Environment monitoring data processing method and system based on big data analysis

Abstract

The invention relates to the technical field of environmental data processing, in particular to an environmental monitoring data processing method and system based on big data analysis. The method comprises the following steps: carrying out statistical marking on an urban environment to-be-monitored area, and obtaining block vegetation type data, block environment humidity data and block soil condition data; carrying out block vegetation growth analysis and vegetation carbon storage influence analysis on the basis of the block vegetation type data to obtain actual block vegetation carbon storage; performing soil microflora abundance analysis and soil organic carbon storage estimation on the block soil condition data to obtain actual organic carbon storage of block soil, and performing time period window division and carbon sink capacity comparison division to obtain an urban green land efficient carbon sink block and an urban green land low-efficiency carbon sink block; and performing targeted optimization adjustment on the urban green land low-efficiency carbon sink block until the urban green land low-efficiency carbon sink block is converted into an urban green land high-efficiency carbon sink block. According to the invention, the regional carbon sink capability can be effectively improved and managed.

Description

Technical Field

[0001] The invention relates to the technical field of environmental data processing, and in particular to an environmental monitoring data processing method and system based on big data analysis. Background Art

[0002] Under the background of increasingly serious global climate change and environmental pollution, environmental monitoring has become an important means to ensure ecological security and sustainable development. The optimization of regional carbon sink capacity has become an important strategy to deal with greenhouse gas emissions. Carbon sink refers to the ability to absorb and store carbon dioxide through natural processes such as vegetation and soil. In order to improve the efficiency of carbon sink, accurate environmental monitoring data processing is particularly important. In recent years, the development of big data analysis technology has brought new opportunities for environmental monitoring. By integrating remote sensing images, meteorological data, soil characteristics, vegetation growth status and other data, a comprehensive environmental monitoring database is established to form a comprehensive monitoring system for carbon sink capacity. By using machine learning and data mining technology, the collected data is deeply analyzed to identify the influencing factors and dynamic changes of carbon sink capacity, and an accurate carbon sink assessment model is generated. Through the established carbon sink assessment model, the carbon sink changes are dynamically tracked, and the data analysis results are promptly fed back to managers to support decision optimization and management measures adjustment. However, the current environmental monitoring processing methods often lack effective data analysis methods, and cannot fully mine and utilize these big data, resulting in inaccurate assessment of the carbon sink capacity of the block and untimely response. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide an environmental monitoring data processing method and system based on big data analysis to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for processing environmental monitoring data based on big data analysis includes the following steps:

[0005] Step S1: Statistically mark the green area blocks existing in the urban environment to be monitored to obtain each urban green area block; obtain the block vegetation type data, block environmental humidity data and block soil condition data corresponding to each urban green area block;

[0006] Step S2: Based on the block vegetation type data corresponding to each urban green area block, a block vegetation growth analysis is performed on the corresponding urban green area block to obtain a block vegetation growth index corresponding to each urban green area block; based on the block vegetation growth index corresponding to each urban green area block, a vegetation carbon storage impact analysis is performed on the corresponding urban green area block to obtain the actual carbon storage of the block vegetation corresponding to each urban green area block;

[0007] Step S3: Perform soil microbial community abundance analysis on the block soil condition data corresponding to each urban green area block to obtain the relative abundance of the block soil microbial community corresponding to each urban green area block; estimate the soil organic carbon storage of the corresponding urban green area block based on the relative abundance of the block soil microbial community corresponding to each urban green area block and the block environmental humidity data to obtain the actual organic carbon storage of the block soil corresponding to each urban green area block;

[0008] Step S4: Divide the actual carbon storage of the block vegetation and the actual organic carbon storage of the block soil corresponding to each urban green area block into time windows to obtain the block vegetation carbon storage component and block soil carbon storage component of each green area block in each time window; compare and divide the carbon sink capacity of the corresponding urban green area blocks based on the block vegetation carbon storage component and block soil carbon storage component of each green area block in each time window to obtain urban green space high-efficiency carbon sink blocks and urban green space low-efficiency carbon sink blocks; carry out targeted optimization and adjustment of the vegetation types and soil communities in the urban green space low-efficiency carbon sink blocks until they are converted into urban green space high-efficiency carbon sink blocks.

[0009] Further, step S1 includes the following steps:

[0010] Step S11: performing urban terrain and land use identification analysis on the urban environment to be monitored area to obtain the terrain conditions and land use types of the urban environment to be monitored area;

[0011] Step S12: Performing an urban regional spatial planning analysis on the urban environment to be monitored area based on the terrain conditions and land use types of the urban to be monitored area to generate a land use planning subdivision map of the urban to be monitored area; performing statistical marking on the green area blocks existing in the land use planning subdivision map of the urban to be monitored area to obtain various urban green area blocks;

[0012] Step S13: Collect vegetation type samples for each urban green area block to obtain vegetation type data corresponding to each urban green area block;

[0013] Step S14: using a humidity sensor to monitor the environmental humidity of each urban green area block in real time, and obtaining the block environmental humidity data corresponding to each urban green area block;

[0014] Step S15: Sampling and analyzing the soil conditions of each urban green area block to obtain block soil condition data corresponding to each urban green area block.

[0015] Further, step S2 includes the following steps:

[0016] Step S21: Performing statistical analysis on the growth characteristics of the vegetation types corresponding to each urban green area block, and obtaining the average growth height, average leaf area, average leaf nitrogen content and average leaf chromaticity of each type of vegetation corresponding to each urban green area block;

[0017] Step S22: Based on the average growth height, average leaf area, average leaf nitrogen content and average leaf chromaticity of each type of vegetation in each urban green area block, the growth index of each type of vegetation in the corresponding urban green area block is calculated using the vegetation growth index calculation formula to obtain the vegetation growth index of each type of vegetation in each urban green area block;

[0018] Step S23: performing arithmetic average calculation of vegetation growth potential for the corresponding urban green area blocks according to the vegetation growth potential indexes of various types of block vegetation corresponding to each urban green area block, so as to obtain the block vegetation growth potential index corresponding to each urban green area block;

[0019] Step S24: Based on the block vegetation growth index corresponding to each urban green area block, a vegetation carbon storage impact analysis is performed on the corresponding urban green area block to obtain the actual carbon storage of the block vegetation corresponding to each urban green area block.

[0020] Furthermore, the vegetation growth index calculation formula described in step S22 is specifically:

[0021]

[0022] Where V i is the vegetation growth index of the i-th type of vegetation in the urban green area block, i is the item measurement parameter of the block vegetation, H i is the average growth height of vegetation in the i-th type of block in the urban green area, A i is the average leaf area of ​​vegetation in the i-th type of block in the urban green area, N i is the average leaf nitrogen content of vegetation in the i-th type of urban green area block, L i is the average leaf chromaticity of vegetation in the i-th type of urban green area block, H ic is the critical value of the growth height of vegetation in the i-th type of block in the urban green area, H i0 is the growth height reference value of the vegetation in the i-th type block corresponding to the urban green area block, α1 is the growth height growth influence coefficient of the block vegetation, A ic is the critical value of leaf area of ​​vegetation in the i-th type of urban green area block, A i0 is the leaf area benchmark value of the vegetation in the i-th type of block corresponding to the urban green area block, α2 is the growth influence coefficient of the leaf area of ​​the vegetation in the block, N ic is the critical value of leaf nitrogen content of vegetation in the i-th type of urban green area block, Ni0 is the reference value of leaf nitrogen content of vegetation in the i-th type of urban green area block, α3 is the growth influence coefficient of leaf nitrogen content of vegetation in the block, L ic is the leaf chromaticity critical value of vegetation in the i-th type of urban green area block, L i0 is the leaf chromaticity reference value of the vegetation in the i-th type block corresponding to the urban green area block, α4 is the growth influence coefficient of leaf chromaticity of vegetation in the block, and η is the correction coefficient of the vegetation growth index.

[0023] Further, step S24 includes the following steps:

[0024] Step S241: Performing statistical analysis on the diameter at breast height and density of vegetation in each urban green area block to obtain the diameter at breast height and density of vegetation in the block corresponding to each urban green area block;

[0025] Step S242: performing statistical calculation of vegetation stock volume for the corresponding urban green area blocks based on the block vegetation diameter at breast height and the block vegetation density corresponding to each urban green area block, so as to obtain the block vegetation stock volume corresponding to each urban green area block;

[0026] Step S243: Calculate the vegetation carbon storage of the corresponding urban green area blocks according to the vegetation stock volume of the blocks corresponding to each urban green area block, and obtain the initial carbon storage of the vegetation of the blocks corresponding to each urban green area block;

[0027] Step S244: Based on the block vegetation growth index corresponding to each urban green area block, the initial carbon storage of the block vegetation corresponding to each urban green area block is evaluated and analyzed for the vegetation growth carbon storage potential, so as to obtain the influencing factor of the block vegetation growth carbon storage potential corresponding to each urban green area block;

[0028] Step S245: Calibrate and calculate the vegetation carbon storage impact of the initial carbon storage of the block vegetation corresponding to each urban green area block according to the carbon storage potential influencing factor of the block vegetation growth corresponding to each urban green area block, and obtain the actual carbon storage of the block vegetation corresponding to each urban green area block.

[0029] Further, step S3 includes the following steps:

[0030] Step S31: collecting soil microbial community samples from the corresponding urban green area blocks based on the block soil condition data corresponding to each urban green area block, and obtaining a block soil microbial community sample set corresponding to each urban green area block;

[0031] Step S32: extracting microbial DNA from the soil microbial community sample set corresponding to each urban green area block to obtain a DNA genome of the microbial community sample corresponding to each urban green area block;

[0032] Step S33: performing high-throughput gene sequencing on the DNA genome of the block microbial community samples corresponding to each urban green area block to obtain a block microbial community gene sequencing sequence set corresponding to each urban green area block;

[0033] Step S34: performing relative abundance statistical analysis on the corresponding block soil microbial community sample set based on the block microbial community gene sequencing sequence set corresponding to each urban green area block, so as to obtain the relative abundance of the block soil microbial community corresponding to each urban green area block;

[0034] Step S35: Based on the relative abundance of soil microbial communities and environmental humidity data of each urban green area block, the soil organic carbon storage of the corresponding urban green area block is estimated to obtain the actual organic carbon storage of the block soil corresponding to each urban green area block.

[0035] Further, step S35 includes the following steps:

[0036] Step S351: setting environmental humidity conditions for corresponding urban green area blocks based on the block environmental humidity data corresponding to each urban green area block, and obtaining the block environmental humidity conditions corresponding to each urban green area block;

[0037] Step S352: analyzing the survival performance of soil microbial communities in each urban green area block based on the block environmental humidity conditions corresponding to each urban green area block, and obtaining the microbial community survival performance data of the soil microbial communities corresponding to each urban green area block under the corresponding environmental humidity conditions;

[0038] Step S353: Based on the microbial community survival performance data of the soil microbial community of each urban green area block under the corresponding environmental humidity conditions and the relative abundance of the soil microbial community of the block, the community ecological adaptability evaluation and analysis is performed on the corresponding urban green area block to obtain the soil microbial community adaptability index corresponding to each urban green area block;

[0039] Step S354: evaluating and analyzing the carbon storage potential of the soil community according to the soil microbial community adaptability index and the environmental humidity data of each urban green area block, and obtaining the carbon storage potential influencing factor of the soil community corresponding to each urban green area block;

[0040] Step S355: Estimate the soil organic carbon storage of the corresponding urban green area blocks according to the soil community carbon storage potential influencing factors corresponding to each urban green area block, and obtain the actual block soil organic carbon storage corresponding to each urban green area block.

[0041] Further, step S4 includes the following steps:

[0042] Step S41: Pre-setting a corresponding time period division window, and dividing the actual carbon storage of the block vegetation and the actual organic carbon storage of the block soil corresponding to each urban green area block according to the time period division window, so as to obtain the block vegetation carbon storage component and block soil carbon storage component of each green area block in each time period window;

[0043] Step S42: using the block carbon sink capacity coefficient calculation formula to quantitatively calculate the carbon sink capacity of the block vegetation carbon storage component and the block soil carbon storage component of each green area block in each time period window, so as to obtain the block carbon sink capacity coefficient of each green area block in each time period window;

[0044] Step S43: Compare and analyze the carbon sink capacity of each green area block in each time window starting from the second time window with the carbon sink capacity coefficient of the block in the previous time window. If the carbon sink capacity coefficient of a green area block in the current time window starting from the second time window is greater than the carbon sink capacity coefficient of the block in the previous time window, then the corresponding urban green area block is divided into an urban green space high-efficiency carbon sink block, otherwise it is divided into an urban green space low-efficiency carbon sink block;

[0045] Step S44: Designing an ecological adaptability adjustment plan for the vegetation types and soil communities in the urban green space inefficient carbon sink block to generate an urban green space inefficient carbon sink vegetation and soil ecological adaptation adjustment configuration plan; optimizing and adjusting the vegetation types and soil communities in the corresponding urban green space inefficient carbon sink block according to the urban green space inefficient carbon sink vegetation and soil ecological adaptation adjustment configuration plan to obtain an urban green space inefficient ecological optimization adjustment block;

[0046] Step S45: Re-dividing the urban green space inefficient ecological optimization and adjustment blocks by comparing their carbon sink capabilities until they are converted into urban green space efficient carbon sink blocks.

[0047] Furthermore, the calculation formula of the block carbon sink capacity coefficient in step S42 is specifically:

[0048]

[0049] In the formula, is the carbon sequestration capacity coefficient of the jth green area block in the kth time window, j is the item measurement parameter of the urban green area block, T k is the kth time period window, M j is the area parameter of the jth green area block, β is the carbon sink impact weight parameter of the green area block, is the vegetation carbon storage component of the jth green area block in the kth time window, is the soil carbon storage component of the jth green area block in the kth time period window, γ is the carbon storage time decay coefficient, and ζ is the correction coefficient of the block carbon sink capacity coefficient.

[0050] Furthermore, the present invention also provides an environmental monitoring data processing system based on big data analysis, which is used to execute the environmental monitoring data processing method based on big data analysis as described above. The environmental monitoring data processing system based on big data analysis includes:

[0051] The urban green area block environmental data monitoring module is used to statistically mark the green area blocks existing in the urban environment to be monitored to obtain each urban green area block; obtain the block vegetation type data, block environmental humidity data and block soil condition data corresponding to each urban green area block;

[0052] The green area block vegetation carbon storage impact analysis module is used to perform block vegetation growth analysis on the corresponding urban green area blocks based on the block vegetation type data corresponding to each urban green area block, so as to obtain the block vegetation growth index corresponding to each urban green area block; based on the block vegetation growth index corresponding to each urban green area block, perform vegetation carbon storage impact analysis on the corresponding urban green area blocks, so as to obtain the actual carbon storage of the block vegetation corresponding to each urban green area block;

[0053] The green area block soil carbon storage impact analysis module is used to perform soil microbial community abundance analysis on the block soil condition data corresponding to each urban green area block, so as to obtain the relative abundance of the block soil microbial community corresponding to each urban green area block; based on the relative abundance of the block soil microbial community corresponding to each urban green area block and the block environmental humidity data, the soil carbon storage impact analysis is performed on the corresponding urban green area block, so as to obtain the actual organic carbon storage of the block soil corresponding to each urban green area block;

[0054] The green area block carbon sink capacity optimization module is used to divide the actual carbon storage of the block vegetation and the actual organic carbon storage of the block soil corresponding to each urban green area block into time windows, so as to obtain the block vegetation carbon storage component and block soil carbon storage component of each green area block in each time window; based on the block vegetation carbon storage component and block soil carbon storage component of each green area block in each time window, the corresponding urban green area blocks are compared and divided in carbon sink capacity to obtain urban green space high-efficiency carbon sink blocks and urban green space inefficient carbon sink blocks; targeted optimization and adjustment of the vegetation types and soil communities in the inefficient carbon sink blocks of urban green spaces are carried out until they are converted into high-efficiency carbon sink blocks of urban green spaces.

[0055] Beneficial effects of the present invention:

[0056] 1. The environmental monitoring data processing method based on big data analysis proposed in the present invention, compared with the prior art, has the beneficial effect that by statistically marking the green area blocks existing in the urban environment to be monitored, the distribution position of the green space in the city can be clarified, thereby providing basic data guarantee for the subsequent carbon sink analysis of the green area blocks. By collecting vegetation species samples from each urban green area block, the composition of the urban green space ecosystem can be deeply understood. This step not only helps to identify the distribution and growth status of different plants, but also provides data support for biodiversity conservation. By using humidity sensors to monitor the environmental humidity of each urban green area block in real time, key environmental data will be provided, which will help analyze the impact of humidity on the carbon storage capacity of microbial communities in the soil, thereby providing data support for subsequent processing processes. At the same time, by sampling and analyzing the soil conditions of each urban green area block, important data can be provided for the subsequent processing process. Soil is the basis for plant growth, and its physical, chemical and biological properties directly affect the growth of vegetation and soil carbon storage capacity. By analyzing indicators such as soil nutrient content, pH, soil structure, etc., data support can be provided for the subsequent evaluation of the carbon sink potential of green area blocks. Secondly, by analyzing the vegetation growth of the corresponding urban green area blocks based on the vegetation type data corresponding to each urban green area block, the growth status and health level of various vegetation in the green area blocks can be deeply evaluated. This analysis process combines multiple indicators such as growth height, leaf area, nitrogen content and leaf chromaticity, and can calculate the actual vegetation growth index in the corresponding urban green area blocks, which can effectively reflect the growth advantage of vegetation under specific ecological conditions, thereby providing basic data guarantee for the subsequent analysis of the carbon storage capacity of vegetation in green area blocks. In addition, by analyzing the impact of vegetation carbon storage on the corresponding urban green area blocks based on the vegetation growth index corresponding to each urban green area block, the contribution of vegetation in urban green area blocks to carbon sequestration can be deeply understood. This analysis process helps to quantify the actual carbon storage of different vegetation types and understand the carbon storage of each green area block, which can provide an important basis for the subsequent carbon sequestration capacity assessment and analysis of green area blocks, ensuring that the corresponding green area blocks can effectively promote carbon emission reduction during urban development. Then, by analyzing the soil microbial community abundance of the block soil condition data corresponding to each urban green area block, it will help to understand the distribution characteristics of different microbial populations in each urban green area block. This analysis can not only identify the dominant microbial species, but also reveal the impact of environmental factors on microbial diversity. By comparing the relative abundance of different samples, the structural changes of the microbial community can be identified and the relationship between it and soil environmental conditions can be explored, thus providing data support for the subsequent soil organic carbon storage capacity analysis process.The soil organic carbon storage of the corresponding urban green area blocks was also estimated based on the relative abundance of soil microbial communities and environmental humidity data of the blocks corresponding to each urban green area block. Microbial communities not only play a key role in the formation and decomposition of soil organic carbon, but also affect the carbon storage capacity of the soil. This step can more accurately estimate the actual organic carbon storage of the soil by combining the relative abundance of microorganisms and environmental humidity data. This result not only provides a quantitative basis for evaluating the ecological value of urban green area blocks, but also provides data support for the subsequent evaluation and analysis of the carbon sequestration capacity of each urban green area block, thereby improving the accuracy and precision of the subsequent block carbon sequestration capacity evaluation process. Finally, by setting a suitable time division window in advance and dividing the actual carbon storage of the vegetation and the actual organic carbon storage of the soil in each urban green area block according to the time division window, the carbon storage of vegetation and soil in different time periods can be systematically analyzed. The advantage of this segmented analysis is that it not only allows observation of how the carbon storage capacity of each green area block changes over time, but also reveals the impact of different seasons or climate conditions on carbon storage, so as to respond to the evaluation and analysis of the carbon sequestration capacity of urban green area blocks in a timely manner. By comparing and dividing the carbon sequestration capacity of the corresponding urban green area blocks based on the carbon storage components of the vegetation and soil in each green area block in each time window, high-efficiency and low-efficiency carbon sequestration blocks can be clearly identified. The key to this process is that it can provide a clear classification basis for the subsequent processing process, help optimize the treatment of urban green areas with low carbon sequestration capacity, and ensure that the carbon sequestration capacity of urban green areas is continuously improved. By carrying out targeted optimization and adjustment of the vegetation types and soil communities in the inefficient carbon sink blocks of urban green spaces, the diversity of vegetation and soil in the inefficient carbon sink green blocks can be improved, and their carbon absorption and storage capacity can be enhanced. This will help improve the carbon sink capacity of urban green area blocks, and by re-evaluating the carbon sink capacity of ecologically optimized inefficient carbon sink blocks, ensure their conversion into efficient carbon sink blocks. The key to this process is that it provides a feedback mechanism that allows urban green space management to be dynamically adjusted based on actual results. This continuous monitoring and evaluation mechanism not only improves the transparency and scientific nature of ecological management, but also provides important experience and data support for future ecological restoration and management of urban green area blocks.

[0057] 2. The environmental monitoring data processing system based on big data analysis proposed in the present invention is generally composed of an urban green area block environmental data monitoring module, a green area block vegetation carbon storage impact analysis module, a green area block soil carbon storage impact analysis module and a green area block carbon sink capacity optimization module. It can realize any environmental monitoring data processing method based on big data analysis described in the present invention, and is used to combine the operations between computer programs running on each module to realize the environmental monitoring data processing method based on big data analysis. The internal structures of the system cooperate with each other, which can greatly reduce duplication of work and manpower investment, and can quickly and effectively provide a more accurate and efficient environmental monitoring data processing process based on big data analysis, thereby simplifying the operating procedures of the environmental monitoring data processing system based on big data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0059] Figure 1 A schematic diagram of the steps of the environmental monitoring data processing method based on big data analysis of the present invention;

[0060] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0061] Figure 3 for Figure 1 Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0062] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0063] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0064] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0065] To achieve this, please refer to Figures 1 to 3 The present invention provides an environmental monitoring data processing method based on big data analysis, the method comprising the following steps:

[0066] Step S1: Statistically mark the green area blocks existing in the urban environment to be monitored to obtain each urban green area block; obtain the block vegetation type data, block environmental humidity data and block soil condition data corresponding to each urban green area block;

[0067] Step S2: Based on the block vegetation type data corresponding to each urban green area block, a block vegetation growth analysis is performed on the corresponding urban green area block to obtain a block vegetation growth index corresponding to each urban green area block; based on the block vegetation growth index corresponding to each urban green area block, a vegetation carbon storage impact analysis is performed on the corresponding urban green area block to obtain the actual carbon storage of the block vegetation corresponding to each urban green area block;

[0068] Step S3: Perform soil microbial community abundance analysis on the block soil condition data corresponding to each urban green area block to obtain the relative abundance of the block soil microbial community corresponding to each urban green area block; estimate the soil organic carbon storage of the corresponding urban green area block based on the relative abundance of the block soil microbial community corresponding to each urban green area block and the block environmental humidity data to obtain the actual organic carbon storage of the block soil corresponding to each urban green area block;

[0069] Step S4: Divide the actual carbon storage of the block vegetation and the actual organic carbon storage of the block soil corresponding to each urban green area block into time windows to obtain the block vegetation carbon storage component and block soil carbon storage component of each green area block in each time window; compare and divide the carbon sink capacity of the corresponding urban green area blocks based on the block vegetation carbon storage component and block soil carbon storage component of each green area block in each time window to obtain urban green space high-efficiency carbon sink blocks and urban green space low-efficiency carbon sink blocks; carry out targeted optimization and adjustment of the vegetation types and soil communities in the urban green space low-efficiency carbon sink blocks until they are converted into urban green space high-efficiency carbon sink blocks.

[0070] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a schematic diagram of the steps of the environmental monitoring data processing method based on big data analysis of the present invention. In this example, the environmental monitoring data processing method based on big data analysis includes the following steps:

[0071] Step S1: Statistically mark the green area blocks existing in the urban environment to be monitored to obtain each urban green area block; obtain the block vegetation type data, block environmental humidity data and block soil condition data corresponding to each urban green area block;

[0072] In the embodiment of the present invention, the green area blocks existing in the urban environment to be monitored are range counted and marked to achieve statistical analysis of the corresponding green land use area range, and mark them as green area blocks, accurately calculate the area and distribution of each green area block, and thus mark each urban green area block. The vegetation type samples of each previously marked urban green area block are randomly collected by adopting a systematic random sampling method, so as to set sampling points, conduct field surveys in each green area block, record the types, quantities and distribution of various types of vegetation, and use plant classification atlases to assist in identifying different vegetation types, so as to obtain the block vegetation type data corresponding to each urban green area block. The environmental humidity conditions in each urban green area block are also monitored in real time by using pre-arranged humidity sensors to continuously record the environmental humidity data in each green area block, so as to obtain the block environmental humidity data corresponding to each urban green area block. Then, the soil conditions of each urban green area block marked previously were sampled and analyzed to ensure that the soil samples in each block were representative by formulating a sampling plan. Soil drilling equipment was used to take samples at different depths, collect soil samples, and bring them back to the laboratory for analysis. Standardized soil analysis methods (such as pH value, nutrient content, and soil texture analysis) were used to test the physical and chemical properties of the soil samples, thereby testing the soil's nutrient content, pH, soil structure and other indicators, and finally obtaining the block soil condition data corresponding to each urban green area block.

[0073] Step S2: Based on the block vegetation type data corresponding to each urban green area block, a block vegetation growth analysis is performed on the corresponding urban green area block to obtain a block vegetation growth index corresponding to each urban green area block; based on the block vegetation growth index corresponding to each urban green area block, a vegetation carbon storage impact analysis is performed on the corresponding urban green area block to obtain the actual carbon storage of the block vegetation corresponding to each urban green area block;

[0074] In the embodiment of the present invention, by statistically analyzing the growth characteristic parameters of each type of vegetation in each urban green area block, a method combining remote sensing technology and ground survey is adopted, and an unmanned aerial vehicle equipped with a high-definition camera is used to collect images of the green area blocks to obtain the growth height of each type of vegetation, and the shape and area of ​​the plant leaves are analyzed by image processing software to calculate the leaf area of ​​each type of vegetation. The nitrogen content is measured by sampling and analysis, and the chemical analysis method (such as Kjeldahl nitrogen determination method) is used for accurate determination, and the leaf chromaticity is obtained by using the vegetation leaf image previously taken and the image processing software to extract the color data of the leaf. The commonly used color spaces are RGB or HSV. By analyzing the leaf chromaticity of the leaf and converting the various types of vegetation into The growth height, leaf area, nitrogen content and leaf chromaticity of the vegetation were summarized through statistical software, and the average growth height, average leaf area, average leaf nitrogen content and average leaf chromaticity of each type of vegetation were calculated by using the arithmetic mean method. The average growth height, average leaf area, average leaf nitrogen content, average leaf chromaticity and related parameters of each type of block vegetation were combined to form a suitable calculation formula to calculate the growth index of each type of block vegetation in the corresponding urban green area block, so as to quantitatively calculate the vegetation growth index of each type of block vegetation, and at the same time perform arithmetic average calculation to quantitatively calculate the corresponding vegetation growth index in each urban green area block, so as to obtain the block vegetation growth index corresponding to each urban green area block.Then, the DBH and density of vegetation in each urban green area block marked previously are counted, random sampling points are selected in each urban green area block, the DBH of each tree is measured one by one using a diameter measurement tool (such as a diameter ruler), and the vegetation distribution data is combined with the DBH measurement results using GIS software to calculate the vegetation density of each block. The vegetation stock volume of the corresponding urban green area block is statistically calculated by combining the DBH and density of the block vegetation obtained by previous statistical calculations, and the measured DBH data and density data are converted into vegetation stock volume, that is, vegetation stock volume = density * DBH. The initial conversion calculation of vegetation carbon storage is performed on the corresponding urban green area block by combining the block vegetation stock volume obtained by previous statistical calculations, so as to convert the vegetation stock volume into vegetation carbon storage. Usually, the initial carbon storage is obtained by multiplying the conversion coefficient (such as 0.5) by the vegetation stock. Finally, the vegetation growth carbon storage potential of the corresponding initial carbon storage is evaluated and analyzed by combining the block vegetation growth index corresponding to each urban green area block obtained by previous quantitative calculation, so as to use the linear regression model to evaluate the influence of growth on carbon storage potential, thereby quantitatively calculating the corresponding carbon storage potential influencing factor. In addition, the vegetation carbon storage is calibrated and calculated for the corresponding block vegetation initial carbon storage by combining the carbon storage potential influencing factor obtained by previous evaluation and analysis, so as to calculate the actual carbon storage of the vegetation in the corresponding urban green area block through the calculation formula: actual carbon storage of vegetation = initial carbon storage of block vegetation * carbon storage potential influencing factor, and finally the actual carbon storage of the block vegetation corresponding to each urban green area block is obtained.

[0075] Step S3: Perform soil microbial community abundance analysis on the block soil condition data corresponding to each urban green area block to obtain the relative abundance of the block soil microbial community corresponding to each urban green area block; estimate the soil organic carbon storage of the corresponding urban green area block based on the relative abundance of the block soil microbial community corresponding to each urban green area block and the block environmental humidity data to obtain the actual organic carbon storage of the block soil corresponding to each urban green area block;

[0076] In an embodiment of the present invention, the sampling location of the microbial community in each urban green area block is determined according to the block soil condition data corresponding to each urban green area block, and the sampling location is determined by using a GPS device. A standard soil sampling tool (such as a soil drill) is used to collect surface soil samples at each sampling point to ensure random sampling in different areas and depths to obtain representative samples, and DNA extraction is performed. By using a commercial DNA extraction kit, the soil sample is weighed and added to an extraction buffer, and is fully mixed and centrifuged to release microbial DNA. At the same time, the DNA of the microbial community sample previously extracted is subjected to high-throughput sequencing to select an Illumina platform for sequencing. First, the DNA library is constructed, including fragmentation and connector connection, and the library is amplified by PCR to ensure a sufficient amount of DNA required for sequencing. After the sequencing is completed, the obtained raw data is subjected to quality control and the connector sequence is removed to generate a corresponding gene sequencing sequence set. In addition, the corresponding block soil microbial community sample set is sequenced and classified using a bioinformatics analysis tool by combining the gene sequencing sequence sets in each block obtained after previous sequencing, so as to use 16S rRNA genes are used for species identification, and by analyzing the abundance data of each microorganism in the sample, the relative abundance is calculated, that is, the proportion of the abundance of a certain microbial community to the total abundance, so as to obtain the relative abundance of the soil microbial community in each urban green area block. Then, by combining the relative abundance of soil microbial communities in blocks corresponding to each urban green area block obtained in the previous analysis and the corresponding block environmental humidity data, the adaptability of the microbial community in the corresponding urban green area block was evaluated and analyzed, and the ecological adaptability model was used to analyze the ecological adaptability index of the microbial community under different humidity conditions. In addition, the carbon storage potential of the soil community was evaluated and analyzed in combination with the environmental humidity data, and the fixation and release rate of carbon by soil microorganisms was calculated by using ecological models (such as the total nitrogen model or the carbon cycle model). The degree of influence of environmental humidity conditions on the carbon storage potential of the soil community was determined by comparing the soil carbon storage under different adaptability indexes, thereby obtaining the corresponding carbon storage potential influencing factors. Finally, the soil organic carbon storage of the corresponding urban green area block was estimated and calculated by combining the carbon storage potential influencing factors obtained by quantitative calculation previously, and the organic carbon content of the soil samples was determined by using the soil sample analysis method and the element analyzer, combined with the existing soil microbial community volume and density data, and the corresponding calculation formula was applied. Calculation is performed, where V is the volume of soil microbial community, ρ is the density of soil microbial community, θ is the factor affecting soil community carbon storage potential, C y The organic carbon content of the soil sample is used to obtain the actual organic carbon storage of each green area block, and finally the actual organic carbon storage of the block soil corresponding to each urban green area block is obtained.

[0077] Step S4: Divide the actual carbon storage of the block vegetation and the actual organic carbon storage of the block soil corresponding to each urban green area block into time windows to obtain the block vegetation carbon storage component and block soil carbon storage component of each green area block in each time window; compare and divide the carbon sink capacity of the corresponding urban green area blocks based on the block vegetation carbon storage component and block soil carbon storage component of each green area block in each time window to obtain urban green space high-efficiency carbon sink blocks and urban green space low-efficiency carbon sink blocks; carry out targeted optimization and adjustment of the vegetation types and soil communities in the urban green space low-efficiency carbon sink blocks until they are converted into urban green space high-efficiency carbon sink blocks.

[0078] In an embodiment of the present invention, each urban green area block within the urban monitoring research area is determined, and a predefined time period division window is set for each green area block, such as every hour, every day, every quarter or every year, and the actual carbon storage of the block vegetation and the actual organic carbon storage of the block soil corresponding to each urban green area block previously obtained by quantitative calculation are divided into data within the time period window by using the previously set time period division window, so as to obtain the block vegetation carbon storage component and block soil carbon storage component of each green area block in each time period window. By combining the block vegetation carbon storage component, the block soil carbon storage component and related parameters to form a suitable calculation formula, the carbon sink capacity of the corresponding urban green area block is quantitatively calculated, so as to quantitatively calculate the carbon sink capacity coefficient of each green area block in each time period window. At the same time, by comparing and analyzing the carbon sequestration capacity coefficient obtained by the previous quantitative calculation starting from the second time period window, the carbon sequestration capacity coefficient of the current time period window is compared with the carbon sequestration capacity coefficient of the previous time period window by using data analysis tools, that is, by subtracting the carbon sequestration capacity coefficient of the previous time period window from the carbon sequestration capacity coefficient of the current time period window to obtain the corresponding comparison result, and the comparison result shows that if the carbon sequestration capacity coefficient of a green area block in all time period windows is greater than that of the previous time period window, that is, the carbon sequestration capacity of the corresponding urban green area block continues to increase during this period of time, then the urban green area block is marked as an urban green space high-efficiency carbon sink block; otherwise, if it is any other comparison situation, it is marked as an urban green space inefficient carbon sink block.Then, by designing ecological adaptability adjustment plans for vegetation types and soil communities in urban green area blocks marked as inefficient carbon sinks, a vegetation adaptability database is established by investigating and analyzing the growth status, ecological adaptability and carbon storage capacity of existing vegetation in the blocks. The information in the database is used in combination with local climate and soil conditions to design suitable and efficient vegetation configuration models, determine vegetation types and combinations that are more suitable for growth, analyze soil types and their microbial communities, formulate targeted soil improvement measures and management strategies, and form a soil optimization strategy, thereby designing and generating a complete set of ecological adaptation adjustment plans. The previously designed ecological adaptation adjustment plans are used to carry out targeted optimization and adjustment of vegetation types and soil communities in the corresponding urban green space inefficient carbon sink blocks, so as to introduce suitable New vegetation in the local environment of the inefficient carbon sink blocks in urban green spaces is introduced, and improvement measures are taken on the microbial community and organic matter content of the soil to improve the overall carbon sink capacity of the inefficient carbon sink blocks. Finally, the carbon sink capacity of the inefficient carbon sink blocks that have been adjusted for ecological adaptability is re-divided to analyze the corresponding vegetation carbon storage and soil carbon storage in the blocks, and the same carbon sink capacity coefficient calculation formula is used to measure and evaluate the optimized and adjusted vegetation and soil characteristics again to observe whether there is an improvement trend. If the optimized and adjusted block has a carbon sink capacity coefficient that is greater than the previous time period window in all time period windows, it will be converted into an urban green space high-efficiency carbon sink block, otherwise it will continue to iterate until it is converted into an urban green space high-efficiency carbon sink block.

[0079] Further, step S1 includes the following steps:

[0080] Step S11: performing urban terrain and land use identification analysis on the urban environment to be monitored area to obtain the terrain conditions and land use types of the urban environment to be monitored area;

[0081] Step S12: Performing an urban regional spatial planning analysis on the urban environment to be monitored area based on the terrain conditions and land use types of the urban to be monitored area to generate a land use planning subdivision map of the urban to be monitored area; performing statistical marking on the green area blocks existing in the land use planning subdivision map of the urban to be monitored area to obtain various urban green area blocks;

[0082] Step S13: Collect vegetation type samples for each urban green area block to obtain vegetation type data corresponding to each urban green area block;

[0083] Step S14: using a humidity sensor to monitor the environmental humidity of each urban green area block in real time, and obtaining the block environmental humidity data corresponding to each urban green area block;

[0084] Step S15: Sampling and analyzing the soil conditions of each urban green area block to obtain block soil condition data corresponding to each urban green area block.

[0085] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0086] Step S11: performing urban terrain and land use identification analysis on the urban environment to be monitored area to obtain the terrain conditions and land use types of the urban environment to be monitored area;

[0087] In an embodiment of the present invention, the terrain and land use of the corresponding urban environment to be monitored area are identified and analyzed, high-resolution satellite images and geographic information system (GIS) data are collected, and remote sensing image processing technology is used to classify the images. A supervised classification algorithm (such as a support vector machine) is applied to identify different terrain features and land use types. When extracting terrain information, the digital elevation model (DEM) is used to analyze the terrain undulation, slope and orientation to obtain detailed terrain conditions, including terrain changes in the area, such as ridges, slopes, rivers and other geographical elements, and land use types, including residential areas, commercial areas, industrial areas, agricultural land and green land, etc., to finally obtain the terrain conditions and land use types of the urban area to be monitored.

[0088] Step S12: Performing an urban regional spatial planning analysis on the urban environment to be monitored area based on the terrain conditions and land use types of the urban to be monitored area to generate a land use planning subdivision map of the urban to be monitored area; performing statistical marking on the green area blocks existing in the land use planning subdivision map of the urban to be monitored area to obtain various urban green area blocks;

[0089] In an embodiment of the present invention, the land use space planning analysis of the corresponding urban environment to be monitored area is carried out by combining the terrain conditions and land use types of the urban area to be monitored obtained by the previous identification and analysis, and the land use planning subdivision map is planned and generated by using the spatial analysis tool through the GIS platform, thereby generating the land use planning subdivision map of the urban area to be monitored. At the same time, the corresponding green land use area range in the land use planning subdivision map of the urban area to be monitored is determined by analysis, and it is marked as a green area block. The urban environment to be monitored area is comprehensively evaluated by spatial overlay analysis to identify green land suitable for protection, and the regional statistical tools are applied to accurately calculate the area and distribution of each green area block, and finally mark each urban green area block.

[0090] Step S13: Collect vegetation type samples for each urban green area block to obtain vegetation type data corresponding to each urban green area block;

[0091] In an embodiment of the present invention, a systematic random sampling method is used to randomly collect vegetation type samples from each previously marked urban green area block, so as to set sampling points, conduct field surveys in each green area block, record the types, quantities and distribution of various types of vegetation, and use plant classification atlases to assist in identifying different vegetation types to ensure the accuracy of the data, and finally obtain the block vegetation type data corresponding to each urban green area block.

[0092] Step S14: using a humidity sensor to monitor the environmental humidity of each urban green area block in real time, and obtaining the block environmental humidity data corresponding to each urban green area block;

[0093] In an embodiment of the present invention, the environmental humidity conditions in each urban green area block are monitored in real time by using pre-arranged humidity sensors, so as to continuously record the environmental humidity data in each green area block, and transmit the data in real time to a central database via a wireless network, so as to finally obtain the block environmental humidity data corresponding to each urban green area block.

[0094] Step S15: Sampling and analyzing the soil conditions of each urban green area block to obtain block soil condition data corresponding to each urban green area block.

[0095] In an embodiment of the present invention, sampling and analysis of the soil conditions of each previously marked urban green area block is performed to ensure that the soil samples of each block are representative by formulating a sampling plan, and sampling is performed at different depths by soil drilling equipment to collect soil samples, which are then brought back to the laboratory for analysis. The soil samples are tested for physical and chemical properties using standardized soil analysis methods (such as pH value, nutrient content, and soil texture analysis), thereby testing and obtaining indicators such as the nutrient content, pH value, and soil structure of the soil, and ultimately obtaining block soil condition data corresponding to each urban green area block.

[0096] Further, step S2 includes the following steps:

[0097] Step S21: Performing statistical analysis on the growth characteristics of the vegetation types corresponding to each urban green area block, and obtaining the average growth height, average leaf area, average leaf nitrogen content and average leaf chromaticity of each type of vegetation corresponding to each urban green area block;

[0098] Step S22: Based on the average growth height, average leaf area, average leaf nitrogen content and average leaf chromaticity of each type of vegetation in each urban green area block, the growth index of each type of vegetation in the corresponding urban green area block is calculated using the vegetation growth index calculation formula to obtain the vegetation growth index of each type of vegetation in each urban green area block;

[0099] Step S23: performing arithmetic average calculation of vegetation growth potential for the corresponding urban green area blocks according to the vegetation growth potential indexes of various types of block vegetation corresponding to each urban green area block, so as to obtain the block vegetation growth potential index corresponding to each urban green area block;

[0100] Step S24: Based on the block vegetation growth index corresponding to each urban green area block, a vegetation carbon storage impact analysis is performed on the corresponding urban green area block to obtain the actual carbon storage of the block vegetation corresponding to each urban green area block.

[0101] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in FIG. 1 , in this embodiment, step S2 includes the following steps:

[0102] Step S21: Performing statistical analysis on the growth characteristics of the vegetation types corresponding to each urban green area block, and obtaining the average growth height, average leaf area, average leaf nitrogen content and average leaf chromaticity of each type of vegetation corresponding to each urban green area block;

[0103] In an embodiment of the present invention, by statistically analyzing the growth characteristic parameters of each type of vegetation in each urban green area block, the vegetation type information of each urban green area block is collected, and a method combining remote sensing technology and ground survey is adopted to collect images of the green area blocks using an unmanned aerial vehicle equipped with a high-definition camera to obtain the growth height of each type of vegetation, and use image processing software to analyze the shape and area of ​​plant leaves to calculate the leaf area of ​​each type of vegetation. The nitrogen content is measured by sampling and analysis, and chemical analysis (such as Kjeldahl nitrogen determination method) is used for accurate determination, and the leaf chromaticity is obtained by using the vegetation leaf image previously taken and using image processing software to extract the color data of the leaves. Commonly used color spaces include RGB or HSV. By analyzing the color distribution of the leaves, and summarizing the growth height, leaf area, nitrogen content and leaf chromaticity of each type of vegetation through statistical software, and calculating the average growth height, average leaf area, average leaf nitrogen content and average leaf chromaticity of each type of vegetation by using the arithmetic average method, the average growth height, average leaf area, average leaf nitrogen content and average leaf chromaticity of each type of vegetation corresponding to each urban green area block are finally obtained.

[0104] Step S22: Based on the average growth height, average leaf area, average leaf nitrogen content and average leaf chromaticity of each type of vegetation in each urban green area block, the growth index of each type of vegetation in the corresponding urban green area block is calculated using the vegetation growth index calculation formula to obtain the vegetation growth index of each type of vegetation in each urban green area block;

[0105] In an embodiment of the present invention, a suitable vegetation growth index calculation formula is formed by combining the average growth height, average leaf area, average leaf nitrogen content, average leaf chroma, growth height critical value, growth height reference value, block vegetation growth height growth influence coefficient, leaf area critical value, leaf area reference value, block vegetation leaf area growth influence coefficient, leaf nitrogen content critical value, leaf nitrogen content reference value, block vegetation leaf nitrogen content growth influence coefficient, leaf chroma critical value, leaf chroma reference value, block vegetation leaf chroma growth influence coefficient and related parameters of various block vegetation to calculate the growth index of various block vegetation in the corresponding urban green area blocks, so as to quantitatively calculate the vegetation growth index of various block vegetation, and finally obtain the vegetation growth index of various block vegetation corresponding to each urban green area block.

[0106] Step S23: performing arithmetic average calculation of vegetation growth potential for the corresponding urban green area blocks according to the vegetation growth potential indexes of various types of block vegetation corresponding to each urban green area block, so as to obtain the block vegetation growth potential index corresponding to each urban green area block;

[0107] In an embodiment of the present invention, the vegetation growth index corresponding to each vegetation type in the urban green area block is arithmetic averaged by combining the vegetation growth index of each type of block vegetation corresponding to each urban green area block obtained by previous quantitative calculation, so as to quantitatively calculate the corresponding vegetation growth index in each urban green area block, avoid deviations caused by different block sizes or vegetation densities, and facilitate intuitive understanding of the vegetation health status of each urban green area block, and finally obtain the block vegetation growth index corresponding to each urban green area block.

[0108] Step S24: Based on the block vegetation growth index corresponding to each urban green area block, a vegetation carbon storage impact analysis is performed on the corresponding urban green area block to obtain the actual carbon storage of the block vegetation corresponding to each urban green area block.

[0109] In an embodiment of the present invention, statistics of the diameter at breast height and density of vegetation are performed on each urban green area block previously marked, random sampling points are selected in each urban green area block, the diameter at breast height of each tree is measured one by one using a diameter measuring tool (such as a diameter ruler), and the vegetation distribution data is combined with the diameter at breast height measurement results using GIS software to calculate the vegetation density of each block. At the same time, the vegetation stock volume of the corresponding urban green area block is statistically calculated by combining the diameter at breast height and density of the block vegetation obtained by previous statistical calculations, that is, the specific biomass of the vegetation, and the measured diameter at breast height data and density data are converted into vegetation biomass. The vegetation stock volume of each block is calculated by the following formula: vegetation stock volume = density * diameter at breast height, and the initial conversion calculation of vegetation carbon storage is performed on the corresponding urban green area block by combining the vegetation stock volume of the block obtained by previous statistical calculations, so as to use the vegetation carbon storage estimation formula to convert the vegetation The accumulated amount is converted into vegetation carbon storage, and the initial carbon storage is usually obtained by multiplying the conversion coefficient (such as 0.5) by the vegetation accumulation amount. Then, the vegetation growth carbon storage potential of the corresponding initial carbon storage is evaluated and analyzed by combining the block vegetation growth index corresponding to each urban green area block obtained by previous quantitative calculation, so as to conduct correlation analysis between the growth index and the initial carbon storage, and use the linear regression model to evaluate the influence of growth on the carbon storage potential, so as to quantitatively calculate the corresponding carbon storage potential influencing factor, and also by combining the carbon storage potential influencing factor obtained by previous evaluation and analysis, the vegetation carbon storage is calibrated and calculated for the corresponding block vegetation initial carbon storage, so as to calculate the actual carbon storage of the vegetation in the corresponding urban green area block through the calculation formula: actual carbon storage of vegetation = initial carbon storage of block vegetation * carbon storage potential influencing factor, and finally the actual carbon storage of the block vegetation corresponding to each urban green area block is obtained.

[0110] Furthermore, the vegetation growth index calculation formula described in step S22 is specifically:

[0111]

[0112] Where V i is the vegetation growth index of the i-th type of vegetation in the urban green area block, i is the item measurement parameter of the block vegetation, H i is the average growth height of vegetation in the i-th type of block in the urban green area, A i is the average leaf area of ​​vegetation in the i-th type of block in the urban green area, N i is the average leaf nitrogen content of vegetation in the i-th type of urban green area block, L i is the average leaf chromaticity of vegetation in the i-th type of urban green area block, H ic is the critical value of the growth height of vegetation in the i-th type of block in the urban green area, H i0is the growth height reference value of the vegetation in the i-th type block corresponding to the urban green area block, α1 is the growth height growth influence coefficient of the block vegetation, A ic is the critical value of leaf area of ​​vegetation in the i-th type of urban green area block, A i0 is the leaf area benchmark value of the vegetation in the i-th type of block corresponding to the urban green area block, α2 is the growth influence coefficient of the leaf area of ​​the vegetation in the block, N ic is the critical value of leaf nitrogen content of vegetation in the i-th type of urban green area block, N i0 is the reference value of leaf nitrogen content of vegetation in the i-th type of urban green area block, α3 is the growth influence coefficient of leaf nitrogen content of vegetation in the block, L ic is the leaf chromaticity critical value of vegetation in the i-th type of urban green area block, L i0 is the leaf chromaticity reference value of the vegetation in the i-th type block corresponding to the urban green area block, α4 is the growth influence coefficient of leaf chromaticity of vegetation in the block, and η is the correction coefficient of the vegetation growth index.

[0113] The present invention obtains a vegetation growth index calculation formula by using a specific mathematical model and verifying it, which is used to calculate the growth index of various types of vegetation in the corresponding urban green area blocks. The vegetation growth index calculation formula combines multiple growth characteristics (such as growth height, leaf area, nitrogen content and leaf chroma). The index can fully reflect the growth status of vegetation and provide a more accurate health assessment than a single indicator. The critical value and reference value in the formula allow the growth potential of various types of vegetation to be quantified, helping to understand the performance of vegetation under different environmental conditions, thereby providing a scientific basis for ecological restoration and management. By adjusting the growth influence coefficient, it can be flexibly adjusted according to the environmental characteristics and vegetation types of a specific city or region, making the index highly adaptable. Using the vegetation growth index, the ecological status of urban green spaces can be regularly monitored, so that potential problems can be discovered in a timely manner. In summary, the formula fully considers the vegetation growth index V of the vegetation in the i-th type of block corresponding to the urban green area block. i , the metric parameter of the block vegetation i, the average growth height H of the vegetation in the urban green area block corresponding to the i-th type of block i , the average leaf area A of the vegetation in the i-th type of urban green area block i , the average leaf nitrogen content N of vegetation in the i-th type of urban green area block i , the average leaf chromaticity L of the vegetation in the i-th type of urban green area block i , the critical value H of the growth height of vegetation in the i-th type of block corresponding to the urban green area block ic , the growth height reference value H of the vegetation in the i-th type of block corresponding to the urban green area block i0 , the growth height influence coefficient of the block vegetation α1, the critical value of the leaf area of ​​the vegetation of the i-th type block in the urban green area block Aic , the leaf area benchmark value A of the vegetation in the i-th type of block corresponding to the urban green area block i0 , the influence coefficient of leaf area growth of block vegetation α2, the critical value of leaf nitrogen content N of the vegetation in the i-th type of block corresponding to the urban green area block ic , the leaf nitrogen content benchmark value N of the vegetation in the i-th type of block corresponding to the urban green area block i0 , the influence coefficient of leaf nitrogen content in the block vegetation growth α3, the critical value of leaf chromaticity of the vegetation in the urban green area block corresponding to the i-th type block L ic , the leaf chromaticity reference value L of the vegetation in the i-th type of block corresponding to the urban green area block i0 , the influence coefficient of leaf chromaticity growth of block vegetation α4, the correction coefficient of vegetation growth index η, according to the vegetation growth index V of the i-th type of block vegetation corresponding to the urban green area block i The correlation between the above parameters constitutes a functional relationship The formula can realize the calculation process of the growth index of various types of vegetation in the corresponding urban green area blocks. At the same time, by introducing the correction coefficient η of the vegetation growth index, it can be adjusted according to the errors occurring in the calculation process, thereby improving the accuracy and applicability of the vegetation growth index calculation formula.

[0114] Further, step S24 includes the following steps:

[0115] Step S241: Performing statistical analysis on the diameter at breast height and density of vegetation in each urban green area block to obtain the diameter at breast height and density of vegetation in the block corresponding to each urban green area block;

[0116] In an embodiment of the present invention, statistics are performed on the DBH and density of vegetation in each previously marked urban green area block, and random sampling points are selected in each urban green area block. The DBH of each tree is measured one by one using a diameter measuring tool (such as a diameter ruler). The collected data needs to be recorded in a spreadsheet, and the vegetation distribution data is combined with the DBH measurement results using GIS software to calculate the vegetation density of each block, that is, the number of trees per unit area, and finally the DBH of the block vegetation and the block vegetation density corresponding to each urban green area block are obtained.

[0117] Step S242: performing statistical calculation of vegetation stock volume for the corresponding urban green area blocks based on the block vegetation diameter at breast height and the block vegetation density corresponding to each urban green area block, so as to obtain the block vegetation stock volume corresponding to each urban green area block;

[0118] In an embodiment of the present invention, the vegetation stock of the corresponding urban green area blocks is statistically calculated by combining the block vegetation breast diameter and block vegetation density corresponding to each urban green area block obtained by previous statistical calculations, that is, the specific biomass of the vegetation. The measured breast diameter data and density data are converted into vegetation biomass using a biomass estimation model, such as a general biomass equation. The vegetation stock of each block is calculated by the following formula: vegetation stock = density * breast diameter, and finally the block vegetation stock corresponding to each urban green area block is obtained.

[0119] Step S243: Calculate the vegetation carbon storage of the corresponding urban green area blocks according to the vegetation stock volume of the blocks corresponding to each urban green area block, and obtain the initial carbon storage of the vegetation of the blocks corresponding to each urban green area block;

[0120] In an embodiment of the present invention, an initial conversion calculation of vegetation carbon storage is performed on the corresponding urban green area blocks by combining the block vegetation stock corresponding to each urban green area block obtained by previous statistical calculation, so as to convert the vegetation stock into vegetation carbon storage using the vegetation carbon storage estimation formula. A conversion coefficient (such as 0.5) is usually used, and the vegetation stock is multiplied by the coefficient to obtain the initial carbon storage. This process needs to take into account the carbon content differences of different tree species. The relevant conversion coefficients are obtained by consulting literature or databases, and finally the initial carbon storage of the block vegetation corresponding to each urban green area block is obtained.

[0121] Step S244: Based on the block vegetation growth index corresponding to each urban green area block, the initial carbon storage of the block vegetation corresponding to each urban green area block is evaluated and analyzed for the vegetation growth carbon storage potential, so as to obtain the influencing factor of the block vegetation growth carbon storage potential corresponding to each urban green area block;

[0122] In an embodiment of the present invention, the vegetation growth carbon storage potential is evaluated and analyzed by combining the block vegetation growth index corresponding to each urban green area block obtained by previous quantitative calculation with the corresponding block vegetation initial carbon storage, so as to perform correlation analysis on the growth index and the initial carbon storage, and use a linear regression model to evaluate the influence of growth on carbon storage potential, and finally obtain the block vegetation growth carbon storage potential influencing factors corresponding to each urban green area block.

[0123] Step S245: Calibrate and calculate the vegetation carbon storage impact of the initial carbon storage of the block vegetation corresponding to each urban green area block according to the carbon storage potential influencing factor of the block vegetation growth corresponding to each urban green area block, and obtain the actual carbon storage of the block vegetation corresponding to each urban green area block.

[0124] In an embodiment of the present invention, the vegetation carbon storage is calibrated and calculated by combining the block vegetation growth carbon storage potential influencing factors corresponding to each urban green area block obtained by previous evaluation and analysis, so as to calculate the actual carbon storage of the vegetation in the corresponding urban green area block through the calculation formula: actual carbon storage of vegetation = initial carbon storage of block vegetation * carbon storage potential influencing factors of block vegetation growth, and finally obtain the actual carbon storage of the block vegetation corresponding to each urban green area block.

[0125] Further, step S3 includes the following steps:

[0126] Step S31: collecting soil microbial community samples from the corresponding urban green area blocks based on the block soil condition data corresponding to each urban green area block, and obtaining a block soil microbial community sample set corresponding to each urban green area block;

[0127] In an embodiment of the present invention, the sampling location of the microbial community in each urban green area block is determined according to the block soil condition data corresponding to each urban green area block, and the GPS device is used for positioning, and a standard soil sampling tool (such as a soil drill) is used to collect surface soil samples at each sampling point, ensuring random sampling in different areas and depths to obtain representative samples, and appropriately marking, recording the sampling time and location information, and finally obtaining a block soil microbial community sample set corresponding to each urban green area block.

[0128] Step S32: extracting microbial DNA from the soil microbial community sample set corresponding to each urban green area block to obtain a DNA genome of the microbial community sample corresponding to each urban green area block;

[0129] In an embodiment of the present invention, DNA is extracted from a set of block soil microbial community samples corresponding to each urban green area block previously obtained by sampling, and a commercial DNA extraction kit is used to weigh the soil sample and add it to an extraction buffer, which is fully mixed and centrifuged to release the microbial DNA. Impurities and inhibitors are removed through multiple washing and centrifugation steps, and the extracted DNA is dissolved in an appropriate buffer. The DNA concentration and purity are determined by using a nanotitrator to ensure that it is suitable for subsequent gene sequencing, and finally a DNA genome of a block microbial community sample corresponding to each urban green area block is obtained.

[0130] Step S33: performing high-throughput gene sequencing on the DNA genome of the block microbial community samples corresponding to each urban green area block to obtain a block microbial community gene sequencing sequence set corresponding to each urban green area block;

[0131] In an embodiment of the present invention, high-throughput sequencing is performed on the DNA genome of the block microbial community samples corresponding to each urban green area block previously extracted, and the Illumina platform is selected for sequencing. First, the DNA library is constructed, including fragmentation and connector connection, and the library is amplified by PCR to ensure a sufficient amount of DNA required for sequencing. After the sequencing is completed, the obtained raw data is subjected to quality control and removal of the connector sequence to generate the corresponding gene sequencing sequence set, and finally the block microbial community gene sequencing sequence set corresponding to each urban green area block is obtained.

[0132] Step S34: performing relative abundance statistical analysis on the corresponding block soil microbial community sample set based on the block microbial community gene sequencing sequence set corresponding to each urban green area block, so as to obtain the relative abundance of the block soil microbial community corresponding to each urban green area block;

[0133] In an embodiment of the present invention, the block microbial community gene sequencing sequence set corresponding to each urban green area block obtained after previous sequencing is combined with a bioinformatics analysis tool to perform sequence comparison and classification on the corresponding block soil microbial community sample set, so as to utilize the 16S rRNA gene for species identification, and by analyzing the abundance data of each microorganism in the sample, the relative abundance is calculated, that is, the proportion of the abundance of a certain microbial community to the total abundance, and finally the relative abundance of the block soil microbial community corresponding to each urban green area block is obtained.

[0134] Step S35: Based on the relative abundance of soil microbial communities and environmental humidity data of each urban green area block, the soil organic carbon storage of the corresponding urban green area block is estimated to obtain the actual organic carbon storage of the block soil corresponding to each urban green area block.

[0135] In an embodiment of the present invention, the adaptability of the microbial community in the corresponding urban green area blocks is evaluated and analyzed by combining the relative abundance of the block soil microbial community corresponding to each urban green area block obtained by previous analysis and the corresponding block environmental humidity data, so as to use the ecological adaptability model to analyze the ecological adaptability of the microbial community under different humidity conditions, and calculate the soil microbial community adaptability index of each green area block according to the adaptability analysis results. At the same time, the soil community carbon storage potential is evaluated and analyzed according to the soil microbial community adaptability index calculated previously and combined with the environmental humidity data, so as to calculate the fixation and release rate of soil microorganisms by adopting an ecological model (such as a total nitrogen model or a carbon cycle model), and by comparing the soil carbon storage under different adaptability indexes, the degree of influence of environmental humidity conditions on the carbon storage potential of the soil community is determined, so as to obtain the corresponding soil community carbon storage potential influencing factor, and then, by combining the soil community carbon storage potential influencing factor obtained by previous quantitative calculation, the soil organic carbon storage of the corresponding urban green area block is estimated and calculated, so as to use the soil sample analysis method and use the element analyzer to determine the organic carbon content of the soil sample, and combine the existing soil microbial community volume and density data, and apply the corresponding calculation formula Calculation is performed, where V is the volume of soil microbial community, ρ is the density of soil microbial community, θ is the factor affecting soil community carbon storage potential, C y The organic carbon content of the soil sample is used to obtain the actual organic carbon storage of each green area block, and finally the actual organic carbon storage of the block soil corresponding to each urban green area block is obtained.

[0136] Further, step S35 includes the following steps:

[0137] Step S351: setting environmental humidity conditions for corresponding urban green area blocks based on the block environmental humidity data corresponding to each urban green area block, and obtaining the block environmental humidity conditions corresponding to each urban green area block;

[0138] In an embodiment of the present invention, a cluster analysis of environmental humidity is performed on the corresponding urban green area blocks by combining the block environmental humidity data corresponding to each urban green area block obtained through previous real-time monitoring to divide a number of environmental humidity categories, and set them as the environmental humidity condition range of the corresponding urban green area blocks, and finally the block environmental humidity conditions corresponding to each urban green area block are obtained.

[0139] Step S352: analyzing the survival performance of soil microbial communities in each urban green area block based on the block environmental humidity conditions corresponding to each urban green area block, and obtaining the microbial community survival performance data of the soil microbial communities corresponding to each urban green area block under the corresponding environmental humidity conditions;

[0140] In an embodiment of the present invention, a statistical analysis of the microbial survival performance of soil microbial communities in corresponding urban green area blocks is performed by combining the block environmental humidity conditions corresponding to each urban green area block obtained by previous division and setting, and molecular biology techniques, such as high-throughput sequencing, are used to analyze the composition and abundance of soil microbial communities, and gene fragments of specific microorganisms are amplified by PCR for sequencing. At the same time, in combination with environmental humidity conditions, ecological statistical software (such as R language and related packages) is used to analyze the survival performance of microorganisms under different humidity conditions, including the diversity index, functional index and environmental factor influence coefficient of the microorganisms, and finally the microbial community survival performance data of the soil microbial communities corresponding to each urban green area block under the corresponding environmental humidity conditions are obtained.

[0141] Step S353: Based on the microbial community survival performance data of the soil microbial community of each urban green area block under the corresponding environmental humidity conditions and the relative abundance of the soil microbial community of the block, the community ecological adaptability evaluation and analysis is performed on the corresponding urban green area block to obtain the soil microbial community adaptability index corresponding to each urban green area block;

[0142] In an embodiment of the present invention, the ecological adaptability of the microbial community in the corresponding urban green area block is evaluated and analyzed by combining the microbial community survival performance data of the corresponding soil microbial community under the corresponding environmental humidity conditions and the relative abundance of the soil microbial community in the block obtained by the previous analysis, so as to use the ecological adaptability model to analyze the ecological adaptability of the microbial community under different humidity conditions, and adopt multivariate statistical analysis methods, such as canonical correspondence analysis (CCA), to evaluate the effect of environmental humidity on the microbial community, and according to the adaptability analysis results, the soil microbial community adaptability index of each green area block is calculated, for example Finally, the soil microbial community adaptability index corresponding to each urban green area block was obtained.

[0143] Step S354: evaluating and analyzing the carbon storage potential of the soil community according to the soil microbial community adaptability index and the environmental humidity data of each urban green area block, and obtaining the carbon storage potential influencing factor of the soil community corresponding to each urban green area block;

[0144] In an embodiment of the present invention, the soil community carbon storage potential is evaluated and analyzed based on the soil microbial community adaptability index corresponding to each urban green area block calculated previously and combined with the environmental humidity data, so as to adopt an ecological model (such as a total nitrogen model or a carbon cycle model) and input relevant data for simulation, and calculate the fixation and release rate of carbon by soil microorganisms. By comparing the soil carbon reserves under different adaptability indexes, the degree of influence of environmental humidity conditions on the soil community carbon storage potential is determined, and finally the soil community carbon storage potential influencing factors corresponding to each urban green area block are obtained.

[0145] Step S355: Estimate the soil organic carbon storage of the corresponding urban green area blocks according to the soil community carbon storage potential influencing factors corresponding to each urban green area block, and obtain the actual block soil organic carbon storage corresponding to each urban green area block.

[0146] In the embodiment of the present invention, the soil organic carbon storage of the corresponding urban green area block is estimated and calculated by combining the soil community carbon storage potential influencing factors corresponding to each urban green area block obtained by quantitative calculation previously, and the organic carbon content of the soil sample is measured by using a soil sample analysis method and an element analyzer, combined with the existing soil microbial community volume and density data, and applying the corresponding calculation formula Calculation is performed, where V is the volume of soil microbial community, ρ is the density of soil microbial community, θ is the factor affecting soil community carbon storage potential, C y The organic carbon content of the soil sample is used to obtain the actual organic carbon storage of each green area block, and finally the actual organic carbon storage of the block soil corresponding to each urban green area block is obtained.

[0147] Further, step S4 includes the following steps:

[0148] Step S41: Pre-setting a corresponding time period division window, and dividing the actual carbon storage of the block vegetation and the actual organic carbon storage of the block soil corresponding to each urban green area block according to the time period division window, so as to obtain the block vegetation carbon storage component and block soil carbon storage component of each green area block in each time period window;

[0149] In an embodiment of the present invention, each urban green area block within the urban research area to be monitored is determined, and a predefined time period division window is set for each green area block, such as every hour, every day, every quarter or every year, and the actual carbon storage of block vegetation and the actual organic carbon storage of block soil corresponding to each urban green area block previously obtained by quantitative calculation are divided into data within the time period window by using the previously set time period division window, and finally the block vegetation carbon storage component and the block soil carbon storage component of each green area block in each time period window are obtained.

[0150] Step S42: using the block carbon sink capacity coefficient calculation formula to quantitatively calculate the carbon sink capacity of the block vegetation carbon storage component and the block soil carbon storage component of each green area block in each time period window, so as to obtain the block carbon sink capacity coefficient of each green area block in each time period window;

[0151] In an embodiment of the present invention, a suitable block carbon sink capacity coefficient calculation formula is formed by combining the item measurement parameters of urban green area blocks, time period windows, area parameters of green area blocks, carbon sink impact weight parameters of green area blocks, block vegetation carbon storage components, block soil carbon storage components, carbon storage time decay coefficients and related parameters to quantitatively calculate the carbon sink capacity of the block vegetation carbon storage components and block soil carbon storage components of each green area block in each time period window, and the relevant coefficients of carbon sink capacity are combined for calculation, and the specific values ​​of the carbon sink capacity of each green area block in each time period window are quantitatively calculated, and finally the block carbon sink capacity coefficients of each green area block in each time period window are obtained.

[0152] Step S43: Compare and analyze the carbon sink capacity of each green area block in each time window starting from the second time window with the carbon sink capacity coefficient of the block in the previous time window. If the carbon sink capacity coefficient of a green area block in the current time window starting from the second time window is greater than the carbon sink capacity coefficient of the block in the previous time window, then the corresponding urban green area block is divided into an urban green space high-efficiency carbon sink block, otherwise it is divided into an urban green space low-efficiency carbon sink block;

[0153] In an embodiment of the present invention, a comparative analysis is performed on the block carbon sequestration capacity coefficients of each green area block in each time period window obtained by the previous quantitative calculation starting from the second time period window, so as to compare the carbon sequestration capacity coefficient of the current time period window with the carbon sequestration capacity coefficient of the previous time period window by using a data analysis tool, that is, by subtracting the carbon sequestration capacity coefficient of the previous time period window from the carbon sequestration capacity coefficient of the current time period window to obtain the corresponding comparison result, and the comparison result shows that if the carbon sequestration capacity coefficient of a green area block in all time period windows is greater than that of the previous time period window, that is, the carbon sequestration capacity of the corresponding urban green area block continues to increase during this period of time, then the urban green area block is marked as an urban green space high-efficiency carbon sink block; otherwise, if it is any other comparison situation, it is marked as an urban green space inefficient carbon sink block.

[0154] Step S44: Designing an ecological adaptability adjustment plan for the vegetation types and soil communities in the urban green space inefficient carbon sink block to generate an urban green space inefficient carbon sink vegetation and soil ecological adaptation adjustment configuration plan; optimizing and adjusting the vegetation types and soil communities in the corresponding urban green space inefficient carbon sink block according to the urban green space inefficient carbon sink vegetation and soil ecological adaptation adjustment configuration plan to obtain an urban green space inefficient ecological optimization adjustment block;

[0155] In an embodiment of the present invention, an ecological adaptability adjustment plan for vegetation types and soil communities is designed for urban green area blocks marked as inefficient carbon sinks, so as to establish a vegetation adaptability database by investigating and analyzing the growth status, ecological adaptability and carbon storage capacity of existing vegetation in the blocks, and using the information in the database, combined with local climate and soil conditions, a suitable high-efficiency vegetation configuration model is designed to determine vegetation types and combinations that are more suitable for growth. At the same time, soil types and their microbial communities are analyzed, targeted soil improvement measures and management strategies are formulated, and soil optimization strategies are formed to support the healthy growth of vegetation, so as to design and generate a complete set of ecological adaptation adjustment plans, thereby generating an urban green space inefficient carbon sink vegetation and soil ecological adaptation adjustment configuration plan. At the same time, the vegetation and soil ecological adaptation adjustment configuration schemes designed previously are used to optimize the vegetation types and soil communities in the corresponding urban green space inefficient carbon sink blocks, introduce new vegetation suitable for the local environment of the urban green space inefficient carbon sink blocks, and take improvement measures on the soil microbial community and organic matter content. Through the above adjustments, we strive to improve the overall carbon sink capacity of the inefficient carbon sink blocks, and finally obtain the urban green space inefficient ecological optimization adjustment blocks.

[0156] Step S45: Re-dividing the urban green space inefficient ecological optimization and adjustment blocks by comparing their carbon sink capabilities until they are converted into urban green space efficient carbon sink blocks.

[0157] In an embodiment of the present invention, the carbon sink capacity of the inefficient carbon sink blocks that have been adjusted for ecological adaptability is re-divided for comparison, so as to analyze the corresponding vegetation carbon storage and soil carbon storage in the blocks, and measure and evaluate the optimized and adjusted vegetation and soil characteristics again by using the same carbon sink capacity coefficient calculation formula to observe whether there is an improvement trend. If the carbon sink capacity coefficient of the optimized and adjusted block is greater than that of the previous time period window in all time period windows, it is converted into an urban green space high-efficiency carbon sink block. Otherwise, it is iterated until it is converted into an urban green space high-efficiency carbon sink block.

[0158] Furthermore, the calculation formula of the block carbon sink capacity coefficient in step S42 is specifically:

[0159]

[0160] In the formula, is the carbon sequestration capacity coefficient of the jth green area block in the kth time window, j is the item measurement parameter of the urban green area block, T k is the kth time period window, M j is the area parameter of the jth green area block, β is the carbon sink impact weight parameter of the green area block, is the vegetation carbon storage component of the jth green area block in the kth time window, is the soil carbon storage component of the jth green area block in the kth time period window, γ is the carbon storage time decay coefficient, and ζ is the correction coefficient of the block carbon sink capacity coefficient.

[0161] The present invention obtains a block carbon sink capacity coefficient calculation formula by using a specific mathematical model and after verification, which is used to quantify the carbon sink capacity of the block vegetation carbon storage component and the block soil carbon storage component of each green area block in each time window. The block carbon sink capacity coefficient calculation formula can quantify the carbon sink capacity of each green area block, provide clear data support, and facilitate scientific decision-making. By setting the time window, the carbon sink capacity can be dynamically monitored, the change trend can be discovered in time, and the management strategy can be optimized. The formula comprehensively considers the carbon storage of vegetation and soil, reflects a more comprehensive carbon sink capacity, and contributes to the overall ecological assessment. Through the carbon sink impact weight parameter, the influence of different green area blocks can be adjusted according to the specific situation, making the assessment more flexible. Secondly, by introducing the time decay coefficient, the assessment is more practical, taking into account the changes in long-term carbon storage. Through the evaluation of inefficient carbon sink blocks, targeted ecological adaptability adjustment plans can be designed to improve the carbon sink efficiency of green space, which helps to clearly divide efficient and inefficient carbon sink blocks, thereby giving priority to resource allocation and implementation of improvement measures, and improving the overall urban green space carbon sink capacity. In summary, this formula fully considers the carbon sink capacity coefficient of the jth green area block in the kth time window. The item measurement parameter j of the urban green area block, the kth time window T k , the area parameter M of the jth green area block j , the carbon sink impact weight parameter of the green area block β, the carbon storage component of the vegetation in the jth green area block in the kth time window The soil carbon storage component of the jth green area block in the kth time window Carbon storage time decay coefficient γ, block carbon sink capacity coefficient correction coefficient ζ, according to the block carbon sink capacity coefficient of the jth green area block in the kth time window The correlation between the above parameters constitutes a functional relationship This formula can realize the quantitative calculation process of the carbon sink capacity of the vegetation carbon storage component and the soil carbon storage component of each green area block in each time period window. At the same time, the introduction of the correction coefficient ζ of the block carbon sink capacity coefficient can be adjusted according to the errors occurring in the calculation process, thereby improving the accuracy and applicability of the calculation formula of the block carbon sink capacity coefficient.

[0162] Furthermore, the present invention also provides an environmental monitoring data processing system based on big data analysis, which is used to execute the environmental monitoring data processing method based on big data analysis as described above. The environmental monitoring data processing system based on big data analysis includes:

[0163] The urban green area block environmental data monitoring module is used to statistically mark the green area blocks existing in the urban environment to be monitored to obtain each urban green area block; obtain the block vegetation type data, block environmental humidity data and block soil condition data corresponding to each urban green area block;

[0164] The green area block vegetation carbon storage impact analysis module is used to perform block vegetation growth analysis on the corresponding urban green area blocks based on the block vegetation type data corresponding to each urban green area block, so as to obtain the block vegetation growth index corresponding to each urban green area block; based on the block vegetation growth index corresponding to each urban green area block, perform vegetation carbon storage impact analysis on the corresponding urban green area blocks, so as to obtain the actual carbon storage of the block vegetation corresponding to each urban green area block;

[0165] The green area block soil carbon storage impact analysis module is used to perform soil microbial community abundance analysis on the block soil condition data corresponding to each urban green area block, so as to obtain the relative abundance of the block soil microbial community corresponding to each urban green area block; based on the relative abundance of the block soil microbial community corresponding to each urban green area block and the block environmental humidity data, the soil carbon storage impact analysis is performed on the corresponding urban green area block, so as to obtain the actual organic carbon storage of the block soil corresponding to each urban green area block;

[0166] The green area block carbon sink capacity optimization module is used to divide the actual carbon storage of the block vegetation and the actual organic carbon storage of the block soil corresponding to each urban green area block into time windows, so as to obtain the block vegetation carbon storage component and block soil carbon storage component of each green area block in each time window; based on the block vegetation carbon storage component and block soil carbon storage component of each green area block in each time window, the corresponding urban green area blocks are compared and divided in carbon sink capacity to obtain urban green space high-efficiency carbon sink blocks and urban green space inefficient carbon sink blocks; targeted optimization and adjustment of the vegetation types and soil communities in the inefficient carbon sink blocks of urban green spaces are carried out until they are converted into high-efficiency carbon sink blocks of urban green spaces.

[0167] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0168] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for processing environmental monitoring data based on big data analysis, characterized in that: The following steps are involved: Step S1: Statistically mark the green area blocks existing in the urban environment to be monitored to obtain each urban green area block; obtain the block vegetation type data, block environmental humidity data and block soil condition data corresponding to each urban green area block; Step S2: Based on the block vegetation type data corresponding to each urban green area block, a block vegetation growth analysis is performed on the corresponding urban green area block to obtain a block vegetation growth index corresponding to each urban green area block; based on the block vegetation growth index corresponding to each urban green area block, a vegetation carbon storage impact analysis is performed on the corresponding urban green area block to obtain the actual carbon storage of the block vegetation corresponding to each urban green area block; Step S3: Perform soil microbial community abundance analysis on the block soil condition data corresponding to each urban green area block to obtain the relative abundance of the block soil microbial community corresponding to each urban green area block; estimate the soil organic carbon storage of the corresponding urban green area block based on the relative abundance of the block soil microbial community corresponding to each urban green area block and the block environmental humidity data to obtain the actual organic carbon storage of the block soil corresponding to each urban green area block; Step S4: Divide the actual carbon storage of the block vegetation and the actual organic carbon storage of the block soil corresponding to each urban green area block into time windows to obtain the block vegetation carbon storage component and block soil carbon storage component of each green area block in each time window; compare and divide the carbon sink capacity of the corresponding urban green area blocks based on the block vegetation carbon storage component and block soil carbon storage component of each green area block in each time window to obtain urban green space high-efficiency carbon sink blocks and urban green space low-efficiency carbon sink blocks; carry out targeted optimization and adjustment of the vegetation types and soil communities in the urban green space low-efficiency carbon sink blocks until they are converted into urban green space high-efficiency carbon sink blocks.

2. The environmental monitoring data processing method based on big data analysis according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: performing urban terrain and land use identification analysis on the urban environment to be monitored area to obtain the terrain conditions and land use types of the urban environment to be monitored area; Step S12: Performing an urban regional spatial planning analysis on the urban environment to be monitored area based on the terrain conditions and land use types of the urban to be monitored area to generate a land use planning subdivision map of the urban to be monitored area; performing statistical marking on the green area blocks existing in the land use planning subdivision map of the urban to be monitored area to obtain various urban green area blocks; Step S13: Collect vegetation type samples for each urban green area block to obtain vegetation type data corresponding to each urban green area block; Step S14: using a humidity sensor to monitor the environmental humidity of each urban green area block in real time, and obtaining the block environmental humidity data corresponding to each urban green area block; Step S15: Sampling and analyzing the soil conditions of each urban green area block to obtain block soil condition data corresponding to each urban green area block.

3. The environmental monitoring data processing method based on big data analysis according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Performing statistical analysis on the growth characteristics of the vegetation types corresponding to each urban green area block, and obtaining the average growth height, average leaf area, average leaf nitrogen content and average leaf chromaticity of each type of vegetation corresponding to each urban green area block; Step S22: Based on the average growth height, average leaf area, average leaf nitrogen content and average leaf chromaticity of each type of vegetation in each urban green area block, the growth index of each type of vegetation in the corresponding urban green area block is calculated using the vegetation growth index calculation formula to obtain the vegetation growth index of each type of vegetation in each urban green area block; Step S23: performing arithmetic average calculation of vegetation growth potential for the corresponding urban green area blocks according to the vegetation growth potential indexes of various types of block vegetation corresponding to each urban green area block, so as to obtain the block vegetation growth potential index corresponding to each urban green area block; Step S24: Based on the block vegetation growth index corresponding to each urban green area block, a vegetation carbon storage impact analysis is performed on the corresponding urban green area block to obtain the actual carbon storage of the block vegetation corresponding to each urban green area block.

4. The environmental monitoring data processing method based on big data analysis according to claim 3 is characterized in that: The vegetation growth index calculation formula described in step S22 is specifically: Where V i is the vegetation growth index of the i-th type of vegetation in the urban green area block, i is the item measurement parameter of the block vegetation, H i is the average growth height of vegetation in the i-th type of block in the urban green area, A i is the average leaf area of ​​vegetation in the i-th type of block in the urban green area, N i is the average leaf nitrogen content of vegetation in the i-th type of urban green area block, L i is the average leaf chromaticity of vegetation in the i-th type of urban green area block, H ic is the critical value of the growth height of vegetation in the i-th type of block in the urban green area, H i0 is the growth height reference value of the vegetation in the i-th type block corresponding to the urban green area block, α1 is the growth height growth influence coefficient of the block vegetation, A ic is the critical value of leaf area of ​​vegetation in the i-th type of urban green area block, A i0 is the leaf area benchmark value of the vegetation in the i-th type of block corresponding to the urban green area block, α2 is the growth influence coefficient of the leaf area of ​​the vegetation in the block, N ic is the critical value of leaf nitrogen content of vegetation in the i-th type of urban green area block, N i0 is the reference value of leaf nitrogen content of vegetation in the i-th type of urban green area block, α3 is the growth influence coefficient of leaf nitrogen content of vegetation in the block, L ic is the leaf chromaticity critical value of vegetation in the i-th type of urban green area block, L i0 is the leaf chromaticity reference value of the vegetation in the i-th type block corresponding to the urban green area block, α4 is the growth influence coefficient of leaf chromaticity of vegetation in the block, and η is the correction coefficient of the vegetation growth index.

5. The environmental monitoring data processing method based on big data analysis according to claim 3 is characterized in that: Step S24 includes the following steps: Step S241: Performing statistical analysis on the diameter at breast height and density of vegetation in each urban green area block to obtain the diameter at breast height and density of vegetation in the block corresponding to each urban green area block; Step S242: performing statistical calculation of vegetation stock volume for the corresponding urban green area blocks based on the block vegetation diameter at breast height and the block vegetation density corresponding to each urban green area block, so as to obtain the block vegetation stock volume corresponding to each urban green area block; Step S243: Calculate the vegetation carbon storage of the corresponding urban green area blocks according to the vegetation stock volume of the blocks corresponding to each urban green area block, and obtain the initial carbon storage of the vegetation of the blocks corresponding to each urban green area block; Step S244: Based on the block vegetation growth index corresponding to each urban green area block, the initial carbon storage of the block vegetation corresponding to each urban green area block is evaluated and analyzed for the vegetation growth carbon storage potential, so as to obtain the influencing factor of the block vegetation growth carbon storage potential corresponding to each urban green area block; Step S245: Calibrate and calculate the vegetation carbon storage impact of the initial carbon storage of the block vegetation corresponding to each urban green area block according to the carbon storage potential influencing factor of the block vegetation growth corresponding to each urban green area block, and obtain the actual carbon storage of the block vegetation corresponding to each urban green area block.

6. The environmental monitoring data processing method based on big data analysis according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: collecting soil microbial community samples from the corresponding urban green area blocks based on the block soil condition data corresponding to each urban green area block, and obtaining a block soil microbial community sample set corresponding to each urban green area block; Step S32: extracting microbial DNA from the soil microbial community sample set corresponding to each urban green area block to obtain a DNA genome of the microbial community sample corresponding to each urban green area block; Step S33: performing high-throughput gene sequencing on the DNA genome of the block microbial community samples corresponding to each urban green area block to obtain a block microbial community gene sequencing sequence set corresponding to each urban green area block; Step S34: performing relative abundance statistical analysis on the corresponding block soil microbial community sample set based on the block microbial community gene sequencing sequence set corresponding to each urban green area block, so as to obtain the relative abundance of the block soil microbial community corresponding to each urban green area block; Step S35: Based on the relative abundance of soil microbial communities and environmental humidity data of each urban green area block, the soil organic carbon storage of the corresponding urban green area block is estimated to obtain the actual organic carbon storage of the block soil corresponding to each urban green area block.

7. The environmental monitoring data processing method based on big data analysis according to claim 6 is characterized in that: Step S35 includes the following steps: Step S351: setting environmental humidity conditions for corresponding urban green area blocks based on the block environmental humidity data corresponding to each urban green area block, and obtaining the block environmental humidity conditions corresponding to each urban green area block; Step S352: analyzing the survival performance of soil microbial communities in each urban green area block based on the block environmental humidity conditions corresponding to each urban green area block, and obtaining the microbial community survival performance data of the soil microbial communities corresponding to each urban green area block under the corresponding environmental humidity conditions; Step S353: Based on the microbial community survival performance data of the soil microbial community of each urban green area block under the corresponding environmental humidity conditions and the relative abundance of the soil microbial community of the block, the community ecological adaptability evaluation and analysis is performed on the corresponding urban green area block to obtain the soil microbial community adaptability index corresponding to each urban green area block; Step S354: evaluating and analyzing the carbon storage potential of the soil community according to the soil microbial community adaptability index and the environmental humidity data of each urban green area block, and obtaining the carbon storage potential influencing factor of the soil community corresponding to each urban green area block; Step S355: Estimate the soil organic carbon storage of the corresponding urban green area blocks according to the soil community carbon storage potential influencing factors corresponding to each urban green area block, and obtain the actual block soil organic carbon storage corresponding to each urban green area block.

8. The environmental monitoring data processing method based on big data analysis according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: Pre-setting a corresponding time period division window, and dividing the actual carbon storage of the block vegetation and the actual organic carbon storage of the block soil corresponding to each urban green area block according to the time period division window, so as to obtain the block vegetation carbon storage component and block soil carbon storage component of each green area block in each time period window; Step S42: using the block carbon sink capacity coefficient calculation formula to quantitatively calculate the carbon sink capacity of the block vegetation carbon storage component and the block soil carbon storage component of each green area block in each time period window, so as to obtain the block carbon sink capacity coefficient of each green area block in each time period window; Step S43: Compare and analyze the carbon sink capacity of each green area block in each time window starting from the second time window with the carbon sink capacity coefficient of the block in the previous time window. If the carbon sink capacity coefficient of a green area block in the current time window starting from the second time window is greater than the carbon sink capacity coefficient of the block in the previous time window, then the corresponding urban green area block is divided into an urban green space high-efficiency carbon sink block, otherwise it is divided into an urban green space low-efficiency carbon sink block; Step S44: Designing an ecological adaptability adjustment plan for the vegetation types and soil communities in the urban green space inefficient carbon sink block to generate an urban green space inefficient carbon sink vegetation and soil ecological adaptation adjustment configuration plan; optimizing and adjusting the vegetation types and soil communities in the corresponding urban green space inefficient carbon sink block according to the urban green space inefficient carbon sink vegetation and soil ecological adaptation adjustment configuration plan to obtain an urban green space inefficient ecological optimization adjustment block; Step S45: Re-dividing the urban green space inefficient ecological optimization and adjustment blocks by comparing their carbon sink capabilities until they are converted into urban green space efficient carbon sink blocks.

9. The environmental monitoring data processing method based on big data analysis according to claim 8 is characterized in that: The specific calculation formula of the block carbon sink capacity coefficient in step S42 is: In the formula, is the carbon sequestration capacity coefficient of the jth green area block in the kth time window, j is the item measurement parameter of the urban green area block, T k is the kth time period window, M j is the area parameter of the jth green area block, β is the carbon sink impact weight parameter of the green area block, is the vegetation carbon storage component of the jth green area block in the kth time window, is the soil carbon storage component of the jth green area block in the kth time period window, γ is the carbon storage time decay coefficient, and ζ is the correction coefficient of the block carbon sink capacity coefficient.

10. An environmental monitoring data processing system based on big data analysis, characterized in that: Used to execute the environmental monitoring data processing method based on big data analysis as claimed in claim 1, the environmental monitoring data processing system based on big data analysis comprises: The urban green area block environmental data monitoring module is used to statistically mark the green area blocks existing in the urban environment to be monitored to obtain each urban green area block; obtain the block vegetation type data, block environmental humidity data and block soil condition data corresponding to each urban green area block; The green area block vegetation carbon storage impact analysis module is used to perform block vegetation growth analysis on the corresponding urban green area blocks based on the block vegetation type data corresponding to each urban green area block, so as to obtain the block vegetation growth index corresponding to each urban green area block; based on the block vegetation growth index corresponding to each urban green area block, perform vegetation carbon storage impact analysis on the corresponding urban green area blocks, so as to obtain the actual carbon storage of the block vegetation corresponding to each urban green area block; The green area block soil carbon storage impact analysis module is used to perform soil microbial community abundance analysis on the block soil condition data corresponding to each urban green area block, so as to obtain the relative abundance of the block soil microbial community corresponding to each urban green area block; based on the relative abundance of the block soil microbial community corresponding to each urban green area block and the block environmental humidity data, the soil carbon storage impact analysis is performed on the corresponding urban green area block, so as to obtain the actual organic carbon storage of the block soil corresponding to each urban green area block; The green area block carbon sink capacity optimization module is used to divide the actual carbon storage of the block vegetation and the actual organic carbon storage of the block soil corresponding to each urban green area block into time windows, so as to obtain the block vegetation carbon storage component and block soil carbon storage component of each green area block in each time window; based on the block vegetation carbon storage component and block soil carbon storage component of each green area block in each time window, the corresponding urban green area blocks are compared and divided in carbon sink capacity to obtain urban green space high-efficiency carbon sink blocks and urban green space inefficient carbon sink blocks; targeted optimization and adjustment of the vegetation types and soil communities in the inefficient carbon sink blocks of urban green spaces are carried out until they are converted into high-efficiency carbon sink blocks of urban green spaces.

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