Intelligent optimization management method for three-dimensional vegetable cultivation

Abstract

The invention discloses an intelligent optimization management method for three-dimensional vegetable cultivation, and relates to the technical field of three-dimensional cultivation management. The method discloses the following steps: Step 3: when it is determined that the current three-dimensional vegetable cultivation strategy is to be optimized, a three-dimensional vegetable simulation cultivation model is constructed, and the three-dimensional vegetable simulation cultivation model generates i simulation three-dimensional cultivation strategies; Step 4: the simulation three-dimensional cultivation strategy with the largest burst fitness value is marked as the optimized three-dimensional cultivation strategy, and the vegetables are three-dimensionally cultivated using the optimized three-dimensional cultivation strategy. Through Step 3 and Step 4, when it is determined that the three-dimensional vegetable cultivation strategy cannot meet the three-dimensional cultivation demand of the vegetables, a three-dimensional vegetable simulation cultivation model is constructed, and a plurality of optimized simulation three-dimensional cultivation strategies are generated through the three-dimensional vegetable simulation cultivation model, and each simulation three-dimensional cultivation strategy is subjected to multi-angle risk resistance analysis, and the simulation three-dimensional cultivation strategy with the best risk resistance is comprehensively analyzed to execute subsequent three-dimensional vegetable cultivation.

Description

Technical Field

[0001] The invention relates to the technical field of stereoscopic cultivation management, and more specifically, to an intelligent optimization management method for stereoscopic cultivation of vegetables. Background Art

[0002] In the field of modern agriculture, vegetable three-dimensional cultivation has been widely used as an important way to efficiently utilize space resources and increase crop yields. However, in actual operation, the management and optimization of vegetable three-dimensional cultivation face many challenges. Traditional cultivation management methods often rely on experience and judgment, and it is difficult to accurately grasp the growth status and needs of each layer of vegetables, thus affecting the cultivation effect and economic benefits.

[0003] In order to solve these problems, in recent years, with the continuous advancement of information technology, the application of intelligent management in vegetable stereoscopic cultivation has become increasingly widespread. Intelligent management systems can monitor the growth data of vegetables in real time and provide a scientific basis for cultivation management. However, the existing intelligent management systems still have shortcomings in the optimization of vegetable stereoscopic cultivation. For example, some systems can only simply record and analyze the growth data of vegetables, but lack the ability to conduct in-depth mining and intelligent optimization of these data. In addition, although some systems can propose optimized cultivation strategies, in the actual cultivation process, the cultivation strategies may be affected by equipment abnormalities, environmental abnormalities and other problems. The system cannot comprehensively evaluate the risk resistance of the cultivation strategy based on these abnormalities, resulting in limited effects in practical applications.

[0004] In view of the deficiencies in the prior art, the present invention proposes an intelligent optimization management method for three-dimensional vegetable cultivation. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide an intelligent optimization management method for three-dimensional vegetable cultivation.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for intelligent optimization management of three-dimensional vegetable cultivation comprises the following steps:

[0008] Step 1: During the three-dimensional cultivation of vegetables, the cultivation management interval is set, and after each cultivation management interval, the growth performance index of each layer of vegetables on the three-dimensional cultivation rack is obtained;

[0009] Step 2: Obtain a three-dimensional cultivation growth value based on the growth performance index of each layer of vegetables on the three-dimensional cultivation rack, and determine whether to optimize the current three-dimensional cultivation strategy of vegetables based on the comparison result between the three-dimensional cultivation growth value and the three-dimensional cultivation growth threshold value;

[0010] Step 3: When it is determined to optimize the current three-dimensional vegetable cultivation strategy, the basic growth data of each layer of vegetables is obtained, the target growth data of each layer of vegetables is set, and a three-dimensional vegetable cultivation model is constructed based on the basic growth data and target growth data of each layer of vegetables. The three-dimensional vegetable cultivation model generates i simulated three-dimensional cultivation strategies;

[0011] Step 4: Obtain the burst fitness value of each simulated stereoscopic cultivation strategy, mark the simulated stereoscopic cultivation strategy with the largest burst fitness value as the optimized stereoscopic cultivation strategy, and use the optimized stereoscopic cultivation strategy to perform stereoscopic cultivation of vegetables.

[0012] Furthermore, the growth performance index of a layer of vegetables is obtained as follows: determine the current time, synchronously collect basic growth data of a layer of vegetables, obtain a growth performance analysis model, combine the basic growth data of the vegetables with the current time into a growth time data group, input the growth time data group into the growth performance analysis model, and the growth performance analysis model outputs the growth performance index of the layer of vegetables.

[0013] Furthermore, based on the comparison result between the stereoscopic cultivation growth value and the stereoscopic cultivation growth boundary value, it is determined whether the current vegetable stereoscopic cultivation strategy is optimized. Specifically, the stereoscopic cultivation growth boundary value is set, and when the stereoscopic cultivation growth value is less than or equal to the stereoscopic cultivation growth boundary value, it is determined that the current vegetable stereoscopic cultivation strategy is optimized.

[0014] Furthermore, the growth value of stereoscopic cultivation is obtained as follows: each layer of vegetables is matched into a heterogeneous growth comparison group, the heterogeneous growth comparison value of each heterogeneous growth comparison group is obtained, and the heterogeneous growth comparison threshold is set. When the heterogeneous growth comparison value of the heterogeneous growth comparison group is greater than the heterogeneous growth comparison threshold, the heterogeneous growth comparison group is marked as a positive comparison group, and the total number of positive comparison groups is marked as Czg. The growth performance index of each layer of vegetables is summed and averaged to obtain the average growth performance index Bny, and the formula is used. The stereoscopic cultivation growth value RPx was obtained, where ha was the positive contrast quantity coefficient and hb was the growth performance average index coefficient.

[0015] Furthermore, the heterogeneous growth comparison value of the heterogeneous growth comparison group is obtained as follows: the growth performance indexes of the two layers of vegetables in the heterogeneous growth comparison group are summed and averaged to obtain the heterogeneous growth performance comprehensive value Ctd; the growth performance indexes of the two layers of vegetables in the heterogeneous growth comparison group are calculated and the absolute value is taken to obtain the heterogeneous growth performance difference value Jse, and the formula is used The heterogeneous growth comparison value Zwa of the heterogeneous growth comparison group is obtained, wherein ca is the heterogeneous growth performance comprehensive coefficient, and cb is the heterogeneous growth performance gap coefficient.

[0016] Furthermore, a three-dimensional simulation cultivation model for vegetables is constructed based on the basic growth data and target growth data of each layer of vegetables. The specific steps are as follows: select simulation software, create a three-dimensional cultivation rack model in the simulation software, and add corresponding vegetables to the three-dimensional cultivation rack model based on the basic growth data of each layer of vegetables, and assign plant height, stem thickness, and leaf area parameters to the vegetables. Based on the target growth data of each layer of vegetables, the three-dimensional cultivation direction of the vegetables is determined to construct a three-dimensional simulation cultivation model for vegetables. The three-dimensional simulation cultivation model for vegetables can generate a simulated three-dimensional cultivation strategy according to the three-dimensional cultivation direction.

[0017] Furthermore, the burst adaptation value of the simulated stereoscopic cultivation strategy is obtained as follows: determine a simulated stereoscopic cultivation strategy, and construct a vegetable stereoscopic simulation cultivation model for each type of abnormal condition based on the simulated stereoscopic cultivation strategy, and then obtain the stereoscopic cultivation growth value of each type of abnormal condition, and mark it as Zmk, m=1, 2, ..., M, m represents the type of abnormal condition, M is the total number of abnormal condition types, set the stereoscopic cultivation growth coefficient to Yk, k=1, 2, 3, ..., k, Y1<Y2<Y3<...<Yk, compare the stereoscopic cultivation growth values ​​of each type of abnormal condition in pairs, calculate the difference between the two compared stereoscopic cultivation growth values ​​and take the absolute value to obtain the stereoscopic cultivation turbulence value, sum all the stereoscopic cultivation turbulence values ​​and take the average to obtain the stereoscopic cultivation turbulence mean Fzg, and use the formula The burst adaptation value Vge of the simulated three-dimensional cultivation strategy is obtained.

[0018] Furthermore, a three-dimensional simulated cultivation model for vegetables of a conditional abnormality type is constructed as follows: determine a simulated three-dimensional cultivation strategy, simultaneously determine abnormal condition parameters of a conditional abnormality type, and input the simulated three-dimensional cultivation strategy and the abnormal condition parameters into the three-dimensional simulated cultivation model for vegetables.

[0019] Furthermore, the method for obtaining the growth value of a stereoscopic cultivation of an abnormal condition type is as follows: after inputting the simulated stereoscopic cultivation strategy and the abnormal condition parameters into the vegetable stereoscopic simulation cultivation model, the vegetable stereoscopic simulation cultivation model performs simulated stereoscopic cultivation for t time, and after the simulated stereoscopic cultivation is completed, the stereoscopic cultivation growth value is obtained.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1. The method of the present invention regularly analyzes the three-dimensional cultivation process of vegetables, performs consistency and comparative analysis on the growth performance of each layer of vegetables, and accurately determines whether the current three-dimensional vegetable cultivation strategy meets the three-dimensional cultivation requirements of vegetables through the above analysis method;

[0022] 2. When the method of the present invention determines that the vegetable three-dimensional cultivation strategy cannot meet the three-dimensional cultivation needs of vegetables, a vegetable three-dimensional simulation cultivation model is constructed, and multiple optimized simulated three-dimensional cultivation strategies are generated through the vegetable three-dimensional simulation cultivation model. Multi-angle risk resistance analysis is performed on each simulated three-dimensional cultivation strategy, and the simulated three-dimensional cultivation strategy with the best risk resistance is comprehensively analyzed to implement subsequent vegetable three-dimensional cultivation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A method flow chart of an intelligent optimization management method for three-dimensional vegetable cultivation;

[0024] Figure 2 It is a flow chart for constructing a three-dimensional simulation cultivation model for vegetables;

[0025] Figure 3 The present invention is a flowchart for constructing a three-dimensional simulation cultivation model of vegetables under abnormal conditions. DETAILED DESCRIPTION

[0026] Reference Figure 1-Figure 3 , a vegetable three-dimensional cultivation intelligent optimization management method, comprising the following steps:

[0027] Step 1: During the three-dimensional cultivation of vegetables, set the cultivation management interval (the cultivation management interval is a preset time period, the length of the time period is set according to the actual cultivation needs, and during the three-dimensional cultivation of vegetables, the cultivation management interval is infinitely cycled), and obtain the growth performance index of each layer of vegetables on the three-dimensional cultivation rack after each cultivation management interval;

[0028] The growth performance index of a layer of vegetables is obtained as follows: determine the current time, synchronously collect the basic growth data of a layer of vegetables (the basic growth data includes plant height, stem thickness, leaf area, etc.), obtain a growth performance analysis model, combine the basic growth data of the vegetables with the current time into a growth time data group, input the growth time data group into the growth performance analysis model, and the growth performance analysis model outputs the growth performance index of the vegetables of this layer;

[0029] The growth performance analysis model is constructed as follows: multiple growth time data groups are collected, each growth time data group contains a basic growth data and a time, wherein the basic growth data can be the real growth data of a layer of vegetables, or it can be the growth data virtually set for constructing the growth performance analysis model, and a deep school model is constructed, and the growth time data group is used as the training data of the deep school model, and a growth performance index is assigned to each training data. The index range of the growth performance index is (1.0~10.0), and the larger the growth performance index, the better the growth condition of the vegetables in this layer, and the smaller the value of the growth performance index, the worse the growth condition of the vegetables in this layer. The training data is divided into a training set and a validation set according to a set ratio of 4:1, and the training set and the validation set are trained. After the training is completed, a growth performance analysis model is constructed;

[0030] Step 2: Based on the growth performance index of each layer of vegetables on the three-dimensional cultivation rack, a three-dimensional cultivation growth value is obtained, and a three-dimensional cultivation growth threshold is set (the three-dimensional cultivation growth threshold is a preset threshold). When the three-dimensional cultivation growth value is less than or equal to the three-dimensional cultivation growth threshold, it is determined to optimize the current vegetable three-dimensional cultivation strategy (when the three-dimensional cultivation growth value is greater than the three-dimensional cultivation growth threshold, no corresponding processing is performed);

[0031] The method for obtaining the growth value of stereoscopic cultivation is as follows: match each layer of vegetables into a heterogeneous growth comparison group, obtain the heterogeneous growth comparison value of each heterogeneous growth comparison group, set the heterogeneous growth comparison threshold (the heterogeneous growth comparison threshold is a preset threshold), and when the heterogeneous growth comparison value of the heterogeneous growth comparison group is greater than the heterogeneous growth comparison threshold, mark the heterogeneous growth comparison group as a positive comparison group (when the heterogeneous growth comparison value of the heterogeneous growth comparison group is less than or equal to the heterogeneous growth comparison threshold, no corresponding processing is performed), mark the total number of positive comparison groups as Czg, sum and average the growth performance index of each layer of vegetables, and obtain the average growth performance index Bny, using the formula The three-dimensional cultivation growth value RPx was obtained, where ha is the positive contrast quantity coefficient and hb is the growth performance average index coefficient, with ha taking the value of 1.14 and hb taking the value of 0.68;

[0032] The heterogeneous growth comparison value of the heterogeneous growth comparison group is obtained as follows: the growth performance indexes of the two layers of vegetables in the heterogeneous growth comparison group are summed and averaged to obtain the heterogeneous growth performance comprehensive value Ctd; the growth performance indexes of the two layers of vegetables in the heterogeneous growth comparison group are calculated and the absolute value is taken to obtain the heterogeneous growth performance difference value Jse. The formula The heterogeneous growth contrast value Zwa of the heterogeneous growth contrast group is obtained, wherein ca is the heterogeneous growth performance comprehensive coefficient, cb is the heterogeneous growth performance gap coefficient, the value of ca is 0.44, and the value of cb is 1.81.

[0033] Through steps one and two, the three-dimensional cultivation process of vegetables is analyzed regularly, and the growth performance of each layer of vegetables is analyzed for consistency and comparison. Through the above analysis method, it can be accurately determined whether the current three-dimensional cultivation strategy of vegetables meets the three-dimensional cultivation needs of vegetables.

[0034] Step 3: When it is determined to optimize the current vegetable stereoscopic cultivation strategy, the basic growth data of each layer of vegetables is obtained, and the target growth data of each layer of vegetables is set (the target growth data is the expected growth data after the completion of the vegetable stereoscopic cultivation). A vegetable stereoscopic simulation cultivation model is constructed based on the basic growth data and target growth data of each layer of vegetables. The vegetable stereoscopic simulation cultivation model generates i simulated stereoscopic cultivation strategies (each simulated stereoscopic cultivation strategy satisfies the requirement that each layer of vegetables grow from the basic growth data to the target growth data, and different simulated stereoscopic cultivation strategies have differences in conditions such as light, temperature, humidity, watering interval, and irrigation method);

[0035] A three-dimensional simulation cultivation model for vegetables is constructed based on the basic growth data and target growth data of each layer of vegetables. The specific steps are as follows: select simulation software such as CropSyst and Unity, create a three-dimensional cultivation rack model in the simulation software, and add corresponding vegetables to the three-dimensional cultivation rack model based on the basic growth data of each layer of vegetables, and assign parameters such as plant height, stem thickness, leaf area, etc. to the vegetables. The three-dimensional cultivation direction of the vegetables is determined based on the target growth data of each layer of vegetables, and a three-dimensional simulation cultivation model for vegetables is constructed. The three-dimensional simulation cultivation model for vegetables can generate a simulated three-dimensional cultivation strategy according to the three-dimensional cultivation direction (using the simulated three-dimensional cultivation strategy can make each layer of vegetables grow from the basic growth data to the target growth data).

[0036] Step 4: Obtain the burst fitness value of each simulated stereoscopic cultivation strategy, mark the simulated stereoscopic cultivation strategy with the largest burst fitness value as the optimized stereoscopic cultivation strategy, and use the optimized stereoscopic cultivation strategy to perform stereoscopic cultivation of vegetables (i.e. cancel the previous vegetable stereoscopic cultivation strategy and use the optimized stereoscopic cultivation strategy for cultivation. The optimized stereoscopic cultivation strategy may have adjustments to light, temperature, humidity, watering interval, irrigation method, etc. compared to the previous vegetable stereoscopic cultivation strategy);

[0037] The burst adaptation value of the simulated stereoscopic cultivation strategy is obtained as follows: determine a simulated stereoscopic cultivation strategy, and build a vegetable stereoscopic simulation cultivation model for each abnormal condition type based on the simulated stereoscopic cultivation strategy (the abnormal condition types include temperature abnormality type, humidity abnormality type, light abnormality type, irrigation abnormality type, etc.), and then obtain the stereoscopic cultivation growth value of each abnormal condition type and mark it as Zmk, m=1, 2, ..., M, m represents the type of abnormal condition, M is the total number of abnormal condition types, and set the stereoscopic cultivation growth coefficient to Yk, k=1, 2, 3, ..., k, Y1<Y2<Y3<... <Yk, each characterization promotion coefficient Yk corresponds to a range of stereoscopic cultivation growth values ​​Zmk, the range of stereoscopic cultivation growth values ​​Zmk includes (0, Zm1], (Zm1, Zm2], …, (Zmk-1, Zmk], when Zmk∈(Zm1, Zm2], the stereoscopic cultivation growth coefficient is Y1, the stereoscopic cultivation growth values ​​of each abnormal condition type are compared in pairs, the difference between the two compared stereoscopic cultivation growth values ​​is calculated and the absolute value is taken to obtain the stereoscopic cultivation turbulence value, all the stereoscopic cultivation turbulence values ​​are summed and averaged to obtain the stereoscopic cultivation turbulence mean Fzg, and the formula is used Obtaining the burst adaptation value Vge of the simulated three-dimensional cultivation strategy;

[0038] A method for constructing a three-dimensional vegetable simulation cultivation model of an abnormal condition type is as follows: determine a simulated three-dimensional cultivation strategy, and simultaneously determine an abnormal condition parameter of an abnormal condition type (such as a temperature abnormality type, determine the temperature parameter of the temperature abnormality type), and input the simulated three-dimensional cultivation strategy and the abnormal condition parameter into the three-dimensional vegetable simulation cultivation model (the three-dimensional vegetable simulation cultivation model adjusts the corresponding parameters to the abnormal condition parameters, and does not adjust the other parameters, and based on the abnormal condition parameters, executes the simulated three-dimensional cultivation strategy to perform simulated three-dimensional cultivation);

[0039] The method for obtaining the growth value of a stereoscopic cultivation of an abnormal condition type is as follows: after inputting the simulated stereoscopic cultivation strategy and abnormal condition parameters into the vegetable stereoscopic simulation cultivation model, the vegetable stereoscopic simulation cultivation model performs simulated stereoscopic cultivation for t time, and after the simulated stereoscopic cultivation is completed, the stereoscopic cultivation growth value is obtained.

[0040] Through steps three and four, when it is determined that the vegetable three-dimensional cultivation strategy cannot meet the three-dimensional cultivation needs of vegetables, a vegetable three-dimensional simulation cultivation model is constructed, and multiple optimized simulated three-dimensional cultivation strategies are generated through the vegetable three-dimensional simulation cultivation model. Each simulated three-dimensional cultivation strategy is analyzed for risk resistance from multiple angles, and the simulated three-dimensional cultivation strategy with the best risk resistance is comprehensively analyzed to implement subsequent vegetable three-dimensional cultivation.

[0041] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0042] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment 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 computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0043] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0044] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0045] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0046] In the several embodiments provided in 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0047] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0048] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for intelligent optimization management of three-dimensional vegetable cultivation, characterized in that: The steps include: Step 1: During the three-dimensional cultivation of vegetables, the cultivation management interval is set, and after each cultivation management interval, the growth performance index of each layer of vegetables on the three-dimensional cultivation rack is obtained; The growth performance index of a layer of vegetables is obtained as follows: determine the current time, synchronously collect basic growth data of a layer of vegetables, obtain a growth performance analysis model, combine the basic growth data of the vegetables with the current time into a growth time data group, input the growth time data group into the growth performance analysis model, and the growth performance analysis model outputs the growth performance index of the vegetables of the layer; Step 2: Obtain a three-dimensional cultivation growth value based on the growth performance index of each layer of vegetables on the three-dimensional cultivation rack, and determine whether to optimize the current three-dimensional cultivation strategy of vegetables based on the comparison result between the three-dimensional cultivation growth value and the three-dimensional cultivation growth threshold value; Match each layer of vegetables into a heterogeneous growth comparison group, obtain the heterogeneous growth comparison value of each heterogeneous growth comparison group, determine the total number of positive comparison groups Czg, obtain the average growth performance index Bny, and use the formula Get the three-dimensional cultivation growth value RPx; The total number of positive comparison groups is obtained as follows: a heterogeneous growth comparison threshold is set, and when the heterogeneous growth comparison value of the heterogeneous growth comparison group is greater than the heterogeneous growth comparison threshold, the heterogeneous growth comparison group is marked as a positive comparison group, and the total number of positive comparison groups is marked as Czg; The average growth performance index Bny is obtained as follows: the growth performance indexes of vegetables in each layer are summed and averaged to obtain the average growth performance index Bny; ha is the coefficient of positive contrast quantity, hb is the coefficient of mean index of growth performance; Get the total value Ctd of heterogeneous growth performance and the difference value Jse of heterogeneous growth performance, and use the formula Obtaining a heterogeneous growth contrast value Zwa of the heterogeneous growth contrast group; The heterogeneous growth performance comprehensive value Ctd is obtained as follows: the growth performance indexes of the two layers of vegetables in the heterogeneous growth comparison group are summed and averaged to obtain the heterogeneous growth performance comprehensive value Ctd; The growth performance index of the two layers of vegetables in the heterogeneous growth comparison group was calculated and the absolute value was taken to obtain the heterogeneous growth performance difference value Jse; ca is the comprehensive coefficient of heterogeneous growth performance, and cb is the difference coefficient of heterogeneous growth performance; Step 3: When it is determined to optimize the current three-dimensional vegetable cultivation strategy, the basic growth data of each layer of vegetables is obtained, the target growth data of each layer of vegetables is set, and a three-dimensional vegetable cultivation model is constructed based on the basic growth data and target growth data of each layer of vegetables. The three-dimensional vegetable cultivation model generates i simulated three-dimensional cultivation strategies; Step 4: Obtain the burst fitness value of each simulated stereoscopic cultivation strategy, mark the simulated stereoscopic cultivation strategy with the largest burst fitness value as the optimized stereoscopic cultivation strategy, and use the optimized stereoscopic cultivation strategy to perform stereoscopic cultivation of vegetables; Determine a simulated three-dimensional cultivation strategy, and build a three-dimensional simulated cultivation model for vegetables of various abnormal conditions based on the simulated three-dimensional cultivation strategy, and then obtain the three-dimensional cultivation growth value of each abnormal condition type, and mark it as Zmk, obtain the three-dimensional cultivation turbulence mean Fzg, and use the formula Obtain the burst adaptation value Vge of the simulated stereoscopic cultivation strategy; m=1, 2, ..., M, m represents the type of abnormal condition, M is the total number of abnormal condition types, set the stereoscopic cultivation growth coefficient to Yk, k=1, 2, 3, ..., k, Y1<Y2<Y3<...<Yk, compare the stereoscopic cultivation growth values ​​of each abnormal condition type in pairs, calculate the difference between the two compared stereoscopic cultivation growth values ​​and take the absolute value to obtain the stereoscopic cultivation turbulence value, sum all the stereoscopic cultivation turbulence values ​​and take the average to obtain the stereoscopic cultivation turbulence mean Fzg.

2. The intelligent optimization management method for three-dimensional vegetable cultivation according to claim 1 is characterized in that: Based on the comparison result between the stereoscopic cultivation growth value and the stereoscopic cultivation growth threshold, it is determined whether to optimize the current vegetable stereoscopic cultivation strategy. Specifically, the stereoscopic cultivation growth threshold is set. When the stereoscopic cultivation growth value is less than or equal to the stereoscopic cultivation growth threshold, it is determined that the current vegetable stereoscopic cultivation strategy is optimized.

3. The intelligent optimization management method for three-dimensional vegetable cultivation according to claim 1 is characterized in that: A three-dimensional vegetable simulation cultivation model is constructed based on the basic growth data and target growth data of vegetables in each layer. The specific steps are as follows: select simulation software, create a three-dimensional cultivation rack model in the simulation software, add corresponding vegetables to the three-dimensional cultivation rack model based on the basic growth data of vegetables in each layer, and assign plant height, stem thickness, and leaf area parameters to the vegetables. Based on the target growth data of vegetables in each layer, the three-dimensional cultivation direction of the vegetables is determined, and a three-dimensional vegetable simulation cultivation model is constructed. The vegetable three-dimensional simulation cultivation model can generate a simulated three-dimensional cultivation strategy according to the three-dimensional cultivation direction.

4. The intelligent optimization management method for three-dimensional vegetable cultivation according to claim 1 is characterized in that: A method for constructing a three-dimensional simulation cultivation model for vegetables with an abnormal condition type is as follows: determine a simulated three-dimensional cultivation strategy, simultaneously determine abnormal condition parameters of a condition abnormal type, input the simulated three-dimensional cultivation strategy and the abnormal condition parameters into the three-dimensional simulation cultivation model for vegetables, and construct a three-dimensional simulation cultivation model for vegetables with the abnormal condition type.

5. The intelligent optimization management method for three-dimensional vegetable cultivation according to claim 1 is characterized in that: The method for obtaining the growth value of a stereoscopic cultivation of an abnormal condition type is as follows: after inputting the simulated stereoscopic cultivation strategy and abnormal condition parameters into the vegetable stereoscopic simulation cultivation model, the vegetable stereoscopic simulation cultivation model performs simulated stereoscopic cultivation for t time, and after the simulated stereoscopic cultivation is completed, the stereoscopic cultivation growth value is obtained.

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