Vegetation Cover Meter

Abstract

The present invention discloses a vegetation coverage measuring instrument, which is used in the field of ecological monitoring, and includes a base plate, a support rod is arranged at the top of the base plate, a controller is arranged at one side of the middle of the support rod, a connecting side rod is arranged at one side of the top of the support rod, a fixing rod is arranged at the bottom end of the connecting side rod, and a measuring instrument body is arranged at the bottom end of the fixing rod; the controller is connected to the measuring instrument body by communication, and the controller includes a data acquisition module, a data processing module, a communication and interface module, an energy and flow management module and a data visualization module. The present invention adopts a dual logistic function fitting method and a dynamic threshold method, and by setting a critical threshold, it can evaluate whether the current ecological state deviates from the normal range and timely identify the potential risks of vegetation growth.

Description

Technical Field

[0001] The present invention relates to the field of ecological monitoring, and in particular to a vegetation coverage measuring instrument. Background Art

[0002] A vegetation cover meter is a tool specifically used to assess and monitor the degree of plant coverage. This instrument usually uses various sensing technologies, such as optical or remote sensing technology, to measure the proportion of ground vegetation coverage to the total surface area. A vegetation cover meter can be a ground-based device or a sensor carried by an unmanned aerial vehicle or satellite. It is used to collect data and process it through image analysis software to generate key ecological parameters such as vegetation cover, NDVI index (vegetation health indicator) and vegetation phenology (seasonal growth characteristics). This type of equipment is of great value to environmental monitoring, agricultural management and ecological research, and can effectively improve the efficiency and accuracy of data collection while reducing the impact of human intervention.

[0003] Traditional vegetation growth simulation methods fail to accurately simulate the different stages of vegetation growth, especially during the rapid growth period and the late saturation period, which can lead to misunderstandings of vegetation ecological responses and inappropriate formulation of management strategies; existing technologies lack the ability to collect and analyze environmental data in a timely manner, especially under extreme climatic conditions. This slow response can lead to insufficient responses to sudden environmental events, thereby affecting the health of vegetation and the entire ecosystem. Due to the lack of the ability to dynamically adjust working modes, traditional systems cannot effectively adapt to changing operating conditions or environmental requirements, resulting in unstable equipment operation or system failures.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In order to overcome the above problems, the present invention aims to propose a vegetation cover measuring instrument, which aims to solve the problem that the existing technology lacks the ability to collect and analyze environmental data in a timely manner. Especially under extreme climatic conditions, this slow response will lead to insufficient response to sudden environmental events, thereby affecting the health of vegetation and the entire ecosystem. Due to the lack of the ability to dynamically adjust the working mode, the traditional system cannot effectively adapt to changing operating conditions or environmental requirements, resulting in unstable equipment operation or system failure.

[0006] To this end, the specific technical solution adopted by the present invention is as follows:

[0007] The vegetation coverage measuring instrument comprises a bottom plate, a support rod is arranged at the top of the bottom plate, a controller is arranged at one side of the middle of the support rod, a connecting side rod is arranged at one side of the top of the support rod, a fixing rod is arranged at the bottom end of the connecting side rod, and a measuring instrument body is arranged at the bottom end of the fixing rod;

[0008] The controller is communicatively connected with the measuring instrument body, and the controller includes a data acquisition module, a data processing module, a communication and interface module, an energy and flow management module and a data visualization module;

[0009] Among them, the data acquisition module is used to obtain vegetation parameters and environmental parameters collected by the measuring instrument body;

[0010] The data processing module is used to analyze the collected vegetation parameters and environmental parameters using the dual logistic function fitting method and the dynamic threshold method, and calculate various ecological and environmental indicators;

[0011] Communication and interface module, used to upload calculation results to big data cloud service platform and mobile application;

[0012] Energy and traffic management module, which monitors power and traffic usage and intelligently adjusts the working mode according to energy status and transmission requirements;

[0013] Data visualization module is used to provide a visual display interface for information input and output.

[0014] Optionally, the measuring instrument body includes a mounting base arranged at the bottom end of the fixed rod, a data sensing component is arranged at the bottom end of the mounting base, and the data sensing component is composed of a front optical zoom lens, a front near-infrared lens, a top-down wide-angle lens and a rear fixed-focus lens.

[0015] Optionally, when the data acquisition module acquires the vegetation parameters and environmental parameters collected by the measuring instrument body, it includes:

[0016] Get a front optical zoom lens to capture the vegetation image in front;

[0017] Acquire the near-infrared image taken by the front near-infrared lens to obtain the near-infrared reflection information of the vegetation;

[0018] Get a wide-angle lens to capture vegetation coverage over a wider area;

[0019] Get a rear fixed focal length lens to capture images of the sky area;

[0020] The vegetation image in front, the near-infrared reflectance information of the vegetation, the vegetation image data within a wide-angle range and the sky area image are integrated to form a complete parameter data set of vegetation parameters and environmental parameters.

[0021] Optionally, the data processing module includes the following steps when analyzing the collected vegetation parameters and environmental parameters using the dual logistic function fitting method and the dynamic threshold method and calculating various ecological indicators and environmental indicators:

[0022] Obtain parameter data sets of vegetation parameters and environmental parameters;

[0023] According to the ecological cycle and growth changes of vegetation, the parameter data set is segmented;

[0024] Based on the segmented parameter data set, a dual logistic function model is constructed, and the model parameters are continuously optimized until the dual logistic function model structure performs best on the validation data set;

[0025] Determine critical thresholds for analyzing vegetation and environmental parameters based on real-time data and predetermined ecological indicators;

[0026] The optimized dual logistic function model was used in combination with critical thresholds to calculate key ecological and environmental indicators.

[0027] Optionally, segmenting the parameter data set according to the ecological cycle and growth changes of the vegetation includes:

[0028] Analyze parameter data sets and identify variables in the parameter data sets that affect vegetation growth;

[0029] The ecological cycle of vegetation is determined using historical data, and data segmentation criteria are set based on the ecological cycle of vegetation and the variables of vegetation growth, and the parameter data set is divided into multiple time windows according to seasons and growth stages;

[0030] According to the set time window and growth stage as the cutting point, the parameter data set is cut into multiple subsets, where the subsets represent specific ecological cycles;

[0031] Label each cut subset and identify the ecological cycle stage and growth stage corresponding to the current subset.

[0032] Optionally, the optimized dual logistic function model is used in combination with critical thresholds to calculate key ecological and environmental indicators including:

[0033] Obtain critical thresholds of vegetation and environmental parameters under different ecological indicators;

[0034] The latest vegetation and environmental parameter data were input into the optimized dual logistic function model;

[0035] The dual logistic function model is used to process the current data, predict the vegetation status, and output the predicted value of the ecological response;

[0036] Compare the predicted value with the pre-set critical threshold to assess whether the ecological status is normal. If the predicted value exceeds the critical threshold, it is judged that there is a risk;

[0037] Calculate ecological indicators based on model predictions and analysis results of critical thresholds;

[0038] Comprehensively analyze ecological indicators and generate comprehensive reports.

[0039] Optionally, the ecological indicators include ecological stress index, normalized health index and rate of change;

[0040] The expression of ecological pressure index is:

[0041]

[0042] The expression of normalized health index is:

[0043]

[0044] The expression for the rate of change is:

[0045]

[0046] In the formula, A represents the ecological pressure index;

[0047] B represents the normalized health index;

[0048] C represents the rate of change;

[0049] y(t) represents the predicted vegetation cover at time t;

[0050] θ represents the critical threshold.

[0051] Optionally, the dual logistic function model is used to process the current data, predict the vegetation state, and output the predicted value of the ecological response including:

[0052] Get current vegetation parameters and environmental parameters;

[0053] Define the form of the bi-logistic function model;

[0054] Based on historical vegetation parameters and historical environmental parameters, the least square method was used to evaluate the parameters of the dual logistic function model.

[0055] Input the current environmental parameters into the verified model, and predict the vegetation status under each environmental parameter;

[0056] The prediction results of all parameters are integrated to output the predicted value of ecological response;

[0057] Among them, the expression of the dual logistic function model is:

[0058]

[0059] In the formula, L1 and L2 represent the maximum values ​​reached by vegetation;

[0060] k1 and k2 represent growth rate parameters;

[0061] x1 and x2 represent the two turning points of the logistic function model;

[0062] f(x) represents the predicted value of the bilogistic function model;

[0063] x represents the independent variable.

[0064] Optionally, the energy and traffic management module includes: monitoring power and traffic usage and intelligently adjusting the working mode according to energy status and transmission requirements;

[0065] Continuously monitor and collect energy generation data of the power supply system and energy consumption data of the meter itself;

[0066] Analyze energy consumption data to determine energy consumption patterns, demand peaks, and flow trends;

[0067] Assess current energy reserve status and flow requirements;

[0068] Intelligently formulate energy adjustment strategies based on energy status and predicted demand changes;

[0069] Implement the developed energy adjustment strategies and monitor the effects after implementation.

[0070] Optionally, analyzing energy consumption data to determine energy consumption patterns, demand peaks, and flow rate trends include:

[0071] Obtain energy consumption data and explore seasonality and trends in energy consumption data through time series analysis;

[0072] Use machine learning algorithms to identify different energy consumption patterns in energy consumption data, both seasonal and trend, and detect demand peaks through statistical methods;

[0073] Based on the analysis results, the data is segmented;

[0074] Fit trend lines to segmented data, analyze cyclical patterns, and separate seasonal components from energy consumption data.

[0075] Compared with the prior art, this application has the following beneficial effects:

[0076] 1. The present invention adopts a dual logistic function fitting method and a dynamic threshold method, which allows accurate modeling of vegetation growth patterns, especially the simulation of the two stages of rapid growth and late saturation period, which is suitable for coping with the complexity of different ecological environments and seasonal changes. By segmenting the data according to the ecological cycle and growth changes of the vegetation, the time series data can be observed and analyzed more carefully, making the ecological model more in line with the actual growth situation; the data collected in real time and instantly predicted through the optimized model are crucial for rapid response to environmental changes and adjustment of management measures, and are particularly suitable for extreme climatic conditions; by setting the critical threshold, it is possible to assess whether the current ecological state deviates from the normal range and promptly identify the potential risks of vegetation growth.

[0077] 2. The present invention realizes real-time monitoring of energy generation and consumption data through sensors connected to the power supply system and measuring instruments, providing key real-time information for energy management and ensuring the stability of operation; by using time series analysis and machine learning algorithms, such as decision trees and neural networks, not only seasonality and trend patterns in energy consumption can be identified, but also demand peaks can be detected. Based on the evaluation of current energy reserves and flow requirements, as well as predicted changes in demand, the module can intelligently formulate energy adjustment strategies, such as demand response management and energy storage scheduling, to improve energy efficiency and reduce waste, while ensuring sufficient energy supply during high-demand periods.

[0078] 3. By implementing intelligent adjustment strategies, the present invention enables the module to dynamically adjust the working status and energy supply of the equipment, optimize load distribution according to actual needs, and ensure that it always operates in the best state. Continuous performance monitoring and feedback loops are the key to optimizing energy use and preventing system failures, ensuring the reliability of the strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] The above characteristics, features and advantages of the present invention and their implementation methods and methods will become more clearly understood in conjunction with the following description of the embodiments, which are described in detail in conjunction with the accompanying drawings. Herein, a schematic diagram is shown:

[0080] Figure 1 is a structural schematic diagram of a vegetation coverage measuring instrument according to an embodiment of the present invention;

[0081] Figure 2 yes Figure 1 A partial enlarged view of the middle A;

[0082] Figure 3 is a schematic structural diagram of a measuring instrument body in a vegetation coverage measuring instrument according to an embodiment of the present invention;

[0083] Figure 4 is a schematic structural diagram of another angle of the measuring instrument body in the vegetation coverage measuring instrument according to an embodiment of the present invention;

[0084] Figure 5 4 is a principle block diagram of a vegetation coverage measuring instrument according to an embodiment of the present invention.

[0085] In the figure:

[0086] 1. Base plate; 2. Support rod; 3. Controller; 301. Data acquisition module; 302. Data processing module; 303. Communication and interface module; 304. Energy and flow management module; 305. Data visualization module; 4. Connecting side rod; 5. Fixing rod; 6. Measuring instrument body; 601. Mounting seat; 602. Front optical zoom lens; 603. Front near-infrared lens; 604. Overhead wide-angle lens; 605. Rear fixed-focus lens. DETAILED DESCRIPTION

[0087] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0088] According to an embodiment of the present invention, a vegetation coverage measuring instrument is provided.

[0089] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1-Figure 5 As shown, the vegetation coverage measuring instrument according to the embodiment of the present invention comprises a base plate 1, characterized in that a support rod 2 is arranged at the top of the base plate 1, a controller 3 is arranged at one side of the middle of the support rod 2, a connecting side rod 4 is arranged at one side of the top of the support rod 2, a fixing rod 5 is arranged at the bottom end of the connecting side rod 4, and a measuring instrument body 6 is arranged at the bottom end of the fixing rod 5;

[0090] Preferably, the measuring instrument body 6 includes a mounting base 601 arranged at the bottom end of the fixed rod 5, and a data sensing component is arranged at the bottom end of the mounting base 601, and the data sensing component is composed of a front optical zoom lens 602, a front near-infrared lens 603, a top-down wide-angle lens 604 and a rear fixed focal length lens 605.

[0091] The vegetation cover meter includes a set of multi-lens front-end monitoring equipment, a big data cloud service platform and a vegetation cover analysis application for mobile terminals; the vegetation cover meter displays monitoring data of ecological factors and habitat status through a comprehensive interface, and supports real-time analysis of vegetation cover, NDVI index and vegetation phenological characteristics.

[0092] Preferably, the data acquisition module 301 includes:

[0093] The front optical zoom lens 602 is used to capture the vegetation image in front;

[0094] Acquire a near-infrared image taken by the front near-infrared lens 603 to obtain near-infrared reflection information of vegetation;

[0095] Obtain a wide-angle lens 604 positioned downward to capture vegetation coverage in a wider range;

[0096] Obtain an image of the sky area captured by the rear fixed focal length lens 605;

[0097] The vegetation image in front, the near-infrared reflectance information of the vegetation, the vegetation image data within a wide-angle range and the sky area image are integrated to form a complete parameter data set of vegetation parameters and environmental parameters.

[0098] It should be explained that the front optical zoom lens 602 is installed in front of the measuring instrument and can focus on objects at different distances, thereby capturing high-definition images of the vegetation in front. The optical zoom lens can adjust the focal length by physically moving the lens, which allows the user to zoom in or out the image in the field of view without changing the camera position, which is suitable for obtaining details of vegetation at different distances.

[0099] The front near-infrared lens 603 is specially used to capture images in the near-infrared band. This type of image can display the near-infrared reflection information of vegetation and is usually used to analyze the health status of vegetation. Near-infrared images are made using the reflection characteristics of vegetation to near-infrared light. When vegetation is healthy, it will reflect more near-infrared light, while sick or dead plants will reflect less, which helps to identify the growth of vegetation.

[0100] The overhead wide-angle lens 604 is installed on the top of the measuring instrument. Looking down, it can cover a wider ground area and capture the coverage of vegetation. The wide-angle lens has a wide viewing angle and is suitable for shooting large areas. The overall coverage of vegetation can be quickly obtained through the wide-angle lens, which is very important for environmental monitoring and vegetation distribution research.

[0101] The rear fixed focal length lens 605 is set at the rear of the measuring instrument to capture the image of the sky area behind it, which helps to record weather conditions and lighting conditions. The fixed focal length lens provides a certain depth of field and image clarity, which is very suitable for capturing constant scenes. By analyzing the cloud layer and light intensity in the sky, additional data support can be provided for environmental monitoring.

[0102] The image data captured by all lenses are integrated through the data acquisition module, and the data from different lenses are analyzed and matched by software to form a vegetation and environmental parameter data set containing comprehensive information. Data integration usually involves image processing and data fusion technology, and data docking is optimized through algorithms to ensure that data from different lenses and sensors can complement each other and provide more comprehensive environmental analysis results.

[0103] The controller 3 is connected to the measuring instrument body 6 in communication, and the controller 3 includes a data acquisition module 301, a data processing module 302, a communication and interface module 303, an energy and flow management module 304 and a data visualization module 305;

[0104] The data acquisition module 301 is used to obtain vegetation parameters and environmental parameters collected by the measuring instrument body 6 .

[0105] It should be explained that the data acquisition module 301 integrates the data from each lens and uses specialized software and algorithms to process and analyze the images, such as image registration, data fusion, etc., to form a comprehensive vegetation and environmental parameter data set. Data fusion is the process of combining data from multiple sensors into a unified information.

[0106] The data processing module 302 is used to analyze the collected vegetation parameters and environmental parameters using the dual logistic function fitting method and the dynamic threshold method, and calculate various ecological indicators and environmental indicators.

[0107] Preferably, the data processing module 302 includes the following steps when analyzing the collected vegetation parameters and environmental parameters using the dual logistic function fitting method and the dynamic threshold method and calculating the ecological indicators and environmental indicators:

[0108] Obtain parameter datasets of vegetation parameters and environmental parameters.

[0109] According to the ecological cycle and growth changes of vegetation, the parameter data set is segmented.

[0110] Preferably, segmenting the parameter data set according to the ecological cycle and growth changes of the vegetation includes:

[0111] Analyze parameter data sets and identify variables in the parameter data sets that affect vegetation growth;

[0112] The ecological cycle of vegetation is determined using historical data, and data segmentation criteria are set based on the ecological cycle of vegetation and the variables of vegetation growth, and the parameter data set is divided into multiple time windows according to seasons and growth stages;

[0113] According to the set time window and growth stage as the cutting point, the parameter data set is cut into multiple subsets, where the subsets represent specific ecological cycles;

[0114] Label each cut subset and identify the ecological cycle stage and growth stage corresponding to the current subset.

[0115] It should be explained that the parameter data set of the vegetation parameters and environmental parameters that have been integrated and pre-processed is obtained from the data acquisition module 301; the parameter data set includes images and measurements collected from different lenses and sensors, and the parameter data set usually contains information on various biological and non-biological factors, such as light, humidity, vegetation coverage, etc., which are key indicators for assessing ecological health and monitoring environmental changes; analyzing the ecological cycle and growth changes of vegetation involves statistical analysis of time series data to identify key growth stages or periodic changes, and then segmenting the data according to these cycles and stages to create data subsets for specific ecological stages. Ecological cycle analysis helps understand how vegetation responds to different seasons and environmental conditions, which is very important for predicting the future growth performance of vegetation and managing ecosystems. By analyzing trends and patterns in historical data sets, the typical ecological cycle of vegetation is determined; it includes using statistical and machine learning methods to identify periodic characteristics and key variables, and according to the ecological cycle The data were then divided into multiple time windows corresponding to different seasons or growth stages. Time window technology is a common method for processing time series data. By focusing on a specific time period, the ecological response and environmental changes of vegetation can be more effectively analyzed and interpreted. Each data subset will be labeled in detail, including its corresponding ecological cycle stage and growth stage, such as spring germination, summer growth, autumn maturity, etc.; the labeling helps to accurately identify and understand the ecological significance of each subset in subsequent analysis. This step involves applying the dual logistic function fitting method and the dynamic threshold method to analyze each data subset. The dual logistic function fitting method is used to simulate the saturation process of vegetation growth, while the dynamic threshold method is used to determine when vegetation growth has changed significantly. After analyzing the data subsets, various ecological and environmental indicators are calculated, such as vegetation index, biomass, coverage, etc.; the indicators help to assess the health and stability of the ecosystem.

[0116] Based on the segmented parameter data set, a dual logistic function model is constructed, and the model parameters are continuously optimized until the dual logistic function model structure performs optimally on the validation data set.

[0117] Based on real-time data and predetermined ecological indicators, critical thresholds for analyzing vegetation and environmental parameters are determined.

[0118] The optimized dual logistic function model was used in combination with critical thresholds to calculate key ecological and environmental indicators.

[0119] Preferably, the optimized dual logistic function model is used in combination with critical thresholds to calculate key ecological and environmental indicators including:

[0120] Obtain critical thresholds of vegetation and environmental parameters under different ecological indicators;

[0121] The latest vegetation and environmental parameter data were input into the optimized dual logistic function model;

[0122] The dual logistic function model is used to process the current data, predict the vegetation status, and output the predicted value of the ecological response;

[0123] Compare the predicted value with the pre-set critical threshold to assess whether the ecological status is normal. If the predicted value exceeds the critical threshold, it is judged that there is a risk;

[0124] Calculate ecological indicators based on model predictions and analysis results of critical thresholds;

[0125] Comprehensively analyze ecological indicators and generate comprehensive reports.

[0126] It should be explained that, based on the segmented parameter data set, a dual logistic function model is constructed to simulate the growth curve and environmental response of vegetation. The model adapts to the characteristics of the data by adjusting parameters. The dual logistic function is a mathematical model used to describe a biological system with two growth stages. It is usually used to fit the initial acceleration and later saturation stages of vegetation growth. The parameters of the dual logistic function model are continuously adjusted through an iterative method, and the performance of the model is evaluated using a validation data set until the performance on the validation set is optimal. Model parameter optimization usually involves algorithms such as gradient descent, and the goal is to minimize the error between the model prediction and the actual data, thereby improving the prediction accuracy of the model. Combine real-time data and predetermined ecological indicators, analyze historical data and vegetation response patterns to determine the corresponding critical thresholds. The critical thresholds are the key points for judging changes in the state of an ecosystem, and their setting Statistical analysis based on ecological and environmental data aims to identify important turning points in ecological status; collect the latest vegetation and environmental parameter data and input them into the optimized dual logistic function model for real-time data processing and prediction. Real-time data processing requires the model to respond quickly to new input data and generate accurate output for immediate ecological assessment. After the model processes the current data, it generates predicted values ​​of vegetation status, which reflect ecological changes or trends. The predicted values ​​of the model are compared with the preset critical thresholds. If the predicted values ​​exceed the critical thresholds, it indicates that there are risks or ecological anomalies. Comparing the predicted results with the critical thresholds is a way to assess whether the current ecological status deviates from the normal range, which helps to take management or intervention measures in a timely manner. Based on the results of model prediction and threshold analysis, various ecological indicators such as biodiversity index, vegetation coverage, etc. are calculated and a comprehensive report is generated.

[0127] Preferably, the ecological indicators include ecological pressure index, normalized health index and change rate;

[0128] The expression of ecological pressure index is:

[0129]

[0130] The expression of normalized health index is:

[0131]

[0132] The expression for the rate of change is:

[0133]

[0134] In the formula, A represents the ecological pressure index;

[0135] B represents the normalized health index;

[0136] C represents the rate of change;

[0137] y(t) represents the predicted vegetation cover at time t;

[0138] θ represents the critical threshold.

[0139] Preferably, the dual logistic function model is used to process the current data, predict the vegetation state, and output the predicted value of the ecological response, including:

[0140] Get current vegetation parameters and environmental parameters;

[0141] Define the form of the bi-logistic function model;

[0142] Based on historical vegetation parameters and historical environmental parameters, the least square method was used to evaluate the parameters of the dual logistic function model.

[0143] Input the current environmental parameters into the verified model, and predict the vegetation status under each environmental parameter;

[0144] The prediction results of all parameters are integrated to output the predicted value of ecological response;

[0145] Among them, the expression of the dual logistic function model is:

[0146]

[0147] In the formula, L1 and L2 represent the maximum values ​​reached by vegetation;

[0148] k1 and k2 represent growth rate parameters;

[0149] x1 and x2 represent the two turning points of the logistic function model;

[0150] f(x) represents the predicted value of the bilogistic function model;

[0151] x represents the independent variable.

[0152] It should be explained that real-time vegetation and environmental data, such as vegetation coverage, humidity, temperature, etc., are usually provided by a sensor network on site. The collection of real-time data is based on various sensors and remote sensing technologies, which can continuously monitor and record changes in the environment and provide the latest input data for the model. The bilogistic function is a mathematical model used to describe the biological growth process with two different growth stages (such as initial rapid growth and later growth slowdown); the model parameters are adjusted by the least squares method to minimize the error between the vegetation growth curve predicted by the model and the actual curve observed in the historical data: the least squares method is a commonly used method in parameter estimation. It The best function match for the data is found by minimizing the sum of squared errors. The environmental parameters collected in real time are input into the verified and adjusted dual logistic function model. The model will predict the corresponding vegetation status based on these parameters. In environmental science, model validation is a key step to ensure the reliability of model predictions. The validation process usually involves comparing the model output with a set of independent validation data, summarizing the model's prediction results for vegetation status under different environmental parameters, and outputting the overall ecological response prediction value after comprehensively considering all factors. Comprehensive analysis uses statistical methods and data fusion technology to summarize and interpret the prediction results of multiple variables in order to provide a comprehensive assessment of the status of the ecosystem.

[0153] The communication and interface module 303 is used to upload the calculation results to the big data cloud service platform and the mobile application.

[0154] It should be explained that before uploading the data, the calculation results must first be formatted and converted into a standard data format, such as JSON or XML, to facilitate parsing and processing by cloud and mobile applications. Data formatting is a key step to ensure that different systems and applications can understand and process data with each other. In order to protect the security and privacy of the data, the communication and interface module 303 will encrypt the data and use security protocols such as SSL / TLS to ensure the security of the data during transmission. Data encryption is an important part of network security. It prevents the data from being stolen or tampered with during transmission by converting the data into a form that only authorized users can interpret. Module 303 will use API (application programming interface) to establish a connection with the big data cloud service platform. Through the authentication and authorization process, it ensures that only authorized users and applications can upload and access data. API is a standard way for software systems to interact. They define the format of requests and the structure of responses, so that different systems can exchange data seamlessly. Through the established secure connection, the communication and interface module 303 uploads the formatted and encrypted data to the cloud service platform. This process usually involves file transfer protocols such as FTP or HTTP POST request, file transfer protocol is a standardized method of transferring files in the network. They allow efficient data transmission between clients and servers. Once the cloud service platform receives the data, it will synchronize it to the mobile application; the mobile application pulls data from the cloud service platform through the built-in interface, or receives data pushed by the cloud service platform.

[0155] The energy and traffic management module 304 is used to monitor power and traffic usage, and intelligently adjust the working mode according to energy status and transmission requirements.

[0156] Preferably, the energy and traffic management module 304 includes: monitoring power and traffic usage, and intelligently adjusting the working mode according to energy status and transmission requirements;

[0157] Continuously monitor and collect energy generation data of the power supply system and energy consumption data of the meter itself.

[0158] Analyze energy consumption data to identify energy consumption patterns, demand peaks, and flow trends.

[0159] Preferably, analyzing energy consumption data to determine energy consumption patterns, demand peaks, and flow rate trends includes:

[0160] Obtain energy consumption data and explore seasonality and trends in energy consumption data through time series analysis;

[0161] Use machine learning algorithms to identify different energy consumption patterns in energy consumption data, both seasonal and trend, and detect demand peaks through statistical methods;

[0162] Based on the analysis results, the data is segmented;

[0163] Fit trend lines to segmented data, analyze cyclical patterns, and separate seasonal components from energy consumption data.

[0164] It should be explained that module 304 monitors energy generation and consumption data in real time through sensors connected to the power supply system and measuring instruments. The monitored energy generation and consumption data include current, voltage, power, energy consumption, etc. Real-time monitoring technology usually involves Internet of Things devices and sensor networks, which can remotely monitor and collect data to provide real-time information for energy management; through data analysis methods, the collected energy consumption data is deeply analyzed to identify patterns and trends in energy use. Data analysis technologies, such as time series analysis, machine learning and statistics, are key tools for understanding and predicting energy consumption behavior. Historical energy consumption data is collected, and time series analysis methods, such as autocorrelation functions and moving average models, are used to identify seasonality. Time series analysis is a branch of statistics that focuses on analyzing data points arranged in chronological order to predict future data values. It uses machine learning algorithms, such as decision trees, random forests, or neural networks, to analyze energy consumption data to identify different consumption patterns and demand peaks. Based on the analysis results, the energy consumption data is segmented into different parts or stages to more accurately analyze energy consumption in specific time periods or conditions. Data segmentation is the process of dividing a data set into different subsets for more in-depth analysis or training machine learning models. Trend lines are fitted on the segmented data to analyze long-term trends in energy consumption, and statistical methods, such as Fourier analysis, are used to identify and separate seasonal components.

[0165] Assess current energy reserve status and flow requirements.

[0166] Intelligently formulate energy adjustment strategies based on energy status and predicted demand changes.

[0167] Implement the developed energy adjustment strategies and monitor the effects after implementation.

[0168] It should be explained that through real-time monitoring systems and analysis tools, the energy reserve status of the power system (such as battery power, solar panel production capacity, etc.) and the current data transmission traffic demand are continuously collected and evaluated. Combined with the real-time data of energy status and traffic demand, prediction algorithms (such as time series prediction, machine learning models, etc.) are used to predict energy demand and traffic changes in the short term; based on these predictions, energy adjustment strategies are intelligently formulated, such as demand response management, energy storage scheduling, and optimized load distribution. The prediction algorithm can analyze historical data and real-time inputs to provide estimates of future conditions. These estimates support strategy selection in the decision-making process. Energy adjustment strategies include peak and valley electricity price adaptation, load balancing, and priority management; after formulating the strategy, deploy the corresponding control system to implement these strategies, such as adjusting the equipment operating status, switching energy supply sources, or regulating energy consumption through automated control systems; after implementation, continue to monitor changes in energy consumption and traffic usage, evaluate the effectiveness of the strategy, and ensure the achievement of the expected goals.

[0169] The specific steps to use machine learning algorithms to identify different energy consumption patterns in seasonality and trends in energy consumption data and detect demand peaks through statistical methods are as follows:

[0170] First, collect historical data on energy consumption, including electricity, fuel usage, etc.; preprocess this data to handle missing values, outliers, and format the data into a format suitable for machine learning processing, perform exploratory data analysis, including drawing time series graphs, calculating basic statistics, and performing correlation analysis to understand the basic characteristics and seasonal patterns of the data, create new features based on the original data, such as moving averages, lag terms, seasonal indicators, etc., to capture the time dependence and periodicity of energy consumption, select a machine learning model suitable for time series prediction, such as random forests, gradient boosting machines, or neural networks, and use historical data to train the model, evaluate the performance of the model through cross-validation and other evaluation indicators; use the trained model to identify seasonal and trend patterns in the data and predict future energy consumption patterns, and apply statistical methods, such as peak detection algorithms (such as threshold methods or more complex statistical tests), to identify abnormal peaks in consumption data, which indicate demand peaks.

[0171] The data visualization module 305 is used to provide a visualization display interface for information input and output.

[0172] It needs to be explained that a user-friendly interface should be designed, including the layout, color scheme and navigation elements of the graphical user interface (GUI); ensure that the interface is concise and easy to operate, and collect the required data from the back-end system, including real-time data and historical data; clean and format the data to ensure that it is suitable for visualization, select appropriate visualization tools and chart types such as bar charts, line charts, scatter plots, heat maps, etc. according to the type of data and user needs, and map the data to the selected chart, including setting coordinate axes, legends and labels; ensure that the chart clearly conveys the key information and trends of the data, and add interactive functions such as clicking, dragging, zooming, etc. to improve the user experience; this allows users to explore different dimensions of the data by directly manipulating the chart, integrate the visualization module into the existing data management system, and conduct sufficient testing, including performance testing and user acceptance testing, to ensure its stability and reliability.

[0173] The specific example background is as follows:

[0174] Suppose there is a large farm located in an area with variable climate; the farm needs to monitor the growth status of vegetation and environmental changes in real time in order to make timely agricultural management decisions.

[0175] The composition is as follows:

[0176] 1) Sensor network: The farm is equipped with a variety of sensors, including soil moisture sensors, temperature sensors, and light sensors, to collect vegetation and environmental parameters in real time;

[0177] 2) Measuring instrument body: installed in the center of the farm, responsible for receiving data from various sensors;

[0178] 3) Controller: The controller is connected to the measuring instrument through a wireless network to collect data and perform preliminary processing.

[0179] The specific application process is as follows:

[0180] Collect data from the sensor network, including parameters such as vegetation coverage, humidity and temperature; these data are initially cleaned and integrated to form a unified data set;

[0181] The data were modeled according to the two stages of vegetation growth (rapid growth stage and mature stage) using the double logistic function fitting method;

[0182] Data segmentation processing: data is divided into seasons and growth stages according to the ecological cycle of vegetation and environmental changes;

[0183] Calculate key ecological indicators such as biomass, vegetation cover and ecological pressure index;

[0184] Upload the processed data and indicators to the cloud service platform through encrypted security protocols for access by farm managers and scientific researchers;

[0185] The mobile application can receive data updates in real time, and farm managers can monitor the farm status through the application and receive push notifications;

[0186] Monitor and adjust energy usage and ensure continuity of data transmission and sensor operations;

[0187] Optimize data transmission frequency and sensor activities based on energy status and data needs;

[0188] Provides a dynamic, multi-functional dashboard that displays real-time data and trend analysis;

[0189] The visualization interface allows users to quickly understand vegetation status and environmental changes through graphics and charts, and supports user interaction, such as selecting a specific time period to view data details.

[0190] In summary, with the aid of the above-mentioned technical scheme of the present invention, the present invention adopts the dual logistic function fitting method and the dynamic threshold method, which allows accurate modeling of vegetation growth patterns, especially the simulation of the two stages of rapid growth and late saturation period, which is suitable for coping with the complexity of different ecological environments and seasonal changes. By segmenting the data according to the ecological cycle and growth changes of the vegetation, the time series data can be observed and analyzed more carefully, so that the ecological model is more in line with the actual growth situation; the data collected in real time and predicted instantly through the optimized model are crucial for rapid response to environmental changes and adjustment of management measures, and are particularly suitable for extreme climatic conditions; by setting the critical threshold, it is possible to evaluate whether the current ecological state deviates from the normal range and promptly identify the potential risks of vegetation growth; the present invention realizes the detection of the current ecological state through sensors connected to the power supply system and the measuring instrument. Real-time monitoring of energy generation and consumption data provides key real-time information for energy management and ensures operational stability. By using time series analysis and machine learning algorithms, such as decision trees and neural networks, not only can seasonal and trend patterns in energy consumption be identified, but demand peaks can also be detected. Based on the evaluation of current energy reserves and flow requirements, as well as predicted changes in demand, the module can intelligently formulate energy adjustment strategies, such as demand response management and energy storage scheduling, to improve energy efficiency and reduce waste, while ensuring sufficient energy supply during high-demand periods. By implementing intelligent adjustment strategies, the present invention enables the module to dynamically adjust the working status and energy supply of the equipment, optimize load distribution according to actual needs, and ensure that it always operates in the best condition. Continuous performance monitoring and feedback loops are the key to optimizing energy use and preventing system failures, ensuring the reliability of the strategy.

[0191] Although the present invention has been disclosed as above with preferred embodiments, the embodiments are merely examples for the convenience of description and are not intended to limit the present invention. Those skilled in the art may make several changes and modifications without departing from the spirit and scope of the present invention. The scope of protection claimed by the present invention shall be based on the claims.

Claims

1. Vegetation coverage measuring instrument, characterized in that: It includes a bottom plate, a support rod is arranged at the top of the bottom plate, a controller is arranged at one side of the middle of the support rod, a connecting side rod is arranged at one side of the top of the support rod, a fixing rod is arranged at the bottom end of the connecting side rod, and a measuring instrument body is arranged at the bottom end of the fixing rod; the controller is connected to the measuring instrument body for communication, and the controller includes a data acquisition module, a data processing module, a communication and interface module, an energy and flow management module and a data visualization module; The data acquisition module is used to obtain the vegetation parameters and environmental parameters collected by the measuring instrument; The data processing module is used to analyze the collected vegetation parameters and environmental parameters using the dual logistic function fitting method and the dynamic threshold method, and calculate various ecological indicators and environmental indicators; including obtaining parameter data sets of vegetation parameters and environmental parameters; and performing segmented processing on the parameter data sets according to the ecological cycle and growth changes of the vegetation; Based on the segmented parameter data set, a dual logistic function model is constructed, and the model parameters are continuously optimized until the dual logistic function model structure performs optimally on the validation data set; based on real-time data and predetermined ecological indicators, critical thresholds for analyzing vegetation and environmental parameters are determined; using the optimized dual logistic function model, combined with critical thresholds, key ecological and environmental indicators are calculated, including obtaining critical thresholds for vegetation and environmental parameters under different ecological indicators; The latest vegetation and environmental parameter data were input into the optimized dual logistic function model; Use the dual logistic function model to process the current data, predict the vegetation status, and output the predicted value of the ecological response; compare the predicted value with the pre-set critical threshold to evaluate whether the ecological status is normal. If the predicted value exceeds the critical threshold, it is judged that there is a risk; calculate the ecological indicators based on the analysis results of the model prediction and the critical threshold; comprehensively analyze the ecological indicators and generate a comprehensive report; Communication and interface module, used to upload calculation results to big data cloud service platform and mobile application; Energy and traffic management module, which monitors power and traffic usage and intelligently adjusts the working mode according to energy status and transmission requirements; Data visualization module is used to provide a visual display interface for information input and output.

2. The vegetation coverage measuring instrument according to claim 1, characterized in that: The measuring instrument body includes a mounting base arranged at the bottom end of a fixed rod, and a data sensing component is arranged at the bottom end of the mounting base. The data sensing component consists of a front optical zoom lens, a front near-infrared lens, a top-down wide-angle lens and a rear fixed-focus lens.

3. The vegetation coverage measuring instrument according to claim 2, characterized in that: The data acquisition module includes the following when acquiring vegetation parameters and environmental parameters collected by the measuring instrument: Get a front optical zoom lens to capture the vegetation image in front; Acquire the near-infrared image taken by the front near-infrared lens to obtain the near-infrared reflection information of the vegetation; Get a wide-angle lens to capture vegetation coverage over a wider area; Get a rear fixed focal length lens to capture images of the sky area; The vegetation image in front, the near-infrared reflectance information of the vegetation, the vegetation image data within a wide-angle range and the sky area image are integrated to form a complete parameter data set of vegetation parameters and environmental parameters.

4. The vegetation coverage measuring instrument according to claim 3, characterized in that: According to the ecological cycle and growth changes of vegetation, the segmentation of parameter data sets includes: Analyze parameter data sets and identify variables in the parameter data sets that affect vegetation growth; The ecological cycle of vegetation is determined using historical data, and data segmentation criteria are set based on the ecological cycle of vegetation and the variables of vegetation growth, and the parameter data set is divided into multiple time windows according to seasons and growth stages; According to the set time window and growth stage as the cutting point, the parameter data set is cut into multiple subsets, and the subsets represent specific ecological cycles; Label each cut subset and identify the ecological cycle stage and growth stage corresponding to the current subset.

5. The vegetation coverage measuring instrument according to claim 4, characterized in that: Using the optimized dual logistic function model and combined with critical thresholds, key ecological and environmental indicators are calculated, including: Obtain critical thresholds of vegetation and environmental parameters under different ecological indicators; The latest vegetation and environmental parameter data were input into the optimized dual logistic function model; The dual logistic function model is used to process the current data, predict the vegetation status, and output the predicted value of the ecological response; Compare the predicted value with the pre-set critical threshold to assess whether the ecological status is normal. If the predicted value exceeds the critical threshold, it is judged that there is a risk; Calculate ecological indicators based on model predictions and analysis results of critical thresholds; Comprehensively analyze ecological indicators and generate comprehensive reports.

6. The vegetation coverage measuring instrument according to claim 5, characterized in that: Ecological indicators include ecological pressure index, normalized health index and rate of change; The expression of ecological pressure index is: ； The expression of normalized health index is: ； The expression for the rate of change is: ； In the formula, A represents the ecological pressure index; B represents the normalized health index; C Indicates the rate of change; Indicates at time t Predicted vegetation cover; θ Indicates the critical threshold.

7. The vegetation coverage measuring instrument according to claim 6, characterized in that: The dual logistic function model is used to process the current data, predict the vegetation status, and output the predicted values ​​of ecological response including: Get current vegetation parameters and environmental parameters; Define the form of the bi-logistic function model; Based on historical vegetation parameters and historical environmental parameters, the least square method was used to evaluate the parameters of the dual logistic function model. Input the current environmental parameters into the verified model, and predict the vegetation status under each environmental parameter; The prediction results of all parameters are integrated to output the predicted value of ecological response; The formal expression of the dual logistic function model is: ； In the formula, and Indicates the maximum value reached by vegetation; and represents the growth rate parameter; x 0 and x 1 represents the two turning points of the dual logistic function model; represents the predicted value of the double logistic function model; x Represents the independent variable.

8. The vegetation coverage measuring instrument according to claim 7, characterized in that: The energy and traffic management module monitors power and traffic usage and intelligently adjusts the working mode according to energy status and transmission requirements, including: Continuously monitor and collect energy generation data of the power supply system and energy consumption data of the meter itself; Analyze energy consumption data to determine energy consumption patterns, demand peaks, and flow trends; Assess current energy reserve status and flow requirements; Intelligently formulate energy adjustment strategies based on energy status and predicted demand changes; Implement the developed energy adjustment strategies and monitor the effects after implementation.

9. The vegetation coverage measuring instrument according to claim 8, characterized in that: Analyze energy consumption data to identify energy consumption patterns, demand peaks, and flow trends including: Obtain energy consumption data and explore seasonality and trends in energy consumption data through time series analysis; Use machine learning algorithms to identify different energy consumption patterns in energy consumption data, both seasonal and trend, and detect demand peaks through statistical methods; Based on the analysis results, the data is segmented; Fit trend lines to segmented data, analyze cyclical patterns, and separate seasonal components from energy consumption data.

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