Vegetation growth data monitoring system and method

Abstract

The invention discloses a vegetation growth data monitoring system and method, relates to the technical field of environment monitoring, and solves the problem of insufficient spatial resolution of a remote sensing technology through a spatial resolution optimization module. Multi-dimensional image data are collected, spatial detail presentation is optimized by combining an image super-resolution processing technology and a multi-angle data collection method, resolution correction is carried out, a high-precision monitoring image with the unified resolution is generated, and accurate recognition of small-scale vegetation changes is achieved; meteorological data and soil monitoring data are collected in real time, data cleaning and formatting processing are carried out in combination with historical data, a unified growth environment data set is constructed, and the accuracy of data fusion and analysis is guaranteed; the complexity and interference problems of environmental factors are solved through an environmental factor analysis module; and calculating and evaluating an interference influence coefficient Eic, generating an early warning and pushing an intervention suggestion, such as adjusting irrigation and soil management, and providing accurate dynamic environment optimization support.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a vegetation growth data monitoring system and method. Background Art

[0002] The vegetation growth data monitoring system originated from the needs of agricultural modernization. The initial monitoring methods mostly relied on manual observation and ground surveys, but this method was inefficient, inaccurate, and limited by time and space. With the introduction of remote sensing technology, satellites and drones have become important tools for vegetation monitoring. By collecting real-time image data of the state of surface vegetation, researchers can have a more comprehensive understanding of vegetation growth. In recent years, with the rapid development of the Internet of Things, cloud computing, and big data analysis technologies, vegetation growth monitoring systems have gradually achieved automation and intelligence. They can accurately collect environmental parameters and conduct real-time analysis, provide decision support, and are widely used in agricultural production, ecological environment protection and other fields. The combination of these technologies not only improves the accuracy of monitoring, but also greatly improves the efficiency and response speed of monitoring.

[0003] However, the existing vegetation growth data monitoring system still has the following technical problems in practical application: 1. Insufficient spatial resolution: Although remote sensing technology can cover a wide range of vegetation monitoring, its spatial resolution is sometimes insufficient, especially in large-scale regional monitoring, and it is impossible to accurately identify small-scale vegetation changes. For example, the resolution of satellite images may be too low, resulting in the inability to accurately reflect small changes or heterogeneity of ground vegetation, affecting the accuracy of prediction.

[0004] 2. Data fusion and standardization issues: Vegetation growth monitoring systems usually rely on multiple data sources (such as remote sensing images, sensor data, etc.), but the standardization of different data sources remains a challenge. The data format, accuracy and acquisition frequency of different platforms and devices may differ, leading to difficulties in data fusion and affecting the reliability and accuracy of comprehensive analysis results.

[0005] 3. Complexity and interference of environmental factors: Vegetation growth is affected by a variety of environmental factors, such as climate, soil type, topography, etc., and the interactions between these factors are relatively complex. Current monitoring systems often find it difficult to fully consider the dynamic changes of all environmental factors. For example, errors in meteorological data or failures in the sensors themselves may lead to deviations in the prediction of vegetation growth status. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a vegetation growth data monitoring system and method, which solves the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a vegetation growth data monitoring system, including a data fusion and standardization module, an environmental factor analysis module, a spatial resolution optimization module, a dynamic monitoring module, a growth prediction module and a report generation module; The data fusion and standardization module is used to collect data related to the growth environment of vegetation, including historical meteorological data and historical soil monitoring data; at the same time, the growth environment related data from different sensors are pre-processed and uniformly formatted, and finally a growth environment related data set is constructed; The environmental factor analysis module is used to analyze the impact of the surrounding environment on the growth of vegetation, including weather, soil type and terrain. Based on the data set related to the growth environment, the interference impact coefficient Eic of the surrounding environment on the growth of vegetation is calculated and evaluated. Finally, an early warning is issued and intervention is carried out according to the evaluation content. The spatial resolution optimization module is used to deploy remote sensing imaging equipment and image sensors around vegetation to collect vegetation images, obtain vegetation multi-dimensional image data in real time, including high-resolution remote sensing images and near-ground image sensor data, and then perform image super-resolution processing on the vegetation multi-dimensional image data, and adjust the spatial resolution of vegetation monitoring in combination with multi-angle data acquisition technology; The dynamic monitoring module is used to analyze the multi-dimensional image data of vegetation collected by remote sensing imaging equipment and image sensors, calculate the vegetation health coefficient Vhc, and perform dynamic evaluation; then, through the combination of sensors and drones, the vegetation growth is dynamically monitored based on time series analysis, and vegetation growth-related data is collected in real time; The growth prediction module is used to analyze the collected vegetation growth-related data and calculate the growth cycle progress value Gcp; by comparing it with the preset growth progress threshold R, the growth trend and future growth status of the vegetation are predicted to assist in decision making; The report generation module is used to receive and analyze the evaluation content of the vegetation health coefficient Vhc and the growth cycle progress value Gcp in real time, and then generate a vegetation growth monitoring report; finally, the vegetation growth monitoring report is pushed to the environmental monitoring terminal and the user operating system through the cloud platform to provide decision support.

[0008] Preferably, the data fusion and standardization module first monitors and collects vegetation growth environment related data in real time by deploying environmental sensors, remote sensing imaging equipment and ground acquisition terminals in the vegetation monitoring area; the growth environment related data also includes real-time meteorological data and real-time soil monitoring data; secondly, the collected growth environment related data from different sources are cleaned and denoised through a preprocessing step to remove redundant and invalid data; then unified formatting is performed to eliminate data inconsistencies caused by differences in device type, data format and timestamp through a standardized algorithm; finally, a growth environment related data set is constructed based on the growth environment related data generated after the formatting process.

[0009] Preferably, the environmental factor analysis module includes an environmental calculation unit and an environmental assessment unit; The environmental calculation unit is used to extract the lower parameters related to meteorology, soil type and terrain based on the growth environment related data set, including the temperature change amplitude Tva, soil moisture content Sfw, light intensity change rate Lir and terrain slope coefficient Tgc, and obtain the interference influence coefficient Eic by calculating the following formula:

[0010] The environmental assessment unit compares and evaluates the preset interference impact threshold Q with the interference impact coefficient Eic, issues an early warning and intervenes; the specific contents are as follows: If the interference impact coefficient Eic≤interference impact threshold Q, it means that the current surrounding environment is normal and there is no interference with the growth of vegetation or the interference is within an acceptable range. At this time, the current data is recorded and a "normal environment" status report is generated; If the interference impact coefficient Eic>interference impact threshold Q, it means that the current surrounding environment is abnormal and the interference to vegetation growth has exceeded the safe range. At this time, intervention measures are taken; including the system automatically generating early warning signals and pushing early warning signals to monitoring terminals and user devices.

[0011] Preferably, the spatial resolution optimization module first deploys high-resolution remote sensing imaging equipment and near-ground image sensors in the vegetation monitoring area to collect multi-dimensional image data of vegetation in the target area, wherein the remote sensing imaging equipment provides a large range of overall image coverage, and the near-ground image sensor captures local high-precision image details; secondly, image super-resolution processing technology is used for fusion to eliminate detail loss and blurring problems caused by data resolution differences; then, based on multi-angle data acquisition technology, the vegetation multi-dimensional image data acquired at different times, angles and devices are integrated, and the spatial detail presentation of the target area is optimized through an angle fusion algorithm; finally, the resolution of the fused vegetation multi-dimensional image data is corrected and adjusted to generate a monitoring image with a uniform resolution.

[0012] Preferably, the dynamic monitoring module includes a health calculation and evaluation unit and a vegetation growth monitoring unit; The health calculation and evaluation unit analyzes the key growth parameters of vegetation, including leaf area Vmj, vegetation coverage Vfg, photosynthetic efficiency Vgh and vegetation canopy surface temperature Vgc, through a feature extraction algorithm based on the multi-dimensional vegetation image data collected by remote sensing imaging equipment and image sensors, and obtains the vegetation health coefficient Vhc by combining the following formula:

[0013] By comparing and evaluating the preset vegetation health threshold W with the vegetation health coefficient Vhc, the following contents are generated: If the vegetation health coefficient Vhc ≥ the vegetation health threshold W, it means that the vegetation growth state is in the healthy range. The system records the current state and marks it as "normal", and monitors the vegetation growth in real time. If the vegetation health coefficient Vhc is less than the vegetation health threshold W, it means that the vegetation growth state is abnormal. At this time, the specific cause of the abnormality should be further analyzed and corresponding measures should be taken.

[0014] Preferably, the vegetation growth monitoring unit works in collaboration with drones by deploying sensors in the vegetation monitoring area. The sensors are used to collect ground growth environment data in real time, while the drones are equipped with multispectral imaging equipment to perform periodic image scanning of the target area to capture multi-dimensional growth image data of the vegetation. The ground growth environment data and the multi-dimensional growth image data are then uploaded to the monitoring platform synchronously, and the dynamic change trends of the continuous ground growth environment data and the multi-dimensional growth image data are analyzed based on time series analysis technology to extract vegetation growth-related data, including vegetation photosynthetic fluctuation rate Pvr, stem thickening rate Scr, root activity Rai, and canopy greenness change rate Cgr.

[0015] Preferably, the growth potential prediction module includes a growth potential calculation unit and a growth potential evaluation unit; The growth potential calculation unit is used to extract the vegetation photosynthetic fluctuation rate Pvr, the stem thickening rate Scr, the root activity Rai and the canopy greenness change rate Cgr, and calculate the growth cycle progress value Gcp in combination with the following formula:

[0016] Preferably, the growth potential assessment unit compares and assesses a preset growth cycle progress threshold R with a growth cycle progress value Gcp to generate the following assessment content: If the growth cycle progress value Gcp ≥ the growth cycle progress threshold R, it means that the growth cycle progress of the vegetation is normal or ahead of schedule; the system marks it as "normal" and records the progress data for subsequent analysis; If the growth cycle progress value Gcp is less than the growth cycle progress threshold R, it means that the growth cycle progress of the vegetation lags behind expectations and there are problems of environmental impact or insufficient management; the system marks it as "abnormal", and further evaluates the causes of the interference, including abnormal photosynthesis efficiency, abnormal soil nutrients, abnormal root development, and canopy damage.

[0017] Preferably, the report generation module is used to conduct a comprehensive analysis of the vegetation health coefficient Vhc and the growth cycle progress value Gcp, and extract key evaluation content, including health status classification, abnormal parameter indicators and growth trend prediction; then, a vegetation growth monitoring report is automatically generated based on the analysis results; wherein the vegetation growth monitoring report content includes the current vegetation health level, growth progress comparison results, the specific location and possible causes of the abnormal area, future growth status prediction trends and intervention measures recommended by the system; then the generated vegetation growth monitoring report is pushed to the environmental monitoring terminal and the user operating system through the cloud platform.

[0018] A method for monitoring vegetation growth data comprises the following steps: Step 1: Collect data related to the vegetation growth environment, including historical meteorological data and historical soil monitoring data; at the same time, pre-process and uniformly format the growth environment data from different sensors, and finally construct a growth environment related data set; Step 2: Analyze the impact of the surrounding environment on the growth of vegetation, including weather, soil type and terrain. Based on the data set related to the growth environment, calculate and evaluate the interference coefficient Eic of the surrounding environment on the growth of vegetation. Finally, issue an early warning and intervene based on the evaluation content. Step 3: Deploy remote sensing imaging equipment and image sensors around vegetation to collect vegetation images, and obtain vegetation multi-dimensional image data in real time, including high-resolution remote sensing images and near-ground image sensor data. Then, perform image super-resolution processing on the vegetation multi-dimensional image data, and adjust the spatial resolution of vegetation monitoring by combining multi-angle data acquisition technology. Step 4: Analyze the multi-dimensional image data of vegetation collected by remote sensing imaging equipment and image sensors, calculate the vegetation health coefficient Vhc, and conduct dynamic evaluation; then, through the combination of sensors and drones, dynamically monitor the growth of vegetation based on time series analysis, and collect relevant data on vegetation growth in real time; Step 5: Analyze the collected vegetation growth-related data and calculate the growth cycle progress value Gcp; by comparing it with the preset growth progress threshold R, predict the growth trend and future growth status of the vegetation to assist in decision-making; Step 6: Receive and analyze the evaluation contents of the vegetation health coefficient Vhc and the growth cycle progress value Gcp in real time, and then generate a vegetation growth monitoring report; finally, push the vegetation growth monitoring report to the environmental monitoring terminal and the user operating system through the cloud platform to provide decision support.

[0019] The present invention provides a vegetation growth data monitoring system and method, which has the following beneficial effects: (1) The vegetation growth data monitoring system and method solve the problem that the current remote sensing technology covers a large area of ​​monitoring but has insufficient spatial resolution through the spatial resolution optimization module; by deploying high-resolution remote sensing imaging equipment in the vegetation monitoring area to work together with near-ground image sensors to collect multi-dimensional image data of the target area; combining image super-resolution processing technology to eliminate the problem of detail loss caused by data resolution differences; using multi-angle data acquisition technology to integrate vegetation multi-dimensional image data acquired at different times, angles and devices, optimizing the spatial detail presentation of the target area through an angle fusion algorithm, and performing resolution correction and adjustment on the fused vegetation multi-dimensional image data to generate high-precision monitoring images with uniform resolution, which can achieve accurate identification of small-scale vegetation changes and comprehensively improve the accuracy and effect of vegetation monitoring.

[0020] (2) The vegetation growth data monitoring system and method solve the formatting and standardization problems of different data sources through the data fusion and standardization module; collect real-time meteorological data and real-time soil monitoring data by deploying environmental sensors, remote sensing imaging equipment and ground acquisition terminals in the vegetation monitoring area, and combine historical meteorological data and historical soil monitoring data to perform unified preprocessing, including data cleaning and denoising to remove redundant data; then eliminate data inconsistencies caused by differences in equipment type, data format and timestamp through a standardized algorithm, and finally construct a growth environment-related data set to provide reliable data support for data analysis in subsequent modules, thereby solving the accuracy and reliability problems of data fusion and analysis.

[0021] (3) The vegetation growth data monitoring system and method solve the complexity and interference problems of environmental factors through the environmental factor analysis module; extract the lower parameters related to meteorology, soil type and terrain, including temperature change amplitude Tva, soil moisture content Sfw, light intensity change rate Lir and terrain slope coefficient Tgc, based on the growth environment related data set through the environmental calculation unit, and obtain the interference influence coefficient Eic by combining the formula calculation; then the interference influence coefficient Eic is evaluated based on the preset interference influence threshold Q through the environmental assessment unit; if Eic is higher than Q, the system generates an early warning signal according to the abnormal parameters and pushes intervention suggestions, including adjusting irrigation frequency and water supply, optimizing soil management and adjusting regional environmental conditions; effectively solve the comprehensive interference analysis and precise management problems of multi-dimensional environmental factors, and provide dynamic environmental optimization support for the healthy growth of vegetation. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a schematic diagram of the framework structure of a vegetation growth data monitoring system of the present invention; Figure 2 The figure is a schematic diagram of the steps of a vegetation growth data monitoring method of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example 1

[0024] See also Figure 1 , the present invention provides a vegetation growth data monitoring system, including a data fusion and standardization module, an environmental factor analysis module, a spatial resolution optimization module, a dynamic monitoring module, a growth prediction module and a report generation module; The data fusion and standardization module is used to collect data related to the growth environment of vegetation, including historical meteorological data and historical soil monitoring data; at the same time, the growth environment related data from different sensors are pre-processed and uniformly formatted, and finally a growth environment related data set is constructed; The environmental factor analysis module is used to analyze the impact of the surrounding environment on the growth of vegetation, including weather, soil type and terrain. Based on the data set related to the growth environment, the interference impact coefficient Eic of the surrounding environment on the growth of vegetation is calculated and evaluated. Finally, an early warning is issued and intervention is carried out according to the evaluation content. The spatial resolution optimization module is used to deploy remote sensing imaging equipment and image sensors around vegetation to collect vegetation images, obtain vegetation multi-dimensional image data in real time, including high-resolution remote sensing images and near-ground image sensor data, and then perform image super-resolution processing on the vegetation multi-dimensional image data, and adjust the spatial resolution of vegetation monitoring in combination with multi-angle data acquisition technology; The dynamic monitoring module is used to analyze the multi-dimensional image data of vegetation collected by remote sensing imaging equipment and image sensors, calculate the vegetation health coefficient Vhc, and perform dynamic evaluation; then, through the combination of sensors and drones, the vegetation growth is dynamically monitored based on time series analysis, and vegetation growth-related data is collected in real time; The growth prediction module is used to analyze the collected vegetation growth-related data and calculate the growth cycle progress value Gcp; by comparing it with the preset growth progress threshold R, the growth trend and future growth status of the vegetation are predicted to assist in decision making; The report generation module is used to receive and analyze the evaluation content of the vegetation health coefficient Vhc and the growth cycle progress value Gcp in real time, and then generate a vegetation growth monitoring report; finally, the vegetation growth monitoring report is pushed to the environmental monitoring terminal and the user operating system through the cloud platform to provide decision support.

[0025] In this embodiment, the data fusion and standardization module collects historical meteorological data and historical soil monitoring data in real time, and combines data cleaning and formatting to build a unified growth environment-related data set to ensure the accuracy and reliability of data fusion; The environmental factor analysis module calculates the interference impact coefficient Eic by extracting the temperature change amplitude Tva, soil moisture content Sfw, light intensity change rate Lir and terrain slope coefficient Tgc, and compares and evaluates it with the preset interference impact threshold Q, so as to timely discover the impact of abnormal environment on vegetation and generate early warning, and provide effective environmental intervention suggestions; The spatial resolution optimization module optimizes the spatial detail presentation of the target area through the collaborative work of high-resolution remote sensing imaging equipment and near-ground image sensors, combined with image super-resolution processing technology and multi-angle data acquisition technology, to generate high-precision vegetation monitoring images with uniform resolution, and achieve accurate monitoring of small-scale vegetation changes; The dynamic monitoring module collects multi-dimensional image data of vegetation in real time through remote sensing imaging equipment and image sensors, and combines drone multi-spectral images with ground sensor data to calculate the vegetation health coefficient Vhc and conduct dynamic evaluation to ensure real-time tracking of vegetation growth; The growth prediction module calculates the growth cycle progress value Gcp by analyzing the vegetation photosynthetic fluctuation rate Pvr, stem thickening rate Scr, root activity Rai and canopy greenness change rate Cgr, and compares it with the growth cycle progress threshold R, accurately predicting the growth trend and future growth status of vegetation, providing support for scientific decision-making; The report generation module generates a monitoring report covering health status classification, growth trend prediction and intervention recommendations by comprehensively analyzing the vegetation health coefficient Vhc and the growth cycle progress value Gcp, and pushes it to the monitoring terminal and user operating system through the cloud platform, providing efficient support for vegetation management. Example 2

[0026] The data fusion and standardization module first monitors and collects vegetation growth environment related data in real time by deploying environmental sensors, remote sensing imaging equipment and ground acquisition terminals in the vegetation monitoring area; the growth environment related data also includes real-time meteorological data and real-time soil monitoring data; secondly, the growth environment related data collected from different sources are cleaned and denoised through a preprocessing step to remove redundant and invalid data; then unified formatting is performed to eliminate data inconsistencies caused by differences in device type, data format and timestamp through a standardized algorithm; finally, a growth environment related data set is constructed based on the growth environment related data generated after the formatting process.

[0027] In this embodiment, by deploying environmental sensors, remote sensing imaging equipment and ground acquisition terminals in the vegetation monitoring area, efficient collection of real-time meteorological data (including temperature, humidity, rainfall, etc.) and real-time soil monitoring data (including soil moisture content, soil nutrient concentration, etc.) is achieved, ensuring the comprehensiveness and timeliness of data sources; redundant and invalid data are removed through data cleaning and denoising, and data quality is improved; through unified formatting processing and standardized algorithms, data inconsistencies caused by differences in device type, data format and timestamp are eliminated, ensuring the fusion accuracy and consistency of multi-source data; the final generated growth environment-related data set provides a unified and reliable high-quality data input for subsequent modules, laying a solid foundation for comprehensive monitoring and analysis of vegetation growth; this module is of great significance in the system, which not only solves the problem of multi-source data fusion, improves the operating efficiency and analysis accuracy of the monitoring system, but also provides solid data support for growth prediction, environmental assessment and intervention decision-making through standardized data management, and is the basic guarantee for the efficient operation of the vegetation growth data monitoring system. Example 3

[0028] The environmental factor analysis module includes an environmental calculation unit and an environmental assessment unit; The environmental calculation unit is used to extract the lower parameters related to meteorology, soil type and terrain based on the growth environment related data set, including the temperature change amplitude Tva, soil moisture content Sfw, light intensity change rate Lir and terrain slope coefficient Tgc, and obtain the interference influence coefficient Eic by calculating the following formula:

[0029] The environmental assessment unit compares and evaluates the preset interference impact threshold Q with the interference impact coefficient Eic, issues an early warning and intervenes; the specific contents are as follows: If the interference impact coefficient Eic≤interference impact threshold Q, it means that the current surrounding environment is normal and there is no interference with the growth of vegetation or the interference is within an acceptable range. At this time, the current data is recorded and a "normal environment" status report is generated; If the interference impact coefficient Eic>interference impact threshold Q, it means that the current surrounding environment is abnormal and the interference to vegetation growth has exceeded the safe range. At this time, intervention measures are taken; including the system automatically generating early warning signals and pushing early warning signals to monitoring terminals and user devices.

[0030] The spatial resolution optimization module first deploys high-resolution remote sensing imaging equipment and near-ground image sensors in the vegetation monitoring area to collect multi-dimensional vegetation image data of the target area, wherein the remote sensing imaging equipment provides a large range of overall image coverage, and the near-ground image sensor captures local high-precision image details; secondly, image super-resolution processing technology is used for fusion to eliminate detail loss and blurring problems caused by data resolution differences; then, based on multi-angle data acquisition technology, the vegetation multi-dimensional image data acquired at different times, angles and devices are integrated, and the spatial detail presentation of the target area is optimized through an angle fusion algorithm; finally, the resolution of the fused vegetation multi-dimensional image data is corrected and adjusted to generate a monitoring image with a uniform resolution.

[0031] The dynamic monitoring module includes a health calculation and evaluation unit and a vegetation growth monitoring unit; The health calculation and evaluation unit analyzes the key growth parameters of vegetation, including leaf area Vmj, vegetation coverage Vfg, photosynthetic efficiency Vgh and vegetation canopy surface temperature Vgc, through a feature extraction algorithm based on the multi-dimensional vegetation image data collected by remote sensing imaging equipment and image sensors, and obtains the vegetation health coefficient Vhc by combining the following formula:

[0032] By comparing and evaluating the preset vegetation health threshold W with the vegetation health coefficient Vhc, the following contents are generated: If the vegetation health coefficient Vhc ≥ the vegetation health threshold W, it means that the vegetation growth state is in the healthy range. The system records the current state and marks it as "normal", and monitors the vegetation growth in real time. If the vegetation health coefficient Vhc is less than the vegetation health threshold W, it means that the vegetation growth state is abnormal. At this time, the specific cause of the abnormality should be further analyzed and corresponding measures should be taken.

[0033] The vegetation growth monitoring unit works in collaboration with drones by deploying sensors in the vegetation monitoring area. The sensors are used to collect ground growth environment data in real time, while the drones are equipped with multispectral imaging equipment to perform periodic image scanning of the target area to capture multi-dimensional growth image data of the vegetation. The ground growth environment data and the multi-dimensional growth image data are then uploaded to the monitoring platform synchronously. The dynamic change trends of the continuous ground growth environment data and the multi-dimensional growth image data are analyzed based on time series analysis technology to extract vegetation growth-related data, including vegetation photosynthetic fluctuation rate Pvr, stem thickening rate Scr, root activity Rai, and canopy greenness change rate Cgr.

[0034] The growth potential prediction module includes a growth potential calculation unit and a growth potential evaluation unit; The growth potential calculation unit is used to extract the vegetation photosynthetic fluctuation rate Pvr, the stem thickening rate Scr, the root activity Rai and the canopy greenness change rate Cgr, and calculate the growth cycle progress value Gcp in combination with the following formula:

[0035] The growth potential assessment unit compares and assesses the preset growth cycle progress threshold R with the growth cycle progress value Gcp to generate the following assessment content: If the growth cycle progress value Gcp ≥ the growth cycle progress threshold R, it means that the growth cycle progress of the vegetation is normal or ahead of schedule; the system marks it as "normal" and records the progress data for subsequent analysis; If the growth cycle progress value Gcp is less than the growth cycle progress threshold R, it means that the growth cycle progress of the vegetation lags behind expectations and there are problems of environmental impact or insufficient management; the system marks it as "abnormal", and further evaluates the causes of the interference, including abnormal photosynthesis efficiency, abnormal soil nutrients, abnormal root development, and canopy damage.

[0036] In this embodiment, through the collaborative work of each module, accurate monitoring, dynamic analysis and scientific decision-making support of vegetation growth are achieved; the environmental factor analysis module extracts the temperature change amplitude Tva, soil moisture content Sfw, light intensity change rate Lir and terrain slope coefficient Tgc through the environmental calculation unit, and uses the formula to calculate the interference influence coefficient Eic to quantify the interference degree of environmental factors on vegetation growth, and compares and evaluates with the preset interference influence threshold Q through the environmental assessment unit, generates environmental warnings and puts forward intervention suggestions to ensure the dynamic optimization of the vegetation growth environment; the spatial resolution optimization module collects multi-dimensional image data through high-resolution remote sensing imaging equipment and near-ground image sensors, and combines image super-resolution processing technology to optimize monitoring accuracy, eliminate the problem of detail loss caused by resolution differences, generate high-precision monitoring images with uniform resolution, and achieve accurate monitoring of small-scale vegetation changes; The dynamic monitoring module calculates the vegetation health coefficient Vhc and performs dynamic evaluation based on the feature extraction of leaf area Vmj, vegetation coverage Vfg, photosynthetic efficiency Vgh and vegetation canopy surface temperature Vgc through the health calculation and evaluation unit, and compares it with the preset vegetation health threshold W to determine the health status of the vegetation. If it is abnormal, the specific parameter problem is analyzed and optimization measures are proposed. At the same time, the vegetation growth monitoring unit combines the vegetation photosynthetic fluctuation rate Pvr, stem thickening rate Scr, root activity Rai and canopy greenness change rate Cgr and other dynamic data collected by sensors and drones to analyze the vegetation growth trend; The growth prediction module calculates the growth cycle progress value Gcp through the growth calculation unit, compares it with the growth cycle progress threshold R, and generates a vegetation growth status assessment and predicts future trends through the growth assessment unit. If the growth cycle progress value Gcp is lower than the threshold R, the cause of the interference is located and the management plan is optimized. Through the dynamic collection, calculation and evaluation of each subordinate parameter, the system can fully grasp the vegetation growth status, accurately diagnose problems and provide scientific intervention and management suggestions, which is an important basis for realizing intelligent monitoring and optimized management of vegetation growth. Example 4

[0037] The report generation module is used to conduct a comprehensive analysis of the vegetation health coefficient Vhc and the growth cycle progress value Gcp, and extract key evaluation content, including health status classification, abnormal parameter indicators, and growth trend prediction; then, a vegetation growth monitoring report is automatically generated based on the analysis results; wherein the vegetation growth monitoring report includes the current vegetation health level, growth progress comparison results, the specific location and possible causes of the abnormal area, the future growth status prediction trend, and the intervention measures recommended by the system; then, the generated vegetation growth monitoring report is pushed to the environmental monitoring terminal and the user operating system through the cloud platform.

[0038] In this embodiment, the report generation module comprehensively analyzes the vegetation health coefficient Vhc and the growth cycle progress value Gcp, comprehensively extracts key evaluation contents such as health status classification, abnormal parameter indicators and growth trend prediction, and realizes systematic summary and intuitive display of vegetation growth status; through the automatically generated vegetation growth monitoring report, it covers the current vegetation health level, growth progress comparison results, the specific location and possible causes of abnormal areas, future growth status prediction trends and intervention measures recommended by the system, providing users with a comprehensive and detailed decision-making reference; the monitoring report is pushed to the environmental monitoring terminal and the user operating system in real time through the cloud platform, ensuring the efficient transmission and transparent management of vegetation growth information; the significance of this module in the system is reflected in the closed loop of data analysis and report generation, which improves the intelligence level of the system and user experience, so that the vegetation monitoring results can be presented in an intuitive and easy-to-understand form, and quickly locates problems and puts forward optimization suggestions, ensuring that users can take scientific decisions and management measures in time, and ultimately achieve accurate control and dynamic optimization of vegetation growth status. Example 5

[0039] See also Figure 2 , a vegetation growth data monitoring method, comprising the following steps: Step 1: Collect data related to the vegetation growth environment, including historical meteorological data and historical soil monitoring data; at the same time, pre-process and uniformly format the growth environment data from different sensors, and finally construct a growth environment related data set; Step 2: Analyze the impact of the surrounding environment on the growth of vegetation, including weather, soil type and terrain. Based on the data set related to the growth environment, calculate and evaluate the interference coefficient Eic of the surrounding environment on the growth of vegetation. Finally, issue an early warning and intervene based on the evaluation content. Step 3: Deploy remote sensing imaging equipment and image sensors around vegetation to collect vegetation images, and obtain vegetation multi-dimensional image data in real time, including high-resolution remote sensing images and near-ground image sensor data. Then, perform image super-resolution processing on the vegetation multi-dimensional image data, and adjust the spatial resolution of vegetation monitoring by combining multi-angle data acquisition technology. Step 4: Analyze the multi-dimensional image data of vegetation collected by remote sensing imaging equipment and image sensors, calculate the vegetation health coefficient Vhc, and conduct dynamic evaluation; then, through the combination of sensors and drones, dynamically monitor the growth of vegetation based on time series analysis, and collect relevant data on vegetation growth in real time; Step 5: Analyze the collected vegetation growth-related data and calculate the growth cycle progress value Gcp; by comparing it with the preset growth progress threshold R, predict the growth trend and future growth status of the vegetation to assist in decision-making; Step 6: Receive and analyze the evaluation contents of the vegetation health coefficient Vhc and the growth cycle progress value Gcp in real time, and then generate a vegetation growth monitoring report; finally, push the vegetation growth monitoring report to the environmental monitoring terminal and the user operating system through the cloud platform to provide decision support.

[0040] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A vegetation growth data monitoring system, characterized by: It includes data fusion and standardization module, environmental factor analysis module, spatial resolution optimization module, dynamic monitoring module, growth prediction module and report generation module; The data fusion and standardization module is used to collect data related to the growth environment of vegetation, including historical meteorological data and historical soil monitoring data; at the same time, the growth environment related data from different sensors are pre-processed and uniformly formatted, and finally a growth environment related data set is constructed; The environmental factor analysis module is used to analyze the impact of the surrounding environment on the growth of vegetation, including weather, soil type and terrain. Based on the data set related to the growth environment, the interference impact coefficient Eic of the surrounding environment on the growth of vegetation is calculated and evaluated. Finally, an early warning is issued and intervention is carried out according to the evaluation content. The spatial resolution optimization module is used to deploy remote sensing imaging equipment and image sensors around vegetation to collect vegetation images, obtain vegetation multi-dimensional image data in real time, including high-resolution remote sensing images and near-ground image sensor data, and then perform image super-resolution processing on the vegetation multi-dimensional image data, and adjust the spatial resolution of vegetation monitoring in combination with multi-angle data acquisition technology; The dynamic monitoring module is used to analyze the multi-dimensional image data of vegetation collected by remote sensing imaging equipment and image sensors, calculate the vegetation health coefficient Vhc, and perform dynamic evaluation; then, through the combination of sensors and drones, the vegetation growth is dynamically monitored based on time series analysis, and vegetation growth-related data is collected in real time; The growth prediction module is used to analyze the collected vegetation growth-related data and calculate the growth cycle progress value Gcp; by comparing it with the preset growth progress threshold R, the growth trend and future growth status of the vegetation are predicted to assist in decision making; The report generation module is used to receive and analyze the evaluation content of the vegetation health coefficient Vhc and the growth cycle progress value Gcp in real time, and then generate a vegetation growth monitoring report; finally, the vegetation growth monitoring report is pushed to the environmental monitoring terminal and the user operating system through the cloud platform to provide decision support.

2. The vegetation growth data monitoring system according to claim 1, characterized in that: The data fusion and standardization module first monitors and collects vegetation growth environment related data in real time by deploying environmental sensors, remote sensing imaging equipment and ground acquisition terminals in the vegetation monitoring area; the growth environment related data also includes real-time meteorological data and real-time soil monitoring data; secondly, the growth environment related data collected from different sources are cleaned and denoised through a preprocessing step to remove redundant and invalid data; then unified formatting is performed to eliminate data inconsistencies caused by differences in device type, data format and timestamp through a standardized algorithm; finally, a growth environment related data set is constructed based on the growth environment related data generated after the formatting process.

3. The vegetation growth data monitoring system according to claim 2, characterized in that: The environmental factor analysis module includes an environmental calculation unit and an environmental assessment unit; The environmental calculation unit is used to extract the lower parameters related to meteorology, soil type and terrain based on the growth environment related data set, including the temperature change amplitude Tva, soil moisture content Sfw, light intensity change rate Lir and terrain slope coefficient Tgc, and obtain the interference influence coefficient Eic by calculating the following formula: ； The environmental assessment unit compares and evaluates the preset interference impact threshold Q with the interference impact coefficient Eic, issues an early warning and intervenes; the specific contents are as follows: If the interference impact coefficient Eic≤interference impact threshold Q, it means that the current surrounding environment is normal and there is no interference with the growth of vegetation or the interference is within an acceptable range. At this time, the current data is recorded and a "normal environment" status report is generated; If the interference impact coefficient Eic>interference impact threshold Q, it means that the current surrounding environment is abnormal and the interference to vegetation growth has exceeded the safe range. At this time, intervention measures are taken; including the system automatically generating early warning signals and pushing early warning signals to monitoring terminals and user devices.

4. The vegetation growth data monitoring system according to claim 3, characterized in that: The spatial resolution optimization module first deploys high-resolution remote sensing imaging equipment and near-ground image sensors in the vegetation monitoring area to collect multi-dimensional vegetation image data in the target area, wherein the remote sensing imaging equipment provides a large-scale overall image coverage, and the near-ground image sensor captures local high-precision image details; Secondly, image super-resolution processing technology is used for fusion to eliminate the problem of detail loss and blur caused by differences in data resolution. Subsequently, based on multi-angle data acquisition technology, the multi-dimensional image data of vegetation acquired at different times, angles and devices are integrated, and the spatial detail presentation of the target area is optimized through the angle fusion algorithm. Finally, the resolution of the fused multi-dimensional image data of vegetation is corrected and adjusted to generate a monitoring image with uniform resolution.

5. The vegetation growth data monitoring system according to claim 4, characterized in that: The dynamic monitoring module includes a health calculation and evaluation unit and a vegetation growth monitoring unit; The health calculation and evaluation unit analyzes the key growth parameters of vegetation, including leaf area Vmj, vegetation coverage Vfg, photosynthetic efficiency Vgh and vegetation canopy surface temperature Vgc, through a feature extraction algorithm based on the multi-dimensional vegetation image data collected by remote sensing imaging equipment and image sensors, and obtains the vegetation health coefficient Vhc by combining the following formula: ； By comparing and evaluating the preset vegetation health threshold W with the vegetation health coefficient Vhc, the following contents are generated: If the vegetation health coefficient Vhc ≥ the vegetation health threshold W, it means that the vegetation growth state is in the healthy range. The system records the current state and marks it as "normal", and monitors the vegetation growth in real time. If the vegetation health coefficient Vhc is less than the vegetation health threshold W, it means that the vegetation growth state is abnormal. At this time, the specific cause of the abnormality should be further analyzed and corresponding measures should be taken.

6. The vegetation growth data monitoring system according to claim 5, characterized in that: The vegetation growth monitoring unit works in collaboration with drones by deploying sensors in the vegetation monitoring area. The sensors are used to collect ground growth environment data in real time, while the drones are equipped with multispectral imaging equipment to perform periodic image scanning of the target area to capture multi-dimensional growth image data of the vegetation. The ground growth environment data and the multi-dimensional growth image data are then uploaded to the monitoring platform synchronously. The dynamic change trends of the continuous ground growth environment data and the multi-dimensional growth image data are analyzed based on time series analysis technology to extract vegetation growth-related data, including vegetation photosynthetic fluctuation rate Pvr, stem thickening rate Scr, root activity Rai, and canopy greenness change rate Cgr.

7. The vegetation growth data monitoring system according to claim 6, characterized in that: The growth potential prediction module includes a growth potential calculation unit and a growth potential evaluation unit; The growth potential calculation unit is used to extract the vegetation photosynthetic fluctuation rate Pvr, the stem thickening rate Scr, the root activity Rai and the canopy greenness change rate Cgr, and calculate the growth cycle progress value Gcp in combination with the following formula: 。 8. The vegetation growth data monitoring system according to claim 7, characterized in that: The growth potential assessment unit compares and assesses the preset growth cycle progress threshold R with the growth cycle progress value Gcp to generate the following assessment content: If the growth cycle progress value Gcp≥growth cycle progress threshold R, it means that the growth cycle progress of the vegetation is normal or ahead of schedule; the system marks it as "normal" and records the progress data for subsequent analysis; If the growth cycle progress value Gcp is less than the growth cycle progress threshold R, it means that the growth cycle progress of the vegetation lags behind expectations and there are problems of environmental impact or insufficient management; the system marks it as "abnormal" and further evaluates the causes of the disturbance, including abnormal photosynthesis efficiency, abnormal soil nutrients, abnormal root development, and canopy damage.

9. The vegetation growth data monitoring system according to claim 8, characterized in that: The report generation module is used to conduct a comprehensive analysis of the vegetation health coefficient Vhc and the growth cycle progress value Gcp, and extract key evaluation contents, including health status classification, abnormal parameter indicators and growth trend prediction; Subsequently, a vegetation growth monitoring report is automatically generated based on the analysis results; the content of the vegetation growth monitoring report includes the current vegetation health level, growth progress comparison results, the specific location and possible causes of abnormal areas, future growth status prediction trends and intervention measures recommended by the system; the generated vegetation growth monitoring report is then pushed to the environmental monitoring terminal and user operating system through the cloud platform.

10. A vegetation growth data monitoring method, applied to a vegetation growth data monitoring system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Collect data related to the vegetation growth environment, including historical meteorological data and historical soil monitoring data; at the same time, pre-process and uniformly format the growth environment data from different sensors, and finally construct a growth environment related data set; Step 2: Analyze the impact of the surrounding environment on the growth of vegetation, including weather, soil type and terrain. Based on the data set related to the growth environment, calculate and evaluate the interference coefficient Eic of the surrounding environment on the growth of vegetation. Finally, issue an early warning and intervene based on the evaluation content. Step 3: Deploy remote sensing imaging equipment and image sensors around vegetation to collect vegetation images, and obtain vegetation multi-dimensional image data in real time, including high-resolution remote sensing images and near-ground image sensor data. Then, perform image super-resolution processing on the vegetation multi-dimensional image data, and adjust the spatial resolution of vegetation monitoring by combining multi-angle data acquisition technology. Step 4: Analyze the multi-dimensional image data of vegetation collected by remote sensing imaging equipment and image sensors, calculate the vegetation health coefficient Vhc, and conduct dynamic evaluation; then, through the combination of sensors and drones, dynamically monitor the growth of vegetation based on time series analysis, and collect relevant data on vegetation growth in real time; Step 5: Analyze the collected vegetation growth-related data and calculate the growth cycle progress value Gcp; by comparing it with the preset growth progress threshold R, predict the growth trend and future growth status of the vegetation to assist in decision-making; Step 6: Receive and analyze the evaluation contents of the vegetation health coefficient Vhc and the growth cycle progress value Gcp in real time, and then generate a vegetation growth monitoring report; finally, push the vegetation growth monitoring report to the environmental monitoring terminal and the user operating system through the cloud platform to provide decision support.

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