Forestry resource efficient scheduling and accurate management system based on multi-technology fusion

Abstract

The invention relates to the field of forestry resource management, and discloses a forestry resource efficient scheduling and accurate management device based on multi-technology fusion, comprising a device body, the outer side of the device body is provided with a plurality of electronic component interfaces for connecting a computer and other devices, and a forestry resource scheduling and management optimization system is carried in the device body. A forestry resource high-efficiency scheduling and precise management system based on multi-technology fusion comprises the following modules: a resource information management module which is responsible for inputting, storing and updating various basic data of forestry resources, comprehensively inputting, updating and querying the forestry resource data through the resource information management module, ensuring that the data is accurate and timely, and managing the forestry resources; the wood yield is predicted by using a linear regression algorithm, and pest and fire hazard threshold values are set according to historical data and a statistical method, so that a scientific basis is provided for felling and planting planning of forestry resources, the resources are reasonably arranged, excessive development or resource waste is avoided, and sustainable development of the forestry resources is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of forestry resource management, and specifically to an efficient scheduling and precise management system for forestry resources based on multi-technology integration. Background Art

[0002] With the continuous development of the forestry industry and the enhancement of ecological protection awareness, the rational dispatch and effective management of forestry resources have become increasingly important. Forestry resources cover forest land, trees and related ancillary resources, which are widely distributed and complex. In actual operation, it is necessary to make precise arrangements for the felling, planting, transportation and other links of forests, while taking into account ecological protection and market demand. For example, during the peak season of timber production, it is necessary to efficiently organize manpower and equipment for felling operations and transport timber to processing plants in a timely manner; in terms of ecological protection, it is necessary to monitor the forest environment in real time, prevent the occurrence of diseases, insect pests and fires and other disasters, and ensure the balance of the forest ecosystem.

[0003] At present, the existing forestry resource scheduling and management system mainly relies on traditional data recording and analysis methods. Through regular manual inspections of forests, information such as tree growth conditions and forest environmental parameters is recorded and then entered into a simple database management system. In terms of scheduling, felling and transportation tasks are usually arranged based on experience and rough resource statistical data, lacking scientific planning and precise calculations. In terms of risk prevention and control, it mainly relies on manual observation and simple sensor monitoring, and the early warning mechanism is relatively lagging. Data analysis is also mostly a simple statistical summary, which makes it difficult to tap the potential information behind the data.

[0004] In actual scenarios, the existing forestry resource scheduling and management systems have many shortcomings. For example, in terms of timber production forecasting, since it relies solely on experience-based estimates, the output estimate deviation is large, which can easily cause companies to make mistakes in production planning, either the output is insufficient to meet market demand, or excessive logging causes waste of resources; in pest and disease control, it is easy to miss the best prevention and control opportunities based on simple threshold judgments. For example, in a forest area, because the humidity and temperature reach certain values, there is no timely warning, which eventually leads to a large-scale outbreak of pests and diseases; in terms of resource scheduling, due to the lack of scientific route planning and resource allocation methods, it often causes problems such as excessively high transportation costs, idle equipment or insufficient manpower, reducing the overall efficiency of forestry production. Therefore, the present invention provides an efficient scheduling and precise management system for forestry resources based on multi-technology integration to address the deficiencies in the prior art. Summary of the invention

[0005] In view of the shortcomings of the existing technology, the present invention provides an efficient scheduling and precise management system for forestry resources based on the integration of multiple technologies, which solves the problem in the existing technology that there is a lack of suitable solutions for the scheduling and management of forestry resources, which easily leads to resource damage.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A device for efficient scheduling and precise management of forestry resources based on multi-technology integration, including the device body, characterized in that a variety of electronic component interfaces are provided on the outer side of the device body for connecting a computer and other devices, and a forestry resource scheduling and management optimization system is installed inside the device body.

[0007] A system for efficient scheduling and precise management of forestry resources based on multi-technology integration, applied to the above-mentioned device for optimizing forestry resource scheduling and management, includes the following modules: Resource information management module: Responsible for inputting, storing, and updating various basic data of forestry resources, including forest land location, area, tree species distribution, number of trees, and growth status information, facilitating query and call at any time; Scheduling plan formulation module: According to the resource status, market demand, and logging and planting task arrangements, generate a scientific and reasonable resource scheduling plan, covering transportation route planning, manpower and equipment allocation; Real-time monitoring module: Utilize sensors and drone technology to collect environmental data of forest land and dynamic changes of resources in real time and feedback them to the system; Analysis and decision-making module: Based on the data accumulated by the system, use data analysis and prediction models to provide decision-making basis for long-term planning and short-term task adjustment of forestry resources, estimate future timber production, and analyze the risk of pests and diseases; Early warning module: Set various index thresholds, and when the monitoring data triggers the thresholds, including the outbreak of pests and diseases, fire hazards, and over-logging of resources, issue early warning notifications to relevant personnel in a timely manner; User management module: Manage the permissions and login information of system users to ensure that personnel in different positions can access and operate corresponding functions according to their permissions; System settings module: Used for basic system management work such as system parameter configuration, data backup, and restoration.

[0008] Preferably, the resource information management module includes the following units: Data input unit: Provide an intuitive data input interface, support manual input or batch import of basic data of forestry resources such as forest land location, area, and tree species distribution, and ensure accurate recording of information; Data update unit: Regularly or according to actual situations, update the information on the growth status of trees and changes in forest land in real time; Data query unit: Users can quickly query the required forestry resource information by forest land number or tree species name, facilitating daily management and decision-making.

[0009] Preferably, the scheduling plan formulation module includes the following units: Task Analysis Unit: Conduct a detailed analysis of logging and planting tasks, determine task priorities and time nodes in combination with the current resource situation and market demand; Resource Allocation Unit: Reasonably arrange human and equipment resources according to task requirements and formulate a detailed allocation plan; Route Planning Unit: Use geographic information system technology to plan the optimal transportation route to reduce transportation costs and time.

[0010] Preferably, the real-time monitoring module includes the following units: Sensor Data Acquisition Unit: Connect various sensors to collect environmental data such as temperature, humidity, and soil moisture in the forest land in real time to provide data support for tree growth; UAV Inspection Unit: Use UAVs to regularly inspect the forest land, obtain high-resolution images, monitor tree growth and pest and disease conditions, and detect abnormalities in a timely manner; Data Transmission and Processing Unit: Quickly transmit the collected data to the system, and perform preprocessing and analysis for subsequent use.

[0011] Preferably, the analysis and decision-making module includes the following units: Data Analysis Unit: Use data mining and statistical analysis methods to deeply analyze the historical data accumulated by the system and explore data value; Prediction Model Unit: Build prediction models for timber yield and pest and disease risk assessment to provide a scientific basis for forest resource planning and management; Decision Support Unit: Generate a decision-making recommendation report based on data analysis and prediction results to assist management personnel in making reasonable decisions.

[0012] Preferably, the early warning module includes the following units: Threshold Setting Unit: Set early warning thresholds for pest and disease damage, fire hazards, and resource logging indicators according to forest resource management standards and experience; Early Warning Trigger Unit: Monitor data in real time, and once the data exceeds the set threshold, immediately trigger the early warning mechanism; Notification Publishing Unit: Notify relevant personnel of the early warning information in a timely manner through text messages and system pop-ups so that countermeasures can be taken.

[0013] Preferably, the user management module includes the following units: User Registration and Login Unit: Provide user registration and login functions to ensure the legal identity of users and guarantee system security; Permission Allocation Unit: Allocate different system operation permissions according to user positions and responsibilities; User Information Management Unit: Manage the basic information and login records of users to facilitate system maintenance and user management.

[0014] Preferably, the system setting module includes the following units: Parameter configuration unit: Set and adjust various parameters of the system to meet different usage requirements; Data backup and recovery unit: Regularly back up system data to prevent data loss; When data problems occur, it can quickly recover data to ensure the normal operation of the system; System log management unit: Record system operation logs to facilitate system monitoring and fault troubleshooting.

[0015] The present invention provides a forestry resource efficient scheduling and precise management system based on multi-technology integration. It has the following beneficial effects: 1. Through the resource information management module of the present invention, forestry resource data is comprehensively input, updated, and queried to ensure accurate and timely data. The linear regression algorithm is used to predict the wood yield, and the thresholds for pests, diseases, and fire hazards are set based on historical data and statistical methods, providing a scientific basis for the logging and planting planning of forestry resources, rationally arranging resources, avoiding overdevelopment or resource waste, and realizing the sustainable development of forestry resources.

[0016] 2. Through the real-time monitoring module of the present invention, data is collected by sensors and drones, and the early warning module issues risk warnings for pests, diseases, fires, etc. in a timely manner. The risk of pests and diseases is evaluated through logistic regression, and the threshold for fire hazards is determined by cluster analysis. Once the data triggers the threshold, relevant personnel are quickly notified to take measures to minimize the disaster losses and effectively protect the forest ecological environment and the achievements of forestry economy.

[0017] 3. The scheduling plan formulation module of the present invention, through task analysis, resource allocation, and route planning, combined with the decision-making support provided by the data analysis unit, comprehensively considers factors such as resource status, market demand, and transportation costs, scientifically arranges manpower, equipment, and transportation routes. At the same time, algorithms such as principal component analysis are used to reduce the dimension of multi-dimensional data, simplify the analysis process, make the scheduling decision more efficient, and improve the coordination of each link in forestry production and the resource utilization efficiency. Brief Description of the Drawings

[0018] Figure 1 It is a schematic diagram of the device body of the present invention; Figure 2 It is a schematic diagram of the system architecture of the present invention; Figure 3 It is a schematic diagram of the resource information management module of the present invention; Figure 4 It is a schematic diagram of the scheduling plan formulation module of the present invention; Figure 5 It is a schematic diagram of the real-time monitoring module of the present invention; Figure 6 It is a schematic diagram of the analysis and decision-making module of the present invention; Figure 7 Schematic diagram of the warning module of the present invention; Figure 8 Schematic diagram of the user management module of the present invention; Figure 9 Schematic diagram of the user management module of the present invention.

[0019] Among them, 1. Device body. Specific implementation mode

[0020] Next, in combination with the attached drawings of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0021] Embodiment 1: Please refer to the attached Figure 1 , the embodiment of the present invention provides a device for efficient scheduling and precise management of forestry resources based on multi-technology integration, including a device body 1. A variety of electronic component interfaces are arranged on the outer side of the device body 1 for connecting a computer and other devices. A forestry resource scheduling and management optimization system is carried inside the device body 1.

[0022] Specifically, the system carried inside the device body 1 is electrically connected to other devices through an external interface, including but not limited to various sensors and unmanned aerial vehicles.

[0023] Embodiment 2: Please refer to the attached Figure 2 - attached Figure 9 , the embodiment of the present invention provides a system for efficient scheduling and precise management of forestry resources based on multi-technology integration, including the following modules: Resource information management module: Responsible for inputting, storing, and updating various basic data of forestry resources, including forest land location, area, tree species distribution, number of trees, and growth status information, for easy query and call at any time; The resource information management module includes the following units: Data input unit: Provides an intuitive data input interface, supports manual input or batch import of basic data of forestry resources such as forest land location, area, and tree species distribution to ensure accurate recording of information; Data update unit: Regularly or according to actual situations, real-time updates information on the growth status of trees and changes in forest land to ensure the timeliness and accuracy of data; Data query unit: Users can quickly query the required forestry resource information by forest land number or tree species name for easy daily management and decision-making.

[0024] Specifically, as the starting link of the entire forestry resource data management, the data entry unit undertakes the key task of introducing a large amount of basic data into the system. For the manual entry method, the interface provides clear field identifiers and detailed prompt information to help users input data accurately. For the batch import function, common data file formats such as CSV and Excel are supported. Before import, the system will automatically perform format verification and data pre-review. Once data format errors or data anomalies are found, it will immediately feedback to the user to prevent incorrect data from entering the system and ensure accurate data recording. The data update unit is the core component for maintaining the freshness of forestry resource data. It has a flexible update mechanism. On the one hand, it can comprehensively update information such as the growth status of forest trees and forest land changes at preset time intervals, such as monthly or quarterly. When updating the growth status of forest trees, with the help of advanced measurement technologies and data analysis means, it accurately records the changes in growth indicators such as tree height, diameter at breast height, and crown width. On the other hand, when sudden forest land change events occur, such as forest land boundary adjustment and the start of new afforestation projects, users can quickly enter the latest information through the emergency update channel of the system at any time. The system will automatically compare and record the data before and after the update to form a data change log for traceability and query, ensuring the timeliness and accuracy of the data. The data query unit provides strong support for the daily management and decision-making of forestry resources. When querying, users can accurately filter through various combination conditions. In addition to common forest land numbers and tree species names, compound queries can also be made according to conditions such as the management type of forest land, forest age range, and soil type. At the same time, the query results support being exported in multiple formats, facilitating further data processing and analysis by users, and greatly facilitating daily management and decision-making.

[0025] Dispatch plan formulation module: Generate a scientific and reasonable resource dispatch plan according to the resource status, market demand, and task arrangements such as logging and planting, covering transportation route planning, manpower and equipment allocation; the dispatch plan formulation module includes the following units: Task analysis unit: Conduct a detailed analysis of logging and planting tasks, and determine task priorities and time nodes in combination with the resource status and market demand; Resource allocation unit: Reasonably arrange manpower and equipment resources according to task requirements, formulate a detailed allocation plan, and improve resource utilization efficiency; Route planning unit: Use geographic information system (GIS) technology to plan the optimal transportation route and reduce transportation costs and time.

[0026] Specifically, when faced with tasks such as logging and planting, the task analysis unit first comprehensively sorts out the goals and requirements of the tasks. Combining with the current resource situation, it deeply investigates information such as the distribution and growth status of target tree species in the current forest land and the surrounding topography and landforms. At the same time, it closely pays attention to market demand, analyzes the market price fluctuations of different wood varieties, recent order demands, and market trend forecasts for a period of time in the future. By comprehensively considering these factors and using scientific methods such as the analytic hierarchy process, it determines the priority of the tasks. For logging tasks that are urgently needed in the market and have a high degree of resource maturity, they are set as high-priority tasks; for planting tasks that contribute to long-term ecological restoration and forest resource cultivation, the priority is determined according to the urgency of the ecological plan. When determining the time nodes, factors such as season, tree growth cycle, and the validity period of logging permits are considered, and a detailed and reasonable task execution schedule is formulated to ensure the orderly progress of each task. The resource allocation unit is a key link to ensure the efficient progress of forestry production. After clarifying the task requirements, for manpower allocation, professional and technical personnel, loggers, planters, etc. are reasonably arranged according to the technical requirements and labor intensity of different tasks. In terms of equipment allocation, the type, performance, and quantity of equipment are comprehensively considered. For logging tasks, appropriate logging machinery, transportation vehicles, etc. are allocated; for planting tasks, seedling cultivation equipment, tree planting machinery, etc. are prepared. At the same time, an equipment maintenance and scheduling log is established to track the usage status and maintenance requirements of the equipment in real time, ensuring that the equipment operates normally at critical moments. By formulating detailed allocation plans such as personnel shift schedules and equipment dispatch plans, the optimal allocation of resources is achieved, the resource utilization efficiency is improved, and resource idleness or overuse is avoided. The route planning unit makes full use of the powerful functions of geographic information system (GIS) technology to realize the intelligent planning of transportation routes. During the planning process, geographical information such as the topography of the forest land, road distribution, and bridge bearing capacity is first incorporated into the system database. Combining the locations of logging and planting tasks and target locations such as wood processing plants and seedling supply locations, path planning algorithms such as Dijkstra algorithm are used, and factors such as transportation distance, road conditions, and transportation costs are comprehensively considered. For the transportation distance, the actual mileage of different routes is accurately calculated; for road conditions, the slope, flatness, and traffic restrictions of the roads are analyzed; for transportation costs, fuel consumption, vehicle wear, etc. are considered. Through multi-dimensional analysis and calculation, the optimal transportation route is selected. At the same time, the route planning is dynamically adjusted according to real-time traffic information and weather conditions. For example, when a certain route is difficult to pass due to road construction or bad weather, the system can quickly re-plan the route to ensure that materials such as wood and seedlings can be transported in a timely and safe manner, effectively reducing transportation costs and time.

[0027] Real-time Monitoring Module: Utilize sensors and drone technology to collect environmental data of forest land and dynamic changes of resources in real time, and feedback them to the system. The real-time monitoring module includes the following units: Sensor Data Acquisition Unit: Connect various sensors to collect environmental data such as temperature, humidity, and soil moisture of forest land in real time, providing data support for the growth of forest trees; Drone Inspection Unit: Use drones to regularly inspect forest land, obtain high-resolution images, monitor the growth of forest trees and the occurrence of pests and diseases, and promptly detect abnormalities; Data Transmission and Processing Unit: Quickly transmit the collected data to the system, and perform preprocessing and analysis for subsequent use.

[0028] Specifically, the sensor data acquisition unit can be compatible with various types of sensors, such as high-precision temperature and humidity sensors, which can accurately measure the temperature and relative humidity of forest land. The temperature measurement accuracy can reach ±0.1°C, and the humidity measurement accuracy can reach ±2%RH, ensuring the accuracy of data. The soil moisture sensor penetrates deep into the soil to measure key parameters such as soil water content, pH value, and nutrient content, providing data support at the soil level for the growth of forest trees. These sensors send the collected data continuously to the data aggregation node through wired or wireless transmission methods, such as low-power wide-area network technologies like LoRa and NB-IoT. During the data acquisition process, the system dynamically adjusts the acquisition frequency according to the characteristics of different sensors and the changes in the forest land environment. For example, when the weather changes drastically, the acquisition frequency of the temperature and humidity sensors is increased from once per hour to once every 15 minutes to obtain environmental data more timely and provide more time-sensitive data support for the growth of forest trees. The drone inspection unit uses advanced drone technology to achieve all-round and high-efficiency monitoring of forest land. The drone conducts regular inspections of the forest land according to the preset flight route and time interval. During the flight, the high-resolution optical camera and multi-spectral camera carried on it work synchronously. The optical camera can take clear images of the forest land with a resolution of up to 48 million pixels, clearly presenting the shape and distribution of forest trees; the multi-spectral camera can capture spectral information in different bands. By analyzing this information, the growth status of forest trees can be accurately monitored, such as determining whether the forest trees are short of water or fertilizer and whether they are attacked by pests and diseases. When the drone encounters signal occlusion or bad weather, it will automatically adjust the flight height and path to ensure the smooth completion of the inspection task. Once abnormal withering or abnormal color changes of trees are found in the forest land, the relevant images and location information will be immediately transmitted back to the system to detect abnormalities in a timely manner, providing key basis for subsequent forestry resource management decisions. The data transmission and processing unit is the data center of the real-time monitoring module, responsible for quickly and accurately transmitting the data collected by sensors and drones into the system and performing effective preprocessing and analysis. During the data transmission stage, a high-speed and stable transmission network, such as a 5G network or a dedicated wireless local area network, is used to ensure that the data is transmitted within a short time and avoid data delay. After the data arrives at the system, format conversion and denoising processing are first carried out to uniformly convert different formats of sensor data and image data into a format recognizable by the system and remove the noise data generated by signal interference, etc. For image data, image recognition and analysis algorithms, such as object detection algorithms based on deep learning, are used to automatically identify information such as the species of forest trees and pest and disease characteristics in the images; for sensor data, by establishing a data model, the change trends and mutual relationships of environmental parameters are analyzed, such as analyzing the correlation between temperature and humidity and soil moisture, providing data basis for the evaluation of the growth environment of forest trees. The preprocessed and analyzed data is stored in the system database for subsequent query, call, and in-depth analysis.

[0029] Analysis and Decision-making Module: Based on the data accumulated by the system, using data analysis and prediction models, it provides decision-making basis for the long-term planning of forestry resources, short-term task adjustment, etc., estimates future timber yields and analyzes pest and disease risks; the analysis and decision-making module includes the following units: Data Analysis Unit: Using data mining and statistical analysis methods, it deeply analyzes the historical data accumulated by the system and mines the data value; Prediction Model Unit: Constructs models such as timber yield prediction and pest and disease risk assessment to provide a scientific basis for forestry resource planning and management; Decision Support Unit: Generates a decision-making recommendation report according to the data analysis and prediction results to assist managers in making reasonable decisions.

[0030] Specifically, the data analysis unit is the key link to deeply mine the data value of forestry resources. In terms of data mining, the association rule mining algorithm is used, and the Pearson correlation coefficient algorithm is adopted to explore the potential connections between different data. The Pearson correlation coefficient is used to measure the linear correlation degree between two variables and analyze the relationship between the tree growth rate and soil nutrient content. The formula is as follows:

[0031] Among them, and are the th observations of variables and respectively, and are the means of variables and respectively, is the number of observations. The value range of

[0032] is [-1, 1]. The closer the value is to 1 or -1, the stronger the linear correlation degree between the two variables; the closer the value is to 0, the weaker the linear correlation degree.

[0032] Principal Component Analysis (PCA) is often used to reduce the dimension of multi-dimensional data. For example, it transforms high-dimensional data containing various environmental factors and tree growth indicators into a few comprehensive indicators for subsequent analysis. The main steps and core formulas are as follows: (1)

[0033] Among them is the value of the th variable of the th sample in the original data matrix, is the mean of the th variable, is the standard deviation of the th variable, It is the standardized data.

[0034] (2)Calculate the covariance matrix:

[0035]

[0036] .

[0037] (3)Calculate the eigenvalues and eigenvectors of the covariance matrix: Solve the equation , where is the eigenvalue, is the corresponding eigenvector.

[0038] (4)Select the principal components: Sort the eigenvectors according to the magnitudes of the eigenvalues, and select the first eigenvectors ( is less than the dimension of the original data). Project the original data onto these eigenvectors to obtain the data after dimensionality reduction. The projection formula is: ; where is the data matrix after dimensionality reduction, is the standardized data matrix, is the matrix composed of the first eigenvectors.

[0039] In statistical analysis, time series analysis methods are adopted to process data such as annual timber yields and pest occurrence frequencies, so as to predict the change trends in the future for a period of time. At the same time, using the clustering analysis algorithm, forest lands in different regions are classified according to factors such as resource characteristics and ecological environments, which is convenient for formulating management strategies targeted. In terms of timber yield prediction, combining the linear regression model and machine learning algorithms, a preliminary prediction model is established using linear regression.

[0040] (1)Timber yield prediction - linear regression. Linear regression assumes that there is a linear relationship between the dependent variable (timber yield ) and independent variables (such as the number of trees , tree age , forest land area , etc.). Its formula is: ; where, is the intercept, , , are the coefficients of each independent variable, representing the degree of influence of each independent variable on wood production. is the error term, representing the random factors that cannot be explained by the independent variables. It is determined by minimizing the sum of squared errors. The

[0041] (2) Pest and disease risk assessment - Logistic regression. Logistic regression is used to predict the probability of pest and disease occurrence, with a value range between 0 and 1. Let the independent variables (such as temperature , humidity , tree species , etc.). The formula is: ; where represents the probability of pest and disease occurrence, is the natural constant. When the probability is greater than the set threshold (such as 0.5), it can be determined that pests and diseases may occur. Similarly, specific algorithms (such as the gradient descent method) are used to determine the values to optimize the prediction accuracy of the probability of pest and disease occurrence of the model.

[0042] The decision support unit generates a targeted and operable decision - making recommendation report based on data analysis and prediction results. During the report generation process, first, the data analysis and prediction results are visually displayed, using intuitive chart forms such as bar charts, line charts, and maps to present information such as wood production trends and pest and disease risk distributions. Then, combined with the goals and actual situations of forest resource management, specific decision - making recommendations are put forward from multiple aspects such as harvesting plans, planting plans, pest and disease control, and resource allocation. The report is written in plain language to facilitate managers' understanding and reference, assisting them in making scientific and reasonable decisions and promoting the sustainable development of forest resources.

[0043] Early warning module: Set various indicator thresholds. When the monitoring data triggers the thresholds, including situations such as pest and disease outbreaks, fire hazards, and over - harvesting of resources, timely warning notifications are sent to relevant personnel; The early warning module includes the following units: Threshold setting unit: Set the early warning thresholds for indicators such as pests and diseases, fire hazards, and resource harvesting according to forest resource management standards and experience; Early warning trigger unit: Real - time monitor data. Once the data exceeds the set threshold, immediately trigger the early warning mechanism; Notification release unit: Through text messages and system pop - ups, timely notify relevant personnel of the warning information so that they can take countermeasures.

[0044] Pest and disease threshold setting: (1) Statistical analysis of historical data: Collect environmental data during pest and disease occurrences over the years; Calculate the mean and standard deviation of these data. For example, the mean of temperature data is: ; The standard deviation is: ; where is the number of data samples, is the th temperature data.

[0045] Generally, the threshold can be set at the mean plus a certain multiple of the standard deviation. For example, commonly ( usually takes 1 - 3 and can be adjusted according to the actual situation) is used as the temperature warning threshold for the occurrence of pests and diseases. When the temperature monitored in real time satisfies, it is considered that there may be a risk of pests and diseases occurring.

[0046] (2) Machine learning method (decision tree): Using historical pest and disease occurrence data and corresponding environmental factors as the training set, a decision tree model is constructed. The decision tree splits the features (such as humidity, temperature, etc.) at each node, so that data of different categories (pests and diseases occurring or not occurring) are divided into different child nodes as much as possible. After the model training is completed, by analyzing the branching conditions of the decision tree, the threshold conditions for the occurrence of pests and diseases under different combinations of environmental factors can be determined.

[0047] Setting the fire hazard threshold: (1) Calculation of the dryness index: Commonly used forest fire danger indices such as the forest fire meteorological index , the formula is: ; where is the relative humidity, is the temperature, is the wind speed, is the precipitation, is a complex functional relationship obtained through a large number of experiments and data fitting.

[0048] According to the corresponding relationship between historical fire data and value, a threshold is determined. When the calculated real-time value exceeds this threshold, a fire warning is issued.

[0049] (2) Clustering analysis method: Collect various environmental data (vegetation type, terrain slope, temperature, humidity, etc.) when a fire occurs.

[0050] Use a clustering algorithm (such as K-Means clustering) to divide the data into different clusters, so that the data within the same cluster has high similarity.

[0051] Analyze the characteristics of each cluster, find out the combination of environmental factors and their value ranges that are closely related to the occurrence of fires, and use this as the basis for setting the fire hazard threshold.

[0052] Specifically, the threshold setting unit is the key starting point of the early warning mechanism, and its setting basis is closely centered around forestry resource management standards and long-term accumulated practical experience. When setting the pest and disease thresholds, refer to the pest and disease control guidelines issued by the forestry department, combine with the actual data of pest and disease occurrences in the local area over the years, analyze the outbreak patterns of different pests and diseases under environmental conditions such as specific temperature, humidity, and vegetation types, so as to determine the early warning thresholds of key indicators such as temperature, humidity, and pest and disease density. For fire hazards, based on the forest fire danger level classification standard, comprehensively consider factors such as the flammability of forest vegetation, terrain slope, and precipitation conditions, and determine the thresholds of indicators such as forest fire meteorological index and fuel load. In terms of resource harvesting, according to the requirements of sustainable forest resource management, combine with the growth potential and ecological carrying capacity of forest land, and set the thresholds of indicators such as timber harvesting volume and harvesting intensity to ensure the reasonable utilization of forest resources. The early warning trigger unit continuously obtains various data of the forest land by being connected in real time with the sensor data acquisition unit, the drone patrol unit, etc. Using high-performance data processing servers and real-time data analysis algorithms, it quickly analyzes the collected data. Once the monitored data exceeds the set threshold, the early warning trigger unit immediately activates the early warning mechanism, records detailed information such as the time, location, and triggered indicators of the early warning occurrence, and quickly transmits the early warning information to the notification release unit. At the same time, it automatically conducts retrospective analysis on the data that triggers the early warning, generates a brief early warning analysis report, and provides data support for subsequent response decisions. The notification release unit is responsible for promptly and accurately conveying the early warning information to relevant personnel to ensure that response measures can be taken quickly. It supports multiple notification methods to meet the needs of different scenarios and personnel. For SMS notifications, by connecting to the SMS interface of the operator, the early warning information is sent to the mobile phones of relevant personnel in a concise and clear format, and the content includes the early warning type, occurrence location, and preliminary measures recommended. System pop-ups will immediately appear on the operation interface of the forestry resource management system to remind the personnel using the system to pay attention to the early warning information. The pop-ups will also flash and be accompanied by a sound alarm to ensure that the personnel will not ignore it. In addition, for some urgent and important early warnings, relevant responsible persons will be directly notified by voice call to ensure the correct transmission of information. The notification release unit also has a notification record function, which details and records information such as the sending time, receiving personnel, and receiving status of each early warning notification for subsequent query and traceability.

[0053] User Management Module: Manages the permissions and login information of system users to ensure that personnel in different positions can access and operate corresponding functions according to their permissions. The User Management Module includes the following units: User Registration and Login Unit: Provides user registration and login functions to ensure the legal identity of users and safeguard system security; Permission Assignment Unit: Assigns different system operation permissions according to the user's position and responsibilities; User Information Management Unit: Manages the basic information and login records of users to facilitate system maintenance and user management.

[0054] Specifically, in the registration process of the user registration and login unit, a concise and standardized registration form is provided, requiring users to fill in real and valid information such as username, password, mobile phone number, and email. To ensure password strength, it is set that the password should contain uppercase and lowercase letters, numbers, and special characters, with a length of not less than 8 digits. At the same time, a dual verification method of SMS verification code and email verification is adopted to verify the mobile phone number and email provided by the user, preventing false information registration. After successful registration, the system will generate a unique user ID for the user, facilitating subsequent management and identification. When logging in, multiple login methods such as username, mobile phone number, or email are supported to meet different usage habits of users. Advanced encryption technologies such as SSL / TLS encryption protocols are used to encrypt the transmission of the account passwords entered by users, preventing data from being stolen during transmission. A verification code mechanism is introduced. After the user continuously makes 3 incorrect login attempts, a verification code input box will automatically pop up to further ensure login security. If a user forgets their password, they can retrieve it through their mobile phone number or email. The system will send an email or SMS containing a password reset link, and the link is valid for 24 hours and will automatically expire after expiration, ensuring the security of the password retrieval process. The permission allocation unit makes a detailed and reasonable permission division based on the user's position and responsibilities. For administrators, they have the highest permissions in the system and can operate on all modules, including data addition, deletion, modification, and query, user management, system settings, etc. They can view and modify all forestry resource information, adjust the scheduling plan, and deeply process warning information, etc. For forestry technicians, they are given the permissions to query data and update some data in the resource information management module, as well as view sensor data and UAV inspection images in the real-time monitoring module, facilitating their forestry resource monitoring and analysis work. At the same time, in the analysis and decision-making module, they can participate in the discussion and optimization of data analysis and prediction models, but do not have the permission to modify core decision-making data. Frontline workers are mainly responsible for actual forestry production operations, so they are only granted basic data entry permissions, such as entering work data such as the daily logging and planting quantities in the resource information management module, and viewing scheduling information related to their own work tasks in the system to ensure the orderly progress of work. The permission allocation unit also supports a flexible permission adjustment function, and can, according to project requirements or personnel position changes, timely perform operations such as adding, modifying, or deleting user permissions. The user information management unit undertakes the comprehensive management responsibility for various types of user information. In terms of basic information management, it not only records the information provided by users during registration, but also includes information such as the user's affiliated department, working years, and forestry-related qualification certificates, facilitating the system to have a more comprehensive understanding and classified management of users. Regularly remind users to update their personal information to ensure the accuracy and timeliness of the information. For user login records, the time, IP address, login method, and login status of each login are detailedly recorded.By analyzing the login records, abnormal login behaviors can be detected in a timely manner, such as multiple off-site login attempts within a short period. The system will automatically trigger a security alert, and the administrator can take measures such as freezing the account according to the situation to ensure the security of the system. In addition, the user information management unit also has the functions of user information backup and recovery, regularly backing up user information to prevent information damage caused by system failures or data loss. When needed, user information can be quickly restored from the backup to ensure the smooth progress of system maintenance and user management work. At the same time, strictly abide by data privacy protection regulations, encrypt and store user information, and strictly prohibit the disclosure of user information without authorization.

[0055] System settings module: Used for basic system management tasks such as system parameter configuration, data backup and recovery, to ensure the stable operation of the system. The system settings module includes the following units: Parameter configuration unit: Sets and adjusts various parameters of the system to meet different usage requirements; Data backup and recovery unit: Regularly backs up system data to prevent data loss; When data problems occur, it can quickly restore the data to ensure the normal operation of the system; System log management unit: Records system operation logs, including user login and data modification information, for system monitoring and troubleshooting.

[0056] Specifically, the parameter configuration unit is a key component to ensure the system's flexible adaptation to different usage scenarios. In terms of setting the data storage path, it supports users to customize the local disk path or connect to a network storage device such as NAS. When the local path is selected, the system will automatically detect the validity of the path and the remaining disk space. If the space is insufficient, it will prompt the user to change the path or clean the disk. For the sensor acquisition frequency, multiple preset options are provided, such as every 5 minutes, 15 minutes, 1 hour, etc. At the same time, users are allowed to accurately set it to collect once per second or at longer intervals according to actual monitoring needs. For example, in key monitoring areas, the acquisition frequency of temperature and humidity sensors can be set to once every 5 minutes to obtain denser data; while in regular areas, setting it to once per hour can meet the basic needs. In addition, the data transmission protocol can also be configured, such as selecting TCP / IP, UDP, etc., to optimize the stability and speed of data transmission according to the network environment and data transmission requirements. For the parameters of data analysis algorithms, they can also be adjusted in this unit. For example, in data mining algorithms, set the support and confidence thresholds of association rules to meet different analysis accuracy requirements. The data backup and recovery unit undertakes the important responsibility of protecting the system data security. In terms of data backup, multiple backup strategies are provided. Full backups can be performed according to a time period, such as a full data backup at 0:00 on Sunday every week, and all data in the system, including resource information, user information, analysis results, etc., are completely copied to the backup storage medium. Incremental backups are also supported, that is, only the data that has changed since the last backup is backed up. This method can greatly reduce the backup time and storage space occupation. For example, incremental backups of the newly added and modified data on the same day are performed every night. The backup storage medium supports multiple types, such as local hard disks, external mobile hard disks, cloud storage, etc. When backing up to cloud storage, encryption transmission and storage technologies are used to ensure data security. When problems such as data loss, damage, or accidental deletion occur, the data recovery function comes into play. The system will quickly retrieve the backup data and select the backup data at a specific time point for recovery according to user needs. During the recovery process, the recovery progress and status information will be displayed in real time, allowing users to clearly understand the recovery situation. At the same time, the current data will be evaluated and backed up before recovery to prevent the existing data from being further damaged due to the recovery operation. The system log management unit details various operation information during the system operation process, providing strong support for system monitoring and troubleshooting. For user login logs, not only the login time, IP address, login method of users are recorded, but also the type of login device and the operating system version used for login are recorded. When abnormal login behaviors are found, such as multiple failed login attempts or off-site logins within a short period of time, the system will automatically mark the login record and send an alert to the administrator. In terms of data modification logs, the time of data modification, the person who modified it, the data content before modification, and the data content after modification are recorded, accurate to the change of each field.For example, when the forest land area data in the resource information management module is modified, the log will record in detail the area values before and after the modification and the reasons for the modification. For internal operations of the system, such as the execution of data analysis tasks and the triggering of warning information, detailed records will also be made. The system log supports querying and filtering according to various conditions such as time range, operation type, user, etc., facilitating administrators to quickly locate the required information. At the same time, the logs are regularly cleaned and archived, and the old logs are stored in a dedicated log storage device to release the system storage space and ensure the efficiency and stability of system log management.

[0057] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A device for efficient dispatching and precise management of forestry resources based on multi-technology integration, comprising a device body (1), characterized in that: The outer side of the device body (1) is provided with a variety of electronic component interfaces for connecting a computer and other equipment, and the interior of the device body (1) is equipped with a forestry resource scheduling and management optimization system.

2. An efficient dispatching and precise management system for forestry resources based on multi-technology integration, characterized by: The device for efficient scheduling and precise management of forestry resources based on multi-technology integration as described in claim 1 comprises the following modules: Resource information management module: responsible for entering, storing and updating various basic data of forestry resources, including forest location, area, tree species distribution, number of trees and growth status information, so as to facilitate query and call at any time; Scheduling plan making module: Generates scientific and reasonable resource scheduling plans based on resource status, market demand, and harvesting and planting task arrangements, covering transportation route planning, manpower and equipment deployment; Real-time monitoring module: using sensors and drone technology to collect forest environmental data and resource dynamics in real time, and feed it back to the system; Analysis and decision-making module: Based on the data accumulated by the system, data analysis and prediction models are used to provide decision-making basis for long-term planning of forestry resources and short-term task adjustment, estimate future timber production and analyze pest and disease risks; Early warning module: Set thresholds for various indicators. When the monitoring data triggers the threshold, including outbreaks of pests and diseases, fire hazards, and over-harvesting of resources, timely issue early warnings to notify relevant personnel; User management module: manages the system users’ permissions and login information, ensuring that personnel in different positions can access and operate corresponding functions according to their permissions; System settings module: used for basic system management tasks such as system parameter configuration, data backup and recovery.

3. The forestry resources efficient scheduling and precise management system based on multi-technology integration according to claim 2 is characterized in that: The resource information management module includes the following units: Data entry unit: provides an intuitive data input interface, supports manual entry or batch import of basic forestry resource data such as forest location, area, and tree species distribution, and ensures accurate recording of information; Data update unit: regularly or according to actual conditions, update the information of forest growth status and forest land changes in real time; Data query unit: Users can quickly query the required forestry resource information by forest land number or tree species name, which is convenient for daily management and decision-making.

4. The forestry resources efficient scheduling and precise management system based on multi-technology integration according to claim 2 is characterized in that: The scheduling plan making module includes the following units: Task analysis unit: Conduct detailed analysis of felling and planting tasks, determine task priorities and time nodes based on resource status and market demand; Resource Allocation Unit: Rationally arrange human and equipment resources according to task requirements and formulate detailed allocation plans; Route planning unit: Use geographic information system technology to plan the best transportation route and reduce transportation costs and time.

5. The forestry resources efficient scheduling and precise management system based on multi-technology integration according to claim 2 is characterized in that: The real-time monitoring module includes the following units: Sensor data acquisition unit: connects to various sensors to collect environmental data such as temperature, humidity and soil moisture of forest land in real time, providing data support for tree growth; UAV inspection unit: Use drones to regularly inspect forests, obtain high-resolution images, monitor tree growth, pests and diseases, and detect abnormalities in a timely manner; Data transmission and processing unit: quickly transmit the collected data to the system, and perform pre-processing and analysis for subsequent use.

6. The forestry resources efficient scheduling and precise management system based on multi-technology integration according to claim 2 is characterized in that: The analysis and decision module includes the following units: Data analysis unit: Use data mining and statistical analysis methods to conduct in-depth analysis of historical data accumulated by the system and explore the value of data; Prediction model unit: construct timber yield prediction and pest risk assessment models to provide a scientific basis for forestry resource planning and management; Decision support unit: Generates decision recommendation reports based on data analysis and prediction results to assist managers in making reasonable decisions.

7. The forestry resources efficient scheduling and precise management system based on multi-technology integration according to claim 2 is characterized in that: The warning module includes the following units: Threshold setting unit: according to forestry resource management standards and experience, set early warning thresholds for pests and diseases, fire hazards, and resource harvesting indicators; Early warning trigger unit: monitors data in real time and immediately triggers an early warning mechanism once the data exceeds the set threshold; Notification issuing unit: The warning information will be promptly notified to relevant personnel through SMS and system pop-up windows so that they can take corresponding measures.

8. The forestry resources efficient scheduling and precise management system based on multi-technology integration according to claim 2 is characterized in that: The user management module includes the following units: User registration and login unit: provides user registration and login functions to ensure the legality of user identities and ensure system security; Permission allocation unit: allocates different system operation permissions according to user positions and responsibilities; User information management unit: manages users' basic information and login records to facilitate system maintenance and user management.

9. The forestry resources efficient scheduling and precise management system based on multi-technology integration according to claim 2 is characterized in that: The system setting module includes the following units: Parameter configuration unit: set and adjust various parameters of the system to meet different usage requirements; Data backup and recovery unit: regularly back up system data to prevent data loss; When data problems occur, data can be quickly restored; System log management unit: records system operation logs to facilitate system monitoring and troubleshooting.

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