A dynamic monitoring system for agricultural meteorological disasters and yield loss assessment

Abstract

The present application discloses a dynamic monitoring and yield loss assessment system for agricultural meteorological disasters, which relates to the field of agricultural monitoring and includes a meteorological monitoring module, a preliminary analysis module, a monitoring point layout module, an image acquisition module, an image analysis module and a comprehensive evaluation module. By monitoring and comprehensively analyzing the crop types and meteorological information of the target area, monitoring points are intelligently selected to effectively improve the accuracy and timeliness of yield loss assessment in the target area. By comprehensively analyzing abnormal crops in the target area, the crop yield losses caused by diseases and meteorological environment are subdivided, thereby improving the accuracy of the assessment of crop yield losses in the target area caused by the meteorological environment.

Description

Technical Field

[0001] The present application relates to the field of agricultural monitoring, and in particular to a system for dynamic monitoring of agricultural meteorological disasters and yield loss assessment. Background Art

[0002] In recent years, the suddenness and extreme nature of agricultural meteorological disasters have become increasingly prominent, regional droughts have become more severe, the frequency of rainstorms and floods has been on the rise, and the frequency and intensity of high temperature heat damage have increased significantly, with their impact on agricultural production becoming increasingly obvious. Monitoring meteorological disasters and assessing yield losses can help farmers reduce losses and quickly assess the damage to crops.

[0003] Existing technologies typically analyze the impact of meteorological disasters on crop yields through surveys, which are often inefficient when dealing with large areas and complex planting environments, prolonging the time required for yield loss assessment.

[0004] Existing technologies are rather one-sided when analyzing the impact of meteorological disasters on crop yields, often ignoring the important factor of disease on crop yields, resulting in large deviations in the analysis of the impact of meteorological disasters on crop yields and reducing the accuracy of the assessment of damage to crops caused by disasters. Summary of the Invention

[0005] The purpose of the present invention is to provide a system for dynamic monitoring of agricultural meteorological disasters and yield loss assessment to solve the problems raised in the above background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a system for dynamic monitoring of agricultural meteorological disasters and yield loss assessment, comprising:

[0007] Meteorological monitoring module: used to detect the meteorological conditions in the target area and establish an environmental parameter model for the target area;

[0008] Preliminary analysis module: used to collect and analyze images of crops in the target area to obtain the corresponding crop information of the target area;

[0009] Monitoring point layout module: used to analyze the environmental parameter model of the target area and the corresponding crop information of the target area, and obtain the number of monitoring points corresponding to each crop planting area in the target area;

[0010] Image acquisition module: used to monitor the images of each crop planting area in the target area, and screen and analyze the number of monitoring points corresponding to each crop planting area in the target area to obtain images of each sampling area corresponding to each crop planting area in the target area;

[0011] Image analysis module: used to perform image analysis on each sampled area image corresponding to each crop planting area in the target area, and obtain the comprehensive environmental yield reduction coefficient corresponding to each crop planting area;

[0012] Comprehensive assessment module: used to conduct yield loss assessment based on the comprehensive environmental yield reduction coefficient corresponding to each crop planting area, and obtain the yield loss assessment results corresponding to each crop planting area.

[0013] In the preferred embodiment of this solution, the specific implementation of the weather monitoring module is as follows:

[0014] Obtain the weather type of the target area through the cloud weather platform;

[0015] Establish a data extraction relationship between the meteorological monitoring module and the database, extract the meteorological monitoring data set corresponding to each meteorological type stored in the database, where the meteorological monitoring data set refers to the various environmental parameters corresponding to each meteorological type and the reference coefficients corresponding to each environmental parameter, and obtain the dedicated equipment corresponding to each environmental parameter through the equipment library;

[0016] Environmental monitoring is carried out using dedicated equipment corresponding to various environmental parameters to obtain various environmental parameters corresponding to the target area, and an environmental parameter model corresponding to the target area is established based on the various environmental parameters corresponding to the target area.

[0017] In the preferred embodiment of this solution, the specific implementation of the preliminary analysis module is as follows:

[0018] Establish a data extraction relationship between the meteorological monitoring module and the database, and extract the remote sensing satellite feature image sets corresponding to various crops in various yield periods stored in the database;

[0019] The target area is imaged by a remote sensing satellite to obtain a remote sensing satellite image corresponding to the target area, and the remote sensing satellite image corresponding to the target area is matched with a set of remote sensing satellite characteristic images corresponding to various crops at various yield periods to obtain various crop planting areas in the target area and the crop types corresponding to each crop planting area;

[0020] The planting area of each crop planting area in the target area is obtained by scaling the remote sensing satellite image corresponding to the target area, and the planting area, crop type and yield period corresponding to each crop planting area in the target area are recorded as the crop information corresponding to the target area.

[0021] In the preferred embodiment of this scheme, the specific implementation method of the monitoring point layout module is as follows:

[0022] Establish a data extraction relationship between the monitoring point layout module and the database, and extract the standard environmental parameter model of each crop in each yield period corresponding to each meteorological type stored in the database, where the standard environmental parameter model includes various standard environmental parameters;

[0023] According to the crop types and yield periods corresponding to the crop planting areas in the target area, a standard environmental parameter model corresponding to each crop planting area in the target area is obtained;

[0024] By calculating the formula , calculate the environmental deviation coefficient corresponding to each crop planting area in the target area ,in Indicates the number of various environmental parameters. Expressed as the number of environmental parameters, Represented as various environmental parameters corresponding to the target area, It is expressed as various standard environmental parameters corresponding to each crop planting area in the target area. Expressed as reference coefficients corresponding to various environmental parameters, It is represented by the number of each crop planting area in the target area;

[0025] By calculating the formula , calculate the monitoring point layout coefficient corresponding to each crop planting area in the target area ,in It is expressed as the planting area of each crop planting area in the target region;

[0026] Extract the number of monitoring points corresponding to each monitoring point layout coefficient interval stored in the database,

[0027] The monitoring point layout coefficients corresponding to each crop planting area in the target area are compared and screened with the monitoring point layout coefficient intervals stored in the database to obtain the number of monitoring point layouts corresponding to each crop planting area in the target area.

[0028] In the preferred embodiment of this solution, the specific implementation of the image acquisition module is as follows:

[0029] By using patrol drones to monitor the crop planting areas in the target area, a full-scale regional image corresponding to each crop planting area in the target area is obtained;

[0030] Extract the unit sampling area corresponding to each type of crop;

[0031] Random sampling is performed based on the number of monitoring points arranged corresponding to each crop planting area in the target area and the unit sampling area corresponding to each crop planting area in the target area, so as to obtain images of each sampling sub-area corresponding to each crop planting area in the target area.

[0032] In the preferred embodiment of this solution, the specific implementation of the image analysis module is as follows:

[0033] Establish a data extraction relationship between the image acquisition module and the database to extract the normal yield model set corresponding to various crops stored in the database;

[0034] Establishing a three-dimensional model of each sampling sub-region image corresponding to each crop planting area, extracting plant models from the three-dimensional model of each sampling sub-region image corresponding to each crop planting area, and obtaining each plant model in each sampling sub-region image corresponding to each crop planting area;

[0035] Comparing each plant model in each sampling sub-region image corresponding to each crop planting area with a set of normal crop yield models corresponding to each crop planting area in the target region, obtaining each conforming plant model and each non-conforming plant model in each sampling sub-region image corresponding to each crop planting area, and obtaining each normal yield plant and each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area;

[0036] Extracting a combined model of plant disease characteristic images corresponding to various diseases of various crops and a yield reduction coefficient corresponding to each disease stored in a database, wherein the plant disease characteristic image model includes characteristic images of diseased leaves of plants, characteristic images of diseased stems of plants, and characteristic images of diseased fruits of plants;

[0037] Extracting image features of each abnormal-yield plant in each sampling sub-region image corresponding to each crop planting area to obtain a feature image combination model of each abnormal-yield plant in each sampling sub-region image corresponding to each crop planting area, wherein the feature image combination model includes plant leaf feature images, plant stem feature images, and plant fruit feature images of the abnormal-yield plant;

[0038] Extracting information from the plant fruit feature images of each abnormally yielding plant in each sampling sub-region image corresponding to each crop planting area, and obtaining the number of fruits of each abnormally yielding plant in each sampling sub-region image corresponding to each crop planting area;

[0039] The model coincidence analysis is performed on the feature image combination model of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area and the feature image combination model of each diseased plant corresponding to each crop type, and the model matching degree of the feature image combination model of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area and the feature image combination model of each diseased plant corresponding to each crop type is obtained, which is recorded as the matching degree of each abnormal yield plant and each disease type. The matching degree of the abnormal yield plant and each disease type is compared with the preset matching degree threshold for analysis, and the screening is carried out. Select each disease type greater than the matching degree threshold, record each disease type greater than the matching degree threshold as each preliminary matching disease type, screen the disease type corresponding to the maximum matching degree and record it as the disease type corresponding to the abnormal yield plant, if there is no disease type greater than the matching degree threshold, it means that the abnormal yield plant is not diseased, statistically obtain each diseased abnormal yield plant and each non-diseased abnormal yield plant corresponding to each sampling sub-region image of each crop planting area, and screen to obtain the number of fruits of each diseased abnormal yield plant and the number of fruits of each non-diseased abnormal yield plant in each sampling sub-region image of each crop planting area;

[0040] By calculating the formula , calculate the first environmental yield reduction coefficient corresponding to each crop planting area ,in It is expressed as the standard yield of a single plant in each crop planting area. It is represented by the number of fruits of each diseased plant with abnormal yield in each sampling area image corresponding to each crop planting area, It is represented by the yield reduction coefficient of each diseased plant with abnormal yield in each sampled area image corresponding to each crop planting area, It represents the number of each sampled area image corresponding to each crop planting area. It represents the number of each diseased plant with abnormal yield in each sampled area image corresponding to each crop planting area, It is represented by the number of sampled area images corresponding to each crop planting area. It is represented by the number of diseased and abnormally yielding plants in each sampled area image corresponding to each crop planting area;

[0041] By calculating the formula , calculate the second yield reduction coefficient corresponding to each crop planting area ,in It is represented by the number of fruits of each non-diseased plant with abnormal yield in each sampling area image corresponding to each crop planting area, It represents the number of each non-diseased plant with abnormal yield in each sampled area image corresponding to each crop planting area, It is represented by the number of non-diseased plants with abnormal yield in each sampled area image corresponding to each crop planting area;

[0042] By calculating the formula , calculate the comprehensive environmental yield reduction coefficient corresponding to each crop planting area .

[0043] In the preferred embodiment of this solution, the comprehensive evaluation module is specifically implemented as follows:

[0044] Establish a data extraction relationship between the comprehensive assessment module and the database to extract various yield loss assessment models stored in the database, where the yield loss assessment model includes crop type, comprehensive environmental yield reduction coefficient and yield reduction ratio;

[0045] By screening the comprehensive environmental yield reduction coefficient corresponding to each crop planting area and the crop types corresponding to each crop planting area, the yield loss assessment model corresponding to each crop planting area is obtained, the yield reduction ratio corresponding to each crop planting area is obtained, and the yield reduction ratio corresponding to each crop planting area is recorded as the yield loss assessment result corresponding to each crop planting area.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention monitors and comprehensively analyzes the crop types and meteorological information of the target area, and intelligently selects monitoring points to effectively improve the accuracy and timeliness of yield loss assessment in the target area.

[0048] The present invention conducts a comprehensive analysis of abnormal crops and subdivides the crop yield losses caused by diseases and meteorological environment, thereby improving the accuracy of assessing crop yield losses in target areas caused by meteorological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0050] Figure 1 This is a schematic diagram of module connections according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] See also Figure 1 , the present invention provides a system for dynamic monitoring of agricultural meteorological disasters and yield loss assessment, the system including a meteorological monitoring module, a preliminary analysis module, a monitoring point layout module, an image acquisition module, an image analysis module and a comprehensive evaluation module;

[0053] The meteorological monitoring module is connected to the preliminary analysis module, the preliminary analysis module is connected to the monitoring point layout module, the monitoring point layout module is connected to the image acquisition module, the image acquisition module is connected to the image analysis module, and the image analysis module is connected to the comprehensive evaluation module.

[0054] The meteorological monitoring module is used to detect the meteorological conditions in the target area and establish an environmental parameter model for the target area;

[0055] Furthermore, the specific implementation of the weather monitoring module is as follows:

[0056] Obtain the weather type of the target area through the cloud weather platform;

[0057] Establish a data extraction relationship between the meteorological monitoring module and the database, extract the meteorological monitoring data set corresponding to each meteorological type stored in the database, where the meteorological monitoring data set refers to the various environmental parameters corresponding to each meteorological type and the reference coefficients corresponding to each environmental parameter, and obtain the dedicated equipment corresponding to each environmental parameter through the equipment library;

[0058] Environmental monitoring is carried out using dedicated equipment corresponding to various environmental parameters to obtain various environmental parameters corresponding to the target area, and an environmental parameter model corresponding to the target area is established based on the various environmental parameters corresponding to the target area.

[0059] The preliminary analysis module is used to collect and analyze images of crops in the target area to obtain the corresponding crop information of the target area;

[0060] Furthermore, the specific execution method of the preliminary analysis module is as follows:

[0061] Establish a data extraction relationship between the meteorological monitoring module and the database, and extract the remote sensing satellite feature image sets corresponding to various crops in various yield periods stored in the database;

[0062] The target area is imaged by a remote sensing satellite to obtain a remote sensing satellite image corresponding to the target area, and the remote sensing satellite image corresponding to the target area is matched with a set of remote sensing satellite characteristic images corresponding to various crops at various yield periods to obtain various crop planting areas in the target area and the crop types corresponding to each crop planting area;

[0063] The planting area of each crop planting area in the target area is obtained by scaling the remote sensing satellite image corresponding to the target area, and the planting area, crop type and yield period corresponding to each crop planting area in the target area are recorded as the crop information corresponding to the target area.

[0064] The monitoring point layout module is used to analyze the environmental parameter model of the target area and the crop information corresponding to the target area, and obtain the number of monitoring points corresponding to each crop planting area in the target area;

[0065] Furthermore, the specific implementation of the monitoring point layout module is as follows:

[0066] Establish a data extraction relationship between the monitoring point layout module and the database, and extract the standard environmental parameter model of each crop in each yield period corresponding to each meteorological type stored in the database, where the standard environmental parameter model includes various standard environmental parameters;

[0067] According to the crop types and yield periods corresponding to the crop planting areas in the target area, a standard environmental parameter model corresponding to each crop planting area in the target area is obtained;

[0068] By calculating the formula , calculate the environmental deviation coefficient corresponding to each crop planting area in the target area ,in Indicates the number of various environmental parameters. Expressed as the number of environmental parameters, Represented as various environmental parameters corresponding to the target area, It is expressed as various standard environmental parameters corresponding to each crop planting area in the target area. Expressed as reference coefficients corresponding to various environmental parameters, It is represented by the number of each crop planting area in the target area;

[0069] By calculating the formula , calculate the monitoring point layout coefficient corresponding to each crop planting area in the target area ,in It is expressed as the planting area of each crop planting area in the target region;

[0070] Extract the number of monitoring points corresponding to each monitoring point layout coefficient interval stored in the database,

[0071] The monitoring point layout coefficients corresponding to each crop planting area in the target area are compared and screened with the monitoring point layout coefficient intervals stored in the database to obtain the number of monitoring point layouts corresponding to each crop planting area in the target area.

[0072] The image acquisition module is used to monitor the images of each crop planting area in the target area, and to screen and analyze the number of monitoring points corresponding to each crop planting area in the target area to obtain images of each sampling area corresponding to each crop planting area in the target area;

[0073] Furthermore, the specific execution mode of the image acquisition module is as follows:

[0074] By using patrol drones to monitor the crop planting areas in the target area, a full-scale regional image corresponding to each crop planting area in the target area is obtained;

[0075] Extract the unit sampling area corresponding to each type of crop;

[0076] Random sampling is performed based on the number of monitoring points arranged corresponding to each crop planting area in the target area and the unit sampling area corresponding to each crop planting area in the target area, so as to obtain images of each sampling sub-area corresponding to each crop planting area in the target area.

[0077] The image analysis module is used to perform image analysis on each sampled area image corresponding to each crop planting area in the target area to obtain the comprehensive environmental yield reduction coefficient corresponding to each crop planting area;

[0078] Furthermore, the specific execution method of the image analysis module is as follows:

[0079] Establish a data extraction relationship between the image acquisition module and the database to extract the normal yield model set corresponding to various crops stored in the database;

[0080] Establishing a three-dimensional model of each sampling sub-region image corresponding to each crop planting area, extracting plant models from the three-dimensional model of each sampling sub-region image corresponding to each crop planting area, and obtaining each plant model in each sampling sub-region image corresponding to each crop planting area;

[0081] Comparing each plant model in each sampling sub-region image corresponding to each crop planting area with a set of normal crop yield models corresponding to each crop planting area in the target region, obtaining each conforming plant model and each non-conforming plant model in each sampling sub-region image corresponding to each crop planting area, and obtaining each normal yield plant and each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area;

[0082] Extracting a combined model of plant disease characteristic images corresponding to various diseases of various crops and a yield reduction coefficient corresponding to each disease stored in a database, wherein the plant disease characteristic image model includes characteristic images of diseased leaves of plants, characteristic images of diseased stems of plants, and characteristic images of diseased fruits of plants;

[0083] Extracting image features of each abnormal-yield plant in each sampling sub-region image corresponding to each crop planting area to obtain a feature image combination model of each abnormal-yield plant in each sampling sub-region image corresponding to each crop planting area, wherein the feature image combination model includes plant leaf feature images, plant stem feature images, and plant fruit feature images of the abnormal-yield plant;

[0084] Extracting information from the plant fruit feature images of each abnormally yielding plant in each sampling sub-region image corresponding to each crop planting area, and obtaining the number of fruits of each abnormally yielding plant in each sampling sub-region image corresponding to each crop planting area;

[0085] The model coincidence analysis is performed on the feature image combination model of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area and the feature image combination model of each diseased plant corresponding to each crop type, and the model matching degree of the feature image combination model of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area and the feature image combination model of each diseased plant corresponding to each crop type is obtained, which is recorded as the matching degree of each abnormal yield plant and each disease type. The matching degree of the abnormal yield plant and each disease type is compared with the preset matching degree threshold for analysis, and the screening is carried out. Select each disease type greater than the matching degree threshold, record each disease type greater than the matching degree threshold as each preliminary matching disease type, screen the disease type corresponding to the maximum matching degree and record it as the disease type corresponding to the abnormal yield plant, if there is no disease type greater than the matching degree threshold, it means that the abnormal yield plant is not diseased, statistically obtain each diseased abnormal yield plant and each non-diseased abnormal yield plant corresponding to each sampling sub-region image of each crop planting area, and screen to obtain the number of fruits of each diseased abnormal yield plant and the number of fruits of each non-diseased abnormal yield plant in each sampling sub-region image of each crop planting area;

[0086] By calculating the formula , calculate the first environmental yield reduction coefficient corresponding to each crop planting area ,in It is expressed as the standard yield of a single plant in each crop planting area. It is represented by the number of fruits of each diseased plant with abnormal yield in each sampling area image corresponding to each crop planting area, It is represented by the yield reduction coefficient of each diseased plant with abnormal yield in each sampled area image corresponding to each crop planting area, It represents the number of each sampled area image corresponding to each crop planting area. It represents the number of each diseased plant with abnormal yield in each sampled area image corresponding to each crop planting area, It is represented by the number of sampled area images corresponding to each crop planting area. It is represented by the number of diseased and abnormally yielding plants in each sampled area image corresponding to each crop planting area;

[0087] By calculating the formula , calculate the second yield reduction coefficient corresponding to each crop planting area ,in It is represented by the number of fruits of each non-diseased plant with abnormal yield in each sampling area image corresponding to each crop planting area, It represents the number of each non-diseased plant with abnormal yield in each sampled area image corresponding to each crop planting area, It is represented by the number of non-diseased plants with abnormal yield in each sampled area image corresponding to each crop planting area;

[0088] By calculating the formula , calculate the comprehensive environmental yield reduction coefficient corresponding to each crop planting area .

[0089] The comprehensive assessment module is used to perform yield loss assessment based on the comprehensive environmental yield reduction coefficient corresponding to each crop planting area, and obtain the yield loss assessment results corresponding to each crop planting area.

[0090] Furthermore, the specific implementation of the comprehensive evaluation module is as follows:

[0091] Establish a data extraction relationship between the comprehensive assessment module and the database to extract various yield loss assessment models stored in the database, where the yield loss assessment model includes crop type, comprehensive environmental yield reduction coefficient and yield reduction ratio;

[0092] By screening the comprehensive environmental yield reduction coefficient corresponding to each crop planting area and the crop types corresponding to each crop planting area, the yield loss assessment model corresponding to each crop planting area is obtained, the yield reduction ratio corresponding to each crop planting area is obtained, and the yield reduction ratio corresponding to each crop planting area is recorded as the yield loss assessment result corresponding to each crop planting area.

[0093] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A system for dynamic monitoring of agricultural meteorological disasters and yield loss assessment, characterized by: include: Meteorological monitoring module: used to detect the meteorological conditions of the target area and establish an environmental parameter model of the target area; Preliminary analysis module: used to collect and analyze images of crops in the target area to obtain the corresponding crop information of the target area; Monitoring point layout module: used to analyze the environmental parameter model of the target area and the crop information corresponding to the target area, and obtain the number of monitoring points corresponding to each crop planting area in the target area; Image acquisition module: used to monitor the images of each crop planting area in the target area, and screen and analyze the number of monitoring points corresponding to each crop planting area in the target area to obtain images of each sampling area corresponding to each crop planting area in the target area; Image analysis module: used to perform image analysis on each sampled area image corresponding to each crop planting area in the target area, and obtain the comprehensive environmental yield reduction coefficient corresponding to each crop planting area; By calculating the formula , calculate the first environmental yield reduction coefficient corresponding to each crop planting area ,in It is expressed as the standard yield of a single plant in each crop planting area. It is represented by the number of fruits of each diseased plant with abnormal yield in each sampling area image corresponding to each crop planting area, It is represented by the yield reduction coefficient of each diseased plant with abnormal yield in each sampled area image corresponding to each crop planting area, It represents the number of each sampled area image corresponding to each crop planting area. It represents the number of each diseased plant with abnormal yield in each sampled area image corresponding to each crop planting area, It is represented by the number of sampled area images corresponding to each crop planting area. It is represented by the number of diseased and abnormally yielding plants in each sampled area image corresponding to each crop planting area; By calculating the formula , calculate the second yield reduction coefficient corresponding to each crop planting area ,in It is represented by the number of fruits of each non-diseased plant with abnormal yield in each sampling area image corresponding to each crop planting area, It represents the number of each non-diseased plant with abnormal yield in each sampled area image corresponding to each crop planting area, It is represented by the number of non-diseased plants with abnormal yield in each sampling area image corresponding to each crop planting area; By calculating the formula , calculate the comprehensive environmental yield reduction coefficient corresponding to each crop planting area ; Comprehensive assessment module: used to conduct yield loss assessment based on the comprehensive environmental yield reduction coefficient corresponding to each crop planting area, and obtain the yield loss assessment results corresponding to each crop planting area.

2. The agricultural meteorological disaster dynamic monitoring and yield loss assessment system according to claim 1 is characterized by: The specific implementation of the meteorological monitoring module is as follows: Obtain the weather type of the target area through the cloud weather platform; Establish a data extraction relationship between the meteorological monitoring module and the database, extract the meteorological monitoring data set corresponding to each meteorological type stored in the database, where the meteorological monitoring data set refers to the various environmental parameters corresponding to each meteorological type and the reference coefficients corresponding to each environmental parameter, and obtain the dedicated equipment corresponding to each environmental parameter through the equipment library; Environmental monitoring is carried out using dedicated equipment corresponding to various environmental parameters to obtain various environmental parameters corresponding to the target area, and an environmental parameter model corresponding to the target area is established based on the various environmental parameters corresponding to the target area.

3. The agricultural meteorological disaster dynamic monitoring and yield loss assessment system according to claim 1 is characterized by: The specific execution method of the preliminary analysis module is as follows: Establish a data extraction relationship between the meteorological monitoring module and the database, and extract the remote sensing satellite feature image sets corresponding to various crops in various yield periods stored in the database; The target area is imaged by a remote sensing satellite to obtain a remote sensing satellite image corresponding to the target area, and the remote sensing satellite image corresponding to the target area is matched with a set of remote sensing satellite characteristic images corresponding to various crops at various yield periods to obtain various crop planting areas in the target area and the crop types corresponding to each crop planting area; The planting area of each crop planting area in the target area is obtained by scaling the remote sensing satellite image corresponding to the target area, and the planting area, crop type and yield period corresponding to each crop planting area in the target area are recorded as the crop information corresponding to the target area.

4. The agricultural meteorological disaster dynamic monitoring and yield loss assessment system according to claim 3 is characterized by: The specific implementation of the monitoring point layout module is as follows: Establish a data extraction relationship between the monitoring point layout module and the database, and extract the standard environmental parameter model of each crop in each yield period corresponding to each meteorological type stored in the database, where the standard environmental parameter model includes various standard environmental parameters; According to the crop types and yield periods corresponding to the crop planting areas in the target area, a standard environmental parameter model corresponding to each crop planting area in the target area is obtained; By calculating the formula , calculate the environmental deviation coefficient corresponding to each crop planting area in the target area ,in Indicates the number of various environmental parameters. Expressed as the number of environmental parameters, Represented as various environmental parameters corresponding to the target area, It is expressed as various standard environmental parameters corresponding to each crop planting area in the target area. Expressed as reference coefficients corresponding to various environmental parameters, It is represented by the number of each crop planting area in the target area; By calculating the formula , calculate the monitoring point layout coefficient corresponding to each crop planting area in the target area ,in It is expressed as the planting area of each crop planting area in the target region; Extract the number of monitoring points corresponding to each monitoring point layout coefficient interval stored in the database, The monitoring point layout coefficients corresponding to each crop planting area in the target area are compared and screened with the monitoring point layout coefficient intervals stored in the database to obtain the number of monitoring point layouts corresponding to each crop planting area in the target area.

5. The agricultural meteorological disaster dynamic monitoring and yield loss assessment system according to claim 4 is characterized by: The specific implementation of the image acquisition module is as follows: By using patrol drones to monitor the crop planting areas in the target area, a full-scale regional image corresponding to each crop planting area in the target area is obtained; Extract the unit sampling area corresponding to each type of crop; Random sampling is performed based on the number of monitoring points arranged corresponding to each crop planting area in the target area and the unit sampling area corresponding to each crop planting area in the target area, so as to obtain images of each sampling sub-area corresponding to each crop planting area in the target area.

6. The agricultural meteorological disaster dynamic monitoring and yield loss assessment system according to claim 5, characterized in that: The specific execution mode of the image analysis module is as follows: Establish a data extraction relationship between the image acquisition module and the database to extract the normal yield model set corresponding to various crops stored in the database; Establishing a three-dimensional model of each sampling sub-region image corresponding to each crop planting area, extracting plant models from the three-dimensional model of each sampling sub-region image corresponding to each crop planting area, and obtaining each plant model in each sampling sub-region image corresponding to each crop planting area; Comparing each plant model in each sampling sub-region image corresponding to each crop planting area with a set of normal crop yield models corresponding to each crop planting area in the target region, obtaining each conforming plant model and each non-conforming plant model in each sampling sub-region image corresponding to each crop planting area, and obtaining each normal yield plant and each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area; Extracting a combined model of plant disease characteristic images corresponding to various diseases of various crops and a yield reduction coefficient corresponding to each disease stored in a database, wherein the plant disease characteristic image model includes characteristic images of diseased leaves of plants, characteristic images of diseased stems of plants, and characteristic images of diseased fruits of plants; Extracting image features of each abnormal-yield plant in each sampling sub-region image corresponding to each crop planting area to obtain a feature image combination model of each abnormal-yield plant in each sampling sub-region image corresponding to each crop planting area, wherein the feature image combination model includes plant leaf feature images, plant stem feature images, and plant fruit feature images of the abnormal-yield plant; Extracting information from the plant fruit feature images of each abnormally yielding plant in each sampling sub-region image corresponding to each crop planting area, and obtaining the number of fruits of each abnormally yielding plant in each sampling sub-region image corresponding to each crop planting area; The model coincidence analysis is performed on the feature image combination model of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area and the feature image combination model of each diseased plant corresponding to each crop type, and the model matching degree of the feature image combination model of each abnormal yield plant in each sampling sub-region image corresponding to each crop planting area and the feature image combination model of each diseased plant corresponding to each crop type is obtained, which is recorded as the matching degree of each abnormal yield plant and each disease type. The matching degree of the abnormal yield plant and each disease type is compared with the preset matching degree threshold for analysis, and the screening is carried out. For each disease type that is greater than the matching degree threshold, each disease type that is greater than the matching degree threshold is recorded as each preliminary matching disease type, and the disease type corresponding to the maximum matching degree is screened and recorded as the disease type corresponding to the abnormal yield plant. If there is no disease type greater than the matching degree threshold, it means that the abnormal yield plant is not sick. The corresponding diseased abnormal yield plants and non-diseased abnormal yield plants in each sampling sub-area image of each crop planting area are statistically obtained, and the number of fruits of each diseased abnormal yield plant and the number of fruits of each non-diseased abnormal yield plant in each sampling sub-area image of each crop planting area are screened to obtain.

7. The agricultural meteorological disaster dynamic monitoring and yield loss assessment system according to claim 6, characterized in that: The specific implementation of the comprehensive evaluation module is as follows: Establish a data extraction relationship between the comprehensive assessment module and the database to extract various yield loss assessment models stored in the database, where the yield loss assessment model includes crop type, comprehensive environmental yield reduction coefficient and yield reduction ratio; By screening the comprehensive environmental yield reduction coefficient corresponding to each crop planting area and the crop types corresponding to each crop planting area, the yield loss assessment model corresponding to each crop planting area is obtained, the yield reduction ratio corresponding to each crop planting area is obtained, and the yield reduction ratio corresponding to each crop planting area is recorded as the yield loss assessment result corresponding to each crop planting area.

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