CN113033994A - Agricultural dangerous case data evaluation method and device, computer equipment and storage medium - Google Patents
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Abstract
The invention relates to the field of data processing, in particular to an agricultural dangerous case data evaluation method, an agricultural dangerous case data evaluation device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring meteorological data and satellite remote sensing data of a designated area to generate a preliminary disaster damage assessment result; setting a plurality of sampling points in a designated area according to the preliminary disaster damage evaluation result; acquiring field investigation data of sampling points and aerial photography remote sensing data of a designated area and sending the field investigation data and the aerial photography remote sensing data to a designated terminal, and obtaining a sampling disaster damage evaluation result by the designated terminal according to the field investigation data and the aerial photography remote sensing data; acquiring sampling spectrum data corresponding to the sampling disaster evaluation result from the aerial photography remote sensing data, and determining a disaster grade threshold according to the sampling spectrum data and the sampling disaster evaluation result; and evaluating the spectral data of the satellite remote sensing data according to the disaster grade threshold value to generate a disaster evaluation result of the specified area. The invention reduces the difficulty of collecting the agricultural dangerous case data and reduces the cost of agricultural dangerous case exploration and damage assessment.
Description
Technical Field
The invention relates to the field of data processing, in particular to an agricultural dangerous case data evaluation method, an agricultural dangerous case data evaluation device, computer equipment and a storage medium.
Background
At present, agricultural dangerous case data needs to be collected when the agricultural insurance is investigated and damage is determined. Most of the agricultural dangerous case data is collected on site by the surveyor at the site of the insurance target. The location of the insurance target is generally distributed in a remote place, so that the agricultural dangerous case data is difficult to collect, high in cost and long in period, and cannot be timely damaged.
Disclosure of Invention
Therefore, it is necessary to provide an agricultural dangerous case data evaluation method, apparatus, computer device and storage medium for solving the problems of difficulty in collecting agricultural dangerous case data, high cost, long period and failure to determine damage in time.
An agricultural dangerous case data evaluation method comprises the following steps:
acquiring meteorological data and satellite remote sensing data of a designated area;
generating a preliminary disaster damage evaluation result according to the meteorological data and the satellite remote sensing data;
setting a plurality of sampling points in the designated area according to the preliminary disaster damage evaluation result;
acquiring field investigation data of the sampling point and aerial photography remote sensing data of the designated area and sending the field investigation data and the aerial photography remote sensing data to a designated terminal, so that the designated terminal performs disaster damage analysis according to the field investigation data and the aerial photography remote sensing data to obtain a sampling disaster damage evaluation result;
receiving the sampling disaster evaluation result sent by the designated terminal, acquiring sampling spectrum data corresponding to the sampling disaster evaluation result from the aerial remote sensing data, and determining a disaster grade threshold according to the sampling spectrum data and the sampling disaster evaluation result;
and evaluating the spectral data of the satellite remote sensing data according to the disaster grade threshold value to generate a disaster evaluation result of the specified area.
An agricultural hazardous situation data evaluation device, comprising:
the data module is used for acquiring meteorological data and satellite remote sensing data of a specified area;
the preliminary disaster damage evaluation result module is used for generating a preliminary disaster damage evaluation result according to the meteorological data and the satellite remote sensing data;
the sampling module is used for setting a plurality of sampling points in the specified area according to the preliminary disaster damage evaluation result;
the sampling evaluation result module is used for acquiring field survey data of the sampling points and aerial photography remote sensing data of the specified area and sending the field survey data and the aerial photography remote sensing data to a specified terminal, so that the specified terminal carries out disaster damage analysis according to the field survey data and the aerial photography remote sensing data to obtain a sampling disaster damage evaluation result;
the disaster damage level threshold module is used for receiving the sampling disaster damage evaluation result sent by the designated terminal, acquiring sampling spectrum data corresponding to the sampling disaster damage evaluation result from the aerial remote sensing data, and determining a disaster damage level threshold according to the sampling spectrum data and the sampling disaster damage evaluation result;
and the disaster damage evaluation result module is used for evaluating the spectral data of the satellite remote sensing data according to the disaster damage grade threshold value so as to generate a disaster damage evaluation result of the specified area.
A computer device comprising a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, the processor implementing the method of agricultural risk data assessment when executing the computer readable instructions.
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 method of agricultural risk data assessment as described above.
The agricultural dangerous case data evaluation method, the agricultural dangerous case data evaluation device, the computer equipment and the storage medium acquire meteorological data and satellite remote sensing data of a designated area; and generating a preliminary damage assessment result according to the meteorological data and the satellite remote sensing data, so that the distribution of a damage range and the damage degree can be rapidly preliminarily known, the exploration place can be clearly guided, and the labor cost and the time cost are saved. And setting a plurality of sampling points in the specified area according to the preliminary disaster damage assessment result, thereby reducing unnecessary investigation of investigation sites. Acquiring field investigation data of the sampling points and aerial photography remote sensing data of the designated area and sending the field investigation data and the aerial photography remote sensing data to a designated terminal, so that the designated terminal carries out disaster damage analysis according to the field investigation data and the aerial photography remote sensing data to obtain a sampling disaster damage evaluation result, an unmanned aerial vehicle can be used for carrying out reconnaissance on the bad terrain, the resolution ratio of the unmanned aerial vehicle is high, and a clearer picture of a disaster damage land block can be obtained. Receiving the sampling disaster evaluation result sent by the designated terminal, acquiring sampling spectrum data corresponding to the sampling disaster evaluation result from the aerial remote sensing data, determining a disaster grade threshold according to the sampling spectrum data and the sampling disaster evaluation result, and improving the accuracy of the disaster grade threshold; and evaluating the spectral data of the satellite remote sensing data according to the disaster grade threshold value to generate a disaster evaluation result of the designated area, and automatically dividing the threshold value according to the disaster grade to determine the disaster grade of the disaster block, thereby reducing labor and time costs. In conclusion, the agricultural dangerous case data collection difficulty is reduced, the agricultural dangerous case exploration and damage assessment cost is reduced, and the disaster damage assessment precision is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a schematic diagram of an application environment of the method for evaluating agricultural risk data according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of the method for evaluating agricultural risk data according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of satellite remote sensing data of different resolutions and the use of drones in an embodiment of the present invention;
FIG. 4 is a schematic view showing the distribution of growth of rice in accordance with one embodiment of the present invention;
FIG. 5 is a schematic diagram of phenological features corresponding to spectral information of rice at different periods according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a sample path and a detailed distribution of sample points for a field survey in accordance with an embodiment of the present invention;
fig. 7 is a schematic diagram illustrating a disaster damage level evaluation result at a parcel level according to an embodiment of the present invention;
FIG. 8 is a schematic view of an agricultural risk data evaluation device according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of a computer device according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The agricultural dangerous case data evaluation method provided by the embodiment can be applied to the application environment shown in fig. 1, wherein the client communicates with the server. The client includes, but is 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 a plurality of servers.
In an embodiment, as shown in fig. 2, an agricultural dangerous situation data evaluation method is provided, which is described by taking the application of the method to the server side in fig. 1 as an example, and includes the following steps:
and S10, acquiring meteorological data and satellite remote sensing data of the specified area.
Understandably, the designated area may be a large area range, such as North China plain; or a small regional area, such as a field area in an agricultural county.
Specifically, meteorological data and satellite remote sensing data of the designated area can be automatically acquired according to preset time. Meteorological data includes, but is not limited to, rainfall, sun exposure, temperature, and wind direction and wind speed for a specified area over a preset time. The satellite remote sensing data comprises satellite pictures and spectrum remote sensing information in the specified area. The satellite remote sensing data is from a satellite covering a specified area, and the satellite remote sensing data with different resolutions has different purposes. Fig. 3 shows satellite remote sensing data of different resolutions and the use of drones. Data sources refer to differently resolved data sources and phases refer to sampling periods. As shown in fig. 3, in an example, the data source GF-6 may acquire satellite remote sensing data with a resolution of 2 meters, the sampling time includes 23 days at 8 months and 27 days at 8 months, and the satellite remote sensing data is used for rice identification and flood assessment.
Optionally, in step S10, the acquiring meteorological data and satellite remote sensing data of the designated area includes:
s101, acquiring initial satellite remote sensing data of the appointed area at appointed time.
Specifically, a plurality of initial satellite remote sensing data with different resolutions and different periods in the designated area can be automatically acquired according to the preset time. The initial satellite remote sensing data can be an unpreprocessed satellite picture and unpreprocessed spectral remote sensing information. The method can acquire initial satellite remote sensing data before occurrence of disaster, before the disaster, in the middle of the disaster and at the later stage of the disaster. The initial satellite remote sensing data refers to satellite remote sensing data which is not preprocessed.
S102, processing the initial satellite remote sensing data according to the preprocessing method to generate preprocessed satellite remote sensing data meeting a preset processing standard, wherein the preset processing standard corresponds to the preprocessing method.
Specifically, the preprocessing method for preprocessing the initial satellite remote sensing data is preset, and the initial satellite remote sensing data is processed by the preprocessing method to generate preprocessed satellite remote sensing data meeting preset processing standards. The preset preprocessing method comprises but is not limited to geometric correction, orthorectification, atmospheric correction, cloud and mist removal and splicing fusion. The preprocessed satellite remote sensing data can be standardized by geometric correction, orthometric correction and atmospheric correction, so that the spectral information can be extracted conveniently. The cloud and fog removal mainly aims at removing noise because the crops cannot be seen clearly due to cloud and fog shielding in the remote sensing data. Splicing and fusion are carried out on the crops spliced into the designated area according to the fusion of the remote sensing data of other periods and the data of the current period. The preprocessed satellite remote sensing data comprise preprocessed satellite pictures and spectrum remote sensing information in a designated area. The preset treatment standard corresponds to the pretreatment method one to one, for example, the standard of the cloud and fog removing pretreatment method can be that crops can be clearly seen.
S103, checking the availability of the preprocessed satellite remote sensing data according to preset checking indexes.
Specifically, the preset inspection index may be cloud content, and the size of the cloud content may be preset as an availability inspection index for inspecting the preprocessed satellite remote sensing data. The image recognition can be carried out on the satellite picture in the designated area, the number of pixel points of the satellite picture is obtained, and the cloud content is judged according to the number of the pixel points. If the cloud content is higher than the preset cloud content threshold value, the fact that crops in the satellite picture area are covered by the cloud fog too much is indicated, and the preprocessed satellite remote sensing data are unavailable. If the cloud content is lower than or equal to the preset cloud content threshold value, the fact that crops in the satellite picture area in the period are less shielded by cloud fog is indicated, and the preprocessed satellite remote sensing data in the period are available.
And S104, determining the preprocessed satellite remote sensing data which passes the usability test as the satellite remote sensing data.
Specifically, preprocessed satellite remote sensing data passing availability inspection is determined as satellite remote sensing data, and the satellite remote sensing data comprises satellite pictures passing availability inspection in a designated area and other related information.
In steps S101 to S104, initial satellite remote sensing data of the designated area at a designated time is obtained, the initial satellite remote sensing data is processed according to the preprocessing method, and preprocessed satellite remote sensing data meeting a preset processing standard is generated, where the preset processing standard corresponds to the preprocessing method, so as to improve accuracy of the satellite remote sensing data. And checking the availability of the preprocessed satellite remote sensing data according to preset checking indexes, determining the preprocessed satellite remote sensing data passing the availability check as the satellite remote sensing data, and improving the availability of the satellite remote sensing data.
And S20, generating a preliminary disaster damage assessment result according to the meteorological data and the satellite remote sensing data.
Specifically, weather disaster preliminary evaluation information is generated according to weather data and administrative division information of a designated area. In one example, the weather disaster preliminary assessment information includes that the flood rating of XX county XX area is medium. And generating remote sensing disaster situation preliminary evaluation information according to the satellite remote sensing data. In one example, the remote sensing disaster condition preliminary assessment information includes that the growth condition of the crops in the XX region of XX county is better. And generating crop distribution information according to the crop spectral information and the satellite remote sensing data. In one example, the crop distribution information includes: XX1 county, rice, 2000 acres; XX2 county, rice, 1800 acres; … … are provided. And finally, generating a preliminary disaster damage evaluation result according to the meteorological disaster preliminary evaluation information, the remote sensing disaster preliminary evaluation information and the crop distribution information.
Optionally, in step S20, the generating a preliminary damage assessment result according to the meteorological data and the satellite remote sensing data includes:
s201, acquiring disaster data of a plurality of designated disaster factors from meteorological data according to preset dimensionality, dividing the disaster data according to administrative division information of designated areas, evaluating the divided disaster data, and generating meteorological disaster preliminary evaluation information, wherein the meteorological disaster preliminary evaluation information comprises preliminary disaster damage degree and preliminary disaster damage range of each administrative area.
Specifically, the disaster data of a plurality of designated disaster factors can be acquired from the meteorological data according to the preset dimensionality, the preliminary disaster degree and the preliminary disaster range are preliminarily determined according to the disaster data and the administrative region information, and the meteorological disaster preliminary evaluation information is generated. In one example, disaster data can be represented as:
TABLE 1 disaster data for a number of specified damage factors
| Disaster data | Amount of rainfall | Temperature of |
| Number of days of |
10 days | 15 days |
| Cumulative value | 200mm | |
| Extremely high value | 30mm | 28 degree |
Understandably, administrative division information of the designated area can be acquired through a GPS positioning system or other ways. The specified damage factors include, but are not limited to, rainfall, insolation, temperature, and wind direction and speed. The predetermined dimensions include, but are not limited to, number of days sustained, cumulative value, cumulative distance flat, extreme high value, extreme low value, average value. For example, the rainfall can be counted by 6 dimensions of continuous day number, cumulative value, cumulative distance average, extreme high value, extreme low value and average value. The accumulated pitch refers to the accumulation of pitch, which is used to represent the difference between the rainfall in a certain period (e.g. one day) and the average rainfall in a long-term period (e.g. one year). The weather disaster condition preliminary evaluation information includes a preliminary disaster damage degree and a preliminary disaster damage range.
Specifically, a designated weather element is screened from meteorological data to serve as a disaster damage factor for judging the disaster damage degree of crops, and disaster data of the disaster damage factor is selected from different dimensions according to a preset dimension. The regional range where the damage is distributed is matched in the meteorological data, and local agricultural experts need to be consulted to obtain the standards of the local crop flood or drought due to different sensitivities of crops in different regions to the disaster. And defining a grade threshold value of the disaster damage degree according to the standard, and dividing flood or drought into 6 grades including but not limited to non-disaster, mild and moderate disaster, moderate and severe disaster. And further evaluating the disaster damage degree of the designated area according to the disaster data with different dimensionalities of the disaster damage factor and the grade threshold of the disaster damage degree. And obtaining the level of the disaster damage degree of the designated area, and obtaining the preliminary evaluation information of the crops from the meteorological viewpoint.
Optionally, the preliminary evaluation of the damage level may also be performed in combination with local geographical conditions, due to the different susceptibility to disasters in different areas.
S202, acquiring spectral data of crops in the designated area from satellite remote sensing data, and evaluating the growth condition of the crops according to the spectral data to generate remote sensing disaster condition preliminary evaluation information; the remote sensing disaster condition preliminary evaluation information comprises the growth condition distribution range of crops.
Understandably, spectrum data of crops in a designated area are obtained from the satellite remote sensing data, and the growth condition of the crops is preliminarily evaluated according to the spectrum data to generate the preliminary evaluation information of the remote sensing disaster. The spectral data needs to be calculated through a normalized vegetation index (NDVI), and the NDVI can well reflect the intensity of vegetation information and is an important index for monitoring the vegetation growth condition. The normalized vegetation index (NDVI) may be calculated from spectral data of the crop, the spectral data including the reflectivity of the crop in both the near infrared and red wavelength bands. The calculation formula of the normalized vegetation index is as follows: NDVI ═ (NIR-R)/(NIR + R), where NIR is the near infrared band and R is the red band. The remote sensing disaster condition preliminary evaluation information comprises the growth condition distribution range of crops. The growth conditions of crops can be divided into 5 dimensions of good, normal, poor and poor. In one example, as shown in fig. 4, the growth status distribution diagram of rice in guanghan city, Sichuan province, 30 days at 6 months is shown, wherein the growth status is good, normal, poor, and the difference is 4.5%, 16.26%, 67.76%, 7.12%, and 4.36%, respectively.
S203, acquiring spectral information of crops in the specified area from the satellite remote sensing data, determining the types of the crops according to the spectral information, and generating crop distribution information.
Understandably, the crop spectrum information shows the phenological phenomena with different characteristics on the spectrum of the crop in different growth periods of germination, leaf expansion, flowering, leaf discoloration, leaf falling and the like. Through operations of marking inspection, investigation, claim settlement and the like, the spectral information of different phenological periods of each crop is gradually precipitated, and a crop spectral information base of rice, corn, wheat and the like is established for automatically and quickly identifying crops in a disaster area. The crop spectral information is the color of the crop expressed in the satellite remote sensing data in different growth periods.
Specifically, spectral information of crops in the designated area is obtained from the satellite remote sensing data, crop spectral information is obtained from the crop spectral information base, the spectral information of the crops in the designated area is automatically identified and compared with the crop spectral information in the crop spectral information base to obtain the types of the crops, and then the distribution range of the crops is generated on the basis of the growth condition distribution range according to the types of the crops.
And S204, combining the meteorological disaster condition preliminary evaluation information, the remote sensing disaster condition preliminary evaluation information and the crop distribution information to generate a preliminary disaster damage evaluation result.
Specifically, a preliminary disaster damage evaluation result is generated according to the preliminary disaster damage degree and the preliminary disaster damage range of the meteorological disaster preliminary evaluation information, the growth condition distribution range of the remote sensing disaster preliminary evaluation information, and the crop type information and the distribution range of the crop distribution information.
Understandably, the preliminary damage evaluation result includes a preliminary damage degree, a preliminary damage range, a growth condition distribution range, crop type information and a distribution range.
In steps S201 to S204, disaster data of a plurality of specified disaster factors are obtained from meteorological data according to preset dimensions, the disaster data are divided according to administrative division information of the specified areas, and are evaluated to generate meteorological disaster preliminary evaluation information, where the meteorological disaster preliminary evaluation information includes preliminary disaster degree and preliminary disaster range of each administrative area, and the preliminary disaster degree and preliminary disaster range can be automatically and quickly obtained from a meteorological perspective, thereby saving time and labor cost. Acquiring spectral data of crops in the designated area from satellite remote sensing data, and evaluating the growth condition of the crops according to the spectral data to generate remote sensing disaster condition preliminary evaluation information; the remote sensing disaster condition preliminary assessment information contains the growth condition distribution range of crops, and the growth condition distribution range can be automatically and rapidly obtained from the satellite remote sensing angle, so that the time and the labor cost are saved. Acquiring spectral information of crops in the specified area from satellite remote sensing data, determining the types of the crops according to the spectral information, and generating crop distribution information; and generating 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, so that the accuracy of the preliminary disaster damage assessment result is improved.
Optionally, in step S203, the obtaining spectral information of crops in the designated area from the satellite remote sensing data, determining the type of the crops according to the spectral information, and generating crop distribution information includes:
s2031, obtaining crop spectrum information and spectrum information in the satellite remote sensing data.
Understandably, the crop spectrum information shows the phenological phenomena with different characteristics on the spectrum of the crop in different growth periods of germination, leaf expansion, flowering, leaf discoloration, leaf falling and the like. Through operations of marking inspection, investigation, claim settlement and the like, the spectral information of different phenological periods of each crop is gradually precipitated, and a crop spectral information base of rice, corn, wheat and the like is established for automatically and quickly identifying crops in a disaster area. The spectral information is the color of the crop expressed in the satellite remote sensing data in different growth periods.
Specifically, spectral information of crops in a specified area is obtained from satellite remote sensing data, and spectral information of the crops is obtained from a crop spectral information base.
S2032, identifying spectral information in the satellite remote sensing data according to the crop spectral information to generate crop distribution information, wherein the crop distribution information comprises crop type information and a crop distribution range.
Specifically, aiming at the phenological characteristics of crops, the multi-temporal spectral information of the crops in the specified area in the satellite remote sensing data is compared and identified with the spectral information of different crops in different periods contained in the phenological characteristic library to determine the types of the crops, and then the distribution range of the crops is obtained on the basis of the distribution range of the growth conditions according to the types of the crops, so that the crop distribution information containing the type information and the distribution range of the crops is generated. The multi-temporal refers to the fusion of satellite remote sensing data at different moments. For example, at a first time, location a of the designated area has a cloud and location B has no cloud; at the second moment, the position B of the designated area has cloud, and the position A has no cloud. Through multi-temporal fusion, satellite remote sensing data of which the position A and the position B are not covered by cloud can be obtained.
The spectral information of crops has great difference in different periods. Taking rice as an example, in the water storage transplanting period of 6 months, the planting range of the rice is represented as a water body; at the early stage of the leaf separating period in the middle 7 th of the month, the rice canopy is not enough to cover the whole ground, and weak vegetation information appears on a remote sensing image; in the late stage of the leaf separating period in late 7 th month, the rice grows vigorously and shows dark green spectral information; in the jointing stage of 8 months, rice shows obvious bright green spectrum information. The crop type information can be identified according to the spectral changes in different periods. FIG. 5 is a characteristic diagram of phenology corresponding to spectral information of rice at different periods. In one example, as shown in fig. 5, the data source GF-1 may collect rice spectral information with a resolution of 2 meters, and the sampling time is 6 months and 17 days, and the phenological features of the rice at the time of the spectral information are water bodies and are in the stage of water storage and transplantation.
In steps S2031 to S2032, spectral information of crops and spectral information in the satellite remote sensing data are acquired, and the spectral information in the satellite remote sensing data is identified according to the spectral information of the crops to generate crop distribution information, where the crop distribution information includes crop type information and a distribution range. The method can automatically and quickly obtain the variety information and the distribution range of crops, clearly guide the exploration land, and save labor cost and time cost.
Optionally, in step S220, the spectral data of the crops in the designated area is obtained from the satellite remote sensing data, and preliminary evaluation is performed on the damage of the growth condition of the crops according to the spectral data to generate preliminary evaluation information of the remote sensing disaster; the remote sensing disaster condition preliminary assessment information contains the growth condition distribution range of crops, and comprises the following steps:
s2021, obtaining spectral data in the satellite remote sensing data, wherein the spectral data comprise a near infrared band and a red light band.
Specifically, spectral data of crops in different wave bands can be obtained from satellite remote sensing data. The spectral data includes a near infrared band and a red light band.
S2022, obtaining the reflectivity of the near infrared band and the reflectivity of the red light band of the crops in the designated area, and processing the reflectivity of the near infrared band and the reflectivity of the red light band through a normalized vegetation index calculation formula to obtain a normalized vegetation index of the designated area.
Understandably, the normalized vegetation index (NDVI) can well reflect the intensity of vegetation information, and is an important index for monitoring the vegetation growth condition, the normalized vegetation index (NDVI) can be obtained by calculating spectral data of crops, and the calculation formula of the normalized vegetation index is as follows: NDVI ═ (NIR-R)/(NIR + R), where NIR is the near infrared band and R is the red band.
Specifically, according to the reflectivity of the near-infrared band and the reflectivity of the red-light band of the crops in the specified area obtained from the satellite remote sensing data, through a calculation formula, the NDVI (NIR-R)/(NIR + R), the reflectivity of the near-infrared band and the reflectivity of the red-light band are processed, and the normalized vegetation index of the crops in the specified area can be obtained.
S2023, determining the growth condition of the crops according to the index range of the normalized vegetation index, and generating remote sensing disaster condition preliminary evaluation information based on the growth condition of the crops, wherein the remote sensing disaster condition preliminary evaluation information comprises the growth condition distribution range of the crops.
Specifically, the damage is preliminarily evaluated according to the index range of the normalized vegetation index (NDVI). And qualitatively counting the growth conditions of the crops in the designated area by 5 dimensions of good, normal, poor and poor to determine the growth conditions of the crops and generate the remote sensing disaster condition preliminary evaluation information comprising the distribution range of the growth conditions. Understandably, the index range of the normalized vegetation index (NDVI) can be expressed as-1 < ═ NDVI < ═ 1, negative values indicate that the ground coverage is cloud, water, snow, etc., highly reflective to visible light; 0 represents rock or bare earth, etc., and NIR and R are approximately equal; positive values indicate vegetation coverage, and NDVI values increase with increasing coverage.
In steps S2021 to S2023, acquiring spectral data in the satellite remote sensing data, where the spectral data includes a near infrared band and a red light band; acquiring the reflectivity of a near infrared band and the reflectivity of a red light band of crops in the designated area, and processing the reflectivity of the near infrared band and the reflectivity of the red light band through a normalized vegetation index calculation formula to obtain a normalized vegetation index of the designated area; the method comprises the steps of determining the growth condition of crops according to the index range of the normalized vegetation index, generating remote sensing disaster condition preliminary evaluation information based on the growth condition of the crops, wherein the remote sensing disaster condition preliminary evaluation information comprises the growth condition distribution range of the crops, rapidly obtaining the growth condition distribution range of the crops in the designated area, and saving labor cost and time cost.
And S30, setting a plurality of sampling points in the specified area according to the preliminary disaster damage assessment result.
Specifically, after the preliminary damage evaluation result is obtained, a plurality of sampling points may be set according to the preliminary damage degree, the preliminary damage range, the growth condition distribution range, the crop type information, and the distribution range. At least one sampling point is arranged on each preliminary disaster damage degree, each growth situation and each crop type. And carrying out field shooting investigation on the crops at the sampling points according to the disaster damage level to obtain field investigation data.
S40, acquiring field investigation data of the sampling points and aerial photography remote sensing data of the designated area and sending the field investigation data and the aerial photography remote sensing data to a designated terminal, so that the designated terminal performs disaster damage analysis according to the field investigation data and the aerial photography remote sensing data to obtain a sampling disaster damage evaluation result.
It is understood that field survey data refers to data that is surveyed on-site by a survey crew to a sampling site. The field survey data includes photographs, time of day, location (including latitude and longitude), person taken, and field records. In one example, there are 224 sampling points, where the field survey data for rice includes: 81 points of non-disaster, 21 points of mild disaster, 18 points of moderate disaster, 30 points of severe disaster and 13 points of dead halt; and 61 points of other crops. In one example, as shown in fig. 6, when the surveying staff performs the on-site survey of sampling points of rice non-disaster points, mild disaster points, moderate disaster points, severe disaster points, dead spots, and other crop points, the sampling route and the specific distribution of the sampling points of the on-site survey are obtained.
The aerial photography remote sensing data may refer to data obtained by aerial photography by an unmanned aerial vehicle. An air route of the unmanned aerial vehicle is planned on the mobile terminal APP aerial view unmanned aerial vehicle system, so that the unmanned aerial vehicle carries out unmanned aerial vehicle aerial photography on crops in the preliminary disaster damage range along the set air route, and aerial photography remote sensing data containing aerial photography pictures and other related information can be obtained. Can carry out multiresolution, many times's unmanned aerial vehicle aerial photography as required.
The designated terminal may refer to a computer device used by an agricultural specialist. After the designated terminal receives the field investigation data and the aerial photography remote sensing data, an agricultural expert can check the field investigation data and the aerial photography remote sensing data through the designated terminal and provide a sampling disaster damage evaluation result. The sampling damage evaluation result comprises the damage grade of the crops. The disaster grade includes but is not limited to 6 grades of no disaster, light and medium disaster, medium and medium disaster, and heavy disaster. Wherein, unmanned aerial vehicle remote sensing data of taking photo by plane contains the picture of taking photo by plane.
Optionally, in step S40, the aerial remote sensing data includes a panoramic image; the acquiring of the field investigation data of the sampling point and the acquiring of the aerial photography remote sensing data of the designated area comprise:
s401, collecting a plurality of aerial pictures according to a preset air route through an unmanned aerial vehicle.
Specifically, the preset air line may refer to an air line of the unmanned aerial vehicle planned on the mobile terminal APP bird's-eye unmanned aerial vehicle system. The unmanned aerial vehicle carries out unmanned aerial vehicle aerial photography to crops in the preliminary disaster damage range along a preset air route, and a plurality of aerial photography live-action pictures are collected. Wherein, can carry out multiresolution, unmanned aerial vehicle aerial photography many times as required.
S402, generating the panoramic image according to the plurality of aerial pictures.
Specifically, the aerial photography live-action pictures of all designated areas are automatically obtained from the aerial photography remote sensing data of the unmanned aerial vehicle, and the aerial photography live-action pictures are automatically spliced into the disaster damage panorama through the technologies of motion structure inference, feature extraction, feature matching, point cloud generation, poisson plane reconstruction and multi-view stereoscopic vision.
Optionally, for the problem of too low stitching speed of the aerial image, a deep structure-from-motion (deep motion recovery structure) algorithm may be introduced, and through GPU parallel operation, the operation efficiency of motion structure inference is significantly improved, so that the stitching speed of the image is improved by 50%.
In steps S401 to S402, the unmanned aerial vehicle acquires a plurality of aerial pictures according to a preset route, and generates the panoramic image according to the plurality of aerial pictures, so that the aerial pictures with high resolution can be obtained, and the panoramic image is automatically generated, thereby increasing the processing speed of the pictures.
S50, receiving the sampling disaster evaluation result sent by the designated terminal, acquiring sampling spectrum data corresponding to the sampling disaster evaluation result from the aerial photography remote sensing data, and determining a disaster grade threshold according to the sampling spectrum data and the sampling disaster evaluation result.
Understandably, because the same disaster damage level is provided with a plurality of sampling points, a plurality of sampling disaster damage evaluation results can be obtained by the same disaster damage level. And acquiring sampling spectrum data of crops with the same damage grade from the aerial remote sensing data, and calculating a plurality of normalized vegetation indexes under the damage grade according to the sampling spectrum data of the crops with the same damage grade. And determining the mean value and the maximum value of the disaster grade according to the plurality of normalized vegetation indexes under the disaster grade, and selecting the mean value and the maximum value to set a threshold value or an interval threshold value of the disaster grade, namely the threshold value of the disaster grade.
And S60, evaluating the spectral data of the satellite remote sensing data according to the disaster level threshold value to generate a disaster evaluation result of the specified area.
Understandably, the designated area is composed of several plots. And automatically identifying the boundaries of all the plots in the designated area by AI image identification and deep learning technology. The same plot may have multiple spectral data, i.e., corresponding to multiple normalized vegetation indices. And acquiring all spectral data from the satellite remote sensing data, and calculating all normalized vegetation indexes in the specified area according to all spectral data.
Further, all the normalized vegetation index values are counted in each plot to obtain the normalized vegetation index mean value of each plot. And further, evaluating the normalized vegetation index mean value of each land block in the designated area according to the threshold value of the disaster damage level to obtain the disaster damage level evaluation result of each land block in the designated area. For example, if the normalized mean vegetation index (NDVI) of a certain region in the designated area is greater than the threshold of the severe disaster damage level, the region disaster damage level is evaluated as severe disaster damage.
Optionally, in step S60, the evaluating the spectral data of the satellite remote sensing data according to the damage level threshold to generate a damage evaluation result of the specified area includes:
s601, satellite remote sensing data of the designated area are obtained, and the satellite remote sensing data meet the requirement of preset resolution.
Understandably, the satellite telemetry data comprises telemetry data of a plurality of satellites at different times and different resolutions. Specifically, the resolution of the satellite remote sensing data is selected according to actual requirements. For example, when the parcel boundary is identified by AI, high definition data with 0.5 m resolution is needed; if data with 10 meters resolution is used, the parcel boundaries cannot meet the accuracy requirements.
The resolution ratio of unmanned aerial vehicle aerial photography is higher than the resolution ratio of satellite remote sensing. In key areas, unmanned aerial vehicle remote sensing data with high resolution can be selected for use.
And S602, processing the satellite remote sensing data through a preset image recognition algorithm to generate cultivated land plot boundary information of the designated area.
Specifically, the preset image recognition algorithm includes an AI image recognition algorithm and a deep learning algorithm. For example, the boundary of each plot in the designated area is automatically identified through an AI image identification algorithm and a deep learning algorithm, so as to generate the boundary information of the cultivated land plots. The area of each cultivated land block can be calculated according to the boundary information. And combining the disaster damage grade and area of the cultivated land plot to obtain the disaster damage area. And a disaster damage visual report can be made according to the disaster damage area and the disaster damage grade evaluation result. In one example, as shown in fig. 7, the evaluation result is a map of the disaster level of a certain village of land blocks, wherein different disaster levels are used for different color standards, and the area of each land block is marked.
S603, extracting the spectrum data of the farmland plot from the farmland plot boundary information.
Specifically, spectral data of each cultivated land block in the designated area is acquired according to the boundary information of the cultivated land blocks.
S604, evaluating the spectral data of the cultivated land blocks according to the disaster level threshold value, and generating disaster evaluation results of the cultivated land blocks, wherein the disaster evaluation results of the designated area comprise the disaster evaluation results of a plurality of cultivated land blocks.
Specifically, the normalized vegetation index (NDVI) of each cultivated land block in the designated area is calculated according to the spectral data of each cultivated land block in the designated area. Further, all normalized vegetation index (NDVI) values are counted into each plot to obtain a normalized vegetation index (NDVI) mean value of each plot in the designated area. And further, evaluating the spectral data of each cultivated land block in the designated area according to the threshold value of the disaster damage grade to obtain the disaster damage grade evaluation result of each land block in the designated area. For example, if the normalized mean vegetation index (NDVI) of a certain region in the designated area is greater than the threshold of the severe disaster damage level, the region disaster damage level is evaluated as severe disaster damage.
In steps S601 to S604, satellite remote sensing data of the designated area is acquired, the satellite remote sensing data meets a preset resolution requirement, the satellite remote sensing data is processed through a preset image recognition algorithm, cultivated land block boundary information of the designated area is generated, spectral data of a cultivated land block is extracted from the cultivated land block boundary information, the spectral data of the cultivated land block is evaluated according to the damage level threshold, a damage evaluation result of the cultivated land block is generated, the damage evaluation result of the designated area includes a plurality of damage evaluation results of the cultivated land block, the damage level of the damaged land block can be determined automatically according to the damage level division threshold, and labor and time costs are reduced.
In summary, in steps S10-S60, the embodiment obtains meteorological data and satellite remote sensing data of a designated area; and generating a preliminary damage assessment result according to the meteorological data and the satellite remote sensing data, so that the distribution of a damage range and the damage degree can be rapidly preliminarily known, the exploration place can be clearly guided, and the labor cost and the time cost are saved. And setting a plurality of sampling points in the specified area according to the preliminary disaster damage assessment result, thereby reducing unnecessary investigation of investigation sites. Acquiring field investigation data of the sampling points and aerial photography remote sensing data of the designated area and sending the field investigation data and the aerial photography remote sensing data to a designated terminal, so that the designated terminal carries out disaster damage analysis according to the field investigation data and the aerial photography remote sensing data to obtain a sampling disaster damage evaluation result, an unmanned aerial vehicle can be used for carrying out reconnaissance on the bad terrain, the resolution ratio of the unmanned aerial vehicle is high, and a clearer picture of a disaster damage land block can be obtained. Receiving the sampling disaster evaluation result sent by the designated terminal, acquiring sampling spectrum data corresponding to the sampling disaster evaluation result from the aerial remote sensing data, determining a disaster grade threshold according to the sampling spectrum data and the sampling disaster evaluation result, and improving the accuracy of the disaster grade threshold; and evaluating the spectral data of the satellite remote sensing data according to the disaster grade threshold value to generate a disaster evaluation result of the designated area, and automatically dividing the threshold value according to the disaster grade to determine the disaster grade of the disaster block, thereby reducing labor and time costs. In conclusion, the agricultural dangerous case data collection difficulty is reduced, the agricultural dangerous case exploration and damage assessment cost is reduced, and the disaster damage assessment precision is improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
In one embodiment, an agricultural dangerous case data evaluation device is provided, and the agricultural dangerous case data evaluation device corresponds to the agricultural dangerous case data evaluation method in the embodiment one to one. As shown in fig. 8, the agricultural dangerous case data evaluation device includes a data module 10, a preliminary evaluation module 20, a sampling point determining module 30, a sampling evaluation module 40, a damage level threshold determining module 50, and a damage evaluation result module 60. The functional modules are explained in detail as follows:
the data module 10 is used for acquiring meteorological data and satellite remote sensing data of a specified area;
the preliminary evaluation module 20 is configured to generate a preliminary disaster damage evaluation result according to the meteorological data and the satellite remote sensing data;
a sampling point determining module 30, configured to set a plurality of sampling points in the designated area according to the preliminary disaster damage assessment result;
the sampling evaluation module 40 is configured to acquire field survey data of the sampling point and aerial photography remote sensing data of the designated area and send the field survey data and the aerial photography remote sensing data to a designated terminal, so that the designated terminal performs disaster damage analysis according to the field survey data and the aerial photography remote sensing data to obtain a sampling disaster damage evaluation result;
a disaster level threshold determining module 50, configured to receive the sampling disaster evaluation result sent by the designated terminal, obtain sampling spectrum data corresponding to the sampling disaster evaluation result from the aerial remote sensing data, and determine a disaster level threshold according to the sampling spectrum data and the sampling disaster evaluation result;
and a damage evaluation result module 60, configured to evaluate the spectral data of the satellite remote sensing data according to the damage level threshold, so as to generate a damage evaluation result of the specified area.
Optionally, in the sampling evaluation module 40, the aerial remote sensing data includes a panoramic image; the acquiring the field investigation data of the sampling point and the aerial photography remote sensing data of the specified area and sending the data to the specified terminal comprises the following steps:
the aerial photo unit is used for acquiring a plurality of aerial photos according to a preset air route by the unmanned aerial vehicle;
and the panoramic image unit is used for generating the panoramic image according to the plurality of aerial pictures.
Optionally, in the preliminary evaluation module 20, the generating a preliminary damage evaluation result according to the meteorological data and the satellite remote sensing data includes:
the system comprises a meteorological data unit 201, a disaster data processing unit and a disaster data processing unit, wherein the meteorological data unit 201 is used for acquiring disaster data of a plurality of specified disaster factors from meteorological data according to preset dimensionality, dividing the disaster data according to administrative division information of specified areas, evaluating the divided disaster data and generating meteorological disaster preliminary evaluation information, and the meteorological disaster preliminary evaluation information comprises preliminary disaster damage degree and preliminary disaster damage range of each administrative area;
the satellite remote sensing data unit 202 is used for acquiring spectral data of crops in the specified area from the satellite remote sensing data, evaluating the growth condition of the crops according to the spectral data and generating remote sensing disaster condition preliminary evaluation information; the remote sensing disaster condition preliminary evaluation information comprises the growth condition distribution range of crops;
a crop distribution information unit 203, configured to obtain spectral information of crops in the specified area from satellite remote sensing data, determine the type of the crops according to the spectral information, and generate crop distribution information;
and the preliminary disaster damage evaluation result unit 204 is used for generating a preliminary disaster damage evaluation result by combining the meteorological disaster preliminary evaluation information, the remote sensing disaster preliminary evaluation information and the crop distribution information.
Optionally, in the crop distribution information unit 203, the obtaining spectral information of crops in the designated area from the satellite remote sensing data, determining the type of the crops according to the spectral information, and generating crop distribution information includes:
the spectral information unit is used for acquiring spectral information of crops and spectral information in the satellite remote sensing data;
and the spectral information identification unit is used for identifying spectral information in the satellite remote sensing data according to the crop spectral information to generate crop distribution information, and the crop distribution information comprises crop type information and a crop distribution range.
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 preliminary evaluation is performed on the growth situation damage of the crops according to the spectral data to generate preliminary evaluation information of the remote sensing disaster; the remote sensing disaster condition preliminary assessment information contains the growth condition distribution range of crops, and comprises the following steps:
the first spectrum data acquisition unit is used for acquiring spectrum data in the satellite remote sensing data, and the spectrum data comprises a near infrared band and a red light band;
the normalized vegetation index unit is used for acquiring the reflectivity of a near infrared band and the reflectivity of a red light band of crops in the designated area, and processing the reflectivity of the near infrared band and the reflectivity of the red light band through a normalized vegetation index calculation formula to obtain a normalized vegetation index of the designated area;
and the remote sensing disaster situation unit is used for determining the growth situation of the crops according to the index range where the normalized vegetation index is located, and generating remote sensing disaster situation preliminary evaluation information based on the growth situation of the crops, wherein the remote sensing disaster situation preliminary evaluation information comprises the growth situation distribution range of the crops.
Optionally, in the data module 10, the acquiring meteorological data and satellite remote sensing data of the designated area includes:
the initial satellite remote sensing data unit is used for acquiring initial satellite remote sensing data of the specified area at specified time;
the preprocessing satellite remote sensing data unit is used for processing the initial satellite remote sensing data according to the preprocessing method to generate preprocessing satellite remote sensing data meeting a preset processing standard, and the preset processing standard corresponds to the preprocessing method;
the inspection unit is used for inspecting the availability of the preprocessed satellite remote sensing data according to a preset inspection index;
and the satellite remote sensing data unit is used for determining the preprocessed satellite remote sensing data passing the usability test as the satellite remote sensing data.
Optionally, in the damage evaluation result module 60, the evaluating the spectral data of the satellite remote sensing data according to the damage level threshold to generate a damage evaluation result of the specified area includes:
the resolution unit is used for acquiring satellite remote sensing data of the specified area, and the satellite remote sensing data meets the requirement of preset resolution;
the boundary information unit is used for processing the satellite remote sensing data through a preset image recognition algorithm to generate cultivated land plot boundary information of the designated area;
the second spectrum data acquisition unit is used for extracting the spectrum data of the cultivated land block from the boundary information of the cultivated land block;
and the disaster evaluation result unit is used for evaluating the spectral data of the cultivated land blocks according to the disaster grade threshold value to generate disaster evaluation results of the cultivated land blocks, and the disaster evaluation results of the designated area comprise a plurality of disaster evaluation results of the cultivated land blocks.
For the specific limitations of the agricultural risk data evaluation device, reference may be made to the above limitations of the agricultural risk data evaluation method, which are not described herein again. The modules in the agricultural dangerous case data evaluation device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 9. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a readable storage medium and 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 operating system and execution of computer-readable instructions in the readable storage medium. The database of the computer equipment is used for storing data related to the agricultural dangerous case data evaluation method. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer readable instructions, when executed by a processor, implement a method for agricultural risk data assessment. The readable storage media provided by the present embodiment include nonvolatile readable storage media and volatile readable storage media.
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, the processor when executing the computer readable instructions implementing the steps of:
acquiring meteorological data and satellite remote sensing data of a designated area;
generating a preliminary disaster damage evaluation result according to the meteorological data and the satellite remote sensing data;
setting a plurality of sampling points in the designated area according to the preliminary disaster damage evaluation result;
acquiring field investigation data of the sampling point and aerial photography remote sensing data of the designated area and sending the field investigation data and the aerial photography remote sensing data to a designated terminal, so that the designated terminal performs disaster damage analysis according to the field investigation data and the aerial photography remote sensing data to obtain a sampling disaster damage evaluation result;
receiving the sampling disaster evaluation result sent by the designated terminal, acquiring sampling spectrum data corresponding to the sampling disaster evaluation result from the aerial remote sensing data, and determining a disaster grade threshold according to the sampling spectrum data and the sampling disaster evaluation result;
and evaluating the spectral data of the satellite remote sensing data according to the disaster grade threshold value to generate a disaster evaluation result of the specified area.
In one embodiment, one or more computer-readable storage media storing computer-readable instructions are provided, the readable storage media provided by the embodiments including non-volatile readable storage media and volatile readable storage media. The readable storage medium has stored thereon computer readable instructions which, when executed by one or more processors, perform the steps of:
acquiring meteorological data and satellite remote sensing data of a designated area;
generating a preliminary disaster damage evaluation result according to the meteorological data and the satellite remote sensing data;
setting a plurality of sampling points in the designated area according to the preliminary disaster damage evaluation result;
acquiring field investigation data of the sampling point and aerial photography remote sensing data of the designated area and sending the field investigation data and the aerial photography remote sensing data to a designated terminal, so that the designated terminal performs disaster damage analysis according to the field investigation data and the aerial photography remote sensing data to obtain a sampling disaster damage evaluation result;
receiving the sampling disaster evaluation result sent by the designated terminal, acquiring sampling spectrum data corresponding to the sampling disaster evaluation result from the aerial remote sensing data, and determining a disaster grade threshold according to the sampling spectrum data and the sampling disaster evaluation result;
and evaluating the spectral data of the satellite remote sensing data according to the disaster grade threshold value to generate a disaster evaluation result of the specified area.
It will be understood by those of ordinary skill in the art that all or part of the processes of the methods of the above embodiments may be implemented by hardware related to computer readable instructions, which may be stored in a non-volatile readable storage medium or a volatile readable storage medium, and when executed, the computer readable instructions may include processes of the above embodiments of the methods. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. An agricultural dangerous case data evaluation method is characterized by comprising the following steps:
acquiring meteorological data and satellite remote sensing data of a designated area;
generating a preliminary disaster damage evaluation result according to the meteorological data and the satellite remote sensing data;
setting a plurality of sampling points in the designated area according to the preliminary disaster damage evaluation result;
acquiring field investigation data of the sampling point and aerial photography remote sensing data of the designated area and sending the field investigation data and the aerial photography remote sensing data to a designated terminal, so that the designated terminal performs disaster damage analysis according to the field investigation data and the aerial photography remote sensing data to obtain a sampling disaster damage evaluation result;
receiving the sampling disaster evaluation result sent by the designated terminal, acquiring sampling spectrum data corresponding to the sampling disaster evaluation result from the aerial remote sensing data, and determining a disaster grade threshold according to the sampling spectrum data and the sampling disaster evaluation result;
and evaluating the spectral data of the satellite remote sensing data according to the disaster grade threshold value to generate a disaster evaluation result of the specified area.
2. The method of assessing agricultural risk data of claim 1, wherein said aerial remote sensing data comprises panoramic images; the acquiring the field investigation data of the sampling point and the aerial photography remote sensing data of the specified area and sending the data to the specified terminal comprises the following steps:
collecting a plurality of aerial pictures according to a preset air route by an unmanned aerial vehicle;
and generating the panoramic image according to the plurality of aerial pictures.
3. The method for evaluating agricultural dangerous situation data according to claim 1, wherein the generating a preliminary damage evaluation result according to the meteorological data and the satellite remote sensing data comprises:
acquiring disaster data of a plurality of specified disaster factors from meteorological data according to preset dimensionality, dividing the disaster data according to administrative division information of specified areas, evaluating the divided disaster data, and generating meteorological disaster preliminary evaluation information, wherein the meteorological disaster preliminary evaluation information comprises preliminary disaster damage degree and preliminary disaster damage range of each administrative area;
acquiring spectral data of crops in the designated area from satellite remote sensing data, and evaluating the growth condition of the crops according to the spectral data to generate remote sensing disaster condition preliminary evaluation information; the remote sensing disaster condition preliminary evaluation information comprises the growth condition distribution range of crops;
acquiring spectral information of crops in the specified area from satellite remote sensing data, determining the types of the crops according to the spectral information, and generating crop distribution information;
and generating 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.
4. The method for evaluating agricultural dangerous situation data according to claim 3, wherein the step of obtaining spectral information of crops in the designated area from the satellite remote sensing data, determining the types of the crops according to the spectral information and generating crop distribution information comprises the steps of:
acquiring spectral information of crops and spectral information in the satellite remote sensing data;
and identifying spectral information in the satellite remote sensing data according to the crop spectral information to generate crop distribution information, wherein the crop distribution information comprises crop type information and a crop distribution range.
5. The agricultural dangerous case data evaluation method of claim 3, wherein the step of obtaining the spectral data of the crops in the designated area from the satellite remote sensing data, evaluating the growth conditions of the crops according to the spectral data, and generating the remote sensing disaster case preliminary evaluation information comprises the steps of:
acquiring spectral data in the satellite remote sensing data, wherein the spectral data comprises a near infrared band and a red light band;
acquiring the reflectivity of a near infrared band and the reflectivity of a red light band of crops in the designated area, and processing the reflectivity of the near infrared band and the reflectivity of the red light band through a normalized vegetation index calculation formula to obtain a normalized vegetation index of the designated area;
and determining the growth condition of the crops according to the index range of the normalized vegetation index, and generating remote sensing disaster condition preliminary evaluation information based on the growth condition of the crops, wherein the remote sensing disaster condition preliminary evaluation information comprises the growth condition distribution range of the crops.
6. The method for evaluating agricultural dangerous situation data according to claim 1, wherein the acquiring meteorological data and satellite remote sensing data of a designated area comprises:
acquiring initial satellite remote sensing data of the designated area at designated time;
processing the initial satellite remote sensing data according to the preprocessing method to generate preprocessed satellite remote sensing data meeting a preset processing standard, wherein the preset processing standard corresponds to the preprocessing method;
checking the availability of the preprocessed satellite remote sensing data according to a preset check index;
and determining the preprocessed satellite remote sensing data which passes the usability test as the satellite remote sensing data.
7. The method for evaluating agricultural dangerous situation data according to claim 1, wherein the evaluating the spectral data of the satellite remote sensing data according to the damage level threshold to generate the damage evaluation result of the designated area comprises:
acquiring satellite remote sensing data of the specified area, wherein the satellite remote sensing data meets the requirement of preset resolution;
processing the satellite remote sensing data through a preset image recognition algorithm to generate cultivated land plot boundary information of the designated area;
extracting the spectral data of the farmland plots from the farmland plot boundary information;
and evaluating the spectral data of the cultivated land blocks according to the disaster grade threshold value to generate disaster evaluation results of the cultivated land blocks, wherein the disaster evaluation results of the designated area comprise the disaster evaluation results of a plurality of cultivated land blocks.
8. An agricultural hazardous situation data evaluation device, characterized by comprising:
the data module is used for acquiring meteorological data and satellite remote sensing data of a specified area;
the preliminary disaster damage evaluation result module is used for generating a preliminary disaster damage evaluation result according to the meteorological data and the satellite remote sensing data;
the sampling module is used for setting a plurality of sampling points in the specified area according to the preliminary disaster damage evaluation result;
the sampling evaluation result module is used for acquiring field survey data of the sampling points and aerial photography remote sensing data of the specified area and sending the field survey data and the aerial photography remote sensing data to a specified terminal, so that the specified terminal carries out disaster damage analysis according to the field survey data and the aerial photography remote sensing data to obtain a sampling disaster damage evaluation result;
the disaster damage level threshold module is used for receiving the sampling disaster damage evaluation result sent by the designated terminal, acquiring sampling spectrum data corresponding to the sampling disaster damage evaluation result from the aerial remote sensing data, and determining a disaster damage level threshold according to the sampling spectrum data and the sampling disaster damage evaluation result;
and the disaster damage evaluation result module is used for evaluating the spectral data of the satellite remote sensing data according to the disaster damage grade threshold value so as to generate a disaster damage evaluation result of the specified area.
9. A computer device comprising a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, wherein the processor when executing the computer readable instructions implements the agricultural risk data assessment method according to any one of claims 1 to 7.
10. 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 method of agricultural risk data assessment of any one of claims 1 to 7.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110308541.0A CN113033994B (en) | 2021-03-23 | 2021-03-23 | Agricultural dangerous case data evaluation method, device, computer equipment and storage medium |
| PCT/CN2021/090311 WO2022198744A1 (en) | 2021-03-23 | 2021-04-27 | Agricultural danger data assessment method and apparatus, computer device, and storage medium |
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| CN202110308541.0A CN113033994B (en) | 2021-03-23 | 2021-03-23 | Agricultural dangerous case data evaluation method, device, computer equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113362192A (en) * | 2021-07-07 | 2021-09-07 | 中国工商银行股份有限公司 | Agricultural insurance underwriting method, system, equipment and storage medium |
| CN113744074A (en) * | 2021-09-06 | 2021-12-03 | 北京超图软件股份有限公司 | Method and device for determining disaster reduction and yield preservation measures of agricultural crops |
| CN117315492A (en) * | 2023-11-29 | 2023-12-29 | 中国平安财产保险股份有限公司 | Planting risk early warning method, system, equipment and medium based on unmanned aerial vehicle technology |
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| CN115346141B (en) * | 2022-10-19 | 2023-03-24 | 山东大学 | Integrated unfavorable geology identification method and system of space-air-ground-tunnel-hole |
| CN115797764B (en) * | 2022-11-18 | 2023-05-30 | 江苏星月测绘科技股份有限公司 | Remote sensing big data interpretation method and system applied to farmland non-agrochemical monitoring |
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| CN116453003B (en) * | 2023-06-14 | 2023-09-01 | 之江实验室 | Method and system for intelligently identifying rice growth vigor based on unmanned aerial vehicle monitoring |
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| CN107169018A (en) * | 2017-04-06 | 2017-09-15 | 河南云保遥感科技有限公司 | A kind of agricultural insurance is surveyed, loss assessment system and its implementation |
| CN108038566A (en) * | 2017-11-30 | 2018-05-15 | 河南云保遥感科技有限公司 | A kind of disaster monitoring damage identification method based on meteorology with Remote Sensing Data Fusion Algorithm |
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| CN113362192A (en) * | 2021-07-07 | 2021-09-07 | 中国工商银行股份有限公司 | Agricultural insurance underwriting method, system, equipment and storage medium |
| CN113744074A (en) * | 2021-09-06 | 2021-12-03 | 北京超图软件股份有限公司 | Method and device for determining disaster reduction and yield preservation measures of agricultural crops |
| CN117315492A (en) * | 2023-11-29 | 2023-12-29 | 中国平安财产保险股份有限公司 | Planting risk early warning method, system, equipment and medium based on unmanned aerial vehicle technology |
| CN117315492B (en) * | 2023-11-29 | 2024-04-02 | 中国平安财产保险股份有限公司 | Planting risk early warning method, system, equipment and medium based on unmanned aerial vehicle technology |
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