Analysis method and device for combining polyglutamic acid with compound fertilizer, computing equipment and storage medium

Abstract

The invention relates to the field of compound fertilizer analysis, and discloses an analysis method and device for combining polyglutamic acid and a compound fertilizer, computing equipment and a storage medium, and the method comprises the following steps: determining a growth cycle and a critical period of crops of which a fertilization scheme needs to be analyzed; determining environment conditions and nutrient absorption characteristics of crop growth; determining types of fertilizer raw materials and fertilization modes according to environmental conditions and nutrient absorption characteristics; combined fertilization schemes corresponding to different growth cycles are generated according to fertilizer raw material types and fertilization modes; inputting the plurality of combined fertilization schemes into a first fertilizer effect prediction model to obtain evaluation results of the plurality of combined fertilization schemes; and determining an optimal combined fertilization scheme according to the evaluation results of the plurality of combined fertilization schemes. According to the application, a polyglutamic acid and compound fertilizer combination scheme is designed according to crop requirements, and then a scheme with an optimal crop growth effect is selected according to the evaluation model, so that crops can obtain required nutrients, and the production cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of compound fertilizer analysis, and more specifically to an analysis method, device, computing equipment, and storage medium for combining polyglutamic acid with compound fertilizer. Background Art

[0002] Compound fertilizer is a type of fertilizer that contains two or more essential plant nutrients, typically nitrogen (N), phosphorus (P), and potassium (K). The basic principle of compound fertilizer production is to combine different raw materials in specific proportions through chemical reactions, physical mixing, and other methods to create a fertilizer that meets specific needs. The key to compound fertilizer production lies in the accuracy of the formula, as different crops have different nutrient requirements. An incorrect formula can lead to poor growth or even death. Formula development must consider factors such as the crop type, growth stage, and soil conditions. Polyglutamic acid (γ-PGA), also known as natto gum or polyglutamic acid, is a water-soluble, biodegradable, and non-toxic biopolymer produced through microbial fermentation. It is a homopolymer amino acid formed by the polymerization of glutamic acid monomers through amide bonds, and has the advantages of strong adsorption and non-toxicity. Polyglutamic acid, as an additive for synergistic fertilizers, can increase fertilizer utilization efficiency from 30-35% to 40-50%, an average 8% increase in fertilizer utilization. This can generally increase crop yields by 10-35%, and root crop yields by over 30-60%. The combination of polyglutamic acid and compound fertilizers is a hot research topic, but effective analytical tools are currently lacking to analyze the effectiveness of different fertilization strategies. Summary of the Invention

[0003] The embodiments of the present application provide an analysis method, apparatus, computing device, and storage medium for combining polyglutamic acid with compound fertilizer. A polyglutamic acid and compound fertilizer combination scheme is designed based on crop needs. The optimal scheme for crop growth is then selected based on an evaluation model, ensuring that crops obtain the required nutrients and reducing production costs.

[0004] In a first aspect, an embodiment of the present application provides an analysis method for combining polyglutamic acid with compound fertilizer, determining the growth cycle and critical period of crops for which fertilization schemes need to be analyzed; determining the environmental conditions and nutrient absorption characteristics of the crop growth; determining the type of fertilizer raw materials and the fertilization method based on the environmental conditions and nutrient absorption characteristics; generating combined fertilization schemes corresponding to different growth cycles based on the type of fertilizer raw materials and the fertilization method, wherein multiple combined fertilization schemes are generated for each growth cycle; inputting the multiple combined fertilization schemes into a first fertilizer effect prediction model to obtain evaluation results of the multiple combined fertilization schemes; and determining the optimal combined fertilization scheme based on the evaluation results of the multiple combined fertilization schemes.

[0005] In one embodiment, the environmental conditions include soil data, and determining the type of fertilizer raw material and the fertilization method based on the environmental conditions and nutrient absorption characteristics includes: determining the type of first fertilizer raw material required by the soil according to the soil data; determining the type of second fertilizer raw material required by the crop according to the nutrient absorption characteristics; The fertilizing method is determined according to the first fertilizer raw material type and the second fertilizer raw material type.

[0006] In one embodiment, the soil data includes soil type, soil pH value, organic matter content, and moisture content. Determining the type of the first fertilizer raw material required by the soil based on the soil data includes: After collecting soil samples where the crops are grown, determining the soil type; Analyzing the soil sample to obtain the pH value, organic matter content, and moisture content of the soil; The type of first fertilizer raw material required by the soil is determined according to the soil type, pH value, organic matter content and moisture content.

[0007] In one embodiment, the fertilization method is a combination of polyglutamic acid and compound fertilizer. The combined fertilization scheme corresponding to different growth cycles is generated according to the type of fertilizer raw materials and the fertilization method, including: According to the type of fertilizer raw materials and the fertilization method, corresponding to each growth cycle, a corresponding combination scheme of polyglutamic acid and compound fertilizer is generated, wherein the fertilization method includes: physical mixing, chemical chelation and coating treatment; The physical mixing is to mix the polyglutamic acid and the compound fertilizer uniformly in a certain proportion; The chemical chelation is to combine polyglutamic acid with the nutrient elements in the compound fertilizer through a chemical reaction to form a stable chelate; The coating treatment is to coat the surface of the compound fertilizer particles with a layer of polyglutamic acid film, wherein the compound fertilizer is compounded according to the types of the fertilizer raw materials.

[0008] In one embodiment, the method of generating a corresponding combination scheme of polyglutamic acid and compound fertilizer for each growth cycle according to the fertilizer type and fertilization method includes: The growth cycle, the nutrient absorption characteristics corresponding to the growth cycle, the type of fertilizer raw materials and the fertilization method are input into a compound fertilizer production formula calculation model to obtain a combination scheme of polyglutamic acid and compound fertilizer corresponding to each growth cycle, wherein the combination scheme also includes the ratio of each fertilizer raw material in the compound fertilizer.

[0009] In one embodiment, before inputting the multiple combined fertilization schemes into the first fertilizer effect prediction model to obtain evaluation results of the multiple combined fertilization schemes, the method includes: Construct crop growth models and soil nutrient transformation models; A first fertilizer effect prediction model is constructed based on the crop growth model and the soil nutrient conversion model.

[0010] In one embodiment, the method further comprises: After fertilizing using the optimal combined fertilization scheme, collecting fertilization data, wherein the fertilization data includes fertilization time data, fertilization amount data, and fertilization area data; Collect crop growth data; Performing correlation analysis on the fertilization data and the crop growth data to obtain correlation data; constructing a second fertilizer effect prediction model based on the fertilization data, the crop growth data, and the correlation data, wherein the first fertilizer effect prediction model and the second fertilizer effect prediction model are different models; The fertilization data is input into a second fertilizer effect prediction model to obtain updated fertilization data, wherein the updated fertilization data is the fertilization data adjusted by model calculation.

[0011] In a second aspect, an embodiment of the present application provides an analytical device for combining polyglutamic acid with compound fertilizer, which has the function of implementing the analytical method for combining polyglutamic acid with compound fertilizer provided in the first aspect. The function can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions, and the modules can be software and / or hardware. In one embodiment, the analytical device for combining polyglutamic acid with compound fertilizer includes: Cycle determination module: determines the growth cycle and critical period of crops that require analysis of fertilization plans; Nutrient determination module: used to determine the environmental conditions for the growth of the crops and the nutrient absorption characteristics; Fertilizer determination module: used to determine the type of fertilizer raw materials and fertilization method according to the environmental conditions and nutrient absorption characteristics; Generation module: used to generate a combined fertilization plan corresponding to different growth cycles according to the type of fertilizer raw materials and fertilization method; Input module: used for inputting the multiple combined fertilization schemes into the first fertilizer effect prediction model to obtain evaluation results of the multiple combined fertilization schemes; Evaluation module: used to determine the optimal combined fertilization scheme based on the evaluation results of the multiple combined fertilization schemes.

[0012] In a third aspect, an embodiment of the present application provides a computing device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the analysis method for combining polyglutamic acid with compound fertilizer as described in the first aspect is implemented.

[0013] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions, which, when executed on a computer, enables the computer to execute the analytical method of combining polyglutamic acid with compound fertilizer as described in the first aspect.

[0014] This application designs a combination of polyglutamic acid and compound fertilizer based on the needs of crops, and then selects the best solution for crop growth based on the evaluation model to ensure that crops obtain the required nutrients and reduce production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 This is a schematic diagram of a scenario of an analysis device for combining polyglutamic acid with compound fertilizer provided in an embodiment of the present application; Figure 2 This is a schematic flow chart of an example of an analytical method for combining polyglutamic acid with compound fertilizer provided in the examples of the present application; Figure 3 This is a schematic structural diagram of an analysis device for combining polyglutamic acid with compound fertilizer according to an embodiment of the present application; Figure 4 This is a schematic structural diagram of an analytical device for combining polyglutamic acid with compound fertilizer according to an embodiment of the present application; Figure 5 A structural diagram of a mobile phone in an embodiment of the present application; Figure 6 This is a structural diagram of a server in an embodiment of the present application.

[0017] In the drawings, the same or corresponding reference numerals denote the same or corresponding parts. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0019] In the following description, the specific embodiments of the present application will be described with reference to steps and symbols performed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be mentioned several times as being performed by a computer, and the computer execution referred to herein includes the operation of a computer processing unit by an electronic signal representing data in a structured form. This operation converts the data or maintains it at a location in the computer's memory system, which can be reconfigured or otherwise change the operation of the computer in a manner familiar to testers in the field. The data structure in which the data is maintained is a physical location in the memory, which has specific characteristics defined by the data format. However, the principles of the present application are described in the above text, which does not represent a limitation, and testers in the field will understand that the various steps and operations described below can also be implemented in hardware.

[0020] As used herein, the terms "module" or "unit" may be considered software objects executed on the computing system. The various components, modules, engines, and services described herein may be considered implementation objects on the computing system. While the devices and methods described herein are preferably implemented in software, they may also be implemented in hardware and are within the scope of protection of this application.

[0021] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0022] The embodiments of the present application provide an analysis method, device, and storage medium for combining polyglutamic acid with compound fertilizer.

[0023] See also Figure 1 , Figure 1 Schematic diagram of a scenario for an analysis device for combining polyglutamic acid with compound fertilizer provided in an embodiment of the present application. The analysis device for combining polyglutamic acid with compound fertilizer may include an analysis system 100 for combining polyglutamic acid with compound fertilizer and a user terminal 200. The analysis system 100 for combining polyglutamic acid with compound fertilizer is connected via a network, and the analysis device for combining polyglutamic acid with compound fertilizer is integrated into the analysis system 100. In the embodiment of the present application, the analysis system 100 for combining polyglutamic acid with compound fertilizer may be a terminal device or a server, and the analysis system 100 for combining polyglutamic acid with compound fertilizer may send a combined fertilization plan to the user terminal 200.

[0024] In the embodiment of the present application, when the analysis system 100 for combining polyglutamic acid with compound fertilizer is a server, the server can be an independent server or a server network or server cluster composed of servers. For example, the server described in the embodiment of the present application includes but is not limited to a computer, a network host, a single network server, a plurality of network server sets or a cloud server composed of a plurality of servers. Wherein, the cloud server is composed of a large number of computers or network servers based on cloud computing. In the embodiment of the present application, communication can be achieved between the server and the client by any communication mode, including but not limited to mobile communications based on the 3rd Generation Partnership Project (3GPP), Long Term Evolution (LTE), Worldwide Interoperability for Microwave Access (WiMAX), or computer network communications based on the TCP / IP protocol suite (TCP / IP), User Datagram Protocol (UDP), etc.

[0025] It is understood that when the analysis system 100 for combining polyglutamic acid with compound fertilizer used in the embodiment of the present application is a terminal device, the terminal device can be a device that includes both receiving hardware and transmitting hardware, that is, a device with receiving and transmitting hardware that can perform two-way communication on a two-way communication link. Such terminal devices may include: cellular or other communication devices, which have a single-line display or a multi-line display or a cellular or other communication device without a multi-line display. The specific analysis system 100 for combining polyglutamic acid with compound fertilizer can be a desktop terminal or a mobile terminal, and the analysis system 100 for combining polyglutamic acid with compound fertilizer can be one of a mobile phone, a tablet computer, a laptop computer, etc.

[0026] The terminal devices involved in the embodiments of the present application may also refer to devices that provide voice and / or data connectivity to users, handheld devices with wireless connectivity, or other processing devices connected to a wireless modem. Examples include mobile phones (or "cellular" phones) and computers with mobile terminals, such as portable, pocket-sized, handheld, built-in, or vehicle-mounted mobile devices that exchange voice and / or data with a wireless access network. Examples include personal communication service (PCs) phones, cordless phones, Session Initiation Protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), and other devices.

[0027] Those skilled in the art will understand that Figure 1 The application environment shown in the figure is only one application scenario of the present application solution and does not constitute a limitation on the application scenario of the present application solution. Other application environments may also include Figure 1 More or fewer computing devices shown in , or computing device network connection relationships, such as Figure 1 Only one computing device is shown. It can be understood that the analytical device for combining polyglutamic acid with compound fertilizer may also include one or more other computing devices, or / and one or more other computing devices connected to the network of the analytical system 100 for combining polyglutamic acid with compound fertilizer, which is not specifically limited here.

[0028] In addition, if Figure 1 As shown, the analysis device for combining polyglutamic acid with compound fertilizer may further include a memory 300 for storing data, such as fertilization data, evaluation results, and the like.

[0029] It should be noted that Figure 1 The scenario diagram of the analytical device for combining polyglutamic acid with compound fertilizer shown is merely an example. The analytical device for combining polyglutamic acid with compound fertilizer and the scenario described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided in the embodiment of the present application. A person skilled in the art will appreciate that, with the evolution of the analytical device for combining polyglutamic acid with compound fertilizer and the emergence of new business scenarios, the technical solution provided in the embodiment of the present application is equally applicable to similar technical problems.

[0030] This application designs a combination of polyglutamic acid and compound fertilizer based on the needs of crops, and then selects the best solution for crop growth based on the evaluation model to ensure that crops obtain the required nutrients and reduce production costs.

[0031] In this embodiment, the analysis method for combining polyglutamic acid with compound fertilizer will be described. The analysis method for combining polyglutamic acid with compound fertilizer can be integrated into an analysis system 100 for combining polyglutamic acid with compound fertilizer.

[0032] The present application provides an analysis method for combining polyglutamic acid with compound fertilizer, and the analysis method for combining polyglutamic acid with compound fertilizer includes: determining the growth cycle and critical period of crops for which fertilization schemes need to be analyzed; determining the environmental conditions and nutrient absorption characteristics of the crop growth; determining the type of fertilizer raw material and the fertilization method based on the environmental conditions and nutrient absorption characteristics; generating combined fertilization schemes corresponding to different growth cycles based on the type of fertilizer raw material and the fertilization method, wherein multiple combined fertilization schemes are generated for each growth cycle; inputting the multiple combined fertilization schemes into a first fertilizer effect prediction model to obtain evaluation results of the multiple combined fertilization schemes; and determining the optimal combined fertilization scheme based on the evaluation results of the multiple combined fertilization schemes.

[0033] See also Figure 2 , Figure 2 20 is a flow chart of an embodiment of an analytical method for combining polyglutamic acid with compound fertilizer in an embodiment of the present application. The analytical method for combining polyglutamic acid with compound fertilizer includes the following steps 201 to 206: 201. Determine the growth cycle and critical period of crops that require analysis of fertilization plans.

[0034] Specifically, combining polyglutamic acid and compound fertilizer can increase the functionality of the compound fertilizer. Through the analysis method, the estimated effects of different combination schemes on actual crops can be obtained to determine the optimal fertilization combination scheme. Specifically, the type of crop for which the fertilization scheme needs to be analyzed can be determined by obtaining the type of crop selected by the user for which the fertilization scheme needs to be analyzed (for example, the terminal display interface displays the optional crop types for the user to select). Optional examples include: rice, wheat, and corn. After determining the type of crop for which the fertilization scheme needs to be analyzed, the growth cycle and critical period of the crop to be analyzed are determined based on the growth characteristics of the crop. Specifically, the growth cycle of a crop generally includes stages such as seed germination, seedling stage, vegetative growth stage, reproductive growth stage, and maturity stage. In the embodiment of the present application, the growth cycle and critical period of each crop can be sorted out in advance. After the type of crop for which the fertilization scheme needs to be analyzed, the growth cycle and critical period of the crop to be analyzed are obtained. After determining the crop's growth cycle, identify key periods within each growth cycle, such as tillering, jointing, heading, flowering, and fruiting. These periods often have a greater demand for nutrients. For example, during its vegetative growth period, wheat experiences a tillering phase, during which nitrogen demand is high, necessitating heavy nitrogen fertilizer application.

[0035] 202. Determine the environmental conditions for the growth of the crops and the nutrient absorption characteristics.

[0036] Specifically, determine the environmental conditions for crop growth, such as the type of soil, the pH value of the soil, the organic matter content and the moisture content, determine the nutrient absorption characteristics, and select appropriate fertilizer types and fertilization methods based on the environmental conditions for crop growth and the nutrient absorption characteristics to improve the stress resistance of crops. Optionally, when determining the environmental conditions for crop growth, soil conditions are taken into consideration. Soil data may include the type of soil, the pH value of the soil, the organic matter content and the moisture content, so as to facilitate subsequent analysis of the soil's demand and response to fertilizers. In an embodiment of the present application, after determining the type of crop to be analyzed, the soil in which the crops are planted is sampled and analyzed to determine the type of soil, the pH value of the soil, the organic matter content and the moisture content. Specifically, different crops have different nutrient absorption ratios and demand characteristics. For example, rice has a greater demand for nitrogen, phosphorus and potassium, while tomatoes have the highest demand for potassium, followed by nitrogen, and less phosphorus. The same crop also has different nutrient requirements at different growth stages. For example, rice is topdressed with nitrogen fertilizer during the tillering stage to promote tillering, and nitrogen, phosphorus, potassium, etc. are topdressed during the jointing stage to promote spike length and spikelet development. In the embodiment of the present application, the nutrient absorption characteristics corresponding to each selectable crop can be sorted and determined in advance by consulting information, etc., and the crop types and corresponding nutrient absorption characteristics are matched one to one. For example, they can be sorted into a table and stored in the memory of the analysis system that combines polyglutamic acid with compound fertilizer. After obtaining the crop types selected by the user at the terminal, the nutrient absorption characteristics corresponding to the crop are determined by searching. Determining the growth environment conditions and nutrient absorption characteristics of the crops is convenient for subsequent generation of a combined fertilization plan.

[0037] 203. Determine the type of fertilizer raw materials and fertilization method based on the environmental conditions and nutrient absorption characteristics.

[0038] Specifically, based on environmental conditions, such as soil data, the first type of fertilizer raw material required for the soil is determined, and based on the nutrient absorption characteristics of the crops, the second type of fertilizer raw material is determined. Based on the first type of fertilizer raw material and the second type of fertilizer raw material, the type of fertilizer raw material ultimately required for crop cultivation and the corresponding fertilization method are determined. In the embodiment of the present application, the correspondence between soil data and fertilizer raw material types, and between nutrient absorption characteristics and fertilizer raw material types can be sorted out in advance. Optionally, the type of raw material corresponding to the nutrient absorption characteristics of the crops can be confirmed by collecting historical growth data and historical fertilization data of the crops. Optionally, the type of nutrients corresponding to the soil characteristic data can also be determined by consulting data.

[0039] Specifically, in the embodiment of the present application, the first fertilizer raw material type is compound fertilizer, and the soil data can be data such as organic matter content, nitrogen content, phosphorus content, potassium content, and pH value. The correspondence between the soil data and the first fertilizer raw material type can be sorted out in advance. The following is an example of a specific correspondence:

[0040] In the embodiment of the present application, the first fertilizer raw material type is polyglutamic acid fertilizer, and the nutrient absorption characteristics can be high nitrogen accumulation type, high phosphorus sensitivity type, high potassium requirement type, and calcium and magnesium high efficiency type. The following is an example of the correspondence between a specific nutrient absorption characteristic and the type of fertilizer raw material:

[0041] Determining the type of fertilizer raw material ultimately required for crop planting and the corresponding fertilization method based on the first fertilizer raw material type and the second fertilizer raw material type can specifically be: inputting the crop type, crop growth period, the first fertilizer raw material type, and the second fertilizer raw material type into a pre-trained fertilizer mixing and proportioning model, and outputting the type of fertilizer raw material ultimately required for crop planting and the corresponding fertilization method. The fertilizer mixing and proportioning model is a neural network model (such as a CNN model).

[0042] The fertilizer mixing ratio model is trained by pre-collecting training set data. Each training set data includes data on crop type, crop growth period, first fertilizer raw material type (compound fertilizer type), and second fertilizer raw material type (polyglutamic acid).

[0043] Specifically, the acquired training set data is first preprocessed, and then the preprocessed training set data is input into the fertilizer mixing ratio model, and the initial fertilizer type and initial fertilization method are output. The system calculates the total error according to the loss function of the neural network model and adjusts the weight of the loss function. After multiple iterative training, when the function value of the loss function meets the preset threshold range, the model training is stopped to obtain a trained fertilizer mixing ratio model.

[0044] In the embodiment of the present application, the fertilizer mixing ratio model may be structured as a convolutional neural network (CNN). Taking the fertilizer mixing ratio model as an example of a CNN structure, the network architecture of the fertilizer mixing ratio model may specifically include the following: Convolutional layer: This layer is primarily used to extract features from the input data (i.e., mapping each training set data point to the hidden feature space). The convolution kernel size can be determined based on the application, for example, (3, 3). Optionally, to reduce computational complexity and improve efficiency, the convolution kernel size of all convolutional layers can also be set to (3, 3). Optionally, to improve the model's expressiveness, nonlinear factors can be introduced by adding an activation function. In this embodiment, the activation function is "ReLU (Rectified Linear Unit)."

[0045] Pooling layer: Alternate with the convolution layer. Specifically, a pooling layer is set after the first convolution layer and after the second convolution layer. The pooling layer is used to perform downsampling (pooling) operations. The downsampling operation is basically the same as the convolution operation, except that the downsampling convolution kernel only takes the maximum value (max pooling) or the average value (mean pooling) of the corresponding position.

[0046] Fully connected layer: It can map the learned "distributed feature representation" to the sample label space. It mainly plays the role of "classifier" in the entire convolutional neural network. Each node in the fully connected layer is connected to all the nodes output by the previous layer. Among them, a node in the fully connected layer is called a neuron in the fully connected layer. The number of neurons in the fully connected layer can be determined according to the needs of the actual application. For example, in this convolutional neural network model, the number of neurons in the fully connected layer can be set to 512, or it can be set to 128, etc. Similar to the convolutional layer, in the fully connected layer, nonlinear factors can be optionally added by adding an activation function. For example, the activation function sigmoid (S-type function) can be added.

[0047] In an embodiment of the present application, one or more fully connected layers can be set in the fertilizer mixing ratio model, and the number of neurons in each fully connected layer can be set to different numbers, which can be set according to actual application needs. For example, the number of neurons in the first fully connected layer can be set to 1024, and the number of neurons in the second fully connected layer can be set to 512.

[0048] Loss layer: For convolutional neural networks, the loss layer is used to calculate the difference between the data prediction value corresponding to each training set data (initial fertilizer type and initial fertilization method) and the actual pre-set data true value (pre-set crop type, crop growth period, first fertilizer raw material type (compound fertilizer type), second fertilizer raw material type (polyglutamic acid) parameters), and continuously correct and optimize the parameters in the fertilizer mixture ratio model through the back propagation algorithm to obtain the fertilizer mixture ratio model. The loss function can use the softmax function or the cross entropy loss function.

[0049] It should be noted that, in an embodiment of the present invention, it may also include an input layer for inputting data and an output layer for outputting data, as well as some other layers that can be set, such as a normalization (BatchNormalization) layer and a linear rectification layer (ReLU), which will not be described in detail here.

[0050] It should be noted that after the model training is completed, the system can also test the validity of the trained model, such as checking whether the model contains the fertilizer components required by the target species (such as polyglutamic acid and compound fertilizer), and verifying whether the fertilizer ratio output by the model is within a safe range (such as whether the amount of polyglutamic acid added meets the requirements and whether various nutrient requirement elements exceed the standard).

[0051] Optionally, the fertilization method refers to a method of combining polyglutamic acid and compound fertilizer, which may include: physical mixing, chemical chelation and coating treatment.

[0052] 204. Generate combined fertilization plans corresponding to different growth cycles based on the types of fertilizer raw materials and fertilization methods, wherein multiple combined fertilization plans are generated for each growth cycle.

[0053] Specifically, the combined fertilization scheme combines polyglutamic acid and compound fertilizer. Based on the fertilizer raw material type and corresponding fertilization method, a corresponding polyglutamic acid and compound fertilizer combination scheme is generated for each growth cycle. Physical mixing involves uniformly mixing polyglutamic acid and compound fertilizer in a specific ratio; chemical chelation involves chemically combining polyglutamic acid with nutrients in the compound fertilizer to form a stable chelate; and coating involves coating the surface of compound fertilizer particles with a polyglutamic acid film. Compound fertilizer is compounded based on the fertilizer raw material type. Specifically, after determining the crop growth cycle, the nutrient absorption characteristics corresponding to that growth cycle, the fertilizer raw material type, and the fertilization method, a compound fertilizer production formula calculation model can be used to generate a polyglutamic acid and compound fertilizer combination scheme for each growth cycle. Optionally, the compound fertilizer production formula calculation model can be a mathematical model or a neural network model. Specifically, when the compound fertilizer production formula calculation model is a neural network model, historical fertilization data can be collected to train the neural network model and achieve convergence to obtain the trained compound fertilizer production formula calculation model. Optionally, historical fertilization data may include fertilization time, fertilizer amount and fertilization plan, and corresponding historical crop growth data may also be collected, and optionally, historical crop growth data may include crop plant height, leaf area and chlorophyll content, etc. The corresponding historical fertilization data and historical crop growth data are collected for data integration to obtain a training set, and the training set can be used to train the compound fertilizer production formula calculation model to converge. After obtaining the trained compound fertilizer production calculation model, the growth cycle, the nutrient absorption characteristics corresponding to the growth cycle, the type of raw fertilizer and the fertilization method can be input into the compound fertilizer production formula calculation model to obtain a combination scheme of polyglutamic acid and compound fertilizer corresponding to each growth cycle, wherein the compound fertilizer production formula calculation model can also output the ratio of each fertilizer raw material of the compound fertilizer. Optionally, the compound fertilizer production formula calculation model and the first fertilizer effect prediction model can be the same model or different models.

[0054] 205. Input the multiple combined fertilization schemes into a first fertilizer effect prediction model to obtain evaluation results of the multiple combined fertilization schemes.

[0055] Optionally, the first fertilizer effect prediction model can be a mathematical model. Before obtaining the evaluation results of multiple combined fertilization schemes, a crop growth model and a soil nutrient content conversion model can be constructed, and the first fertilizer effect prediction model can be constructed based on the crop growth model and the soil nutrient content conversion model. Optionally, the soil nutrient content conversion model can obtain the nutrient content of the soil during the fertilization process. Specifically, the nutrient content of the soil includes the content of essential nutrients such as nitrogen, phosphorus, and potassium in the soil. The content of these elements directly affects the growth and development of plants. In actual operation, the nutrient content is usually determined by soil testing and laboratory analysis. In the process of obtaining the soil nutrient content, the empirical relationship between soil type and organic matter content can be used to calculate the nutrient content. For example, the content of available nitrogen, available phosphorus, and available potassium in the soil can be estimated based on the soil type and organic matter content, thereby obtaining soil nutrient content data. Specifically, the crop growth model may include models such as dry matter accumulation, leaf area development, and root growth. Optionally, the crop growth model describes the accumulation process of crop dry matter, and the dry matter weight of the crop can be calculated based on the soil nutrient content, soil water content, crop dry matter production rate, and crop dry matter respiration rate. According to the crop growth model and the soil nutrient content conversion model, the growth process of the crops in the current combined fertilization scheme can be simulated and calculated, so that the fertilization combination scheme can be evaluated based on the dry matter quality of the crops. Specifically, the crop dry matter quality and the evaluation score can be a mapping function relationship, and the evaluation score can be determined by the crop dry matter quality. Optionally, the first fertilizer effect prediction model can also be a neural network model, which is trained using historical fertilization data and historical crop growth data, and the fertilization combination scheme is input into the trained first fertilizer prediction model to obtain the evaluation score corresponding to the fertilization combination scheme.

[0056] 206. Determine an optimal combined fertilization scheme based on the evaluation results of the multiple combined fertilization schemes.

[0057] Specifically, the evaluation score can reflect the quality of crop growth under the combined fertilization scheme. Optionally, the first fertilizer effect prediction model can simulate the process of crop growth by combining the specific data of the fertilization scheme, thereby obtaining crop growth data, and evaluating the combined fertilization scheme based on the crop growth data. Specifically, if the evaluation results show that the crop growth data is the best, then the combined fertilization scheme is determined to be the optimal combined fertilization scheme. Specifically, when evaluating the combined fertilization scheme in combination with the crop growth data, different evaluation criteria can be set according to different crops. For example, the standard for good wheat growth is different from the standard for good rice growth. Using different evaluation criteria, the crop growth data and the evaluation results are mapped to each other to ensure that the evaluation meets the needs of crop growth.

[0058] The embodiment of the present application designs a combination of polyglutamic acid and compound fertilizer according to the needs of crops, and then selects the solution with the best effect on crop growth based on the evaluation model, ensuring that crops obtain the required nutrients and reducing production costs.

[0059] In one embodiment of the present application, the environmental conditions include soil data, and determining the type of fertilizer raw material and the fertilization method based on the environmental conditions and nutrient absorption characteristics includes: Determine the first fertilizer raw material type required by the soil based on the soil data; determine the second fertilizer raw material type required by the crops based on the nutrient absorption characteristics; and determine the fertilization method based on the first fertilizer raw material type and the second fertilizer raw material type.

[0060] Specifically, based on environmental conditions, such as soil data, the type of the first fertilizer raw material required for the soil is determined, and based on the nutrient absorption characteristics of the crops, the type of the second fertilizer raw material is determined. Based on the first fertilizer raw material type and the second fertilizer raw material type, the type of fertilizer raw material ultimately required for crop planting and the corresponding fertilization method are determined. Optionally, the fertilization method refers to a method of combining polyglutamic acid and compound fertilizer, including: physical mixing, chemical chelation and coating treatment. In a specific embodiment, for example: based on the pH value, organic matter content and moisture content of the soil, it is determined that the soil currently needs nitrogen. When the crop is corn, the nutrient absorption characteristics of corn during the seed germination period are that nitrogen, phosphorus and potassium are required. Then, based on the first fertilizer raw material required by the soil and the second fertilizer raw material determined based on the nutrient absorption characteristics of the crop, it is determined that the type of fertilizer raw material ultimately required is nitrogen, phosphorus and potassium. Furthermore, based on the growth characteristics of corn during the seed germination period, the fertilization method is determined to be physical mixing.

[0061] The embodiment of the present application determines the required fertilizer type based on the fertilizer raw materials required for crop growth in combination with soil conditions and crop needs, thereby improving fertilizer matching.

[0062] In one embodiment of the present application, the soil data includes soil type, soil pH value, organic matter content, and moisture content. Determining the type of the first fertilizer raw material required by the soil based on the soil data includes: After collecting soil samples for planting the crops, the soil type is determined; the soil samples are analyzed to obtain the pH value, organic matter content and moisture content of the soil; and the type of first fertilizer raw material required for the soil is determined based on the soil type, pH value, organic matter content and moisture content.

[0063] Specifically, samples of the soil where crops are to be planted are collected to determine the type of soil. Specifically, soil types include sandy soil, clay, loam, etc., and the soil type can be determined based on geological and agricultural practice experience. After determining the soil type, the soil sample is analyzed to determine the pH value, organic matter content, and moisture content of the soil. After obtaining the pH value, organic matter content, and moisture content of the soil, the water retention capacity and nutrient content of the soil can be calculated. Specifically, the water retention capacity of the soil mainly depends on the particle composition and organic matter content of the soil. Generally, soil with a higher clay content has a stronger water retention capacity, while soil with a higher sand content has a weaker water retention capacity. By obtaining the soil particle composition and organic matter content data in the soil characteristic parameters, the water retention capacity data can be calculated using an empirical formula or a soil moisture retention curve. Optionally, the soil particle composition can be content data of sand particles, silt particles, clay particles, etc.

[0064] Specifically, the nutrient content of the soil includes the content of essential nutrients such as nitrogen, phosphorus, and potassium in the soil. The content of these elements directly affects the growth and development of plants. In actual operation, the nutrient content is usually determined by soil testing and laboratory analysis. During the simulation process, the empirical relationship between soil type and organic matter content can be used to calculate the nutrient content. For example, the content of available nitrogen, available phosphorus, and available potassium in the soil can be estimated based on the soil type and organic matter content to obtain the nutrient content data of the soil. Based on the nutrient content data of the soil, the type of the first fertilizer raw material required by the soil is determined. For example, if the soil nutrient content shows a lack of nitrogen, the first fertilizer raw material type is nitrogen fertilizer.

[0065] In the embodiment of the present application, soil samples are collected to determine soil data and thus the type of the first fertilizer raw material is determined, thereby improving the matching degree of the fertilizer.

[0066] In one embodiment of the present application, the fertilization method is a combination of polyglutamic acid and compound fertilizer. The combined fertilization scheme corresponding to different growth cycles is generated according to the type of fertilizer raw materials and the fertilization method, including: According to the type of fertilizer raw materials and the fertilization method, a corresponding combination scheme of polyglutamic acid and compound fertilizer is generated for each growth cycle, wherein the fertilization method includes: physical mixing, chemical chelation and coating treatment; the physical mixing is to uniformly mix the polyglutamic acid and compound fertilizer in a certain proportion; the chemical chelation is to combine the polyglutamic acid with the nutrient elements in the compound fertilizer through a chemical reaction to form a stable chelate; the coating treatment is to wrap a layer of polyglutamic acid film on the surface of the compound fertilizer particles, wherein the compound fertilizer is compounded according to the type of fertilizer raw materials.

[0067] Specifically, for each crop being analyzed, various polyglutamic acid and compound fertilizer combination schemes are designed, such as physical mixing, chemical chelation, and coating treatment, during each growth cycle or critical period. Specific process parameters are determined for each scheme. Optional fertilization methods include physical mixing, chemical chelation, and coating treatment.

[0068] Physical mixing is to mix polyglutamic acid and compound fertilizer directly in a certain proportion. This method is simple, easy, low-cost, and suitable for most compound fertilizer production lines. Optionally, the mixing ratio of polyglutamic acid and compound fertilizer can be determined according to crop needs and soil conditions. It is generally recommended to add 6-10kg / ton of polyglutamic acid fermentation liquid or a corresponding amount of polyglutamic acid powder to the compound fertilizer. Use a twin-shaft paddle mixer or a drum mixer to ensure uniform mixing. Depending on the equipment performance and raw material characteristics, the mixing time is generally controlled at 10-20 minutes. For example: in a watermelon base, using 300g / mu of 5% polyglutamic acid stock solution mixed with compound fertilizer and then flushing can increase the fruit setting rate and fruit expansion rate of watermelons and increase yield.

[0069] Alternatively, chemical chelation involves chemically combining polyglutamic acid with nutrients in compound fertilizers (such as calcium and magnesium) to form a stable chelate. This bonding method can significantly improve nutrient stability and utilization. Chemical chelation involves heating the polyglutamic acid fermentation broth to 60-80°C, adjusting the pH to 3-3.5, and maintaining the mixture for 6-8 hours to degrade the polyglutamic acid into a suitable molecular weight. Chelation reaction: The treated polyglutamic acid is mixed with calcium nitrate tetrahydrate in a specific ratio, adjusting the pH to 6-7, and stirring for 60-120 minutes. Surfactants (such as sodium dodecylbenzenesulfonate) and preservatives (such as potassium sorbate) can be added as needed. For example, in the preparation of polyglutamic acid chelated calcium fertilizer, chemical chelation combines polyglutamic acid with calcium ions, improving the absorption efficiency and fertilizer effectiveness of the calcium fertilizer.

[0070] Specifically, the coating treatment is to wrap a layer of polyglutamic acid film on the surface of the compound fertilizer particles to form a slow-release effect and prolong the fertilizer effect. This method is suitable for crops that require long-term fertilizer supply. The materials needed for the coating treatment are polyglutamic acid coating agents, which can be polyglutamic acid solutions or powders. Specifically, the compound fertilizer particles are sent into the coating system. The polyglutamic acid coating agent is sprayed on the surface of the particles, and an anti-caking agent is added at the same time. The coating agent is evenly attached to the surface of the particles and solidified by rotation, heating or spraying. The coating thickness is adjusted according to the type of fertilizer and the needs of the crops. Generally, the coating thickness is controlled within a certain range to ensure the slow-release effect. For example: when polyglutamic acid coated compound fertilizer is used in cotton planting, it can improve the cotton's peach setting rate and resistance to wilt by stably releasing nutrients.

[0071] The embodiment of the present application increases crop yield by generating different combinations of polyglutamic acid and compound fertilizer corresponding to different crops.

[0072] In one embodiment of the present application, the method of generating a corresponding combination scheme of polyglutamic acid and compound fertilizer for each growth cycle according to the type of fertilizer and the fertilization method includes: The growth cycle, the nutrient absorption characteristics corresponding to the growth cycle, the type of raw fertilizer and the fertilization method are input into the compound fertilizer production formula calculation model to obtain a combination scheme of polyglutamic acid and compound fertilizer corresponding to each growth cycle, wherein the combination scheme also includes the ratio of each fertilizer raw material of the compound fertilizer.

[0073] Specifically, the compound fertilizer production formula calculation model can improve production efficiency, reduce costs, and optimize crop nutrition management. Through this software, the growth cycle, the corresponding nutrient absorption characteristics of the growth cycle, the type of raw fertilizer, and the fertilization method can be input into the compound fertilizer production formula calculation model. Based on the set target formula and crop needs, the model automatically calculates the optimal production formula and provides detailed formula ratios and cost estimates. Specifically, before using the compound fertilizer production formula calculation model to determine a combination plan, fertilization targets can be preset. The model generates a corresponding combination plan based on the input data and the preset fertilization targets. Specific fertilization targets may include: combining base fertilizer with topdressing, phased fertilization, balanced fertilization, and scientific fertilization methods. Specifically, combining base fertilizer with topdressing means that the base fertilizer is primarily organic fertilizer, supplemented with an appropriate amount of chemical fertilizer to provide a long-term nutrient supply for crops; topdressing is supplemented during critical periods based on the crop's growth needs. Phased fertilization means that fertilizer is applied to the soil in stages based on the crop's growth cycle and nutrient requirements to ensure that crops receive adequate nutrients at different growth stages. Balanced fertilization focuses on ensuring a balanced supply of nutrients like nitrogen, phosphorus, and potassium, while also supplementing with trace elements to meet the needs of comprehensive crop growth. Scientific fertilization involves using appropriate fertilization methods, such as furrow fertilization, hole fertilization, and foliar spraying, to improve fertilizer utilization and minimize nutrient loss.

[0074] Specifically, please refer to Table 1, which shows several embodiments of combining the output of the compound fertilizer production formula calculation model with the fertilization plan.

[0075]

[0076] Table 1 The embodiment of the present application accurately calculates the optimal raw material ratio through a model, avoiding the tediousness and errors of manual calculation.

[0077] In one embodiment of the present application, before inputting the multiple combined fertilization schemes into the first fertilizer effect prediction model to obtain evaluation results of the multiple combined fertilization schemes, the method includes: A crop growth model and a soil nutrient conversion model are constructed; and a first fertilizer effect prediction model is constructed based on the crop growth model and the soil nutrient conversion model.

[0078] Specifically, optionally, the first fertilizer effect prediction model can be a mathematical model. Before obtaining the evaluation results of multiple combined fertilization schemes, a crop growth model and a soil nutrient content conversion model can be constructed, and the first fertilizer effect prediction model can be constructed based on the crop growth model and the soil nutrient content conversion model. Optionally, the soil nutrient content conversion model can obtain the nutrient content of the soil during the fertilization process. Specifically, the nutrient content of the soil includes the content of essential nutrients such as nitrogen, phosphorus, and potassium in the soil. The content of these elements directly affects the growth and development of plants. In actual operation, the nutrient content is usually determined by soil testing and laboratory analysis. In the process of obtaining the soil nutrient content, the empirical relationship between soil type and organic matter content can be used to calculate the nutrient content. For example, the content of available nitrogen, available phosphorus, and available potassium in the soil can be estimated based on the soil type and organic matter content, thereby obtaining soil nutrient content data. Specifically, the crop growth model may include models such as dry matter accumulation, leaf area development, and root growth. Optionally, the crop growth model describes the accumulation process of crop dry matter, and the dry matter weight of the crop can be calculated based on the soil nutrient content, soil water content, crop dry matter production rate, and crop dry matter respiration rate. According to the crop growth model and the soil nutrient content conversion model, the growth process of the crops in the current combined fertilization scheme can be simulated and calculated, so that the fertilization combination scheme can be evaluated based on the dry matter quality of the crops. Specifically, the crop dry matter quality and the evaluation score can be a mapping function relationship, and the evaluation score can be determined by the crop dry matter quality. Optionally, the first fertilizer effect prediction model can also be a neural network model, which is trained using historical fertilization data and historical crop growth data, and the fertilization combination scheme is input into the trained first fertilizer prediction model to obtain the evaluation score corresponding to the fertilization combination scheme.

[0079] The embodiment of the present application constructs a first fertilizer effect prediction model based on the crop growth model, which facilitates the evaluation of the combined fertilization plan and improves the accuracy of the evaluation.

[0080] In one embodiment of the present application, the method further includes: After fertilizing using the optimal combined fertilization scheme, fertilization data is collected, wherein the fertilization data includes fertilization time data, fertilization amount data and fertilization area data; crop growth data is collected; correlation analysis is performed on the fertilization data and the crop growth data to obtain correlation data; a second fertilizer effect prediction model is constructed based on the fertilization data, the crop growth data and the correlation data, wherein the first fertilizer effect prediction model and the second fertilizer effect prediction model are different models; the fertilization data is input into the second fertilizer effect prediction model to obtain updated fertilization data, wherein the updated fertilization data is the fertilization data adjusted by the model calculation.

[0081] Specifically, the crop growth data may include plant height data, leaf area data, and chlorophyll content data of the target crop. Optionally, plant height data, leaf area data, and chlorophyll content data of the crop can be collected in real time using a data collection device. Upon receiving the collection instruction, the data collection device collects the crop growth data in real time and uploads it to a cloud database for storage. Optionally, a correlation analysis is performed on the fertilization data and the crop growth data. Based on the correlation between the crop growth data and the fertilization data, a second fertilizer effect prediction model is constructed. The second fertilizer effect prediction model can be different from the first fertilizer effect prediction model. For example, if the first fertilizer effect model is a neural network model, the second fertilizer effect model can be a mathematical model. The collected fertilization data and crop growth data are cleaned to remove outliers and duplicate data, and then the cleaned data is integrated to form a complete data set. The data set is visualized using charts, images, and other means to more intuitively understand the relationship between fertilization and crop growth. A correlation analysis algorithm is used to determine the degree of correlation between the fertilization data and the crop growth data. For example, the impact of different fertilizer application rates on crop yields can be analyzed to determine the optimal fertilizer application range. A machine learning algorithm is used to train fertilization data to build a predictive model. This model can automatically adjust fertilization plans based on historical fertilization data and crop growth data to increase crop yields. For example, corn has a higher demand for nitrogen fertilizer during the tasseling and grain filling stages. If the output data from the second fertilizer prediction model indicates that the corn leaves are grayish and curled when fertilized according to the current fertilizer combination plan, it indicates a nitrogen deficiency. The amount and ratio of nitrogen fertilizer in the fertilizer combination plan can be adjusted accordingly.

[0082] The embodiment of the present application verifies the fertilization effect of the fertilization combination scheme and adjusts the fertilization scheme to determine the most suitable combination scheme of polyglutamic acid and compound fertilizer for specific crops and soil conditions.

[0083] To facilitate better implementation of the analytical method for combining polyglutamic acid with compound fertilizer provided in the embodiments of the present application, the embodiments of the present application also provide a device based on the analytical method for combining polyglutamic acid with compound fertilizer. The meanings of the terms herein are the same as those in the analytical method for combining polyglutamic acid with compound fertilizer described above. For specific implementation details, reference can be made to the description in the embodiment of the analytical method for combining polyglutamic acid with compound fertilizer.

[0084] The analytical device for combining polyglutamic acid with compound fertilizer in the embodiments of the present application has the function of implementing the analytical method for combining polyglutamic acid with compound fertilizer provided in the above embodiments. The functions can be implemented by hardware or by executing corresponding software implementations in hardware. The hardware or software includes one or more modules corresponding to the above functions, and the modules can be software and / or hardware.

[0085] See also Figure 3 , Figure 3 This is a schematic diagram of the structure of an analysis device for combining polyglutamic acid with compound fertilizer provided in an embodiment of the present application. The analysis device for combining polyglutamic acid with compound fertilizer can be applied to a computing device in a scenario where content push is required. Specifically, the analysis device 300 for combining polyglutamic acid with compound fertilizer may include a cycle determination module 301, a nutrient determination module 302, a fertilizer determination module 303, a generation module 304, an input module 305, and an evaluation module 306, as follows: The cycle determination module 301 is used to determine the growth cycle and critical period of the crops for which the fertilization scheme needs to be analyzed; Nutrient determination module 302: used to determine the environmental conditions for the growth of the crop and the nutrient absorption characteristics; Fertilizer determination module 303: used to determine the type of fertilizer raw materials and fertilization method according to the environmental conditions and nutrient absorption characteristics; Generating module 304: for generating a combined fertilization plan corresponding to different growth cycles according to the type of fertilizer raw materials and fertilization method; Input module 305: used to input the multiple combined fertilization schemes into the first fertilizer effect prediction model to obtain evaluation results of the multiple combined fertilization schemes; Evaluation module 306: used to determine the optimal combined fertilization scheme according to the evaluation results of the multiple combined fertilization schemes.

[0086] In one embodiment, the environmental conditions include soil data, and the fertilizer determination module 303 is specifically configured to: determining the type of first fertilizer raw material required by the soil according to the soil data; determining the type of second fertilizer raw material required by the crop according to the nutrient absorption characteristics; The fertilizing method is determined according to the first fertilizer raw material type and the second fertilizer raw material type.

[0087] In one embodiment, the soil data includes soil type, soil pH value, organic matter content, and water content. The fertilizer determination module 303 is further configured to: After collecting soil samples where the crops are grown, determining the soil type; Analyzing the soil sample to obtain the pH value, organic matter content, and moisture content of the soil; The type of first fertilizer raw material required by the soil is determined according to the soil type, pH value, organic matter content and moisture content.

[0088] In one embodiment, the fertilization method is a combination of polyglutamic acid and compound fertilizer, and the generation module 304 is specifically used to: According to the type of fertilizer raw materials and the fertilization method, corresponding to each growth cycle, a corresponding combination scheme of polyglutamic acid and compound fertilizer is generated, wherein the fertilization method includes: physical mixing, chemical chelation and coating treatment; The physical mixing is to mix the polyglutamic acid and the compound fertilizer uniformly in a certain proportion; The chemical chelation is to combine polyglutamic acid with the nutrient elements in the compound fertilizer through a chemical reaction to form a stable chelate; The coating treatment is to coat the surface of the compound fertilizer particles with a layer of polyglutamic acid film, wherein the compound fertilizer is compounded according to the types of the fertilizer raw materials.

[0089] In one embodiment, the generating module 304 is further configured to: The growth cycle, the nutrient absorption characteristics corresponding to the growth cycle, the type of fertilizer raw materials and the fertilization method are input into a compound fertilizer production formula calculation model to obtain a combination scheme of polyglutamic acid and compound fertilizer corresponding to each growth cycle, wherein the combination scheme also includes the ratio of each fertilizer raw material in the compound fertilizer.

[0090] In one embodiment, the input module is specifically configured to: Construct crop growth models and soil nutrient transformation models; A first fertilizer effect prediction model is constructed based on the crop growth model and the soil nutrient conversion model.

[0091] In one embodiment, the device is further configured to: After fertilizing using the optimal combined fertilization scheme, collecting fertilization data, wherein the fertilization data includes fertilization time data, fertilization amount data, and fertilization area data; Collect crop growth data; Performing correlation analysis on the fertilization data and the crop growth data to obtain correlation data; constructing a second fertilizer effect prediction model based on the fertilization data, the crop growth data, and the correlation data, wherein the first fertilizer effect prediction model and the second fertilizer effect prediction model are different models; The fertilization data is input into a second fertilizer effect prediction model to obtain updated fertilization data, wherein the updated fertilization data is the fertilization data adjusted by model calculation.

[0092] The embodiment of the present application designs a combination of polyglutamic acid and compound fertilizer according to the needs of crops, and then selects the solution with the best effect on crop growth based on the evaluation model, ensuring that crops obtain the required nutrients and reducing production costs.

[0093] The above describes the analytical device for combining polyglutamic acid with compound fertilizer in the embodiment of the present application from the perspective of modular functional entities. The following describes the analytical device for combining polyglutamic acid with compound fertilizer in the embodiment of the present application from the perspective of hardware processing.

[0094] It should be noted that Figure 3 The physical device corresponding to the input module 305 shown may be a transceiver, a radio frequency circuit, a communication module, an input / output (I / O) interface, etc., and the physical device corresponding to the generation module 304 may be a processor.

[0095] Figure 4 The devices shown can all have Figure 3 The structure shown, when Figure 3 The analytical device for combining polyglutamic acid with compound fertilizer has the following features: Figure 4 When the structure shown is Figure 4 The processor and transceiver in the embodiment can realize the same or similar functions as the input module 305 and the generation module 304 provided in the aforementioned device embodiment corresponding to the device. Figure 4 The memory in the storage processor executes the computer program that needs to be called when the above-mentioned analytical method for combining polyglutamic acid with compound fertilizer is executed.

[0096] When the computing device in the embodiment of the present application is a terminal device, the embodiment of the present application also provides a terminal device, such as Figure 5 For ease of explanation, only the parts related to the embodiments of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiments of the present application. The terminal device can be any terminal device including a mobile phone, tablet computer, personal digital assistant (PDA), point of sales (POs), car computer, etc., taking the mobile phone as an example: Figure 5 The block diagram shows a partial structure of a mobile phone related to the terminal device provided in the embodiment of the present application. Figure 5 The mobile phone includes components such as a radio frequency (RF) circuit 1010, a memory 1020, an input unit 1030, a display unit 1040, a sensor 1050, an audio circuit 1060, a wireless fidelity (WiFi) module 1070, a processor 1080, and a power supply 1090. Those skilled in the art will understand that Figure 5 The mobile phone structure shown in the figure does not constitute a limitation to the mobile phone, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0097] The following combination Figure 5 A detailed introduction to the various components of a mobile phone: The RF circuit 1010 can be used to receive and send signals during information transmission or calls. In particular, after receiving downlink information from the base station, it is sent to the processor 1080 for processing; in addition, the designed uplink data is sent to the base station. Generally, the RF circuit 1010 includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (LNA), a duplexer, etc. In addition, the RF circuit 1010 can also communicate with the network and other devices through wireless communication. The above-mentioned wireless communication can use any communication standard or protocol, including but not limited to Global System of Mobile Communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, short messaging service (SMS), etc.

[0098] The memory 1020 can be used to store software programs and modules. The processor 1080 executes the various functional applications and data processing of the mobile phone by running the software programs and modules stored in the memory 1020. The memory 1020 may mainly include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory 1020 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0099] The input unit 1030 can be used to receive input digital or character information, and to generate key signal input related to the user settings and function control of the mobile phone. Specifically, the input unit 1030 may include a touch panel 1031 and other input devices 1032. The touch panel 1031, also known as a touch screen, can collect user touch operations on or near it (such as operations performed by the user using any suitable object or accessory such as a finger, stylus, etc. on or near the touch panel 1031) and drive the corresponding connection device according to a pre-set program. Optionally, the touch panel 1031 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch direction and detects the signal caused by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device and converts it into touch point coordinates, which are then sent to the processor 1080. It can also receive commands sent by the processor 1080 and execute them. In addition, the touch panel 1031 can be implemented using various types such as resistive, capacitive, infrared and surface acoustic wave. In addition to the touch panel 1031, the input unit 1030 may further include other input devices 1032. Specifically, the other input devices 1032 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick.

[0100] The display unit 1040 can be used to display information input by the user or information provided to the user and various menus of the mobile phone. The display unit 1040 may include a display panel 1041. Optionally, the display panel 1041 may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. Further, the touch panel 1031 may cover the display panel 1041. When the touch panel 1031 detects a touch operation on or near it, it is transmitted to the processor 1080 to determine the type of touch event. Subsequently, the processor 1080 provides corresponding visual output on the display panel 1041 according to the type of touch event. Although in Figure 5 In the embodiment, the touch panel 1031 and the display panel 1041 are used as two independent components to realize the input and output functions of the mobile phone, but in some embodiments, the touch panel 1031 and the display panel 1041 can be integrated to realize the input and output functions of the mobile phone.

[0101] The mobile phone may also include at least one sensor 1050, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel 1041 according to the brightness of the ambient light, and the proximity sensor may turn off the display panel 1041 and / or the backlight when the mobile phone is moved to the ear. As a type of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in all directions (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for other sensors that the mobile phone can also be configured with, such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., they will not be described here.

[0102] Audio circuit 1060, speaker 1061, and microphone 1062 provide an audio interface between the user and the phone. Audio circuit 1060 converts received audio data into electrical signals and transmits them to speaker 1061, which then converts them into sound signals for output. Microphone 1062, on the other hand, converts collected sound signals into electrical signals, which are then received by audio circuit 1060 and converted into audio data. The audio data is then processed by processor 1080 and transmitted to, for example, another phone via RF circuit 1010, or stored in memory 1020 for further processing.

[0103] Wi-Fi is a short-range wireless transmission technology. The mobile phone can help users send and receive emails, browse the web, and access streaming media through the Wi-Fi module 1070. It provides users with wireless broadband Internet access. Figure 5 A Wi-Fi module 1070 is shown, but it is understandable that it is not an essential component of the mobile phone and can be omitted as needed without changing the essence of the invention.

[0104] Processor 1080 is the control center of the phone, connecting all parts of the phone using various interfaces and circuits. By running or executing software programs and / or modules stored in memory 1020 and accessing data stored in memory 1020, it executes various phone functions and processes data, thereby providing overall phone monitoring. Optionally, processor 1080 may include one or more processing units; alternatively, processor 1080 may integrate an application processor and a modem processor, with the application processor primarily handling the operating system, user interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into processor 1080.

[0105] The mobile phone also includes a power supply 1090 (such as a battery) for supplying power to various components. Optionally, the power supply can be logically connected to the processor 1080 through a power management system, thereby managing charging, discharging, and power consumption through the power management system.

[0106] Although not shown, the mobile phone may also include a camera, a Bluetooth module, etc., which will not be described in detail here.

[0107] In the embodiment of the present application, the processor 1080 included in the mobile phone also has the function of controlling the execution of the above analysis method process of combining polyglutamic acid with compound fertilizer performed by the analysis device for combining polyglutamic acid with compound fertilizer.

[0108] The present application also provides a server. Figure 6 , Figure 6 This is a schematic diagram of a server structure provided in an embodiment of the present application. The server 1100 may vary significantly due to configuration or performance differences and may include one or more central processing units (CPUs) 1122 (e.g., one or more processors), memory 1132, and one or more storage media 1130 (e.g., one or more mass storage devices) storing application programs 1142 or data 1144. The memory 1132 and storage media 1130 may be either transient or persistent storage. The program stored in the storage medium 1130 may include one or more modules (not shown), each of which may include a series of instruction operations on the server. Furthermore, the CPU 1122 may be configured to communicate with the storage medium 1130 to execute the series of instruction operations in the storage medium 1130 on the server 1100.

[0109] The server 1100 may also include one or more power supplies 1126, one or more wired or wireless network interfaces 1150, one or more input and output interfaces 1158, and / or one or more operating systems 1141, such as Windows server, Mac OS X, Unix, Linux, FreeBSD, etc.

[0110] The steps in the analytical method for combining polyglutamic acid with compound fertilizer in the above embodiment can be based on the Figure 6 The structure of the server 1100 is shown.

[0111] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0112] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0113] In the several embodiments provided in the embodiments of the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules 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 an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0114] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of the present embodiment according to actual needs.

[0115] In addition, the functional modules in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into a module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0116] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0117] The computer program product includes one or more computer instructions. When the computer program is loaded and executed on a computer, the process or function described in the embodiments of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be stored by a computer or a data storage device such as a server or data center that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, hard disk, tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)).

[0118] The above is a detailed introduction to the technical solutions provided in the embodiments of the present application. Specific examples are used in the embodiments of the present application to illustrate the principles and implementation methods of the embodiments of the present application. The description of the above embodiments is only used to help understand the methods and core ideas of the embodiments of the present application. At the same time, for those skilled in the art, according to the ideas of the embodiments of the present application, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the embodiments of the present application.

Claims

1. An analytical method combining polyglutamic acid and compound fertilizer, characterized in that: The analytical method for combining polyglutamic acid and compound fertilizer comprises: Identify the growth cycles and critical periods of crops that require analysis of fertilization programs; determining the environmental conditions and nutrient uptake characteristics for the growth of the crops; Determine the type of fertilizer raw materials and fertilization method based on the environmental conditions and nutrient absorption characteristics; Generating combined fertilization plans corresponding to different growth cycles according to the type of fertilizer raw materials and the fertilization method, wherein multiple combined fertilization plans are generated for each growth cycle; Inputting the plurality of combined fertilization schemes into a first fertilizer effect prediction model to obtain evaluation results of the plurality of combined fertilization schemes; An optimal combined fertilization scheme is determined according to the evaluation results of the multiple combined fertilization schemes.

2. The analytical method for combining polyglutamic acid and compound fertilizer according to claim 1, wherein The environmental conditions include soil data, and determining the type of fertilizer raw materials and the fertilization method based on the environmental conditions and nutrient absorption characteristics includes: determining the type of first fertilizer raw material required by the soil according to the soil data; determining the type of second fertilizer raw material required by the crop according to the nutrient absorption characteristics; The fertilizing method is determined according to the first fertilizer raw material type and the second fertilizer raw material type.

3. The analytical method of combining polyglutamic acid and compound fertilizer according to claim 2, wherein The soil data includes soil type, soil pH value, organic matter content, and water content. The determining of the type of first fertilizer raw material required by the soil based on the soil data includes: After collecting soil samples where the crops are grown, determining the soil type; Analyzing the soil sample to obtain the pH value, organic matter content, and moisture content of the soil; The type of first fertilizer raw material required by the soil is determined according to the soil type, pH value, organic matter content and moisture content.

4. The analytical method for combining polyglutamic acid and compound fertilizer according to claim 3, wherein The fertilization method is a combination of polyglutamic acid and compound fertilizer. According to the type of fertilizer raw materials and the fertilization method, a combined fertilization plan corresponding to different growth cycles is generated, including: According to the type of fertilizer raw materials and the fertilization method, corresponding to each growth cycle, a corresponding combination scheme of polyglutamic acid and compound fertilizer is generated, wherein the fertilization method includes: physical mixing, chemical chelation and coating treatment; The physical mixing is to mix the polyglutamic acid and the compound fertilizer uniformly in a certain proportion; The chemical chelation is to combine polyglutamic acid with the nutrient elements in the compound fertilizer through a chemical reaction to form a stable chelate; The coating treatment is to coat the surface of the compound fertilizer particles with a layer of polyglutamic acid film, wherein the compound fertilizer is compounded according to the types of the fertilizer raw materials.

5. The analytical method for combining polyglutamic acid and compound fertilizer according to claim 4, wherein The method of generating a combination scheme of polyglutamic acid and compound fertilizer corresponding to each growth cycle according to the fertilizer type and fertilization method includes: The growth cycle, the nutrient absorption characteristics corresponding to the growth cycle, the type of fertilizer raw materials and the fertilization method are input into a compound fertilizer production formula calculation model to obtain a combination scheme of polyglutamic acid and compound fertilizer corresponding to each growth cycle, wherein the combination scheme also includes the ratio of each fertilizer raw material in the compound fertilizer.

6. The analytical method for combining polyglutamic acid and compound fertilizer according to claim 1, wherein Before inputting the multiple combined fertilization schemes into the first fertilizer effect prediction model to obtain evaluation results of the multiple combined fertilization schemes, the method includes: Construct crop growth models and soil nutrient transformation models; A first fertilizer effect prediction model is constructed based on the crop growth model and the soil nutrient conversion model.

7. The analytical method for combining polyglutamic acid and compound fertilizer according to claim 1, wherein The method further comprises: After fertilizing using the optimal combined fertilization scheme, collecting fertilization data, wherein the fertilization data includes fertilization time data, fertilization amount data, and fertilization area data; Collect crop growth data; Performing correlation analysis on the fertilization data and the crop growth data to obtain correlation data; constructing a second fertilizer effect prediction model based on the fertilization data, the crop growth data, and the correlation data, wherein the first fertilizer effect prediction model and the second fertilizer effect prediction model are different models; The fertilization data is input into a second fertilizer effect prediction model to obtain updated fertilization data, wherein the updated fertilization data is the fertilization data adjusted by model calculation.

8. An analytical device combining polyglutamic acid and compound fertilizer, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein when the processor executes the computer program, the analytical method for combining polyglutamic acid and compound fertilizer as claimed in any one of claims 1 to 7 is implemented.

9. A computing device, characterized in that The invention comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein when the processor executes the computer program, the analytical method for combining polyglutamic acid and compound fertilizer as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The invention comprises instructions, which, when executed on a computer, enable the computer to execute the analytical method for combining polyglutamic acid and compound fertilizer according to any one of claims 1 to 7.

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