by a three-dimensional wind speed vector ([[]] , , ). It is obtained by interpolating the 1°×1° meteorological field data, and the interpolation step is taken as 0.1° to match the grid resolution. Since , is the horizontal wind speed component, and the horizontal movement is the change of longitude and latitude. Therefore, the horizontal wind speed component can be determined according to the change of longitude and latitude per unit time. Specifically, the horizontal wind speed components , can be obtained through the horizontal motion equation based on the longitude and latitude information contained in the meteorological field data. is the vertical wind speed component, which needs to be calculated through the mass conservation equation shown in formula (2); is the turbulence field data, representing the change of the turbulence field with position and time . The diffusion displacement in the turbulence field data ([[[]] , , ) needs to be calculated through formula (3). , , are the diffusion displacements in the x-axis, y-axis, and z-axis directions respectively. Since the air mass is constantly moving, the wind field and the turbulence field are also constantly changing. Through the above method, the meteorological field data and the turbulence field data can be determined, and further substituting them into formula (1), the position of the air mass at moment can be determined, and the trajectory corresponding to the target time point can be further determined.

[0035] To ensure the accurate height at the end point, it is necessary to limit the height range of all positions in the trajectory. Due to the trajectory motion error, the limited range is 90 - 110 meters. This is because there are certain errors in the calculation of the air mass movement trajectory under different terrains and atmospheric environments. The vertical error of the backward trajectory is usually between 10% and 20%. For example, a nominal height of 100 meters may actually correspond to a physical height of 80 - 120 meters. Therefore, in this embodiment, the calculation result between 90 - 110 meters is regarded as 100 meters, that is, if the calculated height in is between 90 - 110 meters, it is corrected to be equal to 100 meters.

[0036] This embodiment further introduces outlier rejection: During the change of the wind field, if there are sudden changes in the vertical wind speed component and the horizontal wind speed component, it is considered that the calculated position at this time is an outlier and needs to be rejected. The judgment condition for the sudden change of the vertical wind speed component is: ∣Δ ∣> 5 m / s, where Δ is the change value of the vertical wind speed component, reflecting the abnormal movement in the troposphere. The judgment condition for the sudden change of the horizontal wind speed component is: ∣Δ ∣> 20 m / s, where Δ is the change value of the horizontal wind speed component, reflecting the interference of extreme weather such as squall lines. is the horizontal wind speed component of, reflecting the interference of extreme weather such as squall lines.

[0037] The mass conservation equation is: (2); In Equation (2), is the vertical wind speed component, representing the movement speed of the fluid or air in the vertical direction (usually the z-axis), with the unit of m / s; is the height, generally the z-axis coordinate, usually taking the ground ( = 0) as the reference point, with the upward direction being the positive direction, and the unit is m; in the integral upper and lower limits, the lower limit 0 represents the reference height (such as the ground or the top of the boundary layer), and the upper limit represents the height of the desired , and the integral represents the cumulative effect from the reference height to ; and are the horizontal wind speed components in the x-axis (east-west direction) and y-axis (north-south direction) respectively, representing the movement speeds of the fluid or air in the x-axis and y-axis directions respectively, with the unit of m / s; are the x-axis coordinate and y-axis coordinate respectively; is the horizontal velocity gradient along the x-axis, reflecting the velocity change rate in the east-west direction; is The horizontal velocity gradient along the y-axis reflects the rate of velocity change in the north-south direction; is the horizontal divergence, with the unit of 1 / s. If the horizontal divergence is positive, it indicates horizontal divergence (mass loss), which needs to be compensated by vertical upward motion ( <0), conversely, it needs to be compensated by downward motion ( >0).

[0038] In the case of simulating boundary layer perturbations using the random walk model, the calculation formula for the turbulent flow field is: (3); In formula (3), is the diffusion displacement in the direction, that is, the instantaneous displacement of the particle in the direction due to random motion (such as Brownian motion); is the diffusion coefficient, with the unit of m 2 / s, which is related to the medium temperature and particle properties and reflects the diffusion rate, =0.4 , is the friction velocity; is the Gaussian white noise sequence in the direction,

[0039] It should be noted that since the meteorological field data time is the standard UTC time (Coordinated Universal Time), the local time needs to be converted to UTC time (such as subtracting 8 hours from Beijing time) to avoid the wind field misalignment caused by time zone deviation.

[0040] At this time, in this embodiment, for each target time point in the target time period, the air mass movement path is reversely calculated based on the meteorological field data of the source region in the source time period, and the trajectory corresponding to the target time point is obtained.

[0041] Among them, reversely calculating the air mass movement path based on the meteorological field data of the source region in the source time period to obtain the trajectory corresponding to the target time point specifically includes: determining the meteorological field data of the source region in the preset time period based on the meteorological field data of the source region in the source time period. The preset time period is the time period in the source time period before the target time point, and the last time point of the preset time period is the target time point; using the meteorological field data of the source region in the preset time period as the input, and reversely calculating the air mass movement path using the Lagrangian integral method, specifically performing the reverse calculation through formula (1) to obtain the air mass at each preset time point in the preset time period (that is, The position of ); The positions of the air masses at each preset time point within the preset time period are combined to form the trajectory corresponding to the target time point.

[0042] (3) Trajectory concentration allocation.

[0043] When the air mass moves, its trajectory changes. At the same time, the air mass itself has concentration characteristics. In this embodiment, according to the greenhouse gas concentration data of the greenhouse gas monitoring station, it is initially allocated to different air masses according to time. Specifically, the target time period is evenly divided based on the target time point. Each target time point corresponds to a time period. For each target time point, the average value of the greenhouse gas concentration data within the time period corresponding to the target time point is calculated to obtain the average greenhouse gas concentration corresponding to the target time point. Based on the number of trajectory time points in the trajectory corresponding to the target time point, when the preset time period is 72 hours, in this embodiment, the trajectory time points can be set to 72. The time interval between adjacent trajectory time points can be the same. The average greenhouse gas concentration corresponding to the target time point is evenly allocated to obtain the initial greenhouse gas concentration corresponding to each trajectory time point. It is equivalent to evenly distributing the average greenhouse gas concentration corresponding to a certain target time point to each trajectory time point calculated backward. The initial greenhouse gas concentration corresponding to each trajectory time point is equal and is 1 / 72 of the average greenhouse gas concentration.

[0044] After the initial allocation is completed, the initial greenhouse gas concentration corresponding to each trajectory time point in each trajectory can be obtained, and the standardization of the initial greenhouse gas concentration corresponding to each trajectory time point is performed. The calculation formula is: (4); In formula (4), is the initial standardized greenhouse gas concentration corresponding to the th trajectory time point; is the initial greenhouse gas concentration corresponding to the th trajectory time point; is the average value of the initial greenhouse gas concentrations corresponding to the th trajectory time point in all trajectories; is the standard deviation of the initial greenhouse gas concentrations corresponding to the th trajectory time point in all trajectories.

[0045] Since the initial allocation evenly distributes the concentration according to time, and in reality, the greenhouse gas concentration is often greater at the end time. Therefore, it is necessary to adjust the weight on the basis of the initial allocation so that the allocation result is more in line with the actual situation. Therefore, the time weight is added to the initial allocation according to the following formulas (5), (6) and (7) to complete the concentration allocation.

[0046] This embodiment assigns a higher weight to the end time to strengthen the impact of the key period before the arrival of greenhouse gases. That is, since the concentration of greenhouse gases at the transmission end (close to the target city) contributes more significantly to local pollution, the weight of the end time (rank = 1) is higher to strengthen the impact of the key period before the arrival of greenhouse gases. The calculation formula for the initial time weight is: (5); In formula (5), is the initial time weight corresponding to the th trajectory time point; is the rank of the th trajectory time point. All trajectory time points are sorted in the order from the end to the beginning. = 1 represents the end time, = 72 represents the starting time of the traceability; is the attenuation coefficient, = 0.5.

[0047] To ensure that the sum of all initial time weights is equal to 1, the initial time weights obtained from formula (5) are normalized according to formula (6). The calculation formula for normalization is: (6); In formula (6), is the time weight. Through formula (6), it can be ensured that the total weight sum is 1.

[0048] After determining the time weight, according to the obtained time weight, the initially normalized greenhouse gas concentration obtained from formula (4) is adjusted to obtain the greenhouse gas concentration that conforms to the actual situation. The calculation formula is: (7); In formula (7), is the greenhouse gas concentration corresponding to the th trajectory time point.

[0049] At this time, in this embodiment, based on the greenhouse gas concentration data of the target city in the target time period, the average greenhouse gas concentration corresponding to the target time point is determined, and based on the average greenhouse gas concentration corresponding to the target time point, the concentration is allocated to the trajectory corresponding to the target time point to obtain the trajectory concentration vector corresponding to the target time point. The trajectory concentration vector includes the greenhouse gas concentration corresponding to each trajectory time point in the trajectory corresponding to the target time point.

[0050] Among them, allocating the concentration to the trajectory corresponding to the target time point based on the average greenhouse gas concentration corresponding to the target time point to obtain the trajectory concentration vector corresponding to the target time point specifically includes the following steps.

[0051] (1) Based on the number of trajectory time points in the trajectory corresponding to the target time point, evenly distribute the average greenhouse gas concentration corresponding to the target time point to obtain the initial greenhouse gas concentration corresponding to each trajectory time point.

[0052] In this embodiment, the preset time period corresponding to the trajectory can be evenly divided according to user requirements to obtain multiple trajectory time points. The trajectory time points can include the start time point and the end time point of the preset time period, and the time interval between adjacent trajectory time points can be the same. For example, if the preset time period is 3 days, the trajectory time points can be 72 hours within 3 days.

[0053] (2) For each trajectory time point, based on the sorting of the trajectory time points, calculate the initial time weight corresponding to the trajectory time point, specifically calculated by Equation (5), and perform normalization processing on the initial time weight, specifically normalized by Equation (6), to obtain the time weight corresponding to the trajectory time point. Then, based on the initial greenhouse gas concentration and the time weight corresponding to the trajectory time point, calculate the greenhouse gas concentration corresponding to the trajectory time point, specifically calculated by Equation (7).

[0054] (3) Combine the greenhouse gas concentrations corresponding to all trajectory time points to form the trajectory concentration vector corresponding to the target time point. At this time, the dimension of the trajectory concentration vector is 1 72.

[0055] (IV) Calculation of trajectory clusters and average concentrations corresponding to trajectory clusters.

[0056] When normalizing and evaluating the contribution of meteorological conditions, the number of trajectory clusters and the average concentration corresponding to the trajectory clusters need to be obtained. Therefore, clustering is performed based on the trajectories and the trajectory concentration vectors corresponding to the trajectories to calculate the number of trajectory clusters and the average concentration corresponding to the trajectory clusters.

[0057] The objective function shown in the following Equation (8) is the basis for judging whether the clustering result of the trajectory clusters is reasonable. The objective function is used to calculate the objective function value, and the calculation formula of the objective function value is: (8); In Equation (8), is the objective function value; is the initial number of clusters, that is, the number of trajectory clusters; is the th initial trajectory cluster (which is the trajectory set of the th initial trajectory cluster, including all trajectories assigned to this initial trajectory cluster) The th trajectory corresponding trajectory concentration vector; is the th initial trajectory cluster The The trajectory weight corresponding to the trajectory is based on the Initial trajectory cluster The The residence time of each trajectory in the pollution source area of the tracing area is determined; For the Initial trajectory cluster The corresponding average trajectory concentration vector is Initial trajectory cluster The average value vector of the trajectory concentration vector corresponding to each trajectory in , with a dimension of 1×72, represents the typical pollution transmission pattern of the initial trajectory cluster.

[0058] The trajectory weight reflects the The pollution contribution of each trajectory is calculated using either residence time weight or speed weight. Taking residence time weight as an example, the calculation formula for trajectory weight is: (9); In formula (9), It is the set of all trajectory time points that enter the pollution source area (which refers to the source area of pollutant emissions and has an impact on the surrounding environment) in the trajectory, determined by matching the trajectory coordinates with the geo-fence (i.e., geographic coordinate boundary); For The residence time corresponding to each trajectory time point (i.e. each trajectory time point entering the pollution source area), that is, the duration of the trajectory in the pollution source area at the trajectory time point, in hours. If the trajectory continues to stay in the pollution source area at a certain trajectory time point, it is equivalent to the time period corresponding to the trajectory time point (the time period corresponding to the trajectory time point determined by evenly dividing the preset time period based on the trajectory time point) is always in the pollution source area, then = 1 hour. If the trajectory of the time period corresponding to the time point of the trajectory is in the pollution source area for part of the time, the calculation is proportional, specifically the ratio of the part of the time period to the time period corresponding to the trajectory time point (such as 0.5 hours); the numerator represents the total time the trajectory stays in the pollution source area, in hours, and the denominator ( ) represents the total duration of the trajectory (fixed at 72 hours).

[0059] When clustering trajectories, all trajectories are divided into different groups by clustering. Each group is a trajectory cluster. The clustering process includes the following steps.

[0060] (1) Initialize cluster centers.

[0061] The K-means++ algorithm (K-means++ clustering algorithm) is used to select the initial cluster centers to avoid local optimality caused by random initialization. The specific operation is as follows: Randomly select any trajectory as the first initial cluster center. For subsequent initial cluster centers, the selection probability (i.e., the probability of selecting a certain trajectory other than the already selected initial cluster center as the next initial cluster center) is proportional to the square of the distance, that is, select the trajectory with the farthest distance from the already selected initial cluster center as the next initial cluster center.

[0062] (2) Assignment phase.

[0063] For each trajectory, calculate the Euclidean distance between the trajectory and each initial cluster center. The calculation formula is: (10); In formula (10), is the Euclidean distance between the th trajectory and the th initial cluster center; is the greenhouse gas concentration corresponding to the th trajectory at the th trajectory time point; is the average value of the greenhouse gas concentrations corresponding to the th iteration at the th trajectory time point for all trajectories in the th initial trajectory cluster. In the first iteration, it is the greenhouse gas concentration corresponding to the th trajectory in the trajectory corresponding to the th initial cluster center at the

[0064] After calculating the Euclidean distance, the assignment rule is: Assign each trajectory to the trajectory cluster corresponding to the initial cluster center with the smallest Euclidean distance from this trajectory: (11); In formula (11), is the th initial trajectory cluster in the th iteration, which is the set of all trajectories belonging to the th iteration and the th initial trajectory cluster.

[0065] (3) Update phase.

[0066] Recalculate the cluster centers. The calculation formula is as follows: (12); In formula (12), is the The average of the greenhouse gas concentrations corresponding to the th trajectory time point among all trajectories of the th initial trajectory cluster in the th iteration forms the initial cluster center of the th iteration; For the number of trajectories in the th initial trajectory cluster in the

[0067] (4) Convergence determination.

[0068] The iteration termination condition is: the change in the objective function is less than the preset threshold or the number of iterations reaches the maximum number of iterations: Δ Or ≥ 100, Δ is the change in the objective function, is the objective function value of the th iteration, calculated by Equation (8), is the objective function value of the th iteration, calculated by Equation (8).

[0069] Based on the above clustering process, the method for determining the number of trajectory clusters includes the following steps.

[0070] (1) Preliminary screening.

[0071] By calculating the within-cluster sum of squares (WCSS) corresponding to different numbers of clusters , select the number of clusters when the decrease rate of the within-cluster sum of squares decreases significantly as the number of clusters after preliminary screening. Specifically, when the decrease rate of the within-cluster sum of squares < 15%, select the corresponding number of clusters , and the formula for calculating the within-cluster sum of squares is: (13); In Equation (13), is the within-cluster sum of squares corresponding to the number of clusters .

[0072] (2) Re-screening.

[0073] The silhouette score is a comprehensive indicator to measure the within-cluster compactness and between-cluster separation. The closer the value is to 1, the better the clustering effect. The formula for calculating the silhouette score is: (14); In Equation (14), is the silhouette score of the th trajectory; is the distance from the th trajectory to the nearest other initial trajectory cluster (i.e., the other initial trajectory cluster other than the initial trajectory cluster to which the th trajectory belongs, which is the nearest to the th trajectory); is the average distance between the th trajectory and other trajectories in the same cluster (i.e., other trajectories other than the th trajectory that belong to the same initial trajectory cluster as the th trajectory); is the average value of the silhouette coefficients of all trajectories, which measures the global quality of the clustering result; is the number of trajectories.

[0074] This embodiment requires > 0.6 to ensure intra - cluster consistency and inter - cluster separation, indicating that the clustering result is reasonable. Therefore, the number of clusters after preliminary screening with > 0.6 is used as the alternative number, and then the alternative number with the largest is selected as the number of clusters after screening, which is the final number of trajectory clusters.

[0075] After determining the number of trajectory clusters, through clustering, it is possible to obtain how many trajectories each trajectory cluster contains and the average trajectory concentration vector corresponding to each trajectory cluster. Further, the average concentration corresponding to each trajectory cluster is calculated. The formula for calculating the average concentration is: (15); In formula (15), is the average concentration corresponding to the th trajectory cluster; is the number of trajectories included in the th trajectory cluster; is the trajectory concentration vector corresponding to the th trajectory cluster and belonging to the th trajectory.

[0076] At this time, in this embodiment, based on the trajectory concentration vectors corresponding to all target time points, the trajectories corresponding to all target time points are clustered to obtain multiple trajectory clusters and the average concentration corresponding to each trajectory cluster.

[0077] Among them, clustering the trajectories corresponding to all target time points based on the trajectory concentration vectors corresponding to all target time points to obtain multiple trajectory clusters and the average concentration corresponding to each trajectory cluster specifically includes the following steps.

[0078] (1)Randomly determine multiple numbers of clusters, sort all the numbers of clusters in ascending order, and select the first number of clusters as the initial number of clusters.

[0079] (2)Cluster the trajectories corresponding to all target time points based on the initial number of clusters and the trajectory concentration vectors corresponding to all target time points, to obtain multiple initial trajectory clusters corresponding to the initial number of clusters and the average trajectory concentration vector corresponding to each initial trajectory cluster.

[0080] When clustering the trajectories corresponding to all target time points based on the initial number of clusters and the trajectory concentration vectors corresponding to all target time points, the iteration termination condition for clustering is: the change amount of the objective function is less than the preset threshold or the number of iterations reaches the maximum number of iterations. The change amount of the objective function is the difference between the objective function value calculated in the current iteration and the objective function value calculated in the previous iteration. The calculation formula for the objective function value is Equation (8).

[0081] (3)Based on the average trajectory concentration vector corresponding to each initial trajectory cluster corresponding to the initial number of clusters, calculate the within-cluster sum of squares corresponding to the initial number of clusters, specifically calculated by Equation (13), and based on the within-cluster sum of squares corresponding to the initial number of clusters and the within-cluster sum of squares corresponding to the previous number of clusters of the initial number of clusters (that is, the within-cluster sum of squares corresponding when the previous number of clusters of the initial number of clusters is used as the initial number of clusters), calculate the within-cluster sum of squares decline rate corresponding to the initial number of clusters. Specifically, calculate the ratio of the difference (the difference between the within-cluster sum of squares corresponding to the initial number of clusters and the within-cluster sum of squares corresponding to the previous number of clusters of the initial number of clusters) to the within-cluster sum of squares corresponding to the previous number of clusters of the initial number of clusters, to obtain the within-cluster sum of squares decline rate corresponding to the initial number of clusters.

[0082] (4)Judge whether there are unselected numbers of clusters. If so, select the next number of clusters of the initial number of clusters as the initial number of clusters for the next iteration, and return to the step of "cluster the trajectories corresponding to all target time points based on the initial number of clusters and the trajectory concentration vectors corresponding to all target time points". If not, based on the within-cluster sum of squares decline rates corresponding to all initial numbers of clusters, conduct a preliminary screening on all initial numbers of clusters to obtain the numbers of clusters after preliminary screening.

[0083] For each initial number of clusters, if the within-cluster sum of squares decline rate corresponding to the initial number of clusters < 15%, then select this initial number of clusters as the number of clusters after preliminary screening.

[0084] (5)For each number of clusters after preliminary screening, based on the multiple initial trajectory clusters corresponding to the number of clusters after preliminary screening, calculate the silhouette coefficient corresponding to the number of clusters after preliminary screening, specifically calculated by Equation (14).

[0085] (6) Re-screen all the initially screened clustering numbers based on the silhouette coefficients corresponding to all the initially screened clustering numbers to obtain the screened clustering number, and use the multiple initial trajectory clusters corresponding to the screened clustering number and the average trajectory concentration vector corresponding to each initial trajectory cluster as the multiple trajectory clusters and the average trajectory concentration vector corresponding to each trajectory cluster. Based on the average trajectory concentration vector corresponding to the trajectory cluster, calculate the average concentration corresponding to the trajectory cluster, specifically calculated by Equation (15).

[0086] For each initially screened clustering number, if the silhouette coefficient corresponding to the initially screened clustering number > 0.6, then use this initially screened clustering number as an alternative number, and then select the alternative number with the largest silhouette coefficient as the screened clustering number.

[0087] (5) Normalize and evaluate the contribution of meteorological conditions.

[0088] According to the number of trajectory clusters and the average concentration corresponding to the trajectory cluster, quantitatively evaluate the impact of meteorological conditions on the transmission of greenhouse gases in the target city. First, calculate the trajectory cluster occupancy ratio and the trajectory cluster concentration contribution ratio corresponding to each trajectory cluster.

[0089] The calculation formula for the trajectory cluster occupancy ratio is: (16); In Equation (16), is the trajectory cluster occupancy ratio corresponding to the -th trajectory cluster, that is, the percentage of the number of trajectories included in this trajectory cluster in the total number of trajectories; is the number of trajectories included in the -th trajectory cluster; is the number of trajectory clusters.

[0090] The calculation formula for the trajectory cluster concentration contribution ratio is: (17); In Equation (17), is the trajectory cluster concentration contribution ratio corresponding to the -th trajectory cluster; is the average concentration corresponding to the -th trajectory cluster; is the average value of the greenhouse gas concentration data in the target city during the target time period.

[0091] Based on the trajectory cluster occupancy ratio and the trajectory cluster concentration contribution ratio corresponding to each trajectory cluster, a normalized evaluation is carried out on the contribution of the air flow trajectory to the greenhouse gases in the target city after passing through each surrounding area, and the first contribution ratio of each passing province to the greenhouse gases in the target city is obtained. The passing province is any province passed by a trajectory cluster, that is, a certain province passed by the air flow trajectory. The calculation formula for the first contribution ratio is as follows: (18); In formula (18), is the first contribution ratio of the -th passing province to the greenhouse gases in the target city, which is used to characterize the contribution of the passing province to the greenhouse gases in the target city due to meteorological conditions. The numerator represents the concentration contribution of a certain passing province to the greenhouse gases in the target city due to meteorological conditions, which is determined based on the total concentration contribution of each trajectory cluster to a certain passing province (if a trajectory cluster passes through a certain passing province, it is considered to have a concentration contribution to this passing province; if a trajectory cluster does not pass through a certain passing province, it is considered to have no concentration contribution to this passing province, that is, the concentration contribution is 0). The denominator represents the concentration contribution of all passing provinces to the greenhouse gases in the target city due to meteorological conditions; is the number of trajectory clusters passing through the -th passing province; is the trajectory cluster occupancy ratio corresponding to the -th passing province of the -th trajectory cluster; is the trajectory cluster concentration contribution ratio corresponding to the -th passing province of the -th trajectory cluster; is the number of passing provinces.

[0092] At this time, in this embodiment, based on the number of trajectories included in all trajectory clusters and the average concentration corresponding to all trajectory clusters, the first contribution ratio of each passing province to the greenhouse gases in the target city is calculated.

[0093] Among them, based on the number of trajectories included in all trajectory clusters and the average concentration corresponding to all trajectory clusters, calculating the first contribution ratio of each passing province to the greenhouse gases in the target city specifically includes: for each trajectory cluster, based on the number of trajectories included in the trajectory cluster, calculating the trajectory cluster occupancy ratio corresponding to the trajectory cluster, specifically calculated through formula (16), and based on the trajectory cluster occupancy ratio and the average concentration corresponding to the trajectory cluster, calculating the trajectory cluster concentration contribution ratio corresponding to the trajectory cluster, specifically calculated through formula (17); for each passing province, based on the trajectory cluster occupancy ratio and the trajectory cluster concentration contribution ratio corresponding to all trajectory clusters, calculating the first contribution ratio of the passing province to the greenhouse gases in the target city, specifically calculated through formula (18).

[0094] (6) Normalize the assessment of carbon emission contributions.

[0095] To make the quantitative calculation results more accurate, this embodiment additionally introduces the total carbon emissions to further correct the calculation.

[0096] To evaluate the impact of regional carbon emissions on the greenhouse gases of the target city, the total carbon emissions of the whole country in 5 years (2019 - 2023, other durations and time periods can also be selected according to needs) are introduced. Then, the source intensity of the greenhouse gases in the target city can be calculated according to the following formula: (19); (20); In the above formula, is the proportion of the total carbon emissions of the th passing province in the total carbon emissions of the whole country. The numerator represents the total carbon emissions of the th passing province in 5 years (2019 - 2023), and the denominator represents the total carbon emissions of the whole country in 5 years (2019 - 2023); is the annual total carbon emissions of the th passing province; is the annual total carbon emissions of the whole country; is the second contribution ratio of the th passing province to the greenhouse gases of the target city, which is the normalized provincial carbon emissions ratio affecting the source of the greenhouse gases in the target city and is used to characterize the contribution of this passing province to the greenhouse gases of the target city due to carbon emissions; is the total number of passing provinces.

[0097] The calculation formula for the relative contribution ratio of each passing province to the greenhouse gases of the target city is: (21); In formula (21), is the relative contribution ratio of the th passing province to the greenhouse gases of the target city.

[0098] At this time, in this embodiment, based on the total carbon emissions of all passing provinces, the second contribution ratio of each passing province to the greenhouse gases of the target city is calculated, and based on the first contribution ratio and the second contribution ratio, the relative contribution ratio of each passing province to the greenhouse gases of the target city is calculated.

[0099] Among them, based on the total carbon emissions of all passing provinces, the second contribution ratio of each passing province to the greenhouse gases of the target city is calculated, specifically including: for each passing province, calculate the ratio of the total carbon emissions of the passing province to the total carbon emissions of the country to obtain the proportion of carbon emissions corresponding to the passing province, which is specifically calculated by formula (19), and calculate the ratio of the proportion of carbon emissions corresponding to the passing province to the sum value of the proportion of carbon emissions to obtain the second contribution ratio of the passing province to the greenhouse gases of the target city, which is specifically calculated by formula (20), where the sum value of the proportion of carbon emissions is the sum of the proportions of carbon emissions corresponding to all passing provinces.

[0100] Among them, based on the first contribution ratio and the second contribution ratio, the relative contribution ratio of each passing province to the greenhouse gases of the target city is calculated, specifically including: for each passing province, calculate the product of the first contribution ratio of the passing province to the greenhouse gases of the target city and the second contribution ratio of the passing province to the greenhouse gases of the target city to obtain the total contribution ratio corresponding to the passing province, and calculate the ratio of the total contribution ratio corresponding to the passing province to the sum value of the total contribution ratio to obtain the relative contribution ratio of the passing province to the greenhouse gases of the target city, which is specifically calculated by formula (21), where the sum value of the total contribution ratio is the sum of the total contribution ratios corresponding to all passing provinces.

[0101] This embodiment innovatively proposes a quantitative analysis method for the sources of urban greenhouse gases that can be widely promoted and has strong practicability, solving the difficult situation of greenhouse gas source assessment due to the lack of high-quality talents and the imperfect construction of high-precision observation networks.

[0102] This application also provides an application scenario that applies the above-mentioned quantitative analysis method for the sources of urban greenhouse gases. Specifically, the quantitative analysis method for the sources of urban greenhouse gases provided in this embodiment can be applied in a quantitative tracing scenario. The quantitative tracing scenario includes a data acquisition link, a quantitative tracing link, and a display link. The data acquisition link is used to acquire the greenhouse gas concentration data of the target city in the target time period and the meteorological field data of the tracing area in the tracing time period. The quantitative tracing link is used to determine the relative contribution ratio of each passing province to the greenhouse gases of the target city based on the greenhouse gas concentration data and the meteorological field data, and quantify the regional transmission contribution. The display link is used to display the relative contribution ratio of each passing province to the greenhouse gases of the target city to the user, which is conducive to subsequent greenhouse gas emission control. The quantitative analysis method for the sources of urban greenhouse gases provided in this embodiment belongs to the quantitative tracing link.

[0103] Embodiment 2.

[0104] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 3As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a quantitative analysis method for the sources of urban greenhouse gases.

[0105] Those skilled in the art can understand that Figure 3 the structure shown in the figure is only a block diagram of some structures 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 those shown in the figure, or combine certain components, or have different component arrangements.

[0106] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, it implements the quantitative analysis method for the sources of urban greenhouse gases in Embodiment 1.

[0107] Embodiment 3.

[0108] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by the processor, it implements the quantitative analysis method for the sources of urban greenhouse gases in Embodiment 1.

[0109] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0110] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0111] In this text, specific examples are used to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation on the present application.

Claims

1. A quantitative analysis method for urban greenhouse gas sources, characterized in that, The quantitative analysis method for the sources of urban greenhouse gases includes: Obtaining the greenhouse gas concentration data of the target city in the target time period and the meteorological field data of the source region in the source time period; the source region includes the target city, and the source time period includes the target time period and the historical time period before the target time period; For each target time point in the target time period, based on the meteorological field data of the source region in the source time period, the air mass movement path is calculated backwards to obtain the trajectory corresponding to the target time point, the average greenhouse gas concentration corresponding to the target time point is determined based on the greenhouse gas concentration data of the target city in the target time period, and the trajectory corresponding to the target time point is assigned a concentration based on the average greenhouse gas concentration corresponding to the target time point to obtain the trajectory concentration vector corresponding to the target time point; the trajectory concentration vector includes the greenhouse gas concentration corresponding to each trajectory time point in the trajectory corresponding to the target time point; Based on the trajectory concentration vectors corresponding to all the target time points, the trajectories corresponding to all the target time points are clustered to obtain a plurality of trajectory clusters and the average concentration corresponding to each trajectory cluster; Based on the number of trajectories included in all the trajectory clusters and the average concentrations corresponding to all the trajectory clusters, the first contribution ratio of each passing province to the greenhouse gases of the target city is calculated, based on the total carbon emissions of all the passing provinces, the second contribution ratio of each passing province to the greenhouse gases of the target city is calculated, and based on the first contribution ratio and the second contribution ratio, the relative contribution ratio of each passing province to the greenhouse gases of the target city is calculated; the passing province is any province passed by a trajectory cluster.

2. The quantitative analysis method for urban greenhouse gas sources according to claim 1, characterized in that, Calculating the air mass movement path backwards based on the meteorological field data of the source region in the source time period to obtain the trajectory corresponding to the target time point specifically includes: Based on the meteorological field data of the source region in the source time period, determining the meteorological field data of the source region in the preset time period; the preset time period is the time period before the target time point in the source time period, and the last time point of the preset time period is the target time point; Using the meteorological field data of the source region in the preset time period as input, the air mass movement path is calculated backwards by using the Lagrangian integral method to obtain the position of the air mass at each preset time point in the preset time period; The positions of the air mass at each preset time point in the preset time period are combined to form the trajectory corresponding to the target time point.

3. The quantitative analysis method for the sources of urban greenhouse gases according to claim 1, characterized in that, Assigning a concentration to the trajectory corresponding to the target time point based on the average greenhouse gas concentration corresponding to the target time point to obtain the trajectory concentration vector corresponding to the target time point specifically includes: Based on the number of trajectory time points in the trajectory corresponding to the target time point, the average greenhouse gas concentration corresponding to the target time point is evenly distributed to obtain the initial greenhouse gas concentration corresponding to each trajectory time point; For each of the trajectory time points, based on the sorting of the trajectory time points, calculate the initial time weight corresponding to the trajectory time point, perform a normalization process on the initial time weight to obtain the time weight corresponding to the trajectory time point, and calculate the greenhouse gas concentration corresponding to the trajectory time point based on the initial greenhouse gas concentration and the time weight corresponding to the trajectory time point; Form the trajectory concentration vector corresponding to the target time point by using the greenhouse gas concentrations corresponding to all the trajectory time points; Among them, the calculation formula for the initial time weight is: ； Wherein, is the initial time weight corresponding to the -th trajectory time point; is the sorting of the -th trajectory time point, and all trajectory time points are sorted in the order from the latest time to the earliest time; is the attenuation coefficient.

4. The quantitative analysis method for urban greenhouse gas sources according to claim 1, characterized in that Cluster the trajectories corresponding to all the target time points based on the trajectory concentration vectors corresponding to all the target time points to obtain a plurality of trajectory clusters and the average concentration corresponding to each of the trajectory clusters, specifically including: Randomly determine a plurality of clustering numbers, sort all the clustering numbers in ascending order, and select the first clustering number as the initial clustering number; Cluster the trajectories corresponding to all the target time points based on the initial clustering number and the trajectory concentration vectors corresponding to all the target time points to obtain a plurality of initial trajectory clusters corresponding to the initial clustering number and the average trajectory concentration vector corresponding to each of the initial trajectory clusters; Based on the average trajectory concentration vector corresponding to each of the initial trajectory clusters corresponding to the initial clustering number, calculate the within-cluster sum of squares corresponding to the initial clustering number, and calculate the within-cluster sum of squares decline rate corresponding to the initial clustering number based on the within-cluster sum of squares corresponding to the initial clustering number and the within-cluster sum of squares corresponding to the previous clustering number of the initial clustering number; Judge whether there are unselected clustering numbers. If so, select the next clustering number of the initial clustering number as the initial clustering number for the next iteration, and return to the step of "cluster the trajectories corresponding to all the target time points based on the initial clustering number and the trajectory concentration vectors corresponding to all the target time points". If not, perform a preliminary screening on all the initial clustering numbers based on the within-cluster sum of squares decline rates corresponding to all the initial clustering numbers to obtain the clustering numbers after preliminary screening; For each of the clustering numbers after preliminary screening, calculate the silhouette coefficient corresponding to the clustering number after preliminary screening based on the plurality of initial trajectory clusters corresponding to the clustering number after preliminary screening; Perform a re-screening on all the clustering numbers after preliminary screening based on the silhouette coefficients corresponding to all the clustering numbers after preliminary screening to obtain the clustering numbers after screening, and use the plurality of initial trajectory clusters corresponding to the clustering numbers after screening and the average trajectory concentration vector corresponding to each of the initial trajectory clusters as the plurality of trajectory clusters and the average trajectory concentration vector corresponding to each of the trajectory clusters, and calculate the average concentration corresponding to the trajectory cluster based on the average trajectory concentration vector corresponding to the trajectory cluster.

5. The quantitative analysis method for urban greenhouse gas sources according to claim 4, wherein When clustering the trajectories corresponding to all the target time points based on the initial number of clusters and the trajectory concentration vectors corresponding to all the target time points, the iterative termination condition for clustering is that the change in the objective function is less than a preset threshold or the number of iterations reaches the maximum number of iterations. The change in the objective function is the difference between the objective function value calculated in the current iteration and the objective function value calculated in the previous iteration. The calculation formula for the objective function value is: ； Among them, is the objective function value; is the initial number of clusters; is the th initial trajectory cluster the th trajectory concentration vector corresponding to the trajectory in it; is the th initial trajectory cluster the th trajectory weight corresponding to the trajectory in it; is the th initial trajectory cluster corresponding average trajectory concentration vector.

6. The quantitative analysis method for urban greenhouse gas sources according to claim 1, characterized in that Based on the number of trajectories included in all the trajectory clusters and the average concentration corresponding to all the trajectory clusters, calculate the first contribution ratio of each passing province to the greenhouse gases in the target city, specifically including: For each of the trajectory clusters, based on the number of trajectories included in the trajectory cluster, calculate the trajectory cluster occupancy ratio corresponding to the trajectory cluster, and based on the trajectory cluster occupancy ratio and the average concentration corresponding to the trajectory cluster, calculate the trajectory cluster concentration contribution ratio corresponding to the trajectory cluster; For each passing province, based on the trajectory cluster occupancy ratios and the trajectory cluster concentration contribution ratios corresponding to all the trajectory clusters, calculate the first contribution ratio of the passing province to the greenhouse gases in the target city; Among them, the calculation formula for the trajectory cluster occupancy ratio is: ； Among them, is the trajectory cluster occupancy ratio corresponding to the th trajectory cluster; is the number of trajectories included in the th trajectory cluster; is the number of trajectory clusters; The calculation formula for the trajectory cluster concentration contribution ratio is: ； Among them, is the contribution ratio of the trajectory cluster concentration corresponding to the th trajectory cluster; is the average concentration corresponding to the th trajectory cluster; is the average value of the greenhouse gas concentration data of the target city in the target time period; The calculation formula for the first contribution ratio is: ； Among them, is the first contribution ratio of the th province passed through to the greenhouse gases of the target city; is the number of trajectory clusters of the th province passed through; is the trajectory cluster occupancy ratio corresponding to the th trajectory cluster of the th province passed through; is the trajectory cluster concentration contribution ratio corresponding to the th trajectory cluster of the th province passed through; is the number of provinces passed through.

7. The quantitative analysis method for the sources of urban greenhouse gases according to claim 1, characterized in that, Based on the total carbon emissions of all the passing provinces, calculate the second contribution ratio of each passing province to the greenhouse gases in the target city, specifically including: For each of the passing provinces, calculate the ratio of the total carbon emissions of the passing province to the total carbon emissions of the country to obtain the carbon emission ratio corresponding to the passing province, and calculate the ratio of the carbon emission ratio corresponding to the passing province to the sum of the carbon emission ratios to obtain the second contribution ratio of the passing province to the greenhouse gases in the target city; where the sum of the carbon emission ratios is the sum of the carbon emission ratios corresponding to all the passing provinces.

8. The quantitative analysis method for urban greenhouse gas sources according to claim 1, wherein Based on the first contribution ratio and the second contribution ratio, calculate the relative contribution ratio of each passing province to the greenhouse gases in the target city, specifically including: For each of the passing provinces, calculate the product of the first contribution ratio of the passing province to the greenhouse gases in the target city and the second contribution ratio of the passing province to the greenhouse gases in the target city to obtain the total contribution ratio corresponding to the passing province, and calculate the ratio of the total contribution ratio corresponding to the passing province to the sum of the total contribution ratios to obtain the relative contribution ratio of the passing province to the greenhouse gases in the target city; where the sum of the total contribution ratios is the sum of the total contribution ratios corresponding to all the passing provinces.

9. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the quantitative analysis method for the source of urban greenhouse gases according to any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the quantitative analysis method for the source of urban greenhouse gases according to any one of claims 1-8.

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