A method for constructing a spatiotemporal carbon flux digital map

Abstract

The application discloses a kind of construction methods of spatiotemporal carbon flux digital map, including three-dimensional vector information acquisition, establish spatiotemporal carbon flux accounting database, carbon flux spatiotemporal analysis, carbon flux influence factor identification and spatiotemporal carbon flux digital map display, the application obtains geographical space data and influence factor data first, through carbon flux measure system, the real-time, high-precision carbon flux data of target area is calculated and spatiotemporal carbon flux accounting database is constructed, carbon flux time analysis and spatial analysis are determined, and the spatiotemporal distribution trend of the carbon flux of measurement area is determined, using carbon flux spatiotemporal geographic weighting model identifies carbon flux influence factor, and real-time display carbon flux digital map on digital big screen;The application can realize the high time, space precision carbon flux accounting and influence factor identification of measurement area, with accurate quantification, visualization and artificial intelligence mode realizes the detection, management and prediction to target area spatiotemporal carbon flux.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of low-carbon city planning, and particularly relates to a construction method of a spatiotemporal carbon flux digital map. BACKGROUND

[0002] At present, spatial construction, economic activities and human actions are the main sources of carbon emissions, and carbon flux data is a key indicator connecting ecosystems, urban environments and human activities, and is one of the core indicators for measuring carbon peak and carbon neutrality. How to reasonably control carbon flux has become a difficult problem, including how to set up carbon flux measurement equipment, accurately calculate carbon flux, analyze spatiotemporal trends and analyze influencing factors, which have become key problems for the current double carbon target.

[0003] At present, the carbon flux measurement method is mostly based on manual measurement of a single carbon flux measurement device, which has low time resolution and is difficult to capture carbon flux changes in a short time and respond to rapid response and short-term fluctuations. It also has insufficient spatial resolution, which is difficult to capture carbon source and carbon sink space in complex terrain and urban environment, and cannot guarantee full coverage of the measurement area. There is uncertainty in data assimilation and model simulation data, and there is no complex data processing and analysis data integration technology, which cannot identify carbon flux multi-element influencing factors including economic factors and human factors from the perspective of urban planning. There is a lack of data connection with data middle platform and visual screen, which cannot intuitively display the carbon flux measurement results, and cannot accurately translate carbon flux data measurement and spatiotemporal analysis into automatic translation of spatial development problems and spatial planning response schemes.

[0004] Therefore, it is urgent to solve the above problems. SUMMARY

[0005] The purpose of the present application is to provide a construction method of a spatiotemporal carbon flux digital map, which can measure carbon flux with high precision, multiple scales and multiple dimensions, identify influencing factors and display spatiotemporal trends of carbon flux.

[0006] Technical scheme: To achieve the above purpose, the present application discloses a construction method of a spatiotemporal carbon flux digital map, which comprises the following steps:

[0007] (1) Obtain building data, road data and target area boundary data of a target area to form a geographic spatial database, obtain land use data, population data, economic data and industry data to form an influencing factor database, match the geographic spatial database and the influencing factor database to form a vector information data set, and store it;

[0008] (2) Obtain carbon concentration and wind speed in the target area, calculate the carbon flux accounting data of the target area, link the carbon flux accounting data to the vector information data set to form a spatiotemporal carbon flux accounting database;

[0009] (3) According to the spatio-temporal carbon flux accounting database of step (2), the target region is respectively subjected to carbon flux time analysis, spatial analysis and outlier identification, wherein the time analysis includes day-night comparison, quarter comparison, month comparison, annual comparison, identification of extreme values and time of occurrence of extreme values and carbon flux time distribution trend, and the spatial analysis includes overall carbon flux distribution map, identification of extreme values and location of occurrence of extreme values and carbon flux spatial distribution trend;

[0010] (4) According to the influence factor database in step (1) and the spatio-temporal carbon flux accounting database in step (2), the carbon flux measurement result extraction and carbon flux influence factor value calculation of the target region are performed, the extracted data are subjected to spatial matching in a geographic information system, land use index, industry format index, population index and economic index of each grid are calculated, non-numerical index is subjected to numerical processing, and numerical data is subjected to maximum and minimum value standardization processing; according to the carbon flux measurement result of the target region and the influence factor index calculation result, the spatial influence fitting coefficient of each influence factor in each grid is calculated through a carbon flux spatio-temporal geographic weighting model to form a fitting coefficient data set, the first quartile Q1, the second quartile Q2 and the third quartile Q3 in the fitting coefficient data set are set as critical values, the fitting coefficient data set is divided into four intervals: positive first-order influence area U1, positive second-order influence area U2, negative first-order influence area U3 and negative second-order influence area U4, and the influence degree of the carbon flux influence factor on each grid is obtained; the Random Forest algorithm is used to calculate the contribution value of each influence factor to the overall carbon flux of the target region and upload to the data platform;

[0011] (5) According to the spatio-temporal carbon flux accounting database of step (2), the results of carbon flux time analysis, spatial analysis and outlier identification in step (3) and the spatial influence fitting coefficient and contribution value in step (4), different attribute digital map layers are established, and after data superposition, a spatio-temporal carbon flux digital map is formed.

[0012] Optionally, in step (1), the building data, road data and target region boundary data of the target region are obtained from an open map platform to form a geographic spatial basic database; the building data includes the boundary of the building, the number of floors of the building and the function of the building; the road data includes the shape of the road centerline, the length of the road and the grade of the road.

[0013] Optionally, in step (1), the land use data is obtained from satellite remote sensing images, and the land use data includes land properties, land cover types, and land use conditions; the population data is obtained from LBS data of mobile phone operators, and the population data includes population quantity, population distribution, and employment rate; the economic data is obtained from statistical yearbooks, and the economic data includes GDP, per capita income level, and consumption level; and the industry format data is obtained from POI data of open map service providers, and the industry format data includes industry format names, industry format categories, access volumes, and user evaluations.

[0014] Optionally, in step (1), the target region boundary is used to divide 100m×100m grids, each grid is labeled, the geographic space basic data and the influence factor data are matched in coordinates and elevations, data formats are unified, and the data are input into a geographic information system platform to form a vector information data set and stored in a data platform.

[0015] Optionally, in step (2), the measurement points are set in the target area and the carbon flux measurement system is established, which includes the configuration of carbon concentration measurement equipment, measurement point layout, measurement height setting, time resolution setting, spatial resolution setting, wind environment measurement setting and carbon flux calculation; the carbon concentration measurement equipment includes a laser radar device and a drone, the laser radar device with a measurement accuracy of 100m x 100m is used to measure the carbon concentration and wind field of the target area every 30 minutes; and four drones carrying carbon concentration sensors and wind measuring instruments are used to measure the wind speed, wind direction value and carbon concentration inside the grid at a height of 30m and 60m as the reference, to correct and compensate for the missing concentration values outside the circular measurement range of the laser radar device, and to control the measurement accuracy to be less than or equal to 1ppm; the measurement point layout is to set multiple carbon concentration measurement equipment in the target area according to its maximum range to meet the full coverage monitoring of the target area; the measurement height setting is to scan the highest height of the buildings at each measurement point by a three-dimensional laser scanning device, if the highest height is greater than 60m, the measurement height is set to the ground layer, 30m from the ground and 60m from the ground, if the highest height is greater than 30m and less than 60m, the measurement height is set to the ground layer, 30m from the ground, if the highest height is less than 30m, the measurement height is set to the ground layer, the highest building roof layer; the time resolution setting is 30min, and 24 hours is taken as a measurement record; the spatial resolution setting is 100m, which is positionally registered with the 100m grid in step (1); the wind environment measurement setting is a real-time wind field detection mode, including the horizontal wind speed, vertical wind speed, average wind speed and wind direction at the height; the carbon flux calculation means that the carbon concentration measurement equipment is connected with the digital platform through the API interface, the CO2 concentration measurement results of each measurement point are calibrated regularly, the abnormal high value, abnormal low value and blank value are marked, the carbon flux calculation formula is called to calculate the carbon flux accounting data of each measurement point during the measurement period, the carbon flux accounting data set is formed, the vector information data set in step (1) is called, the carbon flux accounting data of each time section is linked to the corresponding grid, the spatio-temporal carbon flux accounting database is formed and stored in the data platform.

[0016] Optionally, in step (3), the abnormal value identification means that according to the spatio-temporal carbon flux accounting data set, five statistical quantities of the spatio-temporal carbon flux accounting data set are calculated, the five statistical quantities include the minimum value x min , the first quartile x 25％ , the second quartile x 50％ , the third quartile x 75％ and the maximum value x max , the interquartile range p is calculated, and the limit of the abnormal value is determined, the formula is:

[0017] p = x 75％ -x 25％

[0018] ε lower =x 25％ -1.5×ρ

[0019] ε upper =x 75％ +1.5×ρ

[0020] Where, ε lower It is the lowest bound for outliers, ε upper The highest limit is the threshold for outliers. Carbon flux measurement data below the lowest limit and above the highest limit are considered outliers. The corresponding time data is used to determine when the outlier occurred.

[0021] Optionally, the carbon flux temporal distribution trend in step (3) refers to the trend based on the spatiotemporal carbon flux accounting data x. ti Where i represents the measurement point number and t represents the measurement time point, the moving average (SMA) is calculated. t The method eliminates random fluctuations in carbon flux data, imports a carbon flux digital map to construct and display a precise and smooth carbon flux time distribution curve, clearly expressing the time distribution trend of carbon flux; the moving average (SMA) method is used. t0 The formula is:

[0022]

[0023] Among them, SMA t It is the moving average of carbon flux at the t-th measurement time point, where n is the moving average calculation window, and x is the moving average value. ti It is the original carbon flux data point of the i-th measurement point at the t-th measurement time point; for the value of the n calculation window, based on the periodicity of the carbon flux data acquisition and the time resolution of 30 minutes, the value of n is determined to be 3-5 carbon flux data points, and multiple iterations are performed.

[0024] Optionally, the spatial distribution trend of carbon flux in step (3) refers to the trend of carbon flux calculated based on spatiotemporal carbon flux accounting data x. ti All observation points adjacent to observation point i, the number of adjacent observation points is N. i The spatial weight w between observation point i and its neighboring observation point j is calculated based on actual distance, land use, building height, green space ratio, and building density data. ij Calculate the aggregate value G of the data from each observation point. i The formula is:

[0025]

[0026] in, L represents the actual distance between observation point i and its adjacent observation point j. α and β are parameters that control the influence of distance on land use, building height, green space ratio, and building density. ijis the difference data of land property, building height, green space ratio, building density between observation point i and adjacent observation point j, is the average value of land property, building height, green space ratio, building density, and γ is a parameter for controlling the influence of the difference of land property, building height, green space ratio, building density;

[0027]

[0028] wherein z j is the standardized value of adjacent observation point j, and j is 1-N i , is the average of the standardized values of all adjacent observation points;

[0029] A clustering value threshold τ is defined, and G i >τ, then observation point i is a high-value cluster; G i <τ, then observation point i is a low-value cluster, and the spatial hot and cold spot distribution map of carbon flux of the measurement area in different periods is superimposed to express the spatial distribution trend of carbon flux.

[0030] Optionally, the primary influence factors in step (4) include land use indexes, industry format indexes, population indexes and economic indexes, the land use indexes include construction land, road, woodland, grassland, water body, farmland and other non-construction land, a total of 7 secondary influence factors; the industry format indexes include business poi index, commercial poi index, residential poi index, public service poi index and industrial poi index, a total of 5 secondary influence factors; the population indexes include residential population and employed population, a total of 2 secondary influence factors; the economic indexes include unit land GDP index and unit population consumption index, a total of 2 secondary influence factors;

[0031]

[0032] wherein IF p is the numerical value of the primary influence factor, IF pk is the numerical value of the secondary influence factor, p is the number of the primary influence factor, K is the number of the secondary influence factor, and k is the number of the secondary influence factor;

[0033] The carbon flux spatiotemporal geographical weighting model refers to using the flux measurement data of each grid and the land use indexes, industry format indexes, population indexes and economic indexes of each grid, and calculating the fitting coefficient of the influence factors and the carbon flux of each grid to capture the spatial and temporal heterogeneity of the carbon flux and clarify the influence degree of the influence factors;

[0034] Each measurement point i corresponds to a grid, so the grid number is also i, and the longitude value of the grid is determined as u i , and the latitude value is v i, the measurement time of the carbon flux is t, and a spatially and temporally weighted model of the carbon flux is constructed, and the formula is:

[0035]

[0036]

[0037] wherein x i is the i-th set of carbon flux values, j is the adjacent grid number of grid i, ε i is a random error, u i is the longitude of the i-th grid, t i is the carbon flux measurement time of the i-th grid, v i is the latitude of the i-th grid, u0 is the longitude of the initial grid, v0 is the latitude of the initial grid, t0 is the initial measurement time, IF ipk is the value of the pk-th influencing factor at the i-th grid, q0(u0, v0, t0) is the fitting coefficient at the initial measurement position longitude, latitude and time, x j (t ij ) is the carbon flux value of the adjacent grid j at time t ij , q pk (u i ,v i ,t i ) is the fitting coefficient of the pk-th influencing factor at the i-th carbon flux measurement grid, e is the constant value of the exponential function, w ij (t ij ) is the weight considering the spatial and temporal distance, x j (t ij ) is the carbon flux value of the adjacent grid j at time t j , d ij is the spatial distance between grid i and grid j, t ij is the time interval of the two measurements, and σ and τ are the spatial and temporal bandwidth parameters, respectively.

[0038] Optionally, the Random Forest algorithm is used in step (4) to calculate the contribution value of each influencing factor to the overall carbon flux of the target region, that is, the land use index IF1, the industry format index IF2, the population index IF3 and the economic index IF4 are used to construct an IF p data set, and four decision trees are constructed based on the data set, and the feature importance value VI pk of the secondary index in each decision tree is calculated, wherein the land use index includes construction land IF 11 , road IF 12 , forest land IF 13 , grassland IF 14 , water body IF 15 , farmland IF 16 and other non-construction land IF17 , and the industry format index includes a business poi index IF 21 , a commercial poi index IF 22 , a residential poi index IF 23 , a public service poi index IF 24 , and an industrial poi index IF 25 , the population index includes a residential population number IF 31 and an employed population number IF 32 , the economic index includes a unit land GDP index IF 41 and a unit population consumption index IE 42 , and the importance value VI pk of all secondary indicators is averaged to obtain the overall contribution value VI p of the secondary indicators, i.e. the primary indicator contribution value, and the formula is:

[0039]

[0040] and the sum is normalized to 1, and the formula is: p is (1, 2, 3, 4), which can explain the contribution degree of each primary indicator to the model prediction result, and the higher the value, the greater the contribution.

[0041] Advantages: Compared with the prior art, the present application has the following significant advantages:

[0042] (1) The vector information data set of the present application breaks through the data information focusing only on a single spatial dimension or time dimension, realizes the spatio-temporal characteristic expression of carbon flux data, and matches the spatio-temporal data with multiple elements, breaking through the difficulty of the traditional carbon flux data results to reflect the connection with economic factors and human factors, providing basic data connection for the identification and analysis of carbon flux influencing factors.

[0043] (2) The present application constructs a carbon flux measurement system to realize real-time measurement, full-coverage carbon flux measurement regardless of terrain and complex urban environment, greatly improves the spatio-temporal accuracy of carbon flux measurement, completes the real-time acquisition, data automatic processing and visualization expression of carbon flux data, and establishes a spatio-temporal carbon flux accounting database, breaking through the limitations of original manual calculation, measurement and control, and using advanced artificial intelligence technology and geographic information system, combined with machine learning algorithm, realizing automatic measurement and quantitative analysis of carbon flux in a specific area, establishing an intelligent and automated process for carbon flux data measurement and processing, and improving the efficiency, accuracy and timeliness of carbon flux data acquisition.

[0044] (3) The application can determine the time distribution trend and the spatial distribution trend of the carbon flux by calling the spatio-temporal carbon flux accounting database information, performing time analysis and spatial analysis of the carbon flux, determining and correcting abnormal values through spatio-temporal geographic weighted analysis, and connecting the digital platform to display the spatio-temporal distribution trend curve of the carbon flux, and constructing different layers according to the time attribute and the spatial attribute, realizing the horizontal and vertical comparison of different time domains and spatial domains, breaking through the limitation of the traditional single time or single space carbon flux comparison, and realizing the visual expression of the spatio-temporal distribution trend of the carbon flux; meanwhile, the application can identify the carbon flux influence factors by calling the influence factor library, and perform influence value calculation through the Random Forest algorithm, determine the factors influencing the carbon flux and the contribution value of each influence factor, and divide the four influence areas, break through the subjective judgment of the traditional carbon flux influence factors, and realize the numerical judgment of the influence of the multiple influence factors including economic factors and human activity factors on the carbon flux from the perspective of urban planning, and improve the comprehensiveness and objectivity of the carbon flux influence factors;

[0045] (4) The application can realize the construction of the digital map platform by establishing different attribute digital map layers and superimposing data and image information according to geographic coordinates, complete the whole process of automatic intelligent processing and display of the carbon flux from data collection, data processing, data analysis, dynamic real-time detection, real-time feedback, strategy response and result output, provide an intuitive, easy-to-understand, convenient and efficient spatial carbon flux display platform for city managers, realize the interactive decision of the spatial planning strategy and the dynamic monitoring and analysis of the carbon flux, and provide the interactive experience of the two-dimensional layer and the three-dimensional digital sand table. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 is a flowchart of the application;

[0047] Figure 2 is a framework diagram of the carbon flux measurement system in the application;

[0048] Figure 3 is a target area carbon flux time analysis result graph in the application;

[0049] Figure 4 is a target area carbon flux spatial analysis result graph in the application;

[0050] Figure 5 is a target area carbon flux influence factor identification result graph in the application;

[0051] Figure 6 is a spatio-temporal carbon flux digital display large screen in the application. DETAILED DESCRIPTION

[0052] The technical solutions of the application will be further described below with reference to the drawings.

[0053] As Figure 1 The application discloses a method for constructing a spatiotemporal carbon flux digital map, comprising the following steps:

[0054] S1. Three-dimensional vector information acquisition: Obtain building data, road data and target area boundary data of the target area to form a geographic space basic database, obtain land use data, population data, economic data and industry data to form an influence factor database, match the geographic space basic database and the influence factor database to form a vector information data set, and store it.

[0055] The building data includes the boundary of the building, the number of floors of the building and the function of the building; the road data includes the road centerline shape, the road length and the road grade. The land use data is obtained from satellite remote sensing images, and includes land properties, land cover types and land use conditions; the population data is obtained from LBS data of mobile phone operators through the following steps: data collection, base station positioning, AFLT positioning, mixed positioning, conversion of latitude and longitude and data transmission, and includes population quantity, population distribution and employment rate; the economic data is obtained from statistical yearbooks, and includes GDP, per capita income level and consumption level; the industry data is obtained from POI data of open map service providers, and includes industry name, industry category, access volume and user evaluation.

[0056] Divide the target area boundary into 100m*100m grids, label each grid, match the geographic space basic data and the influence factor data in coordinates and elevations, unify the data format and input into a geographic information system platform to form a vector information data set, and store it in a data platform.

[0057] S2. Establish a spatiotemporal carbon flux accounting database: Obtain carbon concentration and wind speed in the target area, calculate the carbon flux accounting data of the target area, link the carbon flux accounting data to the vector information data set, and form a spatiotemporal carbon flux accounting database.

[0058] As Figure 2As shown, the measurement points are set in the target area and the carbon flux measurement system is established, the carbon concentration measurement equipment includes a laser radar device and a drone, the laser radar device with a measurement accuracy of 100m*100m is used to measure the carbon concentration and wind field of the target area every 30 minutes; and four drones carrying carbon concentration sensors and wind measuring instruments are used to measure the wind speed, wind direction value and carbon concentration inside the grid at the height of 30m and 60m as the reference, to correct and make up the missing concentration value outside the circular measurement range of the laser radar device, and to control the measurement accuracy to be less than or equal to 1ppm; the carbon concentration measurement equipment is connected with the digital platform through the API interface, the CO2 concentration measurement results of each measurement point are calibrated regularly, the abnormal high value, abnormal low value and blank value are marked, the carbon flux calculation formula is called to calculate the carbon flux accounting data of each measurement point during the measurement period, the carbon flux accounting data set is formed, the vector information data set in S1 is called, the carbon flux accounting data of each time section is linked to the corresponding grid, the spatio-temporal carbon flux accounting database is formed and stored in the data platform;

[0059] The carbon flux measurement system includes carbon concentration measurement equipment, measurement point layout, measurement height setting, time resolution setting, spatial resolution setting, wind environment measurement setting and carbon flux calculation; the measurement point layout is that multiple carbon concentration measurement equipment are set in the target area according to the maximum range, and the laser detection radar and the drone are used to fully meet the full coverage monitoring of the target area; the measurement height setting is that the three-dimensional laser scanning device is used to scan the maximum height of the building at each measurement point, the scanning route is reasonably planned, the overlapping area between the scanning device points is kept at 30%-40%, the scanning accuracy is improved, the data transmission and processing with the numerical control platform are established, if the maximum height is greater than 60m, the measurement height setting is set as the ground layer, 30m away from the ground and 60m away from the ground, if the maximum height is greater than 30m and less than 60m, the measurement height setting is set as the ground layer, 30m away from the ground, if the maximum height is less than 30m, the measurement height setting is set as the ground layer and the top layer of the highest building; the time resolution setting is 30min, and 24 hours are taken as one measurement record; the spatial resolution setting is 100m, which is positionally registered with the 100m grid in step (1); the wind environment measurement setting is the real-time wind field detection mode, which is matched with the intelligent wind measuring instrument carried by the drone to avoid the dead angle of the measurement space and reduce the error, and the wind environment measurement setting includes the horizontal wind speed, vertical wind speed, average wind speed and wind direction at the height; the carbon flux calculation formula is F co2 = p co2 * v * A, wherein F co2 is the CO2 flux, p co2 is the CO2 concentration, v is the wind speed, and A is the cross-sectional area of the measurement area.

[0060] S3: Spatio-temporal analysis of carbon flux: According to the spatio-temporal carbon flux accounting database of step S2, the target region is respectively subjected to carbon flux time analysis, spatial analysis and outlier identification, the time analysis result includes day-night comparison, quarter comparison, month comparison, annual comparison, identification of extreme value and time of extreme value occurrence and carbon flux time distribution trend; the spatial analysis result includes overall carbon flux distribution map, identification of extreme value and location of extreme value occurrence and carbon flux spatial distribution trend;

[0061] Outlier identification refers to calculating and sorting five statistics of the spatio-temporal carbon flux accounting dataset according to the spatio-temporal carbon flux accounting dataset, the five statistics including carbon flux minimum value x min , first quartile x 25％ , second quartile x 50％ , third quartile x 75％ and maximum value x max , calculating quartile interval p to measure the dispersion degree of carbon flux data, and determining the limits of outliers, the formula being:

[0062] p = x 75％ - x 25％

[0063] e lower = x 25％ -1.5xp

[0064] e upper = x 75％ +1.5xp

[0065] Wherein, e lower is the lowest limit of outliers, e upper is the highest limit of outliers, the carbon flux measurement data lower than the lowest limit and higher than the highest limit is an outlier, the corresponding time data is clear about the time of occurrence of the outlier, and further analysis of the extreme event and reason of the occurrence error is carried out, so as to determine whether to retain the data and to build an extreme event set storage data center;

[0066] As shown in Figure 3 , the carbon flux time distribution trend refers to calculating the moving average value SMA t to eliminate the random fluctuations of carbon flux data according to the spatio-temporal carbon flux accounting data x ti , i represents the measurement point number, t represents the measurement time point, importing the carbon flux digital map to construct and display the accurate and smooth carbon flux time distribution curve graph, and clearly expressing the carbon flux time distribution trend; the formula of the moving average value SMA t0 is:

[0067]

[0068] Wherein, SMA tis the carbon flux moving average value of the tth measurement time point, n is the moving average calculation window, x ti is the carbon flux original data point of the ith measurement point at the tth measurement time point; for the value of n calculation window, according to the periodicity and time resolution of 30 minutes of carbon flux data acquisition, the value of n is determined to be 3-5 carbon flux data points, and multiple iterations are carried out;

[0069] As shown in Figure 4 , the carbon flux spatial distribution trend refers to the spatial distribution trend of carbon flux accounting data x ti All observation points adjacent to observation point i, the number of adjacent observation points is N i , the spatial weight w ij of observation point i and its adjacent observation point j is calculated based on the actual distance, land property, building height, green rate, building density data, the aggregation value G i of each observation point data is calculated, and the formula is:

[0070]

[0071] wherein, represents the actual distance between observation point i and adjacent observation point j, α and β are parameters for controlling the distance and land property, building height, green rate, building density, L ij is the difference data of land property, building height, green rate, building density between observation point i and adjacent observation point j, is the average value of land property, building height, green rate, building density, and γ is the parameter for controlling the difference influence of land property, building height, green rate, building density;

[0072]

[0073] wherein, z j is the standardized value of adjacent observation point j, j is 1~N i , is the average of the standardized values of all adjacent observation points;

[0074] The aggregation value threshold τ is defined, G i >τ, then observation point i is high value aggregation; G i <τ, then observation point i is low value aggregation, superimposing the carbon flux spatial cold and hot spot distribution map of measurement area in different periods, the carbon flux spatial distribution trend is accurately expressed;

[0075] S4. Carbon flux influence factor identification: as Figure 5As shown, according to the carbon flux measurement result of the target region and the influence factor index calculation result, the spatial influence fitting coefficient of each influence factor in each grid is calculated through the carbon flux spatio-temporal geographical weighted model, the first quartile Q1, the second quartile Q2 and the third quartile Q3 in the fitting coefficient data set are set as critical values, the data set is divided into four intervals: positive first-order influence area U1, positive second-order influence area U2, negative first-order influence area U3 and negative second-order influence area U4, and the influence degree of the carbon flux influence factor on each grid is obtained.

[0076] Firstly, according to the carbon flux measurement result of the target region and the influence factor index calculation result, the spatial influence fitting coefficient of each influence factor in each grid is calculated through the carbon flux spatio-temporal geographical weighted model, the first quartile Q1, the second quartile Q2 and the third quartile Q3 in the fitting coefficient data set are set as critical values, the data set is divided into four intervals: positive first-order influence area U1, positive second-order influence area U2, negative first-order influence area U3 and negative second-order influence area U4, and the influence degree of the carbon flux influence factor on each grid is obtained.

[0077] Then, the contribution value of each influence factor to the overall carbon flux of the target region is calculated by using the Random Forest algorithm and uploaded to the data platform.

[0078] The first-order influence factors include land use index, industry index, population index and economic index. The land use index includes construction land, road, woodland, grassland, water body, farmland and other non-construction land, a total of 7 second-order influence factors. The industry index includes business poi index, commercial poi index, residential poi index, public service poi index and industrial poi index, a total of 5 second-order influence factors. The population index includes residential population and employed population, a total of 2 second-order influence factors. The economic index includes unit land GDP index and unit population consumption index, a total of 2 second-order influence factors.

[0079]

[0080] Wherein, IF p is the first-order influence factor value, IF pk is the second-order influence factor value, p is the first-order influence factor number, K is the number of second-order influence factors, and k is the second-order influence factor number.

[0081] The carbon flux spatio-temporal geographical weighted model refers to using the flux measurement data of each grid and the land use index, industry index, population index and economic index of each grid to calculate the spatio-temporal comprehensive weight and the fitting coefficient of each grid influence factor and carbon flux, capture the spatial and temporal heterogeneity of carbon flux, and determine the influence degree of the influence factor.

[0082] Each measurement point i corresponds to a grid, so the grid number is also i, and the longitude value of the grid is determined as ui , latitude value is v i , the measurement observation time is t, the spatial and temporal geographical weighted model of carbon flux is constructed, and the formula is:

[0083]

[0084]

[0085] Wherein, x i is the i-th set of carbon flux values, j is the adjacent grid number of grid i, ε i is a random error, u i is the longitude of the i-th grid, t i is the carbon flux measurement time of the i-th grid, v i is the latitude of the i-th grid, u0 is the longitude of the initial grid, v0 is the latitude of the initial grid, t0 is the initial measurement time, IF ipk is the value of the pk-th influencing factor at the i-th grid, q0(u0,v0,t0) is the fitting coefficient at the initial measurement position longitude, latitude and time, x j (t ij ) is the carbon flux value of adjacent grid j at time t ij , q pk (u i ,v i ,t i ) is the fitting coefficient of the pk-th influencing factor at the i-th carbon flux measurement grid, e is the constant value of exponential function, w ij (t ij ) is the weight considering the spatial and temporal distance, x j (t ij ) is the carbon flux value of adjacent grid j at time t j , d ij is the spatial distance between grid i and grid j, t ij is the time interval of two measurements, σ and τ are the spatial and temporal bandwidth parameters respectively;

[0086] According to the calculation of fitting coefficient, the first quartile Q1, the second quartile Q2 and the third quartile Q3 in the fitting coefficient data set are selected, and the data set is divided into four intervals: positive first-order influence area U1, positive second-order influence area U2, negative first-order influence area U3 and negative second-order influence area U4;

[0087] The contribution value of each influencing factor to the overall carbon flux of the target area is calculated by using Random Forest algorithm, that is, the land use index IF1, the industry format index IF2, the population index IF3 and the economic index IF4 are constructed IF pThe dataset was used to construct four types of decision trees, and the feature importance value VI of the secondary indicators in each decision tree was calculated. pk The land use index includes construction land IF 11 , road IF 12 Forest IF 13 Grassland IF 14 Water bodies IF 15 Farmland IF 16 and other non-construction land IF 17 Business type indicators include the Business POI Index (IF). 21 Commercial POI Index (IF) 22 Residential POI Index (IF) 23 Public Service POI Index (IF) 24 and the Industrial POI Index IF 25 Population indicators include the number of residents (IF) 31 and the number of employed people IF 32 Economic indicators include the GDP per unit of land use index (IF). 41 and the per capita consumption index IF 42 And the importance value VI for all secondary indicators. pk The average contribution value VI of the secondary indicators is obtained by calculation. p That is, the contribution value of the primary indicator, the formula is:

[0088]

[0089] And normalize their sum to 1, the formula is: p∈(1,2,3,4) can explain the contribution of each primary indicator to the model's prediction results. The higher the value, the greater the contribution.

[0090] S5. Spatiotemporal carbon flux digital map display: such as Figure 6 As shown, digital map layers with different attributes are established for the carbon flux spatiotemporal data in step S2, the carbon flux spatiotemporal analysis results in step S3, and the carbon flux influencing factor identification results in step S4. After the data and image information are overlaid according to the geographic coordinates, a 24-hour dynamic spatiotemporal carbon flux digital map is displayed on the digital screen. The results of each module of the digital map are output in CSV format according to user needs.

Claims

1. A method for constructing a spatiotemporal carbon flux digital map, characterized by, It comprises the following steps: (1) Obtain building data, road data and target area boundary data of the target area to form a geographic spatial database, obtain land use data, population data, economic data and industry data to form an influence factor database, and match the geographic spatial database and the influence factor database to form a vector information data set; (2) Obtain carbon concentration and wind speed in the target area, calculate the carbon flux accounting data of the target area, link the carbon flux accounting data to the vector information data set, and form a spatio-temporal carbon flux accounting database; (3) According to the spatio-temporal carbon flux accounting database of step (2), the target area is analyzed in time, space and abnormal value identification, wherein the time analysis includes day and night comparison, seasonal comparison, monthly comparison, annual comparison, identification of extreme value and time of extreme value, and carbon flux time distribution trend; the spatial analysis includes carbon flux overall distribution graph, identification of extreme value and location of extreme value, and carbon flux spatial distribution trend; (4) According to the influence factor database in step (1) and the spatio-temporal carbon flux accounting database in step (2), the carbon flux measurement result of the target area is extracted and the carbon flux influence factor value is calculated, the extracted data is spatially matched in the geographic information system, the land use index, industry index, population index and economic index of each grid are calculated, the non-numerical index is numerically processed, and the numerical data is standardized by maximum and minimum value; According to the carbon flux measurement result of the target area and the influence factor index calculation result, the spatial influence fitting coefficient of each influence factor in each grid is calculated by a carbon flux spatio-temporal geographic weighted model, a fitting coefficient data set is formed, the first quartile Q1, the second quartile Q2 and the third quartile Q3 in the fitting coefficient data set are set as critical values, the fitting coefficient data set is divided into four intervals: positive first-order influence area U1, positive second-order influence area U2, negative first-order influence area U3 and negative second-order influence area U4, and the influence degree of carbon flux influence factor on each grid is obtained; the contribution value of each influence factor to the overall carbon flux of the target area is calculated by using Random Forest algorithm; (5) According to the spatio-temporal carbon flux accounting database of step (2), the results of carbon flux time analysis, spatial analysis and abnormal value identification in step (3), and the spatial influence fitting coefficient and contribution value in step (4), digital map layers of different attributes are established, and after data superposition, a spatio-temporal carbon flux digital map is formed.

2. The method of constructing a spatiotemporal carbon flux digital map according to claim 1, wherein: In step (1), the building data, road data and target area boundary data of the target area are obtained from the open map platform to form a geographic spatial database; the building data includes the boundary of the building, the number of floors of the building and the function of the building; the road data includes the shape of the road centerline, the length of the road and the grade of the road.

3. The method of constructing a spatiotemporal carbon flux digital map of claim 1, wherein: The land use data in the step (1) is obtained through satellite remote sensing images, and the land use data includes land properties, land cover types and land use conditions; the population data is obtained by LBS data of mobile phone operators, and the population data includes population quantity, population distribution and employment rate; the economic data is obtained through statistical yearbooks, and the economic data includes GDP, per capita income level and consumption level; the industry format data is obtained through POI data of open map service providers, and the industry format data includes industry format name, industry format category, access volume and user evaluation.

4. The method of constructing a spatiotemporal carbon flux digital map of claim 1, wherein: In the step (1), the target region boundary is divided into 100m*100m grids, each grid is labeled, the geographic space basic data and the influence factor data are matched in coordinates and elevations, the data formats are unified and input into the geographic information system platform to form a vector information data set, and stored in the data platform.

5. The method of constructing a spatiotemporal carbon flux digital map of claim 1, wherein: In the step (2), the measuring points are set in the target region and the carbon flux measurement system is established, the carbon flux measurement system includes carbon concentration measurement equipment, measuring point layout, measurement height setting, time resolution setting, spatial resolution setting, wind environment measurement setting and carbon flux calculation; the carbon concentration measurement equipment includes laser radar equipment and unmanned aerial vehicle, the laser radar equipment with a measurement accuracy of 100m*100m is used to measure the carbon concentration and wind field of the target region every 30 minutes; and four unmanned aerial vehicles carrying carbon concentration sensors and wind measuring instruments are used to measure the wind speed, wind direction value and carbon concentration inside the grid based on the aircraft height of 30m and 60m, to correct and make up the missing concentration values outside the circular measurement range of the laser radar equipment, and to control the measurement accuracy to be less than or equal to 1ppm; the measuring point layout is to set multiple carbon concentration measurement equipment in the target region according to its maximum range to meet the full coverage monitoring of the target region; the measurement height setting is to scan the highest height of the buildings at each measuring point by the three-dimensional laser scanning equipment, if the highest height is greater than 60m, the measurement height is set as the ground layer, 30m away from the ground and 60m away from the ground, if the highest height is greater than 30m and less than 60m, the measurement height is set as the ground layer, 30m away from the ground, if the highest height is less than 30m, the measurement height is set as the ground layer and the highest building roof layer; the time resolution setting is 30min, and 24 hours is taken as a measurement record; the spatial resolution setting is 100m, which is positionally registered with the 100m grid in the step (1); the wind environment measurement setting is a real-time wind field detection mode, including horizontal wind speed, vertical wind speed, average wind speed and wind direction at the height; the carbon flux calculation means that the carbon concentration measurement equipment is connected with the digital platform through the API interface, the CO2 concentration measurement results of each measuring point are calibrated regularly, the abnormal high value, abnormal low value and blank value are marked, the carbon flux calculation formula is called to calculate the carbon flux accounting data of each measuring point during the measurement period, to form a carbon flux accounting data set, and to link each time section carbon flux accounting data to the corresponding grid in the vector information data set in the step (1) to form a spatio-temporal carbon flux accounting database and store it in the data platform.

6. The method of constructing a spatiotemporal carbon flux digital map of claim 1, wherein: The step (3) of identifying outliers refers to calculating five statistics of the spatiotemporal carbon flux accounting dataset according to the spatiotemporal carbon flux accounting dataset, the five statistics including a minimum value x min , a first quartile x 25％ , a second quartile x 50％ , a third quartile x 75％ , and a maximum value x max , calculating a quartile interval p, and determining the limits of outliers, and the formula is: p = x 75％ - x 25％ ε lower = x 25％ -1.5 x p ε upper = x 75％ + 1.5 x p wherein ε lower is the lower limit of outliers, ε upper is the upper limit of outliers, carbon flux measure data below the lower limit and above the upper limit are outliers, corresponding time data, explicitly the time of outliers.

7. The method of constructing a spatiotemporal carbon flux digital map of claim 1, wherein: The carbon flux time distribution trend in step (3) refers to the accounting data x of space-time carbon flux ti , i represents the number of measurement points, t represents the measurement time point, the random fluctuations of carbon flux data are eliminated by calculating the moving average value SMA t , the carbon flux digital map is constructed and the accurate and smooth carbon flux time distribution curve is displayed, and the carbon flux time distribution trend is clearly expressed; the formula of the moving average value SMA t0 is: wherein SMA t is the moving average of carbon flux at the tth measurement time point, n is the moving average calculation window, x ti is the original data point of carbon flux at the ith measurement point at the tth measurement time point; for the value of n calculation window, the value of n is determined to be 3-5 carbon flux data points according to the periodicity and time resolution of 30 minutes of carbon flux data acquisition, and multiple iterations are performed.

8. The method of constructing a spatiotemporal carbon flux digital map according to claim 7, wherein: The carbon flux spatial distribution trend in the step (3) refers to calculating the data x of the carbon flux according to the space-time carbon flux accounting data ti All observation points adjacent to the observation point i, the number of adjacent observation points is N i The spatial weight w of the observation point i and the adjacent observation point j is calculated based on the actual distance, land property, building height, green land rate and building density data ij The aggregation value G of each observation point data is calculated i The formula is: wherein, represents the actual distance between observation point i and adjacent observation point j, and a and β are parameters for controlling the distance and the influence of land properties, building height, green space rate, and building density, ij is the difference data of land properties, building height, green space rate, and building density between observation point i and adjacent observation point j, is the average value of land properties, building height, green space rate, and building density, and γ is a parameter for controlling the influence of the difference of land properties, building height, green space rate, and building density. where z j is the normalized value of the adjacent observation point j, j is 1 ~ N i , is the average of the normalized values of all adjacent observation points; Define the threshold value of the aggregation value τ, G i >τ then the observation point i is a high value aggregation; G i <τ then the observation point i is a low value aggregation, superimpose the carbon flux spatial cold-hot spot distribution map of the measurement area in different periods to express the carbon flux spatial distribution trend.

9. The method of constructing a spatiotemporal carbon flux digital map of claim 8, wherein: The primary influence factors in the step (4) include land use indexes, industry format indexes, population indexes and economic indexes, the land use indexes include construction land, road, woodland, grassland, water body, farmland and other non-construction land, a total of 7 secondary influence factors; the industry format indexes include business poi index, commercial poi index, residential poi index, public service poi index and industrial poi index, a total of 5 secondary influence factors; the population indexes include residential population number and employed population number, a total of 2 secondary influence factors; the economic indexes include unit land GDP index and unit population consumption index, a total of 2 secondary influence factors; wherein, IF p is a first impact factor value, IF pk is a second impact factor value, p is a first impact factor number, K is a second impact factor number, and k is a second impact factor number; The spatiotemporal geographical weighted model of carbon flux refers to using the flux measurement data of each grid and the influence factors of each grid, i.e., land use indexes, industry format indexes, population indexes and economic indexes, and calculating the fitting coefficients of the influence factors and the carbon flux, so as to capture the spatial and temporal heterogeneity of the carbon flux and clarify the influence degree of the influence factors. Each measurement point i corresponds to a grid, so the grid number is also i, and the longitude value of the grid is determined as u i , the latitude value is v i , the measurement observation time is t, and a spatial and temporal geographical weighted model of carbon flux is constructed, and the formula is: where x i is the i-th carbon flux value, j is the adjacent grid number of grid i, ε i is the random error, u i is the longitude of the i-th grid, t i is the measurement time of the i-th grid, v i is the latitude of the i-th grid, u0 is the longitude of the initial grid, v0 is the latitude of the initial grid, t0 is the initial measurement time, IF ipk is the value of the p-th influencing factor at the i-th grid, q0(u0, v0, t0) is the fitting coefficient at the initial measurement position longitude, latitude and time, x j (t ij ) is the carbon flux value of the adjacent grid j at time t ij , q pk (u i ,v i ,t i ) is the fitting coefficient of the p-th influencing factor at the i-th carbon flux measurement grid, e is the constant value of the exponential function, w ij (t ij ) is the weight considering the space-time distance, x j (t ij ) is the carbon flux value of the adjacent grid j at time t j , d ij is the spatial distance between grid i and grid j, t ij is the time interval of the two measurements, σ and τ are the spatial and temporal bandwidth parameters, respectively.

10. The method of constructing a spatiotemporal carbon flux digital map of claim 9, wherein: In step (4), the Random Forest algorithm is used to calculate the contribution of each influencing factor to the overall carbon flux of the target area, that is, to construct the IF1 of land use index, IF2 of business type index, IF3 of population index and IF4 of economic index. p The dataset was used to construct four types of decision trees, and the feature importance value VI of the secondary indicators in each decision tree was calculated. pk The land use index includes construction land IF 11 , road IF 12 Forest IF 13 Grassland IF 14 Water bodies IF 15 Farmland IF 16 and other non-construction land IF 17 Business type indicators include the Business POI Index (IF). 21 Commercial POI Index (IF) 22 Residential POI Index (IF) 23 Public Service POI Index (IF) 24 and the Industrial POI Index IF 25 Population indicators include the number of resident population (IF) 31 and the number of employed people IF 32 Economic indicators include the GDP per unit of land use index (IF). 41 and the per capita consumption index IF 42 And the importance value VI for all secondary indicators. pk The average contribution value VI of the secondary indicators is obtained by calculation. p That is, the contribution value of the primary indicator, the formula is: And its sum is normalized to 1, the formula is: p∈(1,2,3,4), which can explain the contribution of each first-level index to the prediction result of the model. The higher the value, the greater the contribution.

Images