Method and device for adjusting and optimizing power of plant light supplement lamp based on multi-target optimization algorithm

Abstract

The invention relates to power operation optimization of electrical equipment, in particular to a plant light supplement lamp power adjustment optimization method and device based on a multi-target optimization algorithm, and the method comprises the steps: determining the relation between the photosynthetically active radiation value supplied by a light supplement lamp for leaf vegetables in a greenhouse and the dry matter accumulation growth amount in a vigorous growth period; determining a linear relation between the operation power of the light supplement lamp and the illumination intensity of the light supplement lamp; according to the relation between the photosynthetically active radiation value supplied by the light supplement lamp of the leaf vegetables in the greenhouse and the dry matter accumulation and growth amount in the vigorous growth period and the linear relation between the operation power of the light supplement lamp and the illumination intensity of the light supplement lamp, an optimization objective function and constraint conditions of operation of the light supplement lamp microgrid system of the leaf vegetables in the greenhouse are constructed; a hybrid non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm are adopted to optimize and solve the optimization objective function; and controlling the power of the light supplement lamp according to an optimization solution result of the optimization objective function. The energy utilization rate of the agricultural light supplementing system can be effectively improved.

Description

Technical Field

[0001] The present invention relates to power operation optimization of electrical equipment, and in particular to a power adjustment optimization method and device for a plant supplementary light based on a multi-objective optimization algorithm. Background Art

[0002] During the growth process, plants need to photosynthesize to generate energy to sustain their own growth. At present, plant fill-in lights are used for lighting during the seedling, growth, flowering, and fruiting of crops. They can increase the yield and volume of vegetables and fruits, extend the production period, bring them to the market earlier, and enhance their quality and taste. They can also make flowers brighter and prevent them from growing too long. However, when the photosynthetically active radiation reaches a certain value, the growth of leafy vegetables will become very slow. At this time, increasing the light supply will cause energy waste. At the same time, the large-scale use of fill-in lights and other electrical equipment that can assist the growth of crops will greatly increase the agricultural electricity load. Therefore, it is necessary to investigate the load that can be reduced by agricultural fill-in lights, analyze the energy elasticity of fill-in lights equipment, and formulate a power adjustment optimization strategy for plant fill-in lights to reduce the energy consumption in agricultural production.

[0003] However, the existing technologies are more focused on the research of multi-energy coordinated scheduling methods for agricultural integrated energy systems to achieve source-load coordinated optimization operation, or focus on other fields of fill-light LED lamps. There is a lack of research on refined power adjustment strategies specifically for plant fill-light equipment. In terms of the research on power adjustment of fill-lights, Patent No. CN113056068B provides a power adjustment method for fill-lights, which is used for fill-lighting during the shooting of image information on rainy days. It adjusts the power of the fill-light according to the set power change value based on evaluation indicators and impact proportional coefficients; Patent No. CN115734424A provides a power adjustment method for LED power supply that can extend the service life of the power supply while still maintaining sufficient lighting in special scenarios. It judges the status of existing lighting lamps based on three power judgment modules, and then adjusts its power through the control module to realize whether the LED power supply is in an emergency state. In terms of adjustment strategies, most studies only focus on single-target adjustment, such as patent number CN113056068B, which mainly adjusts power for the single target of shooting quality, and the adjustment strategy depends on evaluation indicators and impact proportional coefficients, which may need to be manually set and adjusted; other studies do not combine dynamic real-time conditions to fine-tune the operating state of the fill light, such as patent number CN115734424A, which mainly judges different lighting environment states set in advance and takes different adjustment measures. These manually set environmental thresholds cannot completely cover the operating state of the lighting. Therefore, it is necessary for researchers to conduct fine-tuning power adjustment strategies for plant fill light equipment alone. Summary of the invention

[0004] In view of at least one deficiency in the prior art, the present invention provides a method and device for optimizing the power regulation of plant supplementary lighting lamps based on a multi-objective optimization algorithm. Taking into account the optimal electricity cost per unit output and the optimal plant growth objectives, the power regulation strategy of plant supplementary lighting lamps is studied based on a hybrid non-dominated sorting genetic algorithm and a multi-objective particle swarm (NSGA-II-MOPSO) algorithm, which can effectively improve the energy utilization rate of the agricultural supplementary lighting system.

[0005] To achieve the above object, the technical solution adopted by the present invention includes the following contents:

[0006] In a first aspect, the present invention provides a method for optimizing power regulation of a plant supplementary light based on a multi-objective optimization algorithm, comprising the following steps:

[0007] Determine the relationship between the photosynthetically active radiation value supplied by supplementary lighting for leafy vegetables in greenhouses and the increase in dry matter accumulation during the vigorous growth period;

[0008] Determine the linear relationship between the operating power of the fill light and the light intensity of the fill light;

[0009] According to the relationship between the photosynthetically active radiation value supplied by the supplementary light for the leafy vegetables in the greenhouse and the dry matter accumulation growth during the vigorous growth period and the linear relationship between the operating power of the supplementary light and the light intensity of the supplementary light, the optimization objective function and constraint conditions for the operation of the microgrid system for the supplementary light for the leafy vegetables in the greenhouse are constructed;

[0010] A hybrid non-dominated sorting genetic algorithm and a multi-objective particle swarm algorithm are used to optimize and solve the optimization objective function;

[0011] The power of the fill light is controlled according to the optimization solution result of the optimization objective function.

[0012] In a second aspect, the present invention provides a plant light power adjustment optimization device based on a multi-objective optimization algorithm, comprising:

[0013] A data acquisition unit, which is used to acquire the parameters required for constructing the optimization objective function and constraint conditions of the operation of the microgrid system for supplementary lighting of leafy vegetables in the greenhouse;

[0014] A data processing unit is used to perform the following steps:

[0015] - Determine the relationship between the photosynthetically active radiation value supplied by the supplementary lighting of leafy vegetables in the greenhouse and the increase in dry matter accumulation during the vigorous growth period;

[0016] -Determine the linear relationship between the operating power of the fill light and the light intensity of the fill light;

[0017] -According to the relationship between the photosynthetically active radiation value supplied by the supplementary light for leafy vegetables in the greenhouse and the dry matter accumulation growth during the vigorous growth period and the linear relationship between the operating power of the supplementary light and the light intensity of the supplementary light, the optimization objective function and constraint conditions for the operation of the microgrid system for supplementary light for leafy vegetables in the greenhouse are constructed;

[0018] -Using a hybrid non-dominated sorting genetic algorithm and a multi-objective particle swarm algorithm to optimize and solve the optimization objective function;

[0019] A control execution unit is used to control the power of the fill light according to the optimization solution result of the optimization objective function.

[0020] In a third aspect, the present invention further provides an electronic device, including a processor and a memory;

[0021] The memory is used to store programs;

[0022] The processor executes the program to implement the method described above.

[0023] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the method described above.

[0024] In a fifth aspect, the present invention further provides a computer program product or a computer program, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above method.

[0025] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention studies the refined power adjustment strategy for plant supplementary lighting equipment alone, which can effectively improve the energy utilization rate of the agricultural supplementary lighting system; (2) In view of the nonlinear, non-differentiable and multi-constrained characteristics of the multi-objective model, the present invention proposes a hybrid non-dominated sorting genetic algorithm and multi-objective particle swarm (NSGA-II-MOPSO) algorithm. This method calculates the crowding distance and sorts according to the elite strategy, and updates the speed and position of the particles to obtain the optimal Pareto frontier. On the basis of ensuring the optimal plant growth, the system operation cost is effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 This is a flowchart provided for Example 1 of the present invention.

[0028] Figure 2 Schematic diagram of the power adjustment optimization model of the plant fill light in Example 1 of the present invention.

[0029] Figure 3 This is a diagram of the fill light microgrid system in Example 1 of the present invention.

[0030] Figure 4 Flow chart of the NSGA-II-MOPSO algorithm in Example 1 of the present invention.

[0031] Figure 5 This is a line graph of solar radiation on a typical summer day in Example 1 of the present invention.

[0032] Figure 6 This is a histogram of the microgrid system of the fill light running according to the optimization result of the NSGA-II-MOPSO algorithm in Example 1 of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0034] It should be noted that the terms "including" and "having" and any variations of the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] The present invention studies the power adjustment strategy of plant supplementary lighting lamps based on the hybrid non-dominated sorting genetic algorithm and multi-objective particle swarm optimization (NSGA-II-MOPSO) algorithm, taking into account the minimum overall electricity cost and the optimal plant growth objectives. The hybrid non-dominated sorting genetic algorithm (NSGA-II) can use its non-dominated sorting and crowding distance mechanism to quickly find the Pareto frontier solution; the multi-objective particle swarm optimization algorithm (MOPSO) can improve the diversity and convergence speed of solutions through the global and local search capabilities of the particle swarm; the NSGA-II-MOPSO hybrid algorithm is designed, combined with the excellent gene transfer of the genetic algorithm and the global search characteristics of the particle swarm, and through the iterative calculation of the algorithm, it can achieve multi-objective optimization of the power adjustment of the supplementary lighting lamp in real time and in multiple operating states. This strategy optimizes the power adjustment of the supplementary lighting lamp so that crops can obtain the most suitable lighting conditions during the growth process, while reducing the electricity cost to a minimum, thereby improving the energy utilization rate of the agricultural supplementary lighting system.

[0036] The following will be described through various embodiments:

[0037] Embodiment 1:

[0038] In order to optimize the power regulation requirements of crop fill light, this embodiment provides a plant fill light power regulation optimization method based on a multi-objective optimization algorithm, which includes the following steps:

[0039] Step 1: Determine the relationship between the photosynthetically active radiation value supplied by the supplementary lighting of leafy vegetables in the greenhouse and the increase in dry matter accumulation during the vigorous growth period.

[0040] Preferably, in the above steps, the photosynthetically active radiation value of the leafy vegetables in the greenhouse is first obtained, which includes the solar radiation provided by the external environment and the light intensity supplied by the supplementary light; then, assuming that the solar radiation intensity in the greenhouse is 0, the temperature in the greenhouse is a constant, and the carbon dioxide concentration is a constant, a linear relationship equation is obtained between the photosynthetically active radiation supplied by the supplementary light required for the leafy vegetables in the greenhouse and the growth of dry matter accumulation during the vigorous growth period.

[0041] (1) The photosynthetically active radiation value of leafy vegetables in a greenhouse refers to the solar radiation energy that can be absorbed and converted by chlorophyll during the photosynthesis of green crops. It is the energy source for the life activities, organic matter synthesis and yield formation of crops. Therefore, this patent uses photosynthetically active radiation to describe the effect of light intensity in a greenhouse on leafy vegetables. The photosynthetically active radiation of leafy vegetables in a greenhouse includes the solar radiation provided by the external environment and the light intensity provided by the supplementary light, as shown below:

[0042] I PAR,t =c par G r,t +εI LED,t

[0043] In the formula, c par represents the efficiency of leafy vegetable canopy in absorbing solar radiation, G r,t represents the solar radiation intensity at time t, ε represents the light radiation conversion efficiency of photosynthesis, I LED,t represents the light intensity of the supplementary light in the greenhouse at time t, I PAR,t represents the photosynthetically active radiation of leafy vegetables in the greenhouse at time t;

[0044] (2) Assuming that the solar radiation intensity in the greenhouse is 0, the temperature in the greenhouse is a constant, and the carbon dioxide concentration is a constant, the photosynthetically active radiation I required by the supplementary lighting for crops can be obtained. LED,t The dry matter accumulation growth ΔM during the vigorous growth period d,t The relationship is shown in the following formula. It can be seen that with the increase of photosynthetically active radiation, the growth of dry matter accumulation is getting slower and slower:

[0045]

[0046] I LED,t =I PAR,t / ε

[0047] Where M d,t represents the dry matter accumulation of leafy vegetables per unit area of ​​land at time t, c α represents the conversion rate of carbon dioxide absorbed by photosynthesis into organic matter, c β Represents the growth factor of leafy vegetables during their vigorous growth period, Z T , Z bs They represent the correlation function between temperature, carbon dioxide concentration and dry matter accumulation of leafy vegetables during photosynthesis, which are set as constants here, c k represents the canopy extinction coefficient of leafy vegetables, c d represents the respiration rate of the stem and root of leafy vegetables at the reference temperature, c τ represents the percentage of root dry weight at different growth stages, φ phot,t Represents the photosynthetic rate of leafy vegetables at time t.

[0048] Step 2: Determine the linear relationship between the operating power of the fill light and the light intensity of the fill light;

[0049] Preferably, in the above steps, the greenhouse leafy vegetable supplementary lighting lamp uses an LED lamp as the illuminant, which is an artificial light source that provides the lighting conditions required for photosynthesis of leafy vegetables in the greenhouse. The power adjustment optimization model of the plant supplementary lighting lamp is as follows: Figure 2 As shown, the operating power of the fill light is linearly related to the light intensity:

[0050] P LED,t =I LED,t ·L LED

[0051] Where P LED,t Represents the operating power of the LED fill light at time t, L LED Represents the linearized power-light intensity factor of the LED fill light.

[0052] Step 3: Based on the relationship between the photosynthetically active radiation value supplied by the supplementary lighting lamps for leafy vegetables in the greenhouse and the increase in dry matter accumulation during the vigorous growth period and the linear relationship between the operating power of the supplementary lighting lamps and the light intensity of the supplementary lighting lamps, construct the optimization objective function and constraint conditions for the operation of the microgrid system for supplementary lighting lamps for leafy vegetables in the greenhouse.

[0053] Preferably, in the above steps, the schematic diagram of the fill light microgrid system is as follows: Figure 3 As shown, it includes power purchase from the grid, photovoltaic self-use and supplementary lighting equipment. Among them, the change curves of the power purchase price from the grid and the photovoltaic output on a typical day in summer are known. The multi-objective function that needs to be solved under the constraints consists of minimizing the overall electricity cost and optimizing plant growth. The constraints mentioned are power balance constraints, plant dry matter accumulation constraints, and electrical equipment power constraints:

[0054] (1) The objective function is to minimize the overall electricity cost, which is expressed as:

[0055]

[0056] In the formula, t0 represents the optimization start time, t end represents the optimization termination period, P grid,t represents the electric power obtained from the grid at time t, λ grid,t represents the electricity price at time t;

[0057] (2) The objective function is optimal plant growth, expressed as:

[0058]

[0059] In the formula, t0 represents the optimization start time, t end represents the optimization termination period, and Δt is the optimization time period;

[0060] (3) The constraints mentioned above include power balance constraints. The power consumption on the grid side and the photovoltaic power generation should be equal to the fixed power load in the system and the load of the supplementary lighting that can be reduced. The expression is as follows:

[0061] P grid,t +P PV,t =P LED,t +P L,t

[0062] Where P grid,trepresents the amount of electricity taken from the grid at time t, P PV,t represents the photovoltaic power generation at time t, P LED,t Represents the operating power of the fill light at time t, and the fixed load power in the greenhouse, P L,t represents the fixed power load at time t;

[0063] (4) The constraint condition is the plant dry matter accumulation constraint. The plant dry matter accumulation has upper and lower limits, and the expression is as follows:

[0064]

[0065] In the formula, and They represent the lower and upper limits of the accumulated amount of plant dry matter at the set time t respectively;

[0066] (5) The constraint condition mentioned above includes the power constraint of electrical equipment. The electrical equipment mainly considered in the present invention is the fill light. The fill light has an upper and lower limit of operating power, which is expressed as follows:

[0067]

[0068] In the formula, and Respectively represent the minimum and maximum operating power of the plant light;

[0069] (6) The constraint condition mentioned above includes the power constraint of electrical equipment. The power generation equipment mainly considered in the present invention is solar photovoltaic power generation. Photovoltaic power generation has upper and lower limits of power generation, which are expressed as follows:

[0070]

[0071] In the formula, Represents the maximum power generation of the photovoltaic device.

[0072] Step 4: Use a hybrid non-dominated sorting genetic algorithm and a multi-objective particle swarm algorithm to optimize and solve the optimization objective function.

[0073] Preferably, in the above steps, the embodiment provides a hybrid non-dominated sorting genetic algorithm and multi-objective particle swarm (NSGA-II-MOPSO) algorithm, the algorithm steps are as follows: Figure 4 As shown in the figure, the purpose is to solve the multi-objective function consisting of the minimum overall electricity cost and the optimal plant growth. This objective function has the characteristics of nonlinearity, non-differentiability and multiple constraints. Finally, the optimal daily operation strategy of the fill light is obtained. The algorithm process is mainly divided into 14 steps:

[0074] (1) Set and initialize the initial parameters of the hybrid NSGA-Ⅱ-MOPSO algorithm, with the maximum number of iterations being 120, the population size N being 200, the crossover probability Pc being 0.7, the mutation probability Pm being 0.4, the inertia factor ω being 0.5, the learning factor c1 being 4, and c2 being 1;

[0075] (2) Evaluate the multi-objective function composed of the optimal electricity cost per unit of output and the optimal plant growth;

[0076] (3) When the values ​​of individuals in the population satisfy the constraints, perform non-dominated sorting on the individuals in the population;

[0077] (4) Calculate the crowding distance of each individual and use the elite strategy to generate the next generation population;

[0078] (5) According to the non-dominated sorting results, the population is divided into two parts;

[0079] (6) Using the upper half of the population to create a descendant population through selection, crossover, and mutation, and evaluating the objective function according to the constraint processing mechanism;

[0080] (7) Merge the parent and child populations, perform non-dominated sorting again and calculate the crowding distance, sort the next generation population according to the elite strategy, select the Pareto optimal solution set, and record the crowding distance in ascending order;

[0081] (8) Store the non-dominated Pareto optimal solution and draw the Pareto optimal frontier;

[0082] (9) Initialize the position and velocity of the particles in the lower half, and determine the optimal position of each individual by storing the position of the particles, thereby determining the dominance of the particles;

[0083] (10) Initialize the external repository representative, create a grid and find the grid index, select the leader particle from the external repository, and update the particle position and velocity;

[0084] (11) According to the constraint processing mechanism, evaluate the objective function, apply mutation and calculate the new Pareto solution set;

[0085] (12) adding non-dominant particles to the repository and determining the dominance of the new repository members;

[0086] (13) Keep only the uncontrolled members in the repository, update the adaptive grid and grid index and modify the inertia, thereby creating a new set of particles whose size is half of the total population;

[0087] (14) Use the non-inferior solution set in the repository to fill, combine the population of the non-dominated sorting genetic algorithm and the new particle set of the multi-objective particle swarm algorithm, use the fuzzy membership to traverse the Pareto solution set, and calculate the membership of each objective function in the Pareto solution set by the following formula:

[0088]

[0089] In the formula, F i is the value of the i-th objective function, i∈{1,2}; F i min and F i max are the minimum and maximum values ​​of the objective function respectively; then the weight value in the following formula is used to calculate and determine the Pareto solution with the maximum membership weighted value, and the optimal solution is obtained, and the cycle ends.

[0090]

[0091] Where N p is the number of solutions, N obj is the number of optimization objectives, is the membership of the k-th solution to the i-th target.

[0092] Step 5: Control the power of the fill light according to the optimization result of the optimization objective function.

[0093] Preferably, in the above steps, on a typical summer day, because of sufficient solar radiation, the photovoltaic power generation from 10 a.m. to 3 p.m. is greater than the sum of the power consumption of the fill light and other electrical equipment on one acre of land. Therefore, there is no need to purchase electricity from the grid during this period, and the excess power generation is directly abandoned. In step 5, before and after the multi-objective optimization strategy is adopted, the comparison results of the interaction power between the fill light microgrid system and the grid, the power purchase cost, and the power consumption of the fill light are shown in Table 1, and the optimization results of the power operation of the plant fill light are shown in Table 2. Figure 6 As shown in the figure, it can be clearly seen that the indicators of the microgrid system of the fill light have been significantly improved on a typical summer day: the interactive power with the grid has been reduced by about 9.1%, indicating that the power interaction between the system and the grid has been reduced, and the system operation has reduced its dependence on the grid. The electricity purchase cost has been reduced by about 9.6%, showing a significant cost-saving effect, and the optimization strategy has helped reduce electricity purchase expenses. The power consumption of the fill light has been reduced by about 8.7%, which means that the efficiency of power use has been improved while ensuring the normal operation of the fill light.

[0094] In summary, the multi-objective optimization strategy adopted in this embodiment has a positive effect on the operation of the fill light microgrid system, which can effectively reduce the grid interaction power and electricity purchase costs, while reducing the power consumption of the fill light and improving the overall efficiency of the system.

[0095] Table 1 Interactive power table of microgrid and main grid on a typical day in summer

[0096]

[0097] Embodiment 2:

[0098] The solar radiation on a typical day in summer is shown in Table 2, and the data line graph is shown in Figure 5 shown.

[0099] Table 2 Solar radiation data for typical days in summer

[0100]

[0101]

[0102] Table 3 below shows the specific parameters of the supplementary light microgrid system of the present invention, taking the supplementary light and photovoltaic capacity in one acre of land as a specific example. Figure 3 As shown in the figure, the power grid is connected to the fill light lighting system through an AC / DC converter, and the distributed photovoltaic power source is connected to the fill light lighting system through a DC / DC, only considering self-use and not considering the photovoltaic power generation grid connection. At the same time, in the optimization planning and design, the loss caused by line impedance is ignored.

[0103] Table 3 Specific parameters of the fill light microgrid system

[0104]

[0105] Agricultural electricity prices adopt a tiered pricing method, as shown in Table 4.

[0106] Table 4 Agricultural peak and valley electricity prices (unit: yuan / kWh)

[0107]

[0108] It should be noted here that the specific parameter settings of the microgrid system are not limited to the one provided in this embodiment, and can be specially designed according to the needs of the researcher himself.

[0109] Embodiment 3:

[0110] Based on the same inventive concept, an embodiment of the present invention further provides a plant fill light power adjustment optimization device based on a multi-objective optimization algorithm, comprising:

[0111] A data acquisition unit, which is used to acquire the parameters required for constructing the optimization objective function and constraint conditions of the operation of the microgrid system for supplementary lighting of leafy vegetables in the greenhouse;

[0112] A data processing unit is used to perform the following steps:

[0113] - Determine the relationship between the photosynthetically active radiation value supplied by the supplementary lighting of leafy vegetables in the greenhouse and the increase in dry matter accumulation during the vigorous growth period;

[0114] -Determine the linear relationship between the operating power of the fill light and the light intensity of the fill light;

[0115] -According to the relationship between the photosynthetically active radiation value supplied by the supplementary light for leafy vegetables in the greenhouse and the dry matter accumulation growth during the vigorous growth period and the linear relationship between the operating power of the supplementary light and the light intensity of the supplementary light, the optimization objective function and constraint conditions for the operation of the microgrid system for supplementary light for leafy vegetables in the greenhouse are constructed;

[0116] -Using a hybrid non-dominated sorting genetic algorithm and a multi-objective particle swarm algorithm to optimize and solve the optimization objective function;

[0117] A control execution unit is used to control the power of the fill light according to the optimization solution result of the optimization objective function.

[0118] Since the device is a device corresponding to the plant fill light power adjustment optimization method based on a multi-objective optimization algorithm of an embodiment of the present invention, and the principle of solving the problem by the device is similar to that of the method, the implementation of the device can refer to the implementation process of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0119] Embodiment 4:

[0120] Based on the same inventive concept, an embodiment of the present invention also provides an electronic device, which includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the plant fill light power adjustment optimization method based on the multi-objective optimization algorithm as described above.

[0121] It is understandable that the memory may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory may be used to store instructions, programs, codes, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function, instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data created according to the use of the server, etc.

[0122] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect the various parts of the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Optionally, the processor can be implemented in at least one hardware form of digital signal processing (DSP), field programmable gate array (FPGA), and programmable logic array (PLA). The processor can integrate one or a combination of a central processing unit (CPU) and a modem. Among them, the CPU mainly processes the operating system and application programs; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor, but may be implemented separately through a chip.

[0123] Since the electronic device is the electronic device corresponding to the plant fill light power adjustment optimization method based on the multi-objective optimization algorithm of the embodiment of the present invention, and the principle of solving the problem by the electronic device is similar to that of the method, the implementation of the electronic device can refer to the implementation process of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0124] Embodiment 5:

[0125] Based on the same inventive concept, an embodiment of the present invention also provides a computer-readable storage medium, in which at least one instruction, at least one program, a code set or an instruction set is stored. The at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the plant fill light power adjustment optimization method based on the multi-objective optimization algorithm as described above.

[0126] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, the storage medium including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0127] Since the storage medium is the storage medium corresponding to the plant fill light power adjustment optimization method based on the multi-objective optimization algorithm of the embodiment of the present invention, and the principle of solving the problem by the storage medium is similar to that of the method, the implementation of the storage medium can refer to the implementation process of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0128] Embodiment 6:

[0129] In some possible implementations, various aspects of the method of the embodiments of the present invention may also be implemented in the form of a program product, which includes a program code. When the program product is run on a computer device, the program code is used to enable the computer device to execute the steps of the plant fill light power adjustment optimization method based on a multi-objective optimization algorithm according to various exemplary embodiments of the present application described above in this specification. Among them, the executable computer program code or "code" used to execute each embodiment can be written in a high-level programming language such as C, C++, C#, Smalltalk, Java, JavaScript, Visual Basic, structured query language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0130] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0131] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory. The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable ordinary technicians in the field to understand the content of the present invention and implement it accordingly, and cannot be used to limit the scope of protection of the present invention. Any equivalent changes or modifications made according to the essence of the content of the present invention should be included in the scope of protection of the present invention.

Claims

1. A plant light power adjustment optimization method based on a multi-objective optimization algorithm, characterized in that: The steps include: Determine the relationship between the photosynthetically active radiation value supplied by supplementary lighting for leafy vegetables in greenhouses and the increase in dry matter accumulation during the vigorous growth period; Determine the linear relationship between the operating power of the fill light and the light intensity of the fill light; According to the relationship between the photosynthetically active radiation value supplied by the supplementary light for the leafy vegetables in the greenhouse and the dry matter accumulation growth during the vigorous growth period and the linear relationship between the operating power of the supplementary light and the light intensity of the supplementary light, the optimization objective function and constraint conditions for the operation of the microgrid system for the supplementary light for the leafy vegetables in the greenhouse are constructed; A hybrid non-dominated sorting genetic algorithm and a multi-objective particle swarm algorithm are used to optimize and solve the optimization objective function; The power of the fill light is controlled according to the optimization solution result of the optimization objective function.

2. The plant light power adjustment optimization method based on multi-objective optimization algorithm according to claim 1 is characterized in that: The relationship between the photosynthetically active radiation value supplied by the supplementary lighting lamps for leafy vegetables in the greenhouse and the dry matter accumulation growth during the vigorous growth period is determined as follows: The photosynthetically active radiation of leafy vegetables in the greenhouse includes the solar radiation provided by the external environment and the light intensity provided by the supplementary light: I PAR,t =c par G r,t +εI LED,t In the formula, c par represents the efficiency of leafy vegetable canopy in absorbing solar radiation, G r,t represents the solar radiation intensity at time t, ε represents the solar radiation conversion efficiency of photosynthesis, I LED,t represents the light intensity of the supplementary light in the greenhouse at time t, I PAR,t represents the photosynthetically active radiation of leafy vegetables in the greenhouse at time t; Assuming that the solar radiation intensity in the greenhouse is 0, the temperature in the greenhouse is a constant, and the carbon dioxide concentration is a constant, the photosynthetically active radiation I required by the supplementary lighting for crops can be obtained: LED,t The dry matter accumulation growth ΔM during the vigorous growth period d,t The relationship is shown as follows: Where M d,t represents the dry matter accumulation of leafy vegetables per unit area of ​​land at time t, c α represents the conversion rate of carbon dioxide absorbed by photosynthesis into organic matter, c β Represents the growth factor of leafy vegetables during their vigorous growth period, Z T , Z bs Represent the temperature and carbon dioxide concentration related functions of photosynthesis, which are set as constants here, c k represents the canopy extinction coefficient of leafy vegetables, c d represents the respiration rate of the stem and root of leafy vegetables at the reference temperature, c τ represents the percentage of root dry weight at different growth stages, φ phot,t Represents the photosynthetic rate of leafy vegetables at time t.

3. The plant supplementary light power adjustment optimization method based on multi-objective optimization algorithm according to claim 2 is characterized in that: The linear relationship between the operating power of the fill light and the illumination intensity of the fill light is determined as follows: P LED,t =I LED,t ·L LED Where P LED,t Represents the operating power of the LED fill light at time t, L LED Represents the linearized power-light intensity factor of the LED fill light.

4. The method for optimizing the power regulation of plant supplementary light based on a multi-objective optimization algorithm according to claim 3 is characterized in that: The optimization objective function is specifically: When the microgrid system is running, the electricity cost is the lowest and the plant growth is the best. The lowest electricity cost is obtained according to the following formula: In the formula, t0 represents the optimization start time, t end represents the optimization termination period, P grid,t represents the electric power obtained from the grid at time t, λ grid,t represents the electricity price at time t; The optimal plant growth is obtained according to the following formula: In the formula, t0 represents the optimization start time, t end represents the optimization termination period, and Δt is the optimization time period.

5. The method for optimizing the power regulation of plant supplementary light based on a multi-objective optimization algorithm according to claim 4 is characterized in that: The constraints include: power balance constraint, plant dry matter accumulation constraint, electrical equipment power constraint, among which, The power balance constraint is obtained according to the following formula: P grid,t +P PV,t =P LED,t +P L,t Where P grid,t represents the amount of electricity taken from the grid at time t, P PV,t represents the photovoltaic power generation at time t, P LED,t Represents the operating power of the fill light at time t, and the fixed load power in the greenhouse, P L,t represents the fixed power load at time t; The plant dry matter accumulation constraint is obtained according to the following formula: In the formula, and They represent the lower and upper limits of the accumulated amount of plant dry matter at the set time t respectively; The power constraint of electrical equipment is obtained according to the following formula: In the formula, and Respectively represent the minimum and maximum operating power of the plant light; In the formula, Represents the maximum power generation of the photovoltaic device.

6. The method for optimizing the power regulation of plant supplementary light based on a multi-objective optimization algorithm according to claim 5, characterized in that: The hybrid non-dominated sorting genetic algorithm and multi-objective particle swarm algorithm are used to optimize and solve the optimization objective function, specifically: In the iterative process, the population is sorted by crowding distance, and the population is divided into two parts according to the sorting results based on the elite strategy. The first half of the population is optimized by non-dominated sorting genetic algorithm, while the second half of the population is optimized by multi-objective particle swarm algorithm to converge near the optimal Pareto solution set. After obtaining the Pareto solution set, the satisfaction of each objective function in the Pareto solution set is evaluated according to the fuzzy membership function to determine the optimal output power of the fill light at different times.

7. A plant light power adjustment optimization device based on a multi-objective optimization algorithm, characterized in that: include: A data acquisition unit, which is used to acquire the parameters required for constructing the optimization objective function and constraint conditions of the operation of the microgrid system for supplementary lighting of leafy vegetables in the greenhouse; A data processing unit is used to perform the following steps: - Determine the relationship between the photosynthetically active radiation value supplied by the supplementary lighting of leafy vegetables in the greenhouse and the increase in dry matter accumulation during the vigorous growth period; -Determine the linear relationship between the operating power of the fill light and the light intensity of the fill light; -According to the relationship between the photosynthetically active radiation value supplied by the supplementary light for leafy vegetables in the greenhouse and the dry matter accumulation growth during the vigorous growth period and the linear relationship between the operating power of the supplementary light and the light intensity of the supplementary light, the optimization objective function and constraint conditions for the operation of the microgrid system for supplementary light for leafy vegetables in the greenhouse are constructed; -Using a hybrid non-dominated sorting genetic algorithm and a multi-objective particle swarm algorithm to optimize and solve the optimization objective function; A control execution unit is used to control the power of the fill light according to the optimization solution result of the optimization objective function.

8. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein an instruction and at least one program are stored in the memory, and the at least one instruction and the at least one program are loaded and executed by the processor to implement the plant fill light power adjustment optimization method based on a multi-objective optimization algorithm as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The storage medium stores at least one instruction and at least one program, and the at least one instruction and the at least one program are loaded and executed by the processor to implement the power adjustment optimization method for plant fill light as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by the processor, the plant light power adjustment optimization method based on the multi-objective optimization algorithm as described in any one of claims 1 to 6 is implemented.

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