WO2022198744A1 - Agricultural danger data assessment method and apparatus, computer device, and storage medium - Google Patents

Abstract

An agricultural danger data assessment method and apparatus, a computer device, and a storage medium, which relate to the field of data processing. The method comprises: acquiring meteorological data and satellite remote sensing data of a designated region (10), so as to generate a preliminary disaster damage assessment result (20); according to the preliminary disaster damage assessment result, configuring several sampling points in the designated region (30); acquiring field survey data of the sampling points and aerial remote sensing data of the designated region and sending same to a designated terminal, so that the designated terminal obtains a sampled disaster damage assessment result according to the field survey data and the aerial remote sensing data (40); acquiring, from the aerial remote sensing data, sampled spectral data corresponding to the sampled disaster damage assessment result, and determining a disaster damage level threshold according to the sampled spectral data and the sampled disaster damage assessment result (50); and assessing spectral data of the satellite remote sensing data according to the disaster damage level threshold, so as to generate a disaster damage assessment result of the designated region (60). The method reduces the difficulty of collecting agricultural danger data and reduces the cost of agricultural danger investigation and damage determination.

Description

Agricultural danger data assessment method, device, computer equipment and storage medium

This application claims the priority of the Chinese patent application filed on March 23, 2021 with the application number 202110308541.0 and the invention titled "Agricultural Hazard Data Evaluation Method, Device, Computer Equipment and Storage Medium", the entire content of which is approved by Reference is incorporated in this application.

technical field

The present application relates to the field of data processing, and in particular, to a method, device, computer equipment and storage medium for evaluating agricultural danger data.

Background technique

At present, when carrying out agricultural insurance survey and damage determination, it is necessary to collect agricultural danger data. The inventor realizes that most of these agricultural hazard data are collected by surveyors on the spot where the subject of insurance is located. However, the locations of insurance targets are generally distributed in remote places. The collection of agricultural danger data is difficult, costly, and the cycle is long, and it is impossible to determine the damage in time.

Application content

Based on this, it is necessary to provide an assessment method, device, computer equipment and storage medium for agricultural danger data to solve the above-mentioned problems of difficulty in collecting agricultural danger data, high cost, long cycle, and inability to determine damage in time. .

An agricultural hazard data assessment method, comprising:

Obtain meteorological data and satellite remote sensing data of a designated area;

generating a preliminary disaster damage assessment result according to the meteorological data and the satellite remote sensing data;

Setting up a number of sampling points in the designated area according to the preliminary disaster damage assessment result;

Obtain the field survey data of the sampling point and the aerial photographic remote sensing data of the designated area and send them to the designated terminal, so that the designated terminal can perform disaster damage analysis according to the field investigation data and the aerial photographic remote sensing data to obtain the sampling disaster damage. evaluation result;

Receive the sampled disaster damage assessment result sent by the designated terminal, obtain sampled spectral data corresponding to the sampled disaster damage assessment result from the aerial photography remote sensing data, and determine according to the sampled spectral data and the sampled disaster damage assessment result Disaster damage level threshold;

The spectral data of the satellite remote sensing data is evaluated according to the disaster damage level threshold, so as to generate a disaster damage evaluation result of the designated area.

An agricultural danger data evaluation device, comprising:

The data module is used to obtain meteorological data and satellite remote sensing data of the designated area;

a preliminary disaster damage assessment result module, configured to generate a preliminary disaster damage assessment result according to the meteorological data and the satellite remote sensing data;

a sampling module, configured to set several sampling points in the designated area according to the preliminary disaster damage assessment result;

The sampling evaluation result module is used to obtain the field survey data of the sampling point and the aerial photography remote sensing data of the designated area and send them to the designated terminal, so that the designated terminal can perform a Disaster damage analysis to obtain sampling disaster damage assessment results;

A disaster damage level threshold module, configured to receive the sampled disaster damage assessment result sent by the designated terminal, and obtain sampled spectral data corresponding to the sampled disaster damage assessment result from the aerial photographic remote sensing data, and according to the sampled spectrum Data and sample disaster damage assessment results to determine the disaster damage level threshold;

A disaster damage assessment result module, configured to evaluate the spectral data of the satellite remote sensing data according to the disaster damage grade threshold, so as to generate a disaster damage assessment result of the designated area.

A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer-readable instructions :

Obtain meteorological data and satellite remote sensing data of a designated area;

generating a preliminary disaster damage assessment result according to the meteorological data and the satellite remote sensing data;

Setting up a number of sampling points in the designated area according to the preliminary disaster damage assessment result;

Obtain the field survey data of the sampling point and the aerial photographic remote sensing data of the designated area and send them to the designated terminal, so that the designated terminal can perform disaster damage analysis according to the field investigation data and the aerial photographic remote sensing data to obtain the sampling disaster damage. evaluation result;

Receive the sampled disaster damage assessment result sent by the designated terminal, obtain sampled spectral data corresponding to the sampled disaster damage assessment result from the aerial photography remote sensing data, and determine according to the sampled spectral data and the sampled disaster damage assessment result Disaster damage level threshold;

Evaluate the spectral data of the satellite remote sensing data according to the disaster damage level threshold to generate a disaster damage assessment result of the designated area

One or more readable storage media storing computer readable instructions which, when executed by one or more processors, cause the one or more processors to perform the agricultural hazard data assessment method as described above.

The above-mentioned agricultural danger data assessment method, device, computer equipment and storage medium can obtain meteorological data and satellite remote sensing data in a designated area; A preliminary understanding of the distribution and the degree of disaster damage can clearly guide the survey site, saving labor costs and time costs. According to the preliminary disaster damage assessment results, several sampling points are set in the designated area, which reduces unnecessary surveys of survey sites. Obtain the field survey data of the sampling point and the aerial photographic remote sensing data of the designated area and send them to the designated terminal, so that the designated terminal can perform disaster damage analysis according to the field investigation data and the aerial photographic remote sensing data to obtain the sampling disaster damage. As a result of the assessment, the unmanned aerial vehicle can be used to survey the harsh terrain, and the high resolution of the unmanned aerial vehicle can obtain a clearer picture of the disaster-damaged land. Receive the sampled disaster damage assessment result sent by the designated terminal, obtain sampled spectral data corresponding to the sampled disaster damage assessment result from the aerial photography remote sensing data, and determine according to the sampled spectral data and the sampled disaster damage assessment result Disaster damage level threshold, improve the accuracy of the disaster damage level threshold; according to the disaster damage level threshold, the spectral data of the satellite remote sensing data is evaluated to generate the disaster damage assessment result of the designated area, which can be automatically based on the disaster damage level threshold. The grading threshold determines the hazard level of the disaster-damaged plot, reducing labor and time costs. In conclusion, the present application reduces the difficulty of collecting agricultural danger data, reduces the cost of agricultural danger survey and damage assessment, and improves the accuracy of disaster damage assessment.

The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below, and other features and advantages of the application will become apparent from the description, drawings, and claims.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present application more clearly, the following briefly introduces the drawings that are used in the description of the embodiments of the present application. Obviously, the drawings in the following description are only some embodiments of the present application. , for those of ordinary skill in the art, other drawings can also be obtained from these drawings without creative labor.

Fig. 1 is a schematic diagram of an application environment of the agricultural danger data evaluation method in an embodiment of the present application;

2 is a schematic flowchart of a method for evaluating agricultural danger data in an embodiment of the present application;

3 is a schematic diagram of the use of satellite remote sensing data of different resolutions and an unmanned aerial vehicle in an embodiment of the present application;

4 is a schematic diagram of the distribution of the growth status of rice in an embodiment of the present application;

5 is a schematic diagram of phenological characteristics corresponding to spectral information of rice in different periods in an embodiment of the present application;

6 is a schematic diagram of the specific distribution of sampling routes and sampling points of field investigation in an embodiment of the present application;

FIG. 7 is a schematic diagram of the evaluation result of the disaster damage level at the plot level in an embodiment of the present application;

8 is a schematic structural diagram of a device for evaluating agricultural danger data in an embodiment of the present application;

FIG. 9 is a schematic diagram of a computer device in an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

The agricultural danger data evaluation method provided in this embodiment can be applied in the application environment as shown in FIG. 1 , in which the client terminal communicates with the server terminal. Among them, clients include but are not limited to various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices. The server can be implemented by an independent server or a server cluster composed of multiple servers.

In one embodiment, as shown in FIG. 2 , a method for evaluating agricultural danger data is provided, and the method is applied to the server in FIG. 1 as an example for description, including the following steps:

S10. Acquire meteorological data and satellite remote sensing data of a designated area.

Understandably, the designated area may be a large area, such as the North China Plain; or a small area, such as the farmland area of an agricultural county.

Specifically, meteorological data and satellite remote sensing data of a designated area can be automatically acquired according to a preset time. Meteorological data includes, but is not limited to, rainfall, sunshine, temperature and wind direction and speed in a designated area within a preset time. Satellite remote sensing data contains satellite images and spectral remote sensing information within a specified area. Satellite remote sensing data comes from satellites covering a designated area, and satellite remote sensing data with different resolutions are used for different purposes. Figure 3 shows satellite remote sensing data at different resolutions and the use of drones. Data source refers to data sources with different resolutions, and phase refers to the sampling period. As shown in Figure 3, in an example, the data source GF-6 can collect satellite remote sensing data with a resolution of 2 meters. The sampling time includes August 23 and August 27. The satellite remote sensing data is used for rice identification and Flood Assessment.

Optionally, in step S10, the obtaining meteorological data and satellite remote sensing data of the designated area includes:

S101. Acquire initial satellite remote sensing data of the designated area at a designated time.

Specifically, several initial satellite remote sensing data of different resolutions and different periods of a designated area can be automatically acquired according to a preset time. The initial satellite remote sensing data can be unprocessed satellite images and unprocessed spectral remote sensing information. The initial satellite remote sensing data can be collected before the disaster, the early stage, the middle stage, and the later stage of the disaster. The initial satellite remote sensing data refers to the unprocessed satellite remote sensing data.

S102. Process the initial satellite remote sensing data according to the preprocessing method to generate preprocessing satellite remote sensing data that meets a preset processing standard, where the preset processing standard corresponds to the preprocessing method.

Specifically, a preprocessing method for processing the initial satellite remote sensing data is preset, and after the initial satellite remote sensing data is processed by the preset preprocessing method, preprocessing satellite remote sensing data that meets the preset processing standard is generated. The preset preprocessing methods include, but are not limited to, geometric correction, orthorectification, atmospheric correction, cloud removal and fog removal, and stitching and fusion. Among them, geometric correction, orthorectification, and atmospheric correction can standardize the preprocessing satellite remote sensing data, which is beneficial to extract spectral information. The main reason for removing clouds and fog is that it may exist in the remote sensing data because the crops cannot be clearly seen due to the occlusion of clouds and fog. The purpose of removing clouds and fog is to remove noise. Splicing fusion is based on the fusion of remote sensing data of other periods and the data of this period, and spliced into crops in a designated area. Preprocessed satellite remote sensing data contains preprocessed satellite images and spectral remote sensing information of a specified area. The preset processing standard corresponds to the preprocessing method one-to-one. For example, the standard of the preprocessing method for removing clouds and fogging can be so that crops can be clearly seen.

S103. Check the availability of the preprocessed satellite remote sensing data according to a preset check index.

Specifically, the preset test index may be cloud content, and the size of cloud content may be preset as a usability test index for testing the preprocessed satellite remote sensing data. Image recognition can be performed on satellite images in a designated area to obtain the number of pixels in the satellite images, and the cloud content can be judged according to the number of pixels. If the cloud content is higher than the preset cloud content threshold, it means that the crops in the satellite image area of this period are too obscured by clouds and fog, and the preprocessed satellite remote sensing data in this period is unavailable. If the cloud content is lower than or equal to the preset cloud content threshold, it means that the crops in the satellite image area of this period are less occluded by clouds and fog, and the pre-processed satellite remote sensing data of this period is available.

S104. Determine the preprocessed satellite remote sensing data that has passed the usability check as the satellite remote sensing data.

Specifically, the preprocessed satellite remote sensing data that has passed the usability test is determined as satellite remote sensing data, and the satellite remote sensing data includes satellite images and other relevant information that have passed the usability test in the designated area.

In steps S101-S104, the initial satellite remote sensing data of the specified area at a specified time is obtained, the initial satellite remote sensing data is processed according to the preprocessing method, and the preprocessing satellite remote sensing data satisfying the preset processing standard is generated. The preset processing standard corresponds to the preprocessing method, which improves the accuracy of satellite remote sensing data. The availability of the preprocessed satellite remote sensing data is checked according to a preset test index, and the preprocessed satellite remote sensing data that has passed the availability check is determined as the satellite remote sensing data, thereby improving the availability of the satellite remote sensing data.

S20. Generate a preliminary disaster damage assessment result according to the meteorological data and the satellite remote sensing data.

Specifically, the preliminary assessment information of the meteorological disaster situation is generated according to the meteorological data and the administrative division information of the designated area. In an example, the preliminary assessment information of the meteorological disaster situation includes that the flood level of XX area in XX county is medium. Generate preliminary assessment information of remote sensing disaster situation based on satellite remote sensing data. In an example, the preliminary assessment information of remote sensing disaster situation includes that the crops in XX area of XX county are in good condition. Generate crop distribution information based on crop spectral information and satellite remote sensing data. In an example, the crop distribution information includes: XX1 county, rice, 2000 mu; XX2 county, rice, 1800 mu; . . . Finally, the preliminary disaster damage assessment results are generated according to the preliminary assessment information of meteorological disasters, the preliminary assessment information of remote sensing disasters and the distribution information of crops.

Optionally, in step S20, the generating a preliminary disaster damage assessment result according to the meteorological data and the satellite remote sensing data includes:

S201. Obtain disaster situation data of a number of designated disaster damage factors from meteorological data according to a preset dimension, divide the disaster situation data according to the administrative division information of the designated area, and perform an evaluation to generate preliminary meteorological disaster situation evaluation information. The preliminary meteorological disaster assessment information includes the preliminary disaster damage degree and preliminary disaster damage scope of each administrative region.

Specifically, the disaster situation data of a number of designated disaster damage factors can be obtained from the meteorological data according to the preset dimensions, the preliminary disaster damage degree and the preliminary disaster damage scope can be preliminarily determined according to the disaster situation data and the administrative division information, and the preliminary meteorological disaster situation assessment information can be generated. In one example, the disaster data can be represented as:

Table 1 Disaster data of some designated disaster damage factors

disaster data	rainfall	temperature
duration	10 days	15 days
Cumulative value	200mm
very high value	30mm	28 degrees

Understandably, the administrative division information of the designated area may be obtained through a GPS positioning system or other means. Specified disaster factors include, but are not limited to, rainfall, sunshine, temperature, and wind direction and speed. Preset dimensions include but are not limited to continuous days, cumulative values, cumulative anomalies, extremely high values, extremely low values, and average values. For example, rainfall can be counted through the six dimensions of continuous days, cumulative value, cumulative anomaly, extremely high value, extremely low value and average value. Among them, the cumulative anomaly refers to the accumulation of anomalies, and the anomaly is used to represent the difference between the rainfall in a certain period (such as a day) and the average rainfall in a long-term period (such as a year). The preliminary assessment information of meteorological disasters includes preliminary disaster damage degree and preliminary disaster damage scope.

Specifically, designated weather elements are selected from the meteorological data as disaster damage factors for judging the degree of crop damage, and according to preset dimensions, disaster situation data of disaster failure factors are selected from different dimensions. The area where the disaster is distributed is matched in the meteorological data. Since the sensitivity of crops in different regions to disasters is different, it is necessary to consult local agricultural experts to obtain the standard of flood or drought for local crops. According to this standard, the level threshold of the degree of disaster damage is customized, and floods or droughts are divided into six levels, including but not limited to no disaster, mild disaster, mild to moderate disaster, moderate disaster, moderate to severe disaster, and severe disaster. Then, the disaster damage degree of the designated area is evaluated according to the disaster situation data of different dimensions of the disaster damage factor and the grade threshold of the disaster damage degree. Obtain the level of disaster damage in the designated area, and obtain the preliminary assessment information of crops from the meteorological point of view.

Optionally, since different regions have different sensitivities to disasters, the preliminary assessment of the degree of disaster damage can also be carried out in combination with the local geographical conditions.

S202, obtaining spectral data of crops in the designated area from satellite remote sensing data, evaluating the growth status of crops according to the spectral data, and generating remote sensing disaster preliminary assessment information; the remote sensing disaster preliminary assessment information includes the growing status of crops distribution range.

Understandably, the spectral data of crops in a designated area is obtained from satellite remote sensing data, and preliminary assessment of the growing conditions of crops is performed according to the spectral data to generate preliminary remote sensing disaster assessment information. Among them, the spectral data needs to be calculated and processed by the normalized vegetation index. The normalized vegetation index (NDVI) can well reflect the strength of vegetation information and is an important indicator for monitoring vegetation growth. The normalized vegetation index (NDVI) can be calculated from the spectral data of crops, which includes the reflectance of crops in the near-infrared and red light bands. The calculation formula of the normalized vegetation index is: NDVI=(NIR-R)/(NIR+R), where NIR is the near-infrared band and R is the red light band. The preliminary assessment information of remote sensing disaster situation includes the distribution range of crop growth condition. The growth status of crops can be divided into five dimensions: good, better, normal, poor, and poor. In one example, as shown in Figure 4, it is the distribution map of the growth status of rice in Guanghan City, Sichuan Province on June 30, in which the growth status is good, good, normal, poor, and poor, accounting for 4.5%, 16.26%, and 4.5%, respectively. 67.76%, 7.12%, 4.36%.

S203. Acquire spectral information of crops in the designated area from satellite remote sensing data, determine the types of crops according to the spectral information, and generate crop distribution information.

It is understandable that the spectral information of crops represents the phenological phenomenon that crops produce different characteristics to the spectrum during different growth stages such as germination, leaf expansion, flowering, leaf discoloration, and defoliation. Through operations such as label inspection, survey and claim settlement, the spectral information of each crop in different phenological periods has been gradually accumulated, and a spectral information database of crops such as rice, corn, and wheat has been established, which can be used for automatic and rapid identification of crops in disaster-stricken areas. Crop spectral information is the color of crops in satellite remote sensing data at different growth stages.

Specifically, the spectral information of crops in a designated area is obtained from satellite remote sensing data, and the spectral information of crops is obtained from the spectral information database of crops, and the spectral information of crops in the designated area is compared with the spectral information of crops in the spectral information database of crops. Automatically identify and compare to obtain the type of crops, and then generate the distribution range of crops based on the distribution range of growing conditions according to the type of crops.

S204. Generate the preliminary disaster damage assessment result in combination with the meteorological disaster preliminary assessment information, the remote sensing disaster preliminary assessment information, and the crop distribution information.

Specifically, the preliminary disaster damage assessment result is generated according to the preliminary disaster damage degree and preliminary disaster damage scope of the meteorological disaster preliminary assessment information, the growth state distribution range of the remote sensing disaster preliminary assessment information, and the crop type information and distribution range of the crop distribution information.

It is understandable that the preliminary disaster damage assessment result includes preliminary disaster damage degree, preliminary disaster damage scope, growth condition distribution scope, crop type information and distribution scope.

In steps S201-S204, the disaster situation data of several designated disaster damage factors is obtained from the meteorological data according to the preset dimensions, the disaster situation data is divided according to the administrative division information of the designated area, and the evaluation is performed to generate a preliminary meteorological disaster situation Assessment information, the preliminary meteorological disaster assessment information includes the preliminary disaster damage degree and preliminary disaster damage scope of each administrative region, and the preliminary disaster damage degree and preliminary disaster damage scope can be automatically and quickly obtained from the meteorological point of view, saving time and labor costs. Obtain the spectral data of the crops in the designated area from the satellite remote sensing data, and evaluate the growth condition of the crops according to the spectral data, and generate the preliminary remote sensing disaster assessment information; the remote sensing disaster preliminary assessment information includes the distribution range of the growing condition of the crops , which can automatically and quickly obtain the distribution range of growth conditions from the perspective of satellite remote sensing, saving time and labor costs. Obtain spectral information of crops in the designated area from satellite remote sensing data, determine the types of crops according to the spectral information, and generate crop distribution information; combine the preliminary meteorological disaster assessment information, the remote sensing disaster preliminary assessment information and the The crop distribution information is used to generate the preliminary disaster damage assessment result, so as to improve the accuracy of the preliminary disaster damage assessment result.

Optionally, in step S203, obtaining spectral information of crops in the designated area from satellite remote sensing data, determining the types of crops according to the spectral information, and generating crop distribution information, including:

S2031. Obtain the spectral information of crops and the spectral information in the satellite remote sensing data.

It is understandable that the spectral information of crops represents the phenological phenomenon that crops produce different characteristics to the spectrum during different growth stages such as germination, leaf expansion, flowering, leaf discoloration, and defoliation. Through operations such as label inspection, survey and claim settlement, the spectral information of each crop in different phenological periods has been gradually accumulated, and a spectral information database of crops such as rice, corn, and wheat has been established, which can be used for automatic and rapid identification of crops in disaster-stricken areas. Spectral information is the color of crops in satellite remote sensing data at different growth stages.

Specifically, the spectral information of crops in a designated area is obtained from satellite remote sensing data, and the spectral information of crops is obtained from a spectral information database of crops.

S2032. Identify the spectral information in the satellite remote sensing data according to the crop spectral information, and generate the crop distribution information, where the crop distribution information includes the type information and distribution range of the crops.

Specifically, according to the phenological characteristics of crops, the multi-temporal spectral information of crops in the satellite remote sensing data in the designated area is compared and identified with the spectral information of different crops in different periods contained in the phenological characteristic database to determine the type of crops. Furthermore, according to the types of crops, on the basis of the distribution range of the growing state, the distribution range of the crops is obtained, and the crop distribution information including the type information and the distribution range of the crops is generated. Multi-temporal phase refers to the fusion of satellite remote sensing data at different times. For example, at the first moment, the position A of the designated area has clouds, but the position B has no clouds; at the second moment, the position B of the designated area has clouds, and the position A has no clouds. After multi-temporal fusion, satellite remote sensing data with neither location A nor location B covered by clouds can be obtained.

There are great differences in the spectral information of crops in different periods. Taking rice as an example, during the water storage and transplanting period in June, the rice planting area is represented by water bodies; in the early stage of leaf splitting in mid-July, the rice canopy is not enough to cover the entire ground, and the remote sensing images show weak vegetation information; July In the late stage of leaf splitting, the rice grows vigorously, showing the spectral information of dark green; in the jointing stage in August, the rice shows obvious bright green spectral information. The species information of crops can be identified according to the spectral changes in different periods. Figure 5 is the phenological feature map corresponding to the spectral information of rice in different periods. In an example, as shown in Figure 5, the data source GF-1 can collect the spectral information of rice with a resolution of 2 meters, the sampling time is June 17, and the phenological feature of rice at this time is water, which is in Water storage transplanting period.

In steps S2031-S2032, the spectral information of crops and the spectral information in the satellite remote sensing data are obtained, the spectral information in the satellite remote sensing data is identified according to the spectral information of the crops, and the distribution information of the crops is generated, and the The crop distribution information includes the type information and distribution range of crops. The type information and distribution range of crops can be obtained automatically and quickly, and the survey area can be clearly guided, saving labor costs and time costs.

Optionally, in step S220, the spectral data of the crops in the designated area is obtained from the satellite remote sensing data, and the preliminary assessment information of the remote sensing disaster situation is generated by preliminarily evaluating the growth status and disaster damage of the crops according to the spectral data. ; The preliminary assessment information of remote sensing disaster situation includes the distribution range of the growing condition of crops, including:

S2021. Acquire spectral data in the satellite remote sensing data, where the spectral data includes a near-infrared band and a red light band.

Specifically, the spectral data of crops in different bands can be obtained from satellite remote sensing data. Spectral data includes near-infrared and red light bands.

S2022: Acquire the reflectivity of the near-infrared band and the reflectivity of the red light band of crops in the designated area, and process the reflectivity of the near-infrared band and the reflection of the red light band through a normalized vegetation index calculation formula rate to obtain the normalized vegetation index of the specified area.

It is understandable that the normalized vegetation index (NDVI) can well reflect the strength of vegetation information and is an important indicator for monitoring vegetation growth. The normalized vegetation index (NDVI) can be calculated from the spectral data of crops. The calculation formula of the normalized vegetation index is: NDVI=(NIR-R)/(NIR+R), where NIR is the near-infrared band and R is the red light band.

Specifically, according to the reflectivity of the near-infrared band and the reflectance of the red light band of the crops in the designated area obtained from the satellite remote sensing data, through the calculation formula, NDVI=(NIR-R)/(NIR+R), to process the near-infrared The reflectivity of the band and the reflectivity of the red light band can be used to obtain the normalized vegetation index of crops in the specified area.

S2023, determining the growth status of crops according to the index range where the normalized vegetation index is located, and generating remote sensing disaster preliminary assessment information based on the crop growth status, where the remote sensing disaster preliminary assessment information includes the growth status distribution range of crops .

Specifically, the disaster damage is preliminarily assessed according to the index range of the normalized vegetation index (NDVI). It also conducts qualitative statistics on the growth status of crops in the designated area in five dimensions: good, good, normal, poor, and poor, to determine the growth status of crops, and generate preliminary assessment information of remote sensing disasters including the distribution range of the growth status. Understandably, the index range of the normalized vegetation index (NDVI) can be expressed as -1<=NDVI<=1, and a negative value indicates that the ground is covered with clouds, water, snow, etc., which is highly reflective to visible light; 0 indicates that there are rocks or Bare soil, etc., NIR and R are approximately equal; a positive value indicates that there is vegetation coverage, and the NDVI value increases with the increase of coverage.

In steps S2021-S2023, obtain spectral data in the satellite remote sensing data, the spectral data includes a near-infrared band and a red light band; obtain the reflectivity of the near-infrared band of crops in the designated area, and the red light band The reflectivity of the specified area is obtained by processing the reflectivity of the near-infrared band and the reflectivity of the red light band through the calculation formula of the normalized vegetation index to obtain the normalized vegetation index of the designated area; The index range in which the index is located determines the growth status of the crops, and based on the growth status of the crops, the preliminary assessment information of remote sensing disaster situation is generated. The distribution range of the growing situation saves labor costs and time costs.

S30. Set a number of sampling points in the designated area according to the preliminary disaster damage assessment result.

Specifically, after obtaining the preliminary disaster damage assessment results, several sampling points can be set according to the preliminary disaster damage degree, preliminary disaster damage scope, growth condition distribution scope, crop type information and distribution scope. At least one sampling point is set for each preliminary disaster damage degree, each growing condition, and each crop type. According to the disaster damage level, the crops at the sampling point were photographed and surveyed on the spot, and the field survey data was obtained.

S40. Acquire the field survey data of the sampling point and the aerial photographic remote sensing data of the designated area and send them to the designated terminal, so that the designated terminal performs disaster damage analysis according to the field investigation data and the aerial photographic remote sensing data to obtain sampling Disaster damage assessment results.

It is understandable that the field survey data refers to the data of the surveyor's on-site survey at the sampling point. Field survey data includes photos, shooting time, location (including latitude and longitude), shooting person, and on-site records. In an example, there are 224 sampling points in total, of which the field survey data for rice includes: 81 points that were not affected by disaster, 21 points were mildly affected, 18 points were moderately affected, 30 points were severely affected, and 13 were severely affected. points; other crops 61 points. In an example, as shown in Figure 6, when the surveyor conducts a sampling field survey on the non-disaster-affected, mildly-disaster-affected, moderately-disaster-affected, severely-disaster-affected, dead-yield, and other crop points, the The specific distribution of sampling routes and sampling points.

Aerial photography remote sensing data can refer to data obtained through drone aerial photography. Plan the route of the UAV on the mobile APP bird's-eye UAV system, and make the UAV take aerial photos of the crops within the initial disaster damage area along the set route, and you can get the aerial photos and other related information. aerial remote sensing data. Multi-resolution and multiple drone aerial photography can be performed as needed.

Designated terminals may refer to computer equipment used by agricultural experts. After the designated terminal receives the field survey data and aerial remote sensing data, agricultural experts can view the field survey data and aerial remote sensing data through the designated terminal, and provide sampling disaster damage assessment results. The damage assessment results of sampling include the damage level of crops. Disaster damage levels include but are not limited to six levels: no disaster, mild disaster, mild to moderate disaster, moderate disaster, moderate to severe disaster, and severe disaster. Among them, the remote sensing data of UAV aerial photography includes aerial pictures.

Optionally, in step S40, the aerial photography remote sensing data includes a panoramic image; the acquiring the field survey data of the sampling point, and acquiring the aerial photography remote sensing data of the designated area, includes:

S401 , collecting several aerial pictures according to a preset route by using a drone.

Specifically, the preset route may refer to the route of the UAV planned on the mobile terminal APP bird's-eye UAV system. The drones take drone aerial photography of crops within the initial disaster damage range along the preset route, and collect several aerial photography real pictures. Among them, multi-resolution and multiple drone aerial photography can be performed as needed.

S402. Generate the panoramic image according to the several aerial pictures.

Specifically, automatically obtain aerial real pictures of all designated areas from UAV aerial remote sensing data, including but not limited to motion structure inference, feature extraction, feature matching, point cloud generation, Poisson plane reconstruction, multi-view stereo vision technology Automatically stitch the aerial photos into a panorama of disaster damage.

Optionally, for the problem that the stitching speed of aerial pictures is too slow, the DeepSFM algorithm (deep structure-from-motion, deep motion recovery structure) can be introduced, and the parallel operation of GPU can significantly improve the calculation efficiency of motion structure inference, making the picture more efficient. Stitching speed increased by 50%.

In steps S401-S402, the drone collects several aerial photographs according to the preset route, and generates the panoramic image according to the several aerial photographs, so that a high-resolution aerial photograph can be obtained, and the panoramic image is automatically generated, which improves the picture quality. processing speed.

S50. Receive the sampled disaster damage assessment result sent by the designated terminal, and obtain sampled spectral data corresponding to the sampled disaster damage assessment result from the aerial photographic remote sensing data, and obtain the sampled disaster damage assessment result according to the sampled spectral data and the sampled disaster damage assessment. As a result, the disaster damage level threshold is determined.

It is understandable that since several sampling points are set for the same disaster damage level, several sampling disaster damage assessment results can be obtained for the same disaster damage level. The sampling spectral data of crops with the same disaster damage level are obtained from aerial remote sensing data, and multiple normalized vegetation indices under the disaster damage level are calculated according to the sampling spectral data of crops with the same disaster damage level. Determine the mean and maximum value of the disaster damage level according to multiple normalized vegetation indices under the disaster damage level, and select the mean and maximum value to set the threshold or interval threshold of the disaster damage level, which is the disaster damage level threshold.

S60. Evaluate the spectral data of the satellite remote sensing data according to the disaster damage level threshold to generate a disaster damage evaluation result of the designated area.

Understandably, the designated area consists of several parcels. Through AI image recognition and deep learning technology, the boundaries of each plot in the designated area are automatically recognized. The same plot can have multiple spectral data, that is, there are multiple normalized vegetation indices. Obtain all spectral data from satellite remote sensing data, and calculate all normalized vegetation indices in the specified area based on all spectral data.

Further, all the normalized vegetation index values are counted into each plot to obtain the mean value of the normalized vegetation index of each plot. Furthermore, the mean value of the normalized vegetation index of each plot in the designated area is evaluated according to the threshold value of the disaster damage level, and the evaluation result of the disaster damage level of each plot in the designated area is obtained. For example, if the mean value of the Normalized Vegetation Index (NDVI) of a plot in a designated area is greater than the threshold of the severe disaster damage level, the disaster damage level of the plot is evaluated as a severe disaster damage.

Optionally, in step S60, evaluating the spectral data of the satellite remote sensing data according to the disaster damage level threshold to generate a disaster damage evaluation result of the designated area, including:

S601. Acquire satellite remote sensing data of the designated area, where the satellite remote sensing data meets preset resolution requirements.

Understandably, satellite remote sensing data includes remote sensing data of several satellites in different periods and different resolutions. Specifically, the resolution of satellite remote sensing data is selected according to actual needs. For example, when using AI to identify parcel boundaries, high-definition data with a resolution of 0.5 meters is required; if data with a resolution of 10 meters is used, the boundaries of parcels cannot meet the accuracy requirements.

The resolution of UAV aerial photography is higher than that of satellite remote sensing. In key areas, high-resolution UAV remote sensing data can be selected.

S602. Process the satellite remote sensing data through a preset image recognition algorithm to generate the boundary information of the arable land in the designated area.

Specifically, the preset image recognition algorithm includes an AI image recognition algorithm and a deep learning algorithm. For example, through AI image recognition algorithm and deep learning algorithm, the boundary of each plot in the designated area is automatically identified, and the boundary information of the cultivated land plot is generated. The area of each cultivated land plot can be calculated according to the boundary information. Combined with the disaster damage level and area of the cultivated land plot, the disaster damage area can be obtained. A visual report of disaster damage can be made according to the assessment results of the disaster damage area and the disaster damage level. In an example, as shown in Figure 7, it is a map of the assessment results of the disaster damage level at the plot level of a village, in which different disaster damage levels use different color standards, and the area of the plot is marked in each plot.

S603, extracting the spectral data of the cultivated land block from the border information of the cultivated land block.

Specifically, according to the boundary information of the arable land, the spectral data of each arable land in the designated area is acquired.

S604. Evaluate the spectral data of the cultivated land plot according to the disaster damage level threshold, and generate a disaster damage evaluation result of the cultivated land plot, where the disaster damage evaluation result of the designated area includes several disaster damages of the cultivated land plot. evaluation result.

Specifically, according to the spectral data of each cultivated land plot in the designated area, the normalized vegetation index (NDVI) of each cultivated land plot in the designated area is calculated. Further, all normalized vegetation index (NDVI) values are counted into each plot, and the mean value of normalized vegetation index (NDVI) of each plot in the designated area is obtained. Furthermore, the spectral data of each cultivated land plot in the designated area is evaluated according to the threshold value of the disaster damage level, and the evaluation result of the disaster damage level of each plot in the designated area is obtained. For example, if the mean value of the Normalized Vegetation Index (NDVI) of a plot in a designated area is greater than the threshold of the severe disaster damage level, the disaster damage level of the plot is evaluated as a severe disaster damage.

In steps S601-S604, the satellite remote sensing data of the designated area is obtained, the satellite remote sensing data meets the preset resolution requirements, and the satellite remote sensing data is processed by a preset image recognition algorithm to generate the cultivated land in the designated area block boundary information, extract the spectral data of the cultivated land block from the cultivated land block boundary information, evaluate the spectral data of the cultivated land block according to the disaster damage level threshold, and generate the disaster damage assessment result of the cultivated land block, The disaster damage assessment result of the designated area includes the disaster damage assessment results of several cultivated land plots, and the disaster damage level of the disaster damage plot can be automatically determined according to the disaster damage grade division threshold, thereby reducing labor and time costs.

To sum up, in steps S10-S60, in this embodiment, by acquiring meteorological data and satellite remote sensing data of a designated area; A preliminary understanding of the distribution and the degree of disaster damage can clearly guide the survey site, saving labor costs and time costs. According to the preliminary disaster damage assessment results, several sampling points are set in the designated area, which reduces unnecessary surveys of survey sites. Obtain the field survey data of the sampling point and the aerial photographic remote sensing data of the designated area and send them to the designated terminal, so that the designated terminal can perform disaster damage analysis according to the field investigation data and the aerial photographic remote sensing data to obtain the sampling disaster damage. As a result of the assessment, the unmanned aerial vehicle can be used to survey the harsh terrain, and the high resolution of the unmanned aerial vehicle can obtain a clearer picture of the disaster-damaged land. Receive the sampled disaster damage assessment result sent by the designated terminal, obtain sampled spectral data corresponding to the sampled disaster damage assessment result from the aerial photography remote sensing data, and determine according to the sampled spectral data and the sampled disaster damage assessment result Disaster damage level threshold, improve the accuracy of the disaster damage level threshold; according to the disaster damage level threshold, the spectral data of the satellite remote sensing data is evaluated to generate the disaster damage assessment result of the designated area, which can be automatically based on the disaster damage level threshold. The grading threshold determines the hazard level of the disaster-damaged plot, reducing labor and time costs. In conclusion, the present application reduces the difficulty of collecting agricultural danger data, reduces the cost of agricultural danger survey and damage assessment, and improves the accuracy of disaster damage assessment.

It should be understood that the size of the sequence numbers of the steps in the above embodiments does not mean the sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

In one embodiment, an apparatus for evaluating agricultural danger data is provided, which corresponds one-to-one with the method for evaluating agricultural danger data in the above embodiment. As shown in FIG. 8 , the agricultural danger data evaluation device includes a data module 10 , a preliminary evaluation module 20 , a sampling point determination module 30 , a sampling evaluation module 40 , a disaster damage level threshold determination module 50 and a disaster damage evaluation result module 60 . The detailed description of each functional module is as follows:

The data module 10 is used to obtain meteorological data and satellite remote sensing data of a designated area;

A preliminary assessment module 20, configured to generate a preliminary disaster damage assessment result according to the meteorological data and the satellite remote sensing data;

a sampling point determination module 30, configured to set several sampling points in the designated area according to the preliminary disaster damage assessment result;

The sampling evaluation module 40 is used to obtain the field survey data of the sampling point and the aerial photography remote sensing data of the designated area and send them to the designated terminal, so that the designated terminal can perform a Disaster damage analysis to obtain sampling disaster damage assessment results;

A disaster damage level threshold determination module 50, configured to receive the sampled disaster damage assessment result sent by the designated terminal, and obtain sampled spectral data corresponding to the sampled disaster damage assessment result from the aerial photography remote sensing data, according to the Sampling spectral data and sampling disaster damage assessment results to determine the disaster damage level threshold;

The disaster damage assessment result module 60 is configured to evaluate the spectral data of the satellite remote sensing data according to the disaster damage grade threshold, so as to generate a disaster damage assessment result of the designated area.

Optionally, in the sampling evaluation module 40, the aerial photography remote sensing data includes panoramic images; the aerial photography remote sensing data of the described acquisition of the sampling point and the aerial photography remote sensing data of the designated area are sent to the designated terminal, including:

Aerial photographing unit, used to collect several aerial photographs according to the preset route by the drone;

A panoramic image unit, configured to generate the panoramic image according to the several aerial pictures.

Optionally, in the preliminary assessment module 20, generating a preliminary disaster damage assessment result according to the meteorological data and the satellite remote sensing data includes:

The meteorological data unit 201 is configured to obtain disaster situation data of several designated disaster damage factors from the meteorological data according to preset dimensions, divide the disaster situation data according to the administrative division information of the designated area, and perform an evaluation to generate a preliminary meteorological disaster situation Assessment information, the preliminary meteorological disaster assessment information includes the preliminary disaster damage degree and preliminary disaster damage scope of each administrative region;

The satellite remote sensing data unit 202 is used to obtain the spectral data of crops in the designated area from the satellite remote sensing data, and to evaluate the growth status of the crops according to the spectral data, and to generate preliminary remote sensing disaster assessment information; the remote sensing disaster preliminary assessment The information includes the distribution range of the growing condition of crops;

Crop distribution information unit 203, configured to obtain spectral information of crops in the designated area from satellite remote sensing data, determine the type of crops according to the spectral information, and generate crop distribution information;

The preliminary disaster damage assessment result unit 204 is configured to generate the preliminary disaster damage assessment result by combining the meteorological disaster preliminary assessment information, the remote sensing disaster preliminary assessment information and the crop distribution information.

Optionally, in the crop distribution information unit 203, the spectral information of the crops in the designated area is obtained from the satellite remote sensing data, the type of the crops is determined according to the spectral information, and the crop distribution information is generated, including:

a spectral information unit, used to obtain the spectral information of crops and the spectral information in the satellite remote sensing data;

The spectral information identification unit is configured to identify the spectral information in the satellite remote sensing data according to the crop spectral information, and generate the crop distribution information, where the crop distribution information includes the type information and distribution range of the crops.

Optionally, in the satellite remote sensing data unit 202, the spectral data of the crops in the designated area is obtained from the satellite remote sensing data, and the remote sensing disaster situation is generated by preliminarily evaluating the growth and damage of the crops according to the spectral data. Preliminary assessment information; the preliminary assessment information of remote sensing disaster situation includes the distribution range of the growing condition of crops, including:

a first acquiring spectral data unit, configured to acquire spectral data in the satellite remote sensing data, where the spectral data includes a near-infrared band and a red light band;

The normalized vegetation index unit is used to obtain the reflectance of the near-infrared band and the reflectance of the red light band of crops in the designated area, and the reflectance of the near-infrared band and the reflectance of the near-infrared band are processed by the normalized vegetation index calculation formula. The reflectivity of the red light band, to obtain the normalized vegetation index of the designated area;

The remote sensing disaster situation unit is used to determine the growth status of crops according to the index range in which the normalized vegetation index is located, and generate preliminary remote sensing disaster situation assessment information based on the growth status of the crops, and the remote sensing disaster preliminary assessment information includes the crops. The distribution range of growth conditions.

Optionally, in the data module 10, the obtaining meteorological data and satellite remote sensing data of the designated area includes:

an initial satellite remote sensing data unit, used to obtain initial satellite remote sensing data of the specified area at a specified time;

a unit for preprocessing satellite remote sensing data, configured to process the initial satellite remote sensing data according to the preprocessing method, and generate preprocessing satellite remote sensing data that satisfies a preset processing standard, the preset processing standard corresponding to the preprocessing method;

an inspection unit, configured to inspect the availability of the preprocessed satellite remote sensing data according to a preset inspection index;

A satellite remote sensing data unit, configured to determine the preprocessed satellite remote sensing data that has passed the usability check as the satellite remote sensing data.

Optionally, in the disaster damage assessment result module 60, the spectral data of the satellite remote sensing data is evaluated according to the disaster damage level threshold to generate the disaster damage assessment result of the designated area, including:

a resolution unit for acquiring satellite remote sensing data of the designated area, the satellite remote sensing data meeting preset resolution requirements;

a boundary information unit, configured to process the satellite remote sensing data through a preset image recognition algorithm to generate the boundary information of the arable land in the designated area;

a second acquiring spectral data unit, configured to extract spectral data of the cultivated land block from the border information of the cultivated land block;

A disaster damage assessment result unit, configured to assess the spectral data of the cultivated land plot according to the disaster damage grade threshold, and generate a disaster damage assessment result of the cultivated land plot, and the disaster damage assessment result of the designated area includes a number of the Disaster damage assessment results of cultivated land plots.

For the specific limitation of the agricultural danger data evaluation device, please refer to the limitation of the agricultural danger data evaluation method above, which will not be repeated here. Each module in the above-mentioned agricultural danger data evaluation device can be realized in whole or in part by software, hardware and combinations thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

In one embodiment, a computer device is provided, and the computer device may be a server, and its internal structure diagram may be as shown in FIG. 9 . The computer device includes a processor, memory, a network interface, and a database connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a readable storage medium, an internal memory. The readable storage medium stores an operating system, computer readable instructions and a database. The internal memory provides an environment for the execution of the operating system and computer-readable instructions in the readable storage medium. The database of the computer equipment is used to store the data involved in the agricultural hazard data evaluation method. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer readable instructions, when executed by a processor, implement a method for evaluating agricultural hazard data. The readable storage medium provided by this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

In one embodiment, a computer device is provided, comprising a memory, a processor, and computer-readable instructions stored on the memory and executable on the processor, and the processor implements the following steps when executing the computer-readable instructions:

Obtain meteorological data and satellite remote sensing data of a designated area;

generating a preliminary disaster damage assessment result according to the meteorological data and the satellite remote sensing data;

Setting up a number of sampling points in the designated area according to the preliminary disaster damage assessment results;

Obtain the field survey data of the sampling point and the aerial photographic remote sensing data of the designated area and send them to the designated terminal, so that the designated terminal can perform disaster damage analysis according to the field investigation data and the aerial photographic remote sensing data to obtain the sampling disaster damage. evaluation result;

Receive the sampled disaster damage assessment result sent by the designated terminal, obtain sampled spectral data corresponding to the sampled disaster damage assessment result from the aerial photography remote sensing data, and determine according to the sampled spectral data and the sampled disaster damage assessment result Disaster damage level threshold;

The spectral data of the satellite remote sensing data is evaluated according to the disaster damage level threshold, so as to generate a disaster damage evaluation result of the designated area.

In one embodiment, one or more computer-readable storage media storing computer-readable instructions are provided, and the readable storage media provided in this embodiment include non-volatile readable storage media and volatile readable storage media storage medium. Computer-readable instructions are stored on the readable storage medium, and when the computer-readable instructions are executed by one or more processors, implement the following steps:

Obtain meteorological data and satellite remote sensing data of a designated area;

generating a preliminary disaster damage assessment result according to the meteorological data and the satellite remote sensing data;

Setting up a number of sampling points in the designated area according to the preliminary disaster damage assessment result;

Obtain the field survey data of the sampling point and the aerial photographic remote sensing data of the designated area and send them to the designated terminal, so that the designated terminal can perform disaster damage analysis according to the field investigation data and the aerial photographic remote sensing data to obtain the sampling disaster damage. evaluation result;

Receive the sampled disaster damage assessment result sent by the designated terminal, obtain sampled spectral data corresponding to the sampled disaster damage assessment result from the aerial photography remote sensing data, and determine according to the sampled spectral data and the sampled disaster damage assessment result Disaster damage level threshold;

The spectral data of the satellite remote sensing data is evaluated according to the disaster damage level threshold, so as to generate a disaster damage evaluation result of the designated area.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a non-volatile computer. In the read storage medium or the volatile readable storage medium, the computer-readable instructions, when executed, may include the processes of the foregoing method embodiments. Wherein, any reference to memory, storage, database or other medium used in the various embodiments provided in this application may include non-volatile and/or volatile memory. Nonvolatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Road (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. Module completion, that is, dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above.

The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the above-mentioned embodiments, those of ordinary skill in the art should understand that: it can still be used for the above-mentioned implementations. The technical solutions described in the examples are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions in the embodiments of the application, and should be included in the within the scope of protection of this application.

Claims (20)

1. An agricultural hazard data assessment method, comprising:

   Obtain meteorological data and satellite remote sensing data of a designated area;

   generating a preliminary disaster damage assessment result according to the meteorological data and the satellite remote sensing data;

   Setting up a number of sampling points in the designated area according to the preliminary disaster damage assessment results;

   Obtain the field survey data of the sampling point and the aerial photographic remote sensing data of the designated area and send them to the designated terminal, so that the designated terminal can perform disaster damage analysis according to the field investigation data and the aerial photographic remote sensing data to obtain the sampling disaster damage. evaluation result;

   Receive the sampled disaster damage assessment result sent by the designated terminal, obtain sampled spectral data corresponding to the sampled disaster damage assessment result from the aerial photography remote sensing data, and determine according to the sampled spectral data and the sampled disaster damage assessment result Disaster damage level threshold;

   The spectral data of the satellite remote sensing data is evaluated according to the disaster damage level threshold, so as to generate a disaster damage evaluation result of the designated area.
2. The method for evaluating agricultural danger data according to claim 1, wherein the aerial photographic remote sensing data includes panoramic images; the field survey data of the sampling point and the aerial photographic remote sensing data of the designated area are obtained and sent to a designated terminal, include:

   Collect several aerial pictures according to the preset route by drone;

   The panoramic image is generated according to the several aerial pictures.
3. The agricultural danger data assessment method according to claim 1, wherein the generating a preliminary disaster damage assessment result according to the meteorological data and the satellite remote sensing data comprises:

   Obtain disaster situation data of several designated disaster damage factors from meteorological data according to preset dimensions, divide the disaster situation data according to the administrative division information of the designated area, and perform evaluation to generate preliminary meteorological disaster situation assessment information, the meteorological disaster situation The preliminary assessment information includes the preliminary disaster damage degree and preliminary disaster damage scope of each administrative region;

   Obtain the spectral data of the crops in the designated area from the satellite remote sensing data, and evaluate the growth condition of the crops according to the spectral data, and generate the preliminary remote sensing disaster assessment information; the remote sensing disaster preliminary assessment information includes the distribution range of the growing condition of the crops ;

   Obtain spectral information of crops in the designated area from satellite remote sensing data, determine the type of crops according to the spectral information, and generate crop distribution information;

   The preliminary disaster damage assessment result is generated in combination with the meteorological disaster preliminary assessment information, the remote sensing disaster preliminary assessment information and the crop distribution information.
4. The method for evaluating agricultural danger data according to claim 3, wherein the obtaining spectral information of crops in the designated area from satellite remote sensing data, determining the types of crops according to the spectral information, and generating crop distribution information, comprising:

   Obtaining the spectral information of crops and the spectral information in the satellite remote sensing data;

   The spectral information in the satellite remote sensing data is identified according to the crop spectral information, and the crop distribution information is generated, and the crop distribution information includes the type information and distribution range of the crops.
5. The method for evaluating agricultural danger data according to claim 3, wherein the spectral data of the crops in the designated area is obtained from satellite remote sensing data, and the growing condition of the crops is evaluated according to the spectral data, and a preliminary remote sensing disaster situation is generated. Assessment information, including:

   acquiring spectral data in the satellite remote sensing data, where the spectral data includes a near-infrared band and a red light band;

   Obtain the reflectivity of the near-infrared band of crops in the designated area, and the reflectivity of the red light band, and process the reflectivity of the near-infrared band and the reflectivity of the red light band by a normalized vegetation index calculation formula, obtaining the normalized vegetation index of the designated area;

   According to the index range in which the normalized vegetation index is located, the growth status of the crops is determined, and based on the growth status of the crops, preliminary remote sensing disaster assessment information is generated, and the remote sensing disaster preliminary assessment information includes the distribution range of the growth status of the crops.
6. The method for evaluating agricultural danger data according to claim 1, wherein said obtaining meteorological data and satellite remote sensing data of a designated area comprises:

   obtaining the initial satellite remote sensing data of the designated area at the designated time;

   Process the initial satellite remote sensing data according to the preprocessing method, and generate preprocessing satellite remote sensing data that meets a preset processing standard, the preset processing standard corresponding to the preprocessing method;

   Test the availability of the preprocessed satellite remote sensing data according to the preset test index;

   The preprocessed satellite remote sensing data passing the usability test is determined as the satellite remote sensing data.
7. The method for evaluating agricultural danger data according to claim 1, wherein the evaluating the spectral data of the satellite remote sensing data according to the disaster damage level threshold to generate the disaster damage evaluation result of the designated area, comprising:

   acquiring satellite remote sensing data of the designated area, the satellite remote sensing data meeting preset resolution requirements;

   Process the satellite remote sensing data through a preset image recognition algorithm to generate the boundary information of the arable land in the designated area;

   extracting the spectral data of the cultivated land block from the border information of the cultivated land block;

   The spectral data of the cultivated land plot is evaluated according to the disaster damage level threshold, and the disaster damage evaluation result of the cultivated land plot is generated, and the disaster damage evaluation result of the designated area includes several disaster damage evaluation results of the cultivated land plot. .
8. An agricultural danger data evaluation device, comprising:

   The data module is used to obtain meteorological data and satellite remote sensing data of the designated area;

   a preliminary disaster damage assessment result module, configured to generate a preliminary disaster damage assessment result according to the meteorological data and the satellite remote sensing data;

   a sampling module, configured to set several sampling points in the designated area according to the preliminary disaster damage assessment result;

   The sampling evaluation result module is used to obtain the field survey data of the sampling point and the aerial photography remote sensing data of the designated area and send them to the designated terminal, so that the designated terminal can perform a Disaster damage analysis to obtain sampling disaster damage assessment results;

   A disaster damage level threshold module, configured to receive the sampled disaster damage assessment result sent by the designated terminal, and obtain sampled spectral data corresponding to the sampled disaster damage assessment result from the aerial photographic remote sensing data, and according to the sampled spectrum Data and sample disaster damage assessment results to determine the disaster damage level threshold;

   A disaster damage assessment result module, configured to evaluate the spectral data of the satellite remote sensing data according to the disaster damage grade threshold, so as to generate a disaster damage assessment result of the designated area.
9. The agricultural danger data evaluation device according to claim 8, wherein the sampling evaluation result module comprises:

   Aerial photographing unit, used to collect several aerial photographs according to the preset route by the drone;

   A panoramic image unit, configured to generate the panoramic image according to the several aerial pictures.
10. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer-readable instructions :

    Obtain meteorological data and satellite remote sensing data of a designated area;

    generating a preliminary disaster damage assessment result according to the meteorological data and the satellite remote sensing data;

    Setting up a number of sampling points in the designated area according to the preliminary disaster damage assessment result;

    Obtain the field survey data of the sampling point and the aerial photographic remote sensing data of the designated area and send them to the designated terminal, so that the designated terminal can perform disaster damage analysis according to the field investigation data and the aerial photographic remote sensing data to obtain the sampling disaster damage. evaluation result;

    Receive the sampled disaster damage assessment result sent by the designated terminal, obtain sampled spectral data corresponding to the sampled disaster damage assessment result from the aerial photography remote sensing data, and determine according to the sampled spectral data and the sampled disaster damage assessment result Disaster damage level threshold;

    The spectral data of the satellite remote sensing data is evaluated according to the disaster damage level threshold, so as to generate a disaster damage evaluation result of the designated area.
11. The computer equipment according to claim 10, wherein the aerial photography remote sensing data comprises panoramic images; the acquiring the field survey data of the sampling point and the aerial photography remote sensing data of the designated area and sending them to the designated terminal, comprising:

    Collect several aerial pictures according to the preset route by drone;

    The panoramic image is generated according to the several aerial pictures.
12. The computer device according to claim 10, wherein the generating a preliminary disaster damage assessment result according to the meteorological data and the satellite remote sensing data comprises:

    Obtain disaster situation data of several designated disaster damage factors from meteorological data according to preset dimensions, divide the disaster situation data according to the administrative division information of the designated area, and perform evaluation to generate preliminary meteorological disaster situation assessment information, the meteorological disaster situation The preliminary assessment information includes the preliminary disaster damage degree and preliminary disaster damage scope of each administrative region;

    Obtain the spectral data of the crops in the designated area from the satellite remote sensing data, and evaluate the growth condition of the crops according to the spectral data, and generate the preliminary remote sensing disaster assessment information; the remote sensing disaster preliminary assessment information includes the distribution range of the growing condition of the crops ;

    Obtain spectral information of crops in the designated area from satellite remote sensing data, determine the type of crops according to the spectral information, and generate crop distribution information;

    The preliminary disaster damage assessment result is generated in combination with the meteorological disaster preliminary assessment information, the remote sensing disaster preliminary assessment information and the crop distribution information.
13. The computer device according to claim 12, wherein the obtaining spectral information of crops in the designated area from satellite remote sensing data, determining the types of crops according to the spectral information, and generating crop distribution information, comprising:

    Obtaining the spectral information of crops and the spectral information in the satellite remote sensing data;

    The spectral information in the satellite remote sensing data is identified according to the crop spectral information, and the crop distribution information is generated, and the crop distribution information includes the type information and distribution range of the crops.
14. The computer device according to claim 12, wherein the spectral data of the crops in the designated area is obtained from satellite remote sensing data, and the growing condition of the crops is evaluated according to the spectral data to generate preliminary assessment information of remote sensing disaster situation, include:

    acquiring spectral data in the satellite remote sensing data, where the spectral data includes a near-infrared band and a red light band;

    Obtain the reflectivity of the near-infrared band of crops in the designated area, and the reflectivity of the red light band, and process the reflectivity of the near-infrared band and the reflectivity of the red light band by a normalized vegetation index calculation formula, obtaining the normalized vegetation index of the designated area;

    According to the index range in which the normalized vegetation index is located, the growth status of the crops is determined, and based on the growth status of the crops, preliminary remote sensing disaster assessment information is generated, and the remote sensing disaster preliminary assessment information includes the distribution range of the growth status of the crops.
15. One or more readable storage media storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the following steps:

    Obtain meteorological data and satellite remote sensing data of a designated area;

    generating a preliminary disaster damage assessment result according to the meteorological data and the satellite remote sensing data;

    Setting up a number of sampling points in the designated area according to the preliminary disaster damage assessment results;

    Obtain the field survey data of the sampling point and the aerial photographic remote sensing data of the designated area and send them to the designated terminal, so that the designated terminal can perform disaster damage analysis according to the field investigation data and the aerial photographic remote sensing data to obtain the sampling disaster damage. evaluation result;

    Receive the sampled disaster damage assessment result sent by the designated terminal, obtain sampled spectral data corresponding to the sampled disaster damage assessment result from the aerial photography remote sensing data, and determine according to the sampled spectral data and the sampled disaster damage assessment result Disaster damage level threshold;

    The spectral data of the satellite remote sensing data is evaluated according to the disaster damage level threshold, so as to generate a disaster damage evaluation result of the designated area.
16. The readable storage medium of claim 15, wherein the generating a preliminary disaster damage assessment result according to the meteorological data and the satellite remote sensing data comprises:

    Obtain the disaster situation data of a number of designated disaster damage factors from the meteorological data according to the preset dimensions, divide the disaster situation data according to the administrative division information of the designated area, and perform evaluation, and generate preliminary meteorological disaster situation evaluation information, the meteorological disaster situation The preliminary assessment information includes the preliminary disaster damage degree and preliminary disaster damage scope of each administrative region;

    Obtain the spectral data of the crops in the designated area from the satellite remote sensing data, and evaluate the growth condition of the crops according to the spectral data, and generate the preliminary remote sensing disaster assessment information; the remote sensing disaster preliminary assessment information includes the distribution range of the growing condition of the crops ;

    Obtain spectral information of crops in the designated area from satellite remote sensing data, determine the type of crops according to the spectral information, and generate crop distribution information;

    The preliminary disaster damage assessment result is generated in combination with the meteorological disaster preliminary assessment information, the remote sensing disaster preliminary assessment information and the crop distribution information.
17. The readable storage medium according to claim 16, wherein the obtaining spectral information of crops in the designated area from satellite remote sensing data, determining the types of crops according to the spectral information, and generating crop distribution information, comprising:

    Obtaining the spectral information of crops and the spectral information in the satellite remote sensing data;

    The spectral information in the satellite remote sensing data is identified according to the crop spectral information, and the crop distribution information is generated, and the crop distribution information includes the type information and distribution range of the crops.
18. The readable storage medium according to claim 16, wherein the spectral data of the crops in the designated area is obtained from satellite remote sensing data, and the growing condition of the crops is evaluated according to the spectral data to generate a preliminary assessment of remote sensing disaster situation Information; the preliminary assessment information of remote sensing disaster situation includes the distribution range of crop growth conditions, including:

    acquiring spectral data in the satellite remote sensing data, where the spectral data includes a near-infrared band and a red light band;

    Obtain the reflectivity of the near-infrared band of crops in the designated area, and the reflectivity of the red light band, and process the reflectivity of the near-infrared band and the reflectivity of the red light band by a normalized vegetation index calculation formula, obtaining the normalized vegetation index of the designated area;

    According to the index range in which the normalized vegetation index is located, the growth status of the crops is determined, and based on the growth status of the crops, preliminary remote sensing disaster assessment information is generated, and the remote sensing disaster preliminary assessment information includes the distribution range of the growth status of the crops.
19. The readable storage medium according to claim 15, wherein said obtaining meteorological data and satellite remote sensing data of a designated area comprises:

    obtaining the initial satellite remote sensing data of the designated area at the designated time;

    Process the initial satellite remote sensing data according to the preprocessing method, and generate preprocessing satellite remote sensing data that meets a preset processing standard, the preset processing standard corresponding to the preprocessing method;

    Test the availability of the preprocessed satellite remote sensing data according to the preset test index;

    The preprocessed satellite remote sensing data passing the usability test is determined as the satellite remote sensing data.
20. The readable storage medium according to claim 15, wherein the evaluating the spectral data of the satellite remote sensing data according to the disaster damage level threshold to generate the disaster damage evaluation result of the designated area, comprising:

    acquiring satellite remote sensing data of the designated area, the satellite remote sensing data meeting preset resolution requirements;

    Process the satellite remote sensing data through a preset image recognition algorithm to generate the boundary information of the arable land in the designated area;

    extracting the spectral data of the cultivated land block from the border information of the cultivated land block;

    The spectral data of the cultivated land plot is evaluated according to the disaster damage level threshold, and the disaster damage evaluation result of the cultivated land plot is generated, and the disaster damage evaluation result of the designated area includes several disaster damage evaluation results of the cultivated land plot. .

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