A quantitative analysis method, device and medium for urban greenhouse gas sources

Abstract

The present application discloses a quantitative analysis method, equipment and medium for the source of greenhouse gases in a city, and relates to the field of greenhouse gas tracing technology. The method comprises: based on greenhouse gas concentration data and meteorological field data, determining the average greenhouse gas concentration and trajectory corresponding to the target time point, further performing concentration distribution and clustering, obtaining multiple trajectory clusters and the average concentration corresponding to each trajectory cluster, calculating the first contribution ratio of each passing province to the greenhouse gases of the target city based on the number of trajectories included in all trajectory clusters and the average concentration corresponding to all trajectory clusters, calculating the second contribution ratio of each passing province to the greenhouse gases of the target city based on the total carbon emissions of all passing provinces, and further calculating the relative contribution ratio of each passing province to the greenhouse gases of the target city. The present application can realize the quantitative tracing of greenhouse gases in cities in a low-cost, simple, fast and accurate manner, and can be widely promoted and used.

Description

Technical Field

[0001] The present application relates to the technical field of greenhouse gas source tracing, and in particular to a quantitative analysis method, equipment, and medium for urban greenhouse gas sources. Background Art

[0002] Greenhouse gases (GHGs) are gases that absorb and reradiate heat from the Earth's surface. They are the primary cause of the greenhouse effect, which in turn triggers global climate change. Carbon dioxide (CO2) and methane (CH4), key regulators of the global climate system, contribute 65% and 17% of the total radiative forcing from long-lived greenhouse gases, respectively. Currently, the source and sink mechanisms of atmospheric GHGs are not well understood. CO2 primarily originates from fossil fuel combustion (accounting for 85% of anthropogenic emissions) and is removed through photosynthesis and ocean uptake. CH4 originates from wetlands, agricultural activities, and energy extraction and is primarily removed through hydroxyl radical oxidation. Soils also serve as a significant sink. Cities account for 2% of the world's land area but are home to more than half of the world's population. They emit 70% of all human-generated air pollutants, including 85% of carbon emissions. Therefore, quantitatively tracing and analyzing the sources of greenhouse gases in cities is crucial.

[0003] At present, the greenhouse gas observation network has covered 120 high-precision stations (also known as high-precision observation stations), realizing all-round and high-precision monitoring of greenhouse gases, and 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 greenhouse gas observation network coverage, the density of high-precision stations in cities is insufficient (coverage rate is only 30%), which makes it difficult to effectively distinguish between local emissions and regional transmission contributions, and even more difficult to further quantify regional transmission contributions.

[0004] Traditional methods of quantitative greenhouse gas traceability include: (1) gas isotope ratio zone traceability: Isotopes have unique fingerprint characteristics, and gases from different sources often have unique isotope compositions. By measuring the isotope ratio of gases, their sources and migration paths can be traced. However, this method requires expensive equipment such as accelerator mass spectrometry (the cost of a single carbon 14 test exceeds 10,000 yuan), and on the other hand, it requires complex pre-processing, which takes up to 48 hours. In addition, the isotope fractionation effect during the processing may cause the final quantitative traceability results to be inaccurate. (2) Model simulation method: Using 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 coupledwith Chemistry Model)), the emission sources are inferred by simulating the gas transmission path. However, this method requires a large amount of model data and the computing power of a supercomputer. In addition, the model operation requires high professional knowledge and technical skills, and there is a shortage of talent at urban sites in underdeveloped areas. On the other hand, the model is highly dependent on the quality of meteorological data. If the quality of meteorological data is poor, it will introduce large errors and require 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 quantitative analysis method, equipment and medium for the sources of urban greenhouse gases, which can achieve quantitative traceability of urban greenhouse gases in a low-cost, simple, fast and accurate manner, and can be widely promoted and used.

[0006] To achieve the above objectives, this application provides the following solutions.

[0007] In a first aspect, the present application provides a method for quantitatively analyzing the sources of urban greenhouse gases, the method comprising:

[0008] Obtaining greenhouse gas concentration data for a target city during a target time period and meteorological field data for a source region during the source time period; the source region includes the target city, and the source time period includes the target time period and a historical time period prior to the target time period;

[0009] 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 tracing area during the tracing 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 during the target time period, and assign 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 a 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;

[0010] Clustering 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 an average concentration corresponding to each of the trajectory clusters;

[0011] Based on the number of trajectories included in all the trajectory clusters and the average concentration corresponding to all the trajectory clusters, the first contribution ratio of each passing province to the greenhouse gas 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 gas 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 gas of the target city is calculated; the passing provinces are the provinces passed by any one of the trajectory clusters.

[0012] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for quantitative analysis of urban greenhouse gas sources.

[0013] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for quantitative analysis of urban greenhouse gas sources.

[0014] According to the specific embodiments provided in this application, this application has the following technical effects.

[0015] The present application provides a quantitative analysis method, equipment and medium for the sources of urban greenhouse gases. Based on 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 average greenhouse gas concentration and trajectory corresponding to the target time point are determined, the concentration of the trajectory corresponding to the target time point is distributed based on the average greenhouse gas concentration corresponding to the target time point, and the trajectory concentration vector corresponding to the target time point is obtained. 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. 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 zone tracing method, the present application does not require expensive equipment such as accelerator mass spectrometry, and only needs to obtain greenhouse gas concentration data and meteorological field data. Therefore, it has the advantage of low cost. Since no complex pre-processing is required, only the reverse calculation of air mass movement paths, concentration distribution, clustering and relative contribution ratio calculation need to be completed. Therefore, it has the advantages of simplicity and speed. Since it will not be affected by the isotope fractionation effect during the processing process, it has the advantage of accuracy. Compared with the model simulation method, since there is no need to adopt the global atmospheric chemical transport model and regional model, there is no large amount of data, no complex model operation, no need to be highly dependent on the quality of meteorological data, and no need for a large amount of verification data support, only the reverse calculation of air mass movement paths, concentration distribution, clustering and relative contribution ratio calculation need to be completed. Therefore, it has the advantages of simplicity, speed and accuracy, thereby realizing the quantitative traceability of urban greenhouse gases at low cost, simplicity, speed and accuracy, and can be widely promoted and used. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is an application environment diagram of a quantitative analysis method for urban greenhouse gas sources provided in Example 1 of the present application.

[0018] Figure 2 This is a flow chart of a method for quantitative analysis of urban greenhouse gas sources provided in Example 1 of the present application.

[0019] Figure 3 A schematic diagram of the structure of a computer device provided in Example 2 of the present application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0021] Example 1.

[0022] The quantitative analysis method of urban greenhouse gas sources provided in the embodiments of the present application can be applied to Figure 1 In the application environment 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, or placed on the cloud or other servers. The terminal can send the pending quantitative analysis request to the server. After the server receives the pending quantitative analysis request, for the pending quantitative analysis request, 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, the air mass movement path is reversely calculated 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, and 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 corresponding target time point is calculated based on the average greenhouse gas concentration corresponding to the target time point. The trajectory concentration is assigned to obtain the trajectory concentration vector corresponding to the target time point. The trajectory concentration vectors corresponding to all target time points are then clustered to obtain multiple trajectory clusters and the average concentration corresponding to each trajectory cluster. The first contribution ratio of each province passed through to the target city's greenhouse gas emissions is calculated based on the number of trajectories included in all trajectory clusters and the average concentration corresponding to all trajectory clusters. The second contribution ratio of each province passed through to the target city's greenhouse gas emissions is calculated based on the total carbon emissions of all passed provinces. The relative contribution ratio of each province passed through to the target city's greenhouse gas emissions is calculated based on the first and second contribution ratios. The server can feedback the quantitative analysis results of the relative contribution ratio of each province passed through to the target city's greenhouse gas emissions to the terminal in response to the quantitative analysis request.

[0023] In addition, in some embodiments, the quantitative analysis method of the sources of urban greenhouse gases can also be implemented independently by a server or a terminal. 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.

[0024] The terminals may include, but are not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. The server may be implemented as a standalone server or a server cluster consisting of multiple servers, or as a cloud server.

[0025] In an exemplary embodiment, Figure 2 As shown, a quantitative analysis method for the source of greenhouse gases in a city is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 The following steps are used as an example to illustrate the server.

[0026] Step S1, obtaining 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.

[0027] 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 tracing area in the tracing 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 assign 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 a 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.

[0028] Step S3: clustering the trajectories corresponding to all the target time points based on the trajectory concentration vectors corresponding to all the target time points to obtain multiple trajectory clusters and the average concentration corresponding to each trajectory cluster.

[0029] 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 gas of 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 gas of 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 gas of the target city; the passing province is the province passed by any one of the trajectory clusters.

[0030] Implement the above-mentioned steps S1 to S4. In this embodiment, the air mass movement path is reversely calculated to obtain multiple trajectories. The trajectories are concentrated and clustered to obtain multiple trajectory clusters. The first contribution ratio of each province passed through to the greenhouse gas of the target city is further calculated. At the same time, the total carbon emissions of all passed provinces are introduced to calculate the second contribution ratio of each passed province to the greenhouse gas of the target city. Combining the first contribution ratio and the second contribution ratio, the relative contribution ratio of each passed province to the greenhouse gas of the target city is calculated, thereby completing the quantitative traceability of the greenhouse gas of the target city. Compared with the gas isotope ratio zone tracing method, since it does not require expensive equipment such as accelerator mass spectrometry, it only needs to obtain the greenhouse gas concentration. The method only needs the degree data and meteorological field data, so it has the advantage of low cost. Since it does not require complex pre-processing, it only needs to complete the reverse calculation of the air mass movement path, concentration distribution, clustering and relative contribution ratio calculation. Therefore, it has the advantages of simplicity and speed. Since it will not be affected by the isotope fractionation effect during the processing process, it has the advantage of accuracy. Compared with the model simulation method, since it does not need to adopt the global atmospheric chemical transport model and the regional model, there is no large amount of data, no complex model operation, no need to be highly dependent on the quality of meteorological data, and no need for a large amount of verification data support, it only needs to complete the reverse calculation of the air mass movement path, concentration distribution, clustering and relative contribution ratio calculation. Therefore, it has the advantages of simplicity, speed and accuracy. In summary, this embodiment can realize the quantitative traceability of urban greenhouse gases at low cost, simply, quickly and accurately, and can be widely promoted and used.

[0031] The quantitative analysis method for the sources of urban greenhouse gases used in this embodiment is described in detail below, including the following steps.

[0032] (1) Data collection.

[0033] This embodiment first obtains high-precision greenhouse gas concentration data around the greenhouse gas monitoring station in the target city during the target time period. 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 detector in the greenhouse gas monitoring station. Subsequently, the quantitative traceability of CO2 in the target city can be completed based on the CO2 concentration data, and the quantitative traceability 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 traceability of other types of greenhouse gases. In addition, the meteorological field data of the traceability area during the traceability time period is obtained. Specifically, the meteorological field data of the traceability area during the traceability time period can be obtained on the website of the National Environmental Forecast Center. It should be noted that the target time period and the traceability time period are both determined based on user needs. The traceability time period includes the target time period and the historical time period before the target time period. The target city and the traceability area are both determined based on user needs. The traceability area includes the target city. Generally, the traceability area can be defined as global or national.

[0034] At this time, in this embodiment, 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 are obtained.

[0035] This embodiment further performs statistical analysis on the greenhouse gas concentration data, specifically statistical analysis on the CO2 concentration data and CH4 concentration data in the target time period, to obtain the maximum value, minimum value, and average value, and subsequently further analyzes based on the collected greenhouse gas concentration data and meteorological field data.

[0036] (2) Trajectory calculation.

[0037] The core of trajectory calculation is solving the equation for the time-dependent motion of an air mass (a key concept in meteorology, representing a mass of air occupying a large space with relatively uniform physical properties horizontally). This is primarily achieved through integration of acquired meteorological field data. Assuming the air mass is a passive tracer particle, the core calculation principle is based on the Lagrange integration method. For each target time point within a target time period, the target time point can include the start and end time points of the target time period, and the intervals between adjacent target time points can be the same. For example, if the target time period is one day, the target time points can be any of the 24 hours in that day. To reflect near-surface transmission characteristics, the endpoint of the reverse calculation is chosen to be 100 meters above the ground at the greenhouse gas monitoring station. Therefore, the trajectory ends at the latitude and longitude of the greenhouse gas monitoring station in the target city, 100 meters above the ground. The reverse calculation begins for the air mass's motion path within a preset time period (e.g., 72 hours, the specific duration can be customized) prior to the target time point. 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.

[0038] The calculation formula for the reverse calculation of the air mass movement path is:

[0039] (1);

[0040] In formula (1), For air masses The position at the moment, which is represented by a three-dimensional position vector, which includes longitude ,latitude and height , The calculation step length can be 5s according to user needs; For air masses Position at the moment; Meteorological field data, representing the wind field with location and time The wind speed in the meteorological field data is ( , , ) is represented by the three-dimensional wind speed vector, which is obtained by interpolating the 1°×1° meteorological field data. The interpolation step is 0.1° to match the grid resolution. , The horizontal wind speed component is the horizontal movement. 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 component can be obtained by the horizontal motion equation using the longitude and latitude information contained in the meteorological field data. 、 , is the vertical wind speed component, which needs to be calculated using the mass conservation equation shown in formula (2); is the turbulence field data, representing the turbulence field with position and time The change of diffusion displacement in turbulent field data ( , , ) needs to be calculated by 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 turbulence field are also constantly changing. Through the above method, the meteorological field data and turbulence field data can be determined, and further substituted into formula (1) to determine the air mass in The position at the moment is further determined to determine the trajectory corresponding to the target time point.

[0041] In order to ensure the accuracy of the height of the end point, it is necessary to limit the height range of all positions in the trajectory. Due to the trajectory motion error, the range is limited to 90-110 meters. This is because there is a certain error in the calculation of the air mass motion trajectory under different terrain and atmospheric conditions. The vertical error of the backward trajectory is usually between 10% and 20%. For example, the nominal height of 100 meters may actually correspond to a physical height of 80-120 meters. Therefore, in this embodiment, the calculation results between 90-110 meters are regarded as 100 meters, that is, if the calculated If the altitude is between 90-110 meters, it will be corrected to 100 meters.

[0042] This embodiment further introduces outlier elimination: during the wind field change process, if the vertical wind speed component and the horizontal wind speed component suddenly change, the calculated position at this time is considered to be an outlier and needs to be eliminated. The judgment condition for the sudden change of the vertical wind speed component is: |Δ ∣>5 m / s, Δ is the vertical wind speed component The change value of reflects the abnormal movement of the troposphere. The judgment condition for the sudden change of the horizontal wind speed component is: |Δ ∣>20 m / s, Δ is the horizontal wind speed component The change in the value reflects extreme weather disturbances such as squall lines.

[0043] The mass conservation equation is:

[0044] (2);

[0045] In formula (2), It is the vertical wind speed component, which indicates the velocity of fluid or air in the vertical direction (usually the z-axis), and the unit is m / s; is the height, usually the z-axis coordinate, usually based on the ground ( =0) is the reference point, upward is the positive direction, and the unit is m; in the upper and lower limits of the integral, the lower limit 0 represents the reference height (such as the ground or the top of the boundary layer), and the upper limit Representatives The integral represents the distance from the reference height to the cumulative effect of and They are the horizontal wind speed components, representing the movement speed of fluid or air on the x-axis (east-west direction) and y-axis (north-south direction), respectively, both in m / s; are the x-axis coordinate and the y-axis coordinate respectively; for The horizontal velocity gradient along the x-axis reflects the rate of change of velocity in the east-west direction; for The horizontal velocity gradient along the y-axis reflects the rate of change of velocity in the north-south direction; is the horizontal divergence, in units of 1 / s. If the horizontal divergence is positive, it means horizontal divergence (mass loss), which needs to be compensated by vertical ascent motion ( <0, otherwise, it needs to be compensated by sinking movement ( >0).

[0046] When the random walk model is used to simulate boundary layer disturbances, the calculation formula for the turbulent field is:

[0047] (3);

[0048] In formula (3), For The diffusion displacement in the direction of the particle due to random motion (such as Brownian motion) Instantaneous displacement in direction; is the diffusion coefficient, in m 2 / s, which is related to the medium temperature and particle properties and reflects the diffusion rate. =0.4 , is the friction speed; for Gaussian white noise sequence in the direction, , specifically a random number that obeys a standard Gaussian distribution.

[0049] It should be noted that since the meteorological field data time is the standard UTC time (Coordinated Universal Time, World Coordinated Time or Coordinated Universal Time), the local time needs to be converted to UTC time (such as Beijing time minus 8 hours) to avoid wind field misalignment caused by time zone deviation.

[0050] 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 tracing area in the tracing time period to obtain the trajectory corresponding to the target time point.

[0051] Among them, the air mass movement path is reversely calculated based on the meteorological field data of the tracing area in the tracing time period to obtain the trajectory corresponding to the target time point, specifically including: based on the meteorological field data of the tracing area in the tracing time period, determining the meteorological field data of the tracing area in the preset time period, the preset time period is the time period before the target time point in the tracing time period, and the last time point of the preset time period is the target time point; using the meteorological field data of the tracing area in the preset time period as input, the air mass movement path is reversely calculated using the Lagrange integral method, specifically through formula (1) to perform reverse calculation, and obtain the air mass at each preset time point in the preset time period (i.e. ) position; the position of the air mass at each preset time point in the preset time period is combined into a trajectory corresponding to the target time point.

[0052] (3) Trajectory concentration distribution.

[0053] Air masses change their trajectories as they move. Furthermore, air masses themselves have concentration characteristics. This embodiment initially distributes greenhouse gas concentration data from greenhouse gas monitoring stations to different air masses based on 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 greenhouse gas concentration data within the time period corresponding to that 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, for a preset time period of 72 hours, in this embodiment, the number of trajectory time points can be set to 72. The time intervals between adjacent trajectory time points can be the same. The average greenhouse gas concentrations corresponding to the target time points are evenly distributed to obtain the initial greenhouse gas concentration corresponding to each trajectory time point. This is equivalent to evenly distributing the average greenhouse gas concentration corresponding to a target time point to each trajectory time point in the backward calculation. The initial greenhouse gas concentration corresponding to each trajectory time point is equal, being one-seventy-second of the average greenhouse gas concentration.

[0054] After completing the initial allocation, the initial greenhouse gas concentration corresponding to each trajectory time point in each trajectory can be obtained, and the initial greenhouse gas concentration corresponding to each trajectory time point can be normalized. The calculation formula is:

[0055] (4);

[0056] In formula (4), For the The initial normalized greenhouse gas concentration corresponding to each trajectory time point; For the The initial greenhouse gas concentration corresponding to each trajectory time point; For all trajectories The average value of the initial greenhouse gas concentration corresponding to each trajectory time point; For all trajectories The standard deviation of the initial greenhouse gas concentration corresponding to each trajectory time point.

[0057] Since the initial allocation is to evenly distribute the concentration according to time, and in reality, the concentration of greenhouse gases is often higher at the end moment, it is necessary to adjust the weights based on the initial allocation to make the allocation results more realistic. Therefore, according to the following formulas (5), (6) and (7), the time weight is added to the initial allocation to complete the concentration allocation.

[0058] This embodiment gives higher weight to the terminal time to strengthen the impact of the critical 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 terminal time (sort =1) has a higher weight, which strengthens the impact of the critical period before the arrival of greenhouse gases. The calculation formula for the initial time weight is:

[0059] (5);

[0060] In formula (5), For the The initial time weight corresponding to each trajectory time point; For the Sorting of trajectory time points, sorting all trajectory time points in order from back to front, =1 represents the end moment, =72 represents the starting time of tracing back to the source; is the attenuation coefficient, =0.5.

[0061] In order to ensure that the sum of all initial time weights is equal to 1, the initial time weights obtained by formula (5) are normalized according to formula (6). The calculation formula for normalization is:

[0062] (6);

[0063] In formula (6), is the time weight. By using formula (6), the total weight sum can be guaranteed to be 1.

[0064] After determining the time weight, the initial standardized greenhouse gas concentration obtained by formula (4) is adjusted according to the obtained time weight to obtain the greenhouse gas concentration that conforms to the actual situation. The calculation formula is:

[0065] (7);

[0066] In formula (7), For the Greenhouse gas concentrations corresponding to each trajectory time point.

[0067] At this time, in this embodiment, 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 distributed 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.

[0068] The process of distributing the concentration of 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.

[0069] (1) 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.

[0070] In this embodiment, the preset time period corresponding to the trajectory can be evenly divided according to user needs 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 intervals 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 in 3 days.

[0071] (2) For each trajectory time point, based on the sorting of the trajectory time points, the initial time weight corresponding to the trajectory time point is calculated, specifically by formula (5). The initial time weight is normalized, specifically by formula (6), to obtain the time weight corresponding to the trajectory time point. Based on the initial greenhouse gas concentration and time weight corresponding to the trajectory time point, the greenhouse gas concentration corresponding to the trajectory time point is calculated, specifically by formula (7).

[0072] (3) The greenhouse gas concentrations corresponding to all trajectory time points are combined into a trajectory concentration vector corresponding to the target time point. The dimension of the trajectory concentration vector at this time is 1 72.

[0073] (IV) Calculation of trajectory clusters and the average concentration corresponding to the trajectory clusters.

[0074] When normalizing and evaluating the contribution of meteorological conditions, it is necessary to obtain the number of trajectory clusters and the average concentration corresponding to the trajectory clusters. Therefore, clustering is performed based on the trajectories and the trajectory concentration vectors corresponding to the trajectories, and the number of trajectory clusters and the average concentration corresponding to the trajectory clusters are calculated.

[0075] The objective function shown in the following formula (8) is the basis for judging whether the clustering result of the trajectory cluster is reasonable. The objective function is used to calculate the objective function value. The calculation formula of the objective function value is:

[0076] (8);

[0077] In formula (8), is the objective function value; is the initial cluster number, i.e. the number of trajectory clusters; For the Initial trajectory cluster (It is the first The trajectory set of the initial trajectory cluster, including all the trajectories assigned to the initial trajectory cluster) The trajectory concentration vector corresponding to the trajectory; For the 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.

[0078] 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:

[0079] (9);

[0080] 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).

[0081] 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.

[0082] (1) Initialize cluster centers.

[0083] The 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: any trajectory is randomly selected as the first initial cluster center. For subsequent initial cluster centers, the selection probability (that is, the probability of selecting a trajectory other than the selected initial cluster center as the next initial cluster center) is proportional to the square of the distance. That is, the trajectory with the farthest distance from the selected initial cluster center is selected as the next initial cluster center.

[0084] (2) Allocation stage.

[0085] For each trajectory, the Euclidean distance between the trajectory and each initial cluster center is calculated using the following formula:

[0086] (10);

[0087] In formula (10), For the The trajectory and The Euclidean distance between the initial cluster centers; For the The first track Greenhouse gas concentration corresponding to each trajectory time point; For the In the iteration Among all the trajectories in the initial trajectory cluster The average value of greenhouse gas concentration corresponding to each trajectory time point is the first iteration. The trajectory corresponding to the initial cluster center Greenhouse gas concentrations corresponding to each trajectory time point.

[0088] 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 to the trajectory:

[0089] (11);

[0090] In formula (11), For the In the iteration The initial trajectory cluster is After the iteration, The set of all trajectories of the initial trajectory cluster.

[0091] (3) Update phase.

[0092] Recalculate the cluster center using the following formula:

[0093] (12);

[0094] In formula (12), For the In the iteration Among all the trajectories in the initial trajectory cluster The average value of greenhouse gas concentration corresponding to each trajectory time point constitutes the The initial cluster centers of the iteration; For the In the iteration The number of trajectories in the initial trajectory cluster.

[0095] (4) Convergence determination.

[0096] The iteration termination condition is: the change of 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 of the objective function, For the The objective function value of the iteration is calculated by formula (8): For the The objective function value of the iteration is calculated by formula (8).

[0097] Based on the above clustering process, the method for determining the number of trajectory clusters includes the following steps.

[0098] (1) Preliminary screening

[0099] By calculating the number of different clusters The corresponding within-cluster sum of squares (WCSS) is selected, and the number of clusters is selected when the rate of decrease of the within-cluster sum of squares is significantly reduced. As the number of clusters after preliminary screening, when the intra-cluster square sum decrease rate is <15%, select the corresponding number of clusters , the calculation formula of the sum of squares within the cluster is:

[0100] (13);

[0101] In formula (13), is the number of clusters The corresponding within-cluster sum of squares.

[0102] (2) Rescreening.

[0103] Silhouette Score is a comprehensive indicator that measures the compactness within a cluster and the separation between clusters. The closer the value is to 1, the better the clustering effect. The calculation formula of Silhouette Score is:

[0104] (14);

[0105] In formula (14), For the The silhouette coefficient of the trajectory; For the The trajectory to the nearest other initial trajectory cluster (i.e., the distance from the The nearest track except the The distance to other initial trajectory clusters other than the initial trajectory cluster to which the trajectory belongs; For the The trajectory is compared with other trajectories in the same cluster (i.e. All trajectories belong to the same initial trajectory cluster except the average distance to the other trajectories except the trajectories); is the average value of the silhouette coefficient of all trajectories, which measures the global quality of the clustering results; is the number of trajectories.

[0106] This embodiment requires >0.6, ensuring the consistency within the cluster and the separation between clusters, indicating that the clustering results are reasonable, so we select The number of clusters after preliminary screening of >0.6 is used as the candidate number, and then The maximum number of candidates is used as the number of clusters after screening, which is the final number of trajectory clusters.

[0107] After determining the number of trajectory clusters, clustering can be used to determine the number of trajectories in each trajectory cluster and the average trajectory concentration vector corresponding to each trajectory cluster. The average concentration corresponding to each trajectory cluster can be further calculated using the formula:

[0108] (15);

[0109] In formula (15), For the The average concentration corresponding to the trajectory cluster; For the The number of trajectories included in a trajectory cluster; For the Trajectory clusters No. The trajectory concentration vector corresponding to each trajectory.

[0110] At this time, in this embodiment, the trajectories corresponding to all target time points are clustered 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.

[0111] The trajectories corresponding to all target time points are clustered 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, which specifically includes the following steps.

[0112] (1) Randomly determine the number of clusters, sort all the cluster numbers in ascending order, and select the first cluster number as the initial cluster number.

[0113] (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, and obtain multiple initial trajectory clusters corresponding to the initial number of clusters and the average trajectory concentration vector corresponding to each initial trajectory cluster.

[0114] 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 iterative termination condition of the clustering is: the change in the objective function is less than the 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 of the objective function value is formula (8).

[0115] (3) Based on the average trajectory concentration vector corresponding to each initial trajectory cluster corresponding to the initial cluster number, the intra-cluster sum of squares corresponding to the initial cluster number is calculated. Specifically, it is calculated using formula (13). Based on the intra-cluster sum of squares corresponding to the initial cluster number and the intra-cluster sum of squares corresponding to the previous cluster number of the initial cluster number (that is, the intra-cluster sum of squares corresponding to the previous cluster number of the initial cluster number as the initial cluster number), the intra-cluster sum of squares decrease rate corresponding to the initial cluster number is calculated. Specifically, the ratio of the difference (the difference between the intra-cluster sum of squares corresponding to the initial cluster number and the intra-cluster sum of squares corresponding to the previous cluster number of the initial cluster number) to the intra-cluster sum of squares corresponding to the previous cluster number of the initial cluster number is calculated to obtain the intra-cluster sum of squares decrease rate corresponding to the initial cluster number.

[0116] (4) Determine whether there are any unselected cluster numbers. If so, select the next cluster number of the initial cluster number as the initial cluster number of the next iteration, and return to the step of "clustering the trajectories corresponding to all target time points based on the initial cluster number and the trajectory concentration vectors corresponding to all target time points". If not, perform a preliminary screening of all initial cluster numbers based on the intra-cluster sum of squares decrease rate corresponding to all initial cluster numbers to obtain the number of clusters after preliminary screening.

[0117] For each initial number of clusters, if the decrease rate of the intra-cluster sum of squares corresponding to the initial number of clusters is <15%, then the initial number of clusters is selected as the number of clusters after preliminary screening.

[0118] (5) For each number of clusters after preliminary screening, the silhouette coefficient corresponding to the number of clusters after preliminary screening is calculated based on the multiple initial trajectory clusters corresponding to the number of clusters after preliminary screening, which is specifically calculated using formula (14).

[0119] (6) All the cluster numbers after preliminary screening are rescreened based on the silhouette coefficients corresponding to the cluster numbers after preliminary screening to obtain the number of clusters after screening, and the multiple initial trajectory clusters corresponding to the number of clusters after screening and the average trajectory concentration vector corresponding to each initial trajectory cluster are used as the average trajectory concentration vector corresponding to the multiple trajectory clusters and each trajectory cluster. Based on the average trajectory concentration vector corresponding to the trajectory cluster, the average concentration corresponding to the trajectory cluster is calculated, which is specifically calculated using formula (15).

[0120] For each number of clusters after preliminary screening, if the silhouette coefficient corresponding to the number of clusters after preliminary screening is greater than 0.6, the number of clusters after preliminary screening will be used as the candidate number, and then the candidate number with the largest silhouette coefficient will be selected as the number of clusters after screening.

[0121] (5) Normalized assessment of the contribution of meteorological conditions.

[0122] Based on the number of trajectory clusters and the average concentration corresponding to the trajectory clusters, the impact of meteorological conditions on the transmission of greenhouse gases in the target city is quantitatively evaluated. First, the trajectory cluster ratio and trajectory cluster concentration contribution ratio corresponding to each trajectory cluster are calculated.

[0123] The calculation formula for the trajectory cluster ratio is:

[0124] (16);

[0125] In formula (16), For the The percentage of trajectory clusters corresponding to a trajectory cluster, that is, the percentage of the number of trajectories included in the trajectory cluster to the total number of trajectories; For the The number of trajectories included in a trajectory cluster; is the number of trajectory clusters.

[0126] The calculation formula for the trajectory cluster concentration contribution ratio is:

[0127] (17);

[0128] In formula (17), For the The contribution ratio of trajectory cluster concentration corresponding to each trajectory cluster; For the The average concentration corresponding to the trajectory cluster; It is the average value of greenhouse gas concentration data of the target city during the target time period.

[0129] Then, based on the trajectory cluster proportion and trajectory cluster concentration contribution ratio corresponding to each trajectory cluster, a normalized evaluation is performed on the contribution of the airflow trajectory to the greenhouse gases of the target city after passing through the surrounding areas, and the first contribution ratio of each province passed through to the greenhouse gases of the target city is obtained. The passing province is the province passed by any trajectory cluster, that is, a province passed by the airflow trajectory. The calculation formula for the first contribution ratio is:

[0130] (18);

[0131] In formula (18), For the The first contribution ratio of each passing province to the greenhouse gases of the target city is used to characterize the contribution of the passing province to the greenhouse gases of the target city due to meteorological conditions. The numerator represents the concentration contribution of a passing province to the greenhouse gases of the target city due to meteorological conditions. It is determined based on the total concentration contribution of each trajectory cluster to the passing province (if the trajectory cluster passes through the passing province, it is considered to have a concentration contribution to the passing province; if the trajectory cluster does not pass through the passing province, it is considered to have no concentration contribution to the passing province, that is, the concentration contribution is 0). The denominator represents the concentration contribution of all passing provinces to the greenhouse gases of the target city due to meteorological conditions. For the first The number of trajectory clusters passing through a province; For the first The first province through which The proportion of trajectory clusters corresponding to the trajectory clusters; For the first The first province through which The contribution ratio of trajectory cluster concentration corresponding to each trajectory cluster; is the number of provinces passed through.

[0132] 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 province passed through to the greenhouse gas of the target city is calculated.

[0133] Among them, 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 gas of the target city is calculated, specifically including: for each trajectory cluster, based on the number of trajectories included in the trajectory cluster, the trajectory cluster proportion corresponding to the trajectory cluster is calculated, specifically calculated by formula (16), and based on the trajectory cluster proportion and average concentration corresponding to the trajectory cluster, the trajectory cluster concentration contribution ratio corresponding to the trajectory cluster is calculated, specifically calculated by formula (17); for each passing province, based on the trajectory cluster proportion and trajectory cluster concentration contribution ratio corresponding to all trajectory clusters, the first contribution ratio of the passing province to the greenhouse gas of the target city is calculated, specifically calculated by formula (18).

[0134] (6) Normalized assessment of carbon emission contribution.

[0135] In order to make the quantitative calculation results more accurate, this embodiment additionally introduces total carbon emissions to further correct the calculation.

[0136] To assess the impact of regional carbon emissions on greenhouse gases in target cities, the total carbon emissions of the country over a five-year period (2019-2023, other time periods and time periods can also be selected as needed) are introduced. The greenhouse gas source intensity of the target city can be calculated according to the following formula:

[0137] (19);

[0138] (20);

[0139] In the above formula, For the The total carbon emissions of the provinces it passes through account for the proportion of the country's total carbon emissions. The numerator represents the The denominator represents the total carbon emissions of the provinces in the five years (2019-2023). For the Total annual carbon emissions of the provinces it passes through; is the country’s total annual carbon emissions; For the The second contribution ratio of the passing province to the greenhouse gas of the target city, which is the normalized provincial carbon emission ratio that affects the source of greenhouse gas of the target city, is used to represent the contribution of the passing province to the greenhouse gas of the target city due to carbon emissions; is the total number of provinces passed through.

[0140] The formula for calculating the relative contribution of each province to the greenhouse gas emissions of the target city is:

[0141] (twenty one);

[0142] In formula (21), For the The relative contribution ratio of the passing provinces to the greenhouse gases of the target city.

[0143] 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.

[0144] Among them, based on the total carbon emissions of all passing provinces, the second contribution ratio of each passing province to the greenhouse gas of the target city is calculated, specifically including: for each passing province, calculating the ratio of the total carbon emissions of the passing province to the total carbon emissions of the country, and obtaining the carbon emission proportion corresponding to the passing province, which is specifically calculated by formula (19), and calculating the ratio of the carbon emission proportion corresponding to the passing province to the sum of the carbon emission proportions, and obtaining the second contribution ratio of the passing province to the greenhouse gas of the target city, which is specifically calculated by formula (20), wherein the sum of the carbon emission proportions is the sum of the carbon emission proportions corresponding to all passing provinces.

[0145] 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, 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 is calculated to obtain the total contribution ratio corresponding to the passing province, and the ratio of the total contribution ratio corresponding to the passing province to the total contribution ratio sum is calculated 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), wherein the total contribution ratio sum is the sum of the total contribution ratios corresponding to all passing provinces.

[0146] This embodiment innovatively proposes a quantitative analysis method for urban greenhouse gas sources that can be widely promoted and is highly practical, solving the problem of difficulty in greenhouse gas source assessment due to the lack of high-quality talents and the incomplete construction of high-precision observation networks.

[0147] The present application also provides an application scenario, which applies the above-mentioned quantitative analysis method of the sources of urban greenhouse gases. Specifically, the quantitative analysis method of the sources of urban greenhouse gases provided in this embodiment can be applied in a quantitative traceability scenario. The quantitative traceability scenario includes a data acquisition link, a quantitative traceability link, and a display link. The data acquisition link is used to 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 quantitative traceability link is used to determine the relative contribution ratio of each passing province to the greenhouse gas 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 show the user the relative contribution ratio of each passing province to the greenhouse gas of the target city, which is conducive to the subsequent greenhouse gas emission control. The quantitative analysis method of the sources of urban greenhouse gases provided in this embodiment belongs to the quantitative traceability link.

[0148] Example 2.

[0149] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3As shown. The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, memory and input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. 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 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 an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a quantitative analysis method for the source of urban greenhouse gases is implemented.

[0150] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0151] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the quantitative analysis method of urban greenhouse gas sources in Example 1 when executing the computer program.

[0152] Example 3.

[0153] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the method for quantitatively analyzing urban greenhouse gas sources in Example 1 is implemented.

[0154] 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 used for analysis, stored data, displayed data, etc.) involved in this 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 must comply with relevant regulations.

[0155] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0156] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A quantitative analysis method for the sources of urban greenhouse gases, characterized in that: The quantitative analysis method of the sources of urban greenhouse gases includes: Obtaining greenhouse gas concentration data for a target city during a target time period and meteorological field data for a source region during the source time period; the source region includes the target city, and the source time period includes the target time period and a historical time period prior to the target time period; 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 tracing area during the tracing 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 during the target time period, and assign 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 a 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; Clustering 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 an average concentration corresponding to each of the trajectory clusters; Based on the number of trajectories included in all the trajectory clusters and the average concentration corresponding to all the trajectory clusters, the first contribution ratio of each passing province to the greenhouse gas 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 gas 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 gas of the target city is calculated; the passing provinces are the provinces passed by any one of the trajectory clusters.

2. The method for quantitative analysis of urban greenhouse gas sources according to claim 1, characterized in that: The air mass movement path is reversely calculated based on the meteorological field data of the tracing area during the tracing period to obtain the trajectory corresponding to the target time point, specifically including: Based on the meteorological field data of the tracing area during the tracing time period, determining the meteorological field data of the tracing area during a preset time period; the preset time period is the time period before the target time point in the tracing 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 area in a preset time period as input, the Lagrange integration method is used to reversely calculate the air mass movement path to obtain the position of the air mass at each preset time point in the preset time period; The position of the air mass at each preset time point in the preset time period is combined into a trajectory corresponding to the target time point.

3. The method for quantitative analysis of urban greenhouse gas sources according to claim 1, characterized in that: The method of allocating concentrations of the trajectories corresponding to the target time point based on the average greenhouse gas concentration corresponding to the target time point to obtain a 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, calculating an initial time weight corresponding to the trajectory time point, normalizing the initial time weight to obtain a time weight corresponding to the trajectory time point, and calculating a 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; Combining the greenhouse gas concentrations corresponding to all the trajectory time points into a trajectory concentration vector corresponding to the target time point; The calculation formula of the initial time weight is: ； in, For the The initial time weight corresponding to each trajectory time point; For the Sorting of trajectory time points, sorting all trajectory time points in order from back to front; is the attenuation coefficient.

4. The method for quantitative analysis of urban greenhouse gas sources according to claim 1, characterized in that: Clustering the trajectories corresponding to all the target time points based on the trajectory concentration vectors corresponding to all the target time points to obtain multiple trajectory clusters and the average concentration corresponding to each trajectory cluster specifically includes: Randomly determine multiple cluster numbers, sort all the cluster numbers in ascending order, and select the first cluster number as the initial cluster number; 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, to obtain a plurality of initial trajectory clusters corresponding to the initial number of clusters and an 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 cluster number, the intra-cluster sum of squares corresponding to the initial cluster number is calculated, and based on the intra-cluster sum of squares corresponding to the initial cluster number and the intra-cluster sum of squares corresponding to the previous cluster number of the initial cluster number, the intra-cluster sum of squares decrease rate corresponding to the initial cluster number is calculated; Determine whether there are any unselected cluster numbers. If so, select the next cluster number after the initial cluster number as the initial cluster number for the next iteration, and return to the step of "clustering the trajectories corresponding to all the target time points based on the initial cluster number and the trajectory concentration vectors corresponding to all the target time points." If not, perform a preliminary screening of all the initial cluster numbers based on the intra-cluster sum of squares decrease rate corresponding to all the initial cluster numbers to obtain the number of clusters after preliminary screening. For each of the number of clusters after the preliminary screening, based on a plurality of initial trajectory clusters corresponding to the number of clusters after the preliminary screening, calculating a silhouette coefficient corresponding to the number of clusters after the preliminary screening; All the cluster numbers after the preliminary screening are re-screened based on the silhouette coefficients corresponding to all the cluster numbers after the preliminary screening to obtain the number of clusters after screening, and the multiple initial trajectory clusters corresponding to the number of clusters after screening and the average trajectory concentration vector corresponding to each of the initial trajectory clusters are used as the multiple trajectory clusters and the average trajectory concentration vector corresponding to each of the trajectory clusters. Based on the average trajectory concentration vector corresponding to the trajectory cluster, the average concentration corresponding to the trajectory cluster is calculated.

5. The quantitative analysis method of urban greenhouse gas sources according to claim 4, characterized in that: 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 of the clustering is: the change in the objective function is less than a preset threshold or the number of iterations reaches the maximum number of iterations. The objective function change is the difference between the objective function value calculated by the current iteration and the objective function value calculated by the previous iteration. The calculation formula of the objective function value is: ； in, is the objective function value; is the initial number of clusters; For the Initial trajectory cluster The The trajectory concentration vector corresponding to the trajectory; For the Initial trajectory cluster The The trajectory weight corresponding to the trajectory; For the Initial trajectory cluster The corresponding average trajectory concentration vector.

6. The method for quantitative analysis of 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, the first contribution ratio of each province to the greenhouse gas of the target city is calculated, specifically including: For each of the trajectory clusters, based on the number of trajectories included in the trajectory cluster, the trajectory cluster proportion corresponding to the trajectory cluster is calculated, and based on the trajectory cluster proportion and average concentration corresponding to the trajectory cluster, the trajectory cluster concentration contribution ratio corresponding to the trajectory cluster is calculated; For each province passed through, the first contribution ratio of the province to the greenhouse gas of the target city is calculated based on the trajectory cluster proportion and trajectory cluster concentration contribution ratio corresponding to all the trajectory clusters; The calculation formula of the trajectory cluster ratio is: ； in, For the The proportion of trajectory clusters corresponding to the trajectory clusters; For the The number of trajectories included in a trajectory cluster; is the number of trajectory clusters; The calculation formula for the trajectory cluster concentration contribution ratio is: ； in, For the The contribution ratio of trajectory cluster concentration corresponding to each trajectory cluster; For the The average concentration corresponding to the trajectory cluster; is the average value of greenhouse gas concentration data of the target city during the target time period; The calculation formula for the first contribution ratio is: ； in, For the The first contribution ratio of the passing provinces to the target city’s greenhouse gases; For the first The number of trajectory clusters passing through a province; For the first The first province through which The proportion of trajectory clusters corresponding to the trajectory clusters; For the first The first province through which The contribution ratio of trajectory cluster concentration corresponding to each trajectory cluster; is the number of provinces passed through.

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

8. The method for quantitative analysis of urban greenhouse gas sources according to claim 1, characterized in that: Based on the first contribution ratio and the second contribution ratio, the relative contribution ratio of each province to the greenhouse gas emissions of the target city is calculated, specifically including: For each of the passing provinces, 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 is calculated to obtain the total contribution ratio corresponding to the passing province, and the ratio of the total contribution ratio corresponding to the passing province to the total contribution ratio sum is calculated to obtain the relative contribution ratio of the passing province to the greenhouse gases of the target city; wherein, the total contribution ratio sum 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 in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for quantitative analysis of urban greenhouse gas sources according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for quantitatively analyzing the sources of urban greenhouse gases according to any one of claims 1 to 8 is implemented.

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