Intelligent integrated management system for forest farm

Abstract

The invention discloses an intelligent integrated management system for a forest farm, and the system comprises a sensor network module which is used for carrying out the real-time collection of the data of the growth condition of trees in the forest farm; the Internet of Things transmission module is connected with the sensor network module and is used for transmitting the collected data in real time; the data analysis module is connected with the Internet of Things transmission module and is used for accurately evaluating the data transmitted by the Internet of Things transmission module based on a big data analysis model; the forest farm intelligent integrated management system solves the problems that in the prior art, data transmission is delayed and even lost, the decision support function of the whole system is affected, and the reliability of the system is improved. And a key management decision is delayed.

Description

Technical Field

[0001] The present invention relates to the field of forestry information technology, and in particular to an intelligent integrated management system for a forest farm. Background Art

[0002] In modern forest management, the intelligent integrated management system has become an important tool for improving management efficiency and decision-making quality. The system integrates advanced technologies such as the Internet of Things, cloud computing, and artificial intelligence algorithms, and can achieve full-process monitoring of forest environmental monitoring, forest protection and management, and timber production. However, under existing technical conditions, this intelligent integrated management system faces some key problems in practice, especially in terms of the real-time and accuracy of tree growth monitoring data.

[0003] First, changes in environmental factors, such as fluctuations in climate and soil moisture, have a direct impact on the collection of monitoring data. For example, under extreme weather conditions, sensors may give inaccurate readings or malfunction. Second, the difficulty of sensor deployment also poses a challenge to the comprehensiveness and accuracy of data, especially in large-scale forests with complex terrain. The effective coverage and installation location of sensors will affect the efficiency of data collection. In addition, during wireless transmission, signal attenuation and interruption are also important factors affecting data transmission. These problems lead to delays or even losses in data transmission, which in turn affects the decision-making support function of the entire system and delays key management decisions. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent integrated management system for a forest farm to solve the problem that the prior art may cause delay or even loss of data transmission, thereby affecting the decision support function of the entire system and further delaying key management decisions.

[0005] To achieve the above object, the present invention provides the following technical solution: an intelligent integrated management system for a forest farm, the system comprising: The sensor network module is used to collect data on the growth status of trees in the forest farm in real time; The Internet of Things transmission module connected to the sensor network module is used to transmit the collected data in real time; A data analysis module connected to the IoT transmission module is used to accurately evaluate the data transmitted by the IoT transmission module based on a big data analysis model; An intelligent tuning module connected to the data analysis module is used to tune the forest management plan according to the evaluation results and implement feedback control; Among them, the data analysis module accurately evaluates the growth status of trees based on the big data analysis model, including: establishing an evaluation standard system to identify variables affecting tree growth, using historical data to establish a prediction model, analyzing the factors affecting the current tree growth based on the prediction model, setting normal thresholds and abnormal thresholds based on the evaluation results obtained by the prediction model, and judging whether the evaluation score is lower than the normal threshold and higher than the abnormal threshold. If so, the alarm process is initiated; The intelligent tuning module optimizes the forest management plan according to the evaluation results and implements feedback control, including: automatically identifying key factors affecting tree growth from real-time data through machine learning and data mining technology, updating optimization suggestions in real time according to environmental changes, and correcting management strategies according to actual results to form a closed-loop feedback.

[0006] Preferably, the sensor network module collects data on the growth status of trees in the forest in real time, including: Deploy various special sensors at different growth parts of trees to obtain multi-dimensional growth status data; The data collected by the sensor is transmitted to the local gateway device by wireless transmission means; Implement pre-processing on the local gateway device to remove noise data and perform data screening; Use time series analysis methods to ensure data consistency and real-time updates; Regularly calibrate sensor equipment to maintain long-term high-precision measurement performance.

[0007] Preferably, the sensor network module collects data on the growth status of trees in the forest in real time, further comprising: On the local gateway device, noise data removal and abnormal data screening are performed, and the data is smoothed by taking the median of the data in the local window to remove instantaneous noise; Use Z-Score to determine whether a data point is an outlier, using the formula: , in, It is a data point The standard score of is a specific data point in the data set. is the mean of the data set, This is the standard deviation of the data set, is the sample serial number; The periodic calibration process is achieved through an automatic calibration algorithm: The sensor calibration coefficients are adjusted using stochastic gradient descent, and the loss function It is expressed as: , in, is the loss function, is the total number of samples, is the calibrated sensor output, is the standard reference value, Indicates the sample serial number; Calculate calibration cycle by monitoring deviation trend of historical data : It is calculated by (constant × standard deviation) / data deviation, where the calibration period is dynamically adjusted according to changes in sensor performance.

[0008] Preferably, the Internet of Things transmission module transmits the collected data in real time, including: Establish cloud platform service nodes that match the sensor network and provide high-speed data flow channels; Use IoT protocols to optimize the ratio of upload rate to network resource consumption; Perform redundancy checks on data received during transmission; Automatically adjust the sensor network transmission interval and transmission power to maintain optimal communication link stability; When new data is received, the database on the cloud service platform is updated in real time to ensure that the data analysis module can process the newly arrived information.

[0009] Preferably, the Internet of Things transmission module further comprises: Automatically adjust the sensor network transmission interval and transmission power to maintain optimal communication link stability based on the received signal strength indicator and signal-to-noise ratio , dynamically adjust the sensor's transmission power , the power adjustment formula is: , in, is the transmission power, is the minimum transmission power, is the adjustment factor, is the signal-to-noise ratio, The lowest signal-to-noise ratio.

[0010] Preferably, the data analysis module further comprises: An evaluation standard system was established to identify variables that affect tree growth, using the feature selection method of Lasso regression. The specific formula is: , in, is the estimated regression coefficient, is the sample serial number, Indicates the value of a variable that makes a function reach the minimum value. is the sample size, For the The target value of samples, For the The sample eigenvalues, represents the eigenvalue label, is the regression coefficient, is the regularization parameter; The selected variables are included in the evaluation standard system, and each variable is assigned a corresponding weight through weighted average to evaluate the score. It is expressed as: , in, For variables The weight of Indicates variables, Indicates the variable number, is the number of variables, Score for the assessment.

[0011] Preferably, the data analysis module accurately evaluates the growth status of trees based on the big data analysis model and further includes: After the prediction model training is completed, the data analysis module uses real-time data to analyze the current tree growth status and identify influencing factors. The real-time data collected by the sensor network module is input into the prediction model to obtain the current evaluation score and the contribution of each variable to the growth status. Value analysis, quantify the contribution of each variable to the model prediction results, identify key influencing factors, value It is expressed as: , in, for value, For feature subset The model output is is a feature subset, represents the characteristic variable, is the set of all features, For subset size; Set the normal threshold and abnormal threshold, and use the confidence interval of the normal distribution to set the normal threshold. Under the normal distribution, the normal threshold The calculation formula is: , in, is the normal threshold, is the mean of the evaluation scores, is the standard deviation; Through the historical data of quantile regression, set the abnormal threshold and evaluate the score The distribution of , abnormal threshold It is expressed as: , in, is the abnormal threshold, It indicates the evaluation score The cumulative distribution function of is the set abnormal probability, which ranges from 1% to 5%.

[0012] Preferably, the data analysis module accurately evaluates the growth status of trees based on the big data analysis model and further includes: When the assessment score The following conditions are met: , in, is the abnormal threshold, is the normal threshold, To score the assessment; At this point, it is determined that there is a potential risk in the growth of trees, triggering the early warning mechanism; If the evaluation score Below abnormal threshold , immediately start the abnormal alarm process, Response time under abnormal conditions It is expressed as: , in, is the response time, is the anomaly detection time, For decision time, Time to implement emergency measures.

[0013] Preferably, the intelligent tuning module further includes: First, real-time data is obtained from the sensor network module and the IoT transmission module; The features retained by principal component analysis are calculated by calculating the data covariance matrix , decompose its eigenvalues ​​to obtain the principal component matrix , to achieve dimensionality reduction, the specific formula is: , in, is the covariance matrix, is the sample size, is the sample serial number, It is The feature vector of the samples, is the mean vector of the eigenvectors, is the vector transpose symbol; Based on the identified key factors, a prediction model is constructed using the random forest algorithm to predict the growth trend of trees and potential risk factors. Multiple decision trees are constructed for classification or regression. The output is the average or voting result of the results of multiple trees. The specific formula is: , in, is the final prediction result. is the number of trees, It is A decision tree for input predictions, is the decision tree number, is the input feature vector.

[0014] Preferably, the intelligent tuning module further includes: The optimization suggestions generated by the prediction model are applied to the forest management plan, and the implementation effect is monitored in real time through the sensor network. If there is a difference between the actual effect and the expected result, the optimization suggestions will be automatically adjusted, the feedback controller of the management strategy will be adjusted, and the control signal will be output. The formula is: , in, To output the control signal, For the moment, is the error between the actual effect and the expected target, , , are proportional, integral and differential coefficients respectively; pass The algorithm finds the optimal management strategy, The update formula is: , in, Indicates that in a specific state Next action The expected return when is the learning rate, is the discount factor, For the next state, is the reward value, For status, For action.

[0015] It can be seen from the above technical solution that the present invention has the following beneficial effects: The intelligent integrated management system of the forest farm collects data on the growth conditions of trees in the forest farm in real time through the sensor network module, the Internet of Things transmission module transmits the collected data in real time, the data analysis module accurately evaluates the data transmitted by the Internet of Things transmission module based on the big data analysis model, and the intelligent tuning module optimizes the forest farm management plan according to the evaluation results and implements feedback control, which can significantly improve the real-time and accuracy of data collection and the overall decision-making support capability of the system, significantly improve the data collection and transmission efficiency of forest farm management, ensure the timeliness and accuracy of decision-making support functions, thereby improving the overall efficiency of forest farm management and the ability to respond to emergencies, and solves the problem that the existing technology will cause delays or even losses in data transmission, thereby affecting the decision-making support function of the entire system and delaying key management decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a connection diagram of the system of the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] like Figure 1As shown, an intelligent integrated management system for a forest farm includes a sensor network module, an Internet of Things transmission module connected to the sensor network module, a data analysis module connected to the Internet of Things transmission module, and an intelligent tuning module connected to the data analysis module. The sensor network module collects data on the growth status of trees in the forest farm in real time, the Internet of Things transmission module transmits the collected data in real time, the data analysis module accurately evaluates the data transmitted by the Internet of Things transmission module based on a big data analysis model, the intelligent tuning module optimizes the forest farm management plan according to the evaluation results and implements feedback control, the data analysis module accurately evaluates the growth status of trees based on the big data analysis model, including: establishing an evaluation standard system to identify variables affecting tree growth, using historical data to establish a prediction model, analyzing the influencing factors of the current tree growth based on the prediction model, setting a normal threshold and an abnormal threshold according to the evaluation results obtained by the prediction model, judging whether the evaluation score is lower than the normal threshold and higher than the abnormal threshold, and if so, starting the alarm process, the intelligent tuning module optimizes the forest farm management plan according to the evaluation results and implements feedback control, including: automatically identifying key factors affecting tree growth from real-time data through machine learning and data mining technology, updating optimization suggestions in real time according to environmental changes, and correcting management strategies according to actual effects to form a closed-loop feedback.

[0019] The core technologies of the system include real-time data collection of sensor networks, application of big data analysis models, optimization decision-making of machine learning algorithms, and implementation of feedback control mechanisms. The sensor network module collects multi-dimensional data of tree growth in real time, and transmits these data to the data analysis module for processing through the Internet of Things transmission module. The data analysis module uses big data analysis technology to accurately evaluate the collected data, identify variables that affect tree growth, and set normal and abnormal thresholds through historical data and prediction models. Based on these evaluation results, the intelligent tuning module uses machine learning and data mining technology to optimize and feedback the management plan in real time. The design focus of the system is its integration and intelligence. The sensor network module can deploy various types of sensors to obtain multi-dimensional growth data in real time, such as trunk diameter, chlorophyll content, soil moisture and ambient temperature. The Internet of Things transmission module ensures the instant transmission and efficient processing of these data. The data analysis module conducts in-depth analysis through big data models and sets thresholds based on prediction models and evaluation results to monitor the health of trees. The intelligent tuning module relies on machine learning technology to achieve closed-loop feedback control through continuous learning and optimization of real-time data, so that the management plan can be dynamically adjusted to adapt to environmental changes.

[0020] The sensor network module includes sensors placed at key locations in the forest farm, which can collect tree growth data in real time. Through wireless transmission, the data is promptly transmitted to the IoT transmission module for further processing. The main function of the IoT transmission module is to instantly transmit the data collected by the sensor to the cloud platform or local server. This module ensures efficient data transmission by optimizing the transmission protocol and performs redundancy checks to ensure data integrity. The data analysis module processes the transmitted data based on the big data analysis model, identifies key variables that affect tree growth, and uses historical data to establish a prediction model. By setting normal and abnormal thresholds, the module monitors the health of tree growth and initiates the alarm process when potential risks are found. The intelligent tuning module uses machine learning technology to adjust the management plan in real time based on the evaluation results provided by the data analysis module. This module identifies key growth influencing factors and updates management recommendations based on real-time data to ensure that the forest farm's management plan is always in the best condition. The system collects data in real time through the sensor network module and transmits the data to the cloud platform or local server instantly through the Internet of Things transmission module. The data analysis module analyzes the received data, establishes a prediction model and sets thresholds, and determines the growth status of trees by monitoring the evaluation scores. Once the evaluation score is detected to be within the abnormal range, the system will trigger the alarm process. The intelligent tuning module automatically adjusts the management plan based on these analysis results, and optimizes and continuously improves management through a feedback control mechanism.

[0021] The close integration between the modules enables the system to effectively realize real-time monitoring and optimized management. The efficient data interaction between the sensor network and the IoT transmission module ensures the timely transmission and processing of data. The collaborative work between the data analysis module and the intelligent tuning module enables the analysis results to be directly used for the adjustment of management plans, ensuring that the system can respond quickly to environmental changes, promote the healthy and sustainable development of forest farms, promote the modernization of forest farms, and build a forest farm work management network system that communicates with each other, so as to achieve standardization, informatization, networking and modernization of work.

[0022] The sensor network module collects data on the growth status of trees in the forest in real time, including: Deploy various special sensors at different growth parts of trees to obtain multi-dimensional growth status data; The data collected by the sensor is transmitted to the local gateway device by wireless transmission means; Implement pre-processing on the local gateway device to remove noise data and perform data screening; Use time series analysis methods to ensure data consistency and real-time updates; Regularly calibrate sensor equipment to maintain long-term high-precision measurement performance.

[0023] The core of the sensor network module lies in the combination of multi-dimensional data collection and wireless transmission. Various special sensors are deployed in different growth parts of trees, such as trunks, leaves and roots, to collect different types of growth data (such as trunk diameter, chlorophyll content, soil moisture and ambient temperature). These data are sent to the local gateway device in real time through wireless transmission. Through the time series analysis method, the system can ensure the consistency and timeliness of the data. In addition, in order to ensure the accuracy of long-term data collection, the system also designs a regular sensor calibration mechanism. The core feature of the system design lies in the high precision and real-time performance of the sensor network module. Various sensors are precisely deployed in different growth parts of trees to capture key growth data in various dimensions. Through wireless transmission, the data can be transmitted to the local gateway device in real time, and the local gateway device pre-processes the received data, removes noise and performs preliminary screening. The time series analysis method is used to ensure the consistency and timeliness of the data, and the system also ensures the long-term measurement accuracy of the sensor through regular calibration.

[0024] In the sensor network module, sensors are deployed at different growth parts of trees to obtain multi-dimensional data. Sensors on the trunk monitor diameter changes, sensors on leaves measure chlorophyll content, and ground sensors monitor soil moisture and ambient temperature. Each sensor forms a comprehensive growth status data set for different growth dimensions, and wireless transmission is used to transmit the data collected by the sensor to the local gateway device in real time. During the transmission process, the system ensures the timeliness and integrity of the data by adjusting the signal strength and transmission rate. On the local gateway device, the data is pre-processed in real time. First, the noise data is removed by the denoising algorithm, and secondly, preliminary screening is performed to ensure the validity of the data. The results of data preprocessing directly affect the quality of subsequent analysis. After the data preprocessing is completed, the system uses time series analysis methods to further process the data. Time series analysis not only ensures the consistency of the data, but also identifies trends and anomalies in the data to ensure the accuracy of subsequent analysis and management decisions. In order to maintain the high accuracy of sensor measurements, the system has designed a regular calibration mechanism. Through the automated calibration procedure, the system can adjust the measurement parameters of the sensor in a timely manner to ensure that the data remains high-precision and consistent for a long time.

[0025] The sensor network module collects multi-dimensional data of tree growth in real time by precisely deploying sensors, and sends the data to the local gateway device instantly through wireless transmission. The gateway device pre-processes the data, removes noise and performs preliminary screening to ensure the validity and consistency of the data. Subsequently, the system further processes the data through time series analysis to identify trends and abnormalities and ensure real-time data updates. In order to maintain long-term measurement accuracy, the system also regularly calibrates the sensors and adjusts the parameters of the sensors through automated calibration algorithms to ensure data accuracy and consistency. The close integration of the sensor network module with the IoT transmission module and the data analysis module enables the system to efficiently collect, transmit and process data. The multi-point deployment of sensors and wireless transmission methods ensure that data can be transmitted to the local gateway for processing in a timely and accurate manner. The time series analysis and regular calibration mechanism further improve the reliability and accuracy of data processing, providing strong support for the overall intelligent management of the system.

[0026] The sensor network module collects data on the growth status of trees in the forest in real time, further including: Deploy various special sensors at different growth parts of trees to obtain multi-dimensional growth status data. The collected multi-dimensional growth data includes trunk diameter , chlorophyll content , soil moisture and ambient temperature , the following formula is used for standardization to ensure the uniformity and comparability of different types of data, , in, represents the standardized data, Represents the raw data of a certain dimension, that is: , , ,and , is the mean of the data, is the standard deviation of the data, is the sample serial number; The data collected by the sensor is immediately transmitted to the local gateway device by wireless transmission. During the wireless transmission process, the transmission path loss It is expressed as: , in, Indicates the signal power at the sending end, in units of (dBmW), Indicates the signal power at the receiving end, in units of (dBmW), Represents the path loss in wireless transmission, in decibels ; The following formula is used to ensure that the signal reception strength meets the data transmission requirements at different transmission distances: , in, Represents the path loss in wireless transmission, in decibels , d represents the transmission distance in wireless transmission, is the signal frequency, is the speed of light constant; On the local gateway device, noise data removal and abnormal data screening are performed. The data is smoothed by taking the median of the data in the local window to remove instantaneous noise. For a set of data points x1, x2, …, x n , the output of median filtering is: , in, represents the median operation, is the size of the filter window, is the output data point after median filtering, Indicates the serial number of the data point; Use Z-Score to determine whether a data point is an outlier, using the formula: , in, This is the data point The standard score of is a specific data point in the data set. is the mean of the data set, This is the standard deviation of the data set, is the sample serial number; The time series analysis method is used to predict the trend and update the sensor data in real time. The prediction model expression of time series data is: ,in, is time series data, is the autoregressive coefficient, is the moving average coefficient, It is white noise; Using Kalman filtering to smooth and update real-time data, the state estimation update formula is: , in, is an estimate of the current state, is the Kalman gain, is the measured value, is the measurement matrix, is the state prediction of the previous moment; The periodic calibration process is achieved through an automatic calibration algorithm: The sensor calibration coefficients are adjusted using stochastic gradient descent, and the loss function It is expressed as: , in, is the loss function, is the total number of samples, is the calibrated sensor output, is the standard reference value, Indicates the sample serial number; , in, represents a parameter to be optimized. is the learning rate, is the loss function, which gradually optimizes the calibration coefficients of the sensor to maintain high measurement accuracy; Calculate calibration cycle by monitoring deviation trend of historical data : = (constant × standard deviation) / data deviation. The calibration cycle is dynamically adjusted according to changes in sensor performance.

[0027] The sensor network module of the system adopts a variety of technical means to ensure the accuracy of data and the effectiveness of transmission. First, all collected data are processed through standardized formulas to ensure the comparability between different data dimensions. Wireless transmission technology ensures the integrity of data at different transmission distances by calculating the transmission path loss and the received signal strength. The data processing link includes methods such as median filtering and Z-Score analysis to remove noise and filter abnormal data. Finally, the system uses methods such as time series analysis and Kalman filtering to predict trends and update data in real time to ensure the timeliness and accuracy of data. The design of this system lies in the comprehensiveness and intelligence of its data processing. Through standardized processing, the compatibility between different data dimensions is ensured to avoid errors caused by unit differences. During the wireless transmission process, the system dynamically adjusts according to the signal strength and transmission path loss to ensure the transmission quality of data. Noise removal and abnormal screening technology ensure the purity of data through median filtering and Z-Score analysis. Time series analysis and Kalman filtering methods further improve the reliability of data, especially in real-time monitoring and predictive analysis.

[0028] In actual operation, the sensor network module first collects multi-dimensional data of tree growth through various sensors. After standardized processing, the data is unified and then wirelessly transmitted to the local gateway device. During the wireless transmission process, the system calculates the transmission path loss and signal strength in real time, and automatically adjusts the transmission parameters to ensure the integrity of the data. After the data arrives at the local gateway, the system immediately removes noise and screens anomalies to ensure the purity and validity of the data. Subsequently, through time series analysis and Kalman filtering, the data is updated and trend predicted in real time, providing high-quality input data for subsequent analysis. The regular calibration mechanism ensures the long-term measurement accuracy of the sensor and the continuous reliability of the system. Through tight integration, each functional module realizes efficient collaboration of data collection, transmission, processing and analysis. The coordination of data standardization and wireless transmission ensures the consistency and transmission quality of the data. The combination of data preprocessing and time series analysis ensures the accuracy and real-time nature of the data, providing a reliable basis for subsequent intelligent management and decision-making. The regular calibration mechanism further strengthens the stability of the system and ensures the efficiency and accuracy of the system in long-term operation.

[0029] The IoT transmission module transmits the collected data instantly, including: Establish cloud platform service nodes that match the sensor network and provide high-speed data flow channels; Use IoT protocols to optimize the ratio of upload rate to network resource consumption; Perform redundancy checks on data received during transmission; Automatically adjust the sensor network transmission interval and transmission power to maintain optimal communication link stability; When new data is received, the database on the cloud service platform is updated in real time to ensure that the data analysis module can process the newly arrived information.

[0030] The core of the design of the IoT transmission module is how to efficiently and reliably transmit large-scale, multi-dimensional data from the sensor network to the cloud platform or local server. By setting up a cloud platform service node that matches the sensor network, the system can provide a high-speed data flow channel to ensure the real-time transmission of data. The optimization of the IoT protocol can reduce the consumption of network resources while ensuring the transmission speed. In order to prevent the loss or damage of data during transmission, the system also implements redundancy checks and maintains the stability of the communication link by automatically adjusting the transmission interval and power. Finally, the database on the cloud platform is updated in real time to ensure that the data analysis module processes new data in real time and maintains the timeliness of the data. In the design of the IoT transmission module, the system focuses on solving the transmission efficiency and stability problems of large-scale data. First, by setting up a matching cloud platform service node, the system can provide sufficient bandwidth for data transmission and support high-frequency data upload. Secondly, the optimization of the IoT protocol effectively improves the upload rate and controls the consumption of network resources. In order to ensure the integrity of the data, the system performs redundancy checks during the transmission process to detect and correct transmission errors. In addition, by automatically adjusting the sending interval and transmission power of the sensor network, the system can adapt to different network conditions and maintain the optimal communication link stability. After receiving the data, the system will update the cloud platform database in real time to ensure that the data analysis module can process the latest data instantly and support intelligent decision-making in forest management.

[0031] A cloud platform service node matching the sensor network is established to provide sufficient bandwidth and computing power to support the upload and processing of high-frequency data. The seamless integration of the node and the sensor network ensures efficient data transmission and instant processing. By optimizing the IoT protocol, the system can reduce the consumption of network resources while ensuring the upload rate. Protocol optimization includes measures such as compressing the size of data packets, adjusting the transmission frequency, and optimizing routing selection to achieve a balance between transmission efficiency and resource utilization. During data transmission, the system ensures the integrity and reliability of the data through redundancy checks. The check includes checking the data packets and retransmitting redundant data to ensure the consistency and accuracy of the data during transmission. The system can automatically adjust the transmission interval and transmission power of the sensor network according to the real-time network conditions to maintain the best communication link stability. This dynamic adjustment can effectively respond to changes in network conditions and ensure the smooth transmission of data. After the data is transmitted to the cloud platform, the system will update the database in real time to ensure that all new data can be stored and processed in a timely manner. The real-time update mechanism enables the data analysis module to access the latest data immediately, supporting real-time decision-making and management optimization.

[0032] The IoT transmission module first provides a high-speed channel for data transmission through the established cloud platform service node. The data collected by the sensor network is quickly uploaded to the cloud platform through the optimized IoT protocol. During the transmission process, the system implements redundancy checks to ensure accurate data transmission. In order to adapt to different network conditions, the system will also dynamically adjust the transmission interval and transmission power of the sensor network to ensure the stability of the communication link. After the data arrives at the cloud platform, the system immediately updates the database so that the data analysis module can process the latest data in real time, thereby supporting intelligent forest management. The IoT transmission module is closely integrated with the sensor network module, cloud platform and data analysis module to ensure efficient data collection, transmission and processing. Through the setting of cloud platform service nodes, the system realizes high-speed upload of large-scale data. IoT protocol optimization and redundancy checks ensure the transmission quality and stability of data. The function of automatically adjusting transmission parameters enhances the adaptability of the system, enabling it to cope with changing network conditions. The real-time database update ensures that the data analysis module can obtain and process the latest data in real time, improving the response speed and decision-making accuracy of the overall system.

[0033] The IoT transmission module transmits the collected data instantly, further including: According to the geographical location, network coverage and data traffic requirements of the forest farm, a cloud service provider is selected, the cloud platform service node is configured to support specific IoT protocols, and a high-speed data stream channel is configured. The total bandwidth that the cloud platform service node needs to provide is calculated using the formula: , in, is the data rate uploaded by the sensor node, is the number of sensor nodes, Is the total bandwidth that the cloud platform service node needs to provide ; Optimize the upload rate and network resource consumption ratio using IoT protocols and calculate the average size of data packets and transfer rate , using the formula: , in, is the network resource utilization, is the total transmission time, is the transmission rate, To calculate the average size of a data packet; The data received during the transmission process is checked for redundancy, and the bit error rate of the data packet during the transmission process is , data redundancy Bit Error Rate The relationship between them is: , in, is the bit error rate of the data packet during transmission, is the data redundancy; Success rate of redundancy check It is expressed as: ; in, is the success rate of redundancy check, is the data redundancy, is the bit error rate; Automatically adjust the sensor network transmission interval and transmission power to maintain optimal communication link stability based on the received signal strength indicator and signal-to-noise ratio , dynamically adjust the sensor's transmission power , the power adjustment formula is: , in, is the transmission power, is the minimum transmission power, is the adjustment factor, is the signal-to-noise ratio, is the lowest signal-to-noise ratio; The database on the cloud service platform is updated in real time when new data is received. The average delay of database write operations is The time interval between data arrival is , the update frequency should be related to: , in, The frequency of database updates, is the time interval between data arrivals, The average latency for database write operations.

[0034] The IoT transmission module needs to configure appropriate cloud platform service nodes according to different environmental conditions to ensure that the system can support the data upload requirements of large-scale sensor nodes. The total bandwidth that the cloud platform service node needs to provide Determined by the number of sensor nodes and the upload rate. The system also improves upload efficiency by optimizing the Internet of Things protocol to ensure the rational use of network resources. In order to ensure the integrity of data transmission, the system implements redundancy checks, combined with the calculation of bit error rate and data redundancy to ensure the successful transmission rate of data packets. Dynamic adjustment of transmission parameters, such as transmission power and sending interval, enables the system to maintain the optimal communication link under different network conditions. After the data arrives, the system updates the cloud platform database in real time to support real-time data analysis and decision-making. The design focuses on ensuring the efficiency and stability of data transmission by optimizing the Internet of Things protocol and dynamically adjusting transmission parameters. First, the cloud platform service node needs to be configured according to the specific conditions of the forest farm to ensure that the system has sufficient bandwidth to support data transmission. Through the optimization of the Internet of Things protocol, the system can maintain a high upload rate while reducing network resource consumption. The redundancy check calculates the redundancy and success rate , further improving the reliability of data transmission. Received signal strength and signal-to-noise ratio It is used to dynamically adjust the transmission power to ensure the transmission stability under different conditions. Finally, the system's real-time database update mechanism ensures that the data analysis module can process the transmitted data immediately and ensure the timeliness of the analysis.

[0035] The IoT transmission module configures the cloud platform service node according to the specific conditions of the forest farm to ensure sufficient data transmission bandwidth. During the transmission process, by optimizing the IoT protocol and redundancy check, the system can effectively improve the efficiency and reliability of data transmission. At the same time, the system dynamically adjusts the sensor's transmission power and sending interval according to the real-time signal strength and network conditions to maintain the stability of the communication link. After the data arrives at the cloud platform, the system will immediately update the database to ensure that the data analysis module can instantly obtain and process the latest data, thereby supporting real-time decision-making.

[0036] The data analysis module conducts accurate assessment of tree growth status based on the big data analysis model, further including: An evaluation standard system was established to identify variables that affect tree growth, using the feature selection method of Lasso regression. The specific formula is: , in, is the estimated regression coefficient, is the sample serial number, Indicates the value of a variable that makes a function reach the minimum value. is the sample size, For the The target value of samples, For the The sample eigenvalues, represents the eigenvalue label, is the regression coefficient, is the regularization parameter; The selected variables are included in the evaluation standard system, and each variable is assigned a corresponding weight through weighted average to evaluate the score. It is expressed as: , in, For variables The weight of Indicates variables, Indicates the variable number, is the number of variables, To score the assessment; Select the LSTM model based on the data characteristics. The hidden state update formula in the LSTM model is: , in, is the current input, is the current hidden state, represents the hidden state at the previous time step, is the current memory unit state, represents the state of the memory cell at the previous time step, , , are the activation values ​​of the forget gate, input gate, and output gate, respectively. is the Sigmoid function, is the hyperbolic tangent function, Represent the weight matrices of the forget gate, input gate, candidate memory unit and output gate respectively, They represent the bias items of the forget gate, input gate, candidate memory unit and output gate respectively.

[0037] First, it is necessary to establish an evaluation standard system to identify the variables that have the most significant impact on tree growth. To this end, Lasso regression is used as a feature selection method to improve the generalization ability of the model. Lasso regression can automatically select features while reducing unnecessary complexity by adding L1 regularization terms to the regression model. The selected key variables are included in the evaluation standard system, and the comprehensive evaluation score is calculated by weighted average method. . In order to dynamically predict the growth status of trees, the system uses a long short-term memory (LSTM) model. The LSTM model is suitable for processing time series data and can capture long-term dependencies in the data. Its core is to use forget gates, input gates, and output gates to control the flow of information to ensure that the model can effectively memorize and update key information. The design of the data analysis module focuses on the accuracy of variable selection and the dynamics of model predictions. Through the Lasso regression method, the system can identify and select variables for tree growth and establish an evaluation standard system based on these variables. The weight of each variable in the system is determined by weighted average, and finally summarized into a comprehensive evaluation score To further improve the prediction accuracy, the system uses the LSTM model for time series analysis, which can effectively deal with the long-term dependency problem in the data. The LSTM model controls the storage and update process of information through the combination of forget gate, input gate and output gate, so that the system can dynamically adjust the prediction results according to the latest data.

[0038] First, we use Lasso regression to analyze historical data and automatically identify the variables that have the greatest impact on tree growth. These variables are incorporated into the evaluation criteria system, and the system calculates the comprehensive evaluation score through the weighted average method. Next, the LSTM model is used to dynamically predict real-time data. The LSTM model dynamically adjusts the hidden state and memory unit state through a series of gating mechanisms to make accurate predictions about future tree growth conditions. The entire process ensures that the data analysis module can continuously update the prediction results based on the latest data, providing a reliable basis for management decisions.

[0039] The data analysis module accurately evaluates the growth status of trees based on the big data analysis model and also includes: After the prediction model training is completed, the data analysis module uses real-time data to analyze the current tree growth status and identify influencing factors. The real-time data collected by the sensor network module is input into the prediction model to obtain the current evaluation score and the contribution of each variable to the growth status. Value analysis, quantify the contribution of each variable to the model prediction results, identify key influencing factors, value It is expressed as: , in, for value, For feature subset The model output is is a feature subset, represents the characteristic variable, is the set of all features, For subset size; Set the normal threshold and abnormal threshold, and use the confidence interval of the normal distribution to set the normal threshold. Under the normal distribution, the normal threshold The calculation formula is: , in, is the normal threshold, is the mean of the evaluation scores, is the standard deviation; Through the historical data of quantile regression, set the abnormal threshold and evaluate the score The distribution of , abnormal threshold It is expressed as: , in, is the abnormal threshold, It indicates the evaluation score The cumulative distribution function of is the set abnormal probability, which ranges from 1% to 5%.

[0040] The core is to improve the accuracy of prediction and monitoring through Shapley value analysis and threshold setting. Shapley value analysis provides the system with a method to quantify the contribution of each variable to the model prediction results, so that the system can identify and focus on factors that affect tree growth. The setting of the normal threshold is based on the normal distribution, and the lower limit of the health state is determined by calculating the mean and standard deviation of the evaluation score. The setting of the abnormal threshold is determined by quantile regression, which is used to identify potential risks. After the prediction model training is completed, the data collected by the sensor network module is obtained in real time and input into the prediction model for analysis. Through Shapley value analysis, the system can identify the variables that have the greatest impact on the model prediction results and quantify the contribution of these variables. Subsequently, the system sets the normal threshold based on the mean and standard deviation of the evaluation score, and sets the abnormal threshold through quantile regression. These thresholds are used to monitor the growth of trees in real time, helping the system to discover and deal with potential risks in a timely manner. The data analysis module realizes accurate evaluation of tree growth through real-time data interaction with the sensor network module and close integration with the prediction model. The results of Shapley value analysis directly affect the decision-making of the intelligent tuning module, ensuring that the management plan can be optimized based on real-time data. By setting normal and abnormal thresholds, the system can continuously monitor the growth conditions of trees and ensure healthy management of the forest farm.

[0041] The data analysis module accurately evaluates the growth status of trees based on the big data analysis model and also includes: When the assessment score The following conditions are met: , in, is the abnormal threshold, is the normal threshold, To score the assessment; At this point, it is determined that the tree growth condition has potential risks, but has not yet reached a serious level, triggering the early warning mechanism; If the evaluation score Below abnormal threshold , immediately start the abnormal alarm process, Response time under abnormal conditions It is expressed as: , in, is the response time, is the anomaly detection time, For decision time, Time to implement emergency measures.

[0042] When the data analysis module performs real-time evaluation, it will and anomaly threshold Determine the current growth status. If the evaluation score If the score is between the abnormal threshold and the normal threshold, the system determines that the tree has potential risks but has not yet reached a serious level. At this time, the early warning mechanism is triggered to remind managers to conduct further observation and evaluation. , the system starts the abnormal alarm process and prompts the manager to take emergency measures immediately. The response time of the whole process It is divided into three stages: Anomaly detection time , decision time and the time to implement emergency measures Through real-time analysis and threshold setting, all-round monitoring and management of tree growth conditions are achieved. The triggering and execution of early warning and abnormal alarm processes ensure that the system can respond to evaluation results in a timely manner and seamlessly connect with the management decision-making process. The calculation and control of response time further improves the system's response speed in emergency situations, ensuring the healthy development and management efficiency of the forest farm.

[0043] The intelligent tuning module optimizes the forest management plan according to the evaluation results and implements feedback control, further including: First, real-time data is obtained from the sensor network module and the IoT transmission module, and the Lasso regression algorithm is used to identify the key factors that have a significant impact on tree growth. The specific formula is: , in, is the estimated regression coefficient, is the sample number, Indicates the value of a variable that makes a function reach the minimum value. is the sample size, For the The target value of samples, For the The sample eigenvalues, represents the eigenvalue label, is the regression coefficient, is the regularization parameter; The principal component analysis method is used to retain features by calculating the data covariance matrix , to achieve dimensionality reduction, the specific formula is: , in, is the covariance matrix, is the sample size, is the sample serial number, It is The feature vector of the samples, is the mean vector of the eigenvectors, is the vector transpose symbol; Based on the identified key factors, a prediction model is constructed using the random forest algorithm to predict the growth trend of trees and potential risk factors. Multiple decision trees are constructed for classification or regression. The output is the average or voting result of the results of multiple trees. The specific formula is: , in, is the final prediction result. is the number of trees, It is A decision tree for input predictions, is the decision tree number, is the input feature vector; Use online learning algorithms to update the model in real time.

[0044] After acquiring real-time data, the Lasso regression algorithm is first used to identify the key factors that have a significant impact on tree growth. The identified features are processed by principal component analysis (PCA) for dimensionality reduction to reduce data redundancy. Then, the system uses the random forest algorithm to build a prediction model to predict the growth trend and potential risks of trees. Driven by the real-time data stream, the system uses an online learning algorithm to continuously update the model parameters to ensure that the prediction model is always consistent with the current data. The whole process ensures that the intelligent tuning module can dynamically adjust the management plan according to the latest evaluation results and optimize the plan through the feedback control mechanism. The real-time data interaction between the intelligent tuning module and the data analysis module and the sensor network module can adjust the management plan according to the latest data changes. The combination of Lasso regression and PCA ensures the accurate identification and processing of features, and the application of random forest and online learning algorithms ensures the accuracy and dynamic adaptability of the prediction model. Finally, the intelligent tuning module applies the adjusted management plan to forest management through the feedback control mechanism to ensure that management decisions can respond to changes in the environment and data in a timely manner and improve the efficiency and effectiveness of forest management.

[0045] The intelligent tuning module optimizes the forest management plan according to the evaluation results and implements feedback control, further including: The optimization suggestions generated by the prediction model are applied to the forest management plan, and the implementation effect is monitored in real time through the sensor network. If there is a difference between the actual effect and the expected result, the optimization suggestions will be automatically adjusted, the feedback controller of the management strategy will be adjusted, and the control signal will be output. The formula is: , in, To output the control signal, For the moment, is the error between the actual effect and the expected target, , , are proportional, integral and differential coefficients respectively; The optimal management strategy is found through the Q-learning algorithm. The update formula of Q-learning is: , in, Indicates that in a specific state Next action The expected return when is the learning rate, is the discount factor, For the next state, is the reward value, For status, For action.

[0046] Through the PID controller, the system can accurately adjust the execution of the management plan and promptly correct the difference between the actual effect and the expected goal. The control signal output by the PID controller By error , the integral of the error and the differential of the error to ensure the stability and response speed of the control process. The Q-learning algorithm provides the system with an optimization strategy based on trial and error. By continuously learning and updating the Q value, the system can gradually find the optimal management solution and realize intelligent dynamic adjustment. After applying the optimization suggestions generated by the prediction model, the execution effect of the management solution is monitored in real time through the sensor network. If an error is detected between the actual effect and the expected result, the PID controller will automatically adjust the control signal , in order to correct deviations and optimize management strategies. In the long-term operation of the system, the management strategy is continuously updated through the Q-learning algorithm, and the optimal operation plan is gradually found to ensure that the system is always in the best operating state. The entire operation mechanism combines feedback control and reinforcement learning, so that the forest management plan can dynamically adapt to environmental changes and actual conditions. The intelligent tuning module is seamlessly integrated with the data analysis module and the sensor network module, and the dynamic adjustment of the management plan is realized through real-time data and feedback mechanism. The PID controller ensures that the system can respond to errors quickly and maintain the stability of the management plan. The introduction of the Q-learning algorithm provides the system with the ability of continuous optimization, so that the management strategy can be continuously adjusted according to the actual results. Finally, the intelligent tuning module realizes the precise optimization and intelligent adjustment of the forest management plan through the combination of feedback control and reinforcement learning.

[0047] 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. An intelligent integrated forest management system, characterized in that: The system comprises: The sensor network module is used to collect data on the growth status of trees in the forest farm in real time; The Internet of Things transmission module connected to the sensor network module is used to transmit the collected data in real time; A data analysis module connected to the IoT transmission module is used to accurately evaluate the data transmitted by the IoT transmission module based on a big data analysis model; An intelligent tuning module connected to the data analysis module is used to tune the forest management plan according to the evaluation results and implement feedback control; Among them, the data analysis module accurately evaluates the growth status of trees based on the big data analysis model, including: establishing an evaluation standard system to identify variables affecting tree growth, using historical data to establish a prediction model, analyzing the factors affecting the current tree growth based on the prediction model, setting normal thresholds and abnormal thresholds based on the evaluation results obtained by the prediction model, and judging whether the evaluation score is lower than the normal threshold and higher than the abnormal threshold. If so, the alarm process is initiated; The intelligent tuning module optimizes the forest management plan according to the evaluation results and implements feedback control, including: automatically identifying key factors affecting tree growth from real-time data through machine learning and data mining technology, updating optimization suggestions in real time according to environmental changes, and correcting management strategies according to actual results to form a closed-loop feedback.

2. The intelligent integrated forest management system according to claim 1 is characterized in that: The sensor network module collects data on the growth status of trees in the forest in real time, including: Deploy various special sensors at different growth parts of trees to obtain multi-dimensional growth status data; The data collected by the sensor is transmitted to the local gateway device by wireless transmission means; Implement pre-processing on the local gateway device to remove noise data and perform data screening; Use time series analysis methods to ensure data consistency and real-time updates; Regularly calibrate sensor equipment to maintain long-term high-precision measurement performance.

3. The intelligent integrated forest management system according to claim 2 is characterized in that: The sensor network module collects data on the growth status of trees in the forest in real time, further comprising: On the local gateway device, noise data removal and abnormal data screening are performed, and the data is smoothed by taking the median of the data in the local window to remove instantaneous noise; Use Z-Score to determine whether a data point is an outlier, using the formula: ， in, It is a data point The standard score of is a specific data point in the data set. is the mean of the data set, This is the standard deviation of the data set, is the sample serial number; The periodic calibration process is achieved through an automatic calibration algorithm: The sensor calibration coefficients are adjusted using stochastic gradient descent, and the loss function It is expressed as: ， in, is the loss function, is the total number of samples, is the calibrated sensor output, is the standard reference value, Indicates the sample serial number; Calculate calibration cycle by monitoring deviation trend of historical data : It is calculated by (constant × standard deviation) / data deviation, where the calibration period is dynamically adjusted according to changes in sensor performance.

4. The intelligent integrated forest management system according to claim 3 is characterized in that: The Internet of Things transmission module transmits the collected data in real time, including: Establish cloud platform service nodes that match the sensor network and provide high-speed data flow channels; Use IoT protocols to optimize the ratio of upload rate to network resource consumption; Perform redundancy checks on data received during transmission; Automatically adjust the sensor network transmission interval and transmission power to maintain optimal communication link stability; When new data is received, the database on the cloud service platform is updated in real time to ensure that the data analysis module can process the newly arrived information.

5. The intelligent integrated forest management system according to claim 4 is characterized in that: The Internet of Things transmission module further includes: Automatically adjust the sensor network transmission interval and transmission power to maintain optimal communication link stability based on the received signal strength indicator and signal-to-noise ratio , dynamically adjust the sensor's transmission power , the power adjustment formula is: ， in, is the transmission power, is the minimum transmission power, is the adjustment factor, is the signal-to-noise ratio, The lowest signal-to-noise ratio.

6. The intelligent integrated forest management system according to claim 1 is characterized by: The data analysis module further includes: An evaluation standard system was established to identify variables that affect tree growth, using the feature selection method of Lasso regression. The specific formula is: ， in, is the estimated regression coefficient, is the sample serial number, Indicates the value of a variable that makes a function reach the minimum value. is the sample size, For the The target value of samples, For the The sample eigenvalues, represents the eigenvalue label, is the regression coefficient, is the regularization parameter; The selected variables are included in the evaluation standard system, and each variable is assigned a corresponding weight through weighted average to evaluate the score. It is expressed as: ， in, For variables The weight of Indicates variables, Indicates the variable number, is the number of variables, Score for the assessment.

7. The intelligent integrated forest management system according to claim 6 is characterized by: The data analysis module accurately evaluates the growth status of trees based on the big data analysis model and further includes: After the prediction model training is completed, the data analysis module uses real-time data to analyze the current tree growth status and identify influencing factors. The real-time data collected by the sensor network module is input into the prediction model to obtain the current evaluation score and the contribution of each variable to the growth status. Value analysis, quantify the contribution of each variable to the model prediction results, identify key influencing factors, value It is expressed as: ， in, for value, For feature subset The model output is is a feature subset, represents the characteristic variable, is the set of all features, For subset size; Set the normal threshold and abnormal threshold, and use the confidence interval of the normal distribution to set the normal threshold. Under the normal distribution, the normal threshold The calculation formula is: ， in, is the normal threshold, is the mean of the evaluation scores, is the standard deviation; Through the historical data of quantile regression, set the abnormal threshold and evaluate the score The distribution of , abnormal threshold It is expressed as: ， in, is the abnormal threshold, It indicates the evaluation score The cumulative distribution function of is the set abnormal probability, which ranges from 1% to 5%.

8. The intelligent integrated forest management system according to claim 7 is characterized in that: The data analysis module accurately evaluates the growth status of trees based on the big data analysis model and further includes: When the evaluation score The following conditions are met: ， in, is the abnormal threshold, is the normal threshold, To score the assessment; At this point, it is determined that there is a potential risk in the growth of the trees, triggering the early warning mechanism; If the evaluation score Below abnormal threshold , immediately start the abnormal alarm process, Response time under abnormal conditions It is expressed as: ， in, is the response time, is the anomaly detection time, For decision time, Time to implement emergency measures.

9. The intelligent integrated forest management system according to claim 8, characterized in that: The intelligent tuning module further includes: First, real-time data is obtained from the sensor network module and the IoT transmission module; The features retained by principal component analysis are calculated by calculating the data covariance matrix , decompose its eigenvalues ​​to obtain the principal component matrix , to achieve dimensionality reduction, the specific formula is: ， in, is the covariance matrix, is the sample size, is the sample serial number, It is The feature vector of the samples, is the mean vector of the eigenvectors, is the vector transpose symbol; Based on the identified key factors, a prediction model is constructed using the random forest algorithm to predict the growth trend of trees and potential risk factors. Multiple decision trees are constructed for classification or regression. The output is the average or voting result of the results of multiple trees. The specific formula is: ， in, is the final prediction result. is the number of trees, It is A decision tree for input predictions, is the decision tree number, is the input feature vector.

10. The intelligent integrated forest management system according to claim 9, characterized in that: The intelligent tuning module further includes: The optimization suggestions generated by the prediction model are applied to the forest management plan, and the implementation effect is monitored in real time through the sensor network. If there is a difference between the actual effect and the expected result, the optimization suggestions will be automatically adjusted, the feedback controller of the management strategy will be adjusted, and the control signal will be output. The formula is: ， in, To output the control signal, For the moment, is the error between the actual effect and the expected target, , , are proportional, integral and differential coefficients respectively; pass The algorithm finds the optimal management strategy, The update formula is: ， in, Indicates that in a specific state Next action The expected return when is the learning rate, is the discount factor, For the next state, is the reward value, For status, For action.

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