Optimization method and device for irrigating and fertilizing highland barley and computer readable storage medium

Abstract

The invention discloses a highland barley fertigation optimization method and device and a computer readable storage medium. The method comprises the following steps: acquiring environment information of a highland barley planting area in a preset period; determining a probability value of each highland barley yield interval according to the environment information through a preset yield prediction model; generating corresponding association information according to the environment information and the probability value, wherein the association information is used for indicating an association relationship between the environment information and the highland barley yield; and optimizing the component proportion of the water and fertilizer components for highland barley irrigation and fertilization according to the associated information through a preset proportion model.

Description

Technical Field

[0001] The present application relates to the technical field of crop cultivation and farming, and in particular to an optimization method, device and computer-readable storage medium for highland barley irrigation and fertilization. Background Art

[0002] Highland barley belongs to the genus Hordeum of the Poaceae family and is an ancient cultivated crop. In my country, it is one of the most important specialty crops in Tibetan areas. Its planting area is located in the plateau area with an altitude of 2400-4500m, serving as the staple food for 50% of the people in Tibetan areas. Highland barley has a nutritional composition of "three highs and two lows" (high protein, high soluble fiber elements, high vitamins, low fat and low sugar), and contains relatively rich mineral elements, making it a good cereal crop. In addition, highland barley straw can also be used as high-quality forage, and its grains are important raw materials for the brewing industry and food processing industry. In recent years, as the value of highland barley has been gradually discovered, highland barley products have been favored by consumers, and the demand for highland barley has also increased year by year. At present, for crops such as rice, wheat, and corn, a water and fertilizer management technology model for high-quality, high-yield, green and efficient planting has basically been formed for different crops in different planting areas. Although highland barley also has a long history of cultivation, its production has always been in a backward state due to the limitations of natural conditions and human factors. There are still problems such as the lack of scientific application of chemical fertilizers and the failure to apply fertilizers reasonably according to climatic conditions and environmental factors. This not only increases production costs, but also results in low barley yield, poor quality and low fertilizer utilization rate, which seriously restricts barley production.

[0003] With respect to the technical problems existing in the above-mentioned prior art, such as the incompatibility between factors such as climatic conditions and environment and fertilizers, resulting in high production costs, low barley yields and low fertilizer utilization, no effective solution has been proposed so far. Summary of the invention

[0004] The embodiments of the present application provide a method, device and computer-readable storage medium for optimizing highland barley irrigation and fertilization, so as to at least solve the technical problems existing in the prior art that factors such as climatic conditions and environment are not coordinated with fertilizers, resulting in high production costs, low highland barley yields and low fertilizer utilization rates.

[0005] According to one aspect of an embodiment of the present application, a method for optimizing highland barley irrigation and fertilization is provided, comprising: collecting environmental information of a highland barley planting area within a preset period; determining the probability value of each highland barley yield interval according to the environmental information through a preset yield prediction model; generating corresponding association information according to the environmental information and the probability value, wherein the association information is used to indicate the association relationship between the environmental information and the highland barley yield; and optimizing the component ratio of water and fertilizer components used for highland barley irrigation and fertilization according to the association information through a preset ratio model, wherein the operation of determining the probability value of each highland barley yield interval according to the environmental information through the preset yield prediction model comprises: generating corresponding first feature information according to the environmental information through an RNN model of the yield prediction model; generating corresponding second feature information according to the first feature information through the first fully connected layer of the yield prediction model; and determining the probability value of each highland barley yield interval according to the second feature information through the first classifier of the yield prediction model.

[0006] According to another aspect of the embodiment of the present application, there is also provided an optimization device for highland barley irrigation and fertilization, including: an information collection module for collecting environmental information of a highland barley planting area within a preset period; a probability determination module for determining the probability value of each highland barley yield interval according to the environmental information through a preset yield prediction model; an information generation module for generating corresponding association information according to the environmental information and the probability value, wherein the association information is used to indicate the association relationship between the environmental information and the highland barley yield; and a proportion optimization module for optimizing the component ratio of water and fertilizer components used for highland barley irrigation and fertilization according to the association information through a preset matching model, wherein the probability determination module includes: a first generation submodule for generating corresponding first feature information according to the environmental information through the RNN model of the yield prediction model; a second generation submodule for generating corresponding second feature information according to the first feature information through the first fully connected layer of the yield prediction model; and a first determination submodule for determining the probability value of each highland barley yield interval according to the second feature information through the first classifier of the yield prediction model.

[0007] According to another aspect of an embodiment of the present application, there is also provided an optimization device for highland barley irrigation and fertilization, comprising: a processor; and a memory connected to the processor, for providing the processor with instructions for processing the following processing steps: collecting environmental information of a highland barley planting area within a preset period; determining the probability value of each highland barley yield interval according to the environmental information through a preset yield prediction model; generating corresponding association information according to the environmental information and the probability value, wherein the association information is used to indicate the association relationship between the environmental information and the highland barley yield; and optimizing the component ratio of water and fertilizer components used for highland barley irrigation and fertilization according to the association information through a preset ratio model, wherein the operation of determining the probability value of each highland barley yield interval according to the environmental information through the preset yield prediction model comprises: generating corresponding first feature information according to the environmental information through the RNN model of the yield prediction model; generating corresponding second feature information according to the first feature information through the first fully connected layer of the yield prediction model; and determining the probability value of each highland barley yield interval according to the second feature information through the first classifier of the yield prediction model.

[0008] According to another aspect of an embodiment of the present application, a computer system is also provided, including a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps of the above method.

[0009] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0010] In the embodiment of the present application, the computing device uses the environmental information of the barley planting area of ​​the period suitable for planting barley as the influencing factor affecting the barley yield, and predicts the probability value of each barley yield in the barley planting area according to the environmental information through the yield prediction model. Thus, the technical solution determines the influence relationship of environmental information on barley yield, thereby accurately predicting the yield of barley. And the technical solution optimizes the component ratio of water and fertilizer according to the generated associated information by associating environmental information and corresponding barley yield probability values, thereby avoiding the problems of increased production cost, low barley yield, poor quality, low fertilizer utilization rate, etc. caused by the inability to reasonably fertilize according to factors such as climatic conditions and environment. Thus, it is realized that under the influencing factors of environment and yield, a reasonable component ratio is determined, so that environmental information, yield and fertilizer are more coordinated and more adaptable. Then, the technical problems that factors such as climatic conditions and environment and fertilizers existing in the prior art are not coordinated with each other, resulting in high production cost, low barley yield and low fertilizer utilization rate are solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 is a hardware structure block diagram of a computing device for implementing the method according to Embodiment 1 of the present application; Figure 2 It is a schematic flow chart of the optimization method for highland barley irrigation and fertilization according to the first aspect of Example 1 of the present application; Figure 3 It is a schematic diagram of the overall process of the optimization method of highland barley irrigation and fertilization according to Example 1 of the present application; Figure 4 is a schematic diagram of a yield prediction model according to Example 1 of the present application; Figure 5 is a flow chart of associating environmental information with probability values ​​according to Embodiment 1 of the present application; Figure 6 is a schematic diagram of a ratio model according to Example 1 of the present application; Figure 7 It is a sequential flow chart of the optimization method of highland barley irrigation and fertilization according to Example 1 of the present application; Figure 8 is a schematic diagram of the optimization device for highland barley irrigation and fertilization according to Example 2 of the present application; and Fig. 9 It is a schematic diagram of the optimization device for highland barley irrigation and fertilization according to Example 3 of the present application. DETAILED DESCRIPTION

[0012] In order to enable those skilled in the art to better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only embodiments of a part of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

[0013] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0014] First, some nouns or terms that appear in the description of the embodiments of the present application are subject to the following explanations: Fertigation: It is the process of dissolving fertilizer in irrigation water and irrigating and fertilizing simultaneously through an irrigation system.

[0015] Example 1 According to this embodiment, a method embodiment of an optimization method for highland barley irrigation and fertilization is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0016] The method embodiment provided in this embodiment can be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Figure 1 The hardware structure block diagram of a computing device for implementing an optimization method for highland barley irrigation and fertilization is shown. Figure 1 As shown, the computing device may include one or more processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory for storing data, and a transmission device for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0017] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computing device. As involved in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0018] The memory can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the optimization method of highland barley fertilization in the embodiment of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the optimization method of highland barley fertilization of the above-mentioned application is realized. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to a computing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network and a combination thereof.

[0019] The transmission device is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computing device. In one example, the transmission device includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0020] The display may be, for example, a touch screen liquid crystal display (LCD) that may enable a user to interact with a user interface of the computing device.

[0021] It should be noted that, in some optional embodiments, the above Figure 1 The computing device shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware elements and software elements. Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computing devices described above.

[0022] Under the above operating environment, according to the first aspect of this embodiment, a method for optimizing highland barley irrigation and fertilization is provided, the method comprising: Figure 1 The computing device implementation shown in . Figure 2 A schematic diagram showing the process of the method is shown in FIG. Figure 2 As shown, the method includes: S202: Collecting environmental information of highland barley planting areas within a preset period; S204: Determine the probability value of each highland barley yield range according to the environmental information through a preset yield prediction model; S206: generating corresponding association information according to the environmental information and the probability value, wherein the association information is used to indicate the association relationship between the environmental information and the highland barley yield; and S208: Optimizing the proportion of water and fertilizer components used for highland barley irrigation and fertilization according to the associated information through a preset matching model.

[0023] Specifically, the computing device predicts the environmental information of a certain highland barley planting area within a preset period in the future (for example, March to September 2025) based on historical environmental data through a preset environmental information prediction model. The environmental information prediction model can be any meteorological prediction model in the prior art and is not specifically limited here. The environmental information includes: monthly average air pressure, monthly average temperature, monthly average water vapor pressure, monthly precipitation, monthly sunshine hours, relative humidity and wind speed, etc. m information. Among them, environmental information[ TA ]=[ A 1, A 2,..., A n ].in n represents the number of months in the preset period, and A i Indicates i The environmental feature vector of the month, i =1~ n .For example n =7 (i.e., March to September, a total of 7 months).

[0024] in A 1=[ a 1,1 , a 2,1 ,..., a m,1 ] T ; A 2=[ a 1,2 , a 2,2 ,..., am,2 ] T ; … A n =[ a 1,n , a 2,n ,..., a m,n ] T .

[0025] in a j,i Indicates the first i Month j Environmental information, j =1~ m .

[0026] Furthermore, the computing device is pre-set with a yield prediction model for predicting highland barley yield. Figure 3 As shown, the computing device converts the environmental information [ TA ] is input into the yield prediction model, and the yield prediction model is used to calculate the environmental information[ TA ], and predict the highland barley yield under the influence of this environment. Then the yield prediction model outputs the probability value of the highland barley yield interval corresponding to the highland barley yield P =[ p 1, p 2, p 3, p 4] T The highland barley yield ranges include: 10,000-15,000 tons, 16,000-20,000 tons, 21,000-25,000 tons, and 26,000-30,000 tons. Therefore, the yield prediction model outputs the probability value corresponding to the highland barley yield range of 10,000-15,000 tons. p 1. Probability value corresponding to the highland barley yield range of 16,000 to 20,000 tons p 2. Probability value corresponding to the highland barley production range of 21,000 to 25,000 tons p 3. Probability value corresponding to the highland barley yield range of 26,000 to 30,000 tons p 4.

[0027] Furthermore, the computing device transmits the environmental information [ TA ]=[ A 1, A 2,..., A n ] and probability value P =[ p 1, p 2, p 3, p 4] TTo associate, thereby generating corresponding association information C =[ c 1, c 2,..., c L ] T The correlation information is used to indicate the correlation between environmental information and highland barley yield.

[0028] Furthermore, the computing device is also pre-set with a ratio model for determining the ratio between the various components in the water fertilizer. C =[ c 1, c 2,..., c L ] T Input matching model, and use matching model to match related information C =[ c 1, c 2,..., c L ] T Processing is performed so that the proportion model outputs the proportion of water and fertilizer components of water and fertilizer Z =[ z 1, z 2, z 3, z 4] T The water and fertilizer components of water and fertilizer include nitrogen fertilizer, phosphorus fertilizer, potassium fertilizer and water. Therefore, the proportion model outputs the proportion value of nitrogen fertilizer components. z 1. Component ratio of phosphate fertilizer z 2. The proportion of potash fertilizer components z 3. Water component ratio z 4. And among them z 1+ z 2+ z 3+ z 4=1.

[0029] Therefore, the computing device optimizes and calculates the water and fertilizer component ratios of water and fertilizer used for highland barley irrigation and fertilization according to environmental factors and corresponding outputs, and then mixes the corresponding water and fertilizer components according to the obtained component ratio, so that the mixed water and fertilizer are applied to the highland barley planting area by irrigation.

[0030] As described in the background technology, highland barley belongs to the genus Hordeum in the Poaceae family and is an ancient cultivated crop. In my country, it is one of the most important specialty crops in the Tibetan area, and its planting area is located at an altitude of 2400 to 4500 meters. mHighland areas of Tibet serve as the staple food for 50% of the people in Tibetan areas. Highland barley has the nutritional composition of "three highs and two lows" (high protein, high soluble fiber elements, high vitamins and low fat and sugar), and is rich in mineral elements, making it a good choice among cereal crops. In addition, highland barley straw can also be used as high-quality forage, and its grains are important raw materials for the brewing industry and food processing industry. In recent years, as the value of highland barley has been gradually discovered, highland barley products have been favored by consumers, and the demand for highland barley has also increased year by year. At present, for crops such as rice, wheat, and corn, a water and fertilizer management technology model for high-quality, high-yield, green and efficient planting has basically been formed for different crops in different planting areas. Although highland barley also has a long history of cultivation, its production has always been in a backward state due to the limitations of natural conditions and human factors. There are still problems such as the lack of scientific application of chemical fertilizers and the failure to apply fertilizers reasonably according to climatic conditions and environmental factors. This not only increases production costs, but also results in low barley yield, poor quality and low fertilizer utilization rate, which seriously restricts barley production.

[0031] In view of the technical problems described above, through the technical solutions of the embodiments of the present application, the computing device uses the environmental information of the highland barley planting area of ​​the period suitable for planting highland barley as the influencing factor affecting the highland barley yield, and predicts the probability value of each highland barley yield in the highland barley planting area according to the environmental information through the yield prediction model. Thus, the technical solution determines the influence relationship of environmental information on highland barley yield, thereby accurately predicting the yield of highland barley. And the technical solution optimizes the component ratio of water and fertilizer according to the generated association information by associating environmental information and corresponding highland barley yield probability values, thereby avoiding the problems of increased production costs, low highland barley yield, poor quality, and low fertilizer utilization rate caused by the inability to reasonably apply fertilizer according to factors such as climatic conditions and environment. Thus, it is realized that under the influencing factors of environment and yield, a reasonable component ratio is determined, so that environmental information, yield and fertilizer are more coordinated and more adaptable. This solves the technical problem in the prior art that factors such as climate conditions and environment are not coordinated with fertilizers, resulting in high production costs, low barley yields and low fertilizer utilization rates.

[0032] Optionally, the operation of determining the probability value of each highland barley yield interval according to environmental information by using a preset yield prediction model includes: RNN The model generates corresponding first feature information based on environmental information; generates corresponding second feature information based on the first feature information through the first fully connected layer of the yield prediction model; and determines the probability value of each highland barley yield interval based on the second feature information through the first classifier of the yield prediction model.

[0033] Specifically, refer to Figure 4 As shown, the pre-trained yield prediction model includes RNN model, a fully connected layer (i.e., the first fully connected layer), and a classifier (i.e., the first classifier).

[0034] The computing device transmits environmental information[ TA ] is input into the production prediction model, then the production prediction model is RNN Model for environmental information TA ] to analyze, thereby outputting corresponding feature information (i.e., first feature information). Then, the computing device inputs the first feature information into the fully connected layer (i.e., first fully connected layer) of the yield prediction model, extracts features from the first feature information through the first fully connected layer, and outputs corresponding feature information (i.e., second feature information). Then, the computing device inputs the second feature information into the classifier (i.e., first classifier) ​​of the yield prediction model. The first classifier can be, for example, softmax Classifier. Thus, the yield prediction model classifies the second feature information through the first classifier and outputs the probability value of the highland barley yield interval corresponding to the highland barley yield. P =[ p 1, p 2, p 3, p 4] T .

[0035] The growth of highland barley is continuously affected by multiple environmental factors, which change over time and constitute complex time series data. RNN The model, with its unique memory unit, can capture and utilize the long-term dependencies in these time series, thus more accurately reflecting the impact of environmental changes on barley growth. This enables the model to take into account the cumulative effect of historical environmental information when predicting barley yields, thus improving the accuracy of the prediction.

[0036] Optionally, the operation of generating corresponding association information based on environmental information and probability values ​​includes: determining multiple environmental feature vectors corresponding to the environmental information; fusing the multiple environmental feature vectors to generate third feature information; and splicing the third feature information and the probability value to generate association information.

[0037] Specifically, refer to Figure 5 As shown, the computing device determines a plurality of environmental feature vectors corresponding to the environmental information. A 1~ A n .

[0038] in A 1=[ a 1,1 , a 2,1 ,..., am,1 ] T ; A 2=[ a 1,2 , a 2,2 ,..., a m,2 ] T ; … A n =[ a 1,n , a 2,n ,..., a m,n ] T .

[0039] Furthermore, the computing device calculates A 1~ A n The average value of each feature in b j , thereby fusing multiple environmental feature vectors to generate the third feature information B =[ b 1, b 2,..., b m ] T . The third characteristic information is generated B =[ b 1, b 2,..., b m ] T The calculation formula is: .

[0040] Further, the computing device converts the third feature information B =[ b 1, b 2,..., b m ] T With probability value P =[ p 1, p 2, p 3, p 4] T Perform splicing processing to generate corresponding related information C =[ c 1, c 2,..., c L ] T.Right now: .

[0041] in L = m +4.

[0042] Therefore, this technical solution splices the third characteristic information with the probability value of the yield prediction, and the generated correlation information not only contains a detailed description of the environmental characteristics, but also incorporates the predicted value of highland barley yield, so that each environmental indicator can be directly corresponded to the possible yield result, which greatly facilitates the intuitive understanding and analysis of the correlation between environmental factors and yield.

[0043] Optionally, the operation of optimizing the component ratio of water and fertilizer components used for highland barley irrigation and fertilization according to the association information through a preset matching model includes: generating fourth feature information according to the association information through the second fully connected layer of the matching model; and optimizing the component ratio of water and fertilizer components according to the fourth feature information through the second classifier of the matching model.

[0044] Specifically, refer to Figure 6 As shown, the pre-trained matching model includes a fully connected layer (ie, the second fully connected layer) and a classifier (ie, the second classifier).

[0045] Computing devices will associate information C The matching model extracts the features of the associated information through the fully connected layer (i.e., the second fully connected layer) and outputs the corresponding feature information (i.e., the fourth feature information). The computing device then inputs the fourth feature information output by the second fully connected layer into the classifier of the matching model (i.e., the second classifier). The second classifier can be, for example, softmax Classifier. The matching model then classifies the fourth feature information through the second classifier and outputs the probability value of the highland barley yield interval corresponding to the highland barley yield. P =[ p 1, p 2, p 3, p 4] T .

[0046] Therefore, the technical solution performs deep feature extraction on the associated information through the second fully connected layer of the ratio model, and then converts the fourth feature information into clear water and fertilizer component optimization suggestions through the second classifier, thereby clearly displaying the water and fertilizer ratio plan.

[0047] Optionally, the steps for training the yield prediction model are as follows: collecting sample environment information and the corresponding first sample yield in a sample period; and training the yield prediction model according to the sample environment information and the corresponding first sample yield by a gradient descent algorithm.

[0048] Specifically, before the yield prediction model is put into use, the computing device trains the yield prediction model, wherein the training steps are as follows: The sample environment information and the corresponding first sample output in the sample collection period (e.g., March to September 2014 to March to September 2023) of the computing device are used to construct the first sample set. The first sample set is shown in Table 1: Table 1

[0049] The sample environment information [ STA f ]=[ S 1 f , S 2 f ,..., S n f ].in n represents the number of months for each sample in the sample period, and S i f Indicates f The sample i Monthly sample environment feature vector, i =1~ n , f =1~ h .For example n =7 (i.e., March to September, a total of 7 months), h =10 (10 years from 2014 to 2023).

[0050] in S 1 f =[ s f 1,1 , s f 2,1 ,..., s f m,1 ] T ; S 2 f =[ s f 1,2 , s f 2,2 ,..., s f m,2 ] T ; … S n f =[ s f 1,n , s f 2,n ,..., s f m,n ] T .

[0051] And the first sample yields SP f =[ sp 1 f , sp 2 f , sp 3 f , sp 4 f ] T .in sp 1 f Indicates f The sample probability value corresponding to the highland barley yield range of 10,000 to 15,000 tons; sp 2 f Indicates f The sample probability value corresponding to the highland barley yield range of 16,000 to 20,000 tons; sp 3 f With f The sample probability value corresponding to the highland barley yield range of 21,000 to 25,000 tons; sp 4 f Indicates f The sample probability value corresponding to the highland barley production range of 26,000 to 30,000 tons.

[0052] Furthermore, the computing device trains the yield prediction model according to the sample environment information and the corresponding first sample yield through a gradient descent algorithm, determines the parameters in the yield prediction model, and obtains a yield prediction model that can be put into use.

[0053] Therefore, this technical solution uses a gradient descent algorithm to train the yield prediction model, and by iteratively adjusting the model parameters, minimizes the difference between the prediction results and the actual output, thereby continuously improving the generalization ability and prediction accuracy of the model.

[0054] Optionally, the steps for training the ratio model are as follows: collecting sample environmental information and sample component ratios of corresponding water and fertilizer components in a sample period; inputting the sample environmental information into a trained yield prediction model, and outputting a second sample yield according to the sample environmental information through the yield prediction model; and training the ratio model according to the second sample yield, the sample environmental information and the sample component ratio through a gradient descent algorithm.

[0055] Specifically, before the matching model is put into use, the computing device trains the matching, wherein the training steps are as follows: The computing device collects sample environmental information in the sample period (e.g., from March to September 2014 to March to September 2023) and the sample component ratios of the water and fertilizer components of the water and fertilizer used at that time to construct a second sample set. The second sample set is shown in Table 2: Table 2

[0056] The sample component ratio SZ f =[ sz 1 f , sz 2 f , sz 3 f , sz 4 f ] T .in sz 1 f Indicates f The sample component ratio values ​​of nitrogen fertilizers of samples, sz 2 f Indicates f The sample component ratio values ​​of the phosphate fertilizer of each sample, sz 3 f Indicates f The sample component ratio values ​​of potash fertilizers of samples, sz 4 f Indicates f The sample component ratio values ​​of the water in the sample.

[0057] Further, the computing device sends the sample environment information [ STA f ] is input into the trained yield prediction model, so that the yield prediction model can predict the sample environment information[ STA f ] to process and output the corresponding second sample output YP f =[ yp 1 f , yp 2 f , yp 3 f , yp 4 f ] T .in yp 1 f Indicates f The sample probability value of highland barley yield range of 10,000 to 15,000 tons corresponding to the sample environmental information; yp 2 f Indicates f The sample probability value of highland barley yield range of 16,000 to 20,000 tons corresponding to the sample environmental information; yp 3 f With f The sample probability value of highland barley yield range of 21,000 to 25,000 tons corresponding to the sample environmental information; yp 4 f Indicates f The sample probability value of the highland barley yield range of 26,000 to 30,000 tons corresponding to the sample environmental information.

[0058] Further, the computing device sends the sample environment information [ STA f ] and the second sample yields YP f To generate corresponding sample association information SC f The computing device then uses the gradient descent algorithm to calculate the sample association information. SC f and sample component ratio SZ f , train the matching model, determine the parameters in the matching model, and obtain a matching model that can be put into use.

[0059] Therefore, this technical solution can obtain accurate second sample yield and sample association information through the trained yield prediction model, so that in the subsequent training of the matching model, the parameters of the matching model can be adjusted through the accurate second sample yield and sample association information, thereby improving the applicability between the yield prediction model and the matching model.

[0060] In summary, reference Figure 7 As shown, the sequential steps of the optimization method for highland barley irrigation and fertilization are as follows: S701: The computing device collects environmental information of the highland barley planting area within a preset period; S702: The computing device generates corresponding first feature information according to the environmental information through the RNN model of the yield prediction model; S703: The computing device generates corresponding second feature information according to the first feature information through the first fully connected layer of the yield prediction model; S704: The computing device determines the probability value of each highland barley yield interval according to the second feature information through the first classifier of the yield prediction model; S705: The computing device determines a plurality of environmental feature vectors corresponding to the environmental information; S706: The computing device performs fusion processing on the multiple environmental feature vectors to generate third feature information; S707: The computing device performs splicing processing on the third feature information and the probability value to generate correlation information; S708: The computing device generates fourth feature information according to the association information through the second fully connected layer of the matching model; S709: The computing device optimizes the component ratio of the water and fertilizer components according to the fourth feature information through the second classifier of the matching model.

[0061] In addition, according to a second aspect of this embodiment, a computer system is provided, including a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps of the above method.

[0062] In addition, according to a third aspect of this embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0063] Thus, according to this embodiment, the computing device uses the environmental information of the barley planting area of ​​the cycle suitable for planting barley as an influencing factor affecting the barley yield, and predicts the probability value of each barley yield in the barley planting area according to the environmental information through the yield prediction model. Thus, the technical solution determines the influence relationship of environmental information on barley yield, thereby accurately predicting the yield of barley. And the technical solution optimizes the component ratio of water and fertilizer according to the generated association information by associating environmental information and corresponding barley yield probability values, thereby avoiding the problems of increased production cost, low barley yield, poor quality, low fertilizer utilization rate, etc. caused by the inability to reasonably fertilize according to factors such as climatic conditions and environment. Thus, it is achieved that under the influencing factors of environment and yield, a reasonable component ratio is determined, so that environmental information, yield and fertilizer are more coordinated and more adaptable. Then, the technical problems of high production cost, low barley yield and low fertilizer utilization rate are solved, which are caused by the incoordination of factors such as climatic conditions and environment with fertilizers in the prior art.

[0064] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0065] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0066] Example 2 Figure 8 The apparatus 800 for optimizing highland barley irrigation and fertilization according to the first aspect of this embodiment is shown, and the apparatus 800 corresponds to the method according to the first aspect of embodiment 1. Figure 8 As shown, the device 800 includes: an information collection module 810, which is used to collect environmental information of a highland barley planting area within a preset period; a probability determination module 820, which is used to determine the probability value of each highland barley yield interval according to the environmental information through a preset yield prediction model; an information generation module 830, which is used to generate corresponding correlation information according to the environmental information and the probability value, wherein the correlation information is used to indicate the correlation relationship between the environmental information and the highland barley yield; and a ratio optimization module 840, which is used to optimize the component ratio of water and fertilizer components used for highland barley irrigation and fertilization according to the correlation information through a preset ratio model.

[0067] Optionally, the probability determination module 820 includes: a first generation submodule, used to generate corresponding first feature information according to environmental information through the RNN model of the yield prediction model; a second generation submodule, used to generate corresponding second feature information according to the first feature information through the first fully connected layer of the yield prediction model; and a first determination submodule, used to determine the probability value of each highland barley yield interval according to the second feature information through the first classifier of the yield prediction model.

[0068] Optionally, the information generation module 830 includes: a second determination submodule, used to determine multiple environmental feature vectors corresponding to the environmental information; a third generation submodule, used to fuse the multiple environmental feature vectors to generate third feature information; and a fourth generation submodule, used to splice the third feature information and the probability value to generate related information.

[0069] Optionally, the ratio optimization module 840 includes: a fifth generation submodule, which is used to generate fourth feature information according to the association information through the second fully connected layer of the ratio model; and a sixth generation submodule, which is used to optimize the component ratio of the water and fertilizer components according to the fourth feature information through the second classifier of the ratio model.

[0070] Optionally, the first training module is used to train the yield prediction model in the following steps: a first training submodule is used to collect sample environment information and the corresponding first sample yield in a sample period; and a second training submodule is used to train the yield prediction model according to the sample environment information and the corresponding first sample yield through a gradient descent algorithm.

[0071] Optionally, the second training module is used to train the ratio model in the following steps: a third training submodule is used to collect sample environmental information and sample component ratios of corresponding water and fertilizer components in a sample period; a fourth training submodule is used to input the sample environmental information into a trained yield prediction model, and output a second sample yield according to the sample environmental information through the yield prediction model; and a fifth training submodule is used to train the ratio model according to the second sample yield, the sample environmental information and the sample component ratio through a gradient descent algorithm.

[0072] Thus, according to this embodiment, the computing device uses the environmental information of the barley planting area of ​​the cycle suitable for planting barley as an influencing factor affecting the barley yield, and predicts the probability value of each barley yield in the barley planting area according to the environmental information through the yield prediction model. Thus, the technical solution determines the influence relationship of environmental information on barley yield, thereby accurately predicting the yield of barley. And the technical solution optimizes the component ratio of water and fertilizer according to the generated association information by associating environmental information and corresponding barley yield probability values, thereby avoiding the problems of increased production cost, low barley yield, poor quality, low fertilizer utilization rate, etc. caused by the inability to reasonably fertilize according to factors such as climatic conditions and environment. Thus, it is achieved that under the influencing factors of environment and yield, a reasonable component ratio is determined, so that environmental information, yield and fertilizer are more coordinated and more adaptable. Then, the technical problems of high production cost, low barley yield and low fertilizer utilization rate are solved, which are caused by the incoordination of factors such as climatic conditions and environment with fertilizers in the prior art.

[0073] Example 3 Fig. 9 The apparatus 900 for optimizing highland barley irrigation and fertilization according to the first aspect of this embodiment is shown, and the apparatus 900 corresponds to the method according to the first aspect of embodiment 1. Fig. 9 As shown, the device 900 includes: a processor 910; and a memory 920, which is connected to the processor 910 and is used to provide the processor 910 with instructions for processing the following processing steps: collecting environmental information of a highland barley planting area within a preset period; determining the probability value of each highland barley yield interval according to the environmental information through a preset yield prediction model; generating corresponding association information according to the environmental information and the probability value, wherein the association information is used to indicate the association relationship between the environmental information and the highland barley yield; and optimizing the component ratio of water and fertilizer components used for highland barley irrigation and fertilization according to the association information through a preset ratio model.

[0074] Optionally, the operation of determining the probability value of each highland barley yield interval according to environmental information through a preset yield prediction model includes: generating corresponding first feature information according to the environmental information through the RNN model of the yield prediction model; generating corresponding second feature information according to the first feature information through the first fully connected layer of the yield prediction model; and determining the probability value of each highland barley yield interval according to the second feature information through the first classifier of the yield prediction model.

[0075] Optionally, the operation of generating corresponding association information based on environmental information and probability values ​​includes: determining multiple environmental feature vectors corresponding to the environmental information; fusing the multiple environmental feature vectors to generate third feature information; and splicing the third feature information and the probability value to generate association information.

[0076] Optionally, the operation of optimizing the component ratio of water and fertilizer components used for highland barley irrigation and fertilization according to the association information through a preset matching model includes: generating fourth feature information according to the association information through the second fully connected layer of the matching model; and optimizing the component ratio of water and fertilizer components according to the fourth feature information through the second classifier of the matching model.

[0077] Optionally, the steps for training the yield prediction model are as follows: collecting sample environment information and the corresponding first sample yield in a sample period; and training the yield prediction model according to the sample environment information and the corresponding first sample yield by a gradient descent algorithm.

[0078] Optionally, the steps for training the ratio model are as follows: collecting sample environmental information and sample component ratios of corresponding water and fertilizer components in a sample period; inputting the sample environmental information into a trained yield prediction model, and outputting a second sample yield according to the sample environmental information through the yield prediction model; and training the ratio model according to the second sample yield, the sample environmental information and the sample component ratio through a gradient descent algorithm.

[0079] Thus, according to this embodiment, the computing device uses the environmental information of the barley planting area of ​​the cycle suitable for planting barley as an influencing factor affecting the barley yield, and predicts the probability value of each barley yield in the barley planting area according to the environmental information through the yield prediction model. Thus, the technical solution determines the influence relationship of environmental information on barley yield, thereby accurately predicting the yield of barley. And the technical solution optimizes the component ratio of water and fertilizer according to the generated association information by associating environmental information and corresponding barley yield probability values, thereby avoiding the problems of increased production cost, low barley yield, poor quality, low fertilizer utilization rate, etc. caused by the inability to reasonably fertilize according to factors such as climatic conditions and environment. Thus, it is achieved that under the influencing factors of environment and yield, a reasonable component ratio is determined, so that environmental information, yield and fertilizer are more coordinated and more adaptable. Then, the technical problems of high production cost, low barley yield and low fertilizer utilization rate are solved, which are caused by the incoordination of factors such as climatic conditions and environment with fertilizers in the prior art.

[0080] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0081] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0082] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0083] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0084] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0085] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0086] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing highland barley irrigation and fertilization, characterized in that: include: Collect environmental information of highland barley planting areas within a preset period; Determine the probability value of each highland barley yield interval according to the environmental information through a preset yield prediction model; Generate corresponding association information according to the environmental information and the probability value, wherein the association information is used to indicate the association relationship between the environmental information and the highland barley yield; as well as The component ratio of water and fertilizer components used for highland barley irrigation and fertilization is optimized according to the associated information through a preset matching model, wherein The operation of determining the probability value of each highland barley yield interval according to the environmental information through a preset yield prediction model includes: generating corresponding first feature information according to the environmental information through an RNN model of the yield prediction model; The first fully connected layer of the yield prediction model generates corresponding second feature information based on the first feature information; and the first classifier of the yield prediction model determines the probability value of each highland barley yield interval based on the second feature information.

2. The method according to claim 1, characterized in that: The operation of generating corresponding association information according to the environmental information and the probability value includes: Determining a plurality of environmental feature vectors corresponding to the environmental information; Fusing the multiple environmental feature vectors to generate third feature information; and The third feature information and the probability value are concatenated to generate the association information.

3. The method according to claim 1, characterized in that: The operation of optimizing the component ratio of water and fertilizer components used for highland barley irrigation and fertilization according to the associated information through a preset ratio model includes: generating fourth feature information according to the association information through the second fully connected layer of the matching model; and The component ratio of the water and fertilizer components is optimized according to the fourth feature information through the second classifier of the matching model.

4. The method according to claim 1, characterized in that: The steps for training the yield prediction model are as follows: Collecting sample environment information and corresponding first sample yield in a sample cycle; and The yield prediction model is trained according to the sample environment information and the corresponding first sample yield by using a gradient descent algorithm.

5. The method according to claim 4, characterized in that The steps for training the matching model are as follows: Collecting the sample environment information and the sample component ratio of the corresponding water and fertilizer components in the sample period; Inputting the sample environment information into the trained yield prediction model, and outputting a second sample yield according to the sample environment information through the yield prediction model; as well as The matching model is trained by the gradient descent algorithm according to the second sample yield, the sample environmental information and the sample component ratio.

6. An optimization device for highland barley irrigation and fertilization, characterized in that: include: An information collection module is used to collect environmental information of highland barley planting areas within a preset period; A probability determination module, used to determine the probability value of each highland barley yield interval according to the environmental information through a preset yield prediction model; An information generation module, used to generate corresponding association information according to the environmental information and the probability value, wherein the association information is used to indicate the association relationship between the environmental information and the highland barley yield; as well as A ratio optimization module is used to optimize the ratio of water and fertilizer components used for highland barley irrigation and fertilization according to the associated information through a preset ratio model, wherein The probability determination module includes: a first generation submodule, which is used to generate corresponding first feature information according to the environmental information through the RNN model of the yield prediction model; a second generation submodule, which is used to generate corresponding second feature information according to the first feature information through the first fully connected layer of the yield prediction model; and a first determination submodule, which is used to determine the probability value of each highland barley yield interval according to the second feature information through the first classifier of the yield prediction model.

7. An optimization device for highland barley irrigation and fertilization, characterized in that: include: processor; as well as A memory, connected to the processor, configured to provide the processor with instructions for processing the following processing steps: Collect environmental information of highland barley planting areas within a preset period; Determine the probability value of each highland barley yield interval according to the environmental information through a preset yield prediction model; Generate corresponding association information according to the environmental information and the probability value, wherein the association information is used to indicate the association relationship between the environmental information and the highland barley yield; as well as The component ratio of water and fertilizer components used for highland barley irrigation and fertilization is optimized according to the associated information through a preset matching model, wherein The operation of determining the probability value of each highland barley yield interval according to the environmental information through a preset yield prediction model includes: generating corresponding first feature information according to the environmental information through an RNN model of the yield prediction model; The first fully connected layer of the yield prediction model generates corresponding second feature information based on the first feature information; and the first classifier of the yield prediction model determines the probability value of each highland barley yield interval based on the second feature information.

8. A computer system comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method of claim 1.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 1 are implemented.

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