A forestry pest early warning model construction system and method

Abstract

The present invention discloses a forest pest early warning model construction system and method, which relates to the technical field of forestry early warning model construction. It outputs an impact factor for each block, and re-corrects the initial monitoring intensity of each block based on the impact factor. After regularly conducting a comprehensive analysis of the real-time data of all blocks, the overall performance index of the monitored forest area is obtained, and the overall performance index is combined with dynamic judgment rules to analyze whether it is necessary to optimize and adjust the current monitoring intensity of all blocks. After dynamically generating a personalized plan for each block based on the analysis results, the personalized plan is mapped to the monitoring forest area early warning model for display. The construction system can dynamically adjust the monitoring intensity according to the actual situation of different blocks, improve the pertinence and accuracy of monitoring, and generate an overall performance index by regularly comprehensively analyzing the real-time data of the blocks and combining it with dynamic judgment rules to dynamically adjust the monitoring strategy, so that the system can adapt to environmental and risk changes.

Description

Technical Field

[0001] The present invention relates to the technical field of forestry early warning model construction, and in particular to a forestry pest early warning model construction system and method. Background Art

[0002] With climate change and global warming, the distribution, life cycle and reproduction rate of forest pests may change, new pest populations may invade, such as the gypsy moth, and the spread of existing populations may also intensify. The instability factors brought about by climate change make the monitoring and early warning of forest pests particularly important. The background of building a system is mainly to respond to and prevent the threats of forest pests (such as pests, pathogens, etc.) to forest ecosystems. The invasion of forest pests will not only destroy forest resources and lead to a decrease in timber production, but may also cause forest fires, affect biodiversity, etc., and thus have a wide range of impacts on the ecological environment and social economy.

[0003] The existing technology has the following defects:

[0004] 1. Existing monitoring systems typically use large forest areas as monitoring units, lacking detailed analysis of regional micro-environmental differences. Factors such as topography, climate, and vegetation conditions vary significantly across different areas, but unified monitoring schemes ignore these factors. As a result, monitoring results fail to fully reflect local conditions and make it difficult to conduct precise monitoring and prevention and control of specific risk areas.

[0005] 2. The monitoring strategies in existing technologies are often based on fixed monitoring intensity and frequency, and cannot be dynamically adjusted according to real-time environmental changes and risk levels. For example, when the pest risk suddenly increases, the built system may not be able to respond quickly and still perform monitoring according to the established plan, resulting in insufficient attention to high-risk areas or waste of resources in low-risk areas.

[0006] Based on this, the present invention proposes a forest pest early warning model construction system and method. By refining the monitoring units, it can dynamically adjust the monitoring intensity according to the actual conditions of different blocks, improve the pertinence and accuracy of monitoring, and generate an overall performance index by regularly comprehensively analyzing the real-time data of the blocks and combining it with dynamic judgment rules to dynamically adjust the monitoring strategy, so that the system can adapt to environmental and risk changes. Summary of the Invention

[0007] The purpose of the present invention is to provide a forestry pest early warning model construction system and method to solve the shortcomings of the background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a method for constructing a forest pest early warning model, the method comprising the following steps:

[0009] The construction system divides the monitored forest area into multiple blocks, builds a three-dimensional model for each block, and then combines the three-dimensional models of multiple blocks into a monitoring forest area early warning model;

[0010] Generate an initial monitoring intensity for each patch. After obtaining historical pest data for each patch, including the amplitude of harmful biodiversity and the amplitude of population dynamic change trends, substitute the amplitude of harmful biodiversity and the amplitude of population dynamic change trends into the evaluation model. The evaluation model outputs an impact factor for each patch, and the initial monitoring intensity of each patch is re-adjusted based on the impact factor.

[0011] After regularly conducting comprehensive analysis of the real-time data of all patches, the overall performance index of the monitored forest area is obtained, and the overall performance index is combined with dynamic judgment rules to analyze whether it is necessary to optimize and adjust the current monitoring intensity of all patches. Based on the analysis results, a personalized plan is dynamically generated for each patch, and the personalized plan is mapped to the monitored forest area early warning model for display.

[0012] In a preferred embodiment, after obtaining the historical pest data of each patch, the pest data is substituted into the evaluation model, and the evaluation model outputs an impact factor for each patch, including the following steps:

[0013] Obtain historical pest data for each patch. The historical pest data includes the amplitude of harmful biodiversity and the amplitude of population dynamic change trend. Substitute the amplitude of harmful biodiversity and the amplitude of population dynamic change trend into the evaluation model. The evaluation model expression is:

[0014] , where is the impact factor, is the magnitude of harmful biodiversity, is the amplitude of population dynamics change trend, 、 are the adjustment coefficients of the amplitude of harmful biodiversity and the amplitude of population dynamic change trend, respectively, and 、 Both are greater than 0.

[0015] In a preferred embodiment, re-correcting the initial monitoring intensity of each block by the influencing factor includes the following steps:

[0016] After obtaining the impact factor of each patch, the initial monitoring intensity of each patch is re-corrected by the impact factor. The expression is: , where is the initial data collection frequency, is the corrected data collection frequency, is the impact factor.

[0017] In a preferred embodiment, after regularly analyzing the real-time data of all patches, the overall performance index of the monitored forest area is obtained, which includes the following steps:

[0018] Regularly obtain real-time data for the patch, including vegetation anomaly assignments and pest monitoring frequencies. Normalize the vegetation anomaly assignments and pest monitoring frequencies so that their ranges are mapped to [0, 1]. Obtain the normalized vegetation anomaly assignment values ​​and pest monitoring frequency values, and sum the normalized vegetation anomaly assignment values ​​and pest monitoring frequency values ​​to obtain the performance factor of the patch.

[0019] Obtain the vegetation coverage rate of each block, sum up the vegetation coverage rates of all blocks to obtain the total coverage value, divide the vegetation coverage rate by the total coverage value to obtain the weight coefficient of the block, and calculate the performance factors of all blocks by weighted calculation to obtain the overall performance index of the monitored forest area.

[0020] In a preferred embodiment, the overall performance index is combined with dynamic judgment rules to analyze whether it is necessary to optimize and adjust the current monitoring intensity of all blocks, including the following steps:

[0021] The overall performance index is substituted into the dynamic judgment rule. The rule logic is: compare the obtained overall performance index with the preset performance threshold. The performance threshold is used to determine whether the monitored forest area needs to be managed, that is, dynamically adjust the monitoring intensity of each block. If the overall performance index is greater than or equal to the performance threshold, it is determined that the monitored forest area needs to be managed. If the overall performance index is less than the performance threshold, it is determined that the monitored forest area does not need to be managed.

[0022] In a preferred embodiment, after dynamically generating a personalized solution for each patch based on the analysis results, the personalized solution is mapped to the monitored forest area early warning model display, including the following steps:

[0023] Comparing the performance factor of the patch with a first gradient threshold and a second gradient threshold, where the first gradient threshold is used to determine whether the monitoring intensity of the patch needs to be reduced, and the second gradient threshold is used to determine whether the monitoring intensity of the patch needs to be increased;

[0024] If the performance factor of a patch is less than the first gradient threshold, it is determined that the monitoring intensity of the patch needs to be reduced. The adjustment algorithm is: , where is the data collection frequency after secondary adjustment, is the corrected data collection frequency, is the performance factor;

[0025] If the performance factor of the block is greater than or equal to the first gradient threshold, and the performance factor is less than or equal to the second gradient threshold, it is determined that there is no need to adjust the monitoring intensity of the block;

[0026] If the performance factor of the patch is greater than the second gradient threshold, it is determined that the monitoring intensity of the patch needs to be increased. The adjustment algorithm is: , where is the data collection frequency after secondary adjustment, is the corrected data collection frequency, is the performance factor.

[0027] In a preferred embodiment, the logic for obtaining the vegetation anomaly assignment is: collect remote sensing images of blocks through drones, identify the normal colors and abnormal colors of vegetation in the area through existing image recognition technology, divide the remote sensing images into several 1×1 grids, obtain the number of abnormal color grids, and then divide the number of abnormal color grids by the total number of grids in the remote sensing image to obtain the vegetation anomaly assignment.

[0028] In a preferred embodiment, the calculation logic of the harmful biodiversity amplitude is: based on the database, the number of harmful species, the growth rate of harmful species, and the reduction rate of harmful species in the block are obtained, and the harmful biodiversity amplitude is calculated and obtained, and the expression is: , where is the number of pest species, Indicates time The growth rate of pest species at each moment, Indicates time The rate of reduction of pest species at each moment, Indicates the monitoring time. 、 、 are information gains, and .

[0029] In a preferred embodiment, the calculation logic of the amplitude of the population dynamic change trend is as follows: based on the database, the outflow of pests in the current block is obtained, and when the pests in the current block are emigrated, the inflow of pests in other blocks around the block is obtained, the outflow of pests in the current block and the inflow of pests in other blocks are summed and divided by two to obtain an outflow impact index, the inflow of pests in the current block is obtained, and when the pests in the current block are emigrated, the outflow of pests in other blocks around the block is obtained, the inflow of pests in the current block and the outflow of pests in other blocks are summed and divided by two to obtain an inflow impact index;

[0030] After obtaining the out-migration impact index and the in-migration impact index of multiple time points of the current block, calculate the mean of the out-migration impact index and the standard deviation of the out-migration impact index, calculate the mean of the in-migration impact index and the standard deviation of the in-migration impact index, divide the mean of the out-migration impact index by the standard deviation of the out-migration impact index to obtain the coefficient of variation of the out-migration impact, and divide the mean of the in-migration impact index by the standard deviation of the in-migration impact index to obtain the coefficient of variation of the in-migration impact;

[0031] The coefficient of variation of out-migration effect and the coefficient of variation of in-migration effect are normalized so that their value ranges are mapped to [0,1]. The sum of the normalized coefficient of variation of out-migration effect and the coefficient of variation of in-migration effect is used to obtain the amplitude of population dynamic change trend. The amplitude of population dynamic change trend fully considers the dynamic change trend of pests in the current area in time and space.

[0032] A forest pest early warning model construction system includes a model construction module, a monitoring intensity correction module, and a dynamic optimization module;

[0033] Model building module: Divide the monitored forest area into multiple blocks, build a three-dimensional model for each block, and then combine the three-dimensional models of multiple blocks into a monitoring forest area early warning model;

[0034] Monitoring intensity correction module: Generates an initial monitoring intensity for each patch. After obtaining historical pest data for each patch, the pest data is substituted into the assessment model. The assessment model outputs an impact factor for each patch, and the initial monitoring intensity of each patch is re-corrected based on the impact factor.

[0035] Dynamic optimization module: After regularly conducting comprehensive analysis of the real-time data of all patches, the overall performance index of the monitored forest area is obtained. The overall performance index is combined with dynamic judgment rules to analyze whether it is necessary to optimize and adjust the current monitoring intensity of all patches. Based on the analysis results, personalized plans are dynamically generated for each patch, and the personalized plans are mapped to the monitored forest area early warning model for display.

[0036] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0037] The present invention obtains the historical pest data of each block, substitutes the pest data into the evaluation model, and the evaluation model outputs an impact factor for each block, and re-corrects the initial monitoring intensity of each block based on the impact factor. After regularly conducting a comprehensive analysis of the real-time data of all blocks, the overall performance index of the monitored forest area is obtained, and the overall performance index is combined with dynamic judgment rules to analyze whether it is necessary to optimize and adjust the current monitoring intensity of all blocks. After dynamically generating a personalized plan for each block based on the analysis results, the personalized plan is mapped to the monitoring forest area early warning model for display. By refining the monitoring units, the construction system can dynamically adjust the monitoring intensity according to the actual situation of different blocks, improve the pertinence and accuracy of monitoring, and generate an overall performance index by regularly comprehensively analyzing the real-time data of the blocks and combining it with dynamic judgment rules to dynamically adjust the monitoring strategy, so that the system can adapt to environmental and risk changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0039] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of 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. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0041] Example 1: Please refer to Figure 1 As shown, the method for constructing a forest pest early warning model described in this embodiment includes the following steps:

[0042] The construction system divides the monitored forest area into multiple blocks, and after establishing a three-dimensional model for each block, the three-dimensional models of multiple blocks are combined into a monitoring forest area early warning model, and an initial monitoring intensity is generated for each block. After obtaining the historical pest data of each block, the pest data is substituted into the evaluation model. The evaluation model outputs an impact factor for each block, and the initial monitoring intensity of each block is re-corrected by the impact factor. After regularly conducting a comprehensive analysis of the real-time data of all blocks, the overall performance index of the monitoring forest area is obtained, and the overall performance index is combined with dynamic judgment rules to analyze whether it is necessary to optimize and adjust the current monitoring intensity of all blocks. After dynamically generating a personalized plan for each block based on the analysis results, the personalized plan is mapped to the monitoring forest area early warning model for display.

[0043] This application obtains the historical pest data of each patch, substitutes the pest data into the evaluation model, and the evaluation model outputs the impact factor for each patch, and re-corrects the initial monitoring intensity of each patch based on the impact factor. After regularly conducting a comprehensive analysis of the real-time data of all patches, the overall performance index of the monitored forest area is obtained, and the overall performance index is combined with the dynamic judgment rules to analyze whether it is necessary to optimize and adjust the current monitoring intensity of all patches. After dynamically generating a personalized plan for each patch based on the analysis results, the personalized plan is mapped to the monitoring forest area early warning model for display. By refining the monitoring units, the construction system can dynamically adjust the monitoring intensity according to the actual situation of different patches, improve the pertinence and accuracy of monitoring, and generate an overall performance index by regularly comprehensively analyzing the real-time data of the patches and combining it with dynamic judgment rules to dynamically adjust the monitoring strategy so that the system can adapt to environmental and risk changes.

[0044] Example 2: A system is constructed to divide the monitored forest area into multiple blocks, and after establishing a three-dimensional model for each block, the three-dimensional models of the multiple blocks are combined into a monitored forest area early warning model, and an initial monitoring intensity is generated for each block, including the following steps:

[0045] Obtain basic data on the topography, vegetation, and meteorology of the monitored forest area, and collect high-definition imagery and mapping data through remote sensing, drone photography, or ground surveys. Develop rules for plotting forest areas based on the forest area's topography, climate, vegetation types, and historical pest distribution. These plots can be divided by geographic area (e.g., valleys, slopes), risk level (high, medium, low), or management unit (e.g., administrative boundaries or natural divisions). Use spatial analysis algorithms (e.g., Voronoi diagrams or clustering algorithms) to automatically delineate forest areas, ensuring that the plots have uniform area or specific geographic characteristics.

[0046] Utilizing remote sensing data, LiDAR scans, and ground survey data, we generate detailed terrain, vegetation structure, and environmental characteristics for each plot. Using professional 3D modeling software (such as ArcGIS 3D Analyst and Pix4D), we construct a 3D model of each plot, including the following details: terrain features (slope, aspect, elevation), vegetation layers (tree canopy height and density), and key environmental elements such as water sources and roads.

[0047] Combine the 3D models of each patch into a comprehensive forest model. Use data fusion algorithms (such as Poisson surface reconstruction or spatial interpolation) to ensure smooth transitions and integrity between patches. Calibrate the combined warning model to eliminate boundary errors and improve model continuity and realism.

[0048] An initial monitoring intensity is generated for each patch. The initial monitoring intensity is the data collection frequency, and the initial monitoring intensity is the same for each patch. This initial monitoring intensity is mapped to the 3D early warning model, and an initial monitoring plan is generated. The plan includes: a monitoring intensity distribution map for each patch; a plan for the layout of monitoring resources and equipment; and an initial monitoring schedule and implementation plan. This allows for precise allocation of monitoring intensity across the forest area, providing a foundation for subsequent dynamic adjustments and optimization.

[0049] After obtaining the historical pest data for each patch, the pest data is substituted into the evaluation model, which outputs the impact factor for each patch, including the following steps:

[0050] Obtain historical pest data for each patch. The historical pest data includes the amplitude of harmful biodiversity and the amplitude of population dynamic change trend. Substitute the amplitude of harmful biodiversity and the amplitude of population dynamic change trend into the evaluation model. The evaluation model expression is:

[0051] , where is the impact factor, is the magnitude of harmful biodiversity, is the amplitude of population dynamics change trend, 、 are the adjustment coefficients of the amplitude of harmful biodiversity and the amplitude of population dynamic change trend, respectively, and 、 Both are greater than 0.

[0052] In this application:

[0053] The calculation logic of the harmful biodiversity amplitude is as follows: Based on the database, the number of harmful species, the growth rate of harmful species, and the reduction rate of harmful species in the patch are obtained, and the harmful biodiversity amplitude is calculated. The expression is: , where is the number of pest species, Indicates time The growth rate of pest species at each moment, Indicates time The rate of reduction of pest species at each moment, Indicates the monitoring time. 、 、 are information gains, and .

[0054] The harmful biodiversity amplitude measures the species types and their dynamic changes within the patch. The more species and the more dramatic the changes, the higher the ecological complexity of the patch, and more data collection is needed to capture the changes. The larger the harmful biodiversity amplitude, the richer the species in the patch, and the significant rate of species growth or decrease. The monitoring needs increase accordingly, and the data collection frequency (monitoring intensity) needs to be improved.

[0055] The calculation logic of the amplitude of the population dynamic change trend is as follows: based on the database, the outflow of pests in the current block is obtained, and when the pests in the current block are emigrated, the inflow of pests in other blocks around the block is obtained, the outflow of pests in the current block and the inflow of pests in other blocks are summed and divided by two to obtain the outflow impact index; the inflow of pests in the current block is obtained, and when the pests in the current block are emigrated, the outflow of pests in other blocks around the block is obtained, the inflow of pests in the current block and the outflow of pests in other blocks are summed and divided by two to obtain the inflow impact index;

[0056] After obtaining the out-migration impact index and the in-migration impact index of multiple time points of the current block, calculate the mean of the out-migration impact index and the standard deviation of the out-migration impact index, calculate the mean of the in-migration impact index and the standard deviation of the in-migration impact index, divide the mean of the out-migration impact index by the standard deviation of the out-migration impact index to obtain the coefficient of variation of the out-migration impact, and divide the mean of the in-migration impact index by the standard deviation of the in-migration impact index to obtain the coefficient of variation of the in-migration impact;

[0057] In this application, the standard deviation of the out-migration impact index and the standard deviation of the in-migration impact index are obtained through the general standard deviation calculation formula, which is expressed as follows: , where is the parameter standard deviation, Indicates the number of time points, Indicates the parameter values, represents the parameter mean.

[0058] The calculation expressions of the coefficient of variation of out-migration effect and the coefficient of variation of in-migration effect are:

[0059] , where is the coefficient of variation of the out-migration effect, is the coefficient of variation of immigration effect, is the mean of the out-migration impact index, is the standard deviation of the out-migration impact index, is the mean of the immigration impact index, is the standard deviation of the immigration impact index.

[0060] The coefficient of variation of out-migration effect and the coefficient of variation of in-migration effect are normalized so that their value ranges are mapped to [0,1]. The sum of the normalized coefficient of variation of out-migration effect and the coefficient of variation of in-migration effect is used to obtain the amplitude of population dynamic change trend. The amplitude of population dynamic change trend fully considers the dynamic change trend of pests in the current area in time and space.

[0061] There is a positive correlation between monitoring intensity (i.e., data collection frequency) and the amplitude of population dynamic change trends. That is, when the amplitude of population dynamic change trends increases, the required monitoring intensity will also increase. When the amplitude of population dynamic change is large, it indicates that the migration of pests within the patch is more complicated, and higher-frequency monitoring may be required to capture such changes. If the migration and dynamic changes of pests in the patch are significant, then building a system requires frequently obtaining higher-resolution data to analyze and predict changes in pest behavior.

[0062] When the influx and outflow of pests in a patch is relatively stable and the fluctuation of the migration impact index is small, the amplitude of the population dynamics trend is low. At this time, the monitoring intensity is maintained at the basic level and the collection frequency will not be too high. If the influx and outflow of pests in a patch is more drastic, the amplitude of the population dynamics trend is large, indicating that the ecological changes in the patch are more complex and changeable, and the monitoring frequency needs to be significantly increased. When the amplitude of the population dynamics change of a patch is close to the maximum, it means that the pests in the patch have undergone extremely complex migration patterns, which may involve frequent species flow or drastic population changes between different patches. At this time, the monitoring intensity may increase rapidly at an exponential rate to ensure sufficient data collection and change capture.

[0063] Re-adjust the initial monitoring intensity of each patch using the impact factor, including the following steps:

[0064] The larger the impact factor, the more severe the overall pest situation in the patch, and the more it is necessary to increase the monitoring intensity of the patch. After obtaining the impact factor of each patch, the initial monitoring intensity of each patch is re-adjusted based on the impact factor. The expression is:

[0065] , where is the initial data collection frequency, is the corrected data collection frequency, is the impact factor.

[0066] Regularly analyze the real-time data of all patches to obtain the overall performance index of the monitored forest area, including the following steps:

[0067] Regularly obtain real-time data for the patch, including vegetation anomaly assignments and pest monitoring frequencies. Normalize the vegetation anomaly assignments and pest monitoring frequencies so that their ranges are mapped to [0, 1]. Obtain the normalized vegetation anomaly assignment values ​​and pest monitoring frequency values, and sum the normalized vegetation anomaly assignment values ​​and pest monitoring frequency values ​​to obtain the performance factor of the patch.

[0068] Obtain the vegetation coverage rate of each patch, sum up the vegetation coverage rates of all patches to obtain the total coverage value, divide the vegetation coverage rate by the total coverage value to obtain the weight coefficient of the patch, and calculate the performance factors of all patches by weighted calculation to obtain the overall performance index of the monitored forest area, which is expressed as follows:

[0069] , where is the overall performance index, For the The weight coefficient of each patch, For the The performance factor of each piece.

[0070] The logic for obtaining vegetation anomaly values ​​is as follows: Remote sensing images are collected using drones. Existing image recognition technology is used to identify the normal color of vegetation in the area (for example, green leafy plants, the normal color is green) and abnormal colors (for example, abnormal colors for green leafy plants may be yellow (e.g., withered) or gray (e.g., leafless branches). The remote sensing image is divided into several 1×1 grids. After obtaining the number of abnormal color grids, the number of abnormal color grids is divided by the total number of grids in the remote sensing image to obtain the vegetation anomaly value.

[0071] The calculation logic of the pest monitoring frequency is: obtain the number of pests monitored during the monitoring period, and divide the number of pests by the monitoring duration to obtain the pest monitoring frequency.

[0072] Combine the overall performance index with dynamic judgment rules to analyze whether the current monitoring intensity of all blocks needs to be optimized and adjusted, including the following steps:

[0073] The overall performance index is substituted into the dynamic judgment rule. The rule logic is: compare the obtained overall performance index with the preset performance threshold. The performance threshold is used to determine whether the monitored forest area needs to be managed, that is, dynamically adjust the monitoring intensity of each block. If the overall performance index is greater than or equal to the performance threshold, it is determined that the monitored forest area needs to be managed. If the overall performance index is less than the performance threshold, it is determined that the monitored forest area does not need to be managed. By making an overall judgment on the monitored forest area, it is beneficial to improve the management efficiency of the monitored forest area and avoid excessive management.

[0074] After dynamically generating a personalized plan for each patch based on the analysis results, the personalized plan is mapped to the monitored forest area early warning model display, including the following steps:

[0075] When it is determined that management of the monitored forest area is necessary, the larger the performance factor of a patch, the worse the vegetation health and the more frequent the occurrence of pests (this may be due to insufficient monitoring efforts). The smaller the performance factor, the better the vegetation health and the less frequent the occurrence of pests (in this case, no excessive monitoring is required). The personalized plan for each patch is as follows:

[0076] Therefore, the performance factor of the patch is compared with the first gradient threshold and the second gradient threshold. The first gradient threshold is used to determine whether the monitoring intensity of the patch needs to be reduced, and the second gradient threshold is used to determine whether the monitoring intensity of the patch needs to be increased.

[0077] If the performance factor of a patch is less than the first gradient threshold, it is determined that the monitoring intensity of the patch needs to be reduced. The adjustment algorithm is: , where is the data collection frequency after secondary adjustment, is the corrected data collection frequency, is the performance factor;

[0078] If the performance factor of the block is greater than or equal to the first gradient threshold, and the performance factor is less than or equal to the second gradient threshold, it is determined that there is no need to adjust the monitoring intensity of the block;

[0079] If the performance factor of the patch is greater than the second gradient threshold, it is determined that the monitoring intensity of the patch needs to be increased. The adjustment algorithm is: , where is the data collection frequency after secondary adjustment, is the corrected data collection frequency, is the performance factor;

[0080] Build a comprehensive forest model: Based on the personalized plans for all forest patches, a comprehensive monitoring model for the forest patch will be constructed. Each patch will correspond to a specific monitoring plan, and the early warning model will integrate the personalized needs of all patches.

[0081] A mapping relationship is established between each patch's personalized plan and the corresponding area's location and characteristics in the early warning model. Using algorithms (such as spatial data processing, regional division, and topological modeling), patch data, monitoring intensity, monitoring requirements, and meteorological information are mapped to the overall forest model. This mapped data not only reflects the personalized monitoring strategy but also reveals its interrelationships with other patches and its impact area.

[0082] Visualization tools (such as GIS platforms and 3D modeling tools) should be used to display the forest area early warning model, showing the implementation of personalized plans, monitoring intensity, and early warning information for each patch. The model should be dynamically updated in real time to reflect adjustments to each patch's monitoring plan, implementation results, and real-time data changes. For example, the system can display monitoring intensity, key monitoring areas, and population changes through heat maps, 3D views, and color block variations. Users can interact with the model to view detailed information for each patch, adjust monitoring strategies, identify environmental trends, and respond to emergencies.

[0083] Example 3: Please refer to Figure 1 As shown, the forest pest early warning model construction system described in this embodiment includes a model construction module, a monitoring intensity correction module, and a dynamic optimization module;

[0084] Model building module: Divide the monitored forest area into multiple blocks, build a three-dimensional model for each block, and combine the three-dimensional models of multiple blocks into a monitoring forest area early warning model. The monitoring forest area early warning model is sent to the dynamic optimization module, and the block division results are sent to the monitoring intensity correction module;

[0085] Monitoring intensity correction module: Generates an initial monitoring intensity for each patch. After obtaining historical pest data for each patch, the pest data is substituted into the evaluation model. The evaluation model outputs an impact factor for each patch, and the initial monitoring intensity of each patch is re-corrected based on the impact factor. The initial monitoring intensity correction information is sent to the dynamic optimization module.

[0086] Dynamic optimization module: After regularly conducting comprehensive analysis of the real-time data of all patches, the overall performance index of the monitored forest area is obtained. The overall performance index is combined with dynamic judgment rules to analyze whether it is necessary to optimize and adjust the current monitoring intensity of all patches. Based on the analysis results, personalized plans are dynamically generated for each patch, and the personalized plans are mapped to the monitored forest area early warning model for display.

[0087] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0088] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0089] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for constructing a forest pest early warning model, characterized by: The construction method comprises the following steps: The construction system divides the monitored forest area into multiple blocks, builds a three-dimensional model for each block, and then combines the three-dimensional models of multiple blocks into a monitoring forest area early warning model; Generate an initial monitoring intensity for each patch. After obtaining the historical pest data for each patch, which includes the amplitude of harmful biodiversity and the amplitude of population dynamic change trend, substitute the amplitude of harmful biodiversity and the amplitude of population dynamic change trend into the evaluation model, the expression is: , where is the impact factor, is the magnitude of harmful biodiversity, is the amplitude of population dynamics change trend, 、 are the adjustment coefficients of the amplitude of harmful biodiversity and the amplitude of population dynamic change trend, respectively, and 、 If both are greater than 0, the evaluation model outputs an impact factor for each patch and uses the impact factor to revise the initial monitoring intensity of each patch. After regularly analyzing the real-time data of all patches, the overall performance index of the monitored forest area is obtained. This overall performance index is combined with dynamic judgment rules to analyze whether the current monitoring intensity of all patches needs to be optimized and adjusted. Based on the analysis results, personalized plans are dynamically generated for each patch and mapped to the monitored forest area early warning model for display; The calculation logic of the performance index is as follows: regularly obtain real-time data of the block, which includes vegetation anomaly assignment and pest monitoring frequency, normalize the vegetation anomaly assignment and pest monitoring frequency, map the value range of vegetation anomaly assignment and pest monitoring frequency to [0,1], obtain the normalized value of vegetation anomaly assignment and the normalized value of pest monitoring frequency, sum the normalized value of vegetation anomaly assignment and the normalized value of pest monitoring frequency to obtain the performance factor of the block; obtain the vegetation coverage rate of each block, sum the vegetation coverage rates of all blocks to obtain the total coverage rate, divide the vegetation coverage rate by the total coverage rate to obtain the weight coefficient of the block, and calculate the performance factors of all blocks weightedly to obtain the overall performance index of the monitored forest area; After dynamically generating a personalized plan for each patch based on the analysis results, the personalized plan is mapped to the monitored forest area early warning model display, including the following steps: Comparing the performance factor of the patch with a first gradient threshold and a second gradient threshold, where the first gradient threshold is used to determine whether the monitoring intensity of the patch needs to be reduced, and the second gradient threshold is used to determine whether the monitoring intensity of the patch needs to be increased; If the performance factor of a patch is less than the first gradient threshold, it is determined that the monitoring intensity of the patch needs to be reduced. The adjustment algorithm is: , where is the data collection frequency after secondary adjustment, is the corrected data collection frequency, is the performance factor; If the performance factor of the block is greater than or equal to the first gradient threshold, and the performance factor is less than or equal to the second gradient threshold, it is determined that there is no need to adjust the monitoring intensity of the block; If the performance factor of the patch is greater than the second gradient threshold, it is determined that the monitoring intensity of the patch needs to be increased. The adjustment algorithm is: , where is the data collection frequency after secondary adjustment, is the corrected data collection frequency, is the performance factor.

2. The method for constructing a forest pest early warning model according to claim 1, characterized in that: Re-adjust the initial monitoring intensity of each patch using the impact factor, including the following steps: After obtaining the impact factor of each patch, the initial monitoring intensity of each patch is re-corrected by the impact factor. The expression is: , where is the initial data collection frequency, is the corrected data collection frequency, is the impact factor.

3. The method for constructing a forest pest early warning model according to claim 2, characterized in that: Combine the overall performance index with dynamic judgment rules to analyze whether the current monitoring intensity of all blocks needs to be optimized and adjusted, including the following steps: The overall performance index is substituted into the dynamic judgment rule. The rule logic is: compare the obtained overall performance index with the preset performance threshold. The performance threshold is used to determine whether the monitored forest area needs to be managed, that is, dynamically adjust the monitoring intensity of each block. If the overall performance index is greater than or equal to the performance threshold, it is determined that the monitored forest area needs to be managed. If the overall performance index is less than the performance threshold, it is determined that the monitored forest area does not need to be managed.

4. The method for constructing a forest pest early warning model according to claim 3, characterized in that: The logic for obtaining the vegetation anomaly value is as follows: collect remote sensing images of patches through drones, identify the normal and abnormal colors of vegetation in the area through existing image recognition technology, divide the remote sensing image into several 1×1 grids, obtain the number of abnormal color grids, and then divide the number of abnormal color grids by the total number of grids in the remote sensing image to obtain the vegetation anomaly value.

5. The method for constructing a forest pest early warning model according to claim 4, characterized in that: The calculation logic of the harmful biodiversity amplitude is: based on the database, the number of harmful species, the growth rate of harmful species, and the reduction rate of harmful species in the block are obtained, and the harmful biodiversity amplitude is calculated. The expression is: , where is the number of pest species, Indicates time The growth rate of pest species at each moment, Indicates time The pest species reduction rate at each moment, Indicates the monitoring time. 、 、 are information gains, and .

6. The method for constructing a forest pest early warning model according to claim 5, characterized in that: The calculation logic of the amplitude of the population dynamic change trend is as follows: based on the database, the outflow of pests in the current block is obtained, and when the pests in the current block are emigrated, the inflow of pests in other blocks around the block is obtained, the outflow of pests in the current block and the inflow of pests in other blocks are summed and divided by two to obtain the outflow impact index, the inflow of pests in the current block is obtained, and when the pests in the current block are emigrated, the outflow of pests in other blocks around the block is obtained, the inflow of pests in the current block and the outflow of pests in other blocks are summed and divided by two to obtain the inflow impact index; After obtaining the out-migration impact index and the in-migration impact index of multiple time points of the current block, calculate the mean of the out-migration impact index and the standard deviation of the out-migration impact index, calculate the mean of the in-migration impact index and the standard deviation of the in-migration impact index, divide the mean of the out-migration impact index by the standard deviation of the out-migration impact index to obtain the coefficient of variation of the out-migration impact, and divide the mean of the in-migration impact index by the standard deviation of the in-migration impact index to obtain the coefficient of variation of the in-migration impact; The coefficient of variation of out-migration effect and the coefficient of variation of in-migration effect are normalized so that their value ranges are mapped to [0,1]. The sum of the normalized coefficient of variation of out-migration effect and the coefficient of variation of in-migration effect is used to obtain the amplitude of population dynamic change trend. The amplitude of population dynamic change trend fully considers the dynamic change trend of pests in the current area in time and space.

7. A forest pest early warning model construction system, used to implement the construction method according to any one of claims 1 to 6, characterized in that: Including model building module, monitoring strength correction module, and dynamic optimization module; Model building module: Divide the monitored forest area into multiple blocks, build a three-dimensional model for each block, and then combine the three-dimensional models of multiple blocks into a monitoring forest area early warning model; Monitoring intensity correction module: Generates an initial monitoring intensity for each patch. After obtaining historical pest data for each patch, the pest data is substituted into the assessment model. The assessment model outputs an impact factor for each patch, and the initial monitoring intensity of each patch is re-corrected based on the impact factor. Dynamic optimization module: After regularly conducting comprehensive analysis of the real-time data of all patches, the overall performance index of the monitored forest area is obtained. The overall performance index is combined with dynamic judgment rules to analyze whether it is necessary to optimize and adjust the current monitoring intensity of all patches. Based on the analysis results, personalized plans are dynamically generated for each patch, and the personalized plans are mapped to the monitored forest area early warning model for display.

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