Green smart city carbon sink dynamic measurement method and system

Abstract

The present invention discloses a green smart city carbon sink dynamic measurement method and system, which relates to the field of Internet of Things data collection technology, wherein a monitoring plot and a plurality of measurement monitoring targets are determined in the city as monitoring information, and the distribution information of the measurement monitoring targets is determined in the monitoring plot; the biomass and carbon content coefficient of the plurality of measurement monitoring targets are respectively determined according to the distribution information of the plurality of measurement monitoring targets, and the carbon sink amount of the monitoring plot is obtained; the carbon sink amount of the city is determined according to the carbon sink amount of the monitoring plot, and the carbon sink amount of the city is managed according to the acquisition time of the measurement information to obtain the dynamic measurement of the carbon sink of the city. The present invention associates the feature set of the range block loaded in the matching group with the cloud server of the corresponding monitoring plot, and measures the carbon sink amount within the entire feature range through the cloud server, thereby improving the efficiency and accuracy of the measurement of the carbon sink amount of the city.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things data collection, and in particular to a green smart city carbon sink dynamic measurement method and system. Background Art

[0002] Carbon sink measurement is a dynamic process, and the carbon storage and changes in the ecosystem will change over time. Therefore, continuous carbon sink monitoring is required, and the measurement results are updated in a timely manner, which is helpful to understand the long-term trend of changes in the ecosystem and evaluate the effectiveness of management measures. At present, when dynamic carbon sink measurement is carried out, it is difficult to quickly collect and calculate the data within the scope of carbon sink measurement due to the large scope involved in carbon sink measurement, and the workload is large. If the carbon sink measurement is carried out in a partial range and a large-scale estimation is made, it will lead to inaccurate carbon sink measurement. Therefore, it is difficult to balance the accuracy and efficiency of carbon sink measurement. Summary of the invention

[0003] The purpose of the present invention is to provide a green smart city carbon sink dynamic measurement method and system to solve the deficiencies in the background technology.

[0004] In order to achieve the above object, the present invention provides the following technical solution: a green smart city carbon sink dynamic measurement method, comprising the following steps:

[0005] Determine monitoring plots and multiple measurement monitoring targets in the city as monitoring information, and determine distribution information of the measurement monitoring targets in the monitoring plots;

[0006] According to the distribution information of the multiple measurement monitoring targets, the biomass and carbon content coefficients of the multiple measurement monitoring targets are determined respectively, and the carbon sink amount of the monitoring sample plot is obtained;

[0007] The urban carbon sink is determined based on the carbon sink amount of the monitoring sample site, and the urban carbon sink is managed according to the collection time of the measurement information to obtain the dynamic measurement of urban carbon sink.

[0008] In a preferred embodiment, the step of determining a monitoring plot and a plurality of metering monitoring targets in a city as monitoring information, and determining distribution information of the metering monitoring targets in the monitoring plot comprises:

[0009] A plurality of monitoring plots are determined in the city, corresponding cloud servers are respectively configured for the plurality of monitoring plots, and the cloud servers corresponding to the plurality of monitoring plots are associated to obtain a star server.

[0010] Identify multiple targets participating in the dynamic measurement of urban carbon sinks in the monitoring plots as measurement monitoring targets;

[0011] Use monitoring plots and multiple measurement monitoring targets as monitoring information;

[0012] The distribution information of multiple measurement and monitoring targets in the monitoring sample sites is collected, wherein the distribution information includes the distribution positions and corresponding distribution ranges between the multiple measurement and monitoring targets, and the distribution information and the monitoring information are stored in the corresponding cloud server for storage.

[0013] In a preferred embodiment, the step of determining a plurality of monitoring plots in a city, configuring corresponding cloud servers for the plurality of monitoring plots respectively, and associating the cloud servers corresponding to the plurality of monitoring plots to obtain a star server comprises:

[0014] Divide the ranges in the city according to geographical features to obtain multiple characteristic ranges;

[0015] One monitoring plot is selected in each of the multiple characteristic ranges to obtain multiple monitoring plots in the city;

[0016] Configure the corresponding cloud server for the corresponding monitoring sample site;

[0017] Gridding other ranges except the characteristic range where the monitoring sample plot is located, and associating other ranges after gridding to the cloud server corresponding to the monitoring sample plot;

[0018] The cloud servers corresponding to multiple monitoring plots are associated to obtain a star server.

[0019] In a preferred embodiment, the step of gridding other ranges except the characteristic range where the monitoring plot is located, and associating the other ranges after the gridding process to the cloud server corresponding to the monitoring plot includes:

[0020] Gridding is performed on the ranges other than the characteristic range where the monitoring sample plot is located to obtain multiple range blocks;

[0021] Setting a plurality of feature sets, wherein the feature sets have the same size and shape as the feature range, wherein the feature sets are composed of a plurality of feature blocks, and the feature blocks have the same size as the range blocks;

[0022] Match the range block within the feature range, take the range block that meets the preset matching condition and the matching range in the feature range as a matching group, match the range block to cover the feature range to obtain multiple matching groups, load the range block in the matching group into the feature block in the feature set, wherein the preset matching condition is that the density of the same type of metrology monitoring targets in one or more range blocks is the same as the density of the corresponding type of metrology monitoring targets in the matching range within the feature range;

[0023] If the integrated density of the same type of metrology monitoring targets in multiple range blocks is the same as the density of the corresponding type of metrology monitoring targets in the matching range within the feature range, the multiple range blocks are bound as one range block;

[0024] The feature set of the range block loaded into the matching group is associated with the cloud server of the corresponding monitoring plot.

[0025] In a preferred embodiment, the step of determining the biomass and average root-to-shoot ratio of the multiple measurement monitoring targets respectively according to the measurement information of the multiple measurement monitoring targets to obtain the carbon sequestration amount of the monitoring plot includes:

[0026] In the monitoring plots, multiple single tree diameters at breast height (DBH) of the target trees were collected as independent variables, and tree height (DBH) was used as the independent variable. As the dependent variable, the average density , average root-to-shoot ratio Biomass Expansion Factor As metering information, calculate the accumulation amount based on metering information Measuring and monitoring target biomass and carbon storage C;

[0027] According to the measurement information, the accumulation Calculate, where , , c and n coefficients;

[0028] according to ;

[0029] According to the measured monitoring target biomass and the carbon content coefficient corresponding to the measured monitoring target Get carbon storage , ;

[0030] Sort the types of measurement and monitoring targets in the monitoring plots and calculate the carbon sinks in the monitoring plots. ,in, , To measure the number of species of monitoring targets in the monitoring plots, For the Measure the carbon storage of the monitoring target in the sorted monitoring plots.

[0031] In a preferred embodiment, the step of determining the urban carbon sink amount according to the carbon sink amount of the monitoring sample plot, and managing the urban carbon sink amount according to the collection time of the measurement information to obtain the dynamic measurement of the urban carbon sink includes:

[0032] Establish collection intervals and collect urban carbon sinks according to the intervals;

[0033] Determine the collection time point based on the interval period, and determine the urban carbon sink through multiple measurement monitoring target carbon sinks based on the collection time point;

[0034] The urban carbon sink is recorded according to the collection time point, and the difference between the urban carbon sink at the next collection time point and the urban carbon sink at the previous collection time point is obtained to obtain the carbon sink relationship line corresponding to multiple collection time points as the dynamic measurement of urban carbon sink.

[0035] In a preferred embodiment, the step of determining the urban carbon sink amount by using multiple measurement monitoring target carbon sink amounts based on the collection time point includes:

[0036] Sort the monitoring plots.

[0037] Calculate the carbon sink of the city. ,in, is the urban carbon sink, For the The carbon sink within the characteristic range is The number of feature sets after loading feature sets for the range block in the matching group, The number of monitoring plots.

[0038] The present invention also provides a green smart city carbon sink dynamic measurement system, including:

[0039] A collection module is used to determine monitoring plots and multiple measurement monitoring targets in the city as monitoring information, and to determine distribution information of the measurement monitoring targets in the monitoring plots;

[0040] A calculation module, connected to the acquisition module, is used to determine the biomass and carbon content coefficient of multiple measurement monitoring targets according to the distribution information of multiple measurement monitoring targets, and obtain the carbon sink amount of the monitoring sample site;

[0041] The determination module is connected to the calculation module and is used to determine the urban carbon sink according to the carbon sink of the monitoring sample site, and to manage the urban carbon sink according to the collection time of the measurement information to obtain the dynamic measurement of the urban carbon sink.

[0042] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0043] In the present invention, after the metering monitoring target density of multiple range blocks can be matched with the metering monitoring target density in the monitoring sample plot, it is expressed as the same area in the feature set. Therefore, after the range blocks in the matching group are completely loaded into the feature set, the monitoring sample plot can be represented. By associating the feature set of the range blocks loaded in the matching group with the cloud server of the corresponding monitoring sample plot, the carbon sink amount in the entire feature range is measured by the cloud server, thereby improving the efficiency and accuracy of urban carbon sink measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0045] Figure 1 The present invention is a flow chart of the method.

[0046] Figure 2 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Example 1, please refer to Figure 1 As shown, the green smart city carbon sink dynamic measurement method described in this embodiment includes the following steps:

[0049] S1. Determine a monitoring plot and a plurality of measurement monitoring targets in a city as monitoring information, and determine distribution information of the measurement monitoring targets in the monitoring plot;

[0050] S2. Determine the biomass and carbon content coefficient of the multiple measurement monitoring targets respectively according to the distribution information of the multiple measurement monitoring targets, and obtain the carbon sink amount of the monitoring sample site;

[0051] S3. Determine the urban carbon sink amount according to the carbon sink amount of the monitoring sample site, manage the urban carbon sink amount according to the collection time of the measurement information to obtain the dynamic measurement of urban carbon sink;

[0052] As described in the above steps S1-S3, carbon sink measurement is a dynamic process, and the carbon storage and changes in the ecosystem will change over time. Therefore, it is necessary to carry out continuous carbon sink monitoring, and update the measurement results in time, which is helpful to understand the long-term change trend of the ecosystem and evaluate the effect of management measures. When the dynamic measurement of carbon sink is currently carried out, due to the large scope involved in carbon sink measurement, it is difficult to quickly collect and calculate the data within the scope involved in carbon sink measurement, and the workload is large. If the carbon sink measurement is carried out in a partial range and a large-scale estimation is made, the inaccuracy of carbon sink measurement will be caused. Therefore, it is difficult to take into account the accuracy and efficiency of carbon sink measurement. And in this application, the measurement monitoring target density of multiple range blocks can be matched with the measurement monitoring target density in the monitoring sample plot, and the form in the feature set is expressed as the same area, so the monitoring sample plot can be represented after the range block in the matching group is completely loaded into the feature set, and the feature set of the range block loaded in the matching group is associated with the cloud server of the corresponding monitoring sample plot, and the carbon sink amount in the entire feature range is measured by the cloud server, so as to improve the efficiency and accuracy of the urban carbon sink measurement.

[0053] In one embodiment, the step S1 of determining a monitoring plot and a plurality of metering monitoring targets in a city as monitoring information and determining distribution information of the metering monitoring targets in the monitoring plot includes:

[0054] S11. Determine a plurality of monitoring plots in the city, configure corresponding cloud servers for the plurality of monitoring plots respectively, and associate the cloud servers corresponding to the plurality of monitoring plots to obtain a star server.

[0055] S12. Determine multiple targets participating in the dynamic measurement of urban carbon sinks in the monitoring plot as measurement monitoring targets;

[0056] S13, using the monitoring plot and multiple measurement monitoring targets as monitoring information;

[0057] S14, collecting distribution information of multiple measurement monitoring targets in the monitoring sample plot, wherein the distribution information includes the distribution positions and corresponding distribution ranges between the multiple measurement monitoring targets, and storing the distribution information and the monitoring information in the corresponding cloud server for storage;

[0058] As described in the above steps S11-S14, multiple monitoring plots are determined in the city, and corresponding cloud servers are respectively configured for the multiple monitoring plots. The cloud servers here are used to store the biomass and carbon content coefficient of the metered monitoring targets collected by the corresponding monitoring plots later, and are used to run the calculation of the carbon sink amount of the subsequent detection plots. The cloud servers corresponding to the multiple monitoring plots are linked to obtain a star server, and multiple targets participating in the dynamic metering of urban carbon sinks are determined in the monitoring plots as metering monitoring targets, such as metering monitoring targets such as trees, shrubs and grasslands. The monitoring plots and the corresponding multiple metering monitoring targets are used as monitoring information, and the distribution positions and corresponding distribution ranges of the multiple metering monitoring targets in the monitoring plots are collected as distribution information. Thereafter, the distribution information and the monitoring information are stored in the corresponding cloud servers for storage, which can record information related to urban carbon sinks and facilitate the subsequent calculation of carbon sink dynamics.

[0059] In one embodiment, the step S11 of determining a plurality of monitoring plots in a city, configuring corresponding cloud servers for the plurality of monitoring plots respectively, and associating the cloud servers corresponding to the plurality of monitoring plots to obtain a star server includes:

[0060] S111. Dividing the range in the city according to geographical features to obtain a plurality of feature ranges;

[0061] S112, selecting a monitoring plot in each of the multiple characteristic ranges to obtain multiple monitoring plots in the city;

[0062] S113, configuring a corresponding cloud server for the corresponding monitoring sample site;

[0063] S114, gridding the ranges other than the characteristic range where the monitoring sample plot is located, and associating the other ranges after the gridding process to the cloud server corresponding to the monitoring sample plot;

[0064] S115, associating the cloud servers corresponding to the multiple monitoring plots to obtain a star server;

[0065] As described in the above steps S111-S115, the range in the city is divided according to the geographical features to obtain multiple characteristic ranges. The geographical features here are the distribution patterns of the measurement and monitoring targets in the city that are used to participate in the dynamic measurement of carbon sinks. For example, the measurement and monitoring targets in the mountains are used as a distribution pattern. Since there are too many measurement and monitoring targets in the mountains, the range is divided as a characteristic range. Then, only a certain range needs to be selected in the characteristic range as the monitoring sample plot of the characteristic range. Therefore, if there are multiple characteristic ranges in the city, there will be a corresponding number of monitoring sample plots. Then, the dynamic measurement of carbon sinks is mainly carried out on the monitoring sample plot. Subsequently, the carbon sink dynamics of the range of the monitoring sample plot is used to reflect the dynamic measurement of carbon sinks in the corresponding characteristic range, and then the carbon sink amount of the entire city is obtained according to the dynamic measurement of carbon sinks in multiple characteristic ranges in the city. Therefore, in order to achieve this purpose, the monitoring sample plot needs to be configured with the corresponding cloud server. In order to measure the carbon sink amount in the characteristic range, it is necessary to grid the ranges other than the characteristic range where the monitoring sample plot is located. The other ranges after gridding processing are associated with the cloud server of the corresponding monitoring plot, and the other ranges after gridding processing except the characteristic range where the monitoring plot is located are made to correspond equally with the monitoring plot, so as to obtain the corresponding relationship between the characteristic range and the monitoring plot, and then the cloud servers corresponding to the multiple monitoring plots are associated to obtain a star server. The star server here refers to multiple cloud servers corresponding to the monitoring plots and interconnected, which can collect and summarize information, so as to facilitate the subsequent calculation of carbon sinks and information aggregation by collecting multiple variables of the metering monitoring targets.

[0066] In one embodiment, the step S114 of gridding other ranges except the characteristic range where the monitoring plot is located and associating the other ranges after the gridding process to the cloud server corresponding to the monitoring plot includes:

[0067] S1141, gridding the ranges other than the characteristic range where the monitoring sample plot is located to obtain a plurality of range blocks;

[0068] S1142, setting a plurality of feature sets, wherein the feature sets have the same size and shape as the feature range, wherein the feature sets are composed of a plurality of feature blocks, and the feature blocks have the same size as the range blocks;

[0069] S1143, matching the range block within the feature range, taking the range block that meets the preset matching condition and the matching range in the feature range as a matching group, matching until the range block covers the feature range to obtain multiple matching groups, and loading the range block in the matching group into the feature block in the feature set, wherein the preset matching condition is that the density of the same type of metrology monitoring targets in one or more range blocks is the same as the density of the corresponding type of metrology monitoring targets in the matching range within the feature range;

[0070] S1144. If the integrated density of the same type of metrology monitoring targets in multiple range blocks is the same as the density of the corresponding type of metrology monitoring targets in the matching range within the feature range, the multiple range blocks are bound as one range block;

[0071] S1145, associating the feature set of the range block loaded into the matching group with the cloud server of the corresponding monitoring plot;

[0072] As described in the above steps S1141-S1145, the ranges other than the characteristic range where the monitoring plot is located are gridded to obtain multiple range blocks, and the range blocks here are used for subsequent corresponding matching of the corresponding monitoring plots, and then multiple feature sets are set, and the feature sets have the same size and shape as the feature ranges, wherein the feature sets are composed of multiple feature blocks, and the feature blocks have the same size as the range blocks. The feature sets here represent that the range blocks of the ranges other than the characteristic range where the monitoring plot is located are grouped into a range with the same carbon sequestration amount as the monitoring plot, and a feature set corresponds to a monitoring range after being fully loaded by the range blocks in the matching group, so that after the carbon sequestration amount of the monitoring range is subsequently monitored, it can be equivalent to the feature set. In the set, when matching the range block and the monitoring plot, the range block that meets the preset matching conditions and the matching range in the feature range are used as a matching group, and the range block is matched to cover the feature range to obtain multiple matching groups, and the range block in the matching group is loaded into the feature block in the feature set. The preset matching condition is that the density of the same type of metering and monitoring targets in one or more range blocks is the same as the density of the metering and monitoring targets of the corresponding type in the matching range within the feature range. These two identical metering and monitoring target densities can represent carbon sinks with a small difference. If the integrated density of the same type of metering and monitoring targets in multiple range blocks is the same as the density of the metering and monitoring targets of the corresponding type in the matching range within the feature range, the multiple range blocks are bound as a range block. Here, The same type of metrological monitoring targets in multiple range blocks are transferred and concentrated in one range block, and the same type of metrological monitoring targets in the range blocks are information-pulled (the information traction here is the information stickiness between the range block and the original position of the range block. Here, the ranges other than the characteristic range where the monitoring sample plot is located are gridded to obtain multiple range blocks based on image acquisition. The images within the collected characteristic ranges are gridded based on the geographic grid division of the images. The geographical locations of the multiple range blocks obtained here are the original positions. The information stickiness here is that the range block is a movable image block. The stickiness information is constructed between the range block and the original position to obtain the information stickiness. The stickiness information is set on the range block. An information endpoint is set at the original position, and the second information endpoint is associated with the first information endpoint to establish the sticky information between the range block and the original position. When the same type of metering and monitoring targets in multiple range blocks are transferred and concentrated in one range block, the range block will attach the first information endpoint to the same type of metering and monitoring targets in the range block. This can avoid confusion of range blocks, avoid repeated use of range blocks, and facilitate the disassembly of subsequent range blocks. Therefore, the environment is constantly changing, and the subsequent feature set will change according to the environment to better ensure the accuracy of carbon sink measurement) and point to the original specific location within the feature range. Here, it is not limited to a one-to-one match between the range block and the range in the monitoring plot.Multiple range blocks can be density matched with the ranges in the monitoring plot; if the density of a range block is greater than the density of all ranges in the monitoring plot, then the density of the range block with large density is combined with other range blocks to density cover the density of the metering monitoring target of a certain range in the monitoring plot. Here it is mainly density correspondence, not simple range coverage. After the metering monitoring target density of multiple range blocks can be matched with the metering monitoring target density in the monitoring plot, it is expressed as the same area in the feature set. Therefore, after the range blocks in the matching group are completely loaded into the feature set, the monitoring plot can be represented. For example, there are three feature sets loaded by the range blocks in the matching group in the feature range. Then the subsequent collection of carbon sequestration in the monitoring plot is T, then the carbon sequestration represented by the three feature sets is, , the carbon sink within the characteristic range is , then the feature set of the range block loaded into the matching group is associated with the cloud server of the corresponding monitoring plot, which is used for the subsequent measurement of the carbon sink within the entire feature range through the cloud server, thereby improving the efficiency and accuracy of the urban carbon sink measurement.

[0073] In one embodiment, the step S2 of respectively determining the biomass and average root-to-shoot ratio of the plurality of measurement monitoring targets according to the measurement information of the plurality of measurement monitoring targets to obtain the carbon sequestration amount of the monitoring plot includes:

[0074] S21. Collect multiple single tree diameters at breast height (DBH) of the monitoring target trees in the monitoring plots as independent variables, and tree height (DBH) as the independent variable. As the dependent variable, the average density , average root-to-shoot ratio Biomass Expansion Factor As metering information, calculate the accumulation amount based on metering information Measuring and monitoring target biomass and carbon storage C;

[0075] S22, according to the measurement information, the accumulation amount Calculate, where , , c and n coefficients;

[0076] S23. According to ;

[0077] S24. According to the measured monitoring target biomass and the carbon content coefficient corresponding to the measured monitoring target Get carbon storage , ;

[0078] S25. Sort the types of measurement and monitoring targets in the monitoring plots and calculate the carbon sinks in the monitoring plots. ,in, , To measure the number of species of monitoring targets in the monitoring plots, For the Measure the carbon stocks of the monitoring targets in the sorted monitoring plots;

[0079] As described in the above steps S21-S25, multiple single tree diameters at breast height of the target trees are collected in the monitoring plot. As an independent variable, tree height As the dependent variable, the average density , average root-to-shoot ratio Biomass Expansion Factor As metering information, calculate the accumulation amount based on metering information , Measure and monitor target biomass and carbon storage , where the accumulation is the total volume of wood, average density is the mass of wood per unit volume, and the biomass expansion factor Used to convert wood stock into whole-plant biomass, taking into account other biomass components besides wood (such as bark, branches, leaves, etc.); It indicates the average root-to-shoot ratio, that is, the ratio of underground biomass (mainly tree roots) to aboveground biomass (trunks, branches, leaves, etc.); the biomass of the measurement and monitoring target can be estimated more accurately through factors such as stock volume, wood density, biomass composition and root-to-shoot ratio; in addition, the measurement and monitoring targets of different tree species, different growth stages and different growth environments may have different parameters such as wood density, biomass expansion factor and root-to-shoot ratio, so they need to be selected and adjusted according to specific circumstances in practical applications;

[0080] In one embodiment, the step S3 of determining the urban carbon sink amount according to the carbon sink amount of the monitoring sample plot, and managing the urban carbon sink amount according to the collection time of the measurement information to obtain the dynamic measurement of the urban carbon sink includes:

[0081] S31. Establish collection intervals and collect urban carbon sinks according to the intervals;

[0082] S32, determining a collection time point based on the interval period, and determining the urban carbon sink amount through multiple measurement monitoring target carbon sink amounts based on the collection time point;

[0083] S33, recording the urban carbon sink amount according to the collection time point, obtaining the difference between the urban carbon sink amount at the next collection time point and the urban carbon sink amount at the previous collection time point, and obtaining the carbon sink amount relationship line corresponding to the multiple collection time points as the dynamic measurement of the urban carbon sink;

[0084] In one embodiment, the step S32 of determining the urban carbon sink amount by using multiple measurement monitoring target carbon sink amounts based on the collection time point includes:

[0085] S321. Sort the monitoring plots.

[0086] S322. Calculate the urban carbon sink. ,in, is the urban carbon sink, For the The carbon sink within the characteristic range is The number of feature sets after loading feature sets for the range block in the matching group, To monitor the number of plots;

[0087] As described in the above steps S31-S33, the collection of carbon sinks requires interval collection, and a collection interval period is formulated. The urban carbon sink is collected according to the interval period. The collection time point can be obtained through the interval period. Therefore, at the collection time point, the urban carbon sink is determined by multiple metered monitoring target carbon sinks. The carbon sink of each monitoring plot is obtained here. Therefore, as long as the number of feature sets is loaded into the feature set according to the carbon sink of the monitoring plot and the range block in the matching group, the carbon sink within multiple feature ranges in the city can be obtained. After that, the urban carbon sink is recorded according to the collection time point, and the difference between the urban carbon sink at the next collection time point and the urban carbon sink at the previous collection time point is obtained to obtain the carbon sink relationship line corresponding to multiple collection time points as the urban carbon sink dynamic measurement, which can efficiently and accurately obtain the urban carbon sink dynamics.

[0088] Example 2, please refer to Figure 2 As shown, the green smart city carbon sink dynamic measurement system described in this embodiment includes:

[0089] A collection module is used to determine monitoring plots and multiple measurement monitoring targets in the city as monitoring information, and to determine distribution information of the measurement monitoring targets in the monitoring plots;

[0090] A calculation module, connected to the acquisition module, is used to determine the biomass and carbon content coefficient of multiple measurement monitoring targets according to the distribution information of multiple measurement monitoring targets, and obtain the carbon sink amount of the monitoring sample site;

[0091] A determination module, connected to the calculation module, is used to determine the urban carbon sink amount according to the carbon sink amount of the monitoring sample site, and to manage the urban carbon sink amount according to the collection time of the measurement information to obtain a dynamic measurement of the urban carbon sink;

[0092] It should be noted that after the metering monitoring target density in multiple range blocks can be matched with the metering monitoring target density in the monitoring sample site, it can be expressed as the same area in the feature set. Therefore, after the range blocks in the matching group are completely loaded into the feature set, the monitoring sample site can be represented. The feature set of the range blocks loaded in the matching group is associated with the cloud server of the corresponding monitoring sample site. The carbon sink in the entire feature range is measured through the cloud server, thereby improving the efficiency and accuracy of urban carbon sink measurement.

[0093] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A green smart city carbon sink dynamic measurement method, characterized in that: The following steps are involved: Determine the monitoring plot, configure the corresponding cloud server for the monitoring plot, grid the ranges other than the feature range where the monitoring plot is located to obtain multiple range blocks, establish a matching group of feature ranges and range blocks, associate the monitoring plot with the corresponding cloud server to obtain a star server, associate the feature set of the range block loaded in the matching group with the cloud server corresponding to the monitoring plot, use the monitoring plot in the city and multiple metrological monitoring targets as monitoring information, and determine the distribution information of the metrological monitoring targets in the monitoring plot; According to the distribution information of the multiple measurement monitoring targets, the biomass and carbon content coefficients of the multiple measurement monitoring targets are determined respectively, and the carbon sink amount of the monitoring sample plot is obtained; The urban carbon sink is determined based on the carbon sink of the monitoring plot, and the urban carbon sink is managed based on the collection time of the measurement information to obtain the dynamic measurement of urban carbon sink; The step of respectively determining the biomass and carbon content coefficients of the plurality of measurement monitoring targets according to the distribution information of the plurality of measurement monitoring targets to obtain the carbon sink amount of the monitoring plot comprises: In the monitoring plots, multiple single tree diameters at breast height (DBH) of the target trees were collected as independent variables, and tree height (DBH) was used as the independent variable. As the dependent variable, the average density , average root-to-shoot ratio Biomass Expansion Factor As metering information, calculate the accumulation amount based on metering information Measuring and monitoring target biomass and carbon storage C; According to the measurement information, the accumulation Calculate, where , , c and n coefficients; according to ; According to the measured monitoring target biomass and the carbon content coefficient corresponding to the measured monitoring target Get carbon storage , ; Sort the types of measurement and monitoring targets in the monitoring plots and calculate the carbon sinks in the monitoring plots. ,in, , To measure the number of species of monitoring targets in the monitoring plots, For the Measure the carbon storage of the monitoring target in the sorted monitoring plots.

2. The green smart city carbon sink dynamic measurement method according to claim 1 is characterized by: The step of using the monitoring plots and a plurality of metering monitoring targets in the city as monitoring information and determining the distribution information of the metering monitoring targets in the monitoring plots comprises: Determine a plurality of monitoring plots in the city, configure corresponding cloud servers for the plurality of monitoring plots respectively, and associate the cloud servers corresponding to the plurality of monitoring plots to obtain a star server; Identify multiple targets participating in the dynamic measurement of urban carbon sinks in the monitoring plots as measurement monitoring targets; Use monitoring plots and multiple measurement monitoring targets as monitoring information; The distribution information of multiple measurement and monitoring targets in the monitoring sample sites is collected, wherein the distribution information includes the distribution positions and corresponding distribution ranges between the multiple measurement and monitoring targets, and the distribution information and the monitoring information are stored in the corresponding cloud server for storage.

3. The green smart city carbon sink dynamic measurement method according to claim 2 is characterized by: The step of determining a plurality of monitoring plots in the city, configuring corresponding cloud servers for the plurality of monitoring plots respectively, and associating the cloud servers corresponding to the plurality of monitoring plots to obtain a star server includes: Divide the ranges in the city according to geographical features to obtain multiple characteristic ranges; One monitoring plot is selected in each of the multiple characteristic ranges to obtain multiple monitoring plots in the city; Configure the corresponding cloud server for the corresponding monitoring sample site; Gridding other ranges except the characteristic range where the monitoring sample plot is located, and associating other ranges after gridding to the cloud server corresponding to the monitoring sample plot; The cloud servers corresponding to multiple monitoring plots are associated to obtain a star server.

4. The green smart city carbon sink dynamic measurement method according to claim 3 is characterized by: The step of gridding other ranges except the characteristic range where the monitoring sample plot is located, and associating the other ranges after the gridding process to the cloud server corresponding to the monitoring sample plot includes: Gridding is performed on the ranges other than the characteristic range where the monitoring sample plot is located to obtain multiple range blocks; Setting a plurality of feature sets, wherein the feature sets have the same size and shape as the feature range, wherein the feature sets are composed of a plurality of feature blocks, and the feature blocks have the same size as the range blocks; Match the range block within the feature range, take the range block that meets the preset matching condition and the matching range in the feature range as a matching group, match the range block to cover the feature range to obtain multiple matching groups, load the range block in the matching group into the feature block in the feature set, wherein the preset matching condition is that the density of the same type of metrology monitoring targets in one or more range blocks is the same as the density of the corresponding type of metrology monitoring targets in the matching range within the feature range; If the integrated density of the same type of metrology monitoring targets in multiple range blocks is the same as the density of the corresponding type of metrology monitoring targets in the matching range within the feature range, the multiple range blocks are bound as one range block; The feature set of the range block loaded into the matching group is associated with the cloud server of the corresponding monitoring plot.

5. The green smart city carbon sink dynamic measurement method according to claim 1 is characterized by: The step of determining the urban carbon sink amount according to the carbon sink amount of the monitoring sample plot, and managing the urban carbon sink amount according to the collection time of the measurement information to obtain the dynamic measurement of the urban carbon sink includes: Establish collection intervals and collect urban carbon sinks according to the intervals; Determine the collection time point based on the interval period, and determine the urban carbon sink through multiple measurement monitoring target carbon sinks based on the collection time point; The urban carbon sink is recorded according to the collection time point, and the difference between the urban carbon sink at the next collection time point and the urban carbon sink at the previous collection time point is obtained to obtain the carbon sink relationship line corresponding to multiple collection time points as the dynamic measurement of urban carbon sink.

6. The green smart city carbon sink dynamic measurement method according to claim 5 is characterized by: The step of determining the urban carbon sink amount by using multiple measurement monitoring target carbon sink amounts based on the collection time point includes: Sort the monitoring plots. Calculate the carbon sink of the city. ,in, is the urban carbon sink, For the The carbon sink within the characteristic range is The number of feature sets after loading feature sets for the range block in the matching group, The number of monitoring plots.

7. A green smart city carbon sink dynamic measurement system, used to implement the green smart city carbon sink dynamic measurement method according to any one of claims 1 to 6, comprising: A collection module is used to determine monitoring plots and multiple measurement monitoring targets in the city as monitoring information, and to determine distribution information of the measurement monitoring targets in the monitoring plots; A calculation module, connected to the acquisition module, is used to determine the biomass and carbon content coefficient of multiple measurement monitoring targets according to the distribution information of multiple measurement monitoring targets, and obtain the carbon sink amount of the monitoring sample site; The determination module is connected to the calculation module and is used to determine the urban carbon sink according to the carbon sink of the monitoring sample site, and to manage the urban carbon sink according to the collection time of the measurement information to obtain the dynamic measurement of the urban carbon sink.

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