Modularized system for planting shade-tolerant commercial crops under plateau photovoltaic panel

Abstract

The invention relates to the technical field of agricultural planting, in particular to a modular system for planting shade-tolerant commercial crops under a plateau photovoltaic panel, which comprises a region division unit used for dividing planting regions according to layout parameters supported by the photovoltaic panel, screening shade-tolerant commercial crop varieties according to a shading gradient adaptation principle of partition vegetation configuration, and storing the shade-tolerant commercial crop varieties in a greenhouse; sensing units carried by the planting modules are used for collecting plateau environment data, soil basic data and photovoltaic panel shading characteristic data, and the microenvironment adaptive weight of each planting partition is obtained; and the area judgment unit is used for judging whether each planting partition is in a shade-tolerant crop growth adaptation interval or not according to the microenvironment adaptation weight. According to the method, the soil microenvironment adjusting requirement is calculated according to the microenvironment adaptive weight, the soil adjusting system driven by solar refrigeration is started in time for temperature and humidity regulation, the adaptability of crop growth is improved, dependence on traditional energy is reduced, and the sustainability of the system is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of agricultural planting, and in particular to a highland photovoltaic panel under shade-tolerant economic crop planting modular system. BACKGROUND

[0002] Shade-tolerant economic crops refer to crops that can grow in relatively weak light or dark environments and have certain economic value; such crops can adapt to shade or lower light conditions and still maintain good growth conditions in environments with more shade, such as under photovoltaic panels or in the shade of forests, with high yield and economic benefits.

[0003] Currently, traditional systems often ignore the influence of photovoltaic panel layout and highland-specific environment, such as sunshine duration and diurnal temperature difference, on crop growth, and often use a unified planting scheme, which may not provide the optimal growth conditions for crops in different microenvironments; the inconsistent light, shade, and temperature and humidity conditions in different regions may lead to uneven crop growth, and even affect yield and quality; and traditional systems often lack a soil microenvironment adjustment mechanism based on real-time data analysis; the soil temperature and humidity in highland regions fluctuate greatly, and traditional systems often cannot adjust in real time according to the soil characteristics and environmental changes in different planting zones, resulting in soil conditions that are not suitable for crop growth, and the system cannot effectively intervene and adjust the environment; soil adjustment often relies on traditional energy or relatively crude control methods, and precise control cannot be achieved.

[0004] In addition, the irrigation of traditional systems is often timed and quantified, ignoring real-time changes in soil humidity and specific needs of crop growth, resulting in water waste or insufficient irrigation; water resources are scarce in highland regions, and traditional irrigation modes often cannot precisely adjust according to the microenvironment and soil needs, resulting in uneven or wasted water supply. SUMMARY

[0005] The main purpose of the present application is to provide a highland photovoltaic panel under shade-tolerant economic crop planting modular system to solve the problem of lack of a soil microenvironment adjustment mechanism based on real-time data analysis in the prior art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical scheme: A highland photovoltaic panel under shade-tolerant economic crop planting modular system, comprising: a region division unit for dividing planting zones according to layout parameters of photovoltaic panel supports, screening shade-tolerant economic crop varieties through a shade gradient adaptation principle of zoned vegetation configuration, and collecting highland environment data, soil basic data, and photovoltaic panel shade characteristic data using a sensing unit carried by a planting module to obtain microenvironment adaptation weights of various planting zones; The region determining unit is configured to determine whether each planting region is in a shade-tolerant crop growth adaptation interval according to the micro-environment adaptation weight. The state monitoring unit is configured to monitor the growth state of crops in the planting region in the non-growth adaptation interval, collect crop leaf physiological data and soil temperature and humidity dynamic data in real time through the planting module, and obtain a soil micro-environment adjustment demand value according to the micro-environment adaptation weight. The environment adjusting unit is configured to determine whether the planting region in the non-growth adaptation interval needs to start a soil micro-environment adjusting system driven by solar refrigeration according to the soil micro-environment adjustment demand value. The irrigation evaluation unit is configured to adjust the operation state of the planting module in the corresponding planting region according to the determination result, evaluate and process irrigation parameters of the soil micro-environment adjustment demand value through an intelligent irrigation decision model, and obtain monitoring deviation data. The region layout unit is configured to statistically analyze the monitoring deviation data, determine a micro-environment adaptation abnormal region in the planting region in the non-growth adaptation interval, and adjust a region vegetation configuration scheme and a planting module layout parameter after statistically analyzing crop growth indexes in the micro-environment adaptation abnormal region.

[0007] Preferably, the planting regions are divided according to the layout parameters of the photovoltaic panel support, shade-tolerant economic crop varieties are screened through the shade gradient adaptation principle of the region vegetation configuration, highland environment data, soil basic data and photovoltaic panel shade characteristic data are collected by the sensing unit carried by the planting module, and the micro-environment adaptation weight of each planting region is obtained, including: The installation interval, inclination and height parameters of the photovoltaic panel support are obtained, combined with the environmental characteristics of the plateau region, wherein the environmental characteristics include sunshine duration, wind speed and diurnal temperature difference, a plurality of planting regions are divided according to the shade gradient adaptation principle of the region vegetation configuration, the shade duration distribution and the light intensity variation law of each planting region are dynamically identified, and the shade adaptation deviation of each planting region in the overall planting region is determined. The soil basic data, variety characteristic data and shade characteristic data of each planting region are collected by the sensing unit carried by the planting module, and the micro-environment adaptation weight of each planting region is obtained, wherein the soil basic data includes soil organic matter content, soil porosity and soil water content, the variety characteristic data includes crop leaf shade tolerance threshold and photosynthetic efficiency critical value, and the shade characteristic data includes photovoltaic panel shade period and shade area.

[0008] Preferably, whether each planting region is in a shade-tolerant crop growth adaptation interval is determined according to the micro-environment adaptation weight, including: The soil basic data, the variety characteristic data and the shading characteristic data are comprehensively processed to determine soil environment fluctuation ranges and light condition fluctuation ranges of various planting areas in a continuous monitoring period; If the soil environment fluctuation range or the light condition fluctuation range exceeds a preset adaptive threshold, it is determined that the planting area is in a non-growth adaptive interval; otherwise, it is determined that the planting area is in a growth adaptive interval.

[0009] Preferably, the planting area in the non-growth adaptive interval is subjected to crop growth state monitoring, and crop leaf physiological data and soil temperature and humidity dynamic data are collected in real time by the planting module, and a soil microenvironment adjustment requirement value is obtained according to the microenvironment adaptive weight, including: The crop in the non-growth adaptive interval planting area is subjected to growth state evaluation by a shade-tolerant crop growth state control standard, and growth indexes such as leaf chlorophyll content, leaf water content and plant height growth rate are counted; According to the correlation between the soil basic data and the shading characteristic data in the microenvironment adaptive weight, the coupling relationship between soil temperature, humidity and crop growth indexes is analyzed in combination with the real-time collected soil temperature and humidity dynamic data, and the key influencing factors of the formation of the non-growth adaptive interval are found out; The soil temperature and humidity regulation difference of each non-growth adaptive interval planting area is quantitatively calculated to obtain the soil microenvironment adjustment requirement value.

[0010] Preferably, according to the soil microenvironment adjustment requirement value, it is determined whether the non-growth adaptive interval planting area needs to start the solar refrigeration driven soil microenvironment adjustment system, including: The soil temperature regulation difference in the soil microenvironment adjustment requirement value is analyzed to determine whether the difference is related to soil cumulative temperature anomaly in the photovoltaic panel shading period; The soil humidity regulation difference is counted to determine whether the soil humidity anomaly presents a concentrated distribution characteristic within the photovoltaic panel shading coverage range; if yes, it is determined that the planting area needs to start the solar refrigeration driven soil microenvironment adjustment system; otherwise, it is determined that the planting area does not need to start the solar refrigeration driven soil microenvironment adjustment system.

[0011] Preferably, the running state of the corresponding planting module is adjusted according to the judgment result; the soil microenvironment adjustment requirement value is subjected to irrigation parameter evaluation processing by an intelligent irrigation decision model to obtain monitoring deviation data, including: When the non-growth adaptive interval planting area does not need to start the solar refrigeration driven soil microenvironment adjustment system, the running state of the corresponding planting module is switched to a basic regulation mode according to the soil microenvironment adjustment requirement value; When the planting subzone in the non-growth adaptation interval needs to start the soil microenvironment regulation system driven by solar refrigeration, the operation state of the corresponding planting module is switched to the intensive control mode according to the soil microenvironment regulation demand value, and the solar refrigeration module and the soil temperature and humidity regulation assembly are activated synchronously. According to the shading adaptation deviation of each planting module included in the soil microenvironment regulation demand value, irrigation parameter calculation instructions are sent to the planting modules of each planting subzone, the irrigation parameter calculation instructions are sampled and intercepted by the sensing units of each planting module, and the intelligent irrigation decision model is fed back to obtain monitoring deviation data.

[0012] Preferably, the monitoring deviation data is statistically analyzed to determine the microenvironment adaptation abnormal area in the non-growth adaptation interval planting subzone, including: The monitoring deviation data is analyzed and processed to obtain the monitoring deviation occurrence period of the sensing units of each planting module. The monitoring deviation occurrence period is counted to determine whether there is intelligent irrigation execution delay in the monitoring deviation occurrence period; if there is, the corresponding planting subzone is determined as the microenvironment adaptation abnormal area; the shading adaptation deviation of all the microenvironment adaptation abnormal areas in the non-growth adaptation interval and the shading adaptation deviation of the planting modules of all non-abnormal areas are determined.

[0013] Preferably, after the crop growth index statistics of the microenvironment adaptation abnormal area, the subzone vegetation configuration scheme and the planting module layout parameters are adjusted, including: According to the shading adaptation deviation of the planting modules of all non-abnormal areas in the non-growth adaptation interval, the planting modules of all non-abnormal areas are adaptively calibrated. According to the shading adaptation deviation of all the microenvironment adaptation abnormal areas in the non-growth adaptation interval, the shading adaptation deviation of the planting subzone adjacent to all the abnormal areas is determined, the subzone vegetation configuration type of the adjacent planting subzone is converted to a more resistant variety type, and the crop growth index statistics of all the microenvironment adaptation abnormal areas are performed. According to the crop growth index statistics result, the subzone vegetation configuration scheme and the installation spacing, height and other layout parameters of the planting module are adjusted.

[0014] The application divides different planting sub-zones according to the layout parameters of the photovoltaic panel and the environment data specific to the plateau, and combines the shading gradient adaptation principle, so that the system can screen out the most suitable shade-tolerant crop varieties for growth in a specific microenvironment; and through dynamic identification of the shading distribution and the light change rule, the adaptability of crop growth in the special environment of the plateau can be maximized, and the negative impact on the environment can be reduced; and through the inadaptation of the soil and light environment, the system calculates the soil microenvironment adjustment demand according to the microenvironment adaptation weight, and timely enables the soil adjustment system driven by solar refrigeration to control the temperature and humidity, which not only improves the adaptability of crop growth, but also reduces the dependence on traditional energy and enhances the sustainability of the system; and by combining the intelligent irrigation decision model, the system can automatically adjust the irrigation parameters according to the demand of the soil microenvironment, realize precise irrigation, avoid water waste, optimize the water supply of crops, and ensure that the crops get sufficient water supply in the water-deficient plateau environment; and through statistical analysis of the monitoring deviation data, the system can dynamically adjust the layout of the planting sub-zone, the plant variety configuration and the layout parameters of the photovoltaic panel, so as to ensure that the microenvironment difference between different regions is effectively corrected and optimized, thereby improving the overall growth performance of crops. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 It is a step flowchart of an embodiment of a high-altitude photovoltaic panel under shade-tolerant economic crop planting modular system.

[0016] Reference signs: 1, region division unit; 2, region judgment unit; 3, state monitoring unit; 4, environment adjustment unit; 5, irrigation evaluation unit; 6, region layout unit. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application.

[0018] As shown in the drawings, Figure 1 A high-altitude photovoltaic panel under shade-tolerant economic crop planting modular system proposed by the application includes: The region division unit 1 is used to divide the planting sub-zone based on the layout parameters of the photovoltaic panel support, screen the shade-tolerant economic crop varieties through the sub-zone vegetation configuration principle, and collect the high-altitude environment data, soil basic data and photovoltaic panel shading characteristic data by using the sensing unit carried by the planting module, to obtain the microenvironment adaptation weight of each planting sub-zone; The region judgment unit 2 is used to judge whether each planting sub-zone is in the shade-tolerant crop growth adaptation interval according to the microenvironment adaptation weight; The state monitoring unit 3 is used for monitoring the crop growth state of the planting subzone in the non-growth adaptation interval, collecting the physiological data of the crop leaves and the dynamic data of the soil temperature and humidity in real time through the planting module, and obtaining the soil microenvironment adjustment demand value according to the microenvironment adaptation weight; The environment regulation unit 4 is used for judging whether the soil microenvironment adjustment system driven by the solar refrigeration needs to be started in the planting subzone in the non-growth adaptation interval according to the soil microenvironment adjustment demand value; The irrigation evaluation unit 5 is used for adjusting the operation state of the planting module of the corresponding planting subzone according to the judgment result, and obtaining the monitoring deviation data by performing irrigation parameter evaluation processing on the soil microenvironment adjustment demand value through the intelligent irrigation decision model. The regional layout unit 6 is used for statistically analyzing the monitoring deviation data, determining the microenvironment adaptation abnormal region in the planting subzone in the non-growth adaptation interval, and adjusting the subzone vegetation configuration scheme and the planting module layout parameter after the crop growth index of the microenvironment adaptation abnormal region is counted.

[0019] It should be noted that the region division unit divides the planting region according to the support layout of the photovoltaic panel; each divided region will be configured with plants according to certain principles, and these principles are based on the requirements of crop shade tolerance; the layout of the photovoltaic panel will affect the light condition of each region, so it is crucial to select appropriate shade-tolerant economic crops; by collecting and analyzing the highland environment data, the soil data and the photovoltaic panel shading characteristic data, the system can determine the microenvironment adaptation weight of each region, that is, which crops are suitable for planting in these regions; the region judgment unit judges whether each divided region is suitable for the growth of the selected shade-tolerant crops according to the microenvironment adaptation weight; if the environmental conditions of a certain region are not suitable for the growth of crops, the region will be marked as an unsuitable region, and the system will enter the next stage for processing; for those planting regions in the non-adaptation interval, the state monitoring unit will be started to monitor the crop growth state; through the planting module, the system collects the physiological data of the crop leaves and the temperature and humidity data of the soil in real time; these data will help the system to identify the deviation between the current environmental conditions and the crop demand, and further evaluate whether the soil needs to be adjusted according to the microenvironment adaptation weight; If it is found that the soil environment in a certain area does not meet the needs of crop growth, the environmental regulation unit will determine whether it is necessary to start the soil microenvironment regulation system; this system uses solar-driven refrigeration technology to regulate soil temperature and humidity to ensure that crops can grow in the best environment; this unit can actively adjust the temperature and humidity to keep the crops in suitable growth conditions; the task of the irrigation evaluation unit is to evaluate the current soil microenvironment regulation needs through an intelligent irrigation decision model; the system will adjust the irrigation parameters, such as water quantity and irrigation frequency, according to the regulation needs of the microenvironment to ensure that the soil maintains appropriate humidity; at this time, the irrigation system will automatically adjust according to the system's judgment and generate monitoring deviation data for further analysis and optimization; finally, the regional layout unit will statistically analyze the monitored deviation data to identify areas with poor microenvironment adaptation; through the statistics of crop growth indicators, the system will adjust the vegetation configuration scheme of these areas and optimize the layout parameters of the planting module; in this way, the microenvironment of these areas can be further improved to ensure that all planting areas can provide suitable environmental conditions for crop growth.

[0020] In an optional embodiment, the planting subareas are divided according to the layout parameters of the photovoltaic panel support, the shade gradient adaptation principle of the subarea vegetation configuration is used to screen shade-tolerant economic crop varieties, the high-altitude environment data, soil basic data, and photovoltaic panel shade characteristic data are collected by the sensing unit carried by the planting module to obtain the microenvironment adaptation weight of each planting subarea, including: The installation interval, inclination angle, and height parameters of the photovoltaic panel support are obtained, combined with the environmental characteristics of the plateau region, wherein the environmental characteristics include sunshine duration, wind speed, and diurnal temperature difference, a plurality of planting subareas are divided according to the shade gradient adaptation principle of the subarea vegetation configuration, the shade duration distribution and light intensity variation law of each planting subarea are dynamically identified to determine the shade adaptation deviation of each planting subarea in the overall planting area; The soil basic data, variety characteristic data, and shade characteristic data of each planting subarea are collected by the sensing unit carried by the planting module to obtain the microenvironment adaptation weight of each planting subarea, wherein the soil basic data includes soil organic matter content, soil porosity, and soil water content, the variety characteristic data includes crop leaf shade tolerance threshold and photosynthetic efficiency critical value, and the shade characteristic data includes photovoltaic panel shade period and shade area; The calculation formula of the microenvironment adaptation weight is as follows: ; Wherein, represents the microenvironment adaptation weight, represents the soil basic data weight coefficient, represents the standardized soil basic data, represents the variety characteristic data weight coefficient, representing standardized variety characteristic data, representing shading characteristic data weight coefficients, representing standardized shading characteristic data.

[0021] It should be noted that the distance between the support structures of the photovoltaic panels will affect the light distribution and shading of each area; reasonable spacing can reduce the shading between photovoltaic panels, allowing sunlight to reach the leaf surface of the plants, helping crops to carry out photosynthesis; the inclination angle of the photovoltaic panels affects the efficiency of solar energy capture and also directly affects the shading of each area; in plateau areas, the sunshine duration is longer, so choosing an appropriate inclination angle helps the photovoltaic panels to obtain more solar energy during the day, while avoiding excessive shading of crops; the support height of the photovoltaic panels will directly determine the degree of shading of the ground area; too low height may lead to excessive shading, which is not conducive to the growth of crops; while too high height may result in insufficient solar radiation captured by the photovoltaic panels; plateau areas usually have longer sunshine duration, which is beneficial for both photovoltaic panel power generation and crop growth; however, excessive sunlight may be too intense for some crops, especially those that require shade; changes in sunshine duration will affect the shading effect of photovoltaic panels, and these areas need to be managed carefully; the wind speed in plateau areas is relatively high, which may affect the stability of the installation of photovoltaic panels and the growth of crops; strong winds may cause soil moisture to evaporate too quickly, affecting crop growth; therefore, wind speed is also a key factor in environmental characteristics; the large diurnal temperature difference in plateau areas may affect soil temperature and humidity, thus affecting the growth status of crops; changes in diurnal temperature difference may also affect the physiological activities of crops, such as photosynthesis, respiration, etc., so it is necessary to adjust the soil and environmental conditions to adapt to this feature; Based on the installation parameters of the photovoltaic panels, such as spacing, tilt angle, and height, and environmental characteristics, such as sunshine duration, wind speed, and diurnal temperature range, the system divides the planting area into several planting zones. These zones are divided according to the shading gradient of the photovoltaic panels, meaning each zone receives different amounts of sunlight in terms of intensity and duration. The system selects suitable crops for each zone based on their respective sunlight and shade tolerance requirements, ensuring these crops can grow well in the shaded environment of the photovoltaic panels. The core of the shading gradient adaptation principle is to balance the crop's light requirements with the photovoltaic panels' impact on the environment, ensuring crops receive appropriate sunlight in different areas. The system dynamically identifies the light intensity and shading duration of each planting zone, allowing for real-time monitoring and analysis of the shading and light conditions of each zone. For example, some areas may be shaded by photovoltaic panels during certain times of the day, resulting in reduced light intensity, while other times receive sufficient sunlight. Through dynamic identification, the system can... The system continuously optimizes the light requirements of crops in different zones. Based on this identification, the system calculates the shading adaptation deviation of each zone within the entire planting area. This means that the shading effect of photovoltaic panels may cause a certain area to deviate from its ideal growing environment. This deviation will affect crop growth, so it is necessary to reduce this deviation by adjusting the photovoltaic panels or the soil environment. Soil basic data: Organic matter content affects the soil's water retention capacity and nutrient supply, which is an important factor affecting crop growth. Areas with higher soil organic matter content can better retain water and nutrients, thereby improving the crop's resistance to stress. Soil porosity affects the flow of water and air, which in turn affects root growth and crop nutrient absorption. High-porosity soil can provide better aeration conditions, promoting root health. Soil moisture content directly affects the crop's growth status. Both excessively dry and excessively wet soil will affect crop growth and need to be regulated through appropriate irrigation and soil management. Variety characteristic data: different crops have different light requirements; shade-tolerant crops can maintain normal photosynthesis under low light conditions; the system obtains the shade tolerance threshold data of the crops to determine whether a certain area is suitable for planting a specific crop; photosynthesis is the basis of plant growth; the system monitors the photosynthetic efficiency of the crops and compares it with the critical value to determine whether the environment is suitable for the growth of the crops; shading characteristic data: photovoltaic panel shading period refers to the time period when the photovoltaic panel blocks sunlight within a day; these data help analyze the light changes in a certain area and the impact of the shading mode of the photovoltaic panel on the crops; shading area refers to the shading area of the photovoltaic panel on the ground planting area at different time periods; the size of the shading area directly affects the light receiving amount of the crops, and thus affects the growth; based on the above collected data, the system will calculate the microenvironment adaptation weight of each planting subarea, which is an index indicating whether each area is suitable for the growth of a certain crop under specific environmental conditions; these weights are calculated by weighting the soil data, crop characteristics, and photovoltaic panel shading characteristics to obtain a comprehensive score; through the microenvironment adaptation weight, the system can effectively evaluate whether each subarea is suitable for planting a specific variety.

[0022] In an optional embodiment, according to the microenvironment adaptation weight, it is determined whether each planting subarea is in a shade-tolerant crop growth adaptation interval, comprising: comprehensively processing the soil basic data, variety characteristic data, and shading characteristic data to determine the soil environment fluctuation range and light condition fluctuation range of each planting subarea within a continuous monitoring period; If the soil environment fluctuation range or the light condition fluctuation range exceeds the preset adaptation threshold, it is determined that the planting subarea is in a non-growth adaptation interval; otherwise, it is determined that the planting subarea is in a growth adaptation interval.

[0023] It is to be noted that by integrating the soil base data, the variety characteristic data and the shading characteristic data, the system analyzes the environmental fluctuation of each planting subzone, especially the changes in soil environment and light conditions; the soil environment fluctuation range refers to how the various indicators of the soil, such as soil organic matter content, porosity and water content, change within a continuous monitoring period, such as a week or a day; the soil environment fluctuation is caused by external climate changes, such as precipitation, temperature changes or human factors, such as irrigation, fertilization; for example, the soil water content in a certain area may have a large fluctuation during the day and night, or a slight change in soil organic matter due to different weather conditions; for some sensitive crops, excessive fluctuation may affect their growth, especially during the root development stage of the crops; the light condition fluctuation range refers to the changes in light intensity and sunshine duration within the monitoring period; due to the shading effect of the photovoltaic panels, seasonal changes and different geographical locations, the light conditions of each planting subzone will fluctuate; for example, the shading of the photovoltaic panels may cause the light intensity in some areas to decrease during certain periods, especially at noon and in the afternoon; different light conditions will affect the photosynthetic efficiency of the crops, therefore, the size of the light fluctuation range is crucial to the growth of the crops; The system sets an adaptation threshold, i.e. the soil environment fluctuation and the light condition fluctuation of a certain area within the range required for the growth of the crops; if the environmental fluctuation is too large, for example, the soil moisture fluctuation is too large, the light intensity fluctuation is too large, it may affect the normal growth of the crops, at this time, it is considered that the area is not suitable for the growth of the crops; these thresholds are set according to the growth requirements of the crops and the variety characteristics; for example, some crops are very sensitive to changes in soil moisture, therefore, the fluctuation of the soil water content cannot exceed a certain range; similarly, some crops have poor adaptability to light changes, too many shading periods or too low light intensity may cause the photosynthetic efficiency of the crops to decrease; if the soil environment fluctuation range or the light condition fluctuation range exceeds the preset adaptation threshold, the system will determine that the planting subzone is not suitable for the growth of the crops; that is, the environment is too extreme, which may cause the crops to fail to grow smoothly; if the soil environment fluctuation range and the light condition fluctuation range are within the adaptation threshold range, the planting subzone is within the growth adaptation interval; at this time, the environmental conditions are considered suitable for the normal growth of the crops; Assuming that a planting subzone in a monitoring period, the system collects the following data: in the monitoring period, the soil moisture content fluctuates greatly, from 30% to 70%, the organic matter content changes little, between 10%-12%, and the porosity remains within the standard range; the solar intensity of this area increases from 80% (morning) to 100%, then the photovoltaic panel begins to shade at noon, the light intensity drops to 70%, and at night it is 0%; in this case, the system will calculate the fluctuation range of soil moisture content to see if it exceeds the tolerance range of crops; at the same time, the fluctuation of light intensity will also be monitored to determine whether the crop is suitable for planting in this subzone; if the fluctuation of soil moisture content exceeds the tolerance range of crops, such as crops can only tolerate 10% fluctuation, but now the fluctuation is 40%, or the light intensity fluctuation is too large, resulting in reduced photosynthetic efficiency of crops, then the area will be determined as a non-growth adaptation interval; on the contrary, if the fluctuations are within the normal range, the system will consider that the subzone is in the growth adaptation interval and that the environment is suitable for crop growth.

[0024] In an optional embodiment, the crop growth state of the planting subzone in the non-growth adaptation interval is monitored, and the physiological data of the crop leaves and the dynamic data of the soil temperature and humidity are collected in real time by the planting module, and the soil microenvironment adjustment requirement value is obtained according to the microenvironment adaptation weight, including: The growth state of the crop in the non-growth adaptation interval planting subzone is evaluated by the shade-tolerant crop growth state reference standard, and the growth indicators such as leaf chlorophyll content, leaf water content, and plant height growth rate are counted; According to the correlation between the soil basic data and the shading characteristic data in the microenvironment adaptation weight, combined with the real-time collected soil temperature and humidity dynamic data, the coupling relationship between soil temperature, humidity and crop growth indicators is analyzed, and the key influencing factors of the formation of the non-growth adaptation interval are found out; The soil temperature and humidity regulation difference of each non-growth adaptation interval planting subzone is quantitatively calculated to obtain the soil microenvironment adjustment requirement value.

[0025] It should be noted that shade-tolerant crops refer to crops that can grow in environments with weak light; they have strong adaptability to shade or low light conditions; when some planting zones are determined to be non-growth adaptation intervals, the growth of crops in these areas may be adversely affected; in order to assess the health and growth quality of these crops, standardized growth indicators need to be checked; common growth indicators include: chlorophyll is a key substance for photosynthesis, and its content reflects the photosynthetic efficiency of crops; low chlorophyll content may indicate poor crop growth, which may be related to insufficient light or other environmental factors; leaf water content directly affects the transpiration, photosynthesis and growth state of crops; if the leaf water content is too low, it may be caused by insufficient water supply or unsuitable soil environment; plant height is an important indicator of plant growth, and the growth rate of plant height can directly reflect the growth speed and health status of crops; if the plant height growth is slow, it may indicate that the environment is not suitable or that the growth of crops is limited; by monitoring and statistical analysis of these growth indicators, the actual growth status of crops in non-adaptation intervals can be evaluated, and the influence of soil and light on crop growth can be judged; Micro-environment adaptation weight refers to the degree of influence of various factors such as soil basic data and shade characteristics on crop growth in the planting environment; different factors have different adaptation weights in different planting areas, so to evaluate the growth status of crops, it is necessary to understand the relevance and relative importance of these factors; soil data includes soil temperature, humidity, nutrient composition, etc., while shade characteristic data includes light intensity, shade duration, etc.; these factors are interrelated, soil humidity and light intensity may interact at certain times to affect crop growth; for example, when soil humidity is high, crops may require less light, and vice versa, they may require more light to support photosynthesis; by dynamically monitoring the changes in soil temperature and humidity, the system can track the environmental conditions of the soil in real time and understand whether there are conditions that are not suitable for crop growth; especially in non-growth adaptation intervals, these data can help analyze the fluctuation range of the soil environment and how these fluctuations affect crop growth; Soil temperature and humidity directly affect crop water absorption, nutrient supply, and root development. Crop growth indicators, such as chlorophyll content, leaf water content, and plant height growth rate, are often closely coupled with these environmental factors. By analyzing the coupling relationship between soil temperature and humidity and these growth indicators, key factors leading to non-fitness zones can be identified. For example, excessively high soil temperature or low humidity may cause crop leaves to wither and photosynthetic efficiency to decrease; conversely, excessive humidity may lead to root hypoxia, affecting water and nutrient absorption. This coupling relationship analysis helps to identify which factors are the root causes limiting normal crop growth in non-fitness zones. The soil temperature and humidity regulation difference refers to the gap between the current actual soil temperature and humidity and the ideal soil temperature and humidity required for crop growth. This difference can be quantified... Calculating this difference allows us to understand the degree of soil environment regulation needs in different planting areas. Regulation needs refer to the resources or measures required to adjust the soil environment to conditions suitable for crop growth. For example, if the soil temperature is too high and the humidity is too low, irrigation and cooling measures are needed; if the soil is too wet and the temperature is too low, drainage and heating measures may be necessary. By collecting soil data, such as soil temperature and humidity, and combining it with crop growth needs, mathematical modeling can be performed to calculate the soil microenvironment regulation needs of each planting area. This value reflects the extent of regulation and resource input required for the planting area under different environmental conditions. For example, if the temperature and humidity in a region exceed the crop's optimal growth range, we can calculate how much water and temperature regulation is needed to make the environment suitable for crop growth.

[0026] In an optional embodiment, based on the soil microenvironment regulation requirement value, it is determined whether the planting zone in the non-adapted growth area needs to activate the solar-cooled soil microenvironment regulation system, including: The soil temperature regulation difference in the soil microenvironment regulation demand value was analyzed to determine whether the difference is related to the abnormal soil accumulated temperature during the photovoltaic shading period. The difference in soil moisture regulation is statistically analyzed to determine whether the abnormal soil moisture shows a concentrated distribution characteristic within the shading coverage area of ​​the photovoltaic panel; if so, it is determined that the planting area needs to activate the solar-cooled soil microenvironment regulation system; if not, it is determined that the planting area does not need to activate the solar-cooled soil microenvironment regulation system. The formula for calculating the soil microenvironment regulation requirement is as follows: ; in, This indicates the soil microenvironment regulation requirement value. This represents the temperature control weighting coefficient. Indicates the actual soil temperature. Indicates the target soil temperature. denotes a humidity control weight coefficient, denotes actual soil humidity, denotes target soil humidity.

[0027] It should be noted that the soil temperature control difference refers to the difference between the actual soil temperature and the ideal temperature required for crop growth; for example, when the soil temperature is too high or too low, it will affect the root development and water absorption of crops, and therefore needs to be controlled; it is necessary to analyze whether this difference is related to the abnormal soil cumulative temperature in the shading period of the photovoltaic panel; the purpose of shading the photovoltaic panel is to reduce direct sunlight and reduce soil temperature, so whether the soil temperature in the shading period of the photovoltaic panel reaches an abnormal value, such as being too low or too high, is a key issue; cumulative temperature refers to the average temperature of the soil within a certain period of time, which is usually based on a certain period of time, such as a day, a week or a growing season; if the soil cumulative temperature is abnormal during the shading period of the photovoltaic panel, for example, much lower or much higher than the ideal temperature, it may have a negative impact on crop growth; it is necessary to compare the actual data of the soil temperature with the data in the shading period of the photovoltaic panel to confirm whether the soil temperature difference is large during these periods, and then to determine whether there is soil temperature abnormality due to shading; if the difference is too large, additional temperature control measures may be needed to ensure crop growth; The soil humidity control difference refers to the difference between the actual soil humidity and the ideal humidity required for crop growth; too high or too low humidity will affect crop growth, so it must be controlled within an appropriate range; it is necessary to statistically analyze the soil humidity control difference to confirm whether the soil humidity abnormality is concentrated in the coverage area of the photovoltaic panel shading; concentration distribution means that the area of soil humidity abnormality accounts for a large proportion in the shading area of the photovoltaic panel, and further measures may need to be taken to adjust it; the abnormality of soil humidity may be caused by the reduction of evaporation or excessive accumulation of water due to the shading of the photovoltaic panel; the shading of the photovoltaic panel will affect the evaporation process of the soil, which may cause abnormal fluctuations in the soil humidity; if the humidity in the shading area deviates significantly from the ideal value required for crop growth, it may affect the water absorption or root oxygen supply of crops; the difference in soil humidity between the shading area and the non-shading area is statistically analyzed; if it is found that the soil humidity is abnormal in the shading area and the concentration of these abnormalities has a significant impact, it means that special humidity control measures are needed in this area; according to the analysis results of the soil temperature and humidity control difference, it is determined whether the solar refrigeration driven soil microenvironment regulation system needs to be started; if the analysis results show that the soil temperature and humidity difference is related to the shading period of the photovoltaic panel, and the temperature and humidity abnormalities in these areas are significant, it may mean that the soil microenvironment in this area is not suitable for crop growth, and the regulation system may be needed to correct these abnormalities; if the soil temperature and humidity difference does not appear abnormal or the abnormality is not significant in the shading period of the photovoltaic panel, it can be inferred that the regulation system does not need to be started, because the soil environment is relatively suitable for crop growth; The solar refrigeration driven system is driven by solar panels to provide power for related equipment such as water pumps, fans, heating devices, etc. to adjust the soil temperature and humidity; for example, when the soil temperature is too high, the system can reduce the temperature by increasing water evaporation or cooling equipment; when the soil humidity is too low, the system can increase the soil humidity through an automatic irrigation system; if the soil temperature is too high or too low during the shading period of the photovoltaic panel, and the temperature control difference is large, the adjustment system may need to be started to control the temperature; if the soil humidity anomaly is concentrated in the shading area, and the humidity difference is large, it means that the area may be excessively dry or excessively wet, and the humidity control system may need to be adjusted; if the soil temperature and humidity fluctuate little during the shading period of the photovoltaic panel, and the temperature and humidity difference of these areas is small, it means that the soil microenvironment is suitable for crop growth, and no additional adjustment system needs to be started.

[0028] In an optional embodiment, the operation state of the planting module of the corresponding planting subarea is adjusted according to the judgment result; the soil microenvironment adjustment demand value is subjected to irrigation parameter evaluation processing through the intelligent irrigation decision model to obtain monitoring deviation data, including: When the planting subarea in the non-growth adaptation interval does not need to start the solar refrigeration driven soil microenvironment adjustment system, the operation state of the corresponding planting module is switched to the basic control mode according to the soil microenvironment adjustment demand value; When the planting subarea in the non-growth adaptation interval needs to start the solar refrigeration driven soil microenvironment adjustment system, the operation state of the corresponding planting module is switched to the intensive control mode according to the soil microenvironment adjustment demand value, and the solar refrigeration module and the soil temperature and humidity adjustment assembly are activated synchronously; According to the shading adaptation deviation of each planting module included in the soil microenvironment adjustment demand value, irrigation parameter calculation instructions are respectively sent to the planting modules of each planting subarea, the irrigation parameter calculation instructions of the sensing unit of each planting module are sampled and intercepted, and feedback is given to the intelligent irrigation decision model to obtain monitoring deviation data; The calculation formula of the monitoring deviation data is as follows: ; Wherein, The monitoring deviation data is represented by The actual irrigation parameter is represented by The predicted irrigation parameter is represented by The shading adaptation deviation coefficient is represented by

[0029] It should be noted that when the planting sub-area in the non-growth adaptation interval does not need to start the soil micro-environment adjustment system driven by solar refrigeration, the system will switch the operation state of the planting module to the basic control mode according to the soil micro-environment adjustment demand value; At this time, the control system may only perform basic temperature and humidity control to ensure that the soil environment is maintained within a certain normal range, without large-scale environmental adjustment; When the planting sub-area in the non-growth adaptation interval needs to start the soil micro-environment adjustment system driven by solar refrigeration, the system will switch the operation state of the planting module to the intensive control mode; In this mode, in addition to basic control, solar refrigeration modules and soil temperature and humidity adjustment components will also be activated, which can more accurately adjust the soil temperature and humidity, especially in extreme environmental conditions such as soil overheating or excessive dryness; In the intensive control mode, the solar refrigeration module and the soil temperature and humidity adjustment component will be activated; The solar refrigeration module uses solar energy to drive the cooling equipment to reduce the soil temperature and prevent the soil from overheating; And the soil temperature and humidity adjustment component adjusts the humidity by controlling irrigation, water evaporation or soil aeration to ensure that the soil remains within the ideal humidity range required by crops; The shading adaptation deviation refers to the difference between the actual light shading received by different planting modules in the environment and their ideal shading conditions; Different planting sub-areas may have uneven light and shading conditions due to reasons such as terrain, shading of photovoltaic panels, and climate change; Shading adaptation deviation reflects these differences and affects the control requirements of soil temperature and humidity; According to the shading adaptation deviation and the demand of soil micro-environment, the system will send irrigation parameter calculation instructions to different planting modules; The goal of these instructions is to calculate the required irrigation amount to ensure that the soil humidity is within the appropriate range, thereby improving the efficiency of crop growth; In each planting module, the system will monitor and collect data on the execution of the irrigation parameter calculation instructions through the sensor unit; The system will periodically extract these sensor data to determine whether the instructions are executed as expected; The intelligent irrigation decision-making model is an intelligent decision-making system based on data analysis and machine learning, which can make irrigation decisions based on sensor data; The system will optimize the irrigation strategy based on the collected irrigation parameter data and actual monitoring values such as soil humidity and temperature to ensure more accurate and economical use of water; Through the feedback of the intelligent irrigation decision-making model, the system will obtain monitoring deviation data; These data reflect the deviation between the actual irrigation result and the predetermined target; For example, if the system finds that the irrigation amount is too much or too little, or the soil humidity does not reach the ideal value, it will adjust and optimize future irrigation operations.

[0030] In an optional embodiment, the monitoring deviation data is statistically analyzed to determine the micro-environment adaptation abnormal area in the non-growth adaptation interval planting sub-area, comprising: The monitoring deviation data is analyzed and processed to obtain the monitoring deviation occurrence period of the sensor unit of each planting module; The monitoring deviation occurrence period is counted to determine whether there is an intelligent irrigation execution delay in the monitoring deviation occurrence period; if there is, the corresponding planting partition is determined as a microenvironment adaptation abnormal area; the shading adaptation deviation of all microenvironment adaptation abnormal areas in the non-growth adaptation interval and the shading adaptation deviation of the planting module of all non-abnormal areas are determined.

[0031] It should be noted that the collected monitoring deviation data is analyzed to extract which time periods have monitoring deviations; for example, if the sensor detects that the soil temperature and humidity of a planting module deviates from the preset value, the information that the deviation occurred at this time period can be recorded; all monitoring deviation time periods are counted to identify the deviation occurrence frequency, duration, and other information in each time period; the core purpose of this step is to analyze whether the deviation occurs regularly or there is a delay or other abnormalities; the deviation occurrence period refers to the deviation between the sensor detected environment value and the set value in a certain time period; for example, if the temperature is too high or the humidity is too low in a certain period, the system will record and calculate these deviation periods; intelligent irrigation execution delay refers to the phenomenon that the intelligent irrigation system does not start to adjust in time after detecting the monitoring deviation; for example, after the deviation occurs, the irrigation system should immediately start to adjust the humidity, but due to the delay, it fails to adjust within the expected time; the delay may be caused by sensor failure, communication delay, or delay in the control decision process; by counting the deviation occurrence period, the system can determine whether there is an irrigation execution delay; for example, the monitored soil humidity is too low, but the irrigation system is delayed in starting, resulting in that the humidity is not restored to the target value in time; If there is a delay in the intelligent irrigation system, the system will mark the corresponding planting subarea as a microenvironment adaptation abnormal area; this area may not be timely regulated, resulting in limited crop growth, and even possible damage to crops; these microenvironment adaptation abnormal areas require special attention because their soil environment cannot meet the ideal conditions for crop growth, which may lead to poor growth or resource waste; in these non-growth adaptation intervals, the system will further analyze the shading adaptation deviation of the area; shading adaptation deviation refers to the difference between the actual shading conditions and the ideal shading needs of crops; insufficient shading may lead to high soil temperature or uneven humidity, affecting crop growth; the system will divide the areas within the non-growth adaptation interval into microenvironment adaptation abnormal areas and non-abnormal areas according to these deviations; for each area, the system will measure and record its shading adaptation deviation, i.e., the deviation between the shading conditions and the ideal shading needs; through the statistics and analysis of the deviations, the system can identify which planting areas have microenvironment adaptation problems, whether there are irrigation delays or insufficient shading, etc.; the system can dynamically adjust based on this data to optimize irrigation schemes, shading strategies, and even possibly adjust the layout of planting subareas to ensure that all areas can provide the ideal environment for crop growth.

[0032] In an optional embodiment, after the crop growth index statistics of the microenvironment adaptation abnormal area, the subarea vegetation configuration scheme and the planting module layout parameters are adjusted, including: According to the shading adaptation deviation of the planting module in all non-abnormal areas in the non-growth adaptation interval, the planting module in all non-abnormal areas is subjected to an adaptation calibration process; Wherein, the calculation formula of the planting module adaptation calibration coefficient is as follows: ; Wherein, represents the adaptation calibration coefficient, represents the average shading adaptation deviation of the non-abnormal area, represents the average shading adaptation deviation of the abnormal area; According to the shading adaptation deviation of all microenvironment adaptation abnormal areas in the non-growth adaptation interval, the shading adaptation deviation of the planting subarea adjacent to all abnormal areas is determined, the subarea vegetation configuration type of the adjacent planting subarea is converted to a more stress-tolerant variety type, and the crop growth index statistics of all microenvironment adaptation abnormal areas are performed; According to the crop growth index statistics result, the subarea vegetation configuration scheme and the installation spacing, height, etc. of the planting module layout parameters are adjusted.

[0033] It should be noted that the non-exceptional area refers to the area in these non-growth adaptation intervals where there is no microenvironment adaptation exception, meaning that the environmental conditions of these areas have not temporarily exceeded the preset threshold, although they are still not suitable for crop growth; in order to improve the growth conditions of these non-exceptional areas, the system will calibrate the area according to the shading adaptation deviation; this may include increasing shading measures, such as strengthening the sunshade net or planting more shade-tolerant plants to adjust the environmental conditions of these areas to ensure that they can better adapt to the needs of crop growth; adjacent planting subareas refer to the surrounding areas of these microenvironment adaptation exception areas, which may be affected by similar environmental factors; by analyzing these adjacent areas, identifying their shading adaptation deviation, further adjusting the environmental configuration of these areas; stress-tolerant varieties are those crop varieties that can tolerate extreme environmental conditions such as high temperature, strong light, and low water; for example, in relatively hot or dry areas, drought-tolerant or high-temperature-tolerant plant varieties can be selected; these varieties usually have stronger stress tolerance and can grow well in less-than-ideal environmental conditions; by adjusting the vegetation configuration of the adjacent planting subareas to stress-tolerant varieties, the system can optimize crop configuration around the microenvironment adaptation exception area; this approach can reduce crop growth problems caused by environmental problems and increase the growth resilience and yield of crops; Crop growth indicators refer to various key indicators of crop growth, such as growth rate, leaf growth, root development, fruiting, yield, etc.; these indicators reflect the growth and health of crops under specific environmental conditions; statistical analysis of crops in microenvironment adaptation exception areas can assess their growth and determine the extent of environmental problems on crops; these statistical data will help determine whether further environmental adjustments or different crop varieties are needed; based on the statistical results of the aforementioned crop growth indicators, the system will further optimize the planting area; for example: if the lack of shading in some areas causes the crops to grow slowly, the shading measures of the planting module may need to be adjusted, or shading facilities such as adjusting the position of the sunshade net may be added; adjusting the installation spacing of the planting module according to the growth performance of the crops can ensure that the space between each module can both ensure the comfort of crop growth and avoid excessive competition for resources such as water and sunlight; different crops have different light requirements, and adjusting the height of the planting module can optimize light distribution and improve the growth environment of crops.

[0034] The specific embodiments of the application are described in detail above, but only as examples, and the application is not limited to the specific embodiments described above. Any equivalent modifications or alternatives to the application made by those skilled in the art are also within the scope of the application, therefore, equivalent transformations, modifications, improvements, etc. made without departing from the spirit and principle range of the application should be covered within the scope of the application.

Claims

1. A modular system for the cultivation of shade-tolerant economic crops under high-altitude photovoltaic panels, characterized by, The method comprises the following steps: a region division unit is used to divide planting sub-zones according to the layout parameters of photovoltaic panel supports, screen shade-tolerant economic crop varieties according to the shade gradient adaptation principle of sub-zone vegetation configuration, and collect highland environment data, soil basic data and photovoltaic panel shade characteristic data by using the sensing unit carried by the planting module to obtain the micro-environment adaptation weight of each planting sub-zone; a region judgment unit is used to judge whether each planting sub-zone is in the shade-tolerant crop growth adaptation interval according to the micro-environment adaptation weight; a state monitoring unit is used to monitor the growth state of the planting sub-zone in the non-growth adaptation interval, collect crop leaf physiological data and soil temperature and humidity dynamic data in real time by using the planting module, and obtain the soil micro-environment adjustment requirement value according to the micro-environment adaptation weight; an environment adjustment unit is used to judge whether the soil micro-environment adjustment system driven by solar refrigeration needs to be started for the planting sub-zone in the non-growth adaptation interval according to the soil micro-environment adjustment requirement value; an irrigation evaluation unit is used to adjust the operation state of the planting module of the corresponding planting sub-zone according to the judgment result, evaluate and process the soil micro-environment adjustment requirement value by using an intelligent irrigation decision model to obtain monitoring deviation data; a region layout unit is used to statistically analyze the monitoring deviation data to determine the micro-environment adaptation abnormal region in the planting sub-zone in the non-growth adaptation interval, and adjust the sub-zone vegetation configuration scheme and the layout parameters of the planting module after the crop growth index of the micro-environment adaptation abnormal region is statistically analyzed.

2. The modular system for growing of shade-tolerant cash crops under high altitude photovoltaic panels according to claim 1, wherein, The method comprises the following steps: obtain the installation interval, inclination angle and height parameters of the photovoltaic panel support, combine the environmental characteristics of the plateau region, wherein the environmental characteristics include sunshine duration, wind speed and diurnal temperature difference, divide a plurality of planting sub-zones according to the shade gradient adaptation principle of sub-zone vegetation configuration, dynamically identify the shade duration distribution and light intensity variation law of each planting sub-zone, and determine the shade adaptation deviation of each planting sub-zone in the overall planting region; collect the soil basic data, variety characteristic data and shade characteristic data of each planting sub-zone by using the sensing unit carried by the planting module to obtain the micro-environment adaptation weight of each planting sub-zone, wherein the soil basic data includes soil organic matter content, soil porosity and soil water content, the variety characteristic data includes crop leaf shade tolerance threshold and photosynthetic efficiency critical value, and the shade characteristic data includes photovoltaic panel shade period and shade area.

3. The modular system for growing of shade-tolerant cash crops under high altitude photovoltaic panels according to claim 2, wherein, judge whether each planting sub-zone is in the shade-tolerant crop growth adaptation interval according to the micro-environment adaptation weight, which comprises the following steps: comprehensively process the soil basic data, the variety characteristic data and the shade characteristic data to determine the soil environment fluctuation range and the light condition fluctuation range of each planting sub-zone in a continuous monitoring period; If the fluctuation range of the soil environment or the fluctuation range of the light conditions exceeds the preset adaptation threshold, the planting area is determined to be in the non-growth adaptation range; otherwise, the planting area is determined to be in the growth adaptation range.

4. The modular system for growing of shade-tolerant cash crops under high altitude photovoltaic panels according to claim 3, wherein, For planting zones outside the growth adaptation range, crop growth status is monitored. Real-time physiological data of crop leaves and dynamic data on soil temperature and humidity are collected via the planting module. Soil microenvironmental regulation requirements are obtained based on the microenvironmental adaptation weights, including: The growth status of crops in non-adapted growing areas was assessed by using the growth status control standard for shade-tolerant crops, and growth indicators such as leaf chlorophyll content, leaf water content, and plant height growth rate were statistically analyzed. Based on the correlation between soil basic data and shading characteristic data in the microenvironment adaptation weight, and combined with the real-time collected dynamic data of soil temperature and humidity, the coupling relationship between soil temperature, humidity and crop growth indicators is analyzed to identify the key influencing factors in the formation of non-growth adaptation intervals. The soil temperature and humidity regulation difference in planting zones of non-adapted growth areas was quantitatively calculated to obtain the soil microenvironment regulation requirement value.

5. The modular system for growing of shade-tolerant cash crops under high altitude photovoltaic panels according to claim 4, wherein, Based on the soil microenvironment regulation requirement value, determine whether the planting zone in the non-adapted growth area needs to activate the solar-cooled soil microenvironment regulation system, including: The soil temperature regulation difference in the soil microenvironment regulation demand value was analyzed to determine whether the difference was related to the abnormal soil accumulated temperature during the photovoltaic panel shading period. The difference in soil moisture regulation is statistically analyzed to determine whether the abnormal soil moisture shows a concentrated distribution characteristic within the shading coverage area of ​​the photovoltaic panel. If so, it is determined that the planting area needs to activate the solar-cooled soil microenvironment regulation system; otherwise, it is determined that the planting area does not need to activate the solar-cooled soil microenvironment regulation system.

6. A high altitude photovoltaic panel under shade economic crops plantation modular system according to claim 5, characterized in that, Adjust the operating status of the planting module in the corresponding planting area based on the judgment results; The soil microenvironment regulation demand value is evaluated and processed using an intelligent irrigation decision model to obtain monitoring deviation data, including: When the planting zone in the non-adaptation zone does not require the activation of the solar-cooled soil microenvironment regulation system, the operating status of the corresponding planting module is switched to the basic regulation mode according to the soil microenvironment regulation requirement value. When a planting zone that is not suitable for growth needs to activate the solar-cooled soil microenvironment regulation system, the corresponding planting module's operating status will be switched to enhanced regulation mode according to the soil microenvironment regulation requirement value, and the solar-cooled module and soil temperature and humidity regulation component will be activated simultaneously. Based on the shading adaptation deviations of various planting modules included in the soil microenvironment regulation requirements, irrigation parameter calculation instructions are sent to the planting modules of each planting zone. The irrigation parameter calculation instructions of the sensing units of each planting module are sampled and extracted, and fed back to the intelligent irrigation decision model to obtain monitoring deviation data.

7. A high-altitude photovoltaic panel-under shade economic crop plantation modularized system according to claim 6, characterized in that, Statistical analysis of the monitoring deviation data was performed to identify microenvironmental adaptation anomalies in the planting zones of the non-adaptation growth intervals, including: The monitoring deviation data is analyzed and processed to obtain the time period in which the monitoring deviation occurred for each planting module's sensing unit; The monitoring deviation occurrence period is counted to determine whether there is an intelligent irrigation execution delay in the monitoring deviation occurrence period; if there is, the corresponding planting subarea is determined as a microenvironment adaptation abnormal area; and the shading adaptation deviation of all the microenvironment adaptation abnormal areas in the non-growth adaptation interval and the shading adaptation deviation of the planting modules of all the non-abnormal areas are determined.

8. A high-altitude photovoltaic panel-under shade economic crop plantation modularized system according to claim 7, characterized in that, After the crop growth index statistics of the microenvironment adaptation abnormal areas, the subarea vegetation configuration scheme and the planting module layout parameters are adjusted, including: According to the shading adaptation deviation of the planting modules of all the non-abnormal areas in the non-growth adaptation interval, the adaptability calibration processing is performed on the planting modules of all the non-abnormal areas; According to the shading adaptation deviation of all the microenvironment adaptation abnormal areas in the non-growth adaptation interval, the shading adaptation deviation of the planting subarea adjacent to all the abnormal areas is determined, the subarea vegetation configuration type of the adjacent planting subarea is converted into a more resistant variety type, and the crop growth index statistics of all the microenvironment adaptation abnormal areas are performed; According to the crop growth index statistics result, the subarea vegetation configuration scheme and the installation spacing, height and other layout parameters of the planting modules are adjusted.

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