Land comprehensive improvement quality evaluation method and system based on big data

Abstract

The invention discloses a land comprehensive improvement quality evaluation method and system based on big data, relates to the technical field of land resource management, and solves the problem of lack of effective quantification means for more critical ecosystem function indexes. The invention discloses a land comprehensive improvement quality evaluation method and system based on big data. The method comprises the steps that a remote sensing image module and an optical image module are used for collecting image data of soil surface vegetation; uploading the acquired image data to a cloud platform through a data network, and establishing a soil surface vegetation optical evaluation index database; the carbon dioxide monitoring module and the oxygen monitoring module are used for monitoring the carbon dioxide concentration and oxygen near the vegetation on the soil surface in real time. According to the method, a soil surface vegetation carbon sequestration evaluation index database is established to calculate vegetation net primary productivity, biomass and carbon utilization efficiency, the vegetation net primary productivity, biomass and carbon utilization efficiency are summarized through a software program, and an ecological quality comprehensive index of soil surface vegetation is given.

Description

Technical Field

[0001] This invention relates to the field of land resource management technology, specifically a method for assessing the quality of comprehensive land consolidation based on big data. Background Technology

[0002] Comprehensive land consolidation is a process within a specific area, based on the goals and uses determined by the overall land use plan. It utilizes land consolidation, reclamation, development, and the linkage between urban and rural construction land increases and decreases as a platform to promote the comprehensive improvement of fields, water, roads, forests, and villages. For example, it may involve implementing the conversion of farmland to forest, grassland, and lake in an area to restore the ecological environment, address climate change, and safeguard national ecological security. In the process of ecological environment restoration, big data-driven land consolidation quality assessment methods are needed to evaluate the quality of the restoration process and results.

[0003] However, existing land consolidation quality assessment methods rely excessively on easily obtainable structural indicators such as vegetation coverage and survival rate. They lack effective quantitative means for more critical ecosystem function indicators (such as water conservation performance, soil retention performance, carbon sequestration and oxygen release performance) and stability indicators (such as ecological resilience and resistance to disturbance). As a result, the assessment results cannot truly reflect the overall health status of the ecosystem and therefore do not meet the current needs. In response, we propose a land consolidation quality assessment method based on big data. Summary of the Invention

[0004] The purpose of this invention is to provide a land consolidation quality assessment method based on big data, in order to solve the problem that the land consolidation quality assessment methods proposed in the background art rely too much on easily obtainable structural indicators such as vegetation coverage and survival rate, and lack effective quantitative means for more critical ecosystem function indicators, resulting in the assessment results failing to truly reflect the overall health status of the ecosystem.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a land consolidation quality assessment method based on big data, comprising the steps of a big data-based land consolidation quality assessment method:

[0006] S1, using remote sensing image module and optical image module to collect image data of soil surface vegetation;

[0007] S2, the collected image data is uploaded to the cloud platform through the data network to establish a database of optical evaluation indicators for soil surface vegetation;

[0008] S3 uses carbon dioxide monitoring modules and oxygen monitoring modules to monitor the carbon dioxide and oxygen concentrations near the soil surface vegetation in real time.

[0009] S4 transmits real-time monitoring data of carbon dioxide concentration and oxygen to the cloud platform to establish a database of soil surface vegetation carbon sequestration assessment indicators.

[0010] S5. A data model is established using the soil surface vegetation optical assessment index database and the soil surface vegetation carbon sequestration assessment index database. The data model gives the comprehensive ecological quality index of soil surface vegetation.

[0011] S6 uses data models and comprehensive ecological quality indices to conduct long-term stability assessments of soil surface vegetation, predicts future trends in soil surface vegetation using these assessments, and provides intelligent early warnings for abnormal data in the assessments.

[0012] Preferably, S1 includes the following steps: S1.1, acquiring hydrological data of the soil surface vegetation remediation in the assessment area during the early, middle and late stages; S1.2, deploying ground-based IoT devices in the assessment area and using wide-angle photography equipment and drone photography equipment to conduct real-time video monitoring of the soil surface vegetation in the assessment area; S1.3, using digital elevation models, soil type maps and meteorological grid data to digitally monitor the soil surface vegetation in the assessment area.

[0013] Preferably, based on step S1, step S2 includes the following steps: S2.1, using a cloud platform to calculate the vegetation coverage, leaf area index, and vegetation purification rate of the soil surface vegetation in the assessment area; S2.2, calculating the water conservation capacity and soil retention capacity of the soil surface vegetation in the assessment area; S2.3, calculating the vegetation recovery trend and ecological resilience index of the soil surface vegetation in the assessment area; S2.4, recording the calculated vegetation coverage, leaf area index, vegetation purification rate, water conservation capacity, soil retention capacity, vegetation recovery trend, and ecological resilience index of the soil surface vegetation, and drawing optical curves based on the recorded data using software, using the drawn curves as optical evaluation indicators of the soil surface vegetation, and storing the data of the optical evaluation indicators of the soil surface vegetation in a database, thus forming a database of optical evaluation indicators of soil surface vegetation.

[0014] Preferably, S3 includes the following steps: S3.1, Selecting sampling points for the surface vegetation in the assessment area, avoiding obvious sources of interference, and placing the carbon dioxide monitoring module and oxygen monitoring module at a height inside or above the vegetation canopy; S3.2 Installing the small air pumps in the carbon dioxide monitoring module and oxygen monitoring module in a protective box, connecting them to the air inlet through pipelines, and connecting the air pump outlet to a buffer bottle or directly venting the air to collect air near the surface vegetation in the assessment area; S3.3, using the data collection frequency set in the program, the carbon dioxide monitoring module and oxygen monitoring module collect air every hour and record the CO2 concentration, O2 concentration, temperature, and humidity data.

[0015] Preferably, based on S3, S4 includes the following steps: S4.1, recording and storing the collected CO2 concentration data, and preparing the CO2 concentration data into a data table using software; S4.2, recording and storing the collected O2 concentration data, and preparing the O2 concentration data into a data table using software; S4.3, recording and storing the collected temperature and humidity data, and preparing the temperature and humidity data into a data table using software; S4.4, using software to plot a carbon sequestration curve from the CO2 concentration data table, the O2 concentration data table, and the temperature and humidity data table, using the plotted carbon sequestration curve as an evaluation index for soil surface vegetation carbon sequestration, and storing the data of the soil surface vegetation carbon sequestration evaluation index in a database.

[0016] Preferably, S5 includes the following steps: S5.1, establishing a data model using data from the optical assessment index database and the carbon sequestration assessment index database, and dividing the data model into three hierarchical structures: target layer, criterion layer, and index layer; S5.2, the target layer determines the amount of CO2 that the surface vegetation in the assessment area needs to absorb, the criterion layer specifies the physiological activity and carbon sink function of the surface vegetation in the assessment area, and the index layer determines the final amount of carbon sequestration by the surface vegetation in the assessment area; S5.3, the data model establishes a surface vegetation carbon sequestration assessment index database through the target layer, criterion layer, and index layer, thereby calculating the net primary productivity, biomass, and carbon use efficiency of vegetation. The net primary productivity, biomass, and carbon use efficiency of vegetation are summarized by a software program, and a comprehensive ecological quality index of surface vegetation is given.

[0017] Preferably, S6 includes the following steps: S6.1, recording the comprehensive ecological quality index of the soil surface vegetation and continuously analyzing the ecological quality of the soil surface vegetation to predict future trends; S6.2, by setting a dynamic threshold in the software, if the comprehensive ecological quality index of the soil surface vegetation is not within the dynamic threshold, the system automatically generates an early warning message, which is then transmitted to various terminal devices for display through the cloud platform.

[0018] The evaluation system for land consolidation quality assessment based on big data includes: a remote sensing image module and an optical image module for collecting images of soil surface vegetation and generating image data of soil surface vegetation; a big data integrated processing platform for accessing, registering, and managing various types of data; a soil surface vegetation optical evaluation index database for storing the collected image data; carbon dioxide monitoring modules and oxygen monitoring modules for collecting CO2 concentration, O2 concentration, temperature, and humidity data; a soil surface vegetation carbon sequestration evaluation index database for storing carbon sequestration curves generated by software using CO2 concentration data tables, O2 concentration data tables, temperature and humidity data tables; and an intelligent early warning module for assessing and judging the stability of soil surface vegetation and providing intelligent early warnings for abnormal data in the assessment data.

[0019] The big data integrated processing platform is equipped with a soil surface vegetation optical assessment index database and a soil surface vegetation carbon sequestration assessment index database. The remote sensing image module and the optical image module are connected to the soil surface vegetation optical assessment index database via a wireless network. The carbon dioxide monitoring module and the oxygen monitoring module are connected to the soil surface vegetation carbon sequestration assessment index database via a wireless network. The soil surface vegetation carbon sequestration assessment index database is connected to the intelligent early warning module via a wireless network.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] 1. This invention establishes a data model by utilizing data from an optical evaluation index database and a carbon sequestration evaluation index database, and divides the data model into three hierarchical structures: target layer, criterion layer, and index layer. The target layer determines the amount of CO2 that the surface vegetation in the evaluation area needs to absorb, the criterion layer specifies the physiological activity and carbon sequestration function of the surface vegetation in the evaluation area, and the index layer determines the final amount of carbon sequestration by the surface vegetation in the evaluation area.

[0022] The data model establishes a database of soil surface vegetation carbon sequestration assessment indicators through target layer, criterion layer and indicator layer, thereby calculating vegetation net primary productivity, biomass and carbon use efficiency. The vegetation net primary productivity, biomass and carbon use efficiency are summarized by software program and a comprehensive ecological quality index of soil surface vegetation is given.

[0023] 2. In this invention, the calculated vegetation coverage, leaf area index, vegetation purification rate, water conservation capacity, soil retention capacity, vegetation recovery trend, and ecological resilience index of the soil surface vegetation are recorded, and optical curves are plotted using software based on the recorded data. The plotted curves are used as optical evaluation indicators for soil surface vegetation, and the data of the optical evaluation indicators for soil surface vegetation are stored in a database, thus forming the optical evaluation indicator database for soil surface vegetation.

[0024] Net primary productivity of vegetation is calculated from total solar radiation and the proportion of photosynthetically active radiation absorbed by the vegetation layer; water conservation capacity is calculated from total rainfall minus surface runoff and then evaporation, with the total rainfall and evaporation data derived from meteorological datasets; vegetation recovery trend is obtained by comparing the total area of ​​the assessment area with the mapped vegetation area; the ecological resilience index is calculated using time-series remote sensing data to determine the speed and extent to which the ecosystem recovers to its original state after being disturbed by drought or other disturbances.

[0025] This comprehensive land consolidation quality assessment method can effectively record more critical ecosystem function indicators, such as water conservation performance, soil retention performance, carbon sequestration and oxygen release performance, and stability indicators, such as ecological resilience and resistance to disturbance, so that the assessment results can truly reflect the overall health status of the ecosystem. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the overall system of the present invention;

[0027] Figure 2 This is a flowchart illustrating the overall method steps of the present invention;

[0028] Figure 3 This is a diagram illustrating the steps of method S1 in this invention;

[0029] Figure 4 This is a diagram illustrating the steps of method S2 in this invention;

[0030] Figure 5 This is a diagram illustrating the steps of method S3 in this invention;

[0031] Figure 6 This is a diagram illustrating the steps of method S4 in this invention;

[0032] Figure 7 This is a diagram illustrating the steps of method S5 in this invention;

[0033] Figure 8 This is a diagram illustrating the steps of method S6 in this invention.

[0034] In the diagram: 1. Carbon dioxide monitoring module; 2. Oxygen monitoring module; 3. Remote sensing image module; 4. Big data integrated processing platform; 5. Soil surface vegetation optical assessment index database; 6. Soil surface vegetation carbon sequestration assessment index database; 7. Optical image module; 8. Intelligent early warning module. Detailed Implementation

[0035] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0036] Please see Figures 1 to 8 The present invention provides an embodiment of a land consolidation quality assessment method based on big data, comprising the following steps:

[0037] S1, use remote sensing image module 3 and optical image module 7 to collect image data of soil surface vegetation;

[0038] S2, The collected image data is uploaded to the cloud platform through the data network to establish a database of optical evaluation indicators for soil surface vegetation;

[0039] S3 uses carbon dioxide monitoring module 1 and oxygen monitoring module 2 to monitor the carbon dioxide concentration and oxygen near the soil surface vegetation in real time.

[0040] S4. Real-time monitoring data of carbon dioxide concentration and oxygen are transmitted to the cloud platform to establish a database of soil surface vegetation carbon sequestration assessment indicators.

[0041] S5. A data model is established using the soil surface vegetation optical assessment index database 5 and the soil surface vegetation carbon sequestration assessment index database 6. The data model gives the comprehensive ecological quality index of soil surface vegetation.

[0042] S6 uses data models and comprehensive ecological quality indices to conduct long-term stability assessments of soil surface vegetation, predicts future trends in soil surface vegetation using these assessments, and provides intelligent early warnings for abnormal data in the assessments.

[0043] S1 includes the following steps: S1.1, acquiring hydrological data for the early, middle and late stages of soil surface vegetation remediation in the assessment area; S1.2, deploying ground-based IoT devices in the assessment area and using wide-angle photography equipment and drone photography equipment to conduct real-time video monitoring of soil surface vegetation in the assessment area; S1.3, using digital elevation models, soil type maps and meteorological grid data to digitally monitor soil surface vegetation in the assessment area.

[0044] Hydrological data includes satellite remote sensing data, ground data, and auxiliary data. Satellite remote sensing data was obtained from the official website and underwent radiometric calibration, atmospheric correction, and cloud removal. Ground data was obtained from local automatic weather stations in the assessment area. Auxiliary data was obtained using soil type maps and daily meteorological grid data.

[0045] Based on step S1, S2 includes the following steps: S2.1, using the cloud platform to calculate the vegetation coverage, leaf area index and vegetation purification rate of the soil surface vegetation in the assessment area;

[0046] S2.2 Calculate the water conservation and soil retention capacity of the surface vegetation in the assessment area;

[0047] S2.3, calculate the vegetation restoration trend and ecological resilience index of the soil surface vegetation in the assessment area;

[0048] S2.4 Record the calculated vegetation coverage, leaf area index, vegetation purification rate, water conservation capacity, soil retention capacity, vegetation recovery trend and ecological resilience index of the soil surface vegetation, and draw optical curves based on the recorded data using software. Use the drawn curves as optical evaluation indicators of soil surface vegetation, and store the data of the optical evaluation indicators of soil surface vegetation in the database, thus forming the soil surface vegetation optical evaluation indicator database 5.

[0049] Net primary productivity of vegetation is calculated from total solar radiation and the proportion of photosynthetically active radiation absorbed by the vegetation layer; water conservation capacity is calculated from total rainfall minus surface runoff and then evaporation, with the total rainfall and evaporation data coming from monitoring records in the meteorological dataset; vegetation recovery trend is obtained by comparing the total area of ​​the assessment area with the mapped vegetation area; the ecological resilience index is calculated using time-series remote sensing data to determine the speed and extent to which the ecosystem recovers to its original state after being disturbed by drought or other disturbances.

[0050] S3 includes the following steps: S3.1, Selecting sampling points for the surface vegetation in the assessment area, avoiding obvious sources of interference, and placing the carbon dioxide monitoring module 1 and oxygen monitoring module 2 at a height inside or above the vegetation canopy; S3.2 Installing the small air pumps in the carbon dioxide monitoring module 1 and oxygen monitoring module 2 inside a protective box, connecting them to the air inlet via pipelines, and connecting the air pump outlet to a buffer bottle or directly venting the air to collect air near the surface vegetation in the assessment area; S3.3, using the data collection frequency set in the program, the carbon dioxide monitoring module 1 and oxygen monitoring module 2 collect air every hour and record the CO2 concentration, O2 concentration, temperature, and humidity data.

[0051] Based on S3, S4 includes the following steps: S4.1, recording and storing the collected CO2 concentration data, and preparing the CO2 concentration data into a data table using software; S4.2, recording and storing the collected O2 concentration data, and preparing the O2 concentration data into a data table using software; S4.3, recording and storing the collected temperature and humidity data, and preparing the temperature and humidity data into a data table using software; S4.4, using software to plot a carbon sequestration curve from the CO2 concentration data table, the O2 concentration data table, and the temperature and humidity data table, using the plotted carbon sequestration curve as an evaluation index for soil surface vegetation carbon sequestration, and storing the data of the soil surface vegetation carbon sequestration evaluation index in a database;

[0052] Time series analysis was performed on CO2 concentration data, O2 concentration data, and temperature and humidity data to plot diurnal and seasonal variation curves of CO2 and O2 concentrations. The carbon and oxygen exchange fluxes of the ecosystem were estimated by combining meteorological data such as wind speed.

[0053] S5 includes the following steps: S5.1, establishing a data model using data from the optical assessment index database and the carbon sequestration assessment index database, and dividing the data model into three hierarchical structures: target layer, criterion layer, and index layer; S5.2, the target layer determines the amount of CO2 that the surface vegetation in the assessment area needs to absorb, the criterion layer specifies the physiological activity and carbon sink function of the surface vegetation in the assessment area, and the index layer determines the final amount of carbon sequestration by the surface vegetation in the assessment area; S5.3, the data model establishes a surface vegetation carbon sequestration assessment index database 6 through the target layer, criterion layer, and index layer, and calculates the net primary productivity, biomass, and carbon use efficiency of vegetation. The net primary productivity, biomass, and carbon use efficiency of vegetation are summarized by a software program, and a comprehensive ecological quality index of surface vegetation is given.

[0054] S6 includes the following steps: S6.1, recording the comprehensive ecological quality index of the soil surface vegetation and continuously analyzing the ecological quality of the soil surface vegetation to predict future trends; S6.2, by setting a dynamic threshold in the software, if the comprehensive ecological quality index of the soil surface vegetation is not within the dynamic threshold, the system will automatically generate an early warning message, which will be transmitted to various terminal devices through the cloud platform for display.

[0055] A system for assessing the quality of comprehensive land consolidation based on big data includes: a remote sensing image module 3 and an optical image module 7 for collecting images of soil surface vegetation and generating image data of soil surface vegetation; a big data integrated processing platform 4 for accessing, registering, and managing various types of data; a soil surface vegetation optical assessment index database 5 for storing the collected image data; a carbon dioxide monitoring module 1 and an oxygen monitoring module 2 for collecting CO2 concentration, O2 concentration, temperature, and humidity data; a soil surface vegetation carbon sequestration assessment index database 6 for storing carbon sequestration curves generated by software using CO2 concentration data tables, O2 concentration data tables, temperature and humidity data tables; and an intelligent early warning module 8 for assessing and judging the stability of soil surface vegetation and providing intelligent early warnings for abnormal data in the assessment data.

[0056] The big data integrated processing platform 4 is equipped with a soil surface vegetation optical evaluation index database 5 and a soil surface vegetation carbon sequestration evaluation index database 6. The remote sensing image module 3 and the optical image module 7 are connected to the soil surface vegetation optical evaluation index database 5 via a wireless network. The carbon dioxide monitoring module 1 and the oxygen monitoring module 2 are connected to the soil surface vegetation carbon sequestration evaluation index database 6 via a wireless network. The soil surface vegetation carbon sequestration evaluation index database 6 is connected to the intelligent early warning module 8 via a wireless network.

[0057] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A land consolidation quality assessment method based on big data, characterized by: The steps involved in land consolidation quality assessment methods, including big data analysis: S1: Collect image data of soil surface vegetation using remote sensing image module (3) and optical image module (7); S2: Upload the collected image data to the cloud platform through the data network to establish a database of optical evaluation indicators for soil surface vegetation (5). S3: The carbon dioxide monitoring module (1) and the oxygen monitoring module (2) are used to monitor the carbon dioxide concentration and oxygen near the soil surface vegetation in real time; S4: Real-time monitoring data of carbon dioxide concentration and oxygen are transmitted to the cloud platform to establish a database of soil surface vegetation carbon sequestration assessment indicators (6). S5: A data model is established using the soil surface vegetation optical assessment index database (5) and the soil surface vegetation carbon sequestration assessment index database (6). The data model gives the comprehensive ecological quality index of soil surface vegetation. S6: Data models and comprehensive ecological quality indices are used to assess the long-term stability of soil surface vegetation. The long-term stability assessment data is used to predict the future trend of soil surface vegetation changes and to provide intelligent early warning for abnormal data in the assessment data.

2. The land consolidation quality assessment method based on big data according to claim 1, characterized in that: S1 includes the following steps: S1.1: Obtain hydrological data for the early, middle, and late stages of soil surface vegetation restoration in the assessment area; S1.2: Deploy ground-based IoT devices in the assessment area and use wide-angle photography equipment and drone photography equipment to conduct real-time video monitoring of the soil surface vegetation in the assessment area; S1.3: Digital monitoring of surface vegetation in the assessment area is carried out using digital elevation models, soil type maps, and meteorological grid data.

3. The land consolidation quality assessment method based on big data according to claim 1, characterized in that: Based on step S1, S2 includes the following steps: S2.1: Calculate the vegetation coverage, leaf area index, and vegetation purification rate of the soil surface vegetation in the assessment area using a cloud platform; S2.2: Calculate the water conservation and soil retention capacity of the surface vegetation in the assessment area; S2.3: Calculate the vegetation restoration trend and ecological resilience index of the soil surface vegetation in the assessment area; S2.4: Record the calculated vegetation coverage, leaf area index, vegetation purification rate, water conservation, soil retention, vegetation recovery trend and ecological resilience index of the soil surface vegetation, and draw optical curves based on the recorded data using software. Use the drawn curves as optical evaluation indicators of soil surface vegetation, and store the data of optical evaluation indicators of soil surface vegetation in the database, thus forming the optical evaluation index database of soil surface vegetation (5).

4. The land consolidation quality assessment method based on big data according to claim 1, characterized in that: S3 includes the following steps: S3.1: Select the sampling points for the surface vegetation in the assessment area, avoid obvious sources of interference, and place the carbon dioxide monitoring module (1) and oxygen monitoring module (2) at a height inside or above the vegetation canopy. S3.2: Install the small air pumps in the carbon dioxide monitoring module (1) and oxygen monitoring module (2) in the protective box, connect them to the air inlet through the pipeline, and connect the air pump outlet to a buffer bottle or directly vent it to collect the air near the soil surface vegetation in the assessment area. S3.3: Using the data acquisition frequency set in the program, the carbon dioxide monitoring module (1) and the oxygen monitoring module (2) collect air every hour and record the CO2 concentration, O2 concentration, temperature and humidity data.

5. The land consolidation quality assessment method based on big data according to claim 1, characterized in that: Based on S3, S4 includes the following steps: S4.1: Record and store the collected CO2 concentration data, and use software to prepare the CO2 concentration data into a data table; S4.2: Record and store the collected O2 concentration data, and use software to prepare the O2 concentration data into a data table; S4.3: Record and store the collected temperature and humidity data, and use software to prepare the temperature and humidity data into a data table; S4.4: Use software to plot carbon sequestration curves from the CO2 concentration data table, O2 concentration data table, and temperature and humidity data table. Use the plotted carbon sequestration curves as an assessment index for soil surface vegetation carbon sequestration, and store the data of the soil surface vegetation carbon sequestration assessment index in the database.

6. The land consolidation quality assessment method based on big data according to claim 1, characterized in that: S5 includes the following steps: S5.1: Build a data model using data from the optical evaluation index database and the carbon sequestration index database, and divide the data model into three hierarchical structures: target layer, criterion layer and index layer. S5.2: The target layer determines the amount of CO2 that the surface vegetation in the assessment area needs to absorb, the criterion layer specifies the physiological activity and carbon sequestration function of the surface vegetation in the assessment area, and the indicator layer determines the amount of carbon that the surface vegetation in the assessment area ultimately sequesters. S5.3: The data model establishes a database of carbon sequestration assessment indicators for soil surface vegetation through the target layer, criterion layer and indicator layer (6), thereby calculating the net primary productivity, biomass and carbon utilization efficiency of vegetation. The net primary productivity, biomass and carbon utilization efficiency of vegetation are summarized by the software program, and a comprehensive ecological quality index of soil surface vegetation is given.

7. The land consolidation quality assessment method based on big data according to claim 1, characterized in that: S6 includes the following steps: S6.1: Record the comprehensive ecological quality index of soil surface vegetation and conduct continuous analysis of the high ecological quality of soil surface vegetation to predict future trends. S6.2: By setting a dynamic threshold in the software, if the comprehensive ecological quality index of the soil surface vegetation is not within the dynamic threshold, the system will automatically generate an early warning message, which will then be transmitted to various terminal devices for display via the cloud platform.

8. The evaluation system for the land consolidation quality assessment method based on big data according to any one of claims 1-7, characterized in that: include: Remote sensing image module (3) and optical image module (7): used to collect images of soil surface vegetation and generate image data of soil surface vegetation; Big Data Integrated Processing Platform (4): Used for the access, registration and management of various types of data; Soil surface vegetation optical evaluation index database (5): used for storing collected image data; Carbon dioxide monitoring module (1) and oxygen monitoring module (2): used to collect data on CO2 concentration, O2 concentration, temperature and humidity; Soil surface vegetation carbon sequestration assessment index database (6): Used to store carbon sequestration curves drawn by software for CO2 concentration data tables, O2 concentration data tables, temperature and humidity data tables; Intelligent early warning module (8): used to assess and judge the stability of soil surface vegetation and provide intelligent early warning for abnormal data in the assessment data. The big data integrated processing platform (4) is equipped with a soil surface vegetation optical evaluation index database (5) and a soil surface vegetation carbon sequestration evaluation index database (6). The remote sensing image module (3) and the optical image module (7) are connected to the soil surface vegetation optical evaluation index database (5) via a wireless network. The carbon dioxide monitoring module (1) and the oxygen monitoring module (2) are connected to the soil surface vegetation carbon sequestration evaluation index database (6) via a wireless network. The soil surface vegetation carbon sequestration evaluation index database (6) is connected to the intelligent early warning module (8) via a wireless network.

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