Automatic irrigation control system and method for tea garden based on photovoltaic power generation

Abstract

The invention discloses an automatic irrigation control system and method for a tea garden based on photovoltaic power generation, and particularly relates to the technical field of agricultural irrigation, photovoltaic power generation capacity, tea tree growth irrigation cycle and meteorological condition information are collected in real time through multiple types of sensors, and formatting processing is carried out to ensure data availability; information lag degree analysis is carried out based on the processed data, and feedback lag early signs in photovoltaic power generation and tea garden irrigation cooperative control are identified; according to the lag signs, extracting power supply fluctuation, tea tree irrigation demand urgency and climate environment change characteristics, and generating corresponding fluctuation indexes, demand indexes and change indexes; dividing a feedback sampling period into a high-quality period or a low-quality period by using fuzzy logic, and dynamically adjusting the sampling frequency based on the current sampling frequency in the low-quality period; the feedback sampling frequency can be dynamically adjusted, and the stability and the response speed in the cooperative control process of photovoltaic power generation and tea garden automatic irrigation are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural irrigation, and more specifically, to a tea garden automatic irrigation control system and method based on photovoltaic power generation. Background Art

[0002] Photovoltaic power generation is a technology that uses solar cells to directly convert sunlight into electrical energy. Because it is clean, renewable and environmentally friendly, photovoltaic power generation has been widely used in agriculture, industry, residents' lives and other fields. The output power of photovoltaic power generation systems is affected by factors such as light intensity and weather conditions, and is characterized by volatility and intermittence. In particular, on cloudy days or at night, the power generation may be greatly reduced.

[0003] Automatic irrigation in tea gardens is an intelligent irrigation method that can automatically adjust the irrigation amount and time according to the growth needs of tea trees. The automatic irrigation system can monitor soil moisture, temperature, humidity and other environmental parameters in real time through sensors, and intelligently adjust the irrigation frequency and water volume according to the growth cycle needs of tea trees to achieve precise irrigation, reduce water waste, and improve the growth quality of tea trees. Automatic irrigation systems usually rely on a stable power supply, and it is difficult to ensure the continuity and reliability of irrigation when the power is unstable.

[0004] In tea gardens in remote areas where the power supply from the power grid is unstable or it is inconvenient to lay the power grid, photovoltaic power generation provides a green and sustainable source of electricity for the automatic irrigation system. However, the volatility of photovoltaic power generation has brought new challenges to the automatic irrigation system. In order to ensure the stability of tea garden irrigation, a collaborative control system is needed that can dynamically adjust the sampling frequency when the photovoltaic power generation power fluctuates to ensure that the automatic irrigation system can obtain high-quality data and ensure that the irrigation demand is met in time when it is applied. Therefore, a tea garden automatic irrigation control system and method based on photovoltaic power generation is proposed. Summary of the invention

[0005] To achieve the above object, the present invention provides the following technical solutions: The automatic irrigation control method for tea garden based on photovoltaic power generation comprises the following steps: Multiple types of sensors are used to collect corresponding photovoltaic power generation information, tea tree growth irrigation cycle stage information, and meteorological conditions information in real time, and the collected information is formatted to ensure the availability of the information; Based on the processed information, information hysteresis analysis is performed, and according to the results of information hysteresis analysis, early signs of coupling feedback hysteresis in the coordinated control process of photovoltaic power generation and automatic irrigation in tea gardens are identified; Based on the early signs of coupled feedback lag identified, three types of features were extracted: power supply fluctuations, urgency of tea tree irrigation demand, and climate and environmental change trends. Then, the photovoltaic power generation volatility index, tea tree growth cycle demand index, and climate and environmental change index were generated respectively. The photovoltaic power generation and tea garden automatic irrigation coordinated control process is divided into operations through fuzzy logic, and the current feedback sampling cycle is divided into high-quality feedback cycle and low-quality feedback cycle. For a low-quality feedback cycle, the feedback sampling frequency is re-determined based on the current feedback sampling frequency.

[0006] In a preferred embodiment, information hysteresis analysis refers to: In each feedback sampling cycle t, the following data are recorded: the power generation of photovoltaic power generation Pt, the power demand of automatic irrigation of tea garden Dt, and the response deviation value of the coordinated control process of photovoltaic power generation and automatic irrigation of tea garden is defined as Et in cycle t: ; A feedback hysteresis threshold θ is preset, and the feedback hysteresis value is calculated within a fixed time window W: ; is the feedback hysteresis value.

[0007] In a preferred embodiment, according to the result of information hysteresis analysis, the early signs of coupling feedback hysteresis in the coordinated control process of photovoltaic power generation and automatic irrigation of tea gardens are identified as follows: Feedback hysteresis value Compared with the preset feedback timeliness threshold, if the feedback lags behind the If the feedback lag value is greater than the preset feedback timeliness threshold, an abnormal signal is generated. If it is less than or equal to the preset feedback timeliness threshold, a normal signal is generated. When an abnormal signal is generated, it indicates that there are early signs of coupling feedback lag in the coordinated control process of photovoltaic power generation and automatic irrigation in tea gardens.

[0008] In a preferred embodiment, the logic for obtaining the photovoltaic power generation volatility index is: Assume that the photovoltaic power generation at each time point t is PGt, and the change of power generation is counted within a fixed time window W to obtain the time series of photovoltaic power generation; Calculate the power generation difference between every two adjacent time points in the current time window : ; ; Calculate the standard deviation of power generation fluctuation within the window , and the average fluctuation of power generation : ; ; The formula for calculating the photovoltaic power generation volatility index is: ; Represents the photovoltaic power generation volatility index.

[0009] In a preferred embodiment, the logic for obtaining the tea tree growth cycle demand index is: Assume that the growth cycle of tea trees is C, where C contains multiple stages. The water demand of each stage is different. The demand weight of the i-th stage is wi, and the daily average irrigation demand corresponding to each stage is Di. The deviation of irrigation demand growth ΔDi is calculated: ; ; Indicates the duration of the current phase in days; In all growth stages, calculate the average of the total demand weight WD and the demand growth deviation in each stage : ; ; n represents the total number of stages in the growth cycle of tea trees; The formula for calculating the tea tree growth cycle demand index is: ; Represents the tea tree growth cycle demand index.

[0010] In a preferred embodiment, the climate environment change index acquisition logic is: Assume that the climate parameter at each time point t is Qt. Within a fixed time window W, for each climate parameter Qt, calculate the standard deviation and record it as , and calculate the rate of change ΔQt: ; ; Calculate the average trend rate of change within the current time window And the climate environment shift coefficient Qshift: ; ; The calculation formula of climate environment change coefficient is: ; represents the coefficient of climate environment variation; The single parameter climate change coefficient Perform weighted summation to obtain the climate and environmental change index: ; The number representing the climate parameter, Indicates the number The corresponding single parameter climate change coefficient, Indicates the number The corresponding proportionality coefficient is, Represents the climate environment change index.

[0011] In a preferred embodiment, the photovoltaic power generation and tea garden automatic irrigation coordinated control process is divided by fuzzy logic, and the current feedback sampling cycle is divided into a high-quality feedback cycle and a low-quality feedback cycle. In the process of coordinated control of photovoltaic power generation and automatic irrigation of tea gardens, the photovoltaic power generation volatility index, the tea tree growth cycle demand index, the climate and environmental change index, and the current feedback sampling frequency are taken as input variables, and the current feedback sampling period division type is taken as the output variable. The input variables are fuzzified and the values ​​of the input variables are converted into fuzzy sets. The output variables are fuzzified and the output variables are converted into fuzzy sets. Fuzzy rules are formulated to describe the adaptability of the feedback sampling period type under different data type combinations. The fuzzified input variables are inferred through fuzzy rules to obtain the current feedback sampling period division type in the process of coordinated control of photovoltaic power generation and automatic irrigation of tea gardens.

[0012] In a preferred embodiment, for a low-quality feedback cycle, re-determining the feedback sampling frequency based on the current feedback sampling frequency means: Assume the current feedback sampling frequency is , the photovoltaic power generation volatility index, tea tree growth cycle demand index, and climate environment change index are combined to generate an adjustment factor for the comprehensive feedback frequency: ; is the total number of index types, An index number representing the index type, Indicates that the index number is The corresponding adjustment coefficient is Indicates that the index number is The corresponding preset standard value is Indicates that the index number is The corresponding exponential value is represents the adjustment factor; The formula for re-determining the feedback sampling frequency is: ; represents the feedback sampling frequency after redetermining, It indicates the upper limit of the maximum sampling frequency set in the coordinated control process of photovoltaic power generation and automatic irrigation of tea garden.

[0013] In a preferred embodiment, the automatic irrigation control system for a tea garden based on photovoltaic power generation comprises: The information collection module collects corresponding photovoltaic power generation information, tea tree growth irrigation cycle stage information and meteorological condition information in real time through multiple types of sensors; The information processing module formats the collected information to ensure the usability of the information; The information hysteresis analysis module performs information hysteresis analysis based on the processed information. According to the results of the information hysteresis analysis, the early signs of coupling feedback hysteresis in the coordinated control process of photovoltaic power generation and automatic irrigation of tea gardens are identified; The feature extraction module extracts three types of features, namely power supply fluctuation, urgency of tea tree irrigation demand, and climate and environmental change trend, based on the identified early signs of coupling feedback lag. It then generates the photovoltaic power generation volatility index, tea tree growth cycle demand index, and climate and environmental change index respectively. The feedback sampling cycle division module divides the photovoltaic power generation and tea garden automatic irrigation collaborative control process through fuzzy logic, and divides the current feedback sampling cycle into high-quality feedback cycle and low-quality feedback cycle; The collaborative control optimization and adjustment module re-determines the feedback sampling frequency based on the current feedback sampling frequency for low-quality feedback cycles.

[0014] Technical effects and advantages of the present invention: The present invention uses photovoltaic power generation to provide the main power source for automatic irrigation, effectively reducing dependence on the power grid, and is particularly suitable for application in remote areas with unstable power supply. The clean energy characteristics of photovoltaic power generation make the coordinated control process of photovoltaic power generation and automatic irrigation in tea gardens more environmentally friendly, in line with the development trend of green agriculture, and help reduce greenhouse gas emissions. The volatility of photovoltaic power generation is one of the factors that affect the stability of the irrigation system. The present invention introduces the photovoltaic power generation volatility index, the tea tree growth cycle demand index and the climate environment change index, combined with fuzzy logic control, to dynamically adjust the feedback sampling frequency, ensuring that the photovoltaic power generation and tea garden automatic irrigation coordinated control process can still provide stable irrigation services under conditions of severe operating fluctuations, effectively improving the stability and response speed of the photovoltaic power generation and tea garden automatic irrigation coordinated control process.

[0015] The present invention is not only applicable to the irrigation needs of tea gardens, but can also be applied to other agricultural planting scenarios that require precise irrigation. It is particularly suitable for use in remote areas that lack a stable power supply, and can dynamically adjust sampling to adapt to changes in different seasons, climates, and tea tree growth stages. It has strong environmental adaptability and scalability. By combining clean energy photovoltaic power generation with intelligent irrigation technology, the present invention not only improves irrigation efficiency, but also effectively reduces resource waste, which is in line with the concept of sustainable agriculture. High-quality photovoltaic power generation and tea garden automatic irrigation coordinated control can ensure the growth of tea trees while taking into account environmental protection and energy conservation, and promote tea gardens to move towards green, low-carbon, and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 This is a schematic diagram of the automatic irrigation control method for a tea garden based on photovoltaic power generation in the present invention.

[0017] Figure 2 This is a schematic diagram of the tea garden automatic irrigation control system based on photovoltaic power generation in the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Reference Figure 1 - Figure 2 The following examples are obtained: Embodiment 1: A method for controlling automatic irrigation in a tea garden based on photovoltaic power generation, comprising the following steps: Multiple types of sensors are used to collect corresponding photovoltaic power generation information, tea tree growth irrigation cycle stage information, and meteorological conditions information in real time, and the collected information is formatted to ensure the availability of the information; different sensors collect different types of information in real time, including photovoltaic power generation, tea tree irrigation demand stage, meteorological conditions, etc. These data provide basic information for the real-time operation of the system, and formatting is used to ensure the uniformity and availability of the data, making the subsequent analysis and control process more accurate and reliable, and avoiding errors caused by inconsistent data.

[0020] Based on the processed information, information hysteresis analysis is performed. According to the results of information hysteresis analysis, early signs of coupling feedback hysteresis in the coordinated control of photovoltaic power generation and automatic irrigation in tea gardens are identified; in the coordinated process of photovoltaic power generation and irrigation control, information hysteresis analysis is used to judge the timeliness of feedback. By analyzing the deviation between photovoltaic power generation and irrigation demand, it is possible to identify the mismatch between power supply and irrigation demand, thereby discovering possible feedback hysteresis problems. This analysis can provide early signs of hysteresis, which helps to provide early warning and correction, ensuring that the system can respond quickly when demand changes.

[0021] Based on the early signs of coupled feedback lag identified, three types of features are extracted: power supply fluctuations, urgency of tea tree irrigation needs, and climate and environmental change trends. Then, the photovoltaic power generation volatility index, tea tree growth cycle demand index, and climate and environmental change index are generated respectively. Through feature extraction, the three main factors affecting irrigation demand: power supply fluctuations, urgency of tea tree irrigation needs, and changes in climate conditions are analyzed, and then corresponding indexes are generated respectively. Each index quantitatively represents the current state and change trend of a factor, which helps the system accurately grasp the stability of photovoltaic power generation, the immediate demand for water for tea tree growth, and the impact of climate on irrigation. These indexes provide a basis for subsequent fuzzy logic decision-making, making the control of the system more targeted and accurate.

[0022] The fuzzy logic is used to divide the collaborative control process of photovoltaic power generation and automatic irrigation in tea gardens, and the current feedback sampling cycle is divided into high-quality feedback cycle and low-quality feedback cycle. According to the photovoltaic power generation volatility index, the tea tree growth cycle demand index and the climate and environmental change index, the fuzzy logic is used to judge the effectiveness of the current feedback, and the sampling cycle is divided into high-quality or low-quality cycles. A high-quality feedback cycle means that the system feedback is stable and the response is timely; a low-quality feedback cycle indicates that there is a risk of feedback lag and the sampling frequency needs to be increased. This division allows the system to dynamically adjust the sampling frequency according to actual needs, improve efficiency, and save resources. Automatic irrigation is achieved by adaptively adjusting the irrigation frequency and water volume.

[0023] For low-quality feedback cycles, the feedback sampling frequency is re-determined based on the current feedback sampling frequency. During low-quality feedback cycles, the system needs to increase the feedback sampling frequency to detect environmental and demand changes more frequently, ensuring that the system can quickly adjust irrigation strategies under dynamic conditions. This step recalculates the sampling frequency by integrating the feedback frequency adjustment factor to adapt the sampling frequency to the needs of the current feedback. By increasing the sampling frequency, the system can detect and handle anomalies more promptly, prevent irrigation delays or inaccuracies caused by feedback lag, and effectively improve the system's response speed and irrigation accuracy.

[0024] Information lag analysis refers to: In each feedback sampling cycle t, the following data are recorded: the power generation of photovoltaic power generation Pt, the power demand of automatic irrigation of tea garden Dt, and the response deviation value of the coordinated control process of photovoltaic power generation and automatic irrigation of tea garden is defined as Et in cycle t: ; A feedback hysteresis threshold θ is preset, and the feedback hysteresis value is calculated within a fixed time window W: ; is the feedback hysteresis value. The information hysteresis analysis aims to measure whether the feedback response in the process of coordinated control of photovoltaic power generation and automatic irrigation of tea gardens is timely, especially to judge the degree of coordination between power supply and irrigation demand. In each feedback sampling cycle t, by recording the photovoltaic power generation and the power demand of tea garden irrigation, the response deviation value of the two is calculated, thereby quantifying the gap between photovoltaic power generation and tea garden irrigation demand. The set feedback hysteresis threshold is used as the response standard, indicating the maximum allowable deviation. Then, within the fixed time window W, the feedback hysteresis value is calculated. If the value is large, it indicates that the feedback hysteresis is more serious. This analysis process helps the system to promptly discover the matching between photovoltaic power generation and irrigation demand, so that the system can quickly respond to potential hysteresis risks, thereby ensuring that irrigation demand is met.

[0025] According to the results of information hysteresis analysis, the early signs of coupling feedback hysteresis in the coordinated control of photovoltaic power generation and automatic irrigation of tea gardens are identified as follows: Feedback hysteresis value Compared with the preset feedback timeliness threshold, if the feedback lags behind the If the feedback lag value is greater than the preset feedback timeliness threshold, an abnormal signal is generated. When the feedback hysteresis value is less than or equal to the preset feedback timeliness threshold, a normal signal is generated. When the abnormal signal is generated, it indicates that there are early signs of coupling feedback lag in the process of coordinated control of photovoltaic power generation and automatic irrigation of tea gardens. In the system, the feedback lag value is compared with the preset feedback timeliness threshold to determine whether the feedback process is normal. When it is greater than the preset threshold, an abnormal signal is generated, indicating that there are early signs of coupling feedback lag in the process of coordinated control of photovoltaic power generation and automatic irrigation of tea gardens; and when it is less than or equal to the preset threshold, a normal signal is generated, indicating that the feedback response is good and there is no lag risk. It is of great significance to identify the early signs of coupling feedback lag, which can warn of feedback problems in the system in advance, so that the control system can take adjustment measures before the feedback lag really affects the irrigation effect. The generation of abnormal signals is equivalent to sending an early warning prompt to the system to prevent response delays when the photovoltaic power generation power fluctuates or the demand changes greatly, thereby improving the overall stability and response efficiency of the system.

[0026] The logic for obtaining the photovoltaic power generation volatility index is: Assume that the photovoltaic power generation at each time point t is PGt. The photovoltaic system output power recorded at each sampling time point is the basic data for volatility calculation. The change in power generation is counted within a fixed time window W to obtain the time series of photovoltaic power generation. Calculate the power generation difference between every two adjacent time points in the current time window : ; ; It indicates the variation range of photovoltaic power between adjacent time points, reflecting the real-time fluctuation of photovoltaic power generation.

[0027] Calculate the standard deviation of power generation fluctuation within the window , and the average fluctuation of power generation : ; ; The average value represents the average level of photovoltaic power generation change and is used to measure the overall fluctuation. The standard deviation quantifies the degree of dispersion of power fluctuation. The larger the standard deviation, the more unstable the fluctuation.

[0028] The formula for calculating the photovoltaic power generation volatility index is: ; Represents the photovoltaic power generation volatility index. The photovoltaic power generation volatility index is used to indicate the overall volatility of photovoltaic power generation. This index combines the standard deviation of the fluctuation and the average value of the fluctuation to more comprehensively reflect the degree of power fluctuation. The photovoltaic power generation volatility index is a comprehensive indicator to measure the volatility of photovoltaic power generation over a period of time. The larger the SVI, the stronger the volatility of photovoltaic power generation and the greater the instability of power generation. This indicates that the current photovoltaic system's power supply support to the irrigation system is unstable, which may lead to a decrease in the reliability of the irrigation system.

[0029] The logic for obtaining the tea tree growth cycle demand index is: Assume that the growth cycle of tea trees is C, where C includes multiple stages, such as seedling stage, vigorous growth stage, dormancy stage, etc. The water demand of each stage is different. The demand weight of the i-th stage is wi, which indicates the importance or priority of the i-th stage in the entire growth cycle. The daily average irrigation demand corresponding to each stage is Di, which indicates the daily water demand of tea trees in this stage. The deviation of irrigation demand growth ΔDi is calculated: ; ; Indicates the duration of the current stage; ΔDi is used to quantify the demand growth rate of the i-th stage relative to the previous stage.

[0030] In all growth stages, calculate the average of the total demand weight WD and the demand growth deviation in each stage : ; ; n represents the total number of stages in the tea tree growth cycle; the average value of demand growth deviation represents the average level of demand growth deviation in each stage of the entire growth cycle, and the total demand weight represents the sum of the demand weights of all growth stages.

[0031] The formula for calculating the tea tree growth cycle demand index is: ; It represents the tea tree growth cycle demand index, which is used to comprehensively evaluate the urgency of the tea tree's irrigation demand in the current growth cycle. The higher the value, the more significant the tea tree's demand for water is at the current stage, and the more urgent the irrigation demand is. The system can judge whether it is necessary to increase the sampling frequency or make appropriate adjustments to the irrigation amount to ensure that the tea tree's water demand at different growth stages is met in a timely manner.

[0032] The logic for obtaining the climate and environmental change index is: Assume that the climate parameter at each time point t is Qt. This climate parameter can be a variable reflecting climate change, such as temperature, humidity, and precipitation. Within a fixed time window W, for each climate parameter Qt, the standard deviation is calculated and recorded as , which is used to measure the volatility of climate parameters within the time window W and calculate the rate of change ΔQt, which is used to quantify the degree of climate change between the current time point and the previous time point: ; ; Larger Indicates drastic climate change.

[0033] Calculate the average trend rate of change within the current time window And the climate environment shift coefficient Qshift: ; The average value of the trend change rate represents the average rate of change of the climate parameter within the time window W, and the climate environment deviation coefficient is used to measure the overall deviation of the climate parameter relative to its average value.

[0034] The calculation formula of climate environment change coefficient is: ; It represents the coefficient of climate and environment variation; the coefficient of climate and environment variation indicates the degree of influence of current climate conditions on irrigation demand.

[0035] The single parameter climate change coefficient Perform weighted summation to obtain the climate and environmental change index: ; The number representing the climate parameter, Indicates the number The corresponding single parameter climate change coefficient, Indicates the number The corresponding proportionality coefficient is, Represents the climate and environment change index. The change index of multiple individual climate parameters is weighted and summed to obtain the climate and environment change index, which is used to comprehensively reflect the impact of different climate parameters on irrigation demand. The climate and environment change index indicates the impact of the volatility of current climate conditions on irrigation demand. A high value means that the climate fluctuates greatly, which may lead to increased volatility in irrigation demand, thereby affecting the irrigation control strategy of the system. The climate and environment change index can be used to determine the combined impact of multiple climate factors. The system can dynamically adjust the feedback sampling frequency based on the index to ensure that irrigation demand is met in a timely manner when climate conditions change dramatically.

[0036] The photovoltaic power generation and tea garden automatic irrigation coordinated control process is divided by fuzzy logic, and the current feedback sampling cycle is divided into high-quality feedback cycle and low-quality feedback cycle. In the process of coordinated control of photovoltaic power generation and automatic irrigation of tea gardens, the photovoltaic power generation volatility index, the tea tree growth cycle demand index, the climate and environmental change index, and the current feedback sampling frequency are taken as input variables, and the current feedback sampling period division type is taken as the output variable. The input variables are fuzzified and the values ​​of the input variables are converted into fuzzy sets. The output variables are fuzzified and the output variables are converted into fuzzy sets. Fuzzy rules are formulated to describe the adaptability of the feedback sampling period type under different data type combinations. The fuzzified input variables are inferred through fuzzy rules to obtain the current feedback sampling period division type in the process of coordinated control of photovoltaic power generation and automatic irrigation of tea gardens.

[0037] The photovoltaic power generation volatility index, tea tree growth cycle demand index, climate environment change index and current feedback sampling frequency are selected as input variables. These variables jointly reflect the stability of photovoltaic power generation, the urgency of tea tree irrigation demand and the volatility of climate environment, as well as the current feedback sampling frequency.

[0038] All input variables are fuzzified, that is, the value of each input variable is converted into a fuzzy set. For example, the photovoltaic power generation volatility index can be divided into "low volatility", "medium volatility" and "high volatility". The tea tree growth cycle demand index can be divided into "low demand", "medium demand" and "high demand". The climate environment change index can be divided into "stable", "changing" and "dramatic change". The feedback sampling frequency can be divided into "low frequency", "medium frequency" and "high frequency".

[0039] The division type of the current feedback sampling cycle is used as the output variable and fuzzified. The output variable can be defined as "high-quality feedback cycle" or "low-quality feedback cycle". A series of fuzzy rules are formulated according to the combination of each input variable. These rules describe the division of feedback sampling cycles under different conditions. For example: if the photovoltaic power generation volatility index is "low volatility", the tea tree growth cycle demand index is "low demand", the climate and environmental change index is "stable", and the feedback sampling frequency is "low frequency", then the current feedback sampling cycle is a "high-quality feedback cycle". If the photovoltaic power generation volatility index is "high volatility", the tea tree growth cycle demand index is "high demand", the climate and environmental change index is "dramatic change", and the feedback sampling frequency is "low frequency", then the current feedback sampling cycle is a "low-quality feedback cycle". Similarly, various possible combination conditions are defined to cover different situations. The fuzzified input variables are inferred through fuzzy rules, and the membership of the output variable is determined according to the matching degree of the fuzzy set of the input variables and the rules.

[0040] Based on the comprehensive reasoning results, the current feedback sampling cycle is divided into "high-quality feedback cycle" or "low-quality feedback cycle". If the output is "high-quality feedback cycle", the system can maintain the current sampling frequency; if the output is "low-quality feedback cycle", the system will increase the sampling frequency to ensure the timeliness of feedback. By dividing the feedback sampling cycle through fuzzy logic, the system can flexibly adapt to changes in the real-time environment. By fuzzifying the input variables and reasoning based on fuzzy rules, the system can make adaptive adjustments to the quality of the feedback sampling cycle under complex and changing conditions, thereby more accurately meeting irrigation needs. This division method reduces the system's dependence on a single indicator, making the decision-making process more robust and efficient, and helps to achieve coordinated control of photovoltaic power generation and automatic irrigation of tea gardens.

[0041] For a low-quality feedback cycle, re-determining the feedback sampling frequency based on the current feedback sampling frequency means: Assume the current feedback sampling frequency is , the photovoltaic power generation volatility index, tea tree growth cycle demand index, and climate environment change index are combined to generate an adjustment factor for the comprehensive feedback frequency: ; is the total number of index types, The index number of the index type. The values ​​of h are 1, 2, and 3, which correspond to the photovoltaic power generation volatility index, the tea tree growth cycle demand index, and the climate environment change index, respectively. Indicates that the index number is The corresponding adjustment coefficient is used to indicate the influence of each index on the feedback sampling frequency adjustment. Indicates that the index number is The preset standard value corresponding to the time is used as a benchmark to measure the relative strength of the current index. Indicates that the index number is The corresponding index type value indicates the actual value of each index under the current conditions. Represents the adjustment factor; the adjustment factor of the feedback frequency is generated by weighted combination of the ratio of the actual value of different indexes to their preset standard values. The larger it is, the more dramatic the current environment changes or the demand is more urgent, requiring a higher feedback sampling frequency.

[0042] The formula for re-determining the feedback sampling frequency is: ; represents the feedback sampling frequency after redetermining, It indicates the maximum sampling frequency limit set in the process of photovoltaic power generation and automatic irrigation of tea gardens. This ensures that the redetermined feedback sampling frequency will not exceed the system resource limit. The new feedback sampling frequency is calculated by the above formula. The system can dynamically adjust the feedback sampling frequency according to the current photovoltaic power generation stability, the urgency of tea tree growth needs, and changes in climatic conditions. In the case of drastic environmental changes or high irrigation needs, increasing the sampling frequency can help the system respond to changes quickly, thereby ensuring that irrigation needs are met in a timely manner; in the case of stable environment, the system can maintain the current sampling frequency, and can actually reduce the sampling frequency to save resources. It can be reduced by the same proportion each time until an abnormal signal is generated and the sampling frequency of the last normal signal is used for operation sampling.

[0043] Embodiment 2: A tea garden automatic irrigation control system based on photovoltaic power generation, comprising: The information collection module collects corresponding photovoltaic power generation information, tea tree growth irrigation cycle stage information and meteorological condition information in real time through multiple types of sensors; The information processing module formats the collected information to ensure the usability of the information; The information hysteresis analysis module performs information hysteresis analysis based on the processed information. According to the results of the information hysteresis analysis, the early signs of coupling feedback hysteresis in the coordinated control process of photovoltaic power generation and automatic irrigation of tea gardens are identified; The feature extraction module extracts three types of features, namely power supply fluctuation, urgency of tea tree irrigation demand, and climate and environmental change trend, based on the identified early signs of coupling feedback lag. It then generates the photovoltaic power generation volatility index, tea tree growth cycle demand index, and climate and environmental change index respectively. The feedback sampling cycle division module divides the photovoltaic power generation and tea garden automatic irrigation collaborative control process through fuzzy logic, and divides the current feedback sampling cycle into high-quality feedback cycle and low-quality feedback cycle; The collaborative control optimization and adjustment module re-determines the feedback sampling frequency based on the current feedback sampling frequency for low-quality feedback cycles.

[0044] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0045] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0046] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0047] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

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

Claims

1. A tea garden automatic irrigation control method based on photovoltaic power generation, characterized in that: The following steps are involved: Multiple types of sensors are used to collect corresponding photovoltaic power generation information, tea tree growth irrigation cycle stage information, and meteorological conditions information in real time, and the collected information is formatted to ensure the availability of the information; Based on the processed information, information hysteresis analysis is performed, and according to the results of information hysteresis analysis, early signs of coupling feedback hysteresis in the coordinated control process of photovoltaic power generation and automatic irrigation in tea gardens are identified; Based on the early signs of coupled feedback lag identified, three types of features were extracted: power supply fluctuations, urgency of tea tree irrigation demand, and climate and environmental change trends. Then, the photovoltaic power generation volatility index, tea tree growth cycle demand index, and climate and environmental change index were generated respectively. The photovoltaic power generation and tea garden automatic irrigation coordinated control process is divided into operations through fuzzy logic, and the current feedback sampling cycle is divided into high-quality feedback cycle and low-quality feedback cycle. For a low-quality feedback cycle, the feedback sampling frequency is re-determined based on the current feedback sampling frequency.

2. The automatic irrigation control method for tea garden based on photovoltaic power generation according to claim 1, characterized in that: Information lag analysis refers to: In each feedback sampling cycle t, the following data are recorded: the power generation of photovoltaic power generation Pt, the power demand of automatic irrigation of tea garden Dt, and the response deviation value of the coordinated control process of photovoltaic power generation and automatic irrigation of tea garden is defined as Et in cycle t: ; A feedback hysteresis threshold θ is preset, and the feedback hysteresis value is calculated within a fixed time window W: ; is the feedback hysteresis value.

3. The automatic irrigation control method for tea garden based on photovoltaic power generation according to claim 2 is characterized in that: According to the results of information hysteresis analysis, the early signs of coupling feedback hysteresis in the coordinated control of photovoltaic power generation and automatic irrigation of tea gardens are identified as follows: Feedback hysteresis value Compared with the preset feedback timeliness threshold, if the feedback lags behind the If the feedback lag value is greater than the preset feedback timeliness threshold, an abnormal signal is generated. If it is less than or equal to the preset feedback timeliness threshold, a normal signal is generated. When an abnormal signal is generated, it indicates that there are early signs of coupling feedback lag in the coordinated control process of photovoltaic power generation and automatic irrigation in tea gardens.

4. The automatic irrigation control method for tea garden based on photovoltaic power generation according to claim 3 is characterized in that: The logic for obtaining the photovoltaic power generation volatility index is: Assume that the photovoltaic power generation at each time point t is PGt, and the change of power generation is counted within a fixed time window W to obtain the time series of photovoltaic power generation; Calculate the power generation difference between every two adjacent time points in the current time window : ； ； Calculate the standard deviation of power generation fluctuation within the window , and the average fluctuation of power generation : ; ; The formula for calculating the photovoltaic power generation volatility index is: ; Represents the photovoltaic power generation volatility index.

5. The method for controlling automatic irrigation in a tea garden based on photovoltaic power generation according to claim 4, characterized in that: The logic for obtaining the tea tree growth cycle demand index is: Assume that the growth cycle of tea trees is C, where C contains multiple stages. The water demand of each stage is different. The demand weight of the i-th stage is wi, and the daily average irrigation demand corresponding to each stage is Di. The deviation of irrigation demand growth ΔDi is calculated: ; ; Indicates the duration of the current phase in days; In all growth stages, calculate the average of the total demand weight WD and the demand growth deviation in each stage : ; ; n represents the total number of stages in the tea tree growth cycle; The formula for calculating the tea tree growth cycle demand index is: ; Represents the tea tree growth cycle demand index.

6. The automatic irrigation control method for tea garden based on photovoltaic power generation according to claim 5, characterized in that: The logic for obtaining the climate and environmental change index is: Assume that the climate parameter at each time point t is Qt. Within a fixed time window W, for each climate parameter Qt, calculate the standard deviation and record it as , and calculate the rate of change ΔQt: ; ; Calculate the average trend rate of change within the current time window And the climate environment shift coefficient Qshift: ; ; The calculation formula of climate environment change coefficient is: ; represents the coefficient of climate environment variation; The single parameter climate change coefficient Perform weighted summation to obtain the climate and environmental change index: ; The number representing the climate parameter, Indicates the number The corresponding single parameter climate change coefficient, Indicates the number The corresponding proportionality coefficient is, Represents the climate environment change index.

7. The method for controlling automatic irrigation in a tea garden based on photovoltaic power generation according to claim 6, characterized in that: The photovoltaic power generation and tea garden automatic irrigation coordinated control process is divided by fuzzy logic, and the current feedback sampling cycle is divided into high-quality feedback cycle and low-quality feedback cycle. In the process of coordinated control of photovoltaic power generation and automatic irrigation of tea gardens, the photovoltaic power generation volatility index, the tea tree growth cycle demand index, the climate and environmental change index, and the current feedback sampling frequency are taken as input variables, and the current feedback sampling period division type is taken as the output variable. The input variables are fuzzified and the values ​​of the input variables are converted into fuzzy sets. The output variables are fuzzified and the output variables are converted into fuzzy sets. Fuzzy rules are formulated to describe the adaptability of the feedback sampling period type under different data type combinations. The fuzzified input variables are inferred through fuzzy rules to obtain the current feedback sampling period division type in the process of coordinated control of photovoltaic power generation and automatic irrigation of tea gardens.

8. The automatic irrigation control method for tea garden based on photovoltaic power generation according to claim 7, characterized in that: For a low-quality feedback cycle, re-determining the feedback sampling frequency based on the current feedback sampling frequency means: Assume the current feedback sampling frequency is , the photovoltaic power generation volatility index, tea tree growth cycle demand index, and climate environment change index are combined to generate an adjustment factor for the comprehensive feedback frequency: ; is the total number of index types, An index number representing the index type, Indicates that the index number is The corresponding adjustment coefficient is Indicates that the index number is The corresponding preset standard value is Indicates that the index number is The corresponding exponential value is represents the adjustment factor; The formula for re-determining the feedback sampling frequency is: ; represents the feedback sampling frequency after redetermining, It indicates the upper limit of the maximum sampling frequency set in the coordinated control process of photovoltaic power generation and automatic irrigation of tea garden.

9. A tea garden automatic irrigation control system based on photovoltaic power generation, which is implemented based on the tea garden automatic irrigation control method based on photovoltaic power generation according to any one of claims 1 to 8, characterized in that: include: The information collection module collects corresponding photovoltaic power generation information, tea tree growth irrigation cycle stage information and meteorological condition information in real time through multiple types of sensors; The information processing module formats the collected information to ensure the usability of the information; The information hysteresis analysis module performs information hysteresis analysis based on the processed information. According to the results of the information hysteresis analysis, the early signs of coupling feedback hysteresis in the coordinated control process of photovoltaic power generation and automatic irrigation of tea gardens are identified; The feature extraction module extracts three types of features, namely power supply fluctuation, urgency of tea tree irrigation demand, and climate and environmental change trend, based on the identified early signs of coupling feedback lag. It then generates the photovoltaic power generation volatility index, tea tree growth cycle demand index, and climate and environmental change index respectively. The feedback sampling cycle division module divides the photovoltaic power generation and tea garden automatic irrigation collaborative control process through fuzzy logic, and divides the current feedback sampling cycle into high-quality feedback cycle and low-quality feedback cycle; The collaborative control optimization and adjustment module re-determines the feedback sampling frequency based on the current feedback sampling frequency for low-quality feedback cycles.

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