A method and system for detecting the loss of a target crop due to combined high temperature and drought disasters

Abstract

The present invention relates to the technical field of disaster loss detection, and particularly relates to a method and system for detecting the disaster loss of target crops under compound high-temperature and drought disasters. On the basis of training a machine learning model to obtain a crop yield prediction model using the target crop yield data, geographical coordinate data of several grids in the research area, and the target crop growth factor data of several dates of the grids, the target crop growth factor data corresponding to the ideal weather scenario and the compound high-temperature and drought scenario are constructed, and then the crop yield prediction model is driven respectively to obtain the target crop yield prediction data corresponding to the ideal weather scenario and the compound high-temperature and drought scenario. The obtained target crop yield prediction data corresponding to the ideal weather scenario and the compound high-temperature and drought scenario are subjected to difference processing to obtain the target crop compound high-temperature and drought disaster loss value, which reflects the yield loss of the target crops under the compound high-temperature and drought stress and provides a scientific basis for ensuring national food security.

Description

Technical Field

[0001] The present invention relates to the technical field of disaster loss detection, and particularly to a method, a system, a computer device and a storage medium for detecting the disaster loss of target crops caused by combined high temperature and drought disasters. Background Art

[0002] Temperature and rainfall are the two most critical meteorological elements affecting the growth of target crops. With the intensification of the global warming trend, meteorological disaster events formed by the combination of temperature and rainfall have become more serious and frequent. Among them, the combined high temperature and drought disaster is the most prominent, becoming one of the greatest risks threatening the production safety of future target crops (maize, rice and wheat).

[0003] However, the current academic research on combined meteorological disasters is still in its infancy. Most of the research focuses on evaluating the intensity and frequency of combined meteorological disasters, and there are few quantitative studies on the assessment of the disaster losses of target crops caused by combined meteorological disasters, thus restricting people from taking corresponding agricultural disaster reduction and prevention measures to face the increasingly serious combined high temperature and drought disasters. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide a method, a system, a computer device and a storage medium for detecting the disaster loss of target crops caused by combined high temperature and drought disasters, construct the data of target crop growth factors corresponding to the ideal weather scenario and the combined high temperature and drought scenario, combine the pre-trained machine learning model to predict the yield of target crops corresponding to the ideal weather scenario and the combined high temperature and drought scenario, and perform difference processing on the obtained yield prediction data of target crops corresponding to the ideal weather scenario and the combined high temperature and drought scenario to obtain the disaster loss value of target crops caused by combined high temperature and drought disasters, which reflects the yield loss value of target crops under the stress of combined high temperature and drought disasters, and provides a scientific basis for ensuring national food security.

[0005] In a first aspect, an embodiment of the present application provides a method for detecting the disaster loss of target crops caused by combined high temperature and drought disasters, including the following steps:

[0006] Obtain the yield data of target crops, geographical coordinate data of several grids in the research area, and the data of target crop growth factors of several dates of the grids;

[0007] Input the yield data of target crops, geographical coordinate data of several grids in the research area, and the data of target crop growth factors of several dates of the grids into the crop yield prediction model to be trained for training to obtain the target crop yield prediction model;

[0008] Obtain the geographical coordinate data of several grids in the target area, the ideal target crop growth factor data of these grids for several dates corresponding to the ideal weather scenario, and the stress target crop growth factor data of these grids for several dates corresponding to the compound high-temperature and drought scenario;

[0009] Input the geographical coordinate data of several grids in the target area and the ideal target crop growth factor data into the target crop yield prediction model to obtain the ideal target crop yield prediction data of several grids in the target area corresponding to the ideal weather scenario; input the geographical coordinate data of several grids in the target area and the stress target crop growth factor data into the target crop yield prediction model to obtain the stress target crop yield prediction data of several grids in the target area corresponding to the compound high-temperature and drought scenario;

[0010] Obtain the detection result of the target crop compound high-temperature and drought disaster loss in the target area according to the ideal target crop yield prediction data and the stress target crop yield prediction data.

[0011] In a second aspect, an embodiment of the present application provides a target crop compound high-temperature and drought disaster loss detection system, including:

[0012] A data acquisition module, configured to acquire the target crop yield data, geographical coordinate data of several grids in the research area, and the target crop growth factor data of these grids for several dates;

[0013] A model training module, configured to input the target crop yield data, geographical coordinate data of several grids in the research area, and the target crop growth factor data of these grids for several dates into the crop yield prediction model to be trained for training, and obtain the target crop yield prediction model;

[0014] A scenario construction module, configured to obtain the geographical coordinate data of several grids in the target area, the ideal target crop growth factor data of these grids for several dates corresponding to the ideal weather scenario, and the stress target crop growth factor data of these grids for several dates corresponding to the compound high-temperature and drought scenario;

[0015] The target crop yield prediction module is used to input the geographical coordinate data of several grids in the target area and the ideal target crop growth factor data into the target crop yield prediction model to obtain the ideal target crop yield prediction data corresponding to the ideal weather scenario for several grids in the target area; input the geographical coordinate data of several grids in the target area and the stress target crop growth factor data into the target crop yield prediction model to obtain the stress target crop yield prediction data corresponding to the compound high-temperature and drought scenario for several grids in the target area.

[0016] The target crop loss calculation module is used to obtain the detection result of the target crop compound high-temperature and drought disaster loss in the target area according to the ideal target crop yield prediction data and the stress target crop yield prediction data.

[0017] In a third aspect, an embodiment of the present application provides a computer device, including: a processor, a memory, and a computer program stored on the memory and executable on the processor; when the computer program is executed by the processor, the steps of the target crop compound high-temperature and drought disaster loss detection method as described in the first aspect are implemented.

[0018] In a fourth aspect, an embodiment of the present application provides a storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the target crop compound high-temperature and drought disaster loss detection method as described in the first aspect are implemented.

[0019] In an embodiment of the present application, a method, a system, a computer device, and a storage medium for detecting the loss of a target crop due to a compound high-temperature and drought disaster are provided. The growth factor data of the target crop corresponding to the ideal weather scenario and the compound high-temperature and drought scenario are constructed, and the target crop yield prediction corresponding to the ideal weather scenario and the compound high-temperature and drought scenario is performed in combination with a pre-trained machine learning model. The obtained target crop yield prediction data corresponding to the ideal weather scenario and the compound high-temperature and drought scenario are subjected to difference processing to obtain the target crop compound high-temperature and drought disaster loss value, which reflects the target crop yield loss value under the stress of the compound high-temperature and drought, and provides a scientific basis for ensuring national food security.

[0020] For better understanding and implementation, the present invention will be described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic flowchart of a method for detecting the loss of a target crop due to a compound high-temperature and drought disaster provided by an embodiment of the present application;

[0022] Figure 2Schematic flowchart of S1 in the method for detecting the loss of target crops due to combined high temperature and drought disasters provided by an embodiment of the present application;

[0023] Figure 3 Schematic flowchart of S3 in the method for detecting the loss of target crops due to combined high temperature and drought disasters provided by an embodiment of the present application;

[0024] Figure 4 Schematic flowchart of S31 in the method for detecting the loss of target crops due to combined high temperature and drought disasters provided by an embodiment of the present application;

[0025] Figure 5 Schematic flowchart of S3 in the method for detecting the loss of target crops due to combined high temperature and drought disasters provided by another embodiment of the present application;

[0026] Figure 6 Schematic flowchart of S5 in the method for detecting the loss of target crops due to combined high temperature and drought disasters provided by an embodiment of the present application;

[0027] Figure 7 Schematic structural diagram of the system for detecting the loss of target crops due to combined high temperature and drought disasters provided by an embodiment of the present application;

[0028] Figure 8 Schematic structural diagram of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0029] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0030] The terms used in the present application are for the purpose of describing specific embodiments and are not intended to limit the present application. The singular forms of "a", "the", and "said" used in the present application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0031] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to a determination".

[0032] The data sending end can be a computer device or a mobile terminal device, used to establish a network connection with the data receiving end, capable of encoding the data information sent to the data receiving end, and parsing the data information sent from the data receiving end.

[0033] The data receiving end can be a computer device or a mobile terminal device, used to establish a network connection with the data sending end, capable of encoding the data information sent to the data sending end, and parsing the data information sent from the data sending end.

[0034] Please refer to Figure 1 , Figure 1 which is a schematic flow chart of a method for detecting the loss of a target crop due to combined high temperature and drought disasters provided by an embodiment of this application. The method includes the following steps:

[0035] S1: Obtain the target crop yield data, geographical coordinate data of several grids in the research area, and the target crop growth factor data of several dates of these grids.

[0036] The execution entity of the method for detecting the loss of a target crop due to combined high temperature and drought disasters is the detection device of the method for detecting the loss of a target crop due to combined high temperature and drought disasters (hereinafter referred to as the detection device). In an optional embodiment, the detection device can be a computer device, which can be a server, or a server cluster composed of multiple computer devices.

[0037] In this embodiment, the detection device obtains the target crop yield data, geographical coordinate data of several grids in the research area, and the target crop growth factor data of several dates of these grids. Among them, the date is the date during the crop growth season of the target crop, and the crop growth season refers to the time period from the sowing (or transplanting) date to the maturity date of the target crop. The target crop growth factor data includes weather element data and soil property data. The weather element data includes daily average temperature, daily precipitation, daily solar radiation, daily wind speed, and daily relative humidity; the geographical coordinate data includes longitude coordinates and latitude coordinates.

[0038] Since there may be significant differences in the climate conditions and planting methods of different target crops in different regions, in order to ensure the credibility of the machine learning model, specifically, the detection device will use the city-level unit of the target crop as the regional scale for training and testing the machine learning model, and the 1km grid of the target crop within the city-level unit as the grid scale of the training and testing data.

[0039] The soil attribute data is the soil attribute data of 1km grid provided by the Harmonized World Soil Database (HWSD).

[0040] The weather element data consists of the daily average temperature, daily precipitation, daily solar radiation of 1km grid scale provided by the global meteorological dataset CHELSA-W5E5, and the daily wind speed and daily relative humidity after downscaling the 0.25° ERA5 global climate data to 1km. The weather element data changes with the date and different weather scenarios.

[0041] The geographic coordinate data comes from the centroid position (including longitude and latitude) of the 1km grid of the target crop in the crop growth season data. The crop growth season data comes from the key phenological dates of the target crop at the 1km grid scale provided by the published dataset of the growth periods of the three major target crops (ChinaCropPhen1km), including the sowing date (or transplanting date), heading date, and maturity date.

[0042] For the yield data of the target crop, please refer to Figure 2 , Figure 2 FIG. 0 is a schematic flowchart of S1 in the method for detecting the loss of compound high-temperature and drought disasters of target crops provided by an embodiment of the present application, including steps S11 to S12, specifically as follows:

[0043] S11: Obtain the yield data of the target crop at the regional scale of the research area, the total sum of the vegetation leaf area index of each grid in the research area, and the average value of the total sum of the vegetation leaf area index of several grids.

[0044] The leaf area index (LAI) of vegetation characterizes the growth status of vegetation, and the larger its value, the more lush the vegetation growth.

[0045] In this embodiment, the detection device obtains the regional-scale yield data of the target crop in the research area, the total vegetation leaf area index of each grid in the research area, and the average value of the total vegetation leaf area index of several grids. Specifically, the regional-scale yield data is obtained based on the municipal target crop yield numerical data from 1981 to 2015 provided by national and provincial statistical yearbooks. The total vegetation leaf area index of the grid is obtained by summing the daily values of the vegetation leaf area index in the remote sensing data MODIS GLASS during the growth season of the target crop.

[0046] S12: According to the regional-scale yield data of the target crop in the research area, the total vegetation leaf area index of each grid in the research area, the average value of the total vegetation leaf area index of several grids, and a preset target crop grid-scale yield calculation algorithm, obtain the target crop yield data of several grids in the research area.

[0047] The target crop grid-scale yield calculation algorithm is as follows:

[0048]

[0049] In the formula, is the target crop yield data at the grid scale, is the regional-scale yield data of the target crop in the research area. is the total vegetation leaf area index of a single grid, is the average value of the total vegetation leaf area index of several grids, and the is the sum of several grids of divided by the number of grids.

[0050] In this embodiment, the detection device obtains the target crop yield data of several grids in the research area according to the regional-scale yield data of the target crop in the research area, the total vegetation leaf area index of each grid in the research area, the average value of the total vegetation leaf area index of several grids, and a preset target crop grid-scale yield calculation algorithm.

[0051] S2: Input the target crop yield data of several grids in the research area, geographical coordinate data, and the target crop growth factor data of several dates of the grid into the crop yield prediction model to be trained for training to obtain the target crop yield prediction model.

[0052] In this embodiment, the detection device inputs the target crop yield data, geographical coordinate data of several grids in the research area, and the target crop growth factor data of several dates of the grids into the crop yield prediction model to be trained for training, and obtains the target crop yield prediction model. Among them, the crop yield prediction model is a model constructed by training a machine learning model with the weather element data, geographical coordinate data, and soil attribute data as independent variables and the target crop yield data as the dependent variable.

[0053] The average daily temperature and daily precipitation are the two most important factors affecting the growth of the target crop. In addition, the daily solar radiation, daily wind speed, and daily relative humidity are also meteorological elements affecting the growth of the target crop, and the soil properties and geographical locations at the time of planting each target crop will also affect the growth of the target crop. Specifically, the detection device further divides the target crop yield data, geographical coordinate data of several grids in the research area, and the target crop growth factor data of several dates of the grids into a training set and a test set according to a ratio of 7:3 for machine learning model training and testing. The machine learning model used is XGBoost, which has a better fitting loss function compared with the traditional gradient boosting decision tree algorithm, can reduce possible errors, and optimizes the simulation performance of machine learning.

[0054] S3: Obtain the geographical coordinate data of several grids in the target area, the ideal target crop growth factor data of several dates corresponding to the ideal weather scenario of the grids, and the stress target crop growth factor data of several dates corresponding to the composite high-temperature and drought scenario of the grids.

[0055] The ideal weather scenario is used to indicate that the target crop in the grid has not suffered from high-temperature heat damage or experienced a meteorological drought event during the crop growth season, and the composite high-temperature and drought scenario is used to indicate that the target crop in the grid has suffered from a disaster event in which high-temperature heat damage and meteorological drought occur simultaneously on one or more dates during the crop growth season.

[0056] In this embodiment, the detection device obtains the geographical coordinate data of several grids in the target area, the ideal target crop growth factor data of several dates corresponding to the ideal weather scenario of the grids, and the stress target crop growth factor data of several dates corresponding to the composite high-temperature and drought scenario of the grids. Among them, the ideal target crop growth factor data is used to indicate the growth factor data of the target crop on the date corresponding to the ideal weather scenario. The stress target crop growth factor data is used to indicate the growth factor data of the target crop on the date corresponding to the composite high-temperature and drought scenario.

[0057] For the ideal target crop growth factor data, please refer to Figure 3 , Figure 3Schematic flowchart of step S3 in the method for detecting the loss of target crop due to combined high temperature and drought disasters provided in an embodiment of the present application, including steps S31 to S32, specifically as follows:

[0058] S31: Obtain the target crop growth factor data, relative threshold of high temperature heat damage, and relative threshold of meteorological drought for a number of dates of a number of grids in the target area.

[0059] In this embodiment, the detection device obtains the target crop growth factor data, relative threshold of high temperature heat damage, and relative threshold of meteorological drought for a number of dates of a number of grids in the target area.

[0060] For the relative threshold of high temperature heat damage and the relative threshold of meteorological drought, please refer to Figure 4 , Figure 4 Schematic flowchart of step S31 in the method for detecting the loss of target crop due to combined high temperature and drought disasters provided in an embodiment of the present application, including step S311, specifically as follows:

[0061] S311: According to the daily average temperature and daily precipitation in the target crop growth factor data for a number of dates of a number of grids in the research area, respectively construct a daily average temperature time series and a daily precipitation time series, and extract the value of a preset first quantile of the daily average temperature time series as the relative threshold of high temperature heat damage, and the value of a preset second quantile of the daily precipitation time series as the relative threshold of meteorological drought.

[0062] Considering the actual situation that the absolute thresholds of disaster-causing temperatures for high temperature heat damage and the absolute thresholds of disaster-causing rainfall for meteorological drought vary greatly for different crops in different regions, in this embodiment, the relative threshold of disaster-causing temperature for high temperature heat damage and the relative threshold of disaster-causing rainfall for meteorological drought will be extracted according to the definition of extreme weather. Extreme weather refers to an abnormal weather phenomenon in a certain area that significantly deviates from its historical average state during the same period, and generally uses a certain percentile as the judgment standard for significant deviation.

[0063] In this embodiment, the detection device constructs a daily average temperature time series and a daily precipitation time series respectively according to the daily average temperature and daily precipitation in the target crop growth factor data for a number of dates of a number of grids in the research area, and extracts the value of a preset first quantile of the daily average temperature time series as the relative threshold of high temperature heat damage, and the value of a preset second quantile of the daily precipitation time series as the relative threshold of meteorological drought. Specifically, the first quantile can be set as the 90% quantile, and the second quantile can be set as 10%.

[0064] S32: Traverse the average daily temperature and daily precipitation in the target crop growth factor data. If the average daily temperature is higher than the relative threshold of high-temperature heat damage, replace the value of the average daily temperature with the relative threshold of high-temperature heat damage; if the daily precipitation is lower than the relative threshold of meteorological drought, replace the value of the daily precipitation with the relative threshold of meteorological drought, so as to obtain the ideal target crop growth factor data of several grids in the target area corresponding to several dates in the ideal weather scenario.

[0065] In this embodiment, the detection device traverses the average daily temperature and daily precipitation in the target crop growth factor data. If the average daily temperature is higher than the relative threshold of high-temperature heat damage, replace the value of the average daily temperature with the relative threshold of high-temperature heat damage; if the daily precipitation is lower than the relative threshold of meteorological drought, replace the value of the daily precipitation with the relative threshold of meteorological drought, so as to obtain the ideal target crop growth factor data of several grids in the target area corresponding to several dates in the ideal weather scenario, ensuring that the average daily temperature and daily precipitation data during the crop growth season calculated will not cause high-temperature heat damage and meteorological drought events, that is, forming the ideal weather scenario of the target crop during the crop growth season.

[0066] For the stress target crop growth factor data, please refer to Figure 5 , Figure 5 which is the flowchart of step S3 in the method for detecting the loss of composite high-temperature and drought disasters of target crops provided by another embodiment of this application, including steps S33 - S34, specifically as follows:

[0067] S33: Based on the relative threshold of high-temperature heat damage and the relative threshold of meteorological drought, obtain the record data of composite high-temperature and drought disaster events in the target area.

[0068] In this embodiment, the detection device obtains the record data of composite high-temperature and drought disaster events in the target area based on the relative threshold of high-temperature heat damage and the relative threshold of meteorological drought. Among them, the record data of composite high-temperature and drought disaster events includes the average daily temperature and daily precipitation of several dates when composite high-temperature and drought disasters occur.

[0069] S34: According to the record data of composite high-temperature and drought disaster events, replace the values of the average daily temperature and daily precipitation on the corresponding dates in the ideal target crop growth factor data of several grids in the target area corresponding to several dates in the ideal weather scenario with the values of the average daily temperature and daily precipitation on the corresponding dates in the record data of composite high-temperature and drought disaster events, so as to obtain the stress target crop growth factor data of several grids in the target area corresponding to several dates in the composite high-temperature and drought scenario.

[0070] In this embodiment, the detection device replaces the values of the average daily temperature and the daily precipitation on the corresponding dates in the ideal target crop growth factor data on several dates corresponding to the ideal weather scenario with the values of the average daily temperature and the daily precipitation on the corresponding dates in the composite high-temperature and drought disaster event record data for several grids in the target area, so as to obtain the stress target crop growth factor data for several grids in the target area on several dates corresponding to the composite high-temperature and drought scenario, ensuring that all possible situations can be comprehensively covered by the designed composite high-temperature and drought weather scenario; other meteorological elements remain unchanged.

[0071] S4: Input the geographical coordinate data of several grids in the target area and the ideal target crop growth factor data into the target crop yield prediction model to obtain the ideal target crop yield prediction data for several grids in the target area corresponding to the ideal weather scenario; input the geographical coordinate data of several grids in the target area and the stress target crop growth factor data into the target crop yield prediction model to obtain the stress target crop yield prediction data for several grids in the target area corresponding to the composite high-temperature and drought scenario.

[0072] In this embodiment, the detection device inputs the geographical coordinate data of several grids in the target area and the ideal target crop growth factor data into the target crop yield prediction model to obtain the ideal target crop yield prediction data for several grids in the target area corresponding to the ideal weather scenario; inputs the geographical coordinate data of several grids in the target area and the stress target crop growth factor data into the target crop yield prediction model to obtain the stress target crop yield prediction data for several grids in the target area corresponding to the composite high-temperature and drought scenario.

[0073] S5: Obtain the detection result of the composite high-temperature and drought disaster loss of the target crop in the target area according to the ideal target crop yield prediction data and the stress target crop yield prediction data.

[0074] In this embodiment, the detection device obtains the detection result of the composite high-temperature and drought disaster loss of the target crop in the target area according to the ideal target crop yield prediction data and the stress target crop yield prediction data.

[0075] Please refer to Figure 6 , Figure 6 which is a schematic flowchart of S5 in the method for detecting the loss of composite high-temperature and drought disasters of target crops provided by an embodiment of this application, including step S51, as follows:

[0076] S51: Perform a difference operation on the predicted ideal target crop yield data corresponding to the ideal weather scenario and the predicted stressed target crop yield data corresponding to the high-temperature heat damage scenario for the same grid in the target area, to obtain the target crop loss values of several grids in the target area, which are used as the detection results of the target crop's combined high-temperature and drought disaster losses.

[0077] In this embodiment, the detection device performs a difference operation on the predicted ideal target crop yield data corresponding to the ideal weather scenario and the predicted stressed target crop yield data corresponding to the high-temperature heat damage scenario for the same grid in the target area, to obtain the target crop loss values of several grids in the target area, which are used as the detection results of the target crop's combined high-temperature and drought disaster losses.

[0078] Construct the target crop growth factor data corresponding to the ideal weather scenario and the combined high-temperature and drought scenario, and combine with a pre-trained machine learning model to predict the target crop yield corresponding to the ideal weather scenario and the combined high-temperature and drought scenario. Perform a difference operation on the obtained predicted target crop yield data corresponding to the ideal weather scenario and the combined high-temperature and drought scenario to obtain the target crop's combined high-temperature and drought disaster loss value, which reflects the target crop yield loss value under the combined high-temperature and drought stress, providing a scientific basis for ensuring national food security.

[0079] Please refer to Figure 7 , Figure 7 FIG. is a schematic structural diagram of a detection system for the combined high-temperature and drought disaster losses of target crops provided by an embodiment of the present application. This device can implement all or part of the detection system for the combined high-temperature and drought disaster losses of target crops through software, hardware, or a combination of both. The system 7 includes:

[0080] A data acquisition module 71, configured to acquire the target crop yield data, geographic coordinate data of several grids in the study area, and the target crop growth factor data of several dates of the grids.

[0081] A model training module 72, configured to input the target crop yield data, geographic coordinate data of several grids in the study area, and the target crop growth factor data of several dates of the grids into a crop yield prediction model to be trained, to obtain a target crop yield prediction model.

[0082] A scenario construction module 73, configured to acquire the geographic coordinate data of several grids in the target area, the ideal target crop growth factor data of several dates corresponding to the ideal weather scenario of the grids, and the stressed target crop growth factor data of several dates corresponding to the combined high-temperature and drought scenario of the grids.

[0083] The target crop yield prediction module 74 is used to input the geographical coordinate data of several grids in the target area and the ideal target crop growth factor data into the target crop yield prediction model to obtain the ideal target crop yield prediction data corresponding to the ideal weather scenario for several grids in the target area; input the geographical coordinate data of several grids in the target area and the stress target crop growth factor data into the target crop yield prediction model to obtain the stress target crop yield prediction data corresponding to the compound high temperature and drought scenario for several grids in the target area.

[0084] The target crop loss calculation module 75 is used to obtain the detection result of the target crop compound high temperature and drought disaster loss in the target area according to the ideal target crop yield prediction data and the stress target crop yield prediction data.

[0085] In the embodiment of the present application, a data acquisition module is configured to acquire the target crop yield data, geographical coordinate data of several grids in the research area, and the target crop growth factor data of several dates of the grids; a model training module inputs the target crop yield data, geographical coordinate data of several grids in the research area, and the target crop growth factor data of several dates of the grids into a crop yield prediction model to be trained for training to obtain a target crop yield prediction model; a scenario construction module acquires the geographical coordinate data of several grids in the target area, the ideal target crop growth factor data of several dates corresponding to the ideal weather scenario of the grids, and the stress target crop growth factor data of several dates corresponding to the compound high temperature and drought scenario of the grids; a target crop yield prediction module inputs the geographical coordinate data of several grids in the target area and the ideal target crop growth factor data into the target crop yield prediction model to obtain the ideal target crop yield prediction data corresponding to the ideal weather scenario of several grids in the target area; inputs the geographical coordinate data of several grids in the target area and the stress target crop growth factor data into the target crop yield prediction model to obtain the stress target crop yield prediction data corresponding to the compound high temperature and drought scenario of several grids in the target area; a target crop loss calculation module obtains the target crop compound high temperature and drought disaster loss detection result of the target area according to the ideal target crop yield prediction data and the stress target crop yield prediction data. By constructing the target crop growth factor data corresponding to the ideal weather scenario and the compound high temperature and drought scenario, combining with a pre-trained machine learning model to predict the target crop yield corresponding to the ideal weather scenario and the compound high temperature and drought scenario, and performing difference processing on the obtained target crop yield prediction data corresponding to the ideal weather scenario and the compound high temperature and drought scenario, the target crop compound high temperature and drought disaster loss value is obtained, which reflects the target crop yield loss value under the compound high temperature and drought stress, providing a scientific basis for ensuring national food security.

[0086] Please refer to Figure 8 , Figure 8 which is a schematic structural diagram of a computer device provided by an embodiment of the present application. The computer device 8 includes: a processor 81, a memory 82, and a computer program 83 stored on the memory 82 and executable on the processor 81; the computer device may store multiple instructions, and the instructions are suitable for being loaded and executed by the processor 81 to perform the method steps of the above Figures 1 to 6 shown embodiment. The specific execution process may refer to the specific description of the Figures 1 to 6 shown embodiment and will not be elaborated here.

[0087] Among them, the processor 81 may include one or more processing cores. The processor 81 is connected to various parts within the server through various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 82, and by invoking the data in the memory 82, it executes various functions of the target crop composite high-temperature and drought disaster loss detection system 7 and processes data. Optionally, the processor 81 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 81 may integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the touch display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 81 and may be implemented separately by a single chip.

[0088] Among them, the memory 82 may include a random access memory (RAM), or may also include a read-only memory (ROM). Optionally, the memory 82 includes a non-transitory computer-readable storage medium. The memory 82 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 82 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch instructions, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory 82 may also be at least one storage device located far from the aforementioned processor 81.

[0089] The embodiment of the present application also provides a storage medium, which can store multiple instructions, and the instructions are suitable for being loaded and executed by a processor to perform the method steps of the above Figures 1 to 6 shown embodiments. The specific execution process can refer to the Figures 1 to 6 specific description of the shown embodiments, and will not be elaborated here.

[0090] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other and do not limit the protection scope of the present application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0091] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not described or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0092] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in the form of hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0093] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the device or unit can be in electrical, mechanical or other forms.

[0094] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] In addition, in each embodiment of the present invention, each functional unit may be integrated into one processing unit, may exist physically alone for each unit, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0096] If the above integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above embodiment methods of the present invention, it may also be completed by a computer program instructing relevant hardware. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments may be implemented. Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file, or some intermediate form, etc.

[0097] The present invention is not limited to the above embodiments. If various changes or deformations to the present invention do not deviate from the spirit and scope of the present invention, and if these changes and deformations are within the scope of the claims of the present invention and equivalent technical scope, then the present invention also intends to include these changes and deformations.

Claims

1. A method for detecting the loss of composite high-temperature and drought disasters in target crops, characterized in that, Including the following steps: Obtaining the target crop yield data, geographic coordinate data of several grids in the research area, and target crop growth factor data of several dates of the grids; Inputting the target crop yield data, geographic coordinate data of several grids in the research area, and target crop growth factor data of several dates of the grids into the crop yield prediction model to be trained for training to obtain the target crop yield prediction model; Obtaining the geographic coordinate data of several grids in the target area; obtaining the ideal target crop growth factor data of several dates corresponding to the ideal weather scenario, including obtaining the target crop growth factor data of several dates of several grids in the target area, the relative threshold of high-temperature heat damage, and the relative threshold of meteorological drought; Traversing the daily average temperature and daily precipitation in the target crop growth factor data, if the daily average temperature is higher than the relative threshold of high-temperature heat damage, replacing the value of the daily average temperature with the relative threshold of high-temperature heat damage; if the daily precipitation is lower than the relative threshold of meteorological drought, replacing the value of the daily precipitation with the relative threshold of meteorological drought to obtain the ideal target crop growth factor data of several dates corresponding to the ideal weather scenario of several grids in the target area; Obtaining the stress target crop growth factor data of several dates corresponding to the compound high-temperature and drought scenario, including obtaining the compound high-temperature and drought disaster event record data of the target area based on the relative threshold of high-temperature heat damage and the relative threshold of meteorological drought, wherein the compound high-temperature and drought disaster event record data includes the daily average temperature and daily precipitation of several dates when the compound high-temperature and drought disaster occurs; According to the compound high-temperature and drought disaster event record data, replacing the values of the daily average temperature and daily precipitation of the corresponding dates in the ideal target crop growth factor data of several dates corresponding to the ideal weather scenario of several grids in the target area with the values of the daily average temperature and daily precipitation of the corresponding dates in the compound high-temperature and drought disaster event record data to obtain the stress target crop growth factor data of several dates corresponding to the compound high-temperature and drought scenario of several grids in the target area; Inputting the geographic coordinate data of several grids in the target area and the ideal target crop growth factor data into the target crop yield prediction model to obtain the ideal target crop yield prediction data of several grids in the target area corresponding to the ideal weather scenario; inputting the geographic coordinate data of several grids in the target area and the stress target crop growth factor data into the target crop yield prediction model to obtain the stress target crop yield prediction data of several grids in the target area corresponding to the compound high-temperature and drought scenario; Obtaining the detection result of the compound high-temperature and drought disaster loss of the target crop in the target area according to the ideal target crop yield prediction data and the stress target crop yield prediction data.

2. The method for detecting the loss of the target crop due to the combined high-temperature and drought disasters according to claim 1, wherein, The target crop yield data of several grids in the research area is obtained through the following steps: Obtain the regional-scale yield data of the target crop in the research area, the total vegetation leaf area index of each grid in the research area, and the average value of the total vegetation leaf area index of several grids. According to the regional-scale yield data of the target crop in the research area, the total vegetation leaf area index of each grid in the research area, the average value of the total vegetation leaf area index of several grids, and the preset target crop grid-scale yield calculation algorithm, obtain the target crop yield data of several grids in the research area.

3. The method for detecting the loss of the target crop due to compound high temperature and drought disasters according to claim 1, wherein: The target crop growth factor data includes weather element data and soil property data; the weather element data includes daily average temperature, daily precipitation, daily solar radiation, daily wind speed, and daily relative humidity; the geographic coordinate data includes longitude coordinates and latitude coordinates.

4. The method for detecting the loss of the target crop due to combined high-temperature and drought disasters according to claim 1, wherein: The high-temperature heat damage relative threshold and the meteorological drought relative threshold are obtained through the following steps: According to the daily average temperature and daily precipitation in the target crop growth factor data of several dates of several grids in the research area, construct a daily average temperature time series and a daily precipitation time series respectively, and extract the value of the preset first quantile of the daily average temperature time series as the high-temperature heat damage relative threshold, and the value of the preset second quantile of the daily precipitation time series as the meteorological drought relative threshold.

5. The method for detecting the loss of target crop due to combined high temperature and drought disasters according to claim 4, characterized in that, The step of obtaining the detection result of the loss of the target crop due to combined high-temperature and drought disasters in the target area according to the ideal target crop yield prediction data and the stressed target crop yield prediction data includes: Perform a difference operation on the ideal target crop yield prediction data corresponding to the ideal weather scenario and the stressed target crop yield prediction data corresponding to the combined high-temperature and drought scenario for the same grid in the target area, and obtain the target crop loss values of several grids in the target area as the detection result of the loss of the target crop due to combined high-temperature and drought disasters.

6. A target crop composite high-temperature and drought disaster loss detection system, characterized in that, Including: A data acquisition module for obtaining the target crop yield data, geographic coordinate data of several grids in the research area, and the target crop growth factor data of several dates of the grids. A model training module for inputting the target crop yield data, geographic coordinate data of several grids in the research area, and the target crop growth factor data of several dates of the grids into the crop yield prediction model to be trained for training to obtain the target crop yield prediction model. A scenario construction module for obtaining the geographic coordinate data of several grids in the target area; obtaining the ideal target crop growth factor data of several dates corresponding to the ideal weather scenario of the grids, including obtaining the target crop growth factor data, high-temperature heat damage relative threshold, and meteorological drought relative threshold of several dates of several grids in the target area. Traverse the daily average temperature and daily precipitation in the target crop growth factor data. If the daily average temperature is higher than the relative threshold of high-temperature heat damage, replace the value of the daily average temperature with the relative threshold of high-temperature heat damage; if the daily precipitation is lower than the relative threshold of meteorological drought, replace the value of the daily precipitation with the relative threshold of meteorological drought, so as to obtain the ideal target crop growth factor data of several grids in the target area corresponding to the ideal weather scenario; Obtain the stress target crop growth factor data of several grids in the target area corresponding to the compound high-temperature and drought scenario, including obtaining the compound high-temperature and drought disaster event record data of the target area based on the relative threshold of high-temperature heat damage and the relative threshold of meteorological drought, wherein the compound high-temperature and drought disaster event record data includes the daily average temperature and daily precipitation of several dates when the compound high-temperature and drought disaster occurs; According to the compound high-temperature and drought disaster event record data, replace the values of the daily average temperature and daily precipitation of the corresponding dates in the ideal target crop growth factor data of several grids in the target area corresponding to the ideal weather scenario with the values of the daily average temperature and daily precipitation of the corresponding dates in the compound high-temperature and drought disaster event record data, so as to obtain the stress target crop growth factor data of several grids in the target area corresponding to the compound high-temperature and drought scenario; The target crop yield prediction module is used to input the geographic coordinate data of several grids in the target area and the ideal target crop growth factor data into the target crop yield prediction model to obtain the ideal target crop yield prediction data of several grids in the target area corresponding to the ideal weather scenario; input the geographic coordinate data of several grids in the target area and the stress target crop growth factor data into the target crop yield prediction model to obtain the stress target crop yield prediction data of several grids in the target area corresponding to the compound high-temperature and drought scenario; The target crop loss calculation module is used to obtain the detection result of the target crop compound high-temperature and drought disaster loss in the target area according to the ideal target crop yield prediction data and the stress target crop yield prediction data.

7. A computer device, characterized in that, Comprising: A processor, a memory, and a computer program stored on the memory and executable on the processor; when the computer program is executed by the processor, the steps of the target crop compound high-temperature and drought disaster loss detection method according to any one of claims 1 to 5 are implemented.

8. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the target crop compound high-temperature and drought disaster loss detection method according to any one of claims 1 to 5 are implemented.

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