Method and system for mining risk prediction of pest outbreak process based on multiple source factors

Abstract

The present application relates to the technical field of pest control, and particularly relates to a pest outbreak process mining risk prediction method and system based on multiple source factors. The method comprises the following steps: measuring the regional meteorological data corresponding to the crop pest monitoring point, the regional crop soil fertility degree and the regional pest occurrence data in real time; obtaining the corresponding pesticide spraying usage of the crop pest monitoring point, and performing regional occurrence density accounting and pest outbreak mining analysis on the regional pest occurrence data based on the pesticide spraying usage, to obtain the corresponding regional pest outbreak characteristic mode under the meteorological condition and the correlation characteristic relationship between the regional soil fertility and the pest outbreak; performing multi-source factor risk prediction and prevention and control decision support analysis on the crop pest monitoring point, to generate a pest outbreak area prevention and control suggestion scheme. The present application can provide scientific and accurate pest risk early warning for agricultural production.

Description

Technical Field

[0001] This invention relates to the field of pest and disease control technology, and in particular to a method and system for risk prediction based on the process of pest and disease outbreaks caused by multiple factors. Background Technology

[0002] Pests and diseases pose a serious threat to agricultural production, affecting not only crop growth and yield but also potentially causing large-scale economic losses. The outbreak patterns of pests and diseases are becoming increasingly complex, and monitoring and forecasting based on single factors can no longer meet the needs of modern agricultural production for pest and disease control. Currently, most traditional pest and disease forecasting methods rely on the analysis of single factors such as empirical data, meteorological conditions, and crop planting patterns, employing statistical models, expert systems, or meteorological models for prediction. However, traditional methods fail to fully consider the comprehensive impact of multiple factors, including environment, climate, and soil quality, on pest and disease outbreaks, resulting in poor accuracy and reliability of forecasts and an inability to respond in real time to various unforeseen factors. Summary of the Invention

[0003] Therefore, it is necessary for the present invention to provide a method and system for risk prediction of pest and disease outbreak processes based on multiple factors, in order to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for risk prediction based on the process of pest and disease outbreaks using multiple factors is proposed, comprising the following steps:

[0005] Step S1: Real-time measurement of regional meteorological data, regional crop soil fertility, and regional pest and disease occurrence data corresponding to crop pest and disease monitoring points. The regional pest and disease occurrence data includes the number of pests and diseases, the time of occurrence, and the location of occurrence.

[0006] Step S2: Obtain the corresponding pesticide spraying amount through crop pest and disease monitoring points, and calculate the regional occurrence density based on the pesticide spraying amount to obtain the regional pest and disease occurrence density.

[0007] Step S3: Based on regional meteorological data and the degree of soil fertility of regional crops, conduct pest and disease outbreak analysis on the density of regional pest and disease occurrence to obtain the regional pest and disease outbreak characteristic patterns under meteorological conditions and the correlation characteristics between regional soil fertility and pest and disease outbreak.

[0008] Step S4: Based on the regional pest and disease outbreak characteristic patterns under meteorological conditions and the correlation characteristics between regional soil fertility and pest and disease outbreaks, multi-source factor risk prediction is performed on crop pest and disease monitoring points to obtain regional pest and disease outbreak risk prediction results; based on the regional pest and disease outbreak risk prediction results, prevention and control decision support analysis is performed to generate prevention and control recommendations for pest and disease outbreak areas.

[0009] Furthermore, step S1 includes the following steps:

[0010] Step S11: Measure the regional meteorological data corresponding to the crop pest and disease monitoring points in real time through the meteorological monitoring station network, including the temperature, humidity, precipitation and light intensity of the monitoring point area.

[0011] Step S12: Real-time monitoring and data collection of soil conditions in the area corresponding to the crop pest and disease monitoring point is carried out using soil sensors to obtain regional crop soil condition data, including regional crop soil pH, regional crop soil fertility content index and regional crop soil microbial prosperity.

[0012] Step S13: Based on the regional crop soil condition data, calculate the regional soil fertility of the crop pest and disease monitoring points to obtain the regional crop soil fertility level.

[0013] Step S14: Measure the occurrence data of crop pests and diseases in the area corresponding to the monitoring point in real time through the daily reporting records of pests and diseases, including the number of pests and diseases, the time of occurrence, and the location of occurrence.

[0014] Furthermore, the regional crop soil fertility content indicators mentioned in step S12 specifically include regional soil nitrogen, phosphorus, and potassium content indicators.

[0015] Furthermore, step S13 includes the following steps:

[0016] Step S131: Based on the soil pH of the crop, analyze the soil conditions of the area corresponding to the crop pest and disease monitoring points to obtain the soil pH fertility decline index.

[0017] Step S132: Obtain the corresponding regional crop vegetation coverage through crop pest and disease monitoring points, and calculate the vegetation coverage area index of crop pest and disease monitoring points based on the regional crop vegetation coverage to obtain the regional crop vegetation coverage area index.

[0018] Step S133: Based on the regional soil pH fertility decay index, regional crop vegetation coverage index, regional crop soil fertility content index, and regional crop soil microbial prosperity index, the regional soil fertility calculation formula is used to calculate the regional soil fertility of crop pest and disease monitoring points to obtain the regional crop soil fertility level.

[0019] Furthermore, the formula for calculating regional soil fertility in step S133 is as follows:

[0020] ;

[0021] In the formula, To assess the soil fertility level of regional crops, This refers to the area of ​​the monitoring points for crop diseases and pests. These are the spatial location parameters corresponding to crop pest and disease monitoring points. The regional soil pH and fertility decline index. For regional soil nitrogen content, The nitrogen content fertility influence coefficient. For regional soil phosphorus content, The factor representing the influence of phosphorus content on fertility is... The potassium content of the regional soil, The coefficient representing the effect of potassium content on fertility. In spatial location The corresponding regional crop soil microbial prosperity level Contribution coefficient to regional microbial prosperity This represents the regional crop vegetation cover index. The vegetation cover impact coefficient. This is a correction coefficient for the soil fertility level of regional crops.

[0022] Furthermore, step S2 includes the following steps:

[0023] Step S21: Collect corresponding pesticide use records through crop pest and disease monitoring points, and obtain the corresponding pesticide spraying amount based on the pesticide use records;

[0024] Step S22: Based on the amount of pesticide spraying, conduct a pest and disease distribution attenuation assessment analysis on the corresponding number of pests and diseases in the regional pest and disease occurrence data to obtain the regional pest and disease distribution pesticide use attenuation factor.

[0025] Step S23: Based on the location of pests and diseases within the regional pest and disease occurrence data, perform statistical analysis on the area corresponding to the monitoring points of crop pests and diseases to obtain the size of the regional pest and disease occurrence area.

[0026] Step S24: Based on the amount of pesticide sprayed, the pesticide use attenuation factor of regional pest distribution, and the size of the area where regional pests occur, calculate the regional occurrence density using the regional occurrence density calculation formula to calculate the number of pests and the time of occurrence of pests and diseases, so as to obtain the regional pest and disease occurrence density.

[0027] The specific formula for calculating the regional occurrence density is as follows:

[0028] ;

[0029] In the formula, The density of regional pests and diseases. The time of occurrence of pests and diseases. For time-varying parameters, This refers to the area affected by pests and diseases in the region. This refers to the amount of pesticide used for spraying. In time The corresponding number of pests and diseases. Pesticide use attenuation factor for regional pest and disease distribution. This is a correction factor for the density of regional pest and disease occurrence.

[0030] Furthermore, step S3 includes the following steps:

[0031] Step S31: Perform regional uniform gridding on the spatial area corresponding to the crop pest and disease monitoring points to obtain a uniform grid of the pest and disease occurrence area.

[0032] Step S32: Based on the uniform grid of the pest and disease occurrence area, perform regional temporal outbreak analysis on the regional pest and disease occurrence density to obtain the grid diffusion and outbreak development mode corresponding to the change of regional pest and disease occurrence over time.

[0033] Step S33: Design meteorological conditions based on the temperature, humidity, precipitation, and light intensity corresponding to the regional meteorological data to generate regional meteorological distribution conditions;

[0034] Step S34: Based on the regional meteorological distribution conditions, conduct pest outbreak mining analysis on the grid diffusion and outbreak development patterns corresponding to the changes in regional pest occurrence over time, and obtain the regional pest outbreak characteristic patterns under meteorological conditions.

[0035] Step S35: Based on the uniform grid of the pest and disease occurrence area, conduct soil fertility correlation analysis on the soil fertility level of crops in the region and the density of pest and disease occurrence in the region to obtain the correlation characteristics between regional soil fertility and pest and disease outbreak.

[0036] Furthermore, step S35 includes the following steps:

[0037] Step S351: Based on the uniform grid of the pest and disease occurrence area, divide the soil fertility of crops in the region and the density of pest and disease occurrence in the region into grids to obtain the corresponding values ​​of soil fertility and pest and disease occurrence under the same uniform grid.

[0038] Step S352: Based on the soil fertility and pest and disease occurrence density values ​​of crops under the same uniform grid, the correlation coefficient between soil fertility and pest and disease occurrence density of crops in each uniform grid is calculated.

[0039] Step S353: Based on the correlation coefficient between crop soil fertility and pest and disease occurrence density within each uniform grid, soil fertility correlation analysis is performed on the regional crop soil fertility level and regional pest and disease occurrence density to obtain the correlation characteristics between regional soil fertility and pest and disease outbreak.

[0040] Furthermore, step S4 includes the following steps:

[0041] Step S41: Based on the regional pest and disease outbreak characteristic patterns under meteorological conditions, conduct meteorological pest and disease impact analysis on the geographical distribution patterns of pests and diseases corresponding to crop pest and disease monitoring points to obtain the meteorological impact factors of regional pest and disease outbreaks.

[0042] Step S42: Based on the correlation characteristics between regional soil fertility and pest outbreaks, conduct soil fertility-pest correlation analysis on the geographical distribution patterns of pests and diseases corresponding to crop pest and disease monitoring points to obtain regional soil fertility-pest outbreak correlation factors.

[0043] Step S43: Based on the meteorological influencing factors of regional pest outbreaks and the correlation factors of regional soil fertility pest outbreaks, multi-source risk prediction is carried out for crop pest monitoring points to obtain the regional pest outbreak risk prediction results, including the predicted pest outbreak risk level corresponding to the monitoring point area.

[0044] Step S44: Based on the regional pest and disease outbreak risk prediction results, conduct prevention and control decision support analysis to generate prevention and control recommendations for pest and disease outbreak areas, including the selection of pest and disease pesticides, dosage and duration of application, and implementation of biological control measures.

[0045] Furthermore, the present invention also provides a risk prediction system for identifying pest and disease outbreak processes based on multiple factors, used to execute the risk prediction method for identifying pest and disease outbreak processes based on multiple factors as described above. This risk prediction system for identifying pest and disease outbreak processes based on multiple factors includes:

[0046] The real-time monitoring module for crop regions is used to measure regional meteorological data, regional crop soil fertility, and regional pest and disease occurrence data corresponding to crop pest and disease monitoring points in real time. The regional pest and disease occurrence data includes the number of pests and diseases, the time of occurrence, and the location of occurrence.

[0047] The pest and disease occurrence density calculation module is used to obtain the corresponding pesticide spraying amount through crop pest and disease monitoring points, and to calculate the regional occurrence density based on the pesticide spraying amount to obtain the regional pest and disease occurrence density.

[0048] The pest and disease outbreak feature mining module is used to mine and analyze the outbreak density of pests and diseases in the region based on regional meteorological data and regional crop soil fertility, thereby obtaining the regional pest and disease outbreak feature patterns under meteorological conditions and the correlation feature relationship between regional soil fertility and pest and disease outbreak.

[0049] The multi-source factor risk prediction module for pests and diseases is used to predict the risk of crop pests and diseases at monitoring points based on the regional pest and disease outbreak characteristics under meteorological conditions and the correlation between regional soil fertility and pest and disease outbreaks, so as to obtain the regional pest and disease outbreak risk prediction results. Based on the regional pest and disease outbreak risk prediction results, the module performs prevention and control decision support analysis to generate prevention and control recommendations for pest and disease outbreak areas.

[0050] The beneficial effects of this invention are:

[0051] 1. The multi-source factor-based risk prediction method for pest and disease outbreaks proposed in this invention, compared with existing technologies, has the following advantages: by measuring meteorological data, soil fertility, and pest and disease occurrence data in real time at the corresponding areas of crop pest and disease monitoring points, it can provide important basic information for subsequent pest and disease monitoring and early warning. Meteorological data includes factors such as temperature, humidity, precipitation, and light intensity, which directly affect the occurrence and development of pests and diseases. Soil fertility data reflects the soil's nutritional status, such as pH, nitrogen, phosphorus, potassium content, and microbial community prosperity. Through statistical analysis of the number, time, and location of pest and disease occurrences, the occurrence patterns and regional characteristics of pests and diseases can be understood, providing accurate references for subsequent prediction and control. Real-time data collection ensures the timeliness of monitoring, enabling farmers and farm managers to take timely control measures in the early stages of pest and disease occurrence, reducing potential economic losses. It provides data support for subsequent pest and disease control and agricultural production management, helping agricultural management departments and farmers make more scientific and accurate decisions. Secondly, by obtaining the corresponding pesticide application rates from crop pest and disease monitoring points and calculating the regional pest and disease density based on these rates, the relationship between pesticide use and pest and disease occurrence can be further analyzed. The key to this step is to infer the severity of pest and disease occurrence through pesticide application, thereby achieving a more accurate calculation of pest and disease density. If pesticide application is too high in a region, it means that the pest and disease density is high; conversely, it means that the pest and disease occurrence is relatively mild. Regional density calculation can more clearly identify which areas experience more frequent pest and disease occurrences, thus providing a basis for subsequent prevention and control decisions. Pesticide application data helps determine the spatiotemporal distribution characteristics of pest and disease occurrence and helps assess the effectiveness of current pest and disease control measures. This data calculation also helps provide a more accurate risk assessment for agricultural production and prevention and control work, ensuring the timeliness and effectiveness of pest and disease control measures. Then, by conducting outbreak analysis on the density of pests and diseases based on regional meteorological data and crop soil fertility, the outbreak patterns of pests and diseases under different climatic and soil conditions can be revealed. The beneficial effect of this analysis is that by analyzing the relationship between these factors and the density of pests and diseases, a characteristic pattern of regional pest and disease outbreaks can be constructed, and future pest and disease outbreak trends can be predicted. This allows for a full consideration of the comprehensive impact of multiple factors such as environment, climate, and soil fertility on pest and disease outbreaks.Finally, by conducting multi-source risk prediction of crop pest and disease monitoring points based on regional pest and disease outbreak characteristics under meteorological conditions and the correlation between regional soil fertility and pest and disease outbreaks, a multi-dimensional risk assessment model can be constructed. This model can comprehensively predict the occurrence risk of pests and diseases for different agricultural production areas, providing farmers with early warning information in advance, thus improving the accuracy and reliability of the prediction results. This prediction result can also effectively reduce the damage of pests and diseases to crops and avoid yield losses caused by pest and disease outbreaks. By generating regional prevention and control suggestions, personalized prevention and control strategies can be provided to help farmers take appropriate measures such as pesticide spraying, irrigation, and fertilization at the most suitable time, thereby effectively reducing the occurrence of pests and diseases.

[0052] 2. The pest and disease outbreak risk prediction system based on multi-source factors proposed in this invention consists of a real-time monitoring module for crop areas, a pest and disease occurrence density calculation module, a pest and disease outbreak characteristic mining module, and a pest and disease multi-source factor risk prediction module. It can realize any pest and disease outbreak risk prediction method based on multi-source factors described in this invention. It is used to combine the operations between the computer programs running on each module to realize the pest and disease outbreak risk prediction method based on multi-source factors. The internal structure of the system cooperates with each other, which can greatly reduce repetitive work and manpower input, and can quickly and effectively provide a more accurate and efficient pest and disease outbreak risk prediction process based on multi-source factors, thereby simplifying the operation process of the pest and disease outbreak risk prediction system based on multi-source factors. Attached Figure Description

[0053] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0054] Figure 1 This is a schematic diagram of the steps in the method for predicting the risk of pest and disease outbreaks based on multiple factors, as described in this invention.

[0055] Figure 2 for Figure 1 A detailed flowchart of step S1;

[0056] Figure 3 for Figure 2 A detailed flowchart of step S13. Detailed Implementation

[0057] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0058] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0059] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0060] To achieve the above objectives, please refer to Figures 1 to 2 This invention provides a method for risk prediction of pest and disease outbreaks based on multi-source factors, the method comprising the following steps:

[0061] Step S1: Real-time measurement of regional meteorological data, regional crop soil fertility, and regional pest and disease occurrence data corresponding to crop pest and disease monitoring points. The regional pest and disease occurrence data includes the number of pests and diseases, the time of occurrence, and the location of occurrence.

[0062] Step S2: Obtain the corresponding pesticide spraying amount through crop pest and disease monitoring points, and calculate the regional occurrence density based on the pesticide spraying amount to obtain the regional pest and disease occurrence density.

[0063] Step S3: Based on regional meteorological data and the degree of soil fertility of regional crops, conduct pest and disease outbreak analysis on the density of regional pest and disease occurrence to obtain the regional pest and disease outbreak characteristic patterns under meteorological conditions and the correlation characteristics between regional soil fertility and pest and disease outbreak.

[0064] Step S4: Based on the regional pest and disease outbreak characteristic patterns under meteorological conditions and the correlation characteristics between regional soil fertility and pest and disease outbreaks, multi-source factor risk prediction is performed on crop pest and disease monitoring points to obtain regional pest and disease outbreak risk prediction results; based on the regional pest and disease outbreak risk prediction results, prevention and control decision support analysis is performed to generate prevention and control recommendations for pest and disease outbreak areas.

[0065] In the embodiments of this invention, please refer to Figure 1 The diagram shown is a flowchart illustrating the steps of the method for predicting the risk of pest and disease outbreaks based on multi-source factors according to the present invention. In this example, the method for predicting the risk of pest and disease outbreaks based on multi-source factors includes the following steps:

[0066] Step S1: Real-time measurement of regional meteorological data, regional crop soil fertility, and regional pest and disease occurrence data corresponding to crop pest and disease monitoring points. The regional pest and disease occurrence data includes the number of pests and diseases, the time of occurrence, and the location of occurrence.

[0067] In this embodiment of the invention, meteorological data corresponding to different pest and disease monitoring points in the area are acquired. This meteorological data includes, but is not limited to, information such as temperature, humidity, wind speed, precipitation, and sunshine duration. Meteorological data can be collected through meteorological stations installed in farmland or through historical meteorological records obtained via a network platform. Next, soil fertility data of crops in the area needs to be collected, including soil organic matter content, concentration of major fertilizer elements such as nitrogen, phosphorus, and potassium, pH, and other indicators. This soil data can be obtained by sampling on-site and sending it to a soil analysis laboratory for testing, or by real-time monitoring and recording through farmland sensors. In addition, pest and disease occurrence data in the area also needs to be collected. This data includes information such as the number of pests and diseases, the specific time of occurrence, and the location of occurrence. It is usually obtained through pest and disease monitoring networks, remote sensing data collection, or manual inspection. The number of pests and diseases is usually statistically analyzed based on sample plot surveys. The real-time acquisition of this data ensures the timeliness of subsequent data processing and provides necessary basic information for subsequent analysis.

[0068] Step S2: Obtain the corresponding pesticide spraying amount through crop pest and disease monitoring points, and calculate the regional occurrence density based on the pesticide spraying amount to obtain the regional pest and disease occurrence density.

[0069] In this embodiment of the invention, pesticide spraying data for the region is obtained through crop pest and disease monitoring points. The amount of pesticide sprayed can be obtained by installing monitoring devices on pesticide spraying equipment. The spraying data recorded by the equipment can be transmitted to a cloud platform in real time for storage and processing. Based on the amount of pesticide sprayed, the density of pest and disease occurrence in the region needs to be calculated. The calculation of the regional pest and disease occurrence density depends on the size of the pest and disease occurrence range and the attenuation of the pest and disease distribution by the sprayed pesticide. When performing the calculation, the spatial distribution range of pests and diseases must first be defined, and the pest and disease density in different regions is calculated in combination with the actual effect of the sprayed pesticide. In some special cases, GIS (Geographic Information System) technology can be used to calculate the spatial distribution of pest and disease occurrence density at different monitoring points through spatial analysis functions to obtain a more accurate pest and disease density distribution map, and finally obtain the regional pest and disease occurrence density.

[0070] Step S3: Based on regional meteorological data and the degree of soil fertility of regional crops, conduct pest and disease outbreak analysis on the density of regional pest and disease occurrence to obtain the regional pest and disease outbreak characteristic patterns under meteorological conditions and the correlation characteristics between regional soil fertility and pest and disease outbreak.

[0071] In this embodiment of the invention, by statistically analyzing historical pest and disease occurrence data and meteorological data, a regression model or machine learning model can be established to reveal how meteorological factors (such as temperature, humidity, and precipitation) affect the outbreak of pests and diseases. For example, some pests and diseases are prone to outbreaks under hot and humid climatic conditions, while others are more severe under drought conditions. By mining these correlation patterns, a predictive model of meteorological conditions and pest and disease occurrence can be established, thereby predicting and analyzing the regional pest and disease outbreak characteristics under the corresponding meteorological conditions. Simultaneously, it is also necessary to analyze the impact of regional soil fertility on pest and disease outbreaks. The concentration of fertilizer elements in the soil affects crop growth, which in turn affects the incidence of pests and diseases. High-fertility soil promotes the reproduction of some pests and diseases, while other soils inhibit their growth. By comparing and analyzing soil fertility levels and pest and disease occurrence data in different regions, the specific impact of different soil fertility levels on pest and disease outbreaks can be revealed, ultimately clarifying the correlation characteristics between soil fertility and pest and disease outbreaks.

[0072] Step S4: Based on the regional pest and disease outbreak characteristic patterns under meteorological conditions and the correlation characteristics between regional soil fertility and pest and disease outbreaks, multi-source factor risk prediction is performed on crop pest and disease monitoring points to obtain regional pest and disease outbreak risk prediction results; based on the regional pest and disease outbreak risk prediction results, prevention and control decision support analysis is performed to generate prevention and control recommendations for pest and disease outbreak areas.

[0073] In this embodiment of the invention, the risk of pest and disease outbreaks is predicted based on the regional pest and disease outbreak characteristic patterns under meteorological conditions obtained in the aforementioned steps, as well as the correlation characteristics between soil fertility and pest and disease outbreaks. Based on this data, a multi-source data fusion model can be constructed, considering multiple factors such as meteorological factors, soil fertility, and the density of historical pest and disease occurrences. Statistical models or deep learning algorithms (such as neural networks, decision trees, etc.) are used to predict the risk of pest and disease outbreaks. This process requires weighting multiple factors to obtain the final risk assessment value. After the risk prediction is completed, further data analysis is used to generate pest and disease control recommendations for different regions. These recommendations include specific control measures, such as suggesting increasing the amount of pesticides used or adjusting the spraying time, or taking other non-chemical control measures (such as planting pest and disease resistant crop varieties). At the same time, based on short-term forecasts of meteorological data, timely warnings can be given for impending pest and disease outbreaks in the next few days or weeks, and dynamically adjusted control plans can be provided. These decision support analyses will help farmers or agricultural managers respond more accurately to pest and disease outbreaks, thereby minimizing crop losses and ultimately generating pest and disease outbreak control recommendations.

[0074] Furthermore, step S1 includes the following steps:

[0075] Step S11: Measure the regional meteorological data corresponding to the crop pest and disease monitoring points in real time through the meteorological monitoring station network, including the temperature, humidity, precipitation and light intensity of the monitoring point area.

[0076] Step S12: Real-time monitoring and data collection of soil conditions in the area corresponding to the crop pest and disease monitoring point is carried out using soil sensors to obtain regional crop soil condition data, including regional crop soil pH, regional crop soil fertility content index and regional crop soil microbial prosperity.

[0077] Step S13: Based on the regional crop soil condition data, calculate the regional soil fertility of the crop pest and disease monitoring points to obtain the regional crop soil fertility level.

[0078] Step S14: Measure the occurrence data of crop pests and diseases in the area corresponding to the monitoring point in real time through the daily reporting records of pests and diseases, including the number of pests and diseases, the time of occurrence, and the location of occurrence.

[0079] As an embodiment of the present invention, reference is made to... Figure 2 As shown, Figure 1 A detailed flowchart of step S1 is shown below. In this embodiment, step S1 includes the following steps:

[0080] Step S11: Measure the regional meteorological data corresponding to the crop pest and disease monitoring points in real time through the meteorological monitoring station network, including the temperature, humidity, precipitation and light intensity of the monitoring point area.

[0081] In this embodiment of the invention, meteorological data of the area corresponding to the crop pest and disease monitoring point is measured in real time through a network of meteorological monitoring stations. In this step, multiple meteorological monitoring stations are deployed in the crop planting area to continuously collect meteorological parameters such as temperature, humidity, precipitation, and light intensity. Temperature and humidity are monitored in real time by sensors in the meteorological stations and uploaded to a central database to record precise temperature changes and humidity fluctuations. Precipitation is automatically sensed by rain gauges and the intensity and duration of precipitation are calculated. The data is transmitted to the cloud for analysis in a timely manner. Light intensity is collected by light sensors and the data is uploaded to the monitoring system via a wireless network to finally obtain the regional meteorological data corresponding to the crop pest and disease monitoring point.

[0082] Step S12: Real-time monitoring and data collection of soil conditions in the area corresponding to the crop pest and disease monitoring point is carried out using soil sensors to obtain regional crop soil condition data, including regional crop soil pH, regional crop soil fertility content index and regional crop soil microbial prosperity.

[0083] In this embodiment of the invention, soil sensors are used to monitor the soil conditions in real time in areas corresponding to crop pest and disease monitoring points. In this step, multiple soil sensors are installed in the crop planting area. These sensors can accurately detect the soil pH, fertility content, and microbial prosperity. Soil pH is measured by pH monitoring sensors to monitor whether the soil environment is suitable for crop growth and whether it may affect the occurrence of pests and diseases. Soil fertility content is achieved through a series of chemical sensors, specifically monitoring the content of the three main nutrients in the soil: nitrogen, phosphorus, and potassium. The data is uploaded to the cloud in real time. Soil microbial prosperity is captured by specific microbial sensors. The sensors detect the number and activity of microbial communities in the soil and provide feedback on soil biological activity data. The real-time data will be integrated and analyzed to determine the specific performance of the crop soil conditions in the area, and finally, regional crop soil condition data will be obtained.

[0084] Step S13: Based on the regional crop soil condition data, calculate the regional soil fertility of the crop pest and disease monitoring points to obtain the regional crop soil fertility level.

[0085] In this embodiment of the invention, soil fertility is calculated based on the acquired regional crop soil condition data. The soil fertility of each monitoring point in the region is evaluated by an algorithm. First, the content of elements such as nitrogen, phosphorus, and potassium in the soil is weighted and calculated to obtain a fertility index. In addition, the soil health is further evaluated by combining the soil pH and microbial prosperity, thereby obtaining the comprehensive degree of soil fertility. This process is automatically calculated by a data model to ensure the scientificity and accuracy of the evaluation results. If the soil fertility index is lower than the set standard threshold, the system will generate an early warning message, indicating that the region may face the risk of soil infertility, which will affect the growth environment of crops. Finally, the degree of regional crop soil fertility is obtained.

[0086] Step S14: Measure the occurrence data of crop pests and diseases in the area corresponding to the monitoring point in real time through the daily reporting records of pests and diseases, including the number of pests and diseases, the time of occurrence, and the location of occurrence.

[0087] In this embodiment of the invention, the occurrence data of pests and diseases in the corresponding areas of crop pest and disease monitoring points are measured in real time through daily reporting records. In this step, mobile terminal devices (such as smartphones, tablets, etc.) are used to upload the daily observation and reporting data of pests and diseases by agricultural workers to the system. The number, time and location of pests and diseases are recorded in detail. By periodically summarizing the uploaded pest and disease reporting records, the system identifies high-incidence areas, time periods and types of pests and diseases. For example, if a certain area reports the occurrence of a certain pest or disease for several consecutive days, the system will automatically mark the area as a high-incidence area of ​​pests and diseases. The system will perform correlation analysis with meteorological data and soil conditions to further predict the potential risk of pest and disease outbreaks. All records will be stored in the database for future pest and disease prediction model training, and finally obtain the occurrence data of pests and diseases in the areas corresponding to the crop pest and disease monitoring points.

[0088] Furthermore, the regional crop soil fertility content indicators mentioned in step S12 specifically include regional soil nitrogen, phosphorus, and potassium content indicators.

[0089] Furthermore, step S13 includes the following steps:

[0090] Step S131: Based on the soil pH of the crop, analyze the soil conditions of the area corresponding to the crop pest and disease monitoring points to obtain the soil pH fertility decline index.

[0091] Step S132: Obtain the corresponding regional crop vegetation coverage through crop pest and disease monitoring points, and calculate the vegetation coverage area index of crop pest and disease monitoring points based on the regional crop vegetation coverage to obtain the regional crop vegetation coverage area index.

[0092] Step S133: Based on the regional soil pH fertility decay index, regional crop vegetation coverage index, regional crop soil fertility content index, and regional crop soil microbial prosperity index, the regional soil fertility calculation formula is used to calculate the regional soil fertility of crop pest and disease monitoring points to obtain the regional crop soil fertility level.

[0093] As an embodiment of the present invention, reference is made to... Figure 3 As shown, Figure 2 A detailed flowchart of step S13 is shown in this embodiment. Step S13 includes the following steps:

[0094] Step S131: Based on the soil pH of the crop, analyze the soil conditions of the area corresponding to the crop pest and disease monitoring points to obtain the soil pH fertility decline index.

[0095] In this embodiment of the invention, soil samples are obtained through a Geographic Information System (GIS) based on the location of crop pest and disease monitoring points. For each soil sample, the pH is measured using a soil pH meter. The pH data of each soil sample is then combined with a soil fertility decay model to calculate the degree of influence of soil pH on soil fertility. Considering the influence of pH on soil nutrients, and taking into account factors such as soil texture, fertility level, and nutrient loss rate, a corresponding fertility decay index is derived. Specifically, if the soil pH is below 5.5 (acidic soil), the decay index will increase significantly, indicating difficulties in fertility maintenance and crop growth. Similarly, if the soil pH is above 7.5 (alkaline soil), it will also have an adverse effect on fertility maintenance. Based on this, by analyzing the pH and fertility decay status of different regions, the soil fertility decay index of that region is assessed, and finally, the regional soil pH fertility decay index is obtained.

[0096] Step S132: Obtain the corresponding regional crop vegetation coverage through crop pest and disease monitoring points, and calculate the vegetation coverage area index of crop pest and disease monitoring points based on the regional crop vegetation coverage to obtain the regional crop vegetation coverage area index.

[0097] In this embodiment of the invention, image data of crops around the monitoring point are acquired through remote sensing satellite imagery or drone photography. Image processing software (such as ENVI or ArcGIS) is used to process the image data and extract vegetation information of the crop area. In the process of calculating vegetation coverage, the Normalized Difference Vegetation Index (NDVI) is used to assess the growth of ground vegetation. The formula for calculating NDVI is: NDVI=(NIR-RED) / (NIR+RED), where NIR represents the reflectivity of the near-infrared band and RED is the reflectivity of the red band. The NDVI value calculated by this formula can be used to assess the growth density and health status of crops, thereby reflecting the vegetation coverage of the corresponding area. Furthermore, spatial analysis of the imagery using Geographic Information System (GIS) is conducted to determine the vegetation cover area index (NDVI) of the area corresponding to the monitoring point. Specifically, the vegetation cover distribution is combined with land use and crop types to derive the NDVI of the area, which is calculated as NDVI × land use area. If the NDVI value of the area corresponding to the monitoring point is high, it indicates that the vegetation cover of the area is good and the probability of pests and diseases is relatively low. If the NDVI value is low, it indicates that the vegetation cover is poor and the risk of pests and diseases is high. Finally, the regional crop vegetation cover area index is obtained.

[0098] Step S133: Based on the regional soil pH fertility decay index, regional crop vegetation coverage index, regional crop soil fertility content index, and regional crop soil microbial prosperity index, the regional soil fertility calculation formula is used to calculate the regional soil fertility of crop pest and disease monitoring points to obtain the regional crop soil fertility level.

[0099] In this embodiment of the invention, a suitable regional soil fertility calculation formula is constructed by combining the area of ​​the crop pest and disease monitoring point, spatial location parameters, regional soil pH fertility decay index, regional soil nitrogen content, nitrogen content fertility influence coefficient, regional soil phosphorus content, phosphorus content fertility influence coefficient, regional soil potassium content fertility influence coefficient, regional crop soil microbial prosperity, regional microbial prosperity contribution coefficient, regional crop vegetation coverage area index, vegetation coverage influence coefficient, and related parameters. This formula is used to perform statistical calculations of soil fertility, quantitatively calculate the specific numerical value of the regional crop soil fertility level, and finally obtain the regional crop soil fertility level.

[0100] Furthermore, the formula for calculating regional soil fertility in step S133 is as follows:

[0101] ;

[0102] In the formula, To assess the soil fertility level of regional crops, This refers to the area of ​​the monitoring points for crop diseases and pests. These are the spatial location parameters corresponding to crop pest and disease monitoring points. The regional soil pH and fertility decline index. For regional soil nitrogen content, The nitrogen content fertility influence coefficient. For regional soil phosphorus content, The factor representing the influence of phosphorus content on fertility is... The potassium content of the regional soil, The coefficient representing the effect of potassium content on fertility. In spatial location The corresponding regional crop soil microbial prosperity level Contribution coefficient to regional microbial prosperity This represents the regional crop vegetation cover index. The vegetation cover impact coefficient. This is a correction coefficient for the soil fertility level of regional crops.

[0103] This invention has developed a regional soil fertility calculation formula by using a specific mathematical model and after verification. This formula is used to calculate the regional soil fertility of crop pest and disease monitoring points. The formula quantifies the degree of regional soil fertility by combining multiple factors, including soil pH, nitrogen, phosphorus and potassium content, microbial prosperity and vegetation cover area. This multi-dimensional assessment method is more comprehensive than single soil component detection and can more accurately reflect the overall health status of the soil. For example, the soil pH fertility decay index reflects the impact of soil acidity and alkalinity on soil fertility. Different crops adapt to different acidic and alkaline environments, and the decay index in the formula reflects their negative impact on soil fertility. The fertility influence coefficients of nitrogen, phosphorus, and potassium reflect the direct impact of the concentration of these basic nutrients on soil fertility, helping to reflect the supporting role of regional soil chemical composition on crop growth. Soil microbial prosperity is related to soil biological activity, and microbial prosperity can affect soil fertility and the occurrence of pests and diseases because microbial activity helps nutrient cycling and pathogen inhibition. The vegetation cover index reflects the effect of vegetation cover on soil fertility; when the cover is high, soil erosion and loss are less, and fertility is relatively higher. The formula introduces spatial location parameters, reflecting the spatial heterogeneity of soil fertility within a region. In practical applications, soil fertility levels are often not uniformly distributed. Therefore, considering spatial variability is very important when assessing regional fertility. This allows for the implementation of precise agricultural management and pest and disease control measures for different soil conditions. The crucial role of soil microorganisms in soil fertility is particularly emphasized. Microorganisms not only contribute to nutrient decomposition and cycling but also aid in pest and disease control. Microbial prosperity is listed separately in the formula, and its contribution coefficient reflects its impact on fertility. The diversity and quantity of microbial communities affect soil health; therefore, their role also has an important indirect effect on pest and disease control. Furthermore, correction coefficients in the formula can further adjust the calculation results, making the soil fertility assessment more realistic. These correction coefficients can be calibrated using field monitoring data, ensuring that the soil fertility calculation is closer to reality and avoiding biases that may arise from a single theoretical model. In summary, this formula fully considers the regional crop soil fertility level. Area of ​​crop pest and disease monitoring points Spatial location parameters corresponding to crop pest and disease monitoring points Regional soil pH and fertility decline index Regional soil nitrogen content Nitrogen content fertility influence coefficient Regional soil phosphorus content Phosphorus content fertility influence coefficient Potassium content in regional soil Potassium content fertility influence coefficient In spatial location The corresponding regional crop soil microbial prosperity Contribution coefficient of regional microbial prosperity Regional crop vegetation coverage index Vegetation cover influence coefficient Correction coefficient for regional crop soil fertility According to the soil fertility level of regional crops The interrelationships between the above parameters constitute a functional relationship. This formula enables the calculation of regional soil fertility at crop pest and disease monitoring points, and also allows for the adjustment of regional crop soil fertility levels using correction coefficients. The introduction of this method allows for adjustments based on errors that occur during the calculation process, thereby improving the accuracy and applicability of the regional soil fertility calculation formula.

[0104] Furthermore, step S2 includes the following steps:

[0105] Step S21: Collect corresponding pesticide use records through crop pest and disease monitoring points, and obtain the corresponding pesticide spraying amount based on the pesticide use records;

[0106] In this embodiment of the invention, pesticide usage records are collected through designated crop pest and disease monitoring points. First, using agricultural Internet of Things (IoT) technology, the monitoring point equipment is connected to a data center to collect pesticide spraying records in real time. Each monitoring point regularly records the type of pesticide sprayed, the amount sprayed, the spraying time, and the area where the pesticide was used. By using Geographic Information System (GIS) and remote sensing technology, combined with an agricultural data management system, the area and specific amount of pesticide sprayed can be accurately identified. In addition, the pesticide usage is supplemented and verified by combining crop growth stages, meteorological data, and soil characteristic information to ensure the accuracy and completeness of the data. The data is then uniformly stored and processed through the data center to finally obtain the corresponding pesticide spraying amount.

[0107] Step S22: Based on the amount of pesticide spraying, conduct a pest and disease distribution attenuation assessment analysis on the corresponding number of pests and diseases in the regional pest and disease occurrence data to obtain the regional pest and disease distribution pesticide use attenuation factor.

[0108] In this embodiment of the invention, based on the amount of pesticide sprayed, an assessment and analysis of the attenuation of pest distribution is conducted on the corresponding number of pests and diseases within the regional pest and disease occurrence data. First, a spatial distribution model of the pest and disease occurrence data within the region is modeled using a spatial interpolation algorithm (such as Kriging interpolation). Based on the amount of pesticide sprayed, combined with meteorological conditions (temperature, humidity, wind speed, etc.) and topographic features (slope, elevation, etc.), an attenuation assessment is conducted on the distribution range and occurrence intensity of pests and diseases. The attenuation factor is determined by the combined effect of multiple factors, including the amount of sprayed, the degradation rate of pesticides, and the influence of environmental conditions. Using multivariate regression analysis, a relationship model between pesticide use and pest and disease occurrence is established to quantify the attenuation of pest and disease distribution, and the pesticide use attenuation factor is calculated to evaluate the differences in the effect of pesticide use on pest and disease outbreaks in different regions. Finally, the regional pest and disease distribution pesticide use attenuation factor is obtained.

[0109] Step S23: Based on the location of pests and diseases within the regional pest and disease occurrence data, perform statistical analysis on the area corresponding to the monitoring points of crop pests and diseases to obtain the size of the regional pest and disease occurrence area.

[0110] In this embodiment of the invention, based on the occurrence data of pests and diseases within a region, the area of ​​occurrence of different pests and diseases is statistically analyzed. First, the locations of pests and diseases are spatially labeled using a Geographic Information System (GIS). Remote sensing images or drone aerial photography are used for on-site monitoring and image acquisition to obtain the precise geographical coordinates of the occurrence of pests and diseases. Then, combined with the specific boundary information of the crop planting area, spatial analysis tools (such as buffer analysis, area calculation, etc.) are used to statistically analyze the area of ​​occurrence of pests and diseases. Using standardized area calculation methods and combined with historical occurrence data of crop pests and diseases, the pest and disease occurrence areas are divided in detail to ensure the accuracy and reliability of the measurement. Furthermore, by combining environmental monitoring data (such as soil moisture, rainfall, etc.), the regional distribution of pests and diseases is dynamically adjusted to further optimize the estimation of the area of ​​occurrence of pests and diseases, and finally, the size of the area of ​​occurrence of pests and diseases in the region is obtained.

[0111] Step S24: Based on the amount of pesticide sprayed, the pesticide use attenuation factor of regional pest distribution, and the size of the area where regional pests occur, calculate the regional occurrence density using the regional occurrence density calculation formula to calculate the number of pests and the time of occurrence of pests and diseases, so as to obtain the regional pest and disease occurrence density.

[0112] In this embodiment of the invention, a suitable formula for calculating the regional occurrence density is constructed by combining the amount of pesticide sprayed, the pesticide use attenuation factor of regional pest distribution, the area of ​​regional pest occurrence, the number of pests, the occurrence time of pests, time variable parameters, and related parameters. This formula is used to quantitatively calculate the occurrence density, thereby quantitatively determining the occurrence of pests in the monitoring point area and finally obtaining the regional pest occurrence density.

[0113] The specific formula for calculating the regional occurrence density is as follows:

[0114] ;

[0115] In the formula, The density of regional pests and diseases. The time of occurrence of pests and diseases. For time-varying parameters, This refers to the area affected by pests and diseases in the region. This refers to the amount of pesticide used for spraying. In time The corresponding number of pests and diseases. Pesticide use attenuation factor for regional pest and disease distribution. This is a correction factor for the density of regional pest and disease occurrence.

[0116] This invention, through the use of a specific mathematical model and verification, yields a formula for calculating regional occurrence density. This formula is used to calculate the regional occurrence density of pests and diseases, considering both the quantity and timing of their occurrence. It comprehensively considers multiple key factors (such as the area affected by pests and diseases, the amount of pesticides used, the quantity of pests and diseases, the attenuation factor of pest and disease distribution, and the timing of occurrence), thus comprehensively reflecting the density of pests and diseases within a region. By integrating these factors, the formula helps to accurately describe the spread and development of pests and diseases within the region. The time variable and exponential decay term in the formula dynamically reflect the impact of pesticide spraying on pest and disease occurrence. As time progresses, the effectiveness of pesticides gradually diminishes, thus affecting the quantity of pests and diseases. This dynamic modeling approach allows for detailed analysis of pest and disease occurrence at different time periods, rather than simply providing static results. By introducing a pesticide use attenuation factor, the formula can model the long-term effects of pesticide spraying. This attenuation factor considers the gradual weakening of pesticide effects over time, helping to assess the sustained efficacy of pesticides after application. This allows for precise measurement of pesticide control effects on pests and diseases at different time points, thus enabling more effective optimization of pesticide application strategies. Various parameters in the formula, such as the area affected by pests and diseases, the amount of pesticide used, and the number of pests and diseases, can be quantified. These quantified factors facilitate practical data analysis, providing agricultural managers with precise decision support, optimizing resource allocation, and reducing the risks of pesticide overuse and environmental pollution. Furthermore, the correction coefficient in the formula provides flexible adjustment capabilities for the calculation results. This means that if special circumstances arise (such as changes in weather conditions or farmland environment), the calculation results can be supplemented and corrected by adjusting this correction coefficient, making the prediction results more accurate and consistent with reality. This allows for the determination of reasonable pesticide spraying strategies, pest and disease control timing, and regional control measures based on the predicted regional pest and disease density, thereby improving agricultural production efficiency. In summary, this formula fully considers the regional pest and disease density. Time of occurrence of diseases and pests Time variable parameter Size of the area affected by regional pests and diseases Pesticide spraying usage In time The corresponding number of pests and diseases Regional pest and disease distribution and pesticide use attenuation factor Correction factor for regional pest and disease occurrence density Based on the density of pests and diseases in the region The interrelationships between the above parameters constitute a functional relationship. This formula enables the calculation of regional occurrence density of pests and diseases, including the number and timing of their occurrence. It also includes a correction coefficient for regional pest and disease occurrence density. The introduction of this feature allows for adjustments based on errors that occur during the calculation process, thereby improving the accuracy and applicability of the regional occurrence density calculation formula.

[0117] Furthermore, step S3 includes the following steps:

[0118] Step S31: Perform regional uniform gridding on the spatial area corresponding to the crop pest and disease monitoring points to obtain a uniform grid of the pest and disease occurrence area.

[0119] In this embodiment of the invention, the spatial area corresponding to the crop pest and disease monitoring points is uniformly gridded. In the implementation process, it is first necessary to collect data from the crop pest and disease monitoring points, obtain the spatial coordinates of each monitoring point and relevant information on the occurrence of pests and diseases. Then, GIS (Geographic Information System) tools are used to perform spatial distribution analysis on the monitoring point data, and the monitoring area is divided into uniform grids. The size of the grid should be set according to the scale of the specific area and the distribution density of the monitoring points to ensure that each grid contains an appropriate number of monitoring points, which can comprehensively represent the spatial distribution of pest and disease occurrence. The pest and disease monitoring data can be transformed into the occurrence of pests and diseases in each grid within the area, and finally a uniform grid of the pest and disease occurrence area is obtained.

[0120] Step S32: Based on the uniform grid of the pest and disease occurrence area, perform regional temporal outbreak analysis on the regional pest and disease occurrence density to obtain the grid diffusion and outbreak development mode corresponding to the change of regional pest and disease occurrence over time.

[0121] In this embodiment of the invention, a regional temporal outbreak analysis of pest occurrence density is performed based on a uniform grid of pest occurrence areas. This utilizes time-series data provided by pest monitoring points to analyze the occurrence of pests within each grid, and classifies and sorts them temporally. Based on historical data, temporal analysis methods (such as ARIMA model, seasonal decomposition, etc.) are used to model the temporal characteristics of pest occurrence, identifying the outbreak cycle, outbreak time point, and outbreak intensity of pests. Through this process, the temporal evolution trend and spatial diffusion pattern of pests can be obtained, predicting the occurrence and spread of pests in the future. The model analysis results will show the outbreak development pattern of pests in each grid over a period of time, ultimately obtaining the grid diffusion outbreak development pattern corresponding to the change of regional pest occurrence over time.

[0122] Step S33: Design meteorological conditions based on the temperature, humidity, precipitation, and light intensity corresponding to the regional meteorological data to generate regional meteorological distribution conditions;

[0123] In this embodiment of the invention, meteorological conditions are designed based on the temperature, humidity, precipitation, and light intensity corresponding to regional meteorological data. Meteorological data can be obtained through meteorological stations, satellite remote sensing data, or meteorological simulation systems to acquire specific meteorological data for each monitoring grid area. The core of meteorological condition design is to model the meteorological conditions of different geographical locations and time periods within the region in detail. Meteorological modeling methods, such as interpolation algorithms (e.g., Kriging interpolation or inverse distance weighting), are used to spatially interpolate the regional meteorological data to fill the meteorological gaps between monitoring stations. Through this process, the specific meteorological distribution corresponding to each grid point within the region is obtained, including the spatiotemporal distribution characteristics of elements such as temperature, humidity, precipitation, and light intensity, ultimately generating regional meteorological distribution conditions.

[0124] Step S34: Based on the regional meteorological distribution conditions, conduct pest outbreak mining analysis on the grid diffusion and outbreak development patterns corresponding to the changes in regional pest occurrence over time, and obtain the regional pest outbreak characteristic patterns under meteorological conditions.

[0125] In this embodiment of the invention, pest outbreak mining and analysis is conducted by analyzing the grid-based diffusion and outbreak development patterns of regional pests and diseases over time based on regional meteorological distribution conditions. This step requires in-depth analysis of meteorological data and the spatiotemporal variation patterns of pests and diseases, employing multi-factor analysis methods such as multiple regression analysis, random forest, and support vector machine to uncover the influence of meteorological conditions on pest and disease outbreaks. First, the meteorological data of each grid is matched with the corresponding pest and disease occurrence to construct a relationship model between pest and disease outbreaks and meteorological conditions. By analyzing historical data, the specific impacts of meteorological factors on pest and disease occurrence are identified, such as whether changes in temperature and humidity promote the reproduction and spread of pests and diseases, and whether precipitation leads to the rapid spread of pests and diseases. Through this analysis, the meteorological characteristic patterns of pest and disease outbreaks can be obtained, ultimately yielding the regional pest and disease outbreak characteristic patterns under the corresponding meteorological conditions.

[0126] Step S35: Based on the uniform grid of the pest and disease occurrence area, conduct soil fertility correlation analysis on the soil fertility level of crops in the region and the density of pest and disease occurrence in the region to obtain the correlation characteristics between regional soil fertility and pest and disease outbreak.

[0127] In this embodiment of the invention, soil fertility correlation analysis is performed on the soil fertility level of crops and the density of pests and diseases in the region based on a uniform grid of the pest and disease occurrence area. In this step, soil fertility data of crops in the region needs to be collected first, including soil nutrient content, pH, soil type and other indicators. Soil sampling technology is used to obtain soil data through laboratory analysis or sensor monitoring. Then, combined with the density data of pests and diseases in the region, correlation analysis methods, such as Pearson correlation coefficient or Granger causality test, are used to quantitatively analyze the relationship between soil fertility and pest and disease occurrence. By analyzing the impact of changes in soil fertility on pest and disease outbreaks, it is possible to identify whether the deficiency or excess of certain specific nutrients in the soil promotes the occurrence and spread of pests and diseases. For example, low nitrogen or excessive phosphorus content in some soils leads to the outbreak of specific pests. Finally, the correlation characteristics between regional soil fertility and pest and disease outbreaks are obtained, that is, whether the soil fertility and pest and disease outbreaks in the monitoring area are positively or negatively correlated.

[0128] Furthermore, step S35 includes the following steps:

[0129] Step S351: Based on the uniform grid of the pest and disease occurrence area, divide the soil fertility of crops in the region and the density of pest and disease occurrence in the region into grids to obtain the corresponding values ​​of soil fertility and pest and disease occurrence under the same uniform grid.

[0130] In this embodiment of the invention, a study area is selected and divided into uniform grids. The division of the area should be based on the spatial characteristics of the pest and disease occurrence area and the distribution of crop soil fertility. This ensures that the grid boundaries can effectively cover the pest and disease occurrence density and soil fertility data within the area. The size of each grid should be suitable for the agricultural planting structure of the area. Typically, the grid scale is determined based on the crop planting density and the distribution range of pests and diseases. Remote sensing technology and geographic information system (GIS) tools are used to collect crop planting information and soil fertility data within the area. Through satellite imagery and ground sampling, soil fertility data within each grid is extracted, including the soil organic matter content, the concentration of nutrients such as nitrogen, phosphorus, and potassium, and specific data on pest and disease occurrence. The soil fertility level and pest and disease occurrence density value of each grid are recorded within that grid and used for subsequent correlation analysis. Finally, the corresponding crop soil fertility level and pest and disease occurrence density values ​​under the same uniform grid are obtained.

[0131] Step S352: Based on the soil fertility and pest and disease occurrence density values ​​of crops under the same uniform grid, the correlation coefficient between soil fertility and pest and disease occurrence density of crops in each uniform grid is calculated.

[0132] In this embodiment of the invention, based on the previously obtained soil fertility and pest and disease density values ​​of each grid, an appropriate statistical analysis method is selected for correlation measurement. To accurately calculate the correlation coefficient between soil fertility and pest and disease density, classic correlation analysis methods such as Pearson correlation coefficient and Spearman rank correlation coefficient can be used. Based on the soil fertility value and pest and disease density value in each uniform grid, the correlation is calculated one by one. In this process, the data of each grid is first standardized to eliminate the influence between different dimensions. Then, computer software (such as Python's SciPy library or correlation analysis tools in R language) is used to calculate the correlation to obtain the correlation coefficient in each grid. Through the calculation results, the positive or negative correlation between soil fertility and pest and disease density can be identified, and finally the correlation coefficient between crop soil fertility and pest and disease density in each uniform grid is obtained.

[0133] Step S353: Based on the correlation coefficient between crop soil fertility and pest and disease occurrence density within each uniform grid, soil fertility correlation analysis is performed on the regional crop soil fertility level and regional pest and disease occurrence density to obtain the correlation characteristics between regional soil fertility and pest and disease outbreak.

[0134] In this embodiment of the invention, based on the correlation coefficients within each uniform grid obtained through previous quantitative calculations, an appropriate clustering algorithm is selected to analyze the correlation characteristics between soil fertility and pest and disease density within the region. Hierarchical clustering or K-means clustering methods can be used to classify the correlation coefficients of different grids, grouping grids with similar correlation coefficients into the same category. The correlation between soil fertility characteristics and pest and disease density within these grids is analyzed. Further analysis of the clustering results can reveal the potential relationship between soil fertility and pest and disease outbreaks in different regions. For example, some high-fertility areas are associated with higher pest and disease densities, while some low-fertility areas exhibit lower pest and disease densities. Combining factors such as regional climate and crop type, regression analysis or neural network models are used to further establish a nonlinear relationship model between soil fertility and pest and disease density, ultimately obtaining the correlation characteristics between regional soil fertility and pest and disease outbreaks, i.e., whether the soil fertility and pest and disease outbreaks within the monitoring point region are positively or negatively correlated.

[0135] Furthermore, step S4 includes the following steps:

[0136] Step S41: Based on the regional pest and disease outbreak characteristic patterns under meteorological conditions, conduct meteorological pest and disease impact analysis on the geographical distribution patterns of pests and diseases corresponding to crop pest and disease monitoring points to obtain the meteorological impact factors of regional pest and disease outbreaks.

[0137] In this embodiment of the invention, historical meteorological data from pest and disease monitoring points, including factors such as temperature, humidity, precipitation, wind speed, and light intensity, are collected and organized. Statistical analysis methods (such as regression analysis and principal component analysis) are used to identify the correlation between different meteorological conditions and pest and disease outbreaks. Spatial analysis of meteorological data and monitoring point data is performed using a Geographic Information System (GIS) to match meteorological data with outbreak patterns of different pest and disease types, revealing the distribution characteristics of various pests and diseases under different meteorological conditions. Then, based on the analysis results, meteorological factors affecting pest and disease outbreaks (such as temperature fluctuations, humidity changes, and precipitation frequency) are extracted and their impact on pest and disease outbreaks in different regions is quantified, ultimately generating regional pest and disease outbreak meteorological influencing factors.

[0138] Step S42: Based on the correlation characteristics between regional soil fertility and pest outbreaks, conduct soil fertility-pest correlation analysis on the geographical distribution patterns of pests and diseases corresponding to crop pest and disease monitoring points to obtain regional soil fertility-pest outbreak correlation factors.

[0139] In this embodiment of the invention, the correlation between regional soil fertility and pest outbreaks is analyzed in conjunction with previous analyses to statistically analyze the distribution patterns of corresponding pests in geographic space. This is done by combining historical records of regional pest outbreaks with multivariate statistical analysis methods (such as multiple linear regression or support vector machines) to analyze the relationship between soil fertility factors and pest outbreaks. By comparing and analyzing the outbreak patterns of pests in different soil types, the invention identifies which soil fertility characteristics have a significant impact on pest outbreaks and quantifies the soil fertility factors based on their degree of influence. Then, by combining the analysis results with the geographic distribution of monitoring points using GIS technology, the soil fertility-pest outbreak correlation factor for specific regions is obtained. This factor reveals the possible degree of influence of soil fertility on pest outbreaks in different regions, ultimately yielding the regional soil fertility-pest outbreak correlation factor.

[0140] Step S43: Based on the meteorological influencing factors of regional pest outbreaks and the correlation factors of regional soil fertility pest outbreaks, multi-source risk prediction is carried out for crop pest monitoring points to obtain the regional pest outbreak risk prediction results, including the predicted pest outbreak risk level corresponding to the monitoring point area.

[0141] In this embodiment of the invention, a multi-source data input model is constructed by combining the meteorological influencing factors obtained in step S41 with the soil fertility correlation factors obtained in step S42. This model is trained on historical data of pest and disease outbreaks using machine learning algorithms (such as random forests, decision trees, or deep learning networks) to predict the risk level of pest and disease occurrence under specific meteorological and soil conditions. The input data of the model includes meteorological data, soil fertility data, and historical pest and disease outbreak information for each monitoring point. After training, the model outputs a risk level for each pest and disease monitoring point, indicating the probability of a pest and disease outbreak. This risk level is categorized according to the specific distribution area, such as low risk, medium risk, and high risk, so as to enable targeted decision-making for regional crop pest and disease control and ultimately obtain the regional pest and disease outbreak risk prediction result.

[0142] Step S44: Based on the regional pest and disease outbreak risk prediction results, conduct prevention and control decision support analysis to generate prevention and control recommendations for pest and disease outbreak areas, including the selection of pest and disease pesticides, dosage and duration of application, and implementation of biological control measures.

[0143] In this embodiment of the invention, targeted prevention and control decisions are formulated based on previously obtained pest and disease outbreak risk prediction results, combined with the crop growth cycle and the characteristics of different pest and disease types. In high-risk areas, pesticides targeting specific pests and diseases are recommended, and specific suggestions on dosage and duration of application are given. At this time, the selection of pesticides is based on the sensitivity of pests and diseases, the mechanism of action of pesticides, and changes in meteorological conditions (such as temperature and humidity) to optimize the control effect. For medium- and low-risk areas, biological control measures, such as the introduction of natural enemies and the release of parasites, can be recommended to reduce the use of chemical pesticides and environmental burden. At the same time, in order to improve the control effect, dynamic adjustments are also needed based on changes in meteorological data and soil fertility. Through these prevention and control suggestions, farmers can implement precise measures in actual operations to effectively control the spread of pests and diseases, and finally generate a prevention and control plan for pest and disease outbreak areas.

[0144] Furthermore, the present invention also provides a risk prediction system for identifying pest and disease outbreak processes based on multiple factors, used to execute the risk prediction method for identifying pest and disease outbreak processes based on multiple factors as described above. This risk prediction system for identifying pest and disease outbreak processes based on multiple factors includes:

[0145] The real-time monitoring module for crop regions is used to measure regional meteorological data, regional crop soil fertility, and regional pest and disease occurrence data corresponding to crop pest and disease monitoring points in real time. The regional pest and disease occurrence data includes the number of pests and diseases, the time of occurrence, and the location of occurrence.

[0146] The pest and disease occurrence density calculation module is used to obtain the corresponding pesticide spraying amount through crop pest and disease monitoring points, and to calculate the regional occurrence density based on the pesticide spraying amount to obtain the regional pest and disease occurrence density.

[0147] The pest and disease outbreak feature mining module is used to mine and analyze the outbreak density of pests and diseases in the region based on regional meteorological data and regional crop soil fertility, thereby obtaining the regional pest and disease outbreak feature patterns under meteorological conditions and the correlation feature relationship between regional soil fertility and pest and disease outbreak.

[0148] The multi-source factor risk prediction module for pests and diseases is used to predict the risk of crop pests and diseases at monitoring points based on the regional pest and disease outbreak characteristics under meteorological conditions and the correlation between regional soil fertility and pest and disease outbreaks, so as to obtain the regional pest and disease outbreak risk prediction results. Based on the regional pest and disease outbreak risk prediction results, the module performs prevention and control decision support analysis to generate prevention and control recommendations for pest and disease outbreak areas.

[0149] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0150] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for risk prediction based on the process of pest and disease outbreaks using multiple factors, characterized in that, Includes the following steps: Step S1: Real-time measurement of regional meteorological data, regional crop soil fertility, and regional pest and disease occurrence data corresponding to crop pest and disease monitoring points. The regional pest and disease occurrence data includes the number of pests and diseases, the time of occurrence, and the location of occurrence. Step S2: Obtain the corresponding pesticide spraying amount through crop pest and disease monitoring points, and calculate the regional occurrence density based on the pesticide spraying amount to obtain the regional pest and disease occurrence density. Step S3: Based on regional meteorological data and regional crop soil fertility, conduct pest and disease outbreak analysis on the regional pest and disease occurrence density to obtain the regional pest and disease outbreak characteristic patterns under meteorological conditions and the correlation characteristics between regional soil fertility and pest and disease outbreaks; Step S3 includes the following steps: Step S31: Perform regional uniform gridding on the spatial area corresponding to the crop pest and disease monitoring points to obtain a uniform grid of the pest and disease occurrence area. Step S32: Based on the uniform grid of the pest and disease occurrence area, perform regional temporal outbreak analysis on the regional pest and disease occurrence density to obtain the grid diffusion and outbreak development mode corresponding to the change of regional pest and disease occurrence over time. Step S33: Design meteorological conditions based on the temperature, humidity, precipitation, and light intensity corresponding to the regional meteorological data to generate regional meteorological distribution conditions; Step S34: Based on the regional meteorological distribution conditions, conduct pest outbreak mining analysis on the grid diffusion and outbreak development patterns corresponding to the changes in regional pest occurrence over time, and obtain the regional pest outbreak characteristic patterns under meteorological conditions. Step S35: Based on a uniform grid in the pest and disease occurrence area, perform soil fertility correlation analysis on the soil fertility level of crops in the region and the density of pest and disease occurrence in the region to obtain the correlation characteristics between regional soil fertility and pest and disease outbreaks; where; Step S4: Based on the regional pest and disease outbreak characteristic patterns under meteorological conditions and the correlation characteristics between regional soil fertility and pest and disease outbreaks, multi-source factor risk prediction is performed on crop pest and disease monitoring points to obtain regional pest and disease outbreak risk prediction results; based on the regional pest and disease outbreak risk prediction results, prevention and control decision support analysis is performed to generate prevention and control recommendations for pest and disease outbreak areas.

2. The method for risk prediction of pest and disease outbreaks based on multi-source factors according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Measure the regional meteorological data corresponding to the crop pest and disease monitoring points in real time through the meteorological monitoring station network, including the temperature, humidity, precipitation and light intensity of the monitoring point area. Step S12: Real-time monitoring and data collection of soil conditions in the area corresponding to the crop pest and disease monitoring point is carried out using soil sensors to obtain regional crop soil condition data, including regional crop soil pH, regional crop soil fertility content index and regional crop soil microbial prosperity. Step S13: Based on the regional crop soil condition data, calculate the regional soil fertility of the crop pest and disease monitoring points to obtain the regional crop soil fertility level. Step S14: Measure the occurrence data of crop pests and diseases in the area corresponding to the monitoring point in real time through the daily reporting records of pests and diseases, including the number of pests and diseases, the time of occurrence, and the location of occurrence.

3. The method for risk prediction of pest and disease outbreaks based on multi-source factors according to claim 2, characterized in that, The regional crop soil fertility content indicators mentioned in step S12 specifically include regional soil nitrogen, phosphorus, and potassium content indicators.

4. The method for risk prediction of pest and disease outbreaks based on multi-source factors according to claim 3, characterized in that, Step S13 includes the following steps: Step S131: Based on the soil pH of the crop, analyze the soil conditions of the area corresponding to the crop pest and disease monitoring points to obtain the soil pH fertility decline index. Step S132: Obtain the corresponding regional crop vegetation coverage through crop pest and disease monitoring points, and calculate the vegetation coverage area index of crop pest and disease monitoring points based on the regional crop vegetation coverage to obtain the regional crop vegetation coverage area index. Step S133: Based on the regional soil pH fertility decay index, regional crop vegetation coverage index, regional crop soil fertility content index, and regional crop soil microbial prosperity index, the regional soil fertility calculation formula is used to calculate the regional soil fertility of crop pest and disease monitoring points to obtain the regional crop soil fertility level.

5. The method for risk prediction of pest and disease outbreaks based on multi-source factors according to claim 4, characterized in that, The specific formula for calculating regional soil fertility mentioned in step S133 is as follows: ； In the formula, To assess the soil fertility level of regional crops, This refers to the area of ​​the monitoring points for crop diseases and pests. These are the spatial location parameters corresponding to crop pest and disease monitoring points. The regional soil pH and fertility decline index. For regional soil nitrogen content, The nitrogen content fertility influence coefficient. For regional soil phosphorus content, The factor representing the influence of phosphorus content on fertility is... The potassium content of the regional soil, The coefficient representing the effect of potassium content on fertility. In spatial location The corresponding regional crop soil microbial prosperity level Contribution coefficient to regional microbial prosperity This represents the regional crop vegetation cover index. The vegetation cover impact coefficient is... This is a correction coefficient for the soil fertility level of regional crops.

6. The method for risk prediction of pest and disease outbreaks based on multi-source factors according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Collect corresponding pesticide use records through crop pest and disease monitoring points, and obtain the corresponding pesticide spraying amount based on the pesticide use records; Step S22: Based on the amount of pesticide spraying, conduct a pest and disease distribution attenuation assessment analysis on the corresponding number of pests and diseases in the regional pest and disease occurrence data to obtain the regional pest and disease distribution pesticide use attenuation factor. Step S23: Based on the location of pests and diseases within the regional pest and disease occurrence data, perform statistical analysis on the area corresponding to the monitoring points of crop pests and diseases to obtain the size of the regional pest and disease occurrence area. Step S24: Based on the amount of pesticide sprayed, the pesticide use attenuation factor of regional pest distribution, and the size of the area where regional pests occur, calculate the regional occurrence density using the regional occurrence density calculation formula to calculate the number of pests and the time of occurrence of pests and diseases, so as to obtain the regional pest and disease occurrence density. The specific formula for calculating the regional occurrence density is as follows: ； In the formula, The density of regional pests and diseases. The time of occurrence of pests and diseases. For time-varying parameters, This refers to the area affected by pests and diseases in the region. This refers to the amount of pesticide used for spraying. In time The corresponding number of pests and diseases. Pesticide use attenuation factor for regional pest and disease distribution. This is a correction factor for the density of regional pest and disease occurrence.

7. The method for risk prediction of pest and disease outbreaks based on multi-source factors according to claim 1, characterized in that, Step S35 includes the following steps: Step S351: Based on the uniform grid of the pest and disease occurrence area, divide the soil fertility of crops in the region and the density of pest and disease occurrence in the region into grids to obtain the corresponding values ​​of soil fertility and pest and disease occurrence under the same uniform grid. Step S352: Based on the soil fertility and pest and disease occurrence density values ​​of crops under the same uniform grid, the correlation coefficient between soil fertility and pest and disease occurrence density of crops in each uniform grid is calculated. Step S353: Based on the correlation coefficient between crop soil fertility and pest and disease occurrence density within each uniform grid, soil fertility correlation analysis is performed on the regional crop soil fertility level and regional pest and disease occurrence density to obtain the correlation characteristics between regional soil fertility and pest and disease outbreak.

8. The method for risk prediction of pest and disease outbreaks based on multi-source factors according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Based on the regional pest and disease outbreak characteristic patterns under meteorological conditions, conduct meteorological pest and disease impact analysis on the geographical distribution patterns of pests and diseases corresponding to crop pest and disease monitoring points to obtain the meteorological impact factors of regional pest and disease outbreaks. Step S42: Based on the correlation characteristics between regional soil fertility and pest outbreaks, conduct soil fertility-pest correlation analysis on the geographical distribution patterns of pests and diseases corresponding to crop pest and disease monitoring points to obtain regional soil fertility-pest outbreak correlation factors. Step S43: Based on the meteorological influencing factors of regional pest outbreaks and the correlation factors of regional soil fertility pest outbreaks, multi-source risk prediction is carried out for crop pest monitoring points to obtain the regional pest outbreak risk prediction results, including the predicted pest outbreak risk level corresponding to the monitoring point area. Step S44: Based on the regional pest and disease outbreak risk prediction results, conduct prevention and control decision support analysis to generate prevention and control recommendations for pest and disease outbreak areas, including the selection of pest and disease pesticides, dosage and duration of application, and implementation of biological control measures.

9. A risk prediction system for pest and disease outbreak processes based on multi-source factors, characterized in that, For executing the multi-source factor-based pest and disease outbreak process risk prediction method as described in claim 1, the multi-source factor-based pest and disease outbreak process risk prediction system includes: The real-time monitoring module for crop regions is used to measure regional meteorological data, regional crop soil fertility, and regional pest and disease occurrence data corresponding to crop pest and disease monitoring points in real time. The regional pest and disease occurrence data includes the number of pests and diseases, the time of occurrence, and the location of occurrence. The pest and disease occurrence density calculation module is used to obtain the corresponding pesticide spraying amount through crop pest and disease monitoring points, and to calculate the regional occurrence density based on the pesticide spraying amount to obtain the regional pest and disease occurrence density. The pest and disease outbreak feature mining module is used to mine and analyze the outbreak density of pests and diseases in the region based on regional meteorological data and regional crop soil fertility, thereby obtaining the regional pest and disease outbreak feature patterns under meteorological conditions and the correlation feature relationship between regional soil fertility and pest and disease outbreak. The multi-source factor risk prediction module for pests and diseases is used to predict the risk of crop pests and diseases at monitoring points based on the regional pest and disease outbreak characteristics under meteorological conditions and the correlation between regional soil fertility and pest and disease outbreaks, so as to obtain the regional pest and disease outbreak risk prediction results. Based on the regional pest and disease outbreak risk prediction results, the module performs prevention and control decision support analysis to generate prevention and control recommendations for pest and disease outbreak areas.

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