A method and system for assessing the drought resistance of intercropping of rice and beans.

Abstract

This application relates to the field of smart agriculture technology, specifically to a method and apparatus for assessing the drought resistance capacity of intercropping of rice and beans. The method includes: acquiring daily soil moisture variation data and environmental parameter variation data of the cultivated land to be assessed; decoupling the soil moisture variation data based on the environmental parameter variation data to obtain humidity variation parameters, including transpiration-humidity variation parameters and temperature-humidity variation parameters; inputting the humidity variation parameters, environmental parameter variation data, and daily soil moisture variation data as samples into a preset soil assessment model to update the preset soil drought resistance assessment model; and inputting temperature change prediction data for the time to be predicted and the time to be predicted into the updated preset soil drought resistance assessment model to obtain the assessment result. This method allows for a more accurate assessment of soil drought resistance by inputting future temperature changes into the corrected prediction model.

Description

Technical Field

[0001] This application relates to the field of smart agriculture technology, and in particular to a method and system for assessing the drought resistance of intercropping of rice and beans. Background Technology

[0002] Agricultural drought is a major risk to food security. In my country, the annual drought-affected area of ​​crops is approximately 20-27 million hectares, resulting in an annual loss of 25-30 billion kilograms of grain, accounting for 60% of the total losses from various natural disasters. Therefore, accurately and promptly monitoring the drought process in farmland and reducing its impact and losses is of great significance for ensuring my country's agricultural production.

[0003] The invention patent with publication number CN120102424A provides a method for determining soil drought resistance, comprising: determining a specific drought susceptibility index of the target soil sample under a specific water content state based on the volume, saturated water content, saturated hydraulic conductivity, and soil water holding curve of the target soil sample; the specific drought susceptibility index represents the power required to remove a unit amount of water from a unit volume of the target soil sample to the surface of the crop root system per unit time; and determining the drought resistance of the target soil sample based on the specific drought susceptibility index. This method provides a comprehensive and accurate quantitative assessment of soil drought resistance, offering theoretical basis and practical guidance for comparing and improving soil drought resistance in agricultural production.

[0004] Current drought monitoring methods and systems focus more on soil moisture or soil and crop water content monitoring, neglecting the importance of crop growth for farmland drought assessment, resulting in low accuracy of assessment results. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, system, computer equipment, computer-readable storage medium, and computer program product for assessing the drought resistance capacity of intercropping of rice and beans, which can improve the accuracy of drought resistance assessment, in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a method for assessing the drought resistance of intercropping of rice and beans, the method comprising:

[0007] Obtain daily soil moisture variation data and environmental parameter variation data in the cultivated land to be evaluated;

[0008] Soil moisture change data are decoupled from environmental parameter change data to obtain moisture change parameters, including transpiration-humidity change parameters and temperature-humidity change parameters.

[0009] Humidity change parameters, environmental parameter change data, and daily soil moisture change data are used as samples to input into the preset soil assessment model in order to update the preset soil drought resistance assessment model.

[0010] Input the predicted time and the predicted temperature change data within the predicted time into the updated preset soil drought resistance assessment model to obtain the assessment results.

[0011] In one embodiment, the specific steps for decoupling soil moisture change data based on environmental parameter change data to obtain moisture change parameters include:

[0012] The daily soil moisture change data and environmental parameter change data were sorted according to the collected timestamp information, and the environmental parameter change data included the environmental temperature change data.

[0013] Based on the ambient temperature change data, the ambient temperature at the time of obtaining the soil moisture change data is interpolated to obtain equivalent temperature change data obtained synchronously with the soil moisture change data.

[0014] Obtain a portion of the equivalent temperature change data that is located at noon and whose temperature fluctuation is less than a preset threshold. Also obtain the timestamp information corresponding to the portion of the equivalent temperature change data as strong evaporation timestamps, and the soil moisture change data corresponding to the strong evaporation timestamps.

[0015] Linear regression calculations were performed on the strong transpiration timestamps and their corresponding soil moisture change data to obtain the linear regression equation for transpiration-humidity change, and the slope of the linear regression equation for transpiration-humidity change was used as the parameter for transpiration-humidity change.

[0016] In one embodiment, the daily soil moisture change data includes at least one set of bare soil moisture change data used as a control sample;

[0017] The specific steps for decoupling soil moisture change data based on environmental parameter change data to obtain moisture change parameters also include:

[0018] Obtain the moisture change data of exposed soil from the daily soil moisture change data;

[0019] Extract data on changes in soil moisture in exposed soil corresponding to strong transpiration timestamps;

[0020] Linear regression calculations were performed on the strong transpiration timestamps and their corresponding bare soil moisture change data to obtain the temperature-humidity change linear regression equation, and the slope of the temperature-humidity change linear regression equation was used as the temperature-humidity change parameter.

[0021] In one embodiment, the specific steps for updating the preset soil drought resistance assessment model by inputting humidity change parameters, environmental parameter change data, and daily soil moisture change data as samples include:

[0022] Read ambient temperature change data and extract soil moisture change data during periods of continuous temperature rise and continuous temperature fall from the daily soil moisture change data.

[0023] Based on the timestamp information of the acquired ambient temperature change data and soil moisture change data, a correspondence between the ambient temperature change data and soil moisture change data is established as the temperature-humidity change relationship;

[0024] Input environmental parameter change data into the environmental parameter change data to obtain prediction results;

[0025] The prediction results are compared with the daily soil moisture change data, and it is determined whether the deviation between the prediction results and the daily soil moisture change data is greater than the preset deviation threshold.

[0026] If so, the temperature-humidity change relationship and humidity change parameter are input as new parameters into the preset soil assessment model to obtain the updated preset soil assessment model.

[0027] In one embodiment, the specific steps for reading ambient temperature change data and extracting soil moisture change data during periods of continuous temperature rise and continuous temperature fall from daily soil moisture change data include:

[0028] Read the ambient temperature change data, take the timestamp corresponding to the start point of the continuous temperature rise phase as the heating start timetamp, take the timestamp corresponding to the end point of the continuous temperature rise phase as the heating end timetamp, take the timestamp corresponding to the start point of the continuous temperature fall phase as the cooling start timetamp, and take the timestamp corresponding to the end point of the continuous temperature fall phase as the cooling end timetamp.

[0029] Extract the daily soil moisture change data between the start and end timestamps of the warming phase as the humidity change data during the warming phase.

[0030] The daily soil moisture change data between the start and end timestamps of the cooling period is extracted as the humidity change data during the cooling phase.

[0031] In one embodiment, the specific steps for establishing a correspondence between environmental temperature change data and soil moisture change data as a temperature-humidity change relationship based on the timestamp information of the acquired environmental temperature change data and soil moisture change data include:

[0032] The humidity change data during the heating phase is matched one-to-one with the ambient temperature change data based on the timestamp information, and the humidity change data during the cooling phase is matched one-to-one with the ambient temperature change data based on the timestamp information.

[0033] The temperature-transpiration inhibition relationship between the warming and cooling phases was calculated based on the temperature-humidity change parameters, the transpiration-humidity change parameters, and the corresponding humidity change data and ambient temperature change data.

[0034] The temperature-humidity relationship is calculated based on the temperature-transpiration inhibition relationship, the temperature-humidity change parameter, and the transpiration-humidity change parameter.

[0035] Secondly, this application also provides an assessment system for the drought resistance capacity of intercropping of rice and beans, the system comprising:

[0036] The environmental parameter acquisition module is used to collect environmental change data;

[0037] The soil moisture acquisition module is installed in the soil of the cultivated land to be evaluated to monitor and collect data on changes in soil moisture.

[0038] The data processing module is used to decouple soil moisture change data based on environmental parameter change data to obtain moisture change parameters, and input moisture change parameters, environmental parameter change data and daily soil moisture change data as samples into the preset soil assessment model.

[0039] The prediction model and iteration module are used to obtain the predicted time and temperature change prediction data within the predicted time and input them into the updated preset soil drought resistance assessment model to output the assessment results; it is also used to run the preset soil drought resistance assessment model and receive humidity change parameters, environmental parameter change data and daily soil humidity change data.

[0040] Thirdly, this application also provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0041] Obtain daily soil moisture variation data and environmental parameter variation data in the cultivated land to be evaluated;

[0042] Soil moisture change data are decoupled from environmental parameter change data to obtain moisture change parameters, including transpiration-humidity change parameters and temperature-humidity change parameters.

[0043] Humidity change parameters, environmental parameter change data, and daily soil moisture change data are used as samples to input into the preset soil assessment model in order to update the preset soil drought resistance assessment model.

[0044] Input the predicted time and the predicted temperature change data within the predicted time into the updated preset soil drought resistance assessment model to obtain the assessment results.

[0045] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0046] Obtain daily soil moisture variation data and environmental parameter variation data in the cultivated land to be evaluated;

[0047] Soil moisture change data are decoupled from environmental parameter change data to obtain moisture change parameters, including transpiration-humidity change parameters and temperature-humidity change parameters.

[0048] Humidity change parameters, environmental parameter change data, and daily soil moisture change data are used as samples to input into the preset soil assessment model in order to update the preset soil drought resistance assessment model.

[0049] Input the predicted time and the predicted temperature change data within the predicted time into the updated preset soil drought resistance assessment model to obtain the assessment results.

[0050] Fifthly, this application also provides a computer program product comprising a computer program that, when executed by a processor, performs the following steps:

[0051] Obtain daily soil moisture variation data and environmental parameter variation data in the cultivated land to be evaluated;

[0052] Soil moisture change data are decoupled from environmental parameter change data to obtain moisture change parameters, including transpiration-humidity change parameters and temperature-humidity change parameters.

[0053] Humidity change parameters, environmental parameter change data, and daily soil moisture change data are used as samples to input into the preset soil assessment model in order to update the preset soil drought resistance assessment model.

[0054] Input the predicted time and the predicted temperature change data within the predicted time into the updated preset soil drought resistance assessment model to obtain the assessment results.

[0055] The aforementioned method, apparatus, computer equipment, storage medium, and computer program product for assessing the drought resistance of intercropping of rice and beans analyze environmental parameter changes and soil moisture changes to obtain transpiration-humidity change parameters, which are used to characterize the impact of plant growth stages on soil moisture changes. This corrects the relationship between soil moisture and temperature changes, thereby revising the prediction model. By inputting future temperature changes into the revised prediction model, a more accurate assessment of soil drought resistance can be obtained. Attached Figure Description

[0056] Figure 1 This is an application environment diagram of a method for assessing the drought resistance of intercropping of rice and beans in one embodiment;

[0057] Figure 2 This is a flowchart illustrating a method for assessing the drought resistance of intercropping of grasses and beans in one embodiment.

[0058] Figure 3 This is a flowchart illustrating step A200 in one embodiment;

[0059] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0061] The method for assessing the drought resistance of intercropping of rice and beans provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and IoT devices. IoT devices can be sensors or smart sensors with wireless communication capabilities. Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0062] In one embodiment, such as Figure 2 As shown, a method for assessing the drought resistance of intercropping of rice and beans is provided, and this method is applied to... Figure 1 Taking the server in the example, the following steps are included:

[0063] Step A100: Obtain daily soil moisture variation data and environmental parameter variation data for the cultivated land to be evaluated.

[0064] In this embodiment, the farmland to be evaluated is farmland where a rice-legume intercropping method is implemented. Compared with traditional farming methods, rice-legume intercropping is easier to achieve water retention and nitrogen fixation, and has stronger drought resistance. Multiple soil moisture sensors are installed in the farmland to be evaluated to monitor soil moisture from multiple points. The soil sensors collect soil moisture according to a preset fixed sampling period or frequency and transmit it back to the server, thereby achieving all-weather monitoring of the soil moisture of the farmland to be evaluated. According to the system clock, the dataset of soil moisture data acquired in a natural day is the aforementioned daily soil moisture change data. To facilitate data decoupling and... In contrast, among the multiple soil moisture sensors installed in the farmland to be evaluated, at least one moisture sensor is installed in the bare soil, while the majority of the remaining moisture sensors are installed in the soil covered by crops. Correspondingly, the daily soil moisture change data includes at least one set of bare soil moisture change data used as a control sample. In addition to the soil sensors, temperature sensors are also installed in the farmland to obtain other environmental parameters in the farmland besides soil moisture. The temperature sensors also detect and transmit temperature data in the farmland at a preset sampling period or frequency. The daily temperature data dataset includes environmental parameter change data.

[0065] Step A200: Decouple the soil moisture change data based on the environmental parameter change data to obtain the moisture change parameters.

[0066] The humidity change parameters include transpiration-humidity change parameters and temperature-humidity change parameters, while the environmental parameter change data is the ambient temperature change data. Because plants continuously transpire during their growth, their roots absorb water from the soil and transport it to the top of the plant, ultimately expelling it through the stomata of the leaves. Therefore, compared to bare soil, vegetated soil avoids direct sunlight, resulting in a relatively lower soil temperature, and thus soil humidity changes are less affected by ambient temperature. However, at higher ambient temperatures, plant transpiration is more intense, and soil humidity changes are significantly influenced by transpiration. Therefore, at the same temperature, two opposing (coupled) effects on soil humidity changes exist, making it difficult to predict soil humidity changes solely based on ambient temperature changes. Step A200 enables data decoupling based on the collected ambient temperature change data and the two sets of soil humidity change data (with and without vegetation cover).

[0067] In one embodiment, step A200 specifically includes the following steps:

[0068] Step A210: Sort the daily soil moisture change data and environmental parameter change data according to the collected timestamp information, where the environmental parameter change data is the environmental temperature change data;

[0069] Step A220: Interpolate the ambient temperature at the time the soil moisture change data is obtained based on the ambient temperature change data to obtain equivalent temperature change data obtained synchronously with the soil moisture change data;

[0070] Step A230: Obtain the equivalent temperature change data that is located at noon and whose temperature change fluctuation is less than the preset threshold from the equivalent temperature change data, and obtain the timestamp information corresponding to the equivalent temperature change data as the strong evaporation timestamp, and the soil moisture change data corresponding to the strong evaporation timestamp.

[0071] Step A240: Perform linear regression calculation on the strong transpiration timestamp and its corresponding soil moisture change data to obtain the linear regression equation of transpiration-humidity change, and use the slope of the linear regression equation of transpiration-humidity change as the parameter of transpiration-humidity change.

[0072] In A210 to A220, since the timestamp information of the acquired soil moisture does not coincide with the timestamp of the collected ambient temperature, and considering that the temperature is usually continuously changing and cannot change abruptly under actual conditions, the timestamp of the acquired soil moisture data is used as the benchmark to interpolate the ambient temperature when the soil moisture was acquired, thereby obtaining equivalent temperature change data. When calculating the equivalent temperature change data, the equivalent temperature change data can be obtained in the following way: First, determine the specific timestamp of the equivalent temperature change data to be calculated, find the ambient temperature change data corresponding to the two timestamps with the shortest time interval (i.e., adjacent) with the timestamp to be interpolated from the actual ambient temperature change data, and the equivalent temperature change data is the average of the aforementioned two ambient temperature change data. In step A230, a preset temperature change threshold is used to filter the range where the temperature is relatively stable at midday. In this embodiment, the preset temperature change threshold can be set to 0.5℃, that is, the temperature change data segment with temperature fluctuations less than 0.5℃ is filtered out by the preset temperature threshold. The time periods that meet this condition include midday and evening. By setting time conditions, the midday period can be further filtered out, thereby obtaining the strong transpiration timestamp, and then obtaining the soil moisture change data corresponding to the strong transpiration timestamp. Through step A240, the equivalent temperature change data and soil moisture change data corresponding to the obtained strong transpiration timestamp can be subjected to linear regression analysis, thereby obtaining the period of the day when plant transpiration is strongest and the influence of transpiration on soil moisture. Since the influence of transpiration on soil moisture is strongest at this time, and it is also the stage when transpiration inhibits the natural evaporation of water in the soil most strongly, the change in soil moisture at this time is linearly related to the time of strong transpiration. Therefore, the slope of the linear regression equation of transpiration-humidity change in this period can be used as the transpiration-humidity change parameter.

[0073] In an optional embodiment, the linear regression equation for transpiration-humidity change obtained after linear regression analysis is Wm=Amax·t+C1, where Wm is the humidity value under strong transpiration, t is time, and Amax is the transpiration-humidity change parameter during the strong transpiration stage. After calculation, Amax is a certain value, which represents the rate of change of soil moisture with time during the strong transpiration stage, and also represents the maximum value of the influence of transpiration on humidity in a day. The transpiration-humidity change parameter at other times should vary in the interval (0, Amax).

[0074] The specific steps of step A200 also include:

[0075] Step A250: Obtain the moisture change data of exposed soil from the daily soil moisture change data;

[0076] Step A260: Extract the data on changes in soil moisture in exposed soil corresponding to the strong transpiration timestamp;

[0077] Step A270: Perform linear regression calculation on the strong transpiration timestamps and their corresponding bare soil moisture change data to obtain the temperature-humidity change linear regression equation, and use the slope of the temperature-humidity change linear regression equation as the temperature-humidity change parameter.

[0078] Through steps A250 to A270, we can obtain the humidity change data of exposed soil after being affected by simple temperature changes. Since the temperature corresponding to the strong transpiration time stamp is the temperature change data of the period with the highest temperature at noon, the corresponding humidity data is also the stage where temperature has the greatest direct impact on soil humidity after excluding the influence of strong transpiration. Therefore, by performing linear regression calculation on the strong transpiration time stamp and its corresponding exposed soil humidity change data in step A270, we can obtain the temperature-humidity change parameter.

[0079] In an optional embodiment, the linear regression equation for temperature-humidity change obtained after linear regression analysis is Wn=Bmax·t+C2, where Wn is the humidity value of the exposed soil at noon, t is the time, and Bmax is the temperature-humidity change parameter. Since the strong transpiration timestamps captured correspond to the period of sustained high temperature at noon, the moisture evaporation rate of the exposed soil is the fastest at this time. Therefore, the temperature-humidity change parameter at this time should represent the maximum value of the influence of air temperature on soil humidity change during the day. The temperature-humidity change parameter at other times should vary within the interval (0, Bmax).

[0080] Step A300: Input the humidity change parameter, environmental parameter change data and daily soil moisture change data as samples into the preset soil assessment model to update the preset soil drought resistance assessment model.

[0081] In one embodiment, step A300 specifically includes the following steps:

[0082] Step A310: Read the ambient temperature change data and extract the soil moisture change data during the periods of continuous temperature rise and continuous temperature fall in the daily soil moisture change data.

[0083] Specifically, step A310 also includes the following steps:

[0084] Step A311: Read the ambient temperature change data, take the timestamp corresponding to the starting point of the continuous temperature rise phase as the heating start timetamp, take the timestamp corresponding to the ending point of the continuous temperature rise phase as the heating end timetamp, take the timestamp corresponding to the starting point of the continuous temperature fall phase as the cooling start timetamp, and take the timestamp corresponding to the ending point of the continuous temperature fall phase as the cooling end timetamp.

[0085] Step A312: Extract the daily soil moisture change data between the start time stamp and the end time stamp of the warming phase, as the humidity change data during the warming phase;

[0086] Step A313: Extract the daily soil moisture change data between the start and end timestamps of the cooling period, as the humidity change data during the cooling phase.

[0087] Step A320: Based on the timestamp information of the acquired ambient temperature change data and soil moisture change data, establish the correspondence between the ambient temperature change data and soil moisture change data as the temperature-humidity change relationship.

[0088] Specifically, step A320 also includes the following steps:

[0089] Step A321: Match the humidity change data during the heating phase with the ambient temperature change data according to the timestamp information, and match the humidity change data during the cooling phase with the ambient temperature change data according to the timestamp information.

[0090] Step A322: Calculate the temperature-transpiration inhibition relationship between the warming and cooling phases based on the temperature-humidity change parameters, transpiration-humidity change parameters, and the corresponding humidity change data and ambient temperature change data.

[0091] Step A323: Calculate the temperature-humidity change relationship based on the temperature-transpiration inhibition change relationship, the temperature-humidity change parameter, and the transpiration-humidity change parameter.

[0092] In an optional embodiment, when performing steps A310 to A320 and their sub-steps, the ambient temperature change data and daily soil moisture change data are first used as samples to input the preset humidity equation W=W0-(x·Amax -(1-x)Bmax)·Δt, where W0 and W are soil moisture data collected at two intervals of Δt, and x is the transpiration inhibition parameter. By inputting the daily soil moisture change data and humidity change parameter, the transpiration inhibition parameter corresponding to each timestamp can be obtained. The relationship between the transpiration inhibition parameter and temperature is the temperature-transpiration inhibition relationship. The transpiration inhibition parameter reflects the inhibitory effect of transpiration on the natural evaporation of exposed soil as the temperature increases. The temperature-transpiration inhibition relationship is a mapping relationship or functional relationship between the temperature and the transpiration inhibition parameter over multiple days. The obtained temperature-transpiration inhibition relationship is combined with the aforementioned preset humidity equation to obtain the preset soil assessment model. The preset soil assessment model is obtained by substituting previous data into the aforementioned preset humidity equation.

[0093] Step A330: Input the environmental parameter change data to obtain the prediction results.

[0094] Step A340: Compare the prediction results with the daily soil moisture change data, and determine whether the deviation between the prediction results and the daily soil moisture change data is greater than the preset deviation threshold.

[0095] Step A350: If so, input the temperature-humidity change relationship and humidity change parameter as new parameters into the preset soil assessment model to obtain the updated preset soil assessment model.

[0096] Step A400: Input the predicted time and the predicted temperature change data within the predicted time into the updated preset soil drought resistance assessment model to obtain the assessment results.

[0097] The specific method for inputting the environmental parameter change data into the updated preset soil drought resistance assessment model is as follows: the temperature change data within the time to be predicted is substituted into the temperature-transpiration inhibition in the preset soil drought resistance assessment model to obtain the change data of the transpiration inhibition parameter over time within the time to be predicted. Then, the transpiration inhibition parameter is substituted into the preset humidity equation to obtain the prediction result of the humidity change over time within the time to be predicted, which serves as the data basis for assessing soil drought resistance.

[0098] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0099] Based on the same inventive concept, this application also provides a system for assessing the drought resistance of intercropping of grains and beans, used to implement the aforementioned method for assessing the drought resistance of intercropping of grains and beans. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more embodiments of the system for assessing the drought resistance of intercropping of grains and beans provided below can be found in the limitations of the method for assessing the drought resistance of intercropping of grains and beans described above, and will not be repeated here.

[0100] In one embodiment, such as Figure 3As shown, a system for assessing the drought resistance of intercropping of rice and beans is provided, including: an environmental parameter acquisition module, a soil moisture acquisition module, a data processing module, and a prediction model and iteration module, wherein:

[0101] The environmental parameter acquisition module is used to collect environmental change data;

[0102] The soil moisture acquisition module is installed in the soil of the cultivated land to be evaluated to monitor and collect data on changes in soil moisture.

[0103] The data processing module is used to decouple soil moisture change data based on environmental parameter change data to obtain moisture change parameters, and input moisture change parameters, environmental parameter change data and daily soil moisture change data as samples into the preset soil assessment model.

[0104] The prediction model and iteration module are used to obtain the predicted time and temperature change prediction data within the predicted time and input them into the updated preset soil drought resistance assessment model to output the assessment results; it is also used to run the preset soil drought resistance assessment model and receive humidity change parameters, environmental parameter change data and daily soil humidity change data.

[0105] Each module in the aforementioned assessment system for the drought resistance of intercropping of rice and beans can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0106] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for assessing the drought resistance of intercropping of soybeans and grains. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device casing, or an external keyboard, touchpad, or mouse.

[0107] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0108] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0109] Step A100: Obtain daily soil moisture variation data and environmental parameter variation data for the cultivated land to be evaluated;

[0110] Step A200: Decouple the soil moisture change data based on the environmental parameter change data to obtain the moisture change parameters, including transpiration-humidity change parameters and temperature-humidity change parameters.

[0111] Step A300: Input the humidity change parameters, environmental parameter change data and daily soil moisture change data as samples into the preset soil assessment model to update the preset soil drought resistance assessment model.

[0112] Step A400: Input the predicted time and the predicted temperature change data within the predicted time into the updated preset soil drought resistance assessment model to obtain the assessment results.

[0113] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0114] Step A100: Obtain daily soil moisture variation data and environmental parameter variation data for the cultivated land to be evaluated;

[0115] Step A200: Decouple the soil moisture change data based on the environmental parameter change data to obtain the moisture change parameters, including transpiration-humidity change parameters and temperature-humidity change parameters.

[0116] Step A300: Input the humidity change parameters, environmental parameter change data and daily soil moisture change data as samples into the preset soil assessment model to update the preset soil drought resistance assessment model.

[0117] Step A400: Input the predicted time and the predicted temperature change data within the predicted time into the updated preset soil drought resistance assessment model to obtain the assessment results.

[0118] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0119] Step A100: Obtain daily soil moisture variation data and environmental parameter variation data for the cultivated land to be evaluated;

[0120] Step A200: Decouple the soil moisture change data based on the environmental parameter change data to obtain the moisture change parameters, including transpiration-humidity change parameters and temperature-humidity change parameters.

[0121] Step A300: Input the humidity change parameters, environmental parameter change data and daily soil moisture change data as samples into the preset soil assessment model to update the preset soil drought resistance assessment model.

[0122] Step A400: Input the predicted time and the predicted temperature change data within the predicted time into the updated preset soil drought resistance assessment model to obtain the assessment results.

[0123] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0124] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0125] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for assessing the drought resistance of intercropping of rice and beans, characterized in that: Obtain daily soil moisture variation data and environmental parameter variation data in the cultivated land to be evaluated; The soil moisture change data is decoupled based on the environmental parameter change data to obtain humidity change parameters, which include transpiration-humidity change parameters and temperature-humidity change parameters. The humidity change parameters, environmental parameter change data, and daily soil humidity change data are used as samples to input into the preset soil assessment model in order to update the preset soil drought resistance assessment model. Input the predicted time and the predicted temperature change data within the predicted time into the updated preset soil drought resistance assessment model to obtain the assessment results; The specific steps for decoupling the soil moisture change data based on the environmental parameter change data to obtain moisture change parameters, including transpiration-humidity change parameters and temperature-humidity change parameters, include: Step A210: Sort the daily soil moisture change data and environmental parameter change data according to the collected timestamp information, where the environmental parameter change data is the environmental temperature change data; Step A220: Interpolate the ambient temperature at the time the soil moisture change data is obtained based on the ambient temperature change data to obtain equivalent temperature change data obtained synchronously with the soil moisture change data; Step A230: Obtain the equivalent temperature change data that is located at noon and whose temperature change fluctuation is less than the preset threshold from the equivalent temperature change data, and obtain the timestamp information corresponding to the equivalent temperature change data as the strong evaporation timestamp, and the soil moisture change data corresponding to the strong evaporation timestamp. Step A240: Perform linear regression calculation on the strong transpiration timestamp and its corresponding soil moisture change data to obtain the linear regression equation of transpiration-humidity change, and use the slope of the linear regression equation of transpiration-humidity change as the parameter of transpiration-humidity change.

2. The method for assessing the drought resistance of intercropping of rice and beans according to claim 1, characterized in that: The specific steps for decoupling the soil moisture change data based on the environmental parameter change data to obtain the moisture change parameters include: The daily soil moisture change data and the environmental parameter change data are sorted according to the collected timestamp information, wherein the environmental parameter change data includes environmental temperature change data. Based on the ambient temperature change data, the ambient temperature at the time when the soil moisture change data was obtained is interpolated to obtain equivalent temperature change data that is obtained synchronously with the soil moisture change data. Obtain a portion of the equivalent temperature change data that is located at noon and whose temperature fluctuation is less than a preset threshold from the equivalent temperature change data, and obtain the timestamp information corresponding to the portion of the equivalent temperature change data as the strong evaporation timestamp, and the soil moisture change data corresponding to the strong evaporation timestamp. Linear regression calculations were performed on the strong transpiration timestamps and their corresponding soil moisture change data to obtain the transpiration-humidity change linear regression equation, and the slope of the transpiration-humidity change linear regression equation was used as the transpiration-humidity change parameter.

3. The method for assessing the drought resistance of intercropping of rice and beans according to claim 2, characterized in that: The daily soil moisture change data shall include at least one set of bare soil moisture change data used as a control sample; The specific steps of decoupling the soil moisture change data based on the environmental parameter change data to obtain the moisture change parameters further include: Obtain the bare soil moisture change data from the daily soil moisture change data; Extract the soil moisture change data corresponding to the strong transpiration timestamp; Linear regression calculations were performed on the strong transpiration timestamps and their corresponding bare soil moisture change data to obtain the temperature-humidity change linear regression equation, and the slope of the temperature-humidity change linear regression equation was used as the temperature-humidity change parameter.

4. The method for assessing the drought resistance of intercropping of cereals and legumes according to claim 3, characterized in that: The specific steps for inputting the humidity change parameters, environmental parameter change data, and daily soil moisture change data as samples into the preset soil assessment model to update the preset soil drought resistance assessment model include: Read the ambient temperature change data and extract the soil moisture change data during the periods of continuous temperature rise and continuous temperature fall from the daily soil moisture change data; Based on the timestamp information of the acquired ambient temperature change data and soil moisture change data, a correspondence between the ambient temperature change data and soil moisture change data is established as the temperature-humidity change relationship; Input the environmental parameter change data into the environmental parameter change data to obtain the prediction results; The prediction result is compared with the daily soil moisture change data, and it is determined whether the deviation between the prediction result and the daily soil moisture change data is greater than a preset deviation threshold. If so, the temperature-humidity change relationship and the humidity change parameter are input as new parameters into the preset soil assessment model to obtain the updated preset soil assessment model.

5. The method for assessing the drought resistance of intercropping of cereals and beans according to claim 4, characterized in that, The specific steps for reading ambient temperature change data and extracting soil moisture change data during periods of continuous temperature rise and continuous temperature fall from the daily soil moisture change data include: Read the ambient temperature change data, take the timestamp corresponding to the start point of the continuous temperature rise phase as the heating start timetamp, take the timestamp corresponding to the end point of the continuous temperature rise phase as the heating end timetamp, take the timestamp corresponding to the start point of the continuous temperature fall phase as the cooling start timetamp, and take the timestamp corresponding to the end point of the continuous temperature fall phase as the cooling end timetamp. The daily soil moisture change data between the start and end timestamps of the warming phase is extracted and used as the humidity change data during the warming phase. The daily soil moisture change data between the cooling start time stamp and the cooling end time stamp is extracted as the humidity change data during the cooling phase.

6. The method for assessing the drought resistance of intercropping of cereals and legumes according to claim 5, characterized in that, The specific steps for establishing a correspondence between environmental temperature change data and soil moisture change data as a temperature-humidity change relationship based on the timestamp information of the acquired environmental temperature change data and soil moisture change data include: The humidity change data during the heating phase and the ambient temperature change data are matched one-to-one according to the timestamp information, and the humidity change data during the cooling phase and the ambient temperature change data are matched one-to-one according to the timestamp information. The temperature-transpiration inhibition relationship between the heating and cooling phases is calculated based on the temperature-humidity change parameters, the transpiration-humidity change parameters, and the corresponding humidity change data and ambient temperature change data. The temperature-humidity change relationship is calculated based on the temperature-transpiration inhibition change relationship, the temperature-humidity change parameter, and the transpiration-humidity change parameter.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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