Information acquisition method for monitoring diseases and insect pests of forestry engineering

Abstract

The invention discloses an information acquisition method for forestry engineering disease and insect pest monitoring, relates to the technical field of forestry engineering disease and insect pest monitoring, and comprises an acquisition method for monitoring and acquiring disease and insect pest conditions in real time, analyzing, evaluating and monitoring differences before and after disease and insect pest control, and improving a control strategy. The data acquisition module is responsible for real-time acquisition of environment and pest and disease damage conditions in a monitored area, the prediction and evaluation module is responsible for sequentially calculating and outputting pest and disease damage density HM, pest and disease damage diffusion rate YK and pest and disease damage prevention and control effects FXG, and the analysis and strategy making module is responsible for contrastive analysis according to the pest and disease damage prevention and control effects FXG before and after prevention and control. According to the method, the accuracy of monitoring data, the monitoring efficiency and the evaluation comprehensiveness of the prevention and control effect are improved, and the innovative method not only provides powerful technical support for monitoring and prevention and control of plant diseases and insect pests of forestry engineering, but also lays a solid foundation for healthy and sustainable development of forest resources.

Description

Technical Field

[0001] The present invention relates to the technical field of forestry engineering pest and disease monitoring, in particular to an information collection method for forestry engineering pest and disease monitoring. Background Art

[0002] In forestry projects, the monitoring and prevention of pests and diseases is an important part of ensuring the health and sustainable development of forest resources. Traditional pest and disease monitoring methods often rely on manual field surveys. This method is not only time-consuming and labor-intensive, but also easily affected by human factors, resulting in inaccurate monitoring results. In order to overcome these limitations, in recent years, with the rapid development of information technology, the information collection methods used for pest and disease monitoring in forestry projects have gradually shifted to automation and intelligence.

[0003] Traditional manual field survey methods are easily affected by various factors such as weather and terrain, resulting in inaccurate monitoring data and difficulty in truly reflecting the actual situation of pests and diseases. Manual surveys require a lot of manpower, material resources and time, and the monitoring efficiency is low, making it difficult to meet the monitoring needs of large-scale forestry projects. In addition, with the rapid development of information technology, some existing monitoring methods often only focus on the results of a single monitoring, lack a circular feedback mechanism, and it is difficult to adjust the prevention and control strategies in a timely manner according to the monitoring results. Existing prevention and control effect evaluation methods often only consider the changes in the number of pests and diseases before and after prevention and control, ignoring the influence of multiple factors such as the spread rate and prevention and control costs. Summary of the Invention

[0004] The purpose of the present invention is to provide an information collection method for monitoring plant diseases and insect pests in forestry projects, which solves the problems raised in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solutions, including a collection method for real-time monitoring and collection of pest and disease conditions, analysis and evaluation of the differences before and after monitoring pest and disease control, and improvement of control strategies; It includes data collection module, prediction and evaluation module and analysis and strategy formulation module; The specific implementation steps are as follows: Data collection module: responsible for real-time collection of the environment and pest and disease conditions in the monitoring area; Prediction and evaluation module: responsible for calculating and outputting pest density HM, pest spread rate YK, and pest control effect FXG in sequence; The prediction and evaluation module includes a unit for monitoring and measuring the severity of pests and diseases, a unit for predicting the spread of pests and diseases, and a unit for real-time monitoring and evaluation of control effects. Analysis and strategy formulation module: responsible for improving the pest and disease control strategy based on the comparative analysis of the pest and disease control effect FXG before and after control.

[0006] Optionally, the equipment used in the data acquisition module includes temperature and humidity sensors, light sensors, insect traps, data recorders, and wireless transmission equipment; The equipment used in the prediction and evaluation module includes computers and software systems; The equipment used by the analysis and strategy formulation module includes analysis and control equipment.

[0007] Optionally, the calculation formula for the unit for monitoring and measuring the severity of pests and diseases is as follows: ; HJ=W×A1+S×A2+JY×A3; in: HM is the pest and disease density; M is the area of the monitoring region; BFZ is the total number of pests and diseases occurrence; FZS is the number of pests and diseases eliminated by control. FZS reflects the number of pests and diseases that have been controlled and eliminated naturally. JT is the monitoring period; HJ is the environmental factor influence coefficient; W is temperature, S is humidity, and JY is rainfall. Temperature W, humidity S, and rainfall JY respectively reflect the average degree of accumulation of the three environmental factors during the monitoring period JT in the monitoring area M. A1, A2 and A3 are all weight coefficients, and A1+A2+A3=0. A1, A2 and A3 are weighted according to the influence of temperature W, humidity S and rainfall JY.

[0008] Optionally, the calculation formula for the predicted pest and disease spread trend unit is as follows: ; HY=KM / M; in: YK is the spread rate of pests and diseases; HY is insect activity; KM is the insect active area; DZ is the terrain barrier factor, which reflects the degree of barrier effect of terrain factors on the spread of pests and diseases; JL is the spacing, reflecting the distance between the monitoring area and the source of pests and diseases; K is the average width of the monitoring area.

[0009] Optionally, the calculation formula for the real-time monitoring and evaluation control effect unit is as follows: ; FD=Σ(W i ×LHi ); in: FXG is the pest control effect; HM prev The density of pests and diseases in the previous monitoring period; CT is the duration of prevention and control measures; ZTL is the total cost of prevention and control; FD is the complexity of the monitoring area; Σ is the summation symbol; W i is the weight coefficient of the i-th factor, LH i Quantify the value of the i-th factor; The weight coefficient W of the i-th factor i The value range is {0-1}; Quantitative value LH of the i-th factor i The factors reflected are elevation difference, vegetation coverage, and average wind speed, and the i-th factor weight coefficient W is calculated based on the emphasis factor. i settings.

[0010] Optionally, based on the pest control effect FXG and the pest control effect FXG of the previous monitoring period prev The monitoring analysis and prevention and control adjustments are as follows: If the pest control effect FXG is higher than the pest control effect FXG in the previous monitoring period prev , it reflects that the current monitoring and implementation of prevention and control work is effective. In this case, when the prevention and control efforts are reduced, as the prevention and control efforts are reduced, the total number of pests and diseases BFZ in the current monitoring period JT will decrease during the next monitoring. If the pest control effect FXG is lower than the pest control effect FXG in the previous monitoring period prev , it reflects that the current prevention and control efforts are insufficient, and the prevention and control effects shown by real-time monitoring are not ideal. In this case, it is necessary to increase the prevention and control efforts. Among them, increasing the prevention and control efforts includes increasing the frequency of monitoring and prevention, strengthening prevention and control methods and pesticide measures, thereby shortening the monitoring period JT of the next monitoring.

[0011] Optionally, the terrain obstruction factor DZ is calculated as follows: ; G i is the elevation of the i-th terrain unit; M i is the area of the i-th terrain unit; P i is the slope of the i-th terrain unit; The data acquisition module regularly monitors the elevation G of the i-th terrain unit in the monitoring area M. i , the area of the i-th terrain unit M i and the slope P of the i-th terrain unit i It is not necessary to update it every monitoring cycle JT, and the setting includes an update mechanism once every six months.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention uses an automated and intelligent information collection method to obtain real-time and accurate monitoring data on pests and diseases, avoiding interference from human factors and improving data accuracy. Specifically, the pest density HM is calculated by monitoring and measuring the severity of the pests and diseases, thereby ensuring the accuracy and real-time nature of the data.

[0013] In addition, the automated monitoring method greatly shortens the monitoring cycle JT, improves monitoring efficiency, and makes the monitoring of large-scale forestry projects possible. It predicts the pest and disease spread rate YK by predicting the pest and disease transmission trend unit, and combines terrain and transmission medium factors to quickly locate high-risk areas for pests and diseases and achieve accurate monitoring.

[0014] 2. The present invention forms a complete circular feedback mechanism by real-time monitoring and evaluation of the control effect of the control effect unit, and timely adjustment of the control strategy according to the evaluation results. Among them, if the current pest control effect FXG is greater than the previous pest control effect FXG, it means that the control is effective and the control intensity can be reduced. If the pest control effect FXG is less than the previous pest control effect FXG, the control intensity needs to be increased. This circular feedback mechanism ensures the continuous optimization and adjustment of the control strategy.

[0015] In addition, the comprehensive consideration of the impact of multiple factors such as the spread rate and prevention and control costs on the prevention and control effect makes the evaluation results more comprehensive and accurate. In the real-time monitoring and evaluation of the prevention and control effect unit, by introducing the pest and disease spread rate YK and the total prevention and control cost input ZTL factors, the prevention and control effect can be comprehensively evaluated, thereby ensuring the accuracy and scientificity of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the information collection method for pest and disease monitoring in this forestry project; Figure 2 This is a schematic diagram of the overall structure of the information collection method for pest and disease monitoring in this forestry project; Figure 3 Schematic diagram of the structure of the prediction and evaluation module of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] Regarding the information collection method for pest and disease monitoring in this forestry project, it is different from the existing pest and disease monitoring methods. The existing pest and disease monitoring methods often rely on manual field investigations. This method is not only time-consuming and labor-intensive, but also easily affected by human factors, resulting in inaccurate monitoring results, and the monitoring before and after prevention and control is not accurate and timely enough. In addition, some existing monitoring methods often only focus on the results of a single monitoring and lack a cyclic feedback mechanism. This algorithm unit improves the accuracy of monitoring data, monitoring efficiency and the comprehensiveness of the evaluation of prevention and control effects. This innovative method not only provides strong technical support for pest and disease monitoring and prevention in forestry projects, but also lays a solid foundation for the healthy and sustainable development of forest resources.

[0019] For example 1, please refer to Figures 1 to 3 This implementation provides a method for collecting information on pest and disease monitoring in forestry projects, including real-time monitoring and collection of pest and disease conditions, analysis and evaluation of differences before and after pest and disease control, and collection methods for improving control strategies. It includes data collection module, prediction and evaluation module and analysis and strategy formulation module; The specific implementation steps are as follows: Data collection module: responsible for real-time collection of the environment and pest and disease conditions in the monitoring area; Prediction and evaluation module: responsible for calculating and outputting pest density HM, pest spread rate YK, and pest control effect FXG in sequence; The prediction and evaluation module includes a unit for monitoring and measuring the severity of pests and diseases, a unit for predicting the spread of pests and diseases, and a unit for real-time monitoring and evaluation of control effects. Analysis and strategy formulation module: responsible for improving pest control strategies based on comparative analysis of pest control effects before and after treatment; The equipment used in the data acquisition module includes temperature and humidity sensors, light sensors, insect traps, data recorders, and wireless transmission equipment; The equipment used in the prediction and evaluation module includes computers and software systems; The equipment used by the analysis and strategy formulation module includes analysis and control equipment.

[0020] In this embodiment, the system forms a complete pest and disease monitoring and assessment system through the mutual cooperation of three algorithm units, combining the three operation results of HM, YK and FXG. Specifically, HM is the pest and disease density. This value can not only accurately locate the high-incidence areas of pests and diseases, provide accurate targets for subsequent prevention and control work, but also intuitively see the effectiveness of prevention and control work, provide strong support for timely adjustment of prevention and control strategies, and help to formulate more scientific and reasonable prevention and control decisions, reduce prevention and control errors caused by environmental factors, and YK is the pest and disease spread rate. This value helps to formulate prevention and control plans in advance, avoid large-scale outbreaks of pests and diseases, and help to concentrate limited prevention and control resources on key areas where pests and diseases spread. Key areas and key periods, improve prevention and control efficiency, and more accurately judge the spread path and spread speed of pests and diseases, so as to formulate more targeted prevention and control strategies and improve prevention and control effects. FXG is the effect of pest and disease control. This value can quantify the prevention and control effect, making the effectiveness of prevention and control work more intuitive and measurable, which helps to evaluate the quality and efficiency of prevention and control work and provide improvement direction for subsequent prevention and control work. The calculation results of FXG can also affect the calculation of HM and YK, making the three algorithms of this system interrelated and establishing a circular feedback mechanism between each other. This design not only improves the accuracy and efficiency of monitoring and prevention work, but also provides strong technical support for the protection and management of forest resources.

[0021] See also Figures 1 to 3 The calculation formula for monitoring and measuring the severity of pests and diseases is as follows: ; HJ=W×A1+S×A2+JY×A3; in: HM is the pest and disease density; M is the area of the monitoring region; BFZ is the total number of pests and diseases occurrence; FZS is the number of pests and diseases eliminated by control. FZS reflects the number of pests and diseases that have been controlled and eliminated naturally. JT is the monitoring period; HJ is the environmental factor influence coefficient; W is temperature, S is humidity, and JY is rainfall. Temperature W, humidity S, and rainfall JY respectively reflect the average degree of accumulation of the three environmental factors during the monitoring period JT in the monitoring area M. A1, A2 and A3 are all weight coefficients, and A1+A2+A3=0. A1, A2 and A3 are weighted according to the influence of temperature W, humidity S and rainfall JY.

[0022] In this embodiment: First, in this algorithm unit, "The calculation part calculates the product of the net increase in the number of pests and diseases in the monitoring area and the square value of the monitoring area M," ” is used to standardize the area so that monitoring areas of different sizes can be compared, while “ " represents the net change in the number of pests and diseases, that is, the total number of actual pests and diseases BFS minus the number of pests and diseases eliminated by prevention and control FZS. This product, as the numerator, reflects the relationship between the actual number of pests and diseases and the area within the monitoring area, and is the key part of calculating the pest and disease density HM; “ The calculation part calculates the sum of the monitoring period JT and the coefficient of influence of environmental factors on the number of pests and diseases, which reflects the combined impact of time factors and environmental factors on the number of pests and diseases. The sum of this calculation part is used as the denominator to standardize the net increase in the number of pests and diseases to unit time, thereby obtaining the pest and disease density HM; By combining the monitoring area M and the total number of pests and diseases occurrence BFS, this algorithm unit can accurately locate areas with high incidence of pests and diseases, and provide precise targets for subsequent prevention and control work, which helps to concentrate resources and improve prevention and control efficiency. The introduction of the number of pests and diseases eliminated by prevention and control FZS enables the unit for monitoring and measuring the severity of pests and diseases to reflect the prevention and control effect in real time. By comparing the FZS values of the number of pests and diseases eliminated by prevention and control at different time points, the effectiveness of the prevention and control work can be intuitively seen, providing strong support for timely adjustment of prevention and control strategies. The addition of the environmental factor influence coefficient HJ enables the unit for monitoring and measuring the severity of pests and diseases to more comprehensively consider the impact of environmental factors on the number of pests and diseases, which helps to make more scientific and reasonable prevention and control decisions and reduce prevention and control errors caused by environmental factors.

[0023] See also Figures 1 to 3 , the calculation formula for predicting pest and disease transmission trend units is as follows: ; HY=KM / M; in: YK is the spread rate of pests and diseases; HY is insect activity; KM is the insect active area; DZ is the terrain barrier factor, which reflects the degree of barrier effect of terrain factors on the spread of pests and diseases; JL is the spacing, reflecting the distance between the monitoring area and the source of pests and diseases; K is the average width of the monitoring area; The calculation formula of terrain obstruction factor DZ is as follows: ; G i is the elevation of the i-th terrain unit; Mi is the area of the i-th terrain unit; P i is the slope of the i-th terrain unit; The data acquisition module regularly collects the elevation G of the i-th terrain unit in the monitoring area M. i , the area of the i-th terrain unit M i and the slope P of the i-th terrain unit i It is not necessary to update it every monitoring cycle JT, and the setting includes an update mechanism once every six months.

[0024] In this embodiment, first, The calculation part calculates the product of pest density HM and insect activity HY, which reflects the direct contribution of the transmission medium to the pest spread rate YK. This product of the calculation part is the first item of the unit for predicting the pest spread trend, indicating the possible spread rate of pests under the influence of the transmission medium; “ The calculation part calculates the product of the terrain barrier factor DZ and the distance between the monitoring area and the source of the pests and diseases divided by the square root of the average width K of the monitoring area. This reflects the barrier effect of terrain factors on the spread rate of pests and diseases YK. The longer the distance and the more complex the terrain, the greater the barrier effect. This product of the calculation part is used as the second item of the unit for predicting the spread trend of pests and diseases, and it has a negative sign, indicating that the terrain slows down the spread rate of pests and diseases. “ The calculation part calculates the product of the pest density HM and the relative density of pests in the monitoring area (total number of pests and diseases BFS / area of the monitoring area M). This reflects the contribution of the distribution density of pests and diseases in the monitoring area to their spread rate. This product of the calculation part is the third item of the unit for predicting the spread trend of pests and diseases, indicating the additional rate at which pests and diseases will spread under the influence of the distribution density of pests and diseases. This algorithm unit comprehensively considers multiple factors, enabling the pest and disease spread trend prediction unit to predict the spread trend of pests and diseases, which helps to formulate prevention and control plans in advance and avoid large-scale outbreaks of pests and diseases; The prediction results of the pest and disease spread trend prediction unit can help optimize the allocation of prevention and control resources. Specifically, based on the prediction results, limited prevention and control resources can be concentrated in key areas and key periods of pest spread, thereby improving prevention and control efficiency. In addition, through the prediction results, we can more accurately judge the spread path and speed of pests and diseases, so as to formulate more targeted prevention and control strategies and improve the prevention and control effects.

[0025] See also Figures 1 to 3 The calculation formula for the real-time monitoring and evaluation unit of control effect is as follows: ; FD=Σ(W i ×LH i ); in: FXG is the pest control effect; HM prev The density of pests and diseases in the previous monitoring period; CT is the duration of prevention and control measures; ZTL is the total cost of prevention and control; FD is the complexity of the monitoring area; Σ is the summation symbol; W i is the weight coefficient of the i-th factor, LH i Quantify the value of the i-th factor; The weight coefficient W of the i-th factor i The value range is {0-1}; Quantitative value LH of the i-th factor i The factors reflected are elevation difference, vegetation coverage, and average wind speed, and the i-th factor weight coefficient W is calculated based on the emphasis factor. i settings.

[0026] In this embodiment, the algorithm unit first " The calculation part is to calculate the square root of the pest spread rate YK and the difference in the number density of pests before and after control. This reflects the direct effect of the control measures on the reduction of pests and diseases, as well as the influence of the diffusion rate on the control effect. The product of the calculation part is used as the numerator, which represents the combined effect of the reduction in the number of pests and diseases and the diffusion rate under the action of the control measures. “ The calculation part calculates the sum of the duration of the control measures CT and the pest and disease diffusion rate YK multiplied by the total control cost ZTL divided by the square root of the complexity of the monitoring area FD, which reflects the cost-effectiveness of the control measures and the impact of the diffusion rate on the cost-effectiveness. This calculation part is used as the denominator to standardize the direct effect of the control effect to the combined impact of the cost-effectiveness of the control measures and the diffusion rate, thereby obtaining the pest and disease control effect FXG; This algorithm unit uses formula calculations to quantify the prevention and control effects, making the effectiveness of prevention and control work more intuitive and measurable. This helps evaluate the quality and efficiency of prevention and control work and provides improvement directions for subsequent prevention and control work. Moreover, this algorithm unit can evaluate the effects of different strategies by comparing the pest control effects FXG under different control strategies, thereby optimizing the control strategies, which helps to formulate more scientific and reasonable control plans and improve control efficiency; The results of the real-time monitoring and evaluation of the control effect unit can be fed back to the unit for monitoring and measuring the severity of pests and diseases and the unit for predicting the spread trend of pests and diseases, forming a circular feedback mechanism. By continuously iterating and optimizing the parameters and calculation methods in the formula, the accuracy and efficiency of monitoring and control work can be continuously improved, and the continuous improvement of control work can be promoted. In summary, the unit for monitoring and measuring the severity of pests and diseases, the unit for predicting the spread trend of pests and diseases, and the unit for real-time monitoring and evaluation of control effects play an important role in the information collection method for pest and disease monitoring in forestry projects. They not only provide standardized calculation methods, accurate prediction results and comprehensive evaluation indicators, but also continuously optimize monitoring and control strategies through a circular feedback mechanism, providing strong support for the healthy and sustainable development of forest resources.

[0027] For example 2, please refer to Figures 1 to 3 Based on the pest control effect FXG and the pest control effect FXG of the previous monitoring period prev The monitoring analysis and prevention and control adjustments are as follows: If the pest control effect FXG is higher than the pest control effect FXG in the previous monitoring period prev , it reflects that the current monitoring and implementation of prevention and control work is effective. In this case, when the prevention and control efforts are reduced, as the prevention and control efforts are reduced, the total number of pests and diseases BFZ in the current monitoring period JT will decrease during the next monitoring. If the pest control effect FXG is lower than the pest control effect FXG in the previous monitoring period prev , it reflects that the current prevention and control efforts are insufficient, and the prevention and control effects shown by real-time monitoring are not ideal. In this case, it is necessary to increase the prevention and control efforts. Among them, increasing the prevention and control efforts includes increasing the frequency of monitoring and prevention, strengthening prevention and control methods and pesticide measures, thereby shortening the monitoring period JT of the next monitoring.

[0028] In this embodiment, by comparing the pest control effect FXG with the pest control effect FXG of the previous monitoring period, prev The size of the control effect can be used to determine whether the control effect is effective. If the pest control effect FXG is greater than the pest control effect FXG in the previous monitoring period, prev , indicating that the prevention and control is effective, the prevention and control efforts can be appropriately reduced. If the pest control effect FXG is less than the pest control effect FXG in the previous monitoring period prev, then it is necessary to increase prevention and control efforts. This dynamic adjustment mechanism makes the prevention and control strategy more flexible and effective, and through the circular feedback mechanism, it can continuously optimize the cost of disease and insect pest control. When the prevention and control effect reaches a certain level, the prevention and control efforts and costs can be appropriately reduced. When the prevention and control effect is not good, it is necessary to increase investment to ensure the prevention and control effect. This optimization mechanism helps to improve the economy and sustainability of prevention and control work. In addition, the circular feedback mechanism makes the monitoring and prevention work form a closed-loop system. By continuously optimizing and adjusting the monitoring and prevention strategies, the efficiency and quality of monitoring and prevention can be improved, ensuring the healthy and sustainable development of forest resources. Specifically, by real-time monitoring of the pest control effect (FXG), the latest results of the control work can be obtained in a timely manner, avoiding decision-making errors caused by data lag in traditional methods. At the same time, this real-time monitoring method also improves data accuracy, thereby enabling a more precise understanding of the actual situation of the pest problem. Compared with the previous one-time control, the circular feedback mechanism allows the control strategy to be dynamically adjusted according to the changes in the pest control effect (FXG). This means that the control method, type of pesticide and control frequency can be flexibly selected according to the actual situation of the pest problem, thereby ensuring the targeted and effective control work. Under the circular feedback mechanism, the intensity of pest control can be appropriately reduced according to the growth of pest control effect FXG to save resources and costs. This adjustment not only helps to reduce the economic burden of pest control work, but also reduces interference and damage to the ecological environment, thus achieving the dual goals of pest control and ecological environment protection. Through real-time monitoring and dynamic adjustment of strategies, I can continuously optimize pest control measures and improve control effectiveness. This optimization process not only helps reduce the incidence of pest problems, but also reduces the impact of pests and diseases on forestry projects, ensuring the sustainable development of forestry resources. The feedback loop also promotes the sustainable development of pest and disease control. Through real-time monitoring and dynamic adjustment of strategies, new problems can be discovered in a timely manner and corresponding control measures can be taken, thus avoiding the accumulation and deterioration of pest and disease problems. This preventive approach not only helps protect the ecological environment, but also promotes the sustainable use and development of forestry resources. In summary, the unit for monitoring and measuring the severity of pests and diseases, the unit for predicting the spread trend of pests and diseases, and the unit for real-time monitoring and evaluation of control effects each have significant beneficial effects in the information collection method for monitoring pests and diseases in forestry projects. At the same time, the cyclical influence mechanism of the real-time monitoring and evaluation of control effects unit on the unit for monitoring and measuring the severity of pests and diseases makes the entire monitoring and control system more complete and efficient. Compared with the previous prevention and control, the cyclical feedback mechanism brings real-time and accuracy, dynamic adjustment strategies, resource and cost savings, improved control effects, and promotion of sustainable development. This mechanism not only helps to better deal with pest and disease problems, but also promotes the sustainable development of forestry resources and the protection of the ecological environment, thereby improving the accuracy and efficiency of monitoring and control work, and providing strong technical support for the protection and management of forest resources.

[0029] Various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, all of which are within the scope of the invention.

Claims

1. A method for collecting information for monitoring pests and diseases in forestry projects, characterized in that: Includes real-time monitoring and collection of pest and disease information, analysis and evaluation of differences before and after pest and disease control, and collection methods to improve control strategies; It includes data collection module, prediction and evaluation module and analysis and strategy formulation module; The specific implementation steps are as follows: Data collection module: responsible for real-time collection of the environment and pest and disease conditions in the monitoring area; Prediction and evaluation module: responsible for calculating and outputting pest density HM, pest spread rate YK, and pest control effect FXG in sequence; The prediction and evaluation module includes a unit for monitoring and measuring the severity of pests and diseases, a unit for predicting the spread of pests and diseases, and a unit for real-time monitoring and evaluation of control effects. Analysis and strategy formulation module: responsible for improving the pest and disease control strategy based on the comparative analysis of the pest and disease control effect FXG before and after control.

2. The information collection method for monitoring forestry engineering pests and diseases according to claim 1, characterized in that: The equipment used in the data acquisition module includes temperature and humidity sensors, light sensors, insect traps, data recorders, and wireless transmission equipment; The equipment used in the prediction and evaluation module includes computers and software systems; The equipment used by the analysis and strategy formulation module includes analysis and control equipment.

3. The information collection method for monitoring forestry pests and diseases according to claim 2, characterized in that: The calculation formula for the unit for monitoring and measuring the severity of pests and diseases is as follows: ； HJ=W×A1+S×A2+JY×A3; in: HM is the pest and disease density; M is the area of the monitoring region; BFZ is the total number of pests and diseases occurrence; FZS is the number of pests and diseases eliminated by control. FZS reflects the number of pests and diseases that have been controlled and eliminated naturally. JT is the monitoring period; HJ is the environmental factor influence coefficient; W is temperature, S is humidity, and JY is rainfall. Temperature W, humidity S, and rainfall JY respectively reflect the average degree of accumulation of the three environmental factors during the monitoring period JT in the monitoring area M. A1, A2 and A3 are all weight coefficients, and A1+A2+A3=0. A1, A2 and A3 are weighted according to the influence of temperature W, humidity S and rainfall JY.

4. The information collection method for monitoring forestry pests and diseases according to claim 3, characterized in that: The calculation formula for predicting the pest and disease spread trend unit is as follows: ； HY=KM / M; in: YK is the spread rate of pests and diseases; HY is insect activity; KM is the insect active area; DZ is the terrain barrier factor, which reflects the degree of barrier effect of terrain factors on the spread of pests and diseases; JL is the spacing, reflecting the distance between the monitoring area and the source of pests and diseases; K is the average width of the monitoring area.

5. The information collection method for monitoring pests and diseases in forestry projects according to claim 4 is characterized by: The calculation formula for the real-time monitoring and evaluation control effect unit is as follows: ； FD=Σ(W i ×LH i ); in: FXG is the pest control effect; HM prev The density of pests and diseases in the previous monitoring period; CT is the duration of prevention and control measures; ZTL is the total cost of prevention and control; FD is the complexity of the monitoring area; Σ is the summation symbol; W i is the weight coefficient of the i-th factor, LH i Quantify the value of the i-th factor; The weight coefficient W of the i-th factor i The value range is {0-1}; Quantitative value LH of factor i i The factors reflected are elevation difference, vegetation coverage, and average wind speed, and the i-th factor weight coefficient W is calculated based on the emphasis factor. i settings.

6. The information collection method for monitoring forestry engineering pests and diseases according to claim 5, characterized in that: Based on the pest control effect FXG and the pest control effect FXG of the previous monitoring period prev The monitoring analysis and prevention and control adjustments are as follows: If the pest control effect FXG is higher than the pest control effect FXG in the previous monitoring period prev , it reflects that the current monitoring and implementation of prevention and control work is effective. In this case, when the prevention and control efforts are reduced, as the prevention and control efforts are reduced, the total number of pests and diseases BFZ in the current monitoring period JT will decrease during the next monitoring. If the pest control effect FXG is lower than the pest control effect FXG in the previous monitoring period prev , it reflects that the current prevention and control efforts are insufficient, and the prevention and control effects shown by real-time monitoring are not ideal. In this case, it is necessary to increase the prevention and control efforts. Among them, increasing the prevention and control efforts includes increasing the frequency of monitoring and prevention, strengthening prevention and control methods and pesticide measures, thereby shortening the monitoring period JT of the next monitoring.

7. The information collection method for monitoring forestry pests and diseases according to claim 4, characterized in that: The calculation formula of the terrain obstacle factor DZ is as follows: ； G i is the elevation of the i-th terrain unit; M i is the area of the i-th terrain unit; P i is the slope of the ith terrain unit.

8. The information collection method for monitoring pests and diseases in forestry projects according to claim 7, characterized in that: The data acquisition module regularly monitors the elevation G of the i-th terrain unit in the monitoring area M. i , the area of the i-th terrain unit M i and the slope P of the i-th terrain unit i It is not necessary to update it every monitoring cycle JT, and the setting includes an update mechanism once every six months.

Images