Forestry gridding management system based on big data

Abstract

The invention discloses a forestry gridding management system based on big data, and relates to the technical field of forestry gridding management. The forestry gridding management system based on big data comprises a grid division module, a time sequence data acquisition module, a forest region state analysis module and a forest region state judgment management module, a forest region is divided into a plurality of forest region units, and time sequence data of vegetation physiological activity and ecological carbon-water exchange are continuously acquired; the method comprises the following steps: continuously acquiring vegetation physiological activity time sequence data and ecological carbon water exchange time sequence data at a forest area unit level, analyzing and generating a vegetation health state index and an ecological carbon water cycle index, comparing the indexes with a preset evaluation interval, dynamically judging the state of the forest area unit, and automatically taking corresponding management measures based on an analysis result. The vegetation health state index and the ecological carbon water cycle index are obtained through real-time analysis, and the limitation that traditional forestry management depends on single static sampling and ecological dynamic changes are difficult to capture is effectively overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of forestry grid management, and in particular to a forestry grid management system based on big data. Background Art

[0002] With the continuous enhancement of global awareness of ecological and environmental protection, scientific management and refined monitoring of forestry resources have become important issues in the sustainable development of forestry. Traditional forestry management methods mainly rely on manual inspections, periodic sampling surveys, and analysis and evaluation based on local static data. These methods can reflect the overall situation of forest areas to a certain extent, but there are problems such as long data collection cycle, limited coverage, and delayed response. It is difficult to timely discover and deal with dynamic problems such as local ecological changes, disaster risks, and resource distribution changes in forest areas. Especially in the face of complex environmental backgrounds such as frequent climate change, increased extreme weather, and reduced biodiversity, traditional methods are obviously insufficient in real-time and accuracy in forest ecological security, fire prevention and control, and growth status assessment, which seriously restricts the intelligent and scientific development of forestry management.

[0003] In recent years, with the rapid development of technologies such as remote sensing monitoring, Internet of Things perception, big data processing and artificial intelligence analysis, the forestry big data management model has gradually emerged. By deploying multi-source heterogeneous sensing equipment, combining spatiotemporal continuous data collection and efficient data mining and analysis, big data technology has provided a more real-time, comprehensive and accurate basic condition for forestry resource management. However, most of the existing big data forestry management systems are still at the macro level, lacking a systematic dynamic monitoring and analysis mechanism based on gridded fine-grained spatial units, making it difficult to achieve rapid identification of changes in ecological status within forest areas and local refined intervention.

[0004] Existing technology, such as the invention patent application with announcement number: CN114997565B, discloses a smart city forestry grid management system based on big data, which belongs to the field of smart cities and involves forestry grid management technology. It solves the technical problem in the existing technology that it is difficult to reasonably schedule trees in various regions, and manages forestry in the region to ensure the growth rate of trees in the region while enhancing the efficiency of forestry management in the region, improving the management of trees in the region, and ensuring forestry construction in the region; through the setting of forest scheduling, the efficiency of regional forestry management is improved, and the growth of trees in the region is improved, so that each grid area can be equipped with suitable trees, improving the construction of smart cities while controlling the investment cost of forestry construction; the eligibility of forest scheduling is analyzed to determine whether there is an impact on forest scheduling, so as to ensure the efficiency of forestry management in the grid area and prevent forestry management from affecting the construction of smart cities in various regions.

[0005] Based on the above solution, it was found that the limitations of existing technologies include at least the following problems. First, the existing forestry grid management system mainly relies on single or short-term static data collection for environmental monitoring and forest status assessment, and lacks systematic monitoring of the continuous dynamic changes of forest units, resulting in delayed management strategies and slow responses. It is difficult to identify the potential deterioration trend of the ecological status of the forest area in real time, which can easily lead to a series of problems such as ecological degradation, increased fire hazards and resource allocation errors, thereby reducing the accuracy and effectiveness of forestry management. Especially in the context of frequent climate change and rapid fluctuations in forest growth conditions, traditional models find it difficult to capture key indicators through time series data, such as abnormal change rates of vegetation physiological activity and carbon and water cycle status, resulting in delayed problem discovery and delayed intervention measures. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a forestry grid management system based on big data, which solves the problems in the existing technology that forestry grid management relies on static data, lacks dynamic continuous monitoring, and is difficult to identify and intervene in ecological anomalies in a timely manner.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a forestry grid management system based on big data, comprising: a grid division module, used to divide the forest to be managed into several forest units based on preset division rules; a time series data acquisition module, used to continuously acquire the vegetation physiological activity time series data and ecological carbon-water exchange time series data of each forest unit in the forest to be managed, and perform preprocessing; a forest area status analysis module, used to perform status analysis on the vegetation physiological activity time series data and ecological carbon-water exchange time series data of each forest unit in the forest to be managed after preprocessing, and obtain the vegetation health status index and ecological carbon-water cycle index of each forest unit in the forest to be managed; a forest area status judgment management module, used to judge and analyze the vegetation health status index and ecological carbon-water cycle index of each forest unit in the forest to be managed with the preset status evaluation interval, and take preset management measures based on the judgment and analysis results.

[0008] Furthermore, the vegetation physiological activity time series data includes chlorophyll concentration values, vegetation moisture content, and volatile organic compound release at several time points, and the ecological carbon-water exchange time series data includes evapotranspiration, net carbon exchange, and soil temperature values at several time points.

[0009] Furthermore, the specific steps for obtaining the vegetation health status index of each forest unit in the forest to be managed are as follows: read the chlorophyll concentration value, vegetation moisture content, and volatile organic compound release of each forest unit in the forest to be managed at several time points, and conduct comprehensive analysis respectively to obtain the average chlorophyll concentration, average vegetation moisture content, and average volatile organic compound release of each forest unit in the forest to be managed; read the chlorophyll concentration value and vegetation moisture content of each forest unit in the forest to be managed at several time points, and conduct comprehensive analysis respectively. Carry out change analysis to obtain the chlorophyll concentration change rate and vegetation moisture change rate of each forest unit in the forest to be managed; obtain the maximum parameter value of chlorophyll concentration, the maximum parameter value of vegetation moisture, and the maximum parameter value of volatile organic compound release of each forest unit in the forest to be managed, and conduct a comprehensive analysis based on the average chlorophyll concentration, average vegetation moisture content, average volatile organic compound release, chlorophyll concentration change rate, and vegetation moisture change rate of the corresponding forest units to obtain the vegetation health status index of each forest unit in the forest to be managed.

[0010] Furthermore, the specific formula for calculating the vegetation health index of a forest unit in the managed forest is as follows: ;in, is the vegetation health index of a forest unit in the managed forest. is the mean chlorophyll concentration of a forest unit in the managed forest, is the maximum reference value of chlorophyll concentration in a forest unit to be managed, is the chlorophyll concentration influence coefficient stored in the database, is the chlorophyll concentration change rate of a forest unit in the managed forest, is the chlorophyll concentration change influence coefficient stored in the database, is the average vegetation moisture content of a forest unit in the managed forest, is the maximum reference content of vegetation moisture in a forest unit to be managed, is the vegetation moisture impact coefficient stored in the database, is the vegetation moisture change rate of a certain forest unit in the managed forest, is the vegetation moisture change impact coefficient stored in the database, is the average emission of volatile organic compounds in a forest unit in the managed forest. is the maximum parameter quantity of volatile organic compound emission in a forest unit to be managed. is the release impact coefficient stored in the database.

[0011] Furthermore, the specific steps for obtaining the ecological carbon and water cycle index of each forest unit in the forest to be managed are as follows: read the evapotranspiration, net carbon exchange and soil temperature values of each forest unit in the forest to be managed at several time points, and conduct comprehensive analysis respectively to obtain the average evapotranspiration, average net carbon exchange and soil temperature mean of each forest unit in the forest to be managed; read the evapotranspiration and net carbon exchange at several time points of each forest unit in the forest to be managed, and conduct change analysis respectively to obtain the evapotranspiration change rate and net carbon exchange change rate of each forest unit in the forest to be managed; obtain the maximum parameter quantity of evapotranspiration, the maximum parameter quantity of net carbon exchange and the maximum parameter value of soil temperature of each forest unit in the forest to be managed, and conduct comprehensive analysis in combination with the average evapotranspiration, average net carbon exchange, soil temperature mean, evapotranspiration change rate and net carbon exchange change rate of the corresponding forest units to obtain the ecological carbon and water cycle index of each forest unit in the forest to be managed.

[0012] Furthermore, the specific formula for calculating the ecological carbon-water cycle index of a forest unit in the managed forest is as follows: ;in, is the ecological carbon and water cycle index of a forest unit in the managed forest. is the average evapotranspiration of a forest unit in the managed forest, is the maximum evapotranspiration parameter of a forest unit in the managed forest. is the evapotranspiration change rate of a forest unit in the managed forest, is the evapotranspiration change sensitivity coefficient stored in the database, is the evapotranspiration contribution coefficient stored in the database, is the average net carbon exchange of a forest unit in the managed forest, is the maximum parameter quantity of net carbon exchange of a forest unit in the managed forest. is the net carbon exchange change rate of a forest unit in the managed forest, is the carbon exchange change sensitivity coefficient stored in the database, is the carbon exchange contribution coefficient stored in the database, is the mean soil temperature of a forest unit in the managed forest, is the maximum parameter value of soil temperature in a forest unit to be managed, It is the soil temperature adjustment coefficient stored in the database.

[0013] Furthermore, the vegetation health status index and ecological carbon-water cycle index of each forest unit in the forest to be managed are judged and analyzed respectively with the preset status assessment interval, and the specific steps of taking preset management measures based on the judgment and analysis results are as follows: the vegetation health status index of each forest unit in the forest to be managed is judged and analyzed respectively with the preset vegetation health status interval; the forest unit whose vegetation health status index is outside the preset vegetation health interval is regarded as a forest unit with abnormal vegetation health, and the preset abnormal vegetation health management measures are taken; the ecological carbon-water cycle index of each forest unit in the forest to be managed is judged and analyzed respectively with the preset ecological carbon-water cycle interval; the forest unit whose ecological carbon-water cycle index is outside the preset carbon-water cycle interval is regarded as a forest unit with abnormal ecological carbon-water cycle, and the preset abnormal ecological carbon-water cycle management measures are taken.

[0014] Furthermore, it also includes: a forest fire risk analysis module, which is used to continuously obtain the surface environmental energy time series data of each forest unit in the forest to be managed, and analyze the forest fire risk index of each forest unit in the forest to be managed; a forest fire risk judgment management module, which is used to judge and analyze the forest fire risk index of each forest unit in the forest to be managed with the preset fire risk assessment interval, and regard the forest unit whose forest fire risk index is within the preset fire risk assessment interval as a fire risk forest unit, and send a fire warning notification to relevant staff.

[0015] Furthermore, the surface environmental energy time series data includes surface temperature values, solar radiation intensity values, soil moisture values, and air humidity values at several time points. The specific steps for analyzing the forest fire risk index of each forest unit in the forest to be managed are as follows: reading the surface temperature values, solar radiation intensity values, and soil moisture values of each forest unit in the forest to be managed at several time points, and performing comprehensive analysis respectively to obtain the average surface temperature, average solar radiation intensity, and average soil moisture value of each forest unit in the forest to be managed; reading the surface temperature values, solar radiation intensity values, and soil moisture values of each forest unit in the forest to be managed at several time points; The air humidity values of each forest unit at several time points are collected, and the changes are analyzed separately to obtain the air humidity change rate of each forest unit in the forest to be managed; the maximum parameter values of surface temperature, maximum parameter values of solar radiation intensity, and maximum parameter values of soil moisture at several time points in each forest unit in the forest to be managed are obtained, and a comprehensive analysis is performed in combination with the mean surface temperature, mean solar radiation intensity, mean soil moisture, and air humidity change rate of the corresponding forest units to obtain the forest fire risk index of each forest unit in the forest to be managed.

[0016] Furthermore, the specific formula for calculating the forest fire risk index of a certain forest unit in the managed forest is as follows: ;in, is the forest fire risk index of a forest unit in the managed forest. is the mean surface temperature of a forest unit in the managed forest, is the maximum parameter value of the surface temperature of a forest unit in the managed forest. is the mean solar radiation intensity of a forest unit in the managed forest, is the maximum parameter value of solar radiation intensity of a certain forest unit in the forest to be managed, is the mean soil moisture value of a forest unit in the managed forest, is the maximum parameter value of soil moisture in a forest unit to be managed. is the rate of change of air humidity in a certain forest unit in the managed forest, is the humidity change influence coefficient stored in the database, is the fire risk impact coefficient stored in the database.

[0017] The present invention has the following beneficial effects:

[0018] (1) The forestry grid management system based on big data continuously obtains vegetation physiological activity time series data and ecological carbon and water exchange time series data at the forest unit level, and obtains vegetation health status index and ecological carbon and water cycle index through real-time analysis, which effectively overcomes the limitation of traditional forestry management that relies on single static sampling and is difficult to capture ecological dynamic changes. In particular, by comprehensively considering parameters such as chlorophyll concentration, vegetation moisture content, volatile organic compound release, evaporation, net carbon exchange and soil temperature, and introducing change rate analysis, it can timely identify ecological problems such as vegetation health deterioration and carbon and water cycle abnormalities in forest units, providing fine-grained, high-efficiency, full-link dynamic monitoring capabilities for forest areas. Based on automatic comparison with the preset evaluation interval, the system timely pushes abnormal unit management suggestions, realizing the transformation from passive response to active intervention, significantly improving the accuracy and response speed of forestry grid management, and providing strong support for ensuring the sustainable use of forest resources and the stability of ecological functions.

[0019] (2) The forestry grid management system based on big data has added a forest fire risk analysis and judgment management module. Based on the surface environmental energy time series data, it comprehensively analyzes the surface temperature, solar radiation intensity, soil moisture and air humidity change rate. Through the joint evaluation of standardized parameter values, mean values and change rates, it scientifically quantifies the fire risk index of forest units. Compared with the traditional method of relying on a single indicator of temperature or soil moisture for extensive early warning, it can more comprehensively capture the potential fire formation conditions and dynamically adjust the early warning sensitivity through the pre-trained humidity change influence coefficient and fire risk influence coefficient in the database, greatly improving the accuracy and timeliness of early identification of potential high-risk areas for fires, thereby helping relevant departments to deploy fire prevention measures in advance, reduce the incidence of forest fires and the scale of losses, and greatly improving the intelligent level of disaster prevention and control in forest areas.

[0020] (3) The forestry grid management system based on big data introduced a maximum parameter value standardization mechanism when processing the ecological monitoring data of each forest unit, including the maximum parameter value of chlorophyll concentration, the maximum parameter value of vegetation moisture content, the maximum parameter value of volatile organic compound release, the maximum parameter value of evapotranspiration, the maximum parameter value of net carbon exchange and the maximum parameter value of soil temperature. By normalizing the time series observation parameters and parameter values, the problem of incomparability of original values between forest units with different geographical locations and climatic conditions was effectively solved. Combined with the influence coefficients stored in the database for index comprehensive analysis, the system can make horizontal comparisons and vertical trend tracking of the ecological status of different forest units under a unified evaluation standard, which greatly improves the data consistency, indicator uniformity and management scientificity of the forestry grid management system, and provides a solid data foundation and technical support for large-scale, multi-scale and multi-time period forest ecological monitoring and intelligent decision-making.

[0021] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a block diagram of a forestry grid management system based on big data in the present invention.

[0023] Figure 2 The present invention is a flowchart of the specific steps for obtaining the vegetation health status index of each forest unit in the forest to be managed in a forestry grid management system based on big data.

[0024] Figure 3 This is a time series diagram of the chlorophyll concentration of a certain forest unit in the managed forestry based on the big data-driven distribution network fault perception system of the present invention.

[0025] Figure 4This is a time series diagram of the vegetation moisture content of a certain forest unit in the managed forestry based on the big data-driven distribution network fault perception system of the present invention.

[0026] Figure 5 This is a time series diagram of the volatile organic compound release rate of a certain forest unit in the managed forestry based on the big data-driven distribution network fault perception system of the present invention.

[0027] Figure 6 The present invention provides a flowchart of the specific steps for obtaining the ecological carbon-water cycle index of each forest unit in the forest to be managed in a forestry grid management system based on big data. DETAILED DESCRIPTION

[0028] See also Figure 1 The embodiment of the present invention provides a technical solution: a forestry grid management system based on big data, comprising: a grid division module, used to divide the forest to be managed into a number of forest units based on a preset division rule (for example, by a fixed area, such as every 0.5 square kilometers); a time series data acquisition module, used to continuously acquire the vegetation physiological activity time series data and the ecological carbon-water exchange time series data of each forest unit in the forest to be managed, and perform preprocessing; a forest area status analysis module, used to perform status analysis on the vegetation physiological activity time series data and the ecological carbon-water exchange time series data of each forest unit in the forest to be managed after preprocessing, and obtain the vegetation health status index and the ecological carbon-water cycle index of each forest unit in the forest to be managed; a forest area status judgment management module, used to judge and analyze the vegetation health status index and the ecological carbon-water cycle index of each forest unit in the forest to be managed with a preset status evaluation interval, and take preset management measures based on the judgment and analysis results.

[0029] The vegetation physiological activity time series data includes chlorophyll concentration values, vegetation moisture content, and volatile organic compound release at several time points (in this embodiment, the time point interval is 15 minutes). The ecological carbon-water exchange time series data includes evapotranspiration, net carbon exchange, and soil temperature values at several time points.

[0030] The chlorophyll concentration value refers to the total amount of chlorophyll contained in vegetation leaves per unit area. It is a key physiological indicator for measuring the photosynthetic capacity, health status and growth activity of plants. A high concentration usually indicates good vegetation activity and good ecological health. The specific steps for obtaining it are: first, deploy ground-based handheld or automated chlorophyll measuring instruments in forest units, or use drones or satellite remote sensing systems equipped with multispectral sensors to regularly monitor leaf reflectance characteristics to invert chlorophyll concentration; second, continuously collect chlorophyll concentration data for each grid unit at multiple set time points to ensure coverage of observation days with clear weather and no rain or snow; finally, archive and process the data at each time point to generate a time series dataset of chlorophyll concentration for the unit.

[0031] Vegetation moisture content refers to the proportion of water contained within vegetation tissue, which directly reflects the plant's drought resistance and physiological stress level. It is an important indicator for judging drought stress and health decline. The specific steps for obtaining it are: first, based on remote sensing technology, use multispectral or hyperspectral imaging systems to estimate vegetation moisture conditions through vegetation moisture indices (such as NDWI and NDMI) inversion, or directly measure by installing ground-based plant water potential sensors; then, continuously observe the vegetation moisture content in each forest unit at a uniform time frequency (such as twice a week or adjusted as needed); finally, organize and store the data at all observation time points to form a complete vegetation moisture content time series data.

[0032] Volatile organic compound emissions refer to the total amount of organic molecules released into the atmosphere when vegetation is subjected to environmental stress (such as drought, high temperature, and insect pests). Abnormal release of VOCs can serve as an early warning signal for plant stress. The specific steps for obtaining VOCs are as follows: first, small atmospheric composition sampling sensors or micrometeorological monitoring equipment are deployed in key areas of the forest to detect the main types and concentrations of VOCs in the air in real time; second, combined with time settings, continuous sampling and recording of VOCs release levels at several designated time points. When sampling, the environmental background concentration should be calibrated to improve monitoring accuracy; finally, the observation data at each time point are integrated to generate a time series of volatile organic compound emissions for the unit.

[0033] Evapotranspiration refers to the total amount of water released into the atmosphere from the surface through plant transpiration and soil evaporation per unit time. It is an important parameter for measuring the activity of surface water cycle and ecosystem water metabolism. The specific steps for obtaining it are: first, evapotranspiration is inverted by deploying ground evaporation dishes, automatic evapotranspiration observation instruments, or using remote sensing technology (such as SEBAL, METRIC and other energy balance models); second, continuous measurement or remote sensing inversion is carried out for each forest unit at a preset frequency (such as daily or hourly); finally, the evapotranspiration data at each time point are processed and archived to form an evapotranspiration time series database for subsequent dynamic analysis.

[0034] Net carbon exchange refers to the net value of carbon flux between the ecosystem per unit area and the atmosphere. It reflects the difference between the carbon absorption capacity (photosynthesis) and carbon emission capacity (respiration) of vegetation. It is a core indicator for evaluating the carbon sink function and ecological vitality of forests. The specific steps for obtaining it are as follows: first, a CO2 flux observation tower or a portable carbon flux measurement system is deployed in a typical forest unit to record the net CO2 exchange rate between the surface and the atmosphere per unit time in real time; second, NEE data is automatically collected at set time points or time intervals, and the original data is corrected for temperature, humidity, and wind speed when necessary; finally, the observation records at each time point are sorted out to form a time series data set of net carbon exchange for each forest unit.

[0035] Soil temperature refers to the soil temperature at a certain depth below the ground surface (usually within the range of 10cm to 50cm). It directly affects root metabolic activity, water absorption capacity and surface energy exchange process, and is an important regulatory factor in the energy and water cycle of forest ecosystems. The specific steps for obtaining it are as follows: First, bury underground temperature sensors at preset locations in each grid unit in the forest area, set the monitoring depth (such as 30cm), and connect to the data recording terminal; second, in accordance with the continuous monitoring mode, record soil temperature changes at multiple set time points (or real-time high frequency); finally, classify and archive the soil temperature data at all time points by forest unit to form a complete soil temperature time series record to support subsequent ecological status analysis.

[0036] Specifically, if Figure 2 As shown in FIG, the specific steps for obtaining the vegetation health status index of each forest unit in the forest to be managed are as follows: read the chlorophyll concentration value, vegetation moisture content, and volatile organic compound release of each forest unit in the forest to be managed at several time points, and perform comprehensive analysis (i.e., mean analysis) to obtain the average chlorophyll concentration, average vegetation moisture content, and average volatile organic compound release of each forest unit in the forest to be managed; read the chlorophyll concentration value and vegetation moisture content of each forest unit in the forest to be managed at several time points, and perform comprehensive analysis (i.e., mean analysis) to obtain the average chlorophyll concentration, average vegetation moisture content, and average volatile organic compound release of each forest unit in the forest to be managed; Change analysis (i.e., change rate analysis) is performed to obtain the chlorophyll concentration change rate and vegetation moisture change rate of each forest unit in the forest to be managed; the maximum parameter value of chlorophyll concentration, the maximum parameter value of vegetation moisture, and the maximum parameter value of volatile organic compound release of each forest unit in the forest to be managed are obtained, and a comprehensive analysis is performed on the average chlorophyll concentration, average vegetation moisture content, average volatile organic compound release, chlorophyll concentration change rate, and vegetation moisture change rate of the corresponding forest units to obtain the vegetation health status index of each forest unit in the forest to be managed.

[0037] Among them, the maximum reference value of chlorophyll concentration refers to the highest value of chlorophyll concentration observed for each forest unit in the managed forest within the set reference time period. This value serves as a standardized reference for measuring the photosynthetic capacity and ecological health limit level of vegetation in the unit. The specific steps for obtaining it are: first, deploy a ground-based multispectral chlorophyll meter in each forest unit or use high-resolution remote sensing imaging equipment carried by drones / satellites to continuously and regularly monitor the chlorophyll reflectance characteristics of vegetation; second, continuously record the chlorophyll concentration data at each time point within the set reference period (such as one year or the peak growing season); then, organize all observed data points and extract the highest chlorophyll concentration value of each unit within the reference period as the maximum reference value.

[0038] The maximum reference moisture content of vegetation refers to the highest average level value extracted after continuous monitoring of vegetation moisture content for each forest unit within the reference time period. It reflects the water holding capacity of vegetation in the unit under optimal water supply conditions and is used as the benchmark for moisture normalization analysis. The specific acquisition steps are: first, use ground-mounted plant water potential sensors or remote sensing image inversion moisture indices (such as NDWI and NDMI) to continuously monitor and sample the vegetation moisture status of each forest unit; then, within the selected reference time period (usually the rainy season or wet period is preferred), collect moisture content data at each moment according to the time frequency; then, perform statistical processing on the data of each forest unit, and take out the peak value of the moisture content as the maximum reference moisture content of the unit, which is used as the benchmark for subsequent normalization processing.

[0039] The maximum parameter value of volatile organic compound release refers to the peak level of VOCs gas released by plants, which is continuously monitored and recorded for each forest unit during the reference period. It serves as a standardized benchmark for measuring the extreme response of vegetation to stress. The specific steps for obtaining it are: first, a small air composition analyzer or a micro-meteorological station VOC detection module is deployed in the forest unit to collect the main types and concentrations of volatile organic compounds related to vegetation in the atmosphere in real time; second, data is continuously collected at a set frequency (such as hourly or daily) within a selected time period (such as peak growth period, extreme high temperature period); then, the VOCs release data of all collection moments in each forest unit are counted, and the highest release value is extracted as the maximum parameter value of volatile organic compound release in the unit. At the same time, the data are eliminated for outliers and background concentration is deducted to ensure that the maximum value is representative and stable.

[0040] The specific implementation example of calculating the average chlorophyll concentration, average vegetation moisture content, and average volatile organic compound release of a forest unit in the managed forest is as follows. The following data are available, including the chlorophyll concentration values, vegetation moisture content, and volatile organic compound release at six time points, as shown in Tables 1 and Figure 3-5 As shown:

[0041] Table 1 Example of time series data of vegetation physiological activity in a forest unit under managed forestry

[0042] Chlorophyll concentration (mg / m²) Vegetation moisture content (%) Volatile organic compound emission (μg / m³) Time point 1 437.215 70.312 7.218 Time point 2 437.193 70.308 7.234 Time point 3 437.205 70.315 7.227 Time point 4 437.181 70.298 7.241 Time point 5 437.187 70.298 7.249 Time point 6 437.160 70.289 7.258

[0043] The mean analysis of the data in Table 1 was performed and the following results were obtained:

[0044] The average chlorophyll concentration of a forest unit in the managed forest is approximately: 437.190 mg / m².

[0045] The average vegetation moisture content of a forest unit in the managed forest is approximately: 70.305%.

[0046] The average emission of volatile organic compounds in a forest unit in the managed forest is approximately 7.238μg / m³.

[0047] The specific formula for calculating the vegetation health index of a forest unit in the managed forest is as follows: ;in, is the vegetation health index of a forest unit in the managed forest. is the mean chlorophyll concentration of a forest unit in the managed forest, is the maximum reference value of chlorophyll concentration in a forest unit to be managed, is the chlorophyll concentration influence coefficient stored in the database, is the chlorophyll concentration change rate of a forest unit in the managed forest, is the chlorophyll concentration change influence coefficient stored in the database, is the average vegetation moisture content of a forest unit in the managed forest, is the maximum reference content of vegetation moisture in a forest unit to be managed, is the vegetation moisture impact coefficient stored in the database, is the vegetation moisture change rate of a certain forest unit in the managed forest, is the vegetation moisture change impact coefficient stored in the database, is the average emission of volatile organic compounds in a forest unit in the managed forest. is the maximum parameter quantity of volatile organic compound emission in a forest unit to be managed. is the release impact coefficient stored in the database.

[0048] It should be explained that the chlorophyll concentration influence coefficient stored in the database , chlorophyll concentration change influence coefficient , vegetation moisture impact coefficient , vegetation moisture change impact coefficient , release influence coefficient The specific steps to obtain are as follows:

[0049] For the chlorophyll concentration influence coefficient: Use historical or benchmark forest area data (including the mean chlorophyll concentration and the normalized vegetation index) to establish a standardized multiple linear regression model of the mean chlorophyll concentration and the normalized vegetation index (NDVI), and take the standardized regression coefficient of the mean chlorophyll concentration in the regression model as the chlorophyll concentration influence coefficient.

[0050] For the influence coefficient of chlorophyll concentration change: use the time series data of historical or benchmark forest areas (including the mean chlorophyll concentration and normalized vegetation index of adjacent monitoring periods), calculate the Pearson correlation coefficient or Spearman rank correlation coefficient of the chlorophyll concentration change rate of adjacent monitoring periods (such as monthly, quarterly) and the normalized vegetation index change rate (ΔNDVI / NDVI) during the same period, and take the absolute value of the correlation coefficient (or take a positive / negative value according to the actual correlation direction) as the influence coefficient of chlorophyll concentration change.

[0051] For the vegetation moisture impact coefficient: Collect the following data for historical or baseline forest units (for a specific statistical period, such as a growing season or a year):

[0052] Average vegetation moisture content: Unit: %, obtained through vegetation moisture meter, near-ground remote sensing or microwave remote sensing inversion.

[0053] Tree mortality rate: (number of dead trees during the period / total number of standing trees at the beginning of the period)*100.

[0054] A standardized multiple linear regression model was established between mean vegetation moisture content and tree mortality.

[0055] The standardized regression coefficient of the average vegetation moisture content in the model was taken as the vegetation moisture impact coefficient.

[0056] For the impact coefficient of vegetation moisture change: use the time series data of historical or benchmark forest areas (including vegetation moisture content and pest and disease outbreak index in adjacent monitoring periods), calculate the Pearson correlation coefficient or Spearman rank correlation coefficient of the vegetation moisture change rate in adjacent monitoring periods and the pest and disease outbreak index in the same period (such as the growth rate of new insect population density or the growth rate of diseased area), and take the absolute value of the correlation coefficient (or take a positive / negative value according to the actual correlation direction) as the impact coefficient of vegetation moisture change.

[0057] For the release impact coefficient: use historical forest area data containing records of fire or significant degradation events (including the average amount of volatile organic compound release and the number of thermal anomaly points / fire points) to establish a standardized multiple linear regression model of the average amount of volatile organic compound release and the number of thermal anomaly points / fire points (based on satellite remote sensing) or the frequency / probability of historical fires or the incidence rate (%) of significant crown yellowing / leaf fall observed in a specific period, and take the standardized regression coefficient of the average amount of volatile organic compound release in the regression model as the release impact coefficient.

[0058] In this implementation plan, by continuously acquiring multi-source time series data such as chlorophyll concentration, vegetation moisture content and volatile organic compound release from forest units, combining mean analysis and rate of change analysis, and introducing maximum parameter value normalization processing and database storage influence coefficient weighted modeling, a vegetation health status index was scientifically constructed, achieving accurate quantification and dynamic evaluation of the physiological status of vegetation in forest units. Compared with the traditional single static indicator analysis method, this method can comprehensively reflect the current health level and changing trend of vegetation, timely discover ecological stress and degradation risks, and support targeted management decisions. Through standardization and dynamic weight optimization processing, it not only improves the data comparability and evaluation uniformity between different forest units, but also enhances the adaptability and monitoring accuracy of the system in complex climatic environments, and significantly improves the intelligence and scientific nature of forestry ecological management.

[0059] Specifically, if Figure 6 As shown in the figure, the specific steps for obtaining the ecological carbon and water cycle index of each forest unit in the forest to be managed are as follows: read the evapotranspiration, net carbon exchange and soil temperature values of each forest unit in the forest to be managed at several time points, and perform comprehensive analysis (i.e., mean analysis) to obtain the average evapotranspiration, average net carbon exchange and soil temperature of each forest unit in the forest to be managed; read the evapotranspiration and net carbon exchange at several time points of each forest unit in the forest to be managed, and perform change analysis (i.e., change rate analysis) to obtain the evapotranspiration change rate and net carbon exchange change rate of each forest unit in the forest to be managed; obtain the maximum evapotranspiration parameter quantity, net carbon exchange maximum parameter quantity and soil temperature maximum parameter value of each forest unit in the forest to be managed, and perform comprehensive analysis based on the average evapotranspiration, average net carbon exchange, soil temperature mean, evapotranspiration change rate and net carbon exchange change rate of the corresponding forest units to obtain the ecological carbon and water cycle index of each forest unit in the forest to be managed.

[0060] Among them, the maximum evapotranspiration parameter refers to the highest value of the total evapotranspiration observed through continuous monitoring for each forest unit in the managed forest within a set reference time period. It reflects the water exchange capacity of the surface and vegetation system of the unit under the most active state, and is an important standard reference for assessing the activity of the water cycle. The specific steps for obtaining it are: first, deploy ground evapotranspiration observation instruments (such as evaporation dishes or automatic evapotranspiration monitoring equipment) in each forest unit, or continuously estimate evapotranspiration through remote sensing inversion technology (such as the SEBAL method based on the energy balance model); second, continuously collect evapotranspiration data from each unit at a preset time frequency (such as hourly or daily) within a selected reference time period (such as one year or growing season); then, perform statistics on the data collected at all time points, extract the peak evapotranspiration of each forest unit as the maximum evapotranspiration parameter of the unit, and eliminate abnormal extreme values when necessary.

[0061] The maximum parameter of net carbon exchange refers to the maximum absorption value of net ecosystem carbon exchange (NEE) recorded through continuous monitoring for each forest unit in the managed forest within a reference time period. It reflects the maximum absorption capacity of the unit for atmospheric carbon dioxide under optimal physiological conditions and is an important standard value for assessing the forest carbon sink potential. The specific steps for obtaining it are as follows: first, deploy CO2 flux observation towers or portable ecosystem carbon exchange measuring instruments in representative forest units to monitor the changes in net carbon flux between the unit area ecosystem and the atmosphere in real time; second, continuously collect NEE data at a high frequency (such as once every half hour) within a set time period (usually a complete growth cycle or a whole year); then, organize the observation records at each time point, extract the maximum negative flux of net carbon exchange (i.e., the maximum carbon absorption) for each forest unit as the maximum parameter of net carbon exchange for the unit, and perform necessary meteorological corrections and outlier screening on the data to ensure that the maximum parameter value is accurate and scientific.

[0062] The maximum reference value of soil temperature refers to the highest value of underground soil temperature recorded by continuous monitoring for each forest unit in the managed forest within the reference time period. This value directly reflects the extreme state of the surface and root environment under the influence of high temperature, and is an important benchmark for assessing the energy accumulation of the ecosystem and the potential pressure of root metabolism. The specific steps for obtaining it are: first, bury subsurface temperature sensors at specified depths (such as 10 cm, 30 cm) in each forest unit, establish an automated continuous monitoring system, and collect soil temperature change data in real time; second, continuously record soil temperature at set time intervals (such as every 15 minutes or every hour) within a set reference time period (such as the whole year or the high temperature season); finally, summarize the soil temperature data collected at all time points in each forest unit, extract the highest temperature value as the maximum reference value of soil temperature for the unit, and eliminate extreme abnormal data.

[0063] The specific formula for calculating the ecological carbon and water cycle index of a forest unit in the managed forest is as follows: ;in, is the ecological carbon and water cycle index of a forest unit in the managed forest. is the average evapotranspiration of a forest unit in the managed forest, is the maximum evapotranspiration parameter of a forest unit in the managed forest. is the evapotranspiration change rate of a forest unit in the managed forest, is the evapotranspiration change sensitivity coefficient stored in the database, is the evapotranspiration contribution coefficient stored in the database, is the average net carbon exchange of a forest unit in the managed forest, is the maximum parameter quantity of net carbon exchange of a forest unit in the managed forest. is the net carbon exchange change rate of a forest unit in the managed forest, is the carbon exchange change sensitivity coefficient stored in the database, is the carbon exchange contribution coefficient stored in the database, is the mean soil temperature of a forest unit in the managed forest, is the maximum parameter value of soil temperature in a forest unit to be managed, It is the soil temperature adjustment coefficient stored in the database.

[0064] It should be explained that the sensitivity coefficient of evapotranspiration stored in the database is , evapotranspiration contribution coefficient , carbon exchange change sensitivity coefficient , carbon exchange contribution coefficient , soil temperature adjustment coefficient The specific steps for obtaining the data are as follows: first, continuously monitor the long-term series data of relevant ecological parameters such as evapotranspiration, net carbon exchange and soil temperature in representative forest units, and simultaneously record the changes in ecological function status (such as forest vitality index, health status score, carbon budget changes, etc.); second, for each forest unit, calculate the dynamic correlation between the evapotranspiration change rate and the change of ecological indicators, and between the net carbon exchange change rate and the change of ecological indicators. Through regression analysis, sensitivity analysis or regression fitting methods based on machine learning, quantitatively extract the standardized contribution of the evapotranspiration change rate to the fluctuation of ecological functions as the evapotranspiration change sensitivity coefficient. The regression weight of the absolute value of evapotranspiration on the overall carbon-water cycle level change is used as the evapotranspiration contribution coefficient The sensitivity of net carbon exchange change rate to system carbon dynamic imbalance is extracted as carbon exchange change sensitivity coefficient The regression weight of the total net carbon exchange contribution to the carbon cycle state is extracted as the carbon exchange contribution coefficient At the same time, based on the regression analysis of soil temperature fluctuations and changes in ecological health levels, the adjustment coefficient of the impact of soil temperature deviation from the optimal range on ecological status was extracted as the soil temperature adjustment coefficient Finally, the final values of each coefficient are determined after multi-sample fitting and error optimization processing, and stored in the database to support the real-time dynamic calculation and update of the ecological carbon and water cycle index.

[0065] In this implementation plan, by continuously collecting time series data of evapotranspiration, net carbon exchange and soil temperature, combined with mean analysis, rate of change analysis and maximum parameter value normalization processing, an ecological carbon and water cycle index was scientifically constructed, which systematically reflects the comprehensive ecological functional status of water cycle and dynamic carbon exchange in forest units. Compared with the traditional ecological assessment method based on a single static parameter, this method can dynamically perceive the fluctuations in the carbon and water metabolism process in the forest area, timely identify potential degradation or imbalance trends, and adjust the weights by introducing the sensitivity coefficient and contribution coefficient obtained through training in the database, thereby improving the accuracy and adaptability of the index calculation. This technology significantly enhances the ability to accurately monitor the ecological status of forestry grid units, warn of abnormal changes and hierarchical management, and provides strong support for the protection of forest carbon sink functions, water cycle stability and ecosystem health maintenance.

[0066] Specifically, the vegetation health status index and ecological carbon-water cycle index of each forest unit in the forest to be managed are judged and analyzed with the preset status assessment interval, and the specific steps of taking preset management measures based on the judgment and analysis results are as follows: the vegetation health status index of each forest unit in the forest to be managed is judged and analyzed with the preset vegetation health status interval; the forest unit whose vegetation health status index is outside the preset vegetation health interval is regarded as a forest unit with abnormal vegetation health, and the preset vegetation health abnormality management measures are taken, that is, a vegetation health abnormality notification is sent to relevant staff. Management suggestions include but are not limited to the following measures: initiate a review of the physiological health of vegetation in the forest unit, arrange ground inspections or drone aerial photography to verify the color and moisture status of leaves; based on the actual abnormality type (such as decreased chlorophyll concentration, insufficient vegetation moisture, etc.), it is recommended to adopt local irrigation and water replenishment operations; for areas where signs of pests and diseases are found, it is recommended to apply biological pesticides in advance or carry out pest and disease control operations; based on the severity of the abnormality, it is recommended to replace some degraded vegetation and carry out ecological restoration and replanting; remind staff to follow up and monitor the abnormal units continuously, and adjust the monitoring frequency (such as shortening to daily sampling).

[0067] The ecological carbon and water cycle index of each forest unit in the forest to be managed will be judged and analyzed against the preset ecological carbon and water cycle range; forest units with ecological carbon and water cycle index outside the preset carbon and water cycle range will be regarded as forest units with abnormal ecological carbon and water cycle, and the preset ecological carbon and water cycle abnormality management measures will be taken, that is, ecological carbon and water cycle abnormality management suggestions will be sent to relevant staff, including but not limited to the following measures: it is recommended to strengthen artificial water source scheduling, such as increasing the irrigation frequency of the unit or adjusting the water source configuration to alleviate the imbalance of the water cycle; for areas with serious abnormal evapotranspiration (too high or too low), it is recommended to optimize surface cover (such as adding ground cover plants and setting up water retention layers); for areas with reduced carbon exchange capacity, it is recommended to plant plants with high carbon absorption efficiency, such as increasing the artificial planting density of local dominant tree species; if abnormally high or low soil temperature is detected, it is recommended to take measures such as surface shading, soil insulation, and control of surface albedo; special inspections are required for abnormal units, and continuous changes in carbon exchange, soil moisture and temperature are included in the key monitoring plan.

[0068] In this implementation plan, by conducting real-time judgment and analysis on the vegetation health status index and ecological carbon-water cycle index of forest units, and actively triggering specific management measures under abnormal conditions, the forestry management has effectively achieved the transformation from passive inspection to active response and refined intervention. The system can not only timely detect problems such as vegetation physiological abnormalities and ecological carbon-water imbalance, but also push targeted management suggestions according to different abnormality types, such as local irrigation, pest and disease control, vegetation restoration, water source scheduling, surface optimization, etc., which greatly improves the timeliness and scientificity of abnormality handling. By dynamically adjusting the monitoring frequency and special inspection arrangements, the continuous tracking and risk control of abnormal units are further strengthened, which significantly improves the stability of the forest ecosystem, resource utilization efficiency and disaster prevention and control capabilities, and provides a strong guarantee for the sustainable management of forestry resources.

[0069] Specifically, it also includes: a forest fire risk analysis module, which is used to continuously obtain the surface environmental energy time series data of each forest unit in the forest to be managed, and analyze the forest fire risk index of each forest unit in the forest to be managed; a forest fire risk judgment management module, which is used to judge and analyze the forest fire risk index of each forest unit in the forest to be managed with the preset fire risk assessment interval, and regard the forest unit whose forest fire risk index is within the preset fire risk assessment interval as a fire risk forest unit, and send a fire warning notification to relevant staff.

[0070] The surface environmental energy time series data include surface temperature values, solar radiation intensity values, soil moisture values, and air humidity values at several time points. The specific steps for analyzing the forest fire risk index of each forest unit in the forest to be managed are as follows: read the surface temperature values, solar radiation intensity values, and soil moisture values of each forest unit in the forest to be managed at several time points, and perform comprehensive analysis (i.e., mean analysis) to obtain the mean surface temperature, solar radiation intensity, and soil moisture values of each forest unit in the forest to be managed; read the surface temperature values, solar radiation intensity values, and soil moisture values of each forest unit in the forest to be managed at several time points; The air humidity values of the forest area unit at several time points are collected, and the change analysis (i.e., change rate analysis) is performed respectively to obtain the air humidity change rate of each forest area unit in the forest to be managed; the maximum parameter value of the surface temperature, the maximum parameter value of the solar radiation intensity, and the maximum parameter value of the soil moisture at several time points of each forest area unit in the forest to be managed are obtained, and a comprehensive analysis is performed in combination with the mean surface temperature, mean solar radiation intensity, mean soil moisture, and air humidity change rate of the corresponding forest area unit to obtain the forest fire risk index of each forest area unit in the forest to be managed.

[0071] Among them, the maximum surface temperature parameter value refers to the maximum value of the surface temperature recorded through continuous monitoring for each forest unit in the managed forest within the set historical reference time period, which is used as the standardized benchmark in the subsequent calculation of the fire risk index. The specific acquisition steps are: first, select a suitable reference time interval, usually the monitoring period of the most recent year, or specifically select the fire-prone season in the forest area as the key period; then, continuously collect surface temperature data through thermal infrared remote sensing equipment (such as satellite remote sensing data Landsat, Sentinel series) or ground infrared temperature sensors deployed in each grid unit in the forest area; then, organize the surface temperature values of all collected time points in each unit, and extract the maximum value from them as the maximum surface temperature parameter value of the unit.

[0072] The maximum reference value of solar radiation intensity refers to the highest recorded value of solar radiation intensity per unit area observed for each forest unit during the selected historical monitoring period. It is used to reflect the extreme upper limit of the amount of solar energy received by the area to support standardized analysis in fire risk assessment. The specific steps for obtaining it are as follows: first, determine a representative time period, usually the season with the strongest sunshine throughout the year or a comprehensive observation period of multiple seasons; then, continuously observe and record the solar radiation intensity of each grid unit through solar radiometers in ground meteorological observation stations deployed in the forest area (or use remote sensing data inversion, such as MODIS Surface Radiation products); then, organize the solar radiation intensity data at each time point in each grid unit, and extract the maximum radiation value of the grid unit during the historical observation period as the maximum reference value.

[0073] The maximum soil moisture reference value refers to the highest value in the soil moisture data obtained from continuous monitoring during the reference time period for each grid unit in the forest area. It is used to measure the optimal moisture state of the soil and serve as the basis for moisture standardization in the calculation of fire potential. The specific steps for obtaining it are as follows: first, set an appropriate historical reference time interval, usually selecting the entire year, or use the complete monitoring data during the rainy season as a reference; then, use soil moisture sensors buried on the surface and shallow layers (such as 10 cm and 30 cm depth) of each forest unit, or use satellite remote sensing soil moisture inversion data (such as SMAP satellite products) to observe soil moisture at high frequency and continuously; then, summarize and analyze the soil moisture values collected for each forest unit during the monitoring period, extract the maximum value as the maximum soil moisture reference value for the grid unit, and, if necessary, eliminate abnormal instantaneous high humidity data caused by heavy rain, extreme weather, etc.

[0074] The specific formula for calculating the forest fire risk index of a forest unit in the managed forest is as follows: ;in, is the forest fire risk index of a forest unit in the managed forest. is the mean surface temperature of a forest unit in the managed forest, is the maximum parameter value of the surface temperature of a forest unit in the managed forest. is the mean solar radiation intensity of a forest unit in the managed forest, is the maximum parameter value of solar radiation intensity of a certain forest unit in the forest to be managed, is the mean soil moisture value of a forest unit in the managed forest, is the maximum parameter value of soil moisture in a forest unit to be managed. is the rate of change of air humidity in a certain forest unit in the managed forest, is the humidity change influence coefficient stored in the database, is the fire risk impact coefficient stored in the database.

[0075] It should be explained that the humidity change influence coefficient stored in the database , Fire risk impact coefficient The specific acquisition steps are as follows: first, multiple grid units with different temporal and spatial distributions that are representative of the reference forest area are selected, and based on historical monitoring data, the time series change data of air humidity and fire occurrence records in each unit are continuously collected; second, for each grid unit, the correlation between the air humidity change rate and the characteristic parameters such as the frequency, scale, and intensity of actual fires within a certain period is calculated, and a mathematical relationship model between the humidity change rate and the risk intensity of fire events is established; then, the contribution value of the humidity change rate to the degree of fire risk increase is quantitatively fitted through regression analysis or machine learning methods (such as multiple linear regression, random forest, etc.), where the standardized regression coefficient of humidity change on fire risk change is extracted as the humidity change influence coefficient; further, the fire risk change trend output by the overall model is compared with the measured fire event data, and all samples are integrated, and the error minimization method (such as the least squares method) is used to determine the overall fire risk prediction accuracy adjustment coefficient, which is extracted as the fire risk influence coefficient.

[0076] In this implementation scheme, by continuously collecting the surface temperature, solar radiation intensity, soil moisture and air humidity change rate of forest units, based on mean and change rate analysis, combined with maximum parameter value standardization processing, the forest fire risk index is scientifically calculated, effectively realizing the dynamic quantification and early warning of fire risk. Compared with the traditional method of relying on a single temperature or humidity indicator for extensive judgment, the present invention introduces the humidity change influence coefficient and fire risk influence coefficient of multi-factor comprehensive evaluation and database optimization training, which greatly improves the accuracy and sensitivity of fire risk identification. The system can detect potential high-risk forest areas in advance and send fire warning notifications to management personnel in a timely manner, significantly enhancing the initiative and refinement of forest fire prevention and control, and providing strong technical support for reducing forest fire losses and ensuring ecological security.

[0077] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0078] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A forestry grid management system based on big data, characterized in that: include: A grid division module is used to divide the forest to be managed into a number of forest area units based on preset division rules; The time series data acquisition module is used to continuously acquire the time series data of vegetation physiological activity and ecological carbon and water exchange of each forest unit in the managed forest, and perform preprocessing; The forest area status analysis module is used to perform status analysis on the pre-processed vegetation physiological activity time series data and ecological carbon-water exchange time series data of each forest area unit in the managed forest, and obtain the vegetation health status index and ecological carbon-water cycle index of each forest area unit in the managed forest; The forest area status judgment management module is used to judge and analyze the vegetation health status index and ecological carbon and water cycle index of each forest area unit in the managed forest with the preset status assessment interval, and take preset management measures based on the judgment and analysis results.

2. The forestry grid management system based on big data according to claim 1 is characterized in that: The vegetation physiological activity time series data includes chlorophyll concentration values, vegetation moisture content, and volatile organic compound release at several time points, and the ecological carbon-water exchange time series data includes evapotranspiration, net carbon exchange, and soil temperature values at several time points.

3. The forestry grid management system based on big data according to claim 2 is characterized in that: The specific steps to obtain the vegetation health index of each forest unit in the managed forest are as follows: Read the chlorophyll concentration values, vegetation moisture content, and volatile organic compound release values of each forest unit in the managed forest at several time points, and conduct comprehensive analysis to obtain the average chlorophyll concentration value, average vegetation moisture content, and average volatile organic compound release value of each forest unit in the managed forest; Read the chlorophyll concentration values and vegetation moisture content of each forest unit in the managed forest at several time points, and perform change analysis on each of them to obtain the chlorophyll concentration change rate and vegetation moisture change rate of each forest unit in the managed forest; The maximum parameter value of chlorophyll concentration, the maximum parameter value of vegetation moisture content, and the maximum parameter value of volatile organic compound release of each forest unit in the forest to be managed are obtained, and a comprehensive analysis is performed based on the average chlorophyll concentration, average vegetation moisture content, average volatile organic compound release, chlorophyll concentration change rate, and vegetation moisture change rate of the corresponding forest units to obtain the vegetation health status index of each forest unit in the forest to be managed.

4. The forestry grid management system based on big data according to claim 3 is characterized in that: The specific formula for calculating the vegetation health index of a forest unit in the managed forest is as follows: ； in, 、 、 、 、 、 、 、 、 The following are the vegetation health index, chlorophyll concentration average, chlorophyll concentration maximum reference value, chlorophyll concentration change rate, average vegetation moisture content, vegetation moisture maximum reference value, vegetation moisture change rate, volatile organic compound release average, volatile organic compound release maximum reference value, 、 、 、 、 They are the chlorophyll concentration influence coefficient, chlorophyll concentration change influence coefficient, vegetation moisture influence coefficient, vegetation moisture change influence coefficient, and release influence coefficient stored in the database.

5. The forestry grid management system based on big data according to claim 2 is characterized in that: The specific steps to obtain the ecological carbon and water cycle index of each forest unit in the managed forest are as follows: Read the evapotranspiration, net carbon exchange, and soil temperature values of each forest unit in the managed forest at several time points, and conduct comprehensive analysis to obtain the average evapotranspiration, average net carbon exchange, and average soil temperature of each forest unit in the managed forest; Read the evapotranspiration and net carbon exchange of each forest unit in the managed forest at several time points, and perform change analysis on each of them to obtain the evapotranspiration change rate and net carbon exchange change rate of each forest unit in the managed forest; The maximum parameter value of evapotranspiration, the maximum parameter value of net carbon exchange, and the maximum parameter value of soil temperature of each forest unit in the forest to be managed are obtained, and a comprehensive analysis is performed based on the average evapotranspiration, average net carbon exchange, mean soil temperature, evapotranspiration change rate, and net carbon exchange change rate of the corresponding forest units to obtain the ecological carbon and water cycle index of each forest unit in the forest to be managed.

6. The forestry grid management system based on big data according to claim 5 is characterized in that: The specific formula for calculating the ecological carbon and water cycle index of a forest unit in the managed forest is as follows: ； in, 、 、 、 、 、 、 、 、 The following are the ecological carbon-water cycle index, average evapotranspiration, maximum evapotranspiration parameter, evapotranspiration change rate, average net carbon exchange, maximum net carbon exchange parameter, net carbon exchange change rate, soil temperature mean, and maximum soil temperature parameter of a certain forest unit in the managed forest. 、 、 、 、 They are the evapotranspiration change sensitivity coefficient, evapotranspiration contribution coefficient, carbon exchange change sensitivity coefficient, carbon exchange contribution coefficient, and soil temperature adjustment coefficient stored in the database.

7. The forestry grid management system based on big data according to claim 1 is characterized in that: The specific steps for judging and analyzing the vegetation health index and ecological carbon-water cycle index of each forest unit in the managed forest against the preset status assessment intervals and taking the preset management measures based on the judgment and analysis results are as follows: The vegetation health index of each forest unit in the managed forest is compared with the preset vegetation health status interval; The forest units whose vegetation health status index is outside the preset vegetation health range are regarded as forest units with abnormal vegetation health, and the preset abnormal vegetation health management measures are adopted; The ecological carbon-water cycle index of each forest unit in the managed forest is compared with the preset ecological carbon-water cycle interval; Forest units whose ecological carbon and water cycle index is outside the preset carbon and water cycle range are regarded as forest units with abnormal ecological carbon and water cycle, and preset ecological carbon and water cycle abnormality management measures are taken.

8. The forestry grid management system based on big data according to claim 1 is characterized in that: Also includes: A forest fire risk analysis module is used to continuously obtain the time series data of the surface environmental energy of each forest unit in the managed forest, and analyze the forest fire risk index of each forest unit in the managed forest; The forest fire risk judgment management module is used to judge and analyze the forest fire risk index of each forest unit in the managed forestry with the preset fire risk assessment range, and regard the forest unit whose forest fire risk index is within the preset fire risk assessment range as a fire risk forest unit, and send fire warning notifications to relevant staff.

9. The forestry grid management system based on big data according to claim 8 is characterized in that: The surface environmental energy time series data includes surface temperature values, solar radiation intensity values, soil moisture values, and air humidity values at several time points. The specific steps for analyzing the forest fire risk index of each forest unit in the managed forest are as follows: Read the surface temperature values, solar radiation intensity values, and soil moisture values of each forest unit in the managed forest at several time points, and conduct comprehensive analysis to obtain the average surface temperature, average solar radiation intensity, and average soil moisture value of each forest unit in the managed forest; Read the air humidity values of each forest unit in the managed forest at several time points, and perform change analysis on each of them to obtain the air humidity change rate of each forest unit in the managed forest; The maximum parameter values of surface temperature, solar radiation intensity and soil moisture at several time points of each forest unit in the forest to be managed are obtained, and a comprehensive analysis is performed based on the mean surface temperature, mean solar radiation intensity, mean soil moisture and air humidity change rate of the corresponding forest units to obtain the forest fire risk index of each forest unit in the forest to be managed.

10. The forestry grid management system based on big data according to claim 9 is characterized in that: The specific formula for calculating the forest fire risk index of a forest unit in the managed forest is as follows: ； in, 、 、 、 、 、 、 、 They are the forest fire risk index, mean surface temperature, maximum surface temperature parameter value, mean solar radiation intensity, maximum solar radiation intensity parameter value, mean soil moisture, maximum soil moisture parameter value, and air humidity change rate of a certain forest unit in the managed forest. 、 They are the humidity change impact coefficient and fire risk impact coefficient stored in the database respectively.

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