Anti-skid slow-release nutrition ecological bar formula optimization method in combination with environmental analysis

Abstract

The invention discloses an anti-skid slow-release nutrition ecological rod formula optimization method combined with environmental analysis, and relates to the technical field of nutrition ecological rods, and the method comprises the steps: obtaining the regional terrain information, regional climate condition information and regional growth vegetation type information of a target application region; obtaining a screening memory formula space; performing formula centralized identification in the screening memory formula space, and determining an initial nutrition ecological bar formula, initial region terrain information, initial region climate condition information and initial region growth vegetation type information; determining an optimized formula performance set; and performing directional optimization on the initial nutrition ecological bar formula by taking the optimized formula performance set as an optimization direction to obtain an optimized nutrition ecological bar formula. The technical problems that in the prior art, the fitting degree of a nutrition ecological bar formula and the actual application environment is not high, and formula optimization response is slow are solved. The technical effect of improving the formula optimization efficiency and the formula optimization reliability of the nutritional ecological bar is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of nutritional ecological bars, and in particular to a method for optimizing the formula of an anti-slip slow-release nutritional ecological bar combined with environmental analysis. Background Art

[0002] In the fields of agriculture and environmental protection, anti-slip slow-release nutrient eco-sticks have been widely used as a technical means to improve soil quality and promote plant growth. The formula of this type of eco-stick is usually combined with the physical and chemical properties of the soil, plant growth requirements and environmental conditions (such as climate and topography). Traditional formula design methods often rely on experience or optimization based on a single environmental factor, and it is difficult to take into account the synergistic effects of multiple environmental factors.

[0003] The existing technology has technical problems that the formula of the nutritional eco-bar is not highly compatible with the actual application environment and the formula optimization response is slow. Summary of the invention

[0004] The present application provides a method for optimizing the formula of an anti-slip sustained-release nutritional eco-bar combined with environmental analysis, which is used to solve the technical problems in the prior art that the formula of the nutritional eco-bar is not highly compatible with the actual application environment and the formula optimization response is slow.

[0005] In view of the above problems, the present application provides a method for optimizing the formula of an anti-slip slow-release nutritional ecological bar combined with environmental analysis, the method comprising:

[0006] Obtain regional terrain information, regional climate conditions information and regional vegetation type information of the target application area;

[0007] Using the regional terrain information, regional climate condition information and regional vegetation type information as indexes, the nutritional ecological stick memory formula storage library is screened according to a preset tolerance distance to obtain a screened memory formula space;

[0008] Performing concentrated formula identification in the screening memory formula space to determine the initial nutritional ecological stick formula and initial regional terrain information, initial regional climate condition information, and initial regional vegetation type information;

[0009] Determine an optimized formula performance set according to differences between initial regional terrain information, initial regional climate condition information, initial regional vegetation growth type information, and regional terrain information, regional climate condition information, and regional vegetation growth type information;

[0010] Taking the optimized formula performance set as the optimization direction, the initial nutrition eco-bar formula is directionally optimized to obtain the optimized nutrition eco-bar formula.

[0011] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0012] This application obtains the regional terrain information, regional climate condition information and regional vegetation type information of the target application area, and then uses the regional terrain information, regional climate condition information and regional vegetation type information as indexes, and performs formula screening on the nutrition ecological bar memory formula repository according to the preset tolerance distance, obtains the screening memory formula space, and then performs formula centralized identification in the screening memory formula space, determines the initial nutrition ecological bar formula and the initial regional terrain information, initial regional climate condition information and initial regional vegetation type information, and then determines the optimized formula performance set according to the initial regional terrain information, initial regional climate condition information and initial regional vegetation type information and the regional terrain information, regional climate condition information and regional vegetation type information. The difference between the information, the initial regional terrain information, the initial regional climate condition information and the initial regional vegetation type information and the regional terrain information, the regional climate condition information and the regional vegetation type information determines the optimized formula performance set, takes the optimized formula performance set as the optimization direction, performs direction optimization on the initial nutrition ecological bar formula, and obtains the optimized nutrition ecological bar formula. The optimization of the nutrition ecological bar formula to fit the application environment and improve the technical effect of the optimization quality is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Attached Figure 1 It is a schematic flow chart of a method for optimizing the formula of an anti-slip slow-release nutritional ecological bar combined with environmental analysis provided by an embodiment of the present invention.

[0014] Attached Figure 2 It is a flow chart of determining the initial nutritional eco-bar formula and initial regional terrain information, initial regional climate condition information and initial regional vegetation growth type information in an anti-slip slow-release nutritional eco-bar formula optimization method combined with environmental analysis provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0016] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0017] Embodiment, as attached Figure 1As shown, the present application provides a method for optimizing the formula of an anti-slip slow-release nutritional ecological bar combined with environmental analysis, wherein the method comprises:

[0018] S1: Obtaining regional terrain information, regional climate conditions information and regional vegetation type information of the target application area;

[0019] In a possible embodiment, the target application area refers to a specific area where the nutritional ecological bar is applied, such as farmland, mountainous area, desert, etc. The environmental characteristics of the target application area will determine the optimization direction of the formula. Optionally, by using the geographic information of the target application area, data collection is performed in a geographic information system (GIS) to obtain the regional terrain information, regional climate condition information and regional vegetation type information.

[0020] Among them, the regional terrain information reflects the terrain conditions of the target application area, including important parameters that affect the anti-skid performance, such as slope, altitude, height difference, terrain structure (such as plains, hills, mountains). The regional climate condition information reflects the environmental climate change conditions in the target area, including the temperature, humidity, rainfall, wind speed, etc. of the target area, which are closely related to the slow-release performance and material degradation rate. The regional vegetation type information refers to the differences in plant species (such as grasses, shrubs, trees), growth cycles, root structures and nutrient requirements in the region. This information determines the nutrient composition ratio of the eco-stick.

[0021] By obtaining the regional terrain information, regional climate conditions information and regional vegetation growth type information of the target application area, we can fully understand the basic situation of the target application area, provide accurate indexes for subsequent formula screening, and ensure that the designed eco-stick can adapt to the technical effects of specific application scenarios.

[0022] S2: Using the regional terrain information, regional climate condition information and regional vegetation type information as indexes, the nutritional ecological stick memory formula storage library is screened according to a preset tolerance distance to obtain a screened memory formula space;

[0023] Further, taking the regional terrain information, regional climate condition information and regional vegetation type information as indexes, the nutritional ecological stick memory formula storage library is screened according to a preset tolerance distance to obtain a screened memory formula space. Step S2 of the embodiment of the present application also includes:

[0024] Constructing a three-dimensional space, wherein the x-axis of the three-dimensional space is terrain information, the y-axis is climate condition information, the z-axis is vegetation type information, and the origin of the three-dimensional space is o;

[0025] Integrate the nutrition ecological bar memory formula storage base based on the three-dimensional space to obtain the nutrition ecological bar memory formula storage integration space;

[0026] A plane passing through the regional terrain information and parallel to the yoz plane is used as a first screening reference plane, and planes obtained by parallel shifting the first screening reference plane to the left by a preset tolerance distance and parallel shifting the first screening reference plane to the right by a preset tolerance distance are used as a screening left plane and a screening right plane;

[0027] A plane passing through the regional climate condition information and parallel to the xoz plane is used as a second screening reference plane, and the second screening reference plane is moved forward and backward in parallel by a preset tolerance distance to obtain a plane as a front screening plane and a rear screening plane;

[0028] A plane passing through the region where vegetation type information is grown and parallel to the xoy plane is used as a third screening reference plane, and the third screening reference plane is moved up and down in parallel by a preset tolerance distance to obtain a plane as an upper screening plane and a lower screening plane;

[0029] The space enclosed by the left screening plane, the right screening plane, the front screening plane, the rear screening plane, the upper screening plane and the lower screening plane in the nutritional ecological bar memory formula storage integration space is used as the screening memory formula space.

[0030] In one embodiment, the nutritional eco-bar memory formula repository is a database containing a large number of historical optimized formulas, each formula recording the material composition, ratio, and applicable environmental conditions. The high-dimensional data in the nutritional eco-bar memory formula repository (such as various formula parameters corresponding to different terrains, climates, and vegetation) is reduced in dimension by indexing, and only the data set related to the current environmental information is screened, thereby reducing the search range and obtaining the screened memory formula space. Among them, the screened memory formula space is a subset of formulas obtained by screening, and the formulas in this subset have a high adaptability to the target application area.

[0031] Preferably, a three-dimensional space is formed by setting three coordinate axes. The x-axis represents terrain information, the y-axis represents climate condition information, and the z-axis represents vegetation type information. The origin o is the reference point of the three-dimensional space coordinate system, and all regional environmental information (including regional terrain information, regional climate condition information, and regional vegetation type information) will be mapped through these axes.

[0032] Furthermore, each formula in the nutritional ecological bar memory formula storage library is associated with its corresponding environmental information (topography, climate, vegetation type), and the position of the formula is determined in three-dimensional space based on the terrain information, climate condition information, and growth vegetation type information. For example, if the terrain of a formula is mountainous, the climate is dry, and the plant types are mostly tropical forest plants, then the formula will form a coordinate component at the corresponding positions of the x-axis, y-axis, and z-axis, thereby obtaining the spatial coordinate points of the formula in three-dimensional space. According to the above process, the nutritional ecological bar memory formula storage library is integrated to obtain the sorted nutritional ecological bar memory formula storage integration space. It has achieved the technical effect of laying the foundation for subsequent rapid retrieval based on the index and improving the response rate of formula optimization.

[0033] The plane passing through the regional terrain information and parallel to the yoz plane is used as the first screening reference plane. The plane passing through the regional climate condition information and parallel to the xoz plane is used as the second screening reference plane. The plane passing through the regional vegetation type information and parallel to the xoy plane is used as the third screening reference plane.

[0034] That is, the first screening reference plane is used to screen out the formulas that meet the regional terrain information of the target application area in combination with the preset tolerance distance. This step helps to eliminate the formulas that are not suitable for the target terrain, thereby avoiding poor results caused by the mismatch between the formula and the actual terrain. The second screening reference plane is used to screen out the formulas that meet the climatic conditions in combination with the preset tolerance distance. The third screening reference plane is used to screen out the formulas that meet the vegetation type conditions in combination with the preset tolerance distance.

[0035] Optionally, the preset tolerance distance is the maximum moving distance of the screening reference plane when performing formula screening, which is preset by a person skilled in the art. The planes obtained by parallel shifting the first screening reference plane to the left by the preset tolerance distance and parallel shifting the first screening reference plane to the right by the preset tolerance distance are used as the left screening plane and the right screening plane, and the planes obtained by parallel shifting the second screening reference plane forward by the preset tolerance distance and parallel shifting the second screening reference plane backward by the preset tolerance distance are used as the front screening plane and the rear screening plane, and the planes obtained by parallel shifting the third screening reference plane upward by the preset tolerance distance and parallel shifting the third screening reference plane downward by the preset tolerance distance are used as the upper screening plane and the lower screening plane, thereby obtaining six planes of the screening memory space.

[0036] By using the space enclosed by the left screening plane, the right screening plane, the front screening plane, the rear screening plane, the upper screening plane and the lower screening plane in the nutritional eco-bar memory formula storage integrated space as the screening memory formula space, the technical effect of screening the formulas of the nutritional eco-bar memory formula storage repository and obtaining a formula subset that meets the environmental conditions of the target application area is achieved, while improving the targetedness and optimization response efficiency of subsequent formula optimization.

[0037] S3: performing formula centralized identification in the screening memory formula space to determine the initial nutritional ecological stick formula and initial regional terrain information, initial regional climate condition information, and initial regional vegetation type information;

[0038] Further, such as Figure 2 As shown, in the screening memory formula space, formula centralized identification is performed to determine the initial nutritional ecological bar formula and the initial regional terrain information, the initial regional climate condition information and the initial regional vegetation type information. Step S3 of the embodiment of the present application also includes:

[0039] Randomly extracting a first screening nutrition ecological bar memory formula from the screening memory formula space, and constructing a first centralized identification neighborhood according to a preset centralized identification bandwidth, wherein the distance from any one of the screening nutrition ecological bar memory formulas in the first centralized identification neighborhood to the first screening nutrition ecological bar memory formula is less than or equal to the preset centralized identification bandwidth;

[0040] In the first centralized identification neighborhood, the first screening nutrition ecological bar memory formula is iterated using the neighborhood center iteration formula to determine the first iteration screening nutrition ecological bar memory formula, and the first iteration centralized identification neighborhood is constructed in combination with the preset centralized identification bandwidth;

[0041] The direction from the first step of screening the memory formula of the nutrition ecological bar to the first step of iterative screening the memory formula of the nutrition ecological bar is used as the iteration direction;

[0042] Determining an iteration step size based on a difference in neighborhood density between the first centralized identified neighborhood and the first iterative centralized identified neighborhood, and the preset centralized identified bandwidth;

[0043] In the screening memory formula space, the first iterative screening nutrition eco-bar memory formula is iterated according to the iteration direction and the iteration step to obtain the initial nutrition eco-bar formula and the initial regional terrain information, the initial regional climate condition information and the initial regional vegetation growth type information.

[0044] In a possible embodiment, the screening memory formula space includes multiple screening nutrition eco-bar memory formulas. In order to obtain the most common screening nutrition eco-bar memory formula from the multiple screening nutrition eco-bar memory formulas, it is necessary to perform concentrated formula identification to determine the screening nutrition eco-bar memory formula with the largest formula density clustered around it, and use it as the initial nutrition eco-bar formula.

[0045] And according to the spatial coordinate position of the initial nutritional eco-bar formula in the screening memory formula space, the corresponding initial regional terrain information, initial regional climate condition information and initial regional vegetation type information are determined. The initial nutritional eco-bar formula is the most commonly applicable formula in the screening memory formula space. The initial regional terrain information, initial regional climate condition information and initial regional vegetation type information reflect the application environment of the initial nutritional eco-bar formula.

[0046] Preferably, the first screening nutrition ecological bar memory formula is randomly extracted from the screening memory formula space, and it is used as the starting point for formula centralized identification. The neighborhood is constructed according to the first screening nutrition ecological bar memory formula, and the first iteration screening nutrition ecological bar memory formula with a relatively dense distribution is determined in the constructed neighborhood, so as to achieve the goal of determining the iteration direction. The technical effect of avoiding directionless random iteration in the screening memory formula space and increasing the response time is achieved.

[0047] Then, the first concentrated identification neighborhood is constructed with the first selected nutritional ecological bar memory formula as the center and the preset concentrated identification bandwidth as the radius. Therefore, the distance from any selected nutritional ecological bar memory formula in the first concentrated identification neighborhood to the first selected nutritional ecological bar memory formula is less than or equal to the preset concentrated identification bandwidth. The preset concentrated identification bandwidth is the distance size preset by the technicians in this neighborhood.

[0048] Since the first screening nutrition ecology bar memory formula is randomly selected, the first screening nutrition ecology bar memory formula cannot be guaranteed to be the most commonly applicable screening nutrition ecology bar memory formula in the screening memory formula space. It is necessary to use the neighborhood center iteration formula to perform center iteration in the first concentrated identification neighborhood, determine the most densely distributed screening nutrition ecology bar memory formula in the first concentrated identification neighborhood, and use it as the first iterative screening nutrition ecology bar memory formula. In other words, the first iterative screening nutrition ecology bar memory formula is the most representative in the first concentrated identification neighborhood, that is, the screening nutrition ecology bar memory formula with the most densely gathered screening nutrition ecology bar memory formula. Furthermore, based on the same construction principle as the first concentrated identification neighborhood, according to the preset concentrated identification bandwidth and the first iterative screening nutrition ecology bar memory formula, the first iterative concentrated identification neighborhood is constructed in the screening memory formula space.

[0049] Since the first iterative screening of nutritional ecological bar memory formulas is more generally applicable than the first screening of nutritional ecological bar memory formulas, the direction from the first screening of nutritional ecological bar memory formulas to the first iterative screening of nutritional ecological bar memory formulas is used as the iteration direction. Thus, the goal of determining the iteration direction when performing concentrated identification of formulas in the screening memory formula space is achieved, thereby achieving the technical effect of improving the efficiency of concentrated identification of formulas.

[0050] Furthermore, by analyzing the difference in neighborhood density between the first centralized identification neighborhood and the first iterative centralized identification neighborhood corresponding to the first screening of the nutritional ecological bar memory formula and the first iterative screening of the nutritional ecological bar memory formula, as well as the preset centralized identification bandwidth, the step length of a single iteration is determined. The technical effect of adaptively adjusting the iteration step length and improving the iteration efficiency is achieved.

[0051] Preferably, in the screening memory formula space, the first iterative screening of the nutritional eco-bar memory formula is iterated according to the iteration direction and the iteration step, thereby obtaining the initial nutritional eco-bar formula that best meets the environmental conditions of the target application area, as well as the initial area terrain information, initial area climate condition information and initial area vegetation growth type information.

[0052] Furthermore, the neighborhood center iteration formula is:

[0053]

[0054] Among them, m(x) is the first iteration of the nutritional ecological bar memory formula, h is the preset centralized identification bandwidth, N(x) is the first centralized identification neighborhood, and x i Identify the i-th selected nutritional ecological stick memory formula in the neighborhood in the first episode, is the Gaussian kernel function.

[0055] Further, based on the difference in neighborhood density between the first centralized identified neighborhood and the first iterative centralized identified neighborhood, and the preset centralized identified bandwidth, determining the iteration step, step S3 of the embodiment of the present application further includes:

[0056] Calculate the neighborhood density difference between the identified neighborhood in the first iteration set and the identified neighborhood in the first set, and compare the calculated result with a preset neighborhood density difference threshold to obtain an iteration step coefficient;

[0057] The iteration step length coefficient is multiplied by the preset centralized identification bandwidth to obtain the iteration step length.

[0058] In one embodiment of the present application, the number of the selected nutritional ecological bar memory formulas contained in the first centralized identification neighborhood is counted, and the statistical result is compared with the spatial volume of the first centralized identification neighborhood to obtain the neighborhood density of the first centralized identification neighborhood. Preferably, the first centralized identification neighborhood is a spherical space constructed with the first selected nutritional ecological bar memory formula as the center and the preset centralized identification bandwidth as the radius. By using the spherical volume calculation formula, the spatial volume of the first centralized identification neighborhood can be calculated. Based on the same calculation principle, the neighborhood density of the first iteration centralized identification neighborhood is obtained.

[0059] Furthermore, the difference in neighborhood density between the first iteration concentrated identification neighborhood and the first concentrated identification neighborhood is calculated, and the obtained calculation result reflects the difference in distribution density between the first iteration concentrated identification neighborhood and the first concentrated identification neighborhood for screening the nutritional ecological bar memory formula, and then the calculation result is compared with the preset neighborhood density difference threshold to obtain the iteration step coefficient when the iteration step is adjusted. Among them, the preset neighborhood density difference threshold is the minimum neighborhood density difference for continuing iterative adjustment pre-set by a person skilled in the art. The iteration step coefficient reflects the degree of iteration step adjustment. The larger the iteration step coefficient, the greater the difference in distribution density between the first iteration concentrated identification neighborhood and the first concentrated identification neighborhood for screening the nutritional ecological bar memory formula, and a larger step is needed to find a more generally applicable screening nutritional ecological bar memory formula.

[0060] The iteration step coefficient is multiplied by the preset centralized identification bandwidth to obtain the iteration step. The iteration step is the step of a single iteration in the screening memory formula space starting from the first iteration screening of the nutrition ecological bar memory formula. By determining the iteration step, the technical effect of determining the iteration step according to the distribution of the actual screening nutrition ecological bar memory formula and ensuring the quality of the iteration screening is achieved.

[0061] Further, in the screening memory formula space, the first iterative screening nutrition ecological bar memory formula is iterated according to the iteration direction and the iteration step length until the preset iteration stop condition is met, and the initial nutrition ecological bar formula and the initial regional terrain information, the initial regional climate condition information and the initial regional vegetation type information are obtained. Step S3 of the embodiment of the present application also includes:

[0062] In the screening memory formula space, the first iterative screening of the nutrition ecological bar memory formula is iterated according to the iteration direction and the iteration step length to obtain the second iterative screening of the nutrition ecological bar memory formula;

[0063] Taking the second iteration screening of the nutritional ecological bar memory formula as the center and the iteration step as the radius, constructing a second iteration centralized identification neighborhood in the screening memory formula space;

[0064] Determine whether the neighborhood density of the neighborhood identified in the second iteration set is greater than or equal to the neighborhood density of the neighborhood identified in the first iteration set, and if so, iterate the second iteration screening of the nutritional eco-bar memory formula according to the iteration step and the iteration direction until the preset iteration stop condition is met, and use the iterative screening nutritional eco-bar memory formula obtained in the last iteration as the initial nutritional eco-bar formula, wherein the preset iteration stop condition is that the number of iterations meets the preset maximum number of iterations and / or the neighborhood density difference of the neighborhoods identified in the two iteration sets obtained in two adjacent iterations is less than or equal to the preset neighborhood density difference threshold;

[0065] According to the corresponding coordinates of the initial nutritional eco-bar formula in the screening memory formula space, the initial area terrain information, the initial area climate condition information and the initial area vegetation type information are obtained.

[0066] In one possible embodiment, after determining the iteration direction, iteration step, and the first iteration screening of the nutritional eco-bar formula, the formula is iteratively screened in the screening memory formula space until the preset iteration stop condition is met to obtain the initial nutritional eco-bar formula and the initial regional terrain information, initial regional climate condition information, and initial regional vegetation growth type information.

[0067] Preferably, in the screening memory formula space, the first iterative screening of the nutrition ecological bar memory formula is iterated according to the iteration direction and the iteration step to obtain the second iterative screening of the nutrition ecological bar memory formula. The second iterative screening of the nutrition ecological bar memory formula is obtained based on the iteration step, so when the neighborhood is constructed, the neighborhood radius is the iteration step. By taking the second iterative screening of the nutrition ecological bar memory formula as the center and the iteration step as the radius, a second iterative concentrated identification neighborhood is constructed in the screening memory formula space.

[0068] The neighborhood density of the neighborhood identified in the second iteration is obtained by the same calculation principle as the neighborhood density of the neighborhood identified in the first set. Further, it is determined whether the neighborhood density of the neighborhood identified in the second iteration is greater than or equal to the neighborhood density of the neighborhood identified in the first iteration. If so, it indicates that the second iteration screening of the nutrition ecological bar memory formula is more than the first iteration screening of the nutrition ecological bar memory formula. The screening of the nutrition ecological bar memory formula is more representative, so the second iteration screening of the nutrition ecological bar memory formula is updated to the iteration starting point, and the second iteration screening of the nutrition ecological bar memory formula continues to be iterated according to the iteration step and the iteration direction until the preset iteration stop condition is met, and the iterative screening of the nutrition ecological bar memory formula obtained in the last iteration is used as the initial nutrition ecological bar formula.

[0069] Among them, the preset iteration stop condition is that the number of iterations meets the preset maximum number of iterations and / or the neighborhood density difference of the identified neighborhoods in the two iterative sets obtained in two adjacent iterations is less than or equal to the preset neighborhood density difference threshold. The preset maximum number of iterations is the maximum number of iterations pre-set by those skilled in the art, which may be 90 times, 150 times, etc. Preferably, the applicable premise of the preset iteration stop condition is that the neighborhood density of the identified neighborhood in the iterative set obtained in this iteration is greater than or equal to the neighborhood density of the identified neighborhood in the iterative set obtained in the previous iteration. Under this premise, when the neighborhood density difference of the identified neighborhoods in the two iterative sets obtained in two adjacent iterations is less than or equal to the preset neighborhood density difference threshold, it indicates that a relatively densely distributed area has been searched and it is not necessary to continue iterating.

[0070] Preferably, it is determined whether the neighborhood density of the neighborhood identified in the second iteration set is greater than or equal to the neighborhood density of the neighborhood identified in the first iteration set. If not, it indicates that the formula density clustered around the nutritional eco-bar memory formula screened in the first iteration is higher than the formula density clustered around the nutritional eco-bar memory formula screened in the second iteration. At this time, the iteration is stopped, and the nutritional eco-bar memory formula screened in the first iteration is used as the initial nutritional eco-bar formula.

[0071] After obtaining the initial nutritional eco-bar formula, the initial regional terrain information, initial regional climate condition information and initial regional vegetation type information are obtained according to the corresponding coordinates in the screening memory formula space, thereby providing data support for the subsequent identification and determination of the formula performance that needs to be optimized.

[0072] S4: determining an optimized formula performance set according to differences between initial regional terrain information, initial regional climate condition information, initial regional vegetation growth type information, and regional terrain information, regional climate condition information, and regional vegetation growth type information;

[0073] Further, according to the difference between the initial regional terrain information, the initial regional climate condition information, the initial regional vegetation type information and the regional terrain information, the regional climate condition information and the regional vegetation type information, the optimization formula performance set is determined, and step S4 of the embodiment of the present application also includes:

[0074] Using a formula requirement performance extractor to extract formula requirement performance from the initial regional terrain information, the initial regional climate condition information, the initial regional vegetation type information and the regional terrain information, the regional climate condition information and the regional vegetation type information, to obtain an initial formula requirement performance set and a target formula requirement performance set;

[0075] A one-to-one performance mapping similarity calculation is performed on the initial recipe requirement performance set and the target recipe requirement performance set, and the recipe requirement performance whose calculation results do not meet the preset similarity requirements is added to the optimized recipe performance set.

[0076] In a possible embodiment, an initial formula requirement performance set is first determined based on initial regional terrain information, initial regional climate condition information, and initial regional vegetation type information. A target formula requirement performance set is then determined based on regional terrain information, regional climate condition information, and regional vegetation type information. By comparing the differences between the initial formula requirement performance set and the target formula requirement performance set, an optimized formula performance set that needs to be optimized is determined.

[0077] Preferably, a plurality of sample terrain information, a plurality of sample climate condition information, a plurality of sample growth vegetation type information, and a plurality of corresponding sample formula requirement performance sets are obtained as extractor training sample data. The extractor training sample data is used to perform supervised training on a framework constructed based on a feedforward neural network, and the mapping relationship between the terrain information, climate condition information, growth vegetation type information, and the formula requirement performance set is learned during the training until the training converges, thereby obtaining the trained formula requirement performance extractor.

[0078] Optionally, a formula requirement performance extractor is used to extract formula requirement performance from the initial regional terrain information, initial regional climate condition information, initial regional vegetation type information and regional terrain information, regional climate condition information and regional vegetation type information, to obtain an initial formula requirement performance set and a target formula requirement performance set. The initial formula requirement performance set reflects the performance that the initial nutritional eco-bar formula needs to meet. The target formula requirement performance set reflects the performance that the formula corresponding to the anti-slip slow-release nutritional eco-bar used in the target application area needs to meet.

[0079] The cosine similarity calculation formula is used to perform a one-to-one mapping similarity calculation on the initial formula requirement performance set and the target formula requirement performance set, and then the formula requirement performance whose calculation results do not meet the preset similarity requirement (the minimum similarity pre-set by technicians in this field without optimization) is added to the optimized formula performance set.

[0080] Through detailed analysis of the performance requirements of the formula, the performance characteristics that need to be optimized are accurately identified, thus providing a clear direction and basis for the next step of formula optimization. Through precise performance matching, the efficiency and accuracy of formula optimization are improved, and the technical effect of ensuring the applicability of the final formula in the target application area is achieved.

[0081] S5: Taking the optimized formula performance set as the optimization direction, directionally optimizing the initial nutrition eco-bar formula to obtain an optimized nutrition eco-bar formula.

[0082] Further, taking the optimized formula performance set as the optimization direction, the initial nutrition ecological bar formula is directionally optimized to obtain the optimized nutrition ecological bar formula. Step S5 of the embodiment of the present application further includes:

[0083] Acquire multiple sample initial nutrition eco-bar formulas, multiple sample optimized formula performance sets, and corresponding multiple sample optimized nutrition eco-bar formulas as training data;

[0084] Using the training data to perform supervised training on a framework built based on a feedforward neural network, updating network parameters according to output results during training until convergence, and obtaining a trained directional optimizer;

[0085] The directional optimizer is used to identify the optimized formula performance set and the initial nutritional eco-bar formula to obtain the optimized nutritional eco-bar formula.

[0086] In an embodiment of the present application, multiple sample initial nutrition eco-bar formulas are obtained, and the performance to be optimized and the optimized formula are determined according to the formula design log, and the multiple sample optimized formula performance sets (such as higher anti-slip properties, slower nutrient release rate, etc.) and the corresponding multiple sample optimized nutrition eco-bar formulas are obtained. The multiple sample initial nutrition eco-bar formulas, the multiple sample optimized formula performance sets and the corresponding multiple sample optimized nutrition eco-bar formulas are used as training data.

[0087] Preferably, the input layer, hidden layer and output layer of the neural network are defined. Among them, the input layer is used to accept the initial formula and the optimization performance set as input features. Several fully connected layers are set to learn the complex relationship between the input features and the output targets. The output layer is used to generate the optimized formula component ratio. And the network parameters (weights and bias values) are initialized to random values. The mean square error function is used as a loss function to measure the difference between the neural network prediction formula and the sample optimized formula. The training data is used to supervise the training of the framework composed of the input layer, hidden layer and output layer of the neural network, and the loss function is used to perform loss analysis on the training process, and the network parameters are adjusted according to the analysis results until the output value of the loss function is less than or equal to the preset loss amount (the maximum loss amount when the training converges set by those skilled in the art), the training converges, and the directional optimizer that has been trained is obtained.

[0088] Then, the optimized formula performance set and the initial nutrition eco-bar formula are input into the directional optimizer for formula optimization, and the optimized nutrition eco-bar formula is obtained. The goal of optimizing the nutrition eco-bar formula according to the environmental conditions of the target application area is achieved, and the technical effect of improving the optimization quality and response rate is achieved.

[0089] In summary, the embodiments of the present application have at least the following technical effects:

[0090] 1. This application comprehensively analyzes the regional terrain information, climate conditions and growth vegetation types of the target application area, and screens the nutritional eco-bar memory formula repository in combination with the preset tolerance distance, thereby improving the fit between the formula and the environment. Unlike the traditional single-factor optimization method, this solution realizes the integration of multi-dimensional environmental data, ensuring the adaptability and accuracy of the nutritional eco-bar formula under different soil, climate and plant growth conditions. This method of screening and centralized identification effectively narrows the search scope and achieves the technical effect of improving the efficiency of formula optimization.

[0091] 2. This application analyzes the differences between the initial formula and the target environment, determines the optimized performance set, and performs directional optimization based on this, thereby achieving the technical effect of ensuring the flexibility and reliability of the formula in a specific application environment.

[0092] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0093] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0094] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for optimizing the formula of an anti-slip slow-release nutritional ecological bar combined with environmental analysis, characterized in that: The method comprises: Obtain regional terrain information, regional climate conditions information and regional vegetation type information of the target application area; Using the regional terrain information, regional climate condition information and regional vegetation type information as indexes, the nutritional ecological stick memory formula storage library is screened according to a preset tolerance distance to obtain a screened memory formula space; Performing concentrated formula identification in the screening memory formula space to determine the initial nutritional ecological stick formula and initial regional terrain information, initial regional climate condition information, and initial regional vegetation type information; Determine an optimized formula performance set according to differences between initial regional terrain information, initial regional climate condition information, initial regional vegetation growth type information, and regional terrain information, regional climate condition information, and regional vegetation growth type information; Taking the optimized formula performance set as the optimization direction, the initial nutrition eco-bar formula is directionally optimized to obtain the optimized nutrition eco-bar formula.

2. The method for optimizing the formula of the anti-slip slow-release nutritional ecological bar combined with environmental analysis according to claim 1, characterized in that: Taking the regional terrain information, regional climate condition information and regional vegetation type information as indexes, the nutritional ecological stick memory formula storage library is screened according to the preset tolerance distance to obtain a screened memory formula space, including: Constructing a three-dimensional space, wherein the x-axis of the three-dimensional space is terrain information, the y-axis is climate condition information, the z-axis is vegetation type information, and the origin of the three-dimensional space is o; Integrate the nutrition ecological bar memory formula storage base based on the three-dimensional space to obtain the nutrition ecological bar memory formula storage integration space; A plane passing through the regional terrain information and parallel to the yoz plane is used as a first screening reference plane, and planes obtained by parallel shifting the first screening reference plane to the left by a preset tolerance distance and parallel shifting the first screening reference plane to the right by a preset tolerance distance are used as a screening left plane and a screening right plane; A plane passing through the regional climate condition information and parallel to the xoz plane is used as a second screening reference plane, and the second screening reference plane is moved forward and backward in parallel by a preset tolerance distance to obtain a plane as a front screening plane and a rear screening plane; A plane passing through the region where vegetation type information is grown and parallel to the xoy plane is used as a third screening reference plane, and the third screening reference plane is moved up and down in parallel by a preset tolerance distance to obtain a plane as an upper screening plane and a lower screening plane; The space enclosed by the left screening plane, the right screening plane, the front screening plane, the rear screening plane, the upper screening plane and the lower screening plane in the nutritional ecological bar memory formula storage integration space is used as the screening memory formula space.

3. The method for optimizing the formula of the anti-slip slow-release nutritional ecological bar combined with environmental analysis according to claim 1, characterized in that: In the screening memory formula space, formula centralized identification is performed to determine the initial nutritional ecological bar formula and initial regional terrain information, initial regional climate condition information, and initial regional vegetation type information, including: Randomly extracting a first screening nutrition ecological bar memory formula from the screening memory formula space, and constructing a first centralized identification neighborhood according to a preset centralized identification bandwidth, wherein the distance from any one of the screening nutrition ecological bar memory formulas in the first centralized identification neighborhood to the first screening nutrition ecological bar memory formula is less than or equal to the preset centralized identification bandwidth; In the first centralized identification neighborhood, the first screening nutrition ecological bar memory formula is iterated using the neighborhood center iteration formula to determine the first iteration screening nutrition ecological bar memory formula, and the first iteration centralized identification neighborhood is constructed in combination with the preset centralized identification bandwidth; The direction from the first step of screening the memory formula of the nutrition ecological bar to the first step of iterative screening the memory formula of the nutrition ecological bar is used as the iteration direction; Determining an iteration step size based on a difference in neighborhood density between the first centralized identified neighborhood and the first iterative centralized identified neighborhood, and the preset centralized identified bandwidth; In the screening memory formula space, the first iterative screening nutrition eco-bar memory formula is iterated according to the iteration direction and the iteration step to obtain the initial nutrition eco-bar formula and the initial regional terrain information, the initial regional climate condition information and the initial regional vegetation growth type information.

4. The method for optimizing the formula of the anti-slip slow-release nutritional ecological bar combined with environmental analysis as claimed in claim 3, characterized in that: The neighborhood center iteration formula is: Among them, m(x) is the first iteration of the nutritional ecological bar memory formula, h is the preset centralized identification bandwidth, N(x) is the first centralized identification neighborhood, and x i Identify the i-th selected nutritional ecological stick memory formula in the neighborhood in the first episode, is the Gaussian kernel function.

5. The method for optimizing the formula of the anti-slip slow-release nutritional ecological bar combined with environmental analysis as claimed in claim 3, characterized in that: Determining the iteration step size based on the difference between the neighborhood density of the first centralized identification neighborhood and the neighborhood density of the first iteration centralized identification neighborhood, and the preset centralized identification bandwidth, includes: Calculate the neighborhood density difference between the identified neighborhood in the first iteration set and the identified neighborhood in the first set, and compare the calculated result with a preset neighborhood density difference threshold to obtain an iteration step coefficient; The iteration step length coefficient is multiplied by the preset centralized identification bandwidth to obtain the iteration step length.

6. The method for optimizing the formula of the anti-slip slow-release nutritional ecological bar combined with environmental analysis as claimed in claim 3, characterized in that: In the screening memory formula space, the first iterative screening nutrition eco-bar memory formula is iterated according to the iteration direction and the iteration step until the preset iteration stop condition is met, and the initial nutrition eco-bar formula and initial regional terrain information, initial regional climate condition information and initial regional vegetation type information are obtained, including: In the screening memory formula space, the first iterative screening of the nutrition ecological bar memory formula is iterated according to the iteration direction and the iteration step length to obtain the second iterative screening of the nutrition ecological bar memory formula; Taking the second iteration screening of the nutritional ecological bar memory formula as the center and the iteration step as the radius, constructing a second iteration centralized identification neighborhood in the screening memory formula space; Determine whether the neighborhood density of the neighborhood identified in the second iteration set is greater than or equal to the neighborhood density of the neighborhood identified in the first iteration set, and if so, iterate the second iteration screening of the nutritional eco-bar memory formula according to the iteration step and the iteration direction until the preset iteration stop condition is met, and use the iterative screening nutritional eco-bar memory formula obtained in the last iteration as the initial nutritional eco-bar formula, wherein the preset iteration stop condition is that the number of iterations meets the preset maximum number of iterations and / or the neighborhood density difference of the neighborhoods identified in the two iteration sets obtained in two adjacent iterations is less than or equal to the preset neighborhood density difference threshold; According to the corresponding coordinates of the initial nutritional eco-bar formula in the screening memory formula space, the initial area terrain information, the initial area climate condition information and the initial area vegetation type information are obtained.

7. The method for optimizing the formula of an anti-slip slow-release nutritional ecological bar combined with environmental analysis according to claim 1, characterized in that: According to the differences between the initial regional terrain information, the initial regional climate condition information, the initial regional vegetation type information and the regional terrain information, the regional climate condition information and the regional vegetation type information, the optimization formula performance set is determined, including: Using a formula requirement performance extractor to extract formula requirement performance from the initial regional terrain information, the initial regional climate condition information, the initial regional vegetation type information and the regional terrain information, the regional climate condition information and the regional vegetation type information, to obtain an initial formula requirement performance set and a target formula requirement performance set; A one-to-one performance mapping similarity calculation is performed on the initial recipe requirement performance set and the target recipe requirement performance set, and the recipe requirement performance whose calculation results do not meet the preset similarity requirements is added to the optimized recipe performance set.

8. The method for optimizing the formula of an anti-slip slow-release nutritional ecological bar combined with environmental analysis according to claim 1, characterized in that: Taking the optimized formula performance set as the optimization direction, the initial nutrition eco-bar formula is directionally optimized to obtain the optimized nutrition eco-bar formula, including: Acquire multiple sample initial nutrition eco-bar formulas, multiple sample optimized formula performance sets, and corresponding multiple sample optimized nutrition eco-bar formulas as training data; Using the training data to perform supervised training on a framework built based on a feedforward neural network, updating network parameters according to output results during training until convergence, and obtaining a trained directional optimizer; The directional optimizer is used to identify the optimized formula performance set and the initial nutritional eco-bar formula to obtain the optimized nutritional eco-bar formula.

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