A photovoltaic tea garden rainwater diversion control system and method

Abstract

The application relates to the technical field of injection molding production management, and discloses a photovoltaic tea garden planting rainwater diversion control system and method, which comprises a multidimensional acquisition module and an intelligent control module. The photovoltaic tea garden planting rainwater diversion control system and method acquire tea tree planting data, environmental data and resource storage data through the multidimensional acquisition module, and classify and form data sets, the intelligent control module analyzes the growth state of tea trees in each region, discovers health problems in time, reduces the influence of diseases or adverse environments on tea yield, comprehensively evaluates high precision, the intelligent control module analyzes the change trend of different environmental factors, predicts potential environmental pressure, counts the total storage amount of rainwater, the power supply amount and the predicted clean water amount at the current time point, judges whether the tea trees are healthy, whether the environmental factors cause resource supply shortage, and whether water resources and electric power resources can be complementary, and corresponding supply measures are taken, dynamic balance is realized, and resource utilization rate is high.

Description

Technical Field

[0001] This invention relates to the field of injection molding production management technology, specifically to a control system and method for rainwater diversion in photovoltaic tea gardens. Background Technology

[0002] Photovoltaic tea gardens, as an innovative model of modern agriculture, combine solar photovoltaic power generation with tea cultivation, achieving multiple improvements in ecological, economic, and social benefits. In photovoltaic tea gardens, rows of neatly arranged photovoltaic panels provide tea trees with a suitable shading environment, helping to regulate the microclimate of the tea garden, reduce water evaporation, and mitigate the impact of extreme weather on tea trees, thereby increasing tea yield and quality. Tea tree yields under photovoltaic panels can increase by 20%, and the amino acid and chlorophyll content also significantly improves. Secondly, photovoltaic tea gardens reduce the electricity consumption in tea production processes by utilizing photovoltaic power generation, enabling green power supply for processes such as tea frying, further reducing production costs. Photovoltaic tea gardens also have excellent ecological benefits. Through the rational arrangement of photovoltaic modules, tea gardens can generate a large amount of clean energy without changing the land's properties, reducing carbon emissions. This "agricultural-photovoltaic complementary" model not only overcomes the problem of scarce land resources but also effectively improves land utilization.

[0003] Currently, the rainwater diversion control system for traditional photovoltaic tea gardens relies on human judgment of the tea tree's growth status before resource supply, making it difficult to accurately meet the tea tree's water and nutrient requirements. When environmental factors lead to insufficient photovoltaic power generation, it is impossible to quickly assess the complementary relationship between water and electricity resources, resulting in poor applicability of resource allocation. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides a photovoltaic tea garden rainwater diversion control system and method, which has the advantages of high accuracy in comprehensive assessment and high resource utilization rate through flexible configuration. It solves the problems of traditional photovoltaic tea garden rainwater diversion control systems failing to accurately meet the growth needs of tea trees and having poor applicability in resource allocation.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention provides the following technical solution: a photovoltaic tea garden rainwater diversion control system and method, comprising a multi-dimensional acquisition module and an intelligent control module;

[0008] The multi-dimensional acquisition module consists of a planting data unit, an environmental monitoring unit, and an energy storage data unit. The planting data unit collects planting datasets by connecting to a database via a network. The planting datasets include tea tree planting data for all regions. The environmental monitoring unit collects environmental datasets by connecting to a monitoring device via a network. The environmental datasets include environmental data for all time points. The energy storage data unit collects energy storage datasets by connecting to an energy storage device via a network. The energy storage datasets include resource storage data for all time points.

[0009] The intelligent control module consists of a growth assessment unit, an environmental analysis unit, and a diversion management unit. The growth assessment unit analyzes the growth status of tea trees in each region based on the planting dataset and generates a corresponding growth coefficient Szxs. The environmental analysis unit sets a fixed monitoring period Q and, combined with the environmental dataset, analyzes the changing trends of different environmental factors to generate a corresponding fluctuation data set Bdsj. The diversion management unit, based on the energy storage dataset, calculates the total rainwater storage ZYU, power supply GDL, and predicted net water volume YJS at the current time point and generates a corresponding complementarity coefficient Hbxs. The diversion management unit sets a fixed range of growth thresholds SZY, rainfall threshold YUY, and temperature threshold WDY. Combining the growth coefficient Szxs, fluctuation data set Bdsj, and complementarity coefficient Hbxs, it determines whether the growth status of the tea trees is healthy, whether environmental factors are causing resource supply shortages, and whether water and electricity resources can complement each other, and implements corresponding supply measures.

[0010] Preferably, the expression for the planting dataset is {Z1} q Z2 q Z3 q ... ... Zh q}, Z1 q To Zh q These are the tea tree planting data for the first to the hth regions, respectively. The tea tree planting data includes planting depth, tree age, soil pH value, soil moisture content, and soil organic matter content. q represents the specific number of a single region.

[0011] Preferably, the expression for the environmental dataset is {H1} m H2 m H3 m ... Hj m}, H1 m To Hj m These represent environmental data from the first time point to the j-th time point. The environmental data includes rainfall and temperature, and m represents the specific time when the environmental data was acquired.

[0012] Preferably, the expression for the energy storage dataset is {YU} s DLs}, YU s This represents water resource storage data, specifically rainwater storage, DL. s This represents the stored data of power resources, namely photovoltaic power generation, and s represents the specific time when the stored data of resources was acquired.

[0013] Preferably, the calculation process for the growth coefficient Szxs is as follows:

[0014] Extract tea tree planting data from the i-th region of the planting dataset, and label the planting depth of the i-th region as SD. i The tree age of the i-th region is labeled as SL. i The soil pH value of the i-th region is labeled as pH. i The soil moisture content of the i-th region is denoted as TS. i The soil organic matter content of the i-th region is labeled as TY. i ;

[0015] If the tree age of the i-th region is SL i If the planting depth is less than three years, it means that the tea trees in the area are in their juvenile stage, and the soil pH, soil moisture content and soil organic matter content at the current planting depth can meet the growth needs of the tea trees in their juvenile stage.

[0016] Szxs=α1×SD i +α2×pH i +α3×TS i +α4×TY i

[0017] In the formula, α1 represents the assessment weight for planting depth, α2 represents the assessment weight for soil pH, α3 represents the assessment weight for soil moisture content, and α4 represents the assessment weight for soil organic matter content. α1 + α2 + α3 + α4 = 1, α1 × SD i +α2×pH i +α3×TS i +α4×TY i This represents the growth coefficient of tea trees in the i-th region, calculated by weighting α1, α2, α3, and α4 and taking into account factors such as planting depth, soil pH, soil moisture content, and soil organic matter content.

[0018] If the tree age of the i-th region is SL i If it has been more than three years, it means that the tea trees in the area are in their mature stage. The vertical distribution depth of the root system of mature tea trees is greater than that of the root system in the juvenile stage. The soil pH, soil moisture content and soil organic matter content at the current planting depth have failed to meet the growth requirements of mature tea trees.

[0019] SG i =SDi +μ×SL i

[0020] GpH i =pH i -θ×SL i

[0021] GTS i =TS i -σ×SL i

[0022] GTY i =TY i -τ×SL i

[0023] Szxs=α1×SG i +α2×GpH i +α3×GTS i +α4×GTY i

[0024] In the formula, SG i GpH represents the vertical root depth of the tea tree in the i-th region, μ represents the conversion coefficient for converting tree age to vertical root depth, and GpH represents the vertical root depth. i GTS represents the soil pH value of the tea tree root system in the i-th region, θ represents the attenuation coefficient, used to measure the degree of influence of tree age on the decrease in soil pH value. i GTY represents the soil moisture content of the tea tree root system in the i-th region, σ represents the attenuation coefficient, used to measure the degree of influence of tree age on the decrease in soil moisture content. i α represents the soil organic matter content of the tea tree root system in the i-th region, τ represents the decay coefficient, used to measure the degree of influence of tree age on the decline of soil organic matter content, and α1×SG. i +α2×GpH i +α3×GTS i +α4×GTY i The growth coefficient of the tea tree in the i-th region is obtained by combining the vertical distribution depth of the tea tree roots, the pH value of the tea tree root soil, the water content of the tea tree root soil, and the organic matter content of the tea tree root soil according to the weights of α1, α2, α3, and α4.

[0025] Preferably, the calculation process for the fluctuation data group Bdsj is as follows:

[0026] Based on the environmental dataset, the rainfall in the i-th region within the monitoring period Q is extracted and sequentially labeled as {Yi}. 1 Yi 2 Yi 3 ... Yi e}, Yi1 To Yi e Let {Wi} represent the rainfall from the first time point to the e-th time point, and then extract the temperature of the i-th region within the monitoring period Q, labeling them sequentially as {Wi}. 1 Wi 2 Wi 3 Wi f}, Wi 1 to Wi f These represent the temperatures from the first time point to the f-th time point;

[0027]

[0028] In the formula, Yi k This represents the rainfall in the i-th region at the k-th time point. This represents the average rainfall in the i-th region during the monitoring period Q. This represents the fluctuation rate of rainfall in the i-th region according to the standard deviation formula. maxYi and minYi represent the maximum and minimum rainfall values ​​in the i-th region within the monitoring period Q. maxYi-minYi represents the range of rainfall in the i-th region within the monitoring period Q. Wi g This represents the temperature of the i-th region at the g-th time point. This represents the average temperature of the i-th region within the monitoring period Q. This represents the temperature fluctuation rate of the i-th region obtained according to the standard deviation formula. maxWi and minWi represent the maximum and minimum temperatures of the i-th region within the monitoring period Q. maxWi-minWi represents the temperature range of the i-th region within the monitoring period Q.

[0029] Preferably, the calculation process for the complementarity coefficient Hbxs is as follows:

[0030] S11. Based on the energy storage dataset, calculate the total rainwater storage capacity ZYU at the current time point. The calculation formula is as follows:

[0031]

[0032] In the formula, ∑(YU s The ) represents the total rainfall after statistically analyzing water storage data at all points in time. JS represents the net water volume after rainwater conversion. DW represents the ambient temperature at the current time. This represents the attenuation coefficient, which measures the degree to which temperature increases affect the evaporation rate of rainwater storage.

[0033] S12. Based on the energy storage dataset, calculate the power supply GDL at the current time point. The calculation formula is as follows:

[0034] GDL=Σ(DL s )-YD

[0035] In the formula, ∑(DL s ) represents the total electricity obtained after statistically analyzing the stored electricity resource data at all points in time, and YD represents the electricity consumption. ∑(DL) s YD represents the total electricity supply minus the electricity consumption, which gives the current power supply at the current time.

[0036] S13. Based on the power supply GDL, calculate the predicted net water volume YJS at the current time point. The calculation formula is as follows:

[0037]

[0038] In the formula, v represents the electricity consumption per unit of net water capacity;

[0039] S14. Based on the total rainwater storage ZYU and the predicted net water volume YJS, calculate the complementarity coefficient Hbxs at the current time point. The calculation formula is as follows:

[0040]

[0041] In the formula, It represents the ratio of total rainwater storage to predicted net water volume, which is the complementarity coefficient between water and electricity resources at the current point in time.

[0042] Preferably, when the growth coefficient Szxs is included in or exceeds the growth threshold SZY, it indicates that the tea trees in the area are in a healthy growth state; when the growth coefficient Szxs is lower than the growth threshold SZY, it indicates that the tea trees in the area are in an unhealthy growth state and require priority supply of clean water and fertilizer.

[0043] Preferably, in the fluctuation data group Bdsj, when the fluctuation rate of rainfall exceeds the rainfall threshold YUY, or the fluctuation rate of temperature exceeds the temperature threshold WDY, it indicates that environmental factors have led to a shortage of resource supply, requiring additional scheduling of water and electricity resources to maintain tea tree growth and tea garden electricity consumption. When the complementarity coefficient Hbxs≥1, it indicates that water and electricity resources can complement each other, and the power supply GDL at the current time point can meet the water purification conversion of the total rainwater storage ZYU.

[0044] A method for controlling rainwater runoff in photovoltaic tea gardens includes the following steps:

[0045] Step 1: Connect the database, monitoring devices, and energy storage devices via the network to obtain tea tree planting data for all regions, environmental data for all time points, and resource storage data for all time points, and classify them into planting datasets, environmental datasets, and energy storage datasets;

[0046] Step 2: Based on the planting dataset, analyze the growth status of tea trees in each region and generate the corresponding growth coefficient Szxs;

[0047] Step 3: Set a fixed monitoring period Q, and then combine it with the environmental dataset to analyze the changing trends of different environmental factors and generate the corresponding fluctuation data group Bdsj;

[0048] Step 4: Based on the energy storage dataset, calculate the total rainwater storage ZYU, power supply GDL, and predicted net water volume YJS at the current time point, and generate the corresponding complementary coefficient Hbxs;

[0049] Step 5: Set fixed ranges for growth threshold SZY, rainfall threshold YUY, and temperature threshold WDY. Then, combine the growth coefficient Szxs, fluctuation data group Bdsj, and complementarity coefficient Hbxs to determine whether the tea tree's growth status is healthy, whether environmental factors are causing resource supply shortages, and whether water and electricity resources can complement each other, and take corresponding supply measures.

[0050] Compared with the prior art, the present invention provides a photovoltaic tea garden rainwater diversion control system and method, which has the following beneficial effects:

[0051] 1. This invention uses a multi-dimensional acquisition module to connect to a database, monitoring device, and energy storage device via a network to acquire tea tree planting data, environmental data, and resource storage data for all regions and time points. These data are then categorized into planting datasets, environmental datasets, and energy storage datasets. The intelligent control module analyzes the growth status of tea trees in each region based on the planting dataset. If the trees in a region are less than three years old, they are considered to be in their juvenile stage. If the trees are more than three years old, they are considered to be in their mature stage. The vertical root distribution depth of mature tea trees is greater than that of juvenile tea trees. If the soil pH, soil moisture content, and soil organic matter content at the current planting depth do not meet the growth requirements of mature tea trees, a corresponding growth coefficient Szxs is generated. This allows for timely detection of health problems, reducing the impact of diseases or adverse environments on tea yield, and providing a comprehensive assessment with high accuracy.

[0052] 2. This invention uses an intelligent control module to set a fixed monitoring period, analyze the changing trends of different environmental factors, generate corresponding fluctuation data sets, predict potential environmental pressures, and provide guidance for optimizing environmental control measures. The intelligent control module statistically analyzes the total rainwater storage, power supply, and predicted net water volume at the current time point, and generates corresponding complementary coefficients to determine whether the tea tree growth is healthy, whether environmental factors are causing resource supply shortages, and whether water and power resources can complement each other. Corresponding supply measures are then implemented to achieve a dynamic balance between water and power resources, flexibly allocate resources, and achieve high utilization rates. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the system flow of the present invention;

[0054] Figure 2 This is a diagram illustrating the steps of the method of the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Traditional photovoltaic tea garden rainwater diversion control systems rely on human judgment of tea tree growth status before resource supply, making it difficult to accurately meet the tea trees' water and nutrient requirements. Furthermore, when environmental factors lead to insufficient photovoltaic power generation, the complementary relationship between water and electricity resources cannot be quickly assessed, resulting in poor applicability of resource allocation. Therefore, this paper proposes a photovoltaic tea garden rainwater diversion control system and method. Please refer to [link / reference]. Figure 1 A control system and method for rainwater diversion in photovoltaic tea gardens, comprising a multi-dimensional acquisition module and an intelligent control module;

[0057] The multi-dimensional data acquisition module consists of a planting data unit, an environmental monitoring unit, and an energy storage data unit. The planting data unit collects planting datasets via a network connection to a database. These datasets include tea tree planting data from all regions, and the expression for the planting dataset is {Z1}. q Z2 q Z3 q ... ... Zh q}, Z1 q To Zh q The data represents the tea tree planting data for the first to the hth regions. The tea tree planting data includes planting depth, tree age, soil pH value, soil moisture content, and soil organic matter content. q represents the specific number of a single region. Comprehensive collection of tea tree planting data will help to develop personalized supply strategies for tea trees in different regions in the future.

[0058] The environmental monitoring unit collects environmental datasets via a network connection to monitoring devices. The environmental datasets include environmental data at all time points, and the expression for the environmental dataset is {H1}. m H2 m H3 m ... Hj m}, H1 m To Hj mThese are environmental data from the first time point to the j-th time point. Environmental planting data includes rainfall and temperature. m represents the specific time when the environmental data was acquired. This helps to identify potential risks of changes in environmental factors and provides early warning for planting and resource management.

[0059] The energy storage data unit collects energy storage datasets via a network connection to the energy storage device. The energy storage dataset includes resource storage data at all points in time, and its expression is {YU}. s DL s}, YU s This represents water resource storage data, specifically rainwater storage, DL. s This represents the stored power resources data, namely photovoltaic power generation. s represents the specific time when the stored resources data was acquired. Centralized recording of stored resources data helps to visualize the status of resource utilization and provides data support for resource allocation and emergency management.

[0060] The intelligent control module consists of a growth assessment unit, an environmental analysis unit, and a diversion management unit. The growth assessment unit analyzes the growth status of tea trees in each region based on the planting dataset and generates the corresponding growth coefficient Szxs. The calculation process is as follows:

[0061] Extract tea tree planting data from the i-th region of the planting dataset, and label the planting depth of the i-th region as SD. i The tree age of the i-th region is labeled as SL. i The soil pH value of the i-th region is labeled as pH. i The soil moisture content of the i-th region is denoted as TS. i The soil organic matter content of the i-th region is labeled as TY. i ;

[0062] If the tree age of the i-th region is SL i If the planting depth is less than three years, it means that the tea trees in the area are in their juvenile stage, and the soil pH, soil moisture content and soil organic matter content at the current planting depth can meet the growth needs of the tea trees in their juvenile stage.

[0063] Szxs=α1×SD i +α2×pH i +α3×TS i +α4×TY i

[0064] In the formula, α1 represents the assessment weight for planting depth, α2 represents the assessment weight for soil pH, α3 represents the assessment weight for soil moisture content, and α4 represents the assessment weight for soil organic matter content. α1 + α2 + α3 + α4 = 1, α1 × SD i +α2×pH i+α3×TS i +α4×TY i This represents the growth coefficient of tea trees in the i-th region, calculated by weighting α1, α2, α3, and α4 and taking into account factors such as planting depth, soil pH, soil moisture content, and soil organic matter content.

[0065] If the tree age of the i-th region is SL i If it has been more than three years, it means that the tea trees in the area are in their mature stage. The vertical distribution depth of the root system of mature tea trees is greater than that of the root system in the juvenile stage. The soil pH, soil moisture content and soil organic matter content at the current planting depth have failed to meet the growth requirements of mature tea trees.

[0066] SG i =SD i +μ×SL i

[0067] GpH i =pH i -θ×SL i

[0068] GTS i =TS i -σ×SL i

[0069] GTY i =TY i -τ×SL i

[0070] Szxs=α1×SG i +α2×GpH i +α3×GTS i +α4×GTY i

[0071] In the formula, SG i GpH represents the vertical root depth of the tea tree in the i-th region, μ represents the conversion coefficient for converting tree age to vertical root depth, and GpH represents the vertical root depth. i GTS represents the soil pH value of the tea tree root system in the i-th region, θ represents the attenuation coefficient, used to measure the degree of influence of tree age on the decrease in soil pH value. i GTY represents the soil moisture content of the tea tree root system in the i-th region, σ represents the attenuation coefficient, used to measure the degree of influence of tree age on the decrease in soil moisture content. i α represents the soil organic matter content of the tea tree root system in the i-th region, τ represents the decay coefficient, used to measure the degree of influence of tree age on the decline of soil organic matter content, and α1×SG. i +α2×GpH i +α3×GTS i +α4×GTYi This means that the growth coefficient of tea trees in the i-th region is obtained by taking into account the vertical distribution depth of tea tree roots, pH value of tea tree root soil, water content of tea tree root soil and organic matter content of tea tree root soil according to the weights of α1, α2, α3 and α4. This quantitatively assesses the growth status of tea trees, timely detects health problems, and reduces the impact of diseases or adverse environments on tea yield.

[0072] The environmental analysis unit is set with a fixed monitoring period Q. Combined with the environmental dataset, it analyzes the changing trends of different environmental factors and generates the corresponding fluctuation data set Bdsj. The calculation process is as follows:

[0073] Based on the environmental dataset, the rainfall in the i-th region within the monitoring period Q is extracted and sequentially labeled as {Yi}. 1 Yi 2 Yi 3 ... Yi e}, Yi 1 To Yi e Let {Wi} represent the rainfall from the first time point to the e-th time point, and then extract the temperature of the i-th region within the monitoring period Q, labeling them sequentially as {Wi}. 1 Wi 2 Wi 3 Wi f}, Wi 1 to Wi f These represent the temperatures from the first time point to the f-th time point;

[0074]

[0075] In the formula, Yi k This represents the rainfall in the i-th region at the k-th time point. This represents the average rainfall in the i-th region during the monitoring period Q. This represents the fluctuation rate of rainfall in the i-th region according to the standard deviation formula. maxYi and minYi represent the maximum and minimum rainfall values ​​in the i-th region within the monitoring period Q. maxYi-minYi represents the range of rainfall in the i-th region within the monitoring period Q. Wi g This represents the temperature of the i-th region at the g-th time point. This represents the average temperature of the i-th region within the monitoring period Q. This indicates the temperature fluctuation rate of the i-th region obtained according to the standard deviation formula. maxWi and minWi represent the maximum and minimum temperature values ​​of the i-th region within the monitoring period Q. maxWi-minWi represents the temperature range of the i-th region within the monitoring period Q. This predicts potential environmental pressure and provides guidance for optimizing environmental control measures.

[0076] Based on the energy storage dataset, the diversion management unit calculates the total rainwater storage (ZYU), power supply (GDL), and predicted net water volume (YJS) at the current time point, and generates the corresponding complementary coefficient (Hbxs). The calculation process is as follows:

[0077] S11. Based on the energy storage dataset, calculate the total rainwater storage capacity ZYU at the current time point. The calculation formula is as follows:

[0078]

[0079] In the formula, ∑(YU s The ) represents the total rainfall after statistically analyzing water storage data at all points in time. JS represents the net water volume after rainwater conversion. DW represents the ambient temperature at the current time. This represents the attenuation coefficient, which measures the degree to which temperature increases affect the evaporation rate of rainwater storage.

[0080] S12. Based on the energy storage dataset, calculate the power supply GDL at the current time point. The calculation formula is as follows:

[0081] GDL=∑(DL s )-YD

[0082] In the formula, ∑(DL s ) represents the total electricity obtained after statistically analyzing the stored electricity resource data at all points in time, and YD represents the electricity consumption. ∑(DL) s YD represents the total electricity supply minus the electricity consumption, which gives the current power supply at the current time.

[0083] S13. Based on the power supply GDL, calculate the predicted net water volume YJS at the current time point. The calculation formula is as follows:

[0084]

[0085] In the formula, v represents the electricity consumption per unit of net water capacity;

[0086] S14. Based on the total rainwater storage ZYU and the predicted net water volume YJS, calculate the complementarity coefficient Hbxs at the current time point. The calculation formula is as follows:

[0087]

[0088] In the formula, It represents the ratio of total rainwater storage to predicted net water volume, which is the complementarity coefficient between water and power resources at the current time. Real-time statistics of current rainwater, power supply, and net water volume enable dynamic balance of water and power resources.

[0089] The diversion management unit is equipped with fixed ranges for growth thresholds SZY, rainfall thresholds YUY, and temperature thresholds WDY. Combined with the growth coefficient Szxs, fluctuation data set Bdsj, and complementarity coefficient Hbxs, it determines the health of tea tree growth, whether environmental factors are causing resource shortages, and whether water and electricity resources are complementary. When the growth coefficient Szxs is within or exceeds the growth threshold SZY, it indicates that the tea trees in that area are growing healthily; when the growth coefficient Szxs is below the growth threshold SZY, it indicates that the tea trees in that area are not growing healthily. The tea plants are not growing healthily and require priority supply of clean water and fertilizer. In the fluctuating data group Bdsj, when the fluctuation rate of rainfall exceeds the rainfall threshold YUY, or the fluctuation rate of temperature exceeds the temperature threshold WDY, it indicates that environmental factors are causing a shortage of resources and additional water and electricity resources need to be allocated to maintain the growth of tea trees and the electricity consumption of tea gardens. When the complementarity coefficient Hbxs≥1, it indicates that water and electricity resources can complement each other. The power supply GDL at the current time point can meet the water purification conversion of the total rainwater storage ZYU, and the resource utilization rate is high with flexible allocation.

[0090] Please see Figure 2 A method for controlling rainwater runoff in photovoltaic tea gardens includes the following steps:

[0091] Step 1: Connect the database, monitoring devices, and energy storage devices via the network to obtain tea tree planting data for all regions, environmental data for all time points, and resource storage data for all time points, and classify them into planting datasets, environmental datasets, and energy storage datasets;

[0092] Step 2: Based on the planting dataset, analyze the growth status of tea trees in each region and generate the corresponding growth coefficient Szxs for comprehensive and accurate evaluation;

[0093] Step 3: Set a fixed monitoring period Q, and then combine it with the environmental dataset to analyze the changing trends of different environmental factors and generate the corresponding fluctuation data group Bdsj;

[0094] Step 4: Based on the energy storage dataset, calculate the total rainwater storage ZYU, power supply GDL, and predicted net water volume YJS at the current time point, and generate the corresponding complementary coefficient Hbxs;

[0095] Step 5: Set fixed ranges for growth threshold SZY, rainfall threshold YUY, and temperature threshold WDY. Then, combine the growth coefficient Szxs, fluctuation data group Bdsj, and complementarity coefficient Hbxs to determine whether the tea tree's growth status is healthy, whether environmental factors are causing resource supply shortages, and whether water and electricity resources can complement each other. Implement corresponding supply measures to flexibly allocate resources with high utilization rates.

[0096] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A photovoltaic tea plantation rainwater diversion control system, characterized in that: The multi-dimensional acquisition module and the intelligent control module are connected through a network. The multi-dimensional acquisition module is composed of a planting data unit, an environment monitoring unit and an energy storage data unit. The intelligent control module is composed of a growth evaluation unit, an environment analysis unit and a shunt management unit, the growth evaluation unit analyzes the growth state of tea trees in each area according to the planting data set, and generates the corresponding growth coefficient ; The growth coefficient The calculation proceeds as follows: Extracting the first from the planting dataset Tea tree planting data for each region, and the data for the first region... The planting depth of each area is marked as follows: , will the The tree age of each region is marked as follows: , will the The soil pH value of each region is marked as follows: , will the The soil moisture content of each region is marked as follows: , will the The soil organic matter content of each region is marked as follows: ; If the first Tree age in each area If the planting depth is less than three years, it means that the tea trees in the area are in their juvenile stage, and the soil pH, soil moisture content and soil organic matter content at the current planting depth can meet the growth needs of the tea trees in their juvenile stage. ； In the formula, This indicates the evaluation weight for planting depth. This indicates the assessment weight for soil pH value. This indicates the assessment weight for soil moisture content. This indicates the assessment weight for soil organic matter content. , Indicates according to , , and The weights, taking into account planting depth, soil pH, soil moisture content, and soil organic matter content, are used to obtain the first... Growth coefficient of tea trees in each region; If the first Tree age in each area If it has been more than three years, it means that the tea trees in the area are in their mature stage. The vertical distribution depth of the root system of mature tea trees is greater than that of the root system in the juvenile stage. The soil pH, soil moisture content and soil organic matter content at the current planting depth have failed to meet the growth requirements of mature tea trees. ； ； ； ； ； In the formula, Indicates the first Vertical distribution depth of tea tree roots in each region This represents the conversion factor that transforms tree age into the vertical depth of root distribution. Indicates the first The pH value of the soil around the tea tree roots in each region This represents the attenuation coefficient, used to measure the degree to which tree age affects the decrease in soil pH. Indicates the first Soil moisture content of tea tree root systems in each region This represents the attenuation coefficient, used to measure the degree to which tree age affects the decrease in soil moisture content. Indicates the first The organic matter content of the soil around the tea tree roots in each region This represents the attenuation coefficient, used to measure the degree to which tree age affects the decline in soil organic matter content. Indicates according to , , and The weights are determined by combining the vertical distribution depth of tea tree roots, the pH value of the soil around tea tree roots, the soil moisture content of the soil around tea tree roots, and the organic matter content of the soil around tea tree roots. Growth coefficient of tea trees in each region; The environment analysis unit is provided with a fixed-length monitoring period In combination with the environmental data set, the change trend of different environmental factors is analyzed to generate corresponding fluctuation data set ; The fluctuation data set The calculation proceeds as follows: According to the environmental data set, extract the monitoring period The rainfall of the first region in the monitoring period is sequentially marked in time sequence as , , , , , The temperature of the first region in the monitoring period is sequentially marked in time sequence as , , , , , ； In the formula, Indicates the first At the nth time point, the th Rainfall in each region Indicates the monitoring period within, no. Average rainfall in each region This indicates that, according to the standard deviation formula, the first... Fluctuation rate of rainfall in each region and Indicates the monitoring period within, no. Maximum and minimum rainfall for each region Indicates the monitoring period within, no. The extreme difference in rainfall in each region Indicates the first At the nth time point, the th Temperature of each region Indicates the monitoring period within, no. The average temperature of each region This indicates that, according to the standard deviation formula, the first... Temperature fluctuation rate in each region and Indicates the monitoring period within, no. The maximum and minimum temperatures for each region. Indicates the monitoring period within, no. The temperature range of each region; The shunt management unit calculates the total rainwater storage amount at the current time point according to the energy storage data set , the power supply amount , and the predicted net water amount , and generates the corresponding complementary coefficient ; The complementary coefficients The calculation proceeds as follows: S11. Calculate the total storage of rainwater at the current time point according to the energy storage dataset The calculation formula is as follows: ； In the formula, represents the total amount of rainwater obtained after statistics of water resource storage data at all time points, represents the net water amount after the rainwater is converted, represents the ambient temperature at the current time point, represents the attenuation coefficient for measuring the influence degree of temperature increase on the evaporation speed of rainwater storage. S12. According to the energy storage data set, the power supply amount at the current time point is calculated The calculation formula is as follows: ； In the formula, represents the total amount of electricity obtained after statistics of the power resource storage data of all time points, represents the amount of electricity used, represents the amount of electricity supplied at the current time point obtained by subtracting the amount of electricity used from the total amount of electricity; S13、According to the power supply amount , the predicted net water amount at the current time point is calculated , and the calculation formula is as follows: ； In the formula, represents the power consumption per unit net water capacity; S14、According to the total storage amount of rainwater and the predicted net water amount , the complementary coefficient at the current time point is calculated , and the calculation formula is as follows: ； In the formula, represents the ratio of total rainwater storage and predicted net water, that is, the complementary coefficient between water resources and power resources at the current time point; The shunt management unit is provided with a fixed range of growth threshold values , rainfall threshold values and temperature threshold values , combined with growth coefficients , fluctuation data sets and complementary coefficients to determine whether the growth state of the tea tree is healthy, whether environmental factors cause resource supply shortages, and whether water resources and power resources can be complementary, and take appropriate supply measures.

2. A photovoltaic tea plantation rainwater diversion control system according to claim 1, characterized in that: The expression of the planting data set is , to are tea tree planting data of the first to the th region, respectively, the tea tree planting data including planting depth, tree age, soil pH value, soil water content, and soil organic matter content, represents a specific number of a single region.

3. A photovoltaic tea plantation rainwater diversion control system according to claim 2, characterized in that: The expression of the environmental data set is , to are the environmental data of the first time point to the time point, respectively, and the environmental planting data includes rainfall and temperature, represents the specific time at which the environmental data is obtained.

4. A photovoltaic tea plantation rainwater diversion control system according to claim 3, characterized in that: The expression of the energy storage data set is , represents water resource storage data, i.e. rainwater storage amount, represents power resource storage data, i.e. photovoltaic power generation amount, represents the specific time of obtaining the resource storage data.

5. A photovoltaic tea plantation rainwater diversion control system according to claim 4, characterized in that: the growth coefficient above or below the growth threshold above the growth threshold, the growth coefficient below the growth threshold below the growth threshold, the growth coefficient 6. A photovoltaic tea plantation rainwater diversion control system according to claim 5, characterized in that: The fluctuation data set In the case where the fluctuation rate of rainfall exceeds a rainfall threshold Or the fluctuation rate of temperature exceeds a temperature threshold Indicates that the environment factor causes the resource supply to be tight, and additional water resources and power resources need to be dispatched to maintain the growth of tea trees and the electricity consumption of tea gardens, the complementary coefficient Indicates that the water resources and the power resources can be complementary, and the power supply at the current time point Can meet the total storage amount of rainwater Net water conversion.

7. A photovoltaic tea plantation rainwater diversion control method applied to the photovoltaic tea plantation rainwater diversion control system of any one of claims 1-6, characterized in that, The method comprises the following steps: Step one: connecting the database, monitoring device and energy storage device through the network, obtaining tea tree planting data in all regions, environment data at all time points and resource storage data at all time points, and classifying and forming planting data set, environment data set and energy storage data set; Step two: According to the planting data set, analyze the growth state of each area of tea tree, and generate the corresponding growth coefficient ; Step three: set a fixed monitoring period In combination with the environmental data set, the change trend of different environmental factors is analyzed to generate corresponding fluctuation data set ; Step four: According to the energy storage data set, the total rainwater storage amount at the current time point is counted , the power supply amount and the predicted net water amount , and the corresponding complementary coefficient is generated; Step five: set the fixed range of growth threshold , rainfall threshold and temperature threshold , combined with growth coefficient , fluctuation data set and complementary coefficient , to determine whether the growth state of tea trees is healthy, whether environmental factors lead to resource supply shortage, and whether water resources and power resources can be complementary, and take corresponding supply measures.

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