Organic tea garden photovoltaic illumination auxiliary nutrient solution supply method and system

Abstract

The invention provides an organic tea garden photovoltaic illumination auxiliary nutrient solution supply method, system and device and a medium. The method comprises the steps that an organic tea garden area is mapped into a photovoltaic illumination auxiliary system; selecting an illumination resource and a tea garden area in the photovoltaic illumination auxiliary system to determine an optimal combination of illumination and nutrient solution supply; based on the growth requirements of the tea garden, a supply scheme set meeting the requirements is extracted from the possible supply schemes; executing multiple times of analog simulation; during each simulation, generating illumination and nutrient solution supply parameters in the nutrient solution supply schemes according to the growth state of the tea garden, and obtaining the simulation effect of each supply scheme; based on the simulation effect, determining an optimal supply scheme in the photovoltaic illumination auxiliary system; and taking the optimal supply scheme as an illumination and nutrient solution supply scheme of a corresponding area in the organic tea garden. The intelligent level of tea garden management can be improved, the labor cost is reduced, and the adaptability of the tea garden to environment changes is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of modern agriculture and renewable energy utilization, and more specifically, to a method and system for supplying nutrient solution in an organic tea garden using photovoltaic illumination as an auxiliary. Background Art

[0002] In the development of modern agriculture, organic tea gardens, as an eco-friendly agricultural model, are gaining more and more attention. However, the traditional management methods of organic tea gardens have some limitations, especially in terms of light and nutrient supply. Light, water and nutrient management in tea gardens are key factors affecting the growth and quality of tea. Most existing technologies rely on natural light and artificial irrigation and fertilization, which is not only inefficient but also difficult to achieve precise control, resulting in waste of resources and increased production costs. In addition, with the promotion of environmental protection and sustainable development concepts, the energy consumption and environmental impact of organic tea gardens have also become urgent issues to be addressed. The use of traditional energy not only increases the operating costs of tea gardens, but may also have a negative impact on the environment. In order to improve the production efficiency and tea quality of organic tea gardens while reducing the impact on the environment, a more efficient and environmentally friendly tea garden management technology is needed.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: first, the prior art lacks a system that can combine the use of renewable energy and precision agriculture management to achieve automation and intelligence in the lighting and nutrient supply of tea gardens; second, the prior art lacks scientificity and systematicity in the formulation of lighting and nutrient supply plans, and it is difficult to dynamically adjust according to the real-time growth status and environmental conditions of the tea garden; finally, the prior art lacks an effective evaluation system and method when evaluating the effectiveness of the supply plan, which makes it difficult to select the optimal supply plan. Summary of the invention

[0004] The invention provides a method, system, medium and equipment for supplying nutrient solution in an organic tea garden assisted by photovoltaic illumination.

[0005] In a first aspect of the present invention, a method for supplying nutrient solution in an organic tea garden using photovoltaic illumination is provided, comprising:

[0006] The organic tea garden area is mapped as a photovoltaic lighting auxiliary system; the photovoltaic lighting auxiliary system includes a photovoltaic power generation module and a nutrient solution supply module; the photovoltaic power generation module is responsible for providing energy support; the nutrient solution supply module includes an irrigation submodule and a fertilization submodule; the irrigation submodule and the fertilization submodule are responsible for transporting the nutrient solution to various areas of the tea garden;

[0007] Select the lighting resources and tea garden area in the photovoltaic lighting auxiliary system to determine the optimal combination of lighting and nutrient solution supply; and based on the growth needs of the tea garden, extract a set of supply solutions that meet the needs from possible supply solutions;

[0008] Perform multiple simulations; in each simulation, generate the illumination and nutrient solution supply parameters in the nutrient solution supply scheme according to the growth status of the tea garden, and obtain the simulation effect of each supply scheme;

[0009] Based on the simulation effect, the optimal supply scheme in the photovoltaic lighting auxiliary system is determined; and the optimal supply scheme is used as the lighting and nutrient solution supply scheme for the corresponding area in the organic tea garden.

[0010] Furthermore, the irrigation submodule is used to describe the water transport and irrigation management functions of the nutrient solution supply module;

[0011] The fertilization submodule is used to describe the nutrient supply and fertilization management functions of the nutrient solution supply module.

[0012] Furthermore, the water transport of the irrigation submodule is used to describe the transport process of the irrigation submodule providing the required water to the tea garden;

[0013] The nutrient supply of the fertilization submodule is used to describe the supply process of the fertilization submodule providing the required nutrients to the tea garden;

[0014] The energy support of the photovoltaic power generation module is used to describe the process in which the photovoltaic power generation module provides stable energy for the entire system.

[0015] Further, the determining of the optimal supply scheme in the photovoltaic lighting auxiliary system includes: for each supply scheme, executing:

[0016] According to the simulation results of multiple simulations of the current supply plan, the expected effect and effect variance of the current supply plan are obtained;

[0017] Determine the effect evaluation index of the current supply plan based on the effect expectation and effect variance of the current supply plan;

[0018] The supply plan corresponding to the maximum value of the effect evaluation index is taken as the optimal supply plan.

[0019] Furthermore, the effect evaluation index of each supply scheme is calculated according to formula (1):

[0020] Reffect=αe*eeffect+αv*veffect(1)

[0021] Among them, Reffect represents the effect evaluation index of the supply plan, eeffect and veffect represent the expected effect and effect variance of the supply plan respectively; αe and αv represent the expected effect weight and effect variance weight of the supply plan respectively; αe+αv=1.

[0022] Furthermore, the growth requirements of the tea garden include light requirements, water requirements and nutrient requirements.

[0023] Furthermore, the extracting of a set of supply solutions that meet the demand from possible supply solutions includes: obtaining real-time growth data and environmental parameters of the organic tea garden; the real-time data includes: real-time light intensity, water content and nutrient content of the tea garden; the environmental parameters include light resources and soil fertility;

[0024] For each supply plan, determine whether it meets the growth needs of the tea garden;

[0025] Extract the supply plan that meets the growth demand as the optimal supply plan;

[0026] All the extracted optimal supply plans are summarized to form a supply plan set.

[0027] Further, whether the growth needs of the tea garden are met is determined in the following manner: if the real-time light intensity, water content, and nutrient content of the tea garden all reach the preset growth threshold, then the current supply plan meets the growth needs of the tea garden;

[0028] The supply plan that simultaneously meets the light demand, water demand and nutrient demand is extracted as the optimal supply plan.

[0029] In a second aspect of the present invention, a photovoltaic lighting-assisted nutrient solution supply system for an organic tea garden is provided, comprising:

[0030] A photovoltaic lighting auxiliary system generation module is used to map the organic tea garden area into a photovoltaic lighting auxiliary system; the photovoltaic lighting auxiliary system includes a photovoltaic power generation module and a nutrient solution supply module; the nutrient solution supply module includes an irrigation submodule and a fertilization submodule;

[0031] The supply scheme set determination module is used to select the lighting resources and areas of the organic tea garden to determine the optimal combination of lighting and nutrient solution supply; and based on the growth needs of the tea garden, extract the supply scheme set that meets the needs from the possible supply schemes;

[0032] The simulation effect evaluation module performs multiple simulations; in each simulation, the illumination and nutrient solution supply parameters in the nutrient solution supply scheme are generated according to the growth status of the tea garden, and the simulation effect of each supply scheme is obtained;

[0033] The optimal supply scheme determination module determines the optimal supply scheme in the photovoltaic lighting auxiliary system based on the simulation effect; and uses the optimal supply scheme as the lighting and nutrient solution supply scheme for the corresponding area in the organic tea garden.

[0034] Further, in the optimal supply scheme determination module, determining the optimal supply scheme in the photovoltaic lighting auxiliary system includes: for each supply scheme, executing:

[0035] According to the simulation results of multiple simulations of the current supply plan, the expected effect and effect variance of the current supply plan are obtained;

[0036] Based on the expected effect and effect variance of the current supply plan, determine the effect evaluation index of the current supply plan; calculate the effect evaluation index of each supply plan according to formula (1):

[0037] Reffect=αe*eeffect+αv*veffect(1)

[0038] Among them, Reffect represents the effect evaluation index of the supply plan, eeffect and veffect represent the expected effect and effect variance of the supply plan respectively; αe and αv represent the expected effect weight and effect variance weight of the supply plan respectively; αe+αv=1.

[0039] The supply plan corresponding to the maximum value of the effect evaluation index is taken as the optimal supply plan.

[0040] According to the above-mentioned embodiment of the present invention, at least the following beneficial effects are achieved: the present invention can realize efficient utilization of energy and automation of tea garden management by combining photovoltaic power generation technology with illumination and nutrient solution supply of organic tea garden. The photovoltaic power generation module can not only provide stable energy support for the system, but also reduce dependence on traditional energy, reduce operating costs, and reduce environmental pollution. The nutrient solution supply module can realize precise control of water and nutrients in the tea garden through the coordinated work of the irrigation submodule and the fertilization submodule, and improve the growth quality and yield of tea leaves. In addition, through simulation and effect evaluation, the system can dynamically adjust the supply plan to adapt to the growth needs and environmental changes of the tea garden, and further improve the scientificity and effectiveness of tea garden management. In addition, the present invention can reduce the use of chemical fertilizers and pesticides and enhance the ecological balance and biodiversity of the tea garden through an intelligent supply plan. At the same time, the design of the system takes into account the long-term development of the tea garden. By optimizing illumination and nutrient supply, the disease and pest resistance and adaptability of tea leaves can be improved, and the sensitivity to external environmental changes can be reduced. This intelligent and ecological management method can not only improve the quality and market competitiveness of tea, but also provide strong technical support and practical cases for the promotion and application of organic agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0042] Figure 1 A schematic diagram of a process for supplying nutrient solution in an organic tea garden using photovoltaic illumination as an auxiliary means according to an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of the structure of a photovoltaic lighting-assisted nutrient solution supply system for an organic tea garden provided in one embodiment of the present invention;

[0044] Figure 3 The schematic diagram schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0046] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0047] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0048] Reference below Figure 1 , Figure 1 This is a schematic diagram of a process for providing a method for photovoltaic illumination-assisted nutrient solution in an organic tea garden according to an embodiment of the present invention. Figure 1 As shown, a method 100 for supplying nutrient solution assisted by photovoltaic illumination in an organic tea garden includes:

[0049] Step 101, mapping the organic tea garden area to a photovoltaic lighting auxiliary system; the photovoltaic lighting auxiliary system includes a photovoltaic power generation module and a nutrient solution supply module; the photovoltaic power generation module is responsible for providing energy support; the nutrient solution supply module includes an irrigation submodule and a fertilization submodule; the irrigation submodule and the fertilization submodule are responsible for transporting the nutrient solution to various areas of the tea garden;

[0050] Step 102, selecting the lighting resources in the photovoltaic lighting auxiliary system and the tea garden area to determine the optimal combination of lighting and nutrient solution supply; and based on the growth requirements of the tea garden, extracting a set of supply solutions that meet the requirements from possible supply solutions;

[0051] Step 103, performing multiple simulations; in each simulation, generating illumination and nutrient solution supply parameters in the nutrient solution supply scheme according to the growth status of the tea garden, and obtaining the simulation effect of each supply scheme;

[0052] Step 104: based on the simulation results, determine the optimal supply scheme in the photovoltaic lighting auxiliary system; and use the optimal supply scheme as the lighting and nutrient solution supply scheme for the corresponding area in the organic tea garden.

[0053] In some embodiments, the tea garden area needs to be mapped to the photovoltaic system to ensure that each area can receive appropriate light and nutrient solution supply. The combination of photovoltaic power generation module and nutrient solution supply module provides a self-sufficient energy and nutrient supply system for the tea garden. Through simulation, the system can dynamically adjust the supply plan according to the growth status of the tea garden to achieve the optimal light and nutrient solution supply.

[0054] Specifically, the photovoltaic lighting auxiliary system can be composed of multiple photovoltaic panels, which are arranged according to the geographical location and orientation of the tea garden to maximize the capture of solar energy. The nutrient solution supply module consists of an irrigation submodule and a fertilization submodule, where the irrigation submodule is responsible for the precise delivery of water and the fertilization submodule is responsible for the precise delivery of nutrients. During the implementation process, different water and nutrient supply parameters, such as irrigation frequency, fertilization amount, etc., can be set according to the soil type, tea tree variety and growth stage of the tea garden.

[0055] In some embodiments, the quantitative formula of light intensity and time is:

[0056]

[0057] In the formula, I total It represents the total light energy received by the tea garden in the time interval [t0, t1], and I(t) is the light intensity at time t.

[0058] The quantitative formula for the amount and frequency of nutrient solution supply is:

[0059] N supply =N base ×(1+α×(GG base ))

[0060] Where N supply is the amount of nutrient solution supplied, N baseis the basic supply, G is the current growth status indicator, G base It is an indicator of basic growth status.

[0061] The comprehensive evaluation index of simulation effect is:

[0062] E effect =w1×E grouth +w2×E yield +w3×E quality

[0063] Among them, E effect is a comprehensive simulation effect evaluation index, E qrowth 、E yield and E quality They represent the evaluation values ​​of growth rate, yield and quality respectively; w1, w2 and w3 are the corresponding weight coefficients.

[0064] The formula for determining the optimal supply plan is:

[0065] Among them, S optimal is the optimal supply solution, S set is the set of all possible supply plans, E effect (S) is the simulation effect evaluation value of supply plan S.

[0066] The dynamic adjustment formula for light and nutrient solution supply is:

[0067] I adj (t) = I base ×f(E effect )

[0068] N adj (t) = N base ×g(E effect )

[0069] Among them, I adj (t) and N adj (t) are the adjusted light intensity and nutrient solution supply at time t, respectively, I base and N base is the basic light intensity and nutrient solution supply, f and g are the simulation effect evaluation index E effect Dynamically adjusted function.

[0070] Preferably, the photovoltaic power generation module can be equipped with an intelligent control system to achieve automatic adjustment of the angle and direction of the photovoltaic panel to adapt to the changes in light in different seasons and weather conditions. The irrigation submodule in the nutrient solution supply module can adopt drip irrigation technology to reduce water evaporation and waste. The fertilization submodule can adopt an intelligent fertilization system based on the soil and plant nutrition status to adjust the fertilization plan according to real-time monitoring data. More specifically, the simulation can use a machine learning algorithm to predict the growth trend of the tea garden and optimize the supply plan by analyzing historical data and environmental parameters.

[0071] In some embodiments, the irrigation submodule is used to describe the water delivery and irrigation management functions of the nutrient solution supply module;

[0072] The fertilization submodule is used to describe the nutrient supply and fertilization management functions of the nutrient solution supply module.

[0073] It should be noted that the irrigation submodule is mainly responsible for water delivery and irrigation management, ensuring that the water supply in the tea garden meets the needs of plant growth; the fertilization submodule is responsible for nutrient supply and fertilization management, ensuring that the tea trees get the necessary nutrients. The coordinated work of these two submodules enables the tea garden to obtain a balanced supply of water and fertilizer, promoting healthy growth.

[0074] Specifically, the irrigation submodule can control the time and amount of irrigation by setting timers and flow meters to adapt to the water needs of the tea garden under different seasons and weather conditions. The fertilization submodule can adjust the type and amount of fertilization according to the growth cycle of the tea trees and the results of soil fertility tests. For example, the irrigation submodule can use an intelligent irrigation system to monitor the soil moisture status in real time through a soil moisture sensor and automatically adjust the irrigation amount; the fertilization submodule can use precision fertilization technology to determine the fertilization plan through chlorophyll content measurement and soil nutrient analysis.

[0075] Preferably, the irrigation submodule can further integrate meteorological monitoring equipment, such as rain gauges and light sensors, to collect meteorological data in real time and optimize irrigation plans. The fertilization submodule can use remote sensing technology, such as multispectral cameras carried by drones, to monitor the growth of tea trees to more accurately assess nutrient requirements. More specifically, the irrigation submodule can set multiple irrigation levels and select the most appropriate irrigation strategy according to different growth stages and environmental conditions; the fertilization submodule can customize personalized fertilization plans according to different varieties and growth conditions of tea trees to achieve more efficient nutrient management.

[0076] In some embodiments, the water delivery of the irrigation submodule is used to describe the delivery process of the irrigation submodule providing the required water to the tea garden;

[0077] The nutrient supply of the fertilization submodule is used to describe the supply process of the fertilization submodule providing the required nutrients to the tea garden;

[0078] The energy support of the photovoltaic power generation module is used to describe the process in which the photovoltaic power generation module provides stable energy for the entire system.

[0079] It should be noted that the irrigation submodule is responsible for delivering water to various areas of the tea garden to ensure that the water needs of the plants are met; the fertilization submodule is responsible for delivering nutrients to the tea garden to ensure that the plants obtain the necessary nutrients. The collaborative work of these two submodules provides a comprehensive water and fertilizer management solution for the tea garden, which helps to improve the growth quality and yield of crops.

[0080] Specifically, the irrigation submodule can achieve precise water delivery through the drip irrigation system installed in the tea garden. The system can automatically adjust the irrigation amount and frequency based on the feedback from the soil moisture sensor to adapt to the water needs of different areas. The fertilization submodule can dissolve the fertilizer in water through the liquid fertilizer injection system and evenly deliver it to the roots of the tea garden through the drip irrigation system. Parameter settings include irrigation cycle, duration of each irrigation, and concentration of fertilizer, which can be adjusted according to the real-time growth data and environmental parameters of the tea garden.

[0081] Preferably, the irrigation submodule can be integrated with an intelligent control system that can automatically adjust the irrigation plan according to weather forecasts, soil moisture, and the growth stage of the tea tree. The fertilization submodule can adopt a modular fertilizer formula to adjust the type and proportion of fertilizer according to the needs of different tea varieties and growth cycles. More specifically, the irrigation submodule can set multiple irrigation levels to adapt to different seasons and weather conditions; the fertilization submodule can dynamically adjust the fertilization plan according to indicators such as the leaf color and growth rate of the tea tree to achieve more accurate water and fertilizer management. In addition, it is also possible to consider using a recycled rainwater system as an irrigation water source to improve the sustainability of the system.

[0082] In some embodiments, determining the optimal supply scheme in the photovoltaic lighting auxiliary system includes: for each supply scheme, executing:

[0083] According to the simulation results of multiple simulations of the current supply plan, the expected effect and effect variance of the current supply plan are obtained;

[0084] Determine the effect evaluation index of the current supply plan based on the effect expectation and effect variance of the current supply plan;

[0085] The supply plan corresponding to the maximum value of the effect evaluation index is taken as the optimal supply plan.

[0086] It should be noted that the detailed steps for determining the optimal supply scheme in the photovoltaic lighting-assisted system include performing multiple simulations on each supply scheme to obtain the expected effect and effect variance of the supply scheme. Through these data, the potential effect and risk of each scheme can be evaluated, and then the effect evaluation index of the supply scheme can be determined. Finally, the supply scheme with the largest effect evaluation index is selected as the optimal supply scheme to ensure that the tea garden receives the best light and nutrient solution supply.

[0087] Specifically, the calculation of effect evaluation indicators involves the effect expectation and effect variance of the supply scheme, and these two parameters can be obtained through statistical analysis of simulation results. The effect expectation can be the average performance of tea garden growth in the simulation, while the effect variance reflects the volatility or uncertainty of growth performance. In order to determine these parameters, the number of simulations, the evaluation criteria for growth status, and the method of data collection can be set. For example, the simulation can be set to run 100 times, and each time key indicators of tea garden growth, such as leaf area index, biomass, etc., are collected, and then the average and standard deviation of these indicators are calculated.

[0088] Preferably, the calculation of the effect evaluation index can adopt a weighted average method, in which the effect expectation and effect variance are assigned different weights to reflect their importance in the decision-making process. For example, if stability is more important than average performance, a greater weight can be given to the effect variance. In addition, other evaluation indicators, such as cost-benefit analysis, environmental impact assessment, etc., can be introduced to obtain a more comprehensive evaluation of supply options. More specifically, a decision support system can be developed that combines a multi-objective optimization algorithm to automatically select the supply option that performs best under multiple evaluation indicators. Alternatives may include using different statistical methods to evaluate the effect expectation and effect variance, or adjusting the parameters of the simulation model according to the specific needs of the tea garden.

[0089] In some embodiments, the effect evaluation index of each supply scheme is calculated according to formula (1):

[0090] Reffect=αe*eeffect+αv*veffect(1)

[0091] Among them, Reffect represents the effect evaluation index of the supply plan, eeffect and veffect represent the expected effect and effect variance of the supply plan respectively; αe and αv represent the expected effect weight and effect variance weight of the supply plan respectively; αe+αv=1.

[0092] It should be noted that based on formula (1), the expected effect and the effect variance are considered together, and the final evaluation index is calculated by assigning them different weights. The setting of the weight reflects the importance attached to stability and average performance. α and β in the formula represent the expected effect weight and the effect variance weight respectively, and their sum is 1, which ensures the rationality of the evaluation index.

[0093] Specifically, the effect expectation can be the average growth rate based on historical data or simulation, and the effect variance reflects the volatility of the growth rate. The setting of parameters α and β depends on the preference for the stability of the scheme and the average effect. For example, if the stability of the scheme is more important, the value of β may be set higher, and vice versa.

[0094] In some embodiments, the simulation process may include the following steps:

[0095] In the simulation of the photovoltaic lighting-assisted nutrient solution supply method in the organic tea garden, the specific steps of the algorithm can be carried out according to the following process:

[0096] Step 1: Data Collection

[0097] Collect environmental data such as soil, climate, and light in organic tea gardens.

[0098] Collect plant growth data in tea gardens, including tea growth cycle, growth rate, etc.

[0099] Step 2: Build a simulation model

[0100] The collected data was used to build a simulation model based on physical and biological principles.

[0101] Step 3: Define simulation parameters

[0102] Determine the variables in the simulation, such as light intensity, nutrient solution concentration, irrigation frequency, etc.

[0103] Set the boundary conditions and initial conditions for the simulation.

[0104] Step 4: Design the algorithm

[0105] Design an optimization algorithm, such as a genetic algorithm, particle swarm optimization algorithm, or simulated annealing algorithm, to search for the optimal supply solution.

[0106] Step 5: Generate initial supply plan

[0107] Generate an initial set of lighting and nutrient solution supply plans using a preset algorithm.

[0108] Step 6: Simulation Execution

[0109] Each supply scheme is simulated to simulate its impact on the growth of tea gardens within a certain period.

[0110] Step 7: Evaluate the simulation results

[0111] The simulation results of each scheme are evaluated, including indicators such as tea growth rate and yield.

[0112] Step 8: Fitness calculation

[0113] According to the evaluation results, the fitness value of each supply scheme is calculated.

[0114] Step 9: Algorithm iteration

[0115] Use optimization algorithms to iteratively optimize the supply plan, such as selection, crossover, mutation and other operations.

[0116] Step 10: Convergence judgment

[0117] Determine whether the algorithm has reached convergence conditions, such as when the fitness no longer increases significantly or when the preset number of iterations is reached.

[0118] Step 11: Determine the optimal supply plan

[0119] The solution with the highest fitness is selected from all iterated supply solutions as the optimal supply solution.

[0120] Step 12: Result Verification

[0121] Additional simulation verification is performed on the optimal supply plan to ensure its stability and effectiveness under different conditions.

[0122] Step 13: Record and archive

[0123] Record simulation process and results and archive them for future analysis and reference.

[0124] Exemplarily, the simulation process based on the particle swarm optimization (PSO) algorithm includes the following sub-steps:

[0125] The first sub-step is to initialize the particle swarm: randomly generate a set of initial supply schemes as the particle swarm.

[0126] Specifically, a set of supply schemes are randomly generated, each of which is determined by light and nutrient solution supply parameters, to form an initial particle swarm.

[0127] Sub-step 2: Calculate the fitness of each particle: Evaluate the supply scheme represented by each particle through the simulation model.

[0128] Specifically, the supply scheme represented by each particle is simulated, and the fitness value is calculated based on the simulation results.

[0129] The third sub-step is to update the individual extreme value of the particle: if the fitness of the current particle is better than the historical best, the individual extreme value is updated.

[0130] Sub-step 4: Update the global extreme value of the particle: If the fitness of the current particle is better than the historical best of all particles, update the global extreme value.

[0131] Sub-step 5, updating particle positions: updating particle positions (i.e., supply schemes) based on individual extreme values ​​and global extreme values.

[0132] Specifically, the new velocity and position are calculated based on the individual extremum and the global extremum, as well as the current velocity of the particle.

[0133] New speed = old speed + (individual extreme value - current position) * cognitive coefficient + (global extreme value - current position) * social coefficient

[0134] New position = current position + new speed

[0135] In sub-step 6, steps 2-5 are repeated until the convergence condition is met or the preset number of iterations is reached.

[0136] In the seventh sub-step, the supply scheme corresponding to the particle with the highest fitness is selected as the optimal scheme.

[0137] Each of the above particles represents a potential supply plan, including a combination of parameters such as light intensity, light duration, type of nutrient solution, concentration, and application frequency. A particle swarm consists of multiple particles, each of which is a candidate supply plan. The individual extreme value is the best supply plan found by the particle in the historical search process. The global extreme value is the best of the historical best supply plans of all particles in the particle swarm.

[0138] Through the above steps, combined with the optimization algorithm, the best lighting and nutrient solution supply plan can be effectively found to improve the production efficiency and tea quality of the organic tea garden.

[0139] Preferably, the calculation of the effect evaluation index can be further refined to improve the accuracy of the evaluation by introducing more environmental and growth parameters. For example, seasonal changes, soil types, tea tree varieties and other factors can be taken into account to make weighted adjustments to the effect expectation and effect variance. More specifically, a dynamic adjustment system can be developed to automatically adjust the values ​​of α and β based on real-time environmental monitoring data and tea tree growth status to adapt to changing conditions. Alternatives may include using different mathematical models or algorithms to evaluate supply plans, such as using machine learning techniques to predict and optimize the effects of supply plans.

[0140] In some embodiments, the growth requirements of the tea garden include light requirements, water requirements, and nutrient requirements.

[0141] It should be noted that the growth requirements of tea gardens are multifaceted, including light requirements, water requirements and nutrient requirements. These requirements are the basis for the healthy growth of tea gardens and are also key factors that must be considered when formulating supply plans. By comprehensively considering these requirements, it can ensure that the supply plan is more scientific and reasonable, thereby improving the production efficiency of tea gardens and the quality of tea.

[0142] Specifically, the light demand of the tea garden can be determined by measuring the real-time light intensity of the tea garden, the water demand can be assessed by monitoring the soil moisture content through a soil moisture sensor, and the nutrient demand can be determined through soil and leaf nutrient analysis. For example, the light intensity threshold can be set as the minimum light requirement for tea tree growth, the water content threshold can be set based on the sensitivity of the tea tree to water, and the nutrient demand can be adjusted according to the variety and growth stage of the tea tree. Fertilization plan.

[0143] Preferably, the assessment methods for these needs can be further refined, for example, by using multispectral sensors to measure light intensity more accurately, or by installing a network of soil moisture sensors to monitor the moisture conditions of the entire tea garden in real time. More specifically, an intelligent decision-making system can be developed that can automatically adjust the supply plan based on real-time data to meet the light, water and nutrient needs of the tea garden. In addition, it can also be considered to introduce meteorological forecast data to predict future light and precipitation conditions and adjust the supply plan in advance to cope with possible environmental changes. Alternatives may include using different sensors or monitoring technologies, or adjusting the setting of demand thresholds according to the specific conditions of the tea garden.

[0144] In some embodiments, extracting a set of supply solutions that meet the demand from possible supply solutions includes: acquiring real-time growth data and environmental parameters of the organic tea garden; the real-time data includes: real-time light intensity, water content and nutrient content of the tea garden; the environmental parameters include light resources and soil fertility;

[0145] For each supply plan, determine whether it meets the growth needs of the tea garden;

[0146] Extract the supply plan that meets the growth demand as the optimal supply plan;

[0147] All the extracted optimal supply plans are summarized to form a supply plan set.

[0148] It should be noted that the process of extracting a set of supply plans that meet the growth needs of tea gardens from possible supply plans involves obtaining real-time growth data and environmental parameters of organic tea gardens, and analyzing these data to determine whether each supply plan meets the growth needs of tea gardens. Supply plans that meet the needs will be selected as the optimal supply plans and summarized into a set of supply plans to guide actual tea garden management.

[0149] Specifically, the implementation of this process requires the collection of data such as real-time light intensity, water content, and nutrient content in the tea garden, which can be obtained in real time through various sensors installed in the tea garden. Environmental parameters, such as light resources and soil fertility, can be determined through regular soil and climate tests. The extraction of a set of supply plans includes evaluating each supply plan to determine whether it can make the light intensity, water content, and nutrient content of the tea garden reach the preset growth threshold. For example, the threshold of light intensity can be set at 1000 lux, the threshold of water content can be based on the percentage of water holding capacity of the soil, and the threshold of nutrient content can be based on the analysis of plant nutritional needs.

[0150] Preferably, the extraction process of the supply scheme can be further refined to improve accuracy and efficiency by integrating more advanced data acquisition and analysis tools. For example, the Internet of Things technology can be used to achieve real-time transmission and processing of sensor data. More specifically, a cloud-based data analysis platform can be developed that is capable of storing and analyzing large amounts of growth and environmental data, providing deep learning and pattern recognition capabilities to automatically identify the optimal supply scheme. In addition, a feedback mechanism can be set up to adjust the supply scheme based on the actual growth effect to achieve continuous optimization. Alternatives may include using different data analysis models or algorithms, or adjusting the setting of growth thresholds according to the specific conditions of the tea garden.

[0151] In some embodiments, whether the growth requirements of the tea garden are met is determined in the following manner: if the real-time light intensity, water content, and nutrient content of the tea garden all reach preset growth thresholds, then the current supply scheme meets the growth requirements of the tea garden;

[0152] The supply plan that simultaneously meets the light demand, water demand and nutrient demand is extracted as the optimal supply plan.

[0153] It should be noted that the method for evaluating whether the supply plan meets the growth needs of the tea garden requires comparing the real-time light intensity, water content and nutrient content of the tea garden with the preset growth threshold to judge the effectiveness of the supply plan. If all these indicators meet the predetermined standards, the current supply plan is considered to meet the growth needs of the tea garden. This method helps to ensure that the tea garden receives balanced growth conditions, thereby improving the yield and quality of tea.

[0154] Specifically, implementing this assessment process requires setting specific threshold standards based on the growth characteristics and environmental conditions of the tea garden. For example, the threshold for light intensity can be set as the minimum light level to ensure photosynthesis efficiency, the threshold for moisture content can be based on the optimal water holding capacity of the soil, and the threshold for nutrient content can be determined based on the nutritional needs of tea trees. These thresholds can be determined through historical data analysis, expert advice, or experimental research, and may vary depending on the season, climate, and soil type.

[0155] Preferably, the evaluation process can be further refined to improve the adaptability and accuracy of the supply plan by introducing real-time monitoring and automatic feedback mechanisms. For example, high-precision sensors can be installed to monitor light, water and nutrient levels in real time and connected to the supply system for automatic adjustment. More specifically, an intelligent control system can be developed that can automatically adjust the frequency and amount of irrigation and fertilization based on monitoring data to ensure that the growing conditions in the tea garden are always kept in the best state. In addition, the use of predictive models to predict future environmental changes and adjust the supply plan in advance to cope with possible climate change can also be considered. Alternatives may include using different evaluation criteria or introducing other growth indicators, such as the growth rate of tea trees, leaf health, etc., to obtain more comprehensive evaluation results.

[0156] The above-mentioned embodiments of the present invention have the following beneficial effects: the present invention provides an efficient and intelligent tea garden management scheme, which can optimize the growth environment of tea trees and improve the yield and quality of tea leaves by accurately controlling the supply of light and nutrient solution. At the same time, the use of photovoltaic power generation modules to provide energy support for the system can reduce dependence on traditional energy, reduce operating costs, reduce environmental pollution, and promote sustainable agricultural development. In addition, the present invention monitors the light intensity, moisture content and nutrient content of the tea garden in real time and compares them with the preset growth threshold. The system can adjust the supply plan in time to ensure that the tea garden can obtain suitable growth conditions at different growth stages and under different environmental conditions. This method can not only improve the scientificity and accuracy of tea garden management, but also enhance the adaptability of tea gardens to environmental changes, and provide strong technical support for the modernization and ecological development of organic tea gardens.

[0157] like Figure 2 As shown, in some embodiments, a photovoltaic illumination-assisted nutrient solution supply system 200 for an organic tea garden includes:

[0158] The photovoltaic lighting auxiliary system generation module 201 is used to map the organic tea garden area into a photovoltaic lighting auxiliary system; the photovoltaic lighting auxiliary system includes a photovoltaic power generation module and a nutrient solution supply module; the nutrient solution supply module includes an irrigation submodule and a fertilization submodule;

[0159] The supply scheme set determination module 202 is used to select the lighting resources and regions of the organic tea garden to determine the optimal combination of lighting and nutrient solution supply; and based on the growth requirements of the tea garden, extract a supply scheme set that meets the requirements from possible supply schemes;

[0160] The simulation effect evaluation module 203 performs multiple simulations; in each simulation, the light and nutrient solution supply parameters in the nutrient solution supply scheme are generated according to the growth status of the tea garden, and the simulation effect of each supply scheme is obtained;

[0161] The optimal supply scheme determining module 204 determines the optimal supply scheme in the photovoltaic lighting auxiliary system based on the simulation effect; and uses the optimal supply scheme as the lighting and nutrient solution supply scheme for the corresponding area in the organic tea garden.

[0162] Further, in the optimal supply scheme determination module, determining the optimal supply scheme in the photovoltaic lighting auxiliary system includes: for each supply scheme, executing:

[0163] According to the simulation results of multiple simulations of the current supply plan, the expected effect and effect variance of the current supply plan are obtained;

[0164] Based on the expected effect and effect variance of the current supply plan, determine the effect evaluation index of the current supply plan; calculate the effect evaluation index of each supply plan according to formula (1):

[0165] Reffect=αe*eeffect+αv*veffect(1)

[0166] Among them, Reffect represents the effect evaluation index of the supply plan, eeffect and veffect represent the expected effect and effect variance of the supply plan respectively; αe and αv represent the expected effect weight and effect variance weight of the supply plan respectively; αe+αv=1.

[0167] The supply plan corresponding to the maximum value of the effect evaluation index is taken as the optimal supply plan.

[0168] It is understandable that the modules described in the organic tea garden photovoltaic lighting assisted nutrient solution supply system 200 are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the photovoltaic lighting assisted nutrient solution supply method for an organic tea garden are also applicable to the photovoltaic lighting assisted nutrient solution supply system 200 for an organic tea garden and the modules contained therein, and will not be described in detail here.

[0169] Reference below Figure 3, which shows a schematic diagram of a structure 300 of an electronic device suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0170] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0171] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0172] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0173] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention to form a technical solution.

Claims

1. A method for supplying nutrient solution in an organic tea garden assisted by photovoltaic illumination, characterized in that: include: The organic tea garden area is mapped as a photovoltaic lighting auxiliary system; the photovoltaic lighting auxiliary system includes a photovoltaic power generation module and a nutrient solution supply module; the photovoltaic power generation module is responsible for providing energy support; the nutrient solution supply module includes an irrigation submodule and a fertilization submodule; the irrigation submodule and the fertilization submodule are responsible for transporting the nutrient solution to various areas of the tea garden; Select the lighting resources and tea garden area in the photovoltaic lighting-assisted system to determine the optimal combination of lighting and nutrient solution supply; Based on the growth demand of the tea garden, a set of supply solutions that meet the demand is extracted from possible supply solutions; Perform multiple simulations; in each simulation, generate the illumination and nutrient solution supply parameters in the nutrient solution supply scheme according to the growth status of the tea garden, and obtain the simulation effect of each supply scheme; Based on the simulation effect, the optimal supply scheme in the photovoltaic lighting auxiliary system is determined; and the optimal supply scheme is used as the lighting and nutrient solution supply scheme for the corresponding area in the organic tea garden.

2. The method for supplying nutrient solution in an organic tea garden assisted by photovoltaic illumination according to claim 1, characterized in that: The irrigation submodule is used to describe the water transport and irrigation management functions of the nutrient solution supply module; The fertilization submodule is used to describe the nutrient supply and fertilization management functions of the nutrient solution supply module.

3. The method for supplying nutrient solution in an organic tea garden assisted by photovoltaic illumination according to claim 2, characterized in that: The water transport of the irrigation submodule is used to describe the transport process of the irrigation submodule providing the required water to the tea garden; The nutrient supply of the fertilization submodule is used to describe the supply process of the fertilization submodule providing the required nutrients to the tea garden; The energy support of the photovoltaic power generation module is used to describe the process in which the photovoltaic power generation module provides stable energy for the entire system.

4. The method for supplying nutrient solution assisted by photovoltaic illumination in an organic tea garden according to any one of claims 1 to 3, characterized in that: The determining of the optimal supply scheme in the photovoltaic illumination auxiliary system includes: for each supply scheme, executing: According to the simulation results of multiple simulations of the current supply plan, the expected effect and effect variance of the current supply plan are obtained; Determine the effect evaluation index of the current supply plan based on the effect expectation and effect variance of the current supply plan; The supply plan corresponding to the maximum value of the effect evaluation index is taken as the optimal supply plan.

5. The method for supplying nutrient solution in an organic tea garden assisted by photovoltaic illumination according to claim 4, characterized in that: According to formula (1), the effect evaluation index of each supply plan is calculated: Reffect=αe*eeffect+αv*veffect(1) Among them, Reffect represents the effect evaluation index of the supply plan, eeffect and veffect represent the expected effect and effect variance of the supply plan respectively; αe and αv represent the expected effect weight and effect variance weight of the supply plan respectively; αe+αv=1.

6. The method for supplying nutrient solution in an organic tea garden assisted by photovoltaic illumination according to claim 1, characterized in that: The growth requirements of the tea garden include light requirements, water requirements and nutrient requirements.

7. The method for supplying nutrient solution in an organic tea garden assisted by photovoltaic illumination according to claim 6, characterized in that: The extracting of a set of supply solutions that meet the demand from possible supply solutions includes: obtaining real-time growth data and environmental parameters of the organic tea garden; the real-time data includes: real-time light intensity, water content and nutrient content of the tea garden; the environmental parameters include light resources and soil fertility; For each supply plan, determine whether it meets the growth needs of the tea garden; Extract the supply plan that meets the growth demand as the optimal supply plan; All the extracted optimal supply plans are summarized to form a supply plan set.

8. The method for supplying nutrient solution in an organic tea garden assisted by photovoltaic illumination according to claim 6, characterized in that: Whether the growth needs of the tea garden are met is determined in the following way: if the real-time light intensity, water content and nutrient content of the tea garden all reach the preset growth threshold, the current supply plan meets the growth needs of the tea garden; The supply plan that simultaneously meets the light demand, water demand and nutrient demand is extracted as the optimal supply plan.

9. An organic tea garden photovoltaic lighting assisted nutrient solution supply system, characterized in that: include: A photovoltaic lighting auxiliary system generation module is used to map the organic tea garden area into a photovoltaic lighting auxiliary system; the photovoltaic lighting auxiliary system includes a photovoltaic power generation module and a nutrient solution supply module; the nutrient solution supply module includes an irrigation submodule and a fertilization submodule; The supply scheme set determination module is used to select the lighting resources and areas of the organic tea garden to determine the optimal combination of lighting and nutrient solution supply; and based on the growth needs of the tea garden, extract the supply scheme set that meets the needs from the possible supply schemes; The simulation effect evaluation module performs multiple simulations; in each simulation, the light and nutrient solution supply parameters in the nutrient solution supply scheme are generated according to the growth status of the tea garden, and the simulation effect of each supply scheme is obtained; The optimal supply scheme determination module determines the optimal supply scheme in the photovoltaic lighting auxiliary system based on the simulation effect; and uses the optimal supply scheme as the lighting and nutrient solution supply scheme for the corresponding area in the organic tea garden.

10. The organic tea garden photovoltaic lighting assisted nutrient solution supply system according to claim 9, characterized in that: In the optimal supply scheme determination module, determining the optimal supply scheme in the photovoltaic lighting auxiliary system includes: for each supply scheme, executing: According to the simulation results of multiple simulations of the current supply plan, the expected effect and effect variance of the current supply plan are obtained; Based on the expected effect and effect variance of the current supply plan, determine the effect evaluation index of the current supply plan; calculate the effect evaluation index of each supply plan according to formula (1): Reffect=αe*eeffect+αv*veffect(1) Among them, Reffect represents the effect evaluation index of the supply plan, eeffect and veffect represent the expected effect and effect variance of the supply plan respectively; αe and αv represent the expected effect weight and effect variance weight of the supply plan respectively; αe+αv=1. The supply plan corresponding to the maximum value of the effect evaluation index is taken as the optimal supply plan.

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