Crop energy-saving light supplement regulation and control method and system based on multi-objective optimization, electronic equipment and storage medium

Abstract

The invention provides a crop energy-saving light supplement regulation and control method and system based on multi-objective optimization, electronic equipment and a storage medium, and relates to the technical field of crop light supplement regulation and control. According to the crop energy-saving light-supplementing regulation and control method based on multi-objective optimization, crops are taken as objects, a light-temperature interaction model is combined, a cooperative influence rule of illumination intensity and temperature on a photosynthetic rate is found, then a multi-objective optimization algorithm is adopted, a Pareto frontier with double objectives of electric energy consumption and the photosynthetic rate is output, and through step search, a light-supplementing regulation and control function is realized. And the optimal light supplementing point of the light supplementing lamp is found, so that accurate dynamic regulation and control of the light supplementing lamp are realized. Under the condition of reducing electric energy consumption as much as possible, the photosynthesis efficiency of the grape color changing period is improved, and then the fruit quality and yield are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of crop supplementary lighting control, and in particular to a crop energy-saving supplementary lighting control method, system, electronic equipment and storage medium based on multi-objective optimization. Background Art

[0002] The color change period of crops (such as grapes and other fruits) is a key stage in the formation of crop quality (such as fruit quality). Light and temperature have an important impact on photosynthesis efficiency, fruit sugar accumulation, and anthocyanin synthesis. Traditional methods mostly rely on plant growth regulators and pesticides to promote color change, but there is a risk of excessive drug residues, affecting food safety.

[0003] In the existing technology, crop light regulation mostly adopts a fixed fill light mode, but this fixed fill light mode has many shortcomings. On the one hand, the fixed fill light mode fails to fully consider the synergistic effect of light intensity and temperature, resulting in energy waste and low photosynthetic efficiency; on the other hand, the fixed fill light mode is mostly for plants with constant light intensity in greenhouses, and it is impossible to perform dynamic and intelligent fill light on the premise of giving priority to external natural light.

[0004] Based on this, there is an urgent need to propose an intelligent lighting technology that can accurately control light and temperature intensity to take into account both photosynthetic efficiency and energy consumption. Summary of the invention

[0005] 1. Technical issues to be resolved In view of the shortcomings of the prior art, the present invention provides a crop energy-saving supplementary lighting control method, system, electronic device and storage medium based on multi-objective optimization, which solves the problem that the prior art cannot accurately control light and temperature to take into account both photosynthetic efficiency and energy consumption.

[0006] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: In the first aspect, the present application first proposes a crop energy-saving supplementary lighting control method based on multi-objective optimization, the method comprising: A nonlinear relationship between temperature and photosynthetic rate is established based on a double sigmoid function, and a light-temperature interaction model is constructed based on the nonlinear relationship and the relationship between light intensity and photosynthetic rate; A multi-objective optimization model is constructed based on the light-temperature interaction model, and the multi-objective optimization model takes maximizing photosynthetic rate and minimizing electric energy consumption as optimization objectives; Solve the multi-objective optimization model to obtain the optimal solution, and formulate the optimal fill light strategy under the current light intensity and temperature data based on the optimal solution.

[0007] In one embodiment, the light-temperature interaction model can be expressed as follows: in, It is the net photosynthetic rate of crops related to photosynthetically active radiation and temperature; α is the initial slope of the light response curve; β and γ is the coefficient; I is the total photosynthetically active radiation; is the dark respiration rate; L is the maximum value of the curve; k To control the rate of growth; It is the center point of the curve, which determines the starting point of growth; is the point where the descent begins; T The current environment temperature.

[0008] In one embodiment, the objective function of the multi-objective optimization model can be expressed as: in, represents the maximum photosynthetic rate; It means minimizing the power consumption; T represents the set of variables including temperature; It means supplemental photosynthetically active radiation; represents natural photosynthetically active radiation; t Indicates fill light time; P n represents the photosynthetic rate; E el Indicates the power consumption of the fill light; The constraints of the objective function are: in, I sat Indicates the light saturation point; I represents the total photosynthetically active radiation; Indicates the power consumption of the fill light.

[0009] In one embodiment, solving the multi-objective optimization model to obtain the optimal solution includes: S31. Finding the Pareto front of the multi-objective optimization model based on the SPEA-2 multi-objective optimization algorithm.

[0010] In a preferred embodiment, formulating an optimal fill light strategy under current light intensity and temperature data based on the optimal solution includes: S32, determining the best fill light point under the current light intensity and temperature data based on step search; Among them, S32 includes: S321, generating a data list containing multiple data points based on the Pareto front, and determining the target coordinates based on the light intensity value and temperature value of the current environment; S322, traversing the data list to check whether there is a data point that completely matches the target coordinates, and if so, returning the target parameter corresponding to the data point; if no completely matching data point is found, initializing a search radius; S323, by calculating the Euclidean distance between the data point and the target coordinates, all data points within the current search radius are screened out; if there is only one data point within the radius, the target parameter of the point is returned; if there are multiple data points, the target parameter of the data point with the largest photosynthetic rate is returned; if no data point is found, the search radius is gradually expanded; S324, repeat S323 until a data point that meets the conditions is found and its target parameter is returned; The target parameter includes a fill light value required for fill light.

[0011] In one embodiment, the relationship between light intensity and photosynthetic rate is fitted based on a rectangular hyperbola correction model.

[0012] In one embodiment, the method further includes: dynamically adjusting the fill light intensity of the fill light based on the optimal fill light strategy.

[0013] In the second aspect, the present application also proposes a crop energy-saving supplementary lighting control system based on multi-objective optimization, the system comprising: A data acquisition module is configured to collect natural light intensity and temperature data in real time; The light-temperature interaction model acquisition module is configured to determine the nonlinear relationship between temperature and photosynthetic rate based on the double sigmoid function, and acquire the light-temperature interaction model based on the nonlinear relationship and the relationship between light intensity and photosynthetic rate; A multi-objective optimization model acquisition module is configured to acquire a multi-objective optimization model based on the light-temperature interaction model, wherein the multi-objective optimization model takes maximizing photosynthetic rate and minimizing power consumption as optimization objectives; The optimal fill-light strategy output module is configured to solve the multi-objective optimization model to obtain an optimal solution, and output an optimal fill-light strategy under current light intensity and temperature data based on the optimal solution.

[0014] In the third aspect, the present application further proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the crop energy-saving and supplementary lighting control method based on multi-objective optimization as described in any one of the above items are implemented.

[0015] In a fourth aspect, the present application finally proposes a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the crop energy-saving and supplementary lighting control method based on multi-objective optimization as described in any of the above items are implemented.

[0016] (III) Beneficial effects The present invention provides a crop energy-saving supplementary lighting control method, system, electronic device and storage medium based on multi-objective optimization. Compared with the prior art, it has the following beneficial effects: The crop energy-saving supplementary lighting control method based on multi-objective optimization proposed in this application first establishes a nonlinear relationship between temperature and photosynthetic rate based on the double sigmoid function, and based on this nonlinear relationship, combined with the relationship between light intensity and photosynthetic rate, constructs a light-temperature interaction model; then, based on the light-temperature interaction model, a multi-objective optimization model is constructed with maximizing photosynthetic rate and minimizing power consumption as the optimization goals; finally, the multi-objective optimization model is solved to obtain the optimal solution, and the optimal supplementary lighting strategy under the current light intensity and temperature data is formulated based on the optimal solution. This application can determine the optimal supplementary lighting point required for the supplementary lighting lamp under the premise of natural lighting, realize accurate dynamic control of the supplementary lighting lamp, and comprehensively consider power consumption and photosynthesis efficiency during the grape color change period, thereby improving fruit quality and yield. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of the crop energy-saving and supplementary lighting control method based on multi-objective optimization of the present invention; Figure 2 This is a diagram showing an embodiment of a crop energy-saving supplementary lighting control method based on multi-objective optimization in an embodiment of the present invention; Figure 3 Schematic diagram of the fitting results of the light-temperature interaction model in an embodiment of the present invention; Figure 4 A flowchart of finding the Pareto front using SPEA-2 in an embodiment of the present invention; Figure 5A schematic diagram of determining target parameters by using step search in an embodiment of the present invention; Figure 6 is the photosynthetic rate of the grape leaves in the embodiment of the present invention ( P n ) and fill light power consumption ( E el ) real-time data display chart. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] The embodiments of the present application provide a crop energy-saving and supplementary lighting control method, system, electronic device and storage medium based on multi-objective optimization, thereby solving the problem that the prior art cannot accurately control light and temperature to take into account both photosynthetic efficiency and energy consumption, and realizing crop energy-saving and supplementary lighting control based on multi-objective optimization.

[0021] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows: The crop energy-saving supplementary lighting control technology based on multi-objective optimization proposed in this application aims to improve the photosynthesis efficiency of crops such as grapes during the color change period by optimizing the light intensity of the supplementary lighting, while minimizing the consumption of electricity, thereby improving the quality and yield of crops. In the embodiment of this application, "Sunshine Rose" grapes are used as the object, and the light-temperature interaction model is combined to find the synergistic influence of light intensity and temperature on the photosynthetic rate. Then, a multi-objective optimization algorithm is used to output the Pareto frontier of the dual objectives of power consumption and photosynthetic rate, and through step search, the optimal supplementary lighting point of the supplementary lighting is found to achieve precise control of the supplementary lighting.

[0022] It should be noted that the crop energy-saving supplementary lighting control technology based on multi-objective optimization proposed in each embodiment of the present application includes but is not limited to light management applicable to the grape color change period, and can also be extended to the intelligent supplementary lighting control of the entire growth cycle of grapes and other crops, providing an efficient and energy-saving solution for light and temperature optimization in precision agriculture. In order to facilitate the description of the implementation process of the embodiments and preferred embodiments of the present application, the "Sunshine Rose" grape is used as the object for description in the embodiments of the present application, but it does not limit the scope of protection of the technical solution and claims of the present application.

[0023] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0024] Embodiment 1: In the first aspect, the present invention first proposes a crop energy-saving supplementary lighting control method based on multi-objective optimization, see Figure 1-2 , the method comprising: S1. A nonlinear relationship between temperature and photosynthetic rate is established based on a double sigmoid function, and a light-temperature interaction model is constructed based on the nonlinear relationship and the relationship between light intensity and photosynthetic rate; S2. constructing a multi-objective optimization model based on the light-temperature interaction model, wherein the multi-objective optimization model takes maximizing photosynthetic rate and minimizing electric energy consumption as optimization objectives; S3. Solve the multi-objective optimization model to obtain an optimal solution, and formulate an optimal fill light strategy under current light intensity and temperature data based on the optimal solution.

[0025] The crop energy-saving supplementary lighting control method based on multi-objective optimization proposed in this embodiment first establishes a nonlinear relationship between temperature and photosynthetic rate based on the double sigmoid function, and based on this nonlinear relationship, combines the relationship between light intensity and photosynthetic rate to build a light-temperature interaction model; then, based on the light-temperature interaction model, a multi-objective optimization model is built with maximizing photosynthetic rate and minimizing power consumption as the optimization goals; finally, the multi-objective optimization model is solved to obtain the optimal solution, and the optimal supplementary lighting strategy under the current light intensity and temperature data is formulated based on the optimal solution. The method of this embodiment can determine the optimal supplementary lighting point of the supplementary light under the premise of natural lighting, and realize accurate dynamic control of the supplementary light.

[0026] The following is combined with Figure 1-6 , and explanations of specific steps S1-S3 are provided to describe in detail the implementation process of an embodiment of the present invention.

[0027] S1. A nonlinear relationship between temperature and photosynthetic rate is established based on a double sigmoid function, and based on the nonlinear relationship, a light-temperature interaction model is constructed in combination with the relationship between light intensity and photosynthetic rate.

[0028] The photosynthesis efficiency of grapes during the color change period is affected by the synergistic effects of light intensity and temperature, but the existing technology lacks a model that can accurately describe the synergistic effect of the interaction between the two on photosynthesis efficiency.

[0029] In one embodiment, a light-temperature interaction model is constructed using existing experimental data, including: using a rectangular hyperbola correction model to fit the relationship between light intensity and photosynthetic rate, and using a double sigmoid (D-sigmoid) function to describe the nonlinear effect of temperature on photosynthetic rate. By combining the response curves of light intensity and temperature to photosynthetic rate, a light-temperature interaction model is established to fully reflect the synergistic effect of light intensity and temperature on photosynthetic rate, providing a theoretical basis for subsequent precise regulation.

[0030] In a preferred embodiment, the light-temperature interaction model expression is as follows: in, is the net photosynthetic rate of grapes related to photosynthetically active radiation and temperature; α is the initial slope of the light response curve; β and γ is the coefficient; I is the total photosynthetically active radiation; is the dark respiration rate; L is the maximum value of the curve, i.e. the peak value; k To control the rate of growth; It is the center point of the curve, which determines the starting point of growth; is the point where the descent begins; T is the temperature of the current environment. The fitting results of the light-temperature interaction model are as follows: Figure 3 shown.

[0031] In this embodiment, the D-Sigmoid function is used to fit the relationship between temperature and photosynthetic rate, and the photosynthetic rate model of light-temperature interaction is coupled. Through the above light-temperature interaction model, the influence of temperature and light conditions on photosynthetic rate can be more accurately described, providing a reliable basis for subsequent supplementary light control.

[0032] S2. Constructing a multi-objective optimization model based on the light-temperature interaction model, wherein the multi-objective optimization model takes maximizing photosynthetic rate and minimizing electric energy consumption as optimization objectives.

[0033] In grape cultivation, the use of supplementary lights needs to find the best balance between increasing photosynthetic rate and reducing energy consumption. However, the existing technology mostly adopts single-objective optimization, which is difficult to take into account both photosynthetic efficiency and energy consumption. Based on this, in order to take into account both photosynthetic efficiency and energy consumption, this embodiment constructs a multi-objective optimization model based on the above-mentioned light-temperature interaction model, wherein the multi-objective optimization model takes maximizing photosynthetic rate and minimizing energy consumption as optimization goals, and achieves goal optimization by adjusting the supplementary light parameters. The objective function expression is: in, represents the maximum photosynthetic rate; It means minimizing the power consumption; T represents the set of variables including temperature; It means supplemental photosynthetically active radiation; represents natural photosynthetically active radiation; t Indicates fill light time. Pn represents the photosynthetic rate; E el Indicates the power consumption of the fill light.

[0034] In practice, supplementing photosynthetically active radiation must meet the following constraints: Supplementing photosynthetically active radiation value Must be greater than natural photosynthetically active radiation (Right now ), and must not exceed the light saturation point I sat Photosynthetically active radiation The difference (i.e. ). In addition, according to actual needs, the power consumption of the fill light, the natural light intensity, the fill light intensity, etc. all have a certain range. Therefore, the constraint condition can be expressed by the formula: in, I represents the total photosynthetically active radiation; Indicates the power consumption of the fill light; 780 indicates the maximum power consumption of the fill light; 500 indicates the maximum natural light intensity; 200 and 1800 indicate the minimum and maximum fill light intensity, respectively.

[0035] S3. Solve the multi-objective optimization model to obtain an optimal solution, and formulate an optimal fill light strategy under current light intensity and temperature data based on the optimal solution.

[0036] When solving the multi-objective optimization model constructed above, the existing multi-objective optimization algorithm (such as NSGA-II, MOEA / D, etc.) can also be used to solve the problem. However, the existing multi-objective optimization algorithm still has deficiencies in solution accuracy, solution efficiency, and maintaining solution diversity. Based on this, in order to better solve the multi-objective optimization model constructed in this embodiment, in one embodiment, the solution process of the multi-objective optimization model includes the following steps: S31. Finding the Pareto front of the multi-objective optimization model based on the SPEA-2 multi-objective optimization algorithm.

[0037] like Figure 4 As shown, it is a flowchart of using SPEA-2 multi-objective optimization algorithm to find the Pareto frontier (also called Pareto optimal boundary, Pareto Frontier) of the multi-objective optimization model, see Figure 4 , the process includes: Step 1: Start by initializing parameters.

[0038] The initialization parameters include but are not limited to: initialization population P , population P The number of N ; Initialize the file A ; Initialize the number of iterations G =1; preset maximum number of iterations G max ; Step 2: Randomly select an individual and evaluate the fitness value of the individual.

[0039] Step 3: Determine whether it is a Pareto optimal solution. If so, go to step 4; if not, go to step 2; Step 4: Judgement A Is the individual larger than the population? P Number of N ; If so, use the selection mechanism to replace the file A , and go to step 5; if not, go directly to step 5; Step 5: Update the profile A ; Step 6: Judgement G > G max Is it true? If so, go to step 7; if not, perform crossover and mutation, and let G = G +1, and go to step 2; Step 7: Draw the Pareto front, and we are done.

[0040] Among them, the Pareto front (i.e., the Pareto optimal solution set) represents a set of points that maximize the photosynthetic rate and minimize the power consumption, that is, the balance point between the power consumption of the fill light and the photosynthetic rate.

[0041] It should be noted that the SPEA-2 (Strength Pareto Evolutionary Algorithm 2) multi-objective optimization algorithm used in this embodiment to find the Pareto frontier has significant advantages in the following aspects compared to traditional multi-objective optimization algorithms (such as NSGA-II, MOEA / D, etc.): First, by introducing an external archiving mechanism and a strength-based fitness allocation strategy, SPEA-2 can more accurately identify and retain non-dominated solutions, thereby more efficiently approaching the true Pareto frontier. Secondly, SPEA-2 can effectively maintain the diversity of the solution set, avoid the algorithm from converging to the local optimal area too early, and ensure that the global optimal equilibrium solution is found under multivariate constraints such as temperature, supplementary light time, natural photosynthetic active radiation and supplementary photosynthetic active radiation; In addition, SPEA-2 shows stronger robustness when dealing with high-dimensional target spaces and complex nonlinear problems, and can better balance the complex relationship between maximizing photosynthetic rate and minimizing power consumption. Compared with traditional multi-objective optimization algorithms such as NSGA-II, SPEA-2 performs better in the distribution and convergence of solution sets, and is particularly suitable for multi-variable and multi-constrained optimization scenarios. Therefore, SPEA-2 can not only provide a variety of Pareto solution sets (Pareto frontiers), but also flexibly adjust algorithm parameters according to actual needs, significantly improving the optimization effect of fill light parameter adjustment.

[0042] S32: Determine the best fill light point under the current light intensity and temperature data based on step search.

[0043] According to the above-obtained "Pareto Frontier" (also known as the Pareto optimal boundary, Pareto Frontier) analysis results, the data set with the best energy consumption can be screened out. However, in order to ensure the scientific significance and application value of the research, the photosynthetic rate is further introduced as a key screening indicator, and all the photosynthetic rates corresponding to the data set with the best energy consumption are taken as candidate points. By comparing and analyzing the photosynthetic rate data in all these candidate points, the data point with the largest photosynthetic rate is finally selected as the optimal solution. Finally, based on the fill light intensity corresponding to the data point corresponding to the optimal solution, the actual fill light is adjusted to provide the best fill light strategy for the vineyard.

[0044] However, in practical applications, the control of the fill light needs to be dynamically adjusted according to real-time environmental data (mainly referring to the current light intensity and temperature data), and the existing technology lacks an efficient real-time optimization method. In order to obtain the parameters corresponding to the best target from a given data list, in one embodiment, a target parameter determination method based on step search is proposed to obtain the best target parameters from a given data list. The specific steps of step search are as follows: S321, first, input a data list containing multiple data points (i.e., the Pareto front generated and drawn as described above), wherein each data point includes multiple attributes (i.e., variables such as temperature, light intensity, and photosynthetic rate), and a target coordinate (the light intensity value and temperature value measured in the current environment); S322. Next, traverse the data list to check whether there is a data point that completely matches the target coordinates. If so, directly return the target parameters of the point; if no completely matching point is found, initialize a search radius and use step search to search: S323, in each iteration, the Euclidean distance between the data point and the target coordinates is calculated, and all data points within the current search radius are screened out; if there is only one data point within the radius, the target parameter of the point is returned; if there are multiple data points, the target parameter of the data point with the largest photosynthetic rate is returned; if no data point is found, the search radius is gradually expanded; S324, repeat the above process until a data point that meets the conditions is found and its target parameter (the fill light value that needs to be filled in by the fill light) is returned.

[0045] The target parameter determination method based on step search can efficiently and accurately determine the optimal target parameters and is suitable for parameter matching and optimization in complex data environments.

[0046] During the measurement process, Figure 5 As shown in the middle point A, in the third round of search, three data points were found to be within the preset search radius. By comparing and analyzing the photosynthetic rate data of these three candidate points, the data point with the largest photosynthetic rate was finally selected as the optimal solution. According to the fill light intensity corresponding to this point, the actual fill light is adjusted to provide the optimal fill light strategy for the vineyard.

[0047] When the above embodiment is implemented, firstly, an air temperature, humidity and light recorder (such as the YM-18 air temperature, humidity and light recorder) is used to collect and store the natural light intensity and temperature data in real time. Then, based on the above-mentioned SPEA-2 multi-objective optimization algorithm, the optimal photosynthetic efficiency set for each time period is determined, and combined with the real-time monitored natural photosynthetic active radiation and temperature data, the light intensity of the fill light is dynamically adjusted to optimize the photosynthetic efficiency and reduce energy consumption.

[0048] When natural light is insufficient or the temperature is not ideal, the fill light intensity is automatically increased; conversely, when light is sufficient or the temperature is appropriate, the fill light intensity is reduced, thereby achieving efficient use of electrical energy.

[0049] In addition, a dynamic feedback mechanism is used to monitor changes in plant photosynthesis in real time and fine-tune light intensity to ensure that photosynthesis efficiency is maximized while reducing energy waste.

[0050] Photosynthetic rate of grape leaves ( P n ) and fill light power consumption ( E el ) real-time data such as Figure 6 shown.

[0051] Although the photosynthetic rate decreased by 21.65% compared with the light saturation point, the light energy consumption was significantly reduced by 68.63% under the same conditions. It can be seen that although the photosynthetic efficiency decreased slightly, the optimized lighting conditions can effectively reduce energy consumption, thereby achieving a better balance between energy utilization efficiency and photosynthetic efficiency. This regulation strategy not only significantly improved photosynthetic efficiency, but also greatly reduced electricity consumption, providing important support for energy utilization efficiency and sustainable development of agricultural production.

[0052] It should be noted that in this embodiment, the step search is combined with the real-time collected light intensity and temperature data to quickly find the fill light point that best matches the current environmental conditions in the Pareto optimal solution set. The algorithm gradually expands the search range through an adaptive radius expansion strategy to ensure that the optimal solution is found in the shortest time, thereby achieving precise control of the fill light and minimizing energy consumption while ensuring photosynthesis efficiency.

[0053] Embodiment 2: In a second aspect, the present invention further provides a crop energy-saving supplementary lighting control system based on multi-objective optimization, the system comprising: A data acquisition module is configured to collect natural light intensity and temperature data in real time; The light-temperature interaction model acquisition module is configured to determine the nonlinear relationship between temperature and photosynthetic rate based on the double sigmoid function, and acquire the light-temperature interaction model based on the nonlinear relationship and the relationship between light intensity and photosynthetic rate; A multi-objective optimization model acquisition module is configured to acquire a multi-objective optimization model based on the light-temperature interaction model, wherein the multi-objective optimization model takes maximizing photosynthetic rate and minimizing power consumption as optimization objectives; The optimal fill-light strategy output module is configured to solve the multi-objective optimization model to obtain an optimal solution, and output an optimal fill-light strategy under current light intensity and temperature data based on the optimal solution.

[0054] The light-temperature interaction model acquisition module, the multi-objective optimization model acquisition module, and the optimal supplementary lighting strategy output module execute the steps of the crop energy-saving supplementary lighting control method based on multi-objective optimization described in the above-mentioned embodiment 1 and its preferred embodiments.

[0055] It can be understood that the crop energy-saving and supplementary lighting control system based on multi-objective optimization provided in the embodiment of the present invention corresponds to the above-mentioned crop energy-saving and supplementary lighting control method based on multi-objective optimization. The explanations, examples, beneficial effects and other parts of the relevant contents can refer to the corresponding contents in the crop energy-saving and supplementary lighting control method based on multi-objective optimization, and will not be repeated here.

[0056] Embodiment 3: In a third aspect, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the crop energy-saving supplementary lighting control method based on multi-objective optimization as described in any one of the above embodiments and preferred implementations thereof are implemented, and the method mainly includes: S1. A nonlinear relationship between temperature and photosynthetic rate is established based on a double sigmoid function, and a light-temperature interaction model is constructed based on the nonlinear relationship and the relationship between light intensity and photosynthetic rate; S2. constructing a multi-objective optimization model based on the light-temperature interaction model, wherein the multi-objective optimization model takes maximizing photosynthetic rate and minimizing electric energy consumption as optimization objectives; S3. Solve the multi-objective optimization model to obtain an optimal solution, and formulate an optimal fill light strategy under current light intensity and temperature data based on the optimal solution.

[0057] It can be understood that the crop energy-saving and supplementary lighting control electronic device based on multi-objective optimization provided in the embodiment of the present invention corresponds to the above-mentioned crop energy-saving and supplementary lighting control method and system based on multi-objective optimization. The explanations, examples, beneficial effects and other parts of the relevant contents can refer to the corresponding contents in the crop energy-saving and supplementary lighting control method and system based on multi-objective optimization, and will not be repeated here.

[0058] Embodiment 4: In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the crop energy-saving supplementary lighting control method based on multi-objective optimization as described in any of the above embodiments and preferred implementations thereof are implemented. The method mainly includes: S1. A nonlinear relationship between temperature and photosynthetic rate is established based on a double sigmoid function, and a light-temperature interaction model is constructed based on the nonlinear relationship and the relationship between light intensity and photosynthetic rate; S2. constructing a multi-objective optimization model based on the light-temperature interaction model, wherein the multi-objective optimization model takes maximizing photosynthetic rate and minimizing electric energy consumption as optimization objectives; S3. Solve the multi-objective optimization model to obtain an optimal solution, and formulate an optimal fill light strategy under current light intensity and temperature data based on the optimal solution.

[0059] It can be understood that the crop energy-saving and supplementary lighting control storage medium based on multi-objective optimization provided in the embodiment of the present invention corresponds to the above-mentioned crop energy-saving and supplementary lighting control method and system based on multi-objective optimization, and the explanations, examples, beneficial effects and other parts of the relevant contents can refer to the corresponding contents in the crop energy-saving and supplementary lighting control method and system based on multi-objective optimization, and will not be repeated here.

[0060] In summary, compared with the prior art, the present invention has the following beneficial effects: 1. The crop energy-saving supplementary lighting control technology based on multi-objective optimization proposed in this application first establishes a nonlinear relationship between temperature and photosynthetic rate based on the double sigmoid function, and then builds a light-temperature interaction model based on the nonlinear relationship and the relationship between light intensity and photosynthetic rate; then, based on the light-temperature interaction model, a multi-objective optimization model is built with maximizing photosynthetic rate and minimizing power consumption as the optimization goals; finally, the multi-objective optimization model is solved to obtain the optimal solution, and the optimal supplementary lighting strategy under the current light intensity and temperature data is formulated based on the optimal solution. This application can determine the optimal supplementary lighting point of the supplementary lighting lamp under the premise of natural lighting, realize the precise dynamic control of the supplementary lighting lamp, and comprehensively consider the power consumption and photosynthesis efficiency of crops such as grapes during the color change period, thereby improving the fruit quality and yield.

[0061] 2. The crop energy-saving supplementary lighting control technology based on multi-objective optimization proposed in this application uses the D-Sigmoid function to fit the relationship between temperature and photosynthetic rate, and couples the photosynthetic rate model of light-temperature interaction. Through this model, the influence of temperature and light conditions on photosynthetic rate can be more accurately described, providing a reliable basis for the subsequent precise control of supplementary lighting.

[0062] 3. The crop energy-saving supplementary lighting control technology based on multi-objective optimization proposed in this application uses the SPEA-2 algorithm in multi-objective optimization and successfully solves the balance point between the power consumption of the supplementary lighting lamp and the photosynthetic rate. By drawing the Pareto frontier, the trade-off relationship between power consumption and photosynthetic rate under different supplementary lighting strategies is reflected, providing a variety of optional solutions for practical applications.

[0063] 4. The crop energy-saving supplementary lighting control technology based on multi-objective optimization proposed in this application uses step search to monitor environmental data in real time and dynamically adjust the supplementary lighting intensity, thereby ensuring the maximization of photosynthesis efficiency and reducing energy waste. This strategy has shown high response efficiency and accuracy in practical applications, providing reliable technical support for intelligent lighting control systems.

[0064] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0065] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A crop energy-saving supplementary lighting control method based on multi-objective optimization, characterized in that: The method comprises: A nonlinear relationship between temperature and photosynthetic rate is established based on a double sigmoid function, and a light-temperature interaction model is constructed based on the nonlinear relationship and the relationship between light intensity and photosynthetic rate. A multi-objective optimization model is constructed based on the light-temperature interaction model, and the multi-objective optimization model takes maximizing photosynthetic rate and minimizing electric energy consumption as optimization objectives; Solve the multi-objective optimization model to obtain the optimal solution, and formulate the optimal fill light strategy under the current light intensity and temperature data based on the optimal solution.

2. The method according to claim 1, characterized in that The light-temperature interaction model can be expressed as follows: in, It is the net photosynthetic rate of crops related to photosynthetically active radiation and temperature; α is the initial slope of the light response curve; β and γ is the coefficient; I is the total photosynthetically active radiation; is the dark respiration rate; L is the maximum value of the curve; k To control the rate of growth; It is the center point of the curve, which determines the starting point of growth; is the point where the descent begins; T The current ambient temperature.

3. The method according to claim 1, characterized in that The objective function of the multi-objective optimization model can be expressed as follows: in, represents the maximum photosynthetic rate; It means minimizing the power consumption; T represents the set of variables including temperature; It means supplemental photosynthetically active radiation; represents natural photosynthetically active radiation; t Indicates fill light time; P n represents the photosynthetic rate; E el Indicates the power consumption of the fill light; The constraints of the objective function are: in, I sat Indicates the light saturation point; I represents the total photosynthetically active radiation; Indicates the power consumption of the fill light.

4. The method according to claim 1, characterized in that When solving the multi-objective optimization model to obtain the optimal solution, it includes: S31. Finding the Pareto front of the multi-objective optimization model based on the SPEA-2 multi-objective optimization algorithm.

5. The method according to claim 4, characterized in that Based on the optimal solution, an optimal fill light strategy under the current light intensity and temperature data is formulated, including: S32, determining the best fill light point under the current light intensity and temperature data based on step search; Among them, S32 includes: S321, generating a data list containing multiple data points based on the Pareto front, and determining the target coordinates based on the light intensity value and temperature value of the current environment; S322, traversing the data list to check whether there is a data point that completely matches the target coordinates, and if so, returning the target parameter corresponding to the data point; if no completely matching data point is found, initializing a search radius; S323, by calculating the Euclidean distance between the data point and the target coordinates, all data points within the current search radius are screened out; if there is only one data point within the radius, the target parameter of the point is returned; if there are multiple data points, the target parameter of the data point with the largest photosynthetic rate is returned; if no data point is found, the search radius is gradually expanded; S324, repeat S323 until a data point that meets the conditions is found and its target parameter is returned; The target parameter includes a fill light value required for fill light.

6. The method according to claim 1, characterized in that The relationship between light intensity and photosynthetic rate was fitted based on the rectangular hyperbola correction model.

7. The method according to any one of claims 1 to 6, characterized in that: The method further includes: dynamically adjusting the fill light illumination intensity of the fill light based on the optimal fill light strategy.

8. A crop energy-saving supplementary lighting control system based on multi-objective optimization, characterized in that: The system comprises: A data acquisition module is configured to collect natural light intensity and temperature data in real time; The light-temperature interaction model acquisition module is configured to determine the nonlinear relationship between temperature and photosynthetic rate based on the double sigmoid function, and acquire the light-temperature interaction model based on the nonlinear relationship and the relationship between light intensity and photosynthetic rate; A multi-objective optimization model acquisition module is configured to acquire a multi-objective optimization model based on the light-temperature interaction model, wherein the multi-objective optimization model takes maximizing photosynthetic rate and minimizing power consumption as optimization objectives; The optimal fill-light strategy output module is configured to solve the multi-objective optimization model to obtain an optimal solution, and output an optimal fill-light strategy under current light intensity and temperature data based on the optimal solution.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the crop energy-saving and supplementary lighting control method based on multi-objective optimization are implemented as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the crop energy-saving and supplementary lighting control method based on multi-objective optimization as described in any one of claims 1 to 7 are implemented.

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