Desert photovoltaic field rainwater resource utilization and sand land sand fixation integration method

Abstract

The invention discloses a desert photovoltaic field rainwater resource utilization and sand fixation integration method, and relates to the technical field of desert control and new energy comprehensive utilization. The system comprises the following components: S1, photovoltaic panel array layout and rainwater collection system design, S2, sand stabilization vegetation optimization and configuration, S3, intelligent drip irrigation system construction and operation, and S4, sand ecological monitoring and optimization management. By means of the integration method, rainwater resources of the desert photovoltaic field are fully utilized, the rainwater resources are effectively collected and utilized by accurately analyzing the rainfall characteristics of the desert area and designing a reasonable photovoltaic panel angle and a rainwater collecting system, a stable water source is provided for the photovoltaic field, and meanwhile, by combining optimization and configuration of sand stabilization vegetation, the sand stabilization effect of the desert photovoltaic field is improved. The ecological environment of the sand land is improved, the vegetation coverage rate and the soil fertility of the sand land are improved, and aggravation of the desertification process can be prevented.

Description

Technical Field

[0001] The invention relates to the technical field of desert control and comprehensive utilization of new energy, and in particular to an integrated method for rainwater resource utilization and sand fixation in a desert photovoltaic field. Background Art

[0002] As an important area of ​​new energy development, the construction and operation of desert photovoltaic sites face many challenges. Among them, water shortage and fragile ecological environment of sand are two particularly prominent problems. Rainfall in desert areas is scarce and unevenly distributed, which brings great water resource pressure to the operation of photovoltaic sites. At the same time, the soil in the sand is poor and the vegetation coverage rate is low, which is prone to wind and sand invasion, affecting the stability and service life of photovoltaic equipment.

[0003] Traditional technologies have shortcomings. On the one hand, traditional methods often ignore the precise analysis of rainfall characteristics in desert areas, resulting in low rainwater collection efficiency and inability to meet the water needs of photovoltaic sites. On the other hand, traditional sand fixation technologies usually adopt simple vegetation planting methods, lack scientific vegetation configuration and effective irrigation systems, resulting in low vegetation survival rates and difficulty in forming a stable ecological barrier.

[0004] In summary, traditional technologies have many limitations and shortcomings in the utilization of rainwater resources and sand fixation in desert photovoltaic areas. Therefore, it is particularly important to develop an integrated method for the utilization of rainwater resources and sand fixation in desert photovoltaic areas. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and to provide an integrated method for rainwater resource utilization and sand fixation in desert photovoltaic areas. It can accurately analyze the rainfall characteristics in desert areas, design the angle of photovoltaic panels and the rainwater collection system, effectively collect and utilize rainwater resources, and provide a stable water source for the photovoltaic area. At the same time, combined with the optimization and configuration of sand-fixing vegetation and the construction and operation of the intelligent drip irrigation system, it improves the ecological environment of the sand and increases the vegetation coverage and soil fertility of the sand.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for integrating rainwater resource utilization and sand fixation in desert photovoltaic areas, the system comprising the following components:

[0007] S1. Photovoltaic panel array layout and rainwater collection system design:

[0008] Accurately analyze the data of sunshine duration, solar azimuth change, rainfall time distribution, raindrop size distribution and falling speed in desert areas over the years, and use the algorithm in the intelligent control unit to adjust the angle of photovoltaic panels. Set the sunshine duration influencing factor as T f , the rate of change of the solar azimuth angle is A r, the rainfall time distribution weight is R t The average size of raindrops is D s , the falling speed is V s , the power generation efficiency optimization coefficient is C p , the rain collection efficiency optimization coefficient is C r , then the calculation formula of the photovoltaic panel angle θ is: During non-rainy periods, the intelligent control unit calculates the optimal power generation angle of the photovoltaic panel based on the sunshine duration influencing factors and the rate of change of the solar azimuth angle. During rainfall, the rain collection angle is determined and the photovoltaic panel is adjusted based on the rainfall time distribution weight, the average size of the raindrop particle size and the falling speed.

[0009] A rainwater diversion structure is constructed around and at the bottom of the photovoltaic panel array. The rainwater diversion groove at the edge of the photovoltaic panel is made of composite materials. This material has high strength, self-cleaning and wind and sand wear resistance. The diversion curve of the diversion groove is based on the desert wind and sand flow characteristics and the principle of rainwater fluid mechanics. The horizontal coordinate of the diversion groove is x, the vertical coordinate is y, and the lateral interference coefficient of wind and sand on water flow is F. x , the longitudinal interference coefficient is F y , then the equation of the bottom curve of the diversion channel is y = -(0.02 + F x )×x 2 +(1.2+0.3F y )×x, to ensure that rainwater can be efficiently gathered and flow into the rainwater collection pipe. The rainwater collection pipe adopts a modular splicing design. There is a sealed and easily disassembled connection structure between each module. The pipeline network is optimized according to the slope, slope direction and sandy geological permeability characteristics of the photovoltaic field area. The rainwater is introduced into the rainwater storage pool. The storage pool is equipped with a high-precision liquid level sensor, a multi-parameter water quality monitoring sensor and an intelligent drainage and water replenishment control device. The liquid level sensor has an accuracy of ±0.2 cm. The water quality monitoring sensor can accurately detect the concentration of suspended particles and microbial content parameters in the water. When the liquid level in the storage pool reaches the preset upper limit, drainage is automatically started. When the liquid level is lower than the lower limit and there is a rainfall forecast, water is automatically replenished;

[0010] S2. Optimization and configuration of sand-fixing vegetation:

[0011] Comprehensive collection of desert ecological big data, including data on the photosynthetic rate of various desert plants as a function of light intensity and temperature r , Water use efficiency data W u Adaptability data for different soil types and adaptation strategies for special climate events in the desert. At the same time, a high-precision sensor network is used to monitor the microenvironment of the photovoltaic field to obtain three-dimensional distribution data of light intensity in different areas, layered dynamic change data of soil temperature and humidity, spatiotemporal change data of wind speed and direction, and microscopic distribution data of soil nutrients;

[0012] Based on the above data, the big data analysis and ecological model fusion algorithm were used to screen the sand-fixing vegetation combination. First, preliminary candidate plants were screened based on the plant adaptability data to the desert climate. The photosynthetic rate was required to be P under high temperature and strong light. r ≥10 μmol·m -2 ·s -1 , water use efficiency W u ≥2. It can grow normally in a specific soil type and its recovery period after a special climate event is ≤15 days. Then, an evaluation model of the synergistic effect of sand-fixing vegetation and photovoltaic facilities is constructed. The adaptability index of plants to the shadow of photovoltaic panels is S a , the adjustment coefficient of photovoltaic panels on the microenvironment of plant growth is C e , the contribution index of plants to wind and sand protection in photovoltaic areas is C p , calculate the synergy score S s =S a ×C e +C p , filter out the synergy score S s ≥0.7 plant combination, and then determine the optimal planting density and layout through simulation experiments, set up a variety of different planting densities and spatial layout schemes, observe the root growth angle and depth distribution data of the file in the experiment, and the aboveground biomass accumulation rate B r , the species diversity index of vegetation community D i As well as the impact data on the stability of sandy soil structure, these indicators are used to construct a comprehensive evaluation function E = α × R g +β×B r +γ×D i +δ×S s , where α=0.25、β=0.25γ=0.25、δ=0.25 are the weights of each indicator respectively, R g As a comprehensive index of root growth, the planting density and layout pattern with the highest comprehensive evaluation function value were selected;

[0013] S3. Construction and operation of intelligent drip irrigation system:

[0014] Build an intelligent drip irrigation system closely connected to the rainwater storage tank. The pipe network of the drip irrigation system is made of polymer materials with excellent weather resistance and wear resistance. The pipe diameter d and wall thickness t are determined according to the area A of the drip irrigation area and the complexity coefficient C of the terrain. t And the required drip irrigation flow Q is accurately calculated and selected. The calculation formula is: Where v is the flow velocity in the pipe, P is the pressure on the pipe, [σ] is the allowable stress of the pipe, and high-precision intelligent flow control valves and pressure sensors are evenly distributed on the drip irrigation pipe. The sensor monitors the water flow and pressure changes in the pipe in real time and transmits the data to the central control system. The central control system uses an intelligent algorithm based on artificial intelligence deep learning to calculate the drip irrigation water volume and time interval. The soil moisture target value is H t , the current soil moisture is H c , the dynamic coefficient of vegetation water demand is W v The comprehensive meteorological impact factor is M c , then drip irrigation water volume V = (H t -H c )×A×W v ×M c , drip irrigation time interval The central control system sends control instructions to the intelligent flow control valve based on the calculation results to achieve precise adjustment;

[0015] Establish an intelligent irrigation decision-making system based on in-depth analysis of meteorological big data and ultra-short-term meteorological forecasts. The system obtains massive meteorological data, including hourly rainfall data for many years, daily and annual temperature change curves, seasonal humidity change trends, and ultra-short-term rainfall probability P in the future. f and rainfall forecast R f The machine learning algorithm that combines deep neural network and genetic algorithm is used to deeply mine and analyze the data. The input layer of the deep neural network is the meteorological data features. The number of hidden layer nodes is dynamically adjusted according to the data complexity and training effect. The output layer is the predicted precise change in soil moisture ΔH. The genetic algorithm is used to optimize the weights and thresholds of the neural network. When it is predicted that there will be rainfall and the rainfall amount R f When the rainfall is ≥3 mm, the central control system adjusts the drip irrigation strategy 6 hours in advance, reduces or suspends drip irrigation operations. After the rainfall ends, drip irrigation is resumed and drip irrigation parameters are adjusted in a timely manner based on soil moisture monitoring data and vegetation growth requirements.

[0016] S4. Sandy land ecological monitoring and optimized management:

[0017] A comprehensive and multi-level sand ecological monitoring network is built in the desert photovoltaic field. Ground monitoring stations are evenly distributed in various areas of the photovoltaic field. Each monitoring station is equipped with a series of sensor equipment. The vegetation growth status monitoring adopts multi-spectral imaging technology combined with lidar technology to measure the leaf area index L of the vegetation. a , plant height distribution H d , biomass density B d The physical and chemical properties of soil are monitored by using a high-precision soil bulk density meter, porosity analyzer, soil moisture rapid tester, and soil nutrient ion selective electrode to measure soil bulk density ρ, porosity n, and water content H s, concentration of various nutrient ions, meteorological environmental parameters monitoring using high-precision temperature sensors, humidity sensors, three-dimensional wind speed and direction instruments, solar radiation spectrometers to measure temperature T, humidity H, wind speed v w 、Wind direction D w , Solar radiation spectrum distribution I s The intensity of wind and sand activity is monitored by using a laser dust particle counter and a wind and sand erosion in-situ monitor to measure the dust particle concentration C s , wind and sand erosion depth change rate E r The monitoring data is collected every 15 minutes and transmitted to the data processing center through a high-speed wireless communication network. The aerial monitoring platform uses drones equipped with ultra-high-resolution multispectral cameras, thermal infrared imagers, and high-precision lidar equipment. The multispectral camera obtains the fine spectral feature data of vegetation for analyzing the health status of vegetation and species identification. The thermal infrared imager monitors the temperature field distribution and changes on the surface of the sand. The lidar obtains high-precision three-dimensional point cloud data of the sandy landforms for analyzing the evolution of the terrain. Regular aerial monitoring of the entire photovoltaic field is carried out;

[0018] Establish a complete data analysis and optimization management system. The data processing center uses big data analysis technology, geographic information system and ecological model simulation to conduct comprehensive analysis and processing of data. By comparing monitoring data from different regions and time periods, the rationality and effectiveness of the current photovoltaic panel array layout, sand-fixing vegetation configuration and drip irrigation system operating parameters are evaluated. According to the analysis results, an optimization adjustment plan is formulated through continuous monitoring, analysis and optimization adjustment.

[0019] Furthermore, in the photovoltaic panel array layout and rainwater collection system design, the hardware architecture of the intelligent control unit adopts a combination of a multi-core processor and a field programmable gate array. The multi-core processor is responsible for complex algorithm calculations, photovoltaic panel angle calculations and meteorological data processing, and the FPGA is used to quickly process sensor data acquisition and control signal output. The two are connected through a high-speed data bus with a data transmission rate of more than 10Gbps to ensure that the intelligent control unit responds quickly and accurately to the photovoltaic panel angle adjustment. In this architecture, the number of cores of the multi-core processor is determined according to the amount and complexity of the data processing task. Assume that the amount of data processing task is M, the task complexity is C, and the number of cores is The multi-core processor uses dynamic frequency adjustment technology to adjust the operating frequency according to the real-time task load. L , Operating frequency F = F max ×(1-L), where F max The maximum frequency of the processor to reduce energy consumption and improve system stability.

[0020] Furthermore, in the optimization and configuration of the sand-fixing vegetation, the sand-fixing vegetation combination adopts seed pretreatment technology when planting, and the seeds are soaked in a specific biostimulant solution. The concentration of the biostimulant solution is 10-50ppm, and the soaking time is 12-24 hours. The biostimulant can promote seed germination and seedling root growth, improve the germination rate of seeds and the survival rate of seedlings in sandy environments, and enhance the initial construction effect of the sand-fixing vegetation community. During the seed pretreatment process, the temperature of the biostimulant solution is maintained at 20-30°C, and a constant temperature control device is used to maintain a stable temperature. The solution needs to be continuously stirred at a speed of 50-100rpm to ensure that the seeds are evenly treated with the drug and improve the pretreatment effect.

[0021] Furthermore, during the construction and operation of the intelligent drip irrigation system, the drip irrigation pipe network is wrapped with a thermal insulation layer when it is laid. The thermal insulation layer is made of aerogel material with a thickness of 2-5 mm. The material has extremely low thermal conductivity, which can reduce the impact of high temperature in the sand on the temperature of the drip irrigation water flow, prevent excessive water temperature from damaging the roots of plants, and reduce water evaporation losses, thereby improving the effective utilization rate of water for drip irrigation. A layer of anti-ultraviolet coating is coated on the outer ring of the thermal insulation layer. The thickness of the anti-ultraviolet coating is 0.1-0.3 mm, which can effectively prevent ultraviolet rays from aging the thermal insulation layer and the aerogel material, extend their service life, and ensure the long-term and stable operation of the drip irrigation system.

[0022] Furthermore, in the above-mentioned sand ecological monitoring and optimization management, the sensor equipment of the ground monitoring station adopts a self-cleaning protective device. The self-cleaning protective device combines a solar-driven vibration motor with a hydrophobic coating. The vibration motor is started regularly with a frequency of 1-2 times / day and an amplitude of 1-3 mm to remove dust from the sensor surface. The hydrophobic coating can prevent dust and water droplets from adhering, ensuring the long-term stable operation of the sensor and reducing the manual maintenance cost. The power of the solar panel in the self-cleaning protective device is determined according to the energy consumption demand of the sensor equipment. Assume that the daily average energy consumption of the sensor equipment is E d The local annual average sunshine duration is T s , the conversion efficiency of the solar panel is η, then the power of the solar panel is To ensure that the solar panels can continue to power the vibration motor and sensor equipment.

[0023] Furthermore, the central control system of the intelligent drip irrigation system has data backup and recovery functions, and adopts distributed storage technology to back up the drip irrigation system operation data to multiple storage nodes. The storage nodes adopt a mixed storage method of solid-state hard disk and mechanical hard disk. The solid-state hard disk is used to store recent high-frequency access data, and the mechanical hard disk is used to store historical data. When the system fails or data is lost, the data can be quickly restored to ensure the continuity and stability of the drip irrigation system operation. The number of storage nodes is determined according to the data volume and backup frequency. Suppose the data volume is D and the backup frequency is F.b , the storage capacity of a single storage node is C n , then the number of storage nodes Each storage node is connected by a redundant link, and the link redundancy is R. To improve the reliability of data transmission.

[0024] Furthermore, in the preferred configuration of the sand-fixing vegetation, a microbial community regulation system is constructed in the sand-fixing vegetation community, by adding a specific combination of microbial agents to the soil, including phosphate-solubilizing bacteria, potassium-solubilizing bacteria and growth-promoting bacteria, and the amount of the agent added is 10 5 -10 7 The active bacteria can regulate the structure of soil microbial communities, promote soil nutrient circulation and release, improve the absorption and utilization efficiency of soil nutrients by sand-fixing vegetation, and improve the fertility of sandy soil. The ratio of the microbial agent combination is determined according to the soil nutrient status. Assuming the phosphorus content in the soil is P s , potassium content is K s , the organic matter content is O s , the ratio of phosphate-solubilizing bacteria, potassium-solubilizing bacteria and growth-promoting bacteria is When adding bacterial agents, adopt a layered application method and divide the agents into three layers and mix them evenly into the soil. The surface layer depth is 0-5 cm, the middle layer depth is 5-15 cm, and the bottom layer depth is 15-30 cm. The application ratio of each layer is 3:5:2, so as to improve the uniformity of distribution and effect of the bacterial agents at different soil depths, and promote the effective absorption of nutrients by the roots of sand-fixing vegetation in each layer of soil.

[0025] Furthermore, in the photovoltaic panel array layout and rainwater collection system design, the top of the rainwater storage pool adopts an openable awning structure. The awning is made of reflective and heat-insulating materials. The awning can be closed during the high temperature period in summer to reduce rainwater evaporation and the rise in water temperature in the storage pool. The opening and closing of the awning is automatically controlled by an intelligent control system according to the temperature and solar radiation intensity. When the temperature is higher than 35°C and the solar radiation intensity is greater than 800W / m 2 It will automatically shut down when the temperature is below 30℃ and the solar radiation intensity is less than 500W / m 2 The awning opens automatically when the sunshade is opened. The opening and closing mechanism of the awning is driven by an electric push rod. The thrust of the electric push rod is determined according to the area and weight of the awning. Suppose the area of ​​the awning is S, the weight per unit area is W, and the wind resistance coefficient is C. w , then the electric push rod thrust F = S × W × C w ×1.2 to ensure that the awning can be opened and closed stably under different meteorological conditions, effectively protecting the rainwater resources in the rainwater storage tank.

[0026] Furthermore, in the above-mentioned sand ecological monitoring and optimization management, the data processing center adopts the architecture of edge computing and cloud computing collaboration. The edge computing equipment is deployed near the photovoltaic field and is responsible for the preliminary processing of data with high real-time requirements, cleaning and simple analysis of sensor data. The cloud computing platform is used for large-scale data storage, complex model calculation and overall system optimization decision-making. The edge computing equipment and the cloud computing platform are connected through a dedicated network, and the data transmission delay is less than 100ms, which improves the data processing efficiency and system response speed. Assume that the amount of data processed in real time is D r , the processing time requirement is T r , the data processing complexity is C p , then the computing power of the edge computing device In addition, the cloud computing platform adopts an elastic computing resource allocation strategy, dynamically allocating computing resources according to the overall data processing load of the system to reduce costs and ensure efficient operation of the system.

[0027] Compared with the existing technology, this integrated method of rainwater resource utilization and sand fixation in desert photovoltaic areas has the following beneficial effects:

[0028] 1. The present invention makes full use of rainwater resources in desert photovoltaic fields through an integrated method. This method accurately analyzes the rainfall characteristics in desert areas, designs reasonable photovoltaic panel angles and rainwater collection systems, effectively collects and utilizes rainwater resources, and provides a stable water source for photovoltaic fields. At the same time, combined with the optimization and configuration of sand-fixing vegetation and the construction and operation of intelligent drip irrigation systems, the ecological environment of sandy land is improved, the vegetation coverage rate and soil fertility of sandy land are increased, and it helps prevent the aggravation of desertification.

[0029] 2. The present invention constructs an all-round, multi-level sand ecological monitoring network to monitor the microenvironment, vegetation growth conditions, soil physical and chemical properties, and meteorological environmental parameters of the photovoltaic area in real time, providing data support for the optimal management of the photovoltaic area. Based on these data, the rationality and effectiveness of the current photovoltaic panel array layout, sand-fixing vegetation configuration, and drip irrigation system operating parameters can be evaluated, and an optimization adjustment plan can be formulated. Through continuous monitoring, analysis, and optimization and adjustment, the entire desert photovoltaic area is ensured to be in the best operating state at all times, thereby improving the overall operating efficiency and sustainability.

[0030] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 This is a process operation diagram of an integrated method for rainwater resource utilization and sand fixation in desert photovoltaic areas. DETAILED DESCRIPTION

[0033] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0034] Embodiment 1

[0035] This embodiment describes a small desert photovoltaic power station located on the edge of the desert with an area of ​​about 5 hectares. The area has sufficient sunshine, with an average annual sunshine duration of about 3,000 hours, relatively stable changes in the solar azimuth angle, and rare rainfall concentrated in summer. The annual rainfall is about 150 mm, the raindrop particle size is small, the falling speed is slow, the soil is severely sandy, the nutrient content is low, the vegetation is sparse, and the wind and sand activities are frequent.

[0036] The intelligent control unit calculates the photovoltaic panel angle adjustment scheme based on the sunshine duration influencing factor, solar azimuth change rate, rainfall time distribution weight, average raindrop particle size and falling speed in the area. Suppose the sunshine duration influencing factor is T f , the rate of change of the solar azimuth angle is A r , the rainfall time distribution weight is R t The average size of raindrops is D s , the falling speed is V s , the power generation efficiency optimization coefficient is C p , the rain collection efficiency optimization coefficient is C r , then the calculation formula of the photovoltaic panel angle θ is: During non-rainy periods, the photovoltaic panels are adjusted to the optimal power generation angle to improve power generation efficiency. During rainy periods, they are adjusted to the rain collection angle to ensure effective collection of rainwater.

[0037] A rainwater diversion structure is constructed around and at the bottom of the photovoltaic panel array. The diversion trough is made of high-strength, self-cleaning and wind-sand wear-resistant composite materials. Its diversion curve is designed based on the local wind-sand flow characteristics and rainwater fluid mechanics principles. The lateral coordinate of the diversion trough is x, the longitudinal coordinate is y, and the lateral interference coefficient of wind and sand on water flow is F. x, the longitudinal interference coefficient is F y , then the equation of the bottom curve of the diversion channel is y = -(0.02 + F x )×x 2 +(1.2+0.3F y )×x, rainwater is gathered through the diversion trough and flows into the modular and connectable rainwater collection pipe. The pipeline network is laid out according to the terrain slope, slope direction and sandy geological permeability characteristics of the site, and finally leads to the rainwater storage pool. The storage pool is equipped with a high-precision liquid level sensor (accuracy up to ± 0.2 cm), a multi-parameter water quality monitoring sensor and an intelligent drainage and water replenishment control device. When the liquid level reaches the preset upper limit, it will automatically drain the water, and when it is below the lower limit and there is a rainfall forecast, it will automatically replenish the water.

[0038] Collect local desert ecological big data, including data on the changes in photosynthetic rate of various types of desert plants with light intensity and temperature, water use efficiency data, adaptability to soil types, and adaptation strategy data for special climate events. At the same time, use a high-precision sensor network to monitor the microenvironment of the photovoltaic field to obtain the three-dimensional distribution of light intensity, dynamic changes in soil temperature and humidity stratification, spatiotemporal changes in wind speed and direction, and microscopic distribution data of soil nutrients.

[0039] Based on the data on plant adaptability to desert climate, preliminary candidate plants were selected, such as sea buckthorn and caragana. These plants had photosynthetic rates of ≥10 μmol·m-2·s-1 under high temperature and strong light, water use efficiency ≥2, could adapt to local soil types, and had a recovery period of ≤15 days after special climate events. Then, an evaluation model for the synergistic effect of sand-fixing vegetation and photovoltaic facilities was constructed, and the synergistic effect score was calculated. The adaptability index of plants to the shadow of photovoltaic panels was set as S. a , the adjustment coefficient of photovoltaic panels on the microenvironment of plant growth is C e , the contribution index of plants to wind and sand protection in photovoltaic areas is C p , calculate the synergy score S s =S a ×C e +C p , screen out plant combinations with a score ≥ 0.7, such as seabuckthorn and caragana korshinskii mixed in a certain proportion, determine the optimal planting density and layout through simulation experiments, set different planting densities and spatial layout plans, and observe the root growth angle and depth distribution data of the file in the experiment, the aboveground biomass accumulation rate B r , the species diversity index of vegetation community D i As well as the impact data on the stability of sandy soil structure, these indicators are used to construct a comprehensive evaluation function E = α × R g +β×B r +γ×D i +δ×S s, where α=0.25、β=0.25γ=0.25、δ=0.25 are the weights of each indicator respectively, R g It is a comprehensive index of root growth, and the optimal model is selected based on the comprehensive evaluation function.

[0040] Build an intelligent drip irrigation system connected to the rainwater storage tank. The drip irrigation pipe is made of high polymer materials with excellent weather resistance and wear resistance. According to the area of ​​the drip irrigation area, the complexity coefficient of the terrain and the required drip irrigation flow, the pipe diameter and wall thickness are accurately calculated and selected. High-precision intelligent flow control valves and pressure sensors are evenly distributed on the drip irrigation pipe. The formula is: Where v is the flow velocity in the pipe, P is the pressure on the pipeline, [σ] is the allowable stress of the pipe, and the water flow and pressure changes are monitored in real time and the data is transmitted to the central control system.

[0041] The central control system uses an intelligent algorithm based on artificial intelligence deep learning to calculate the drip irrigation water volume and time interval, setting the soil moisture target value to H t , the current soil moisture is H c , the dynamic coefficient of vegetation water demand is W v The comprehensive meteorological impact factor is M c , then drip irrigation water volume V = (H t -H c )×A×W v ×M c , drip irrigation time interval For example, when the target soil moisture is 30%, the current soil moisture is 20%, the dynamic coefficient of vegetation water demand is 0.8, and the comprehensive meteorological impact factor is 0.9, the drip irrigation water volume is calculated and the drip irrigation time interval is controlled. At the same time, an intelligent irrigation decision-making system based on deep analysis of meteorological big data and ultra-short-term meteorological forecasts is established to obtain meteorological data and conduct in-depth mining and analysis. When it is predicted that there will be rainfall and the rainfall is ≥3 mm, the drip irrigation strategy is adjusted 6 hours in advance to reduce or suspend drip irrigation operations. After the rainfall ends, drip irrigation is resumed and parameters are adjusted in a timely manner according to soil moisture monitoring data and vegetation growth needs.

[0042] A sand ecological monitoring network is set up in the photovoltaic field area. The ground monitoring stations are evenly distributed and equipped with advanced sensor equipment. The vegetation growth status monitoring adopts the combination of multi-spectral imaging technology and lidar technology to measure the vegetation leaf area index, plant height distribution, and biomass density. The physical and chemical properties of the soil are monitored by high-precision instruments to measure the soil bulk density, porosity, water content, and nutrient ion concentration. The meteorological environmental parameters are monitored by high-precision sensors to measure the temperature, humidity, wind speed, wind direction, and solar radiation spectrum distribution. The intensity of wind and sand activity is monitored by using laser dust particle counters and wind and sand erosion in-situ monitors to measure the dust particle concentration and the change rate of wind and sand erosion depth. The monitoring data is collected every 15 minutes and transmitted to the data processing center.

[0043] The data processing center uses big data analysis technology, geographic information systems and ecological model simulation methods to conduct comprehensive analysis and processing of data, compare monitoring data from different regions and time periods, evaluate the rationality and effectiveness of the current photovoltaic panel array layout, sand-fixing vegetation configuration and drip irrigation system operating parameters, and formulate optimization and adjustment plans to ensure that the system is always in the best operating state. For example, if it is found that the vegetation growth in a certain area is poor, the analysis reason may be insufficient drip irrigation water or the shadow of the photovoltaic panel, and the drip irrigation parameters can be adjusted accordingly or the photovoltaic panel layout can be optimized.

[0044] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for integrating rainwater resource utilization and sand fixation in desert photovoltaic areas, characterized in that: The specific steps of this method are: S1. Photovoltaic panel array layout and rainwater collection system design: Accurately analyze the data of sunshine duration, solar azimuth change, rainfall time distribution, raindrop size distribution and falling speed in desert areas over the years, and use the algorithm in the intelligent control unit to adjust the angle of photovoltaic panels. Set the sunshine duration influencing factor as T f , the rate of change of the solar azimuth angle is A r , the rainfall time distribution weight is R t The average size of raindrops is D s , the falling speed is V s , the power generation efficiency optimization coefficient is C p , the rain collection efficiency optimization coefficient is C r , then the calculation formula of the photovoltaic panel angle θ is: During non-rainy periods, the intelligent control unit calculates the optimal power generation angle of the photovoltaic panel based on the sunshine duration influencing factor and the rate of change of the solar azimuth angle. During rainfall, the rain collection angle is determined and the photovoltaic panel is adjusted based on the rainfall time distribution weight, the average size of the raindrop particle size and the falling speed. A rainwater diversion structure is constructed around and at the bottom of the photovoltaic panel array. The rainwater diversion groove at the edge of the photovoltaic panel is made of composite materials. This material has high strength, self-cleaning and wind and sand wear resistance. The diversion curve of the diversion groove is based on the desert wind and sand flow characteristics and the principle of rainwater fluid mechanics. The horizontal coordinate of the diversion groove is x, the vertical coordinate is y, and the lateral interference coefficient of wind and sand on water flow is F. x , the longitudinal interference coefficient is F y , then the equation of the bottom curve of the diversion channel is y = -(0.02 + F x )×x 2 +(1.2+0.3F y )×x, to ensure that rainwater can be efficiently gathered and flow into the rainwater collection pipe. The rainwater collection pipe adopts a modular splicing design. There is a sealed and easily disassembled connection structure between each module. The pipeline network is optimized according to the slope, slope direction and sandy geological permeability characteristics of the photovoltaic field area. The rainwater is introduced into the rainwater storage pool. The storage pool is equipped with a high-precision liquid level sensor, a multi-parameter water quality monitoring sensor and an intelligent drainage and water replenishment control device. The liquid level sensor has an accuracy of ±0.2 cm. The water quality monitoring sensor can accurately detect the concentration of suspended particles and microbial content parameters in the water. When the liquid level in the storage pool reaches the preset upper limit, drainage is automatically started. When the liquid level is lower than the lower limit and there is a rainfall forecast, water is automatically replenished; S2. Optimization and configuration of sand-fixing vegetation: Comprehensive collection of desert ecological big data, including data on the photosynthetic rate of various desert plants as a function of light intensity and temperature r , Water use efficiency data W u Adaptability data for different soil types and adaptation strategies for special climate events in the desert. At the same time, a high-precision sensor network is used to monitor the microenvironment of the photovoltaic field to obtain three-dimensional distribution data of light intensity in different areas, layered dynamic change data of soil temperature and humidity, spatiotemporal change data of wind speed and direction, and microscopic distribution data of soil nutrients; Based on the above data, the big data analysis and ecological model fusion algorithm were used to screen the sand-fixing vegetation combination. First, preliminary candidate plants were screened based on the plant adaptability data to the desert climate. The photosynthetic rate was required to be P under high temperature and strong light. r ≥10 μmol·m -2 ·s -1 , water use efficiency W u ≥2. It can grow normally in a specific soil type and its recovery period after a special climate event is ≤15 days. Then, an evaluation model of the synergistic effect of sand-fixing vegetation and photovoltaic facilities is constructed. The adaptability index of plants to the shadow of photovoltaic panels is S a , the adjustment coefficient of photovoltaic panels on the microenvironment of plant growth is C e , the contribution index of plants to wind and sand protection in photovoltaic areas is C p , calculate the synergy score S s =S a ×C e +C p , filter out the synergy score S s ≥0.7 plant combination, and then determine the optimal planting density and layout through simulation experiments, set up a variety of different planting densities and spatial layout schemes, observe the root growth angle and depth distribution data of the file in the experiment, and the aboveground biomass accumulation rate B r , the species diversity index of vegetation community D i As well as the impact data on the stability of sandy soil structure, these indicators are used to construct a comprehensive evaluation function E = α × R g +β×B r +γ×D i +δ×S s , where α=0.25、β=0.25γ=0.25、δ=0.25 are the weights of each indicator respectively, R g As a comprehensive index of root growth, the planting density and layout pattern with the highest comprehensive evaluation function value were selected; S3. Construction and operation of intelligent drip irrigation system: Build an intelligent drip irrigation system closely connected to the rainwater storage tank. The pipe network of the drip irrigation system is made of polymer materials with excellent weather resistance and wear resistance. The pipe diameter d and wall thickness t are determined according to the area A of the drip irrigation area and the complexity coefficient C of the terrain. t And the required drip irrigation flow Q is accurately calculated and selected. The calculation formula is: Where v is the flow velocity of water in the pipe, P is the pressure on the pipe, [σ] is the allowable stress of the pipe, and high-precision intelligent flow control valves and pressure sensors are evenly distributed on the drip irrigation pipe. The sensor monitors the water flow and pressure changes in the pipe in real time and transmits the data to the central control system. The central control system uses an intelligent algorithm based on artificial intelligence deep learning to calculate the drip irrigation water volume and time interval. The soil moisture target value is H t , the current soil moisture is H c , the dynamic coefficient of vegetation water demand is W v The comprehensive meteorological impact factor is M c , then drip irrigation water volume V = (H t -H c )×A×W v ×M c , drip irrigation time interval The central control system sends control instructions to the intelligent flow control valve based on the calculation results to achieve precise adjustment; Establish an intelligent irrigation decision-making system based on in-depth analysis of meteorological big data and ultra-short-term meteorological forecasts. The system obtains massive meteorological data, including hourly rainfall data for many years, daily and annual temperature change curves, seasonal change trends of humidity, and ultra-short-term rainfall probability P in the future. f and rainfall forecast R f The machine learning algorithm that combines deep neural network and genetic algorithm is used to deeply mine and analyze the data. The input layer of the deep neural network is the meteorological data features. The number of hidden layer nodes is dynamically adjusted according to the data complexity and training effect. The output layer is the predicted precise change in soil moisture ΔH. The genetic algorithm is used to optimize the weights and thresholds of the neural network. When it is predicted that there will be rainfall and the rainfall amount R f When the rainfall is ≥3 mm, the central control system adjusts the drip irrigation strategy 6 hours in advance, reduces or suspends drip irrigation operations. After the rainfall ends, drip irrigation is resumed and drip irrigation parameters are adjusted in a timely manner according to soil moisture monitoring data and vegetation growth requirements. S4. Sandy land ecological monitoring and optimized management: A comprehensive and multi-level sand ecological monitoring network is built in the desert photovoltaic field. Ground monitoring stations are evenly distributed in various areas of the photovoltaic field. Each monitoring station is equipped with a series of sensor equipment. The vegetation growth status monitoring adopts multi-spectral imaging technology combined with lidar technology to measure the leaf area index L of the vegetation. a , plant height distribution H d , biomass density B d The physical and chemical properties of soil are monitored by using a high-precision soil bulk density meter, porosity analyzer, soil moisture rapid tester, and soil nutrient ion selective electrode to measure soil bulk density ρ, porosity n, and water content H s , concentration of various nutrient ions, meteorological environmental parameters monitoring using high-precision temperature sensors, humidity sensors, three-dimensional wind speed and direction instruments, solar radiation spectrometers to measure temperature T, humidity H, wind speed v w 、Wind direction D w , Solar radiation spectrum distribution I s The intensity of wind and sand activity is monitored by using a laser dust particle counter and a wind and sand erosion in-situ monitor to measure the dust particle concentration C s , wind and sand erosion depth change rate E r The monitoring data is collected every 15 minutes and transmitted to the data processing center through a high-speed wireless communication network. The aerial monitoring platform uses drones equipped with ultra-high-resolution multispectral cameras, thermal infrared imagers, and high-precision lidar equipment. The multispectral camera obtains the fine spectral feature data of vegetation for analyzing the health status of vegetation and species identification. The thermal infrared imager monitors the temperature field distribution and changes on the surface of the sand. The lidar obtains high-precision three-dimensional point cloud data of the sandy landforms for analyzing the evolution of the terrain. Regular aerial monitoring of the entire photovoltaic field is carried out; Establish a complete data analysis and optimization management system. The data processing center uses big data analysis technology, geographic information system and ecological model simulation to conduct comprehensive analysis and processing of data. By comparing monitoring data from different regions and time periods, the rationality and effectiveness of the current photovoltaic panel array layout, sand-fixing vegetation configuration and drip irrigation system operating parameters are evaluated. According to the analysis results, an optimization adjustment plan is formulated through continuous monitoring, analysis and optimization adjustment.

2. The integrated method for rainwater resource utilization and sand fixation in desert photovoltaic areas according to claim 1 is characterized in that: In the photovoltaic panel array layout and rainwater collection system design, the hardware architecture of the intelligent control unit adopts a combination of a multi-core processor and a field programmable gate array. The multi-core processor is responsible for complex algorithm calculations, photovoltaic panel angle calculations and meteorological data processing, and the FPGA is used to quickly process sensor data acquisition and control signal output. The two are connected through a high-speed data bus. In this architecture, the number of cores of the multi-core processor is determined according to the amount and complexity of data processing tasks. Assume that the amount of data processing tasks is M, the task complexity is C, and the number of cores is The multi-core processor uses dynamic frequency adjustment technology to adjust the operating frequency according to the real-time task load. L , Operating frequency F = F max ×(1-L), where F max The maximum frequency of the processor.

3. The integrated method for rainwater resource utilization and sand fixation in desert photovoltaic areas according to claim 1 is characterized in that: In the optimization and configuration of the sand-fixing vegetation, the sand-fixing vegetation combination adopts seed pretreatment technology when planting, and the seeds are soaked in a specific biostimulant solution. The concentration of the biostimulant solution is 10-50ppm, and the soaking time is 12-24 hours. The biostimulant can promote seed germination and seedling root growth. During the seed pretreatment process, the temperature of the biostimulant solution is maintained at 20-30°C, and a constant temperature control device is used to maintain a stable temperature. The solution needs to be continuously stirred at a stirring speed of 50-100rpm.

4. The integrated method for rainwater resource utilization and sand fixation in desert photovoltaic areas according to claim 1 is characterized in that: During the construction and operation of the intelligent drip irrigation system, the drip irrigation pipeline network is wrapped with a thermal insulation layer when it is laid. The thermal insulation layer is made of aerogel material with a thickness of 2-5 mm. The material has extremely low thermal conductivity. The outer ring of the thermal insulation layer is coated with an anti-ultraviolet coating with a thickness of 0.1-0.3 mm.

5. The integrated method for rainwater resource utilization and sand fixation in desert photovoltaic areas according to claim 1 is characterized in that: In the above-mentioned sand ecological monitoring and optimization management, the sensor equipment of the ground monitoring station adopts a self-cleaning protective device. The self-cleaning protective device combines a solar-driven vibration motor with a hydrophobic coating. The vibration motor is started regularly with a frequency of 1-2 times / day and an amplitude of 1-3 mm to remove dust from the sensor surface. The hydrophobic coating can prevent dust and water droplets from adhering. The power of the solar panel in the self-cleaning protective device is determined according to the energy consumption demand of the sensor equipment. Assume that the daily average energy consumption of the sensor equipment is E d The local annual average sunshine duration is T s , the conversion efficiency of the solar panel is η, then the power of the solar panel is 6. The integrated method for rainwater resource utilization and sand fixation in desert photovoltaic areas according to claim 1 is characterized in that: The central control system of the intelligent drip irrigation system has data backup and recovery functions. It adopts distributed storage technology to back up the operation data of the drip irrigation system to multiple storage nodes. The storage nodes adopt a mixed storage method of solid-state hard disk and mechanical hard disk. The solid-state hard disk is used to store recent high-frequency access data, and the mechanical hard disk is used to store historical data. When the system fails or data is lost, the data can be quickly restored to ensure the continuity and stability of the drip irrigation system. The number of storage nodes is determined according to the data volume and backup frequency. Let the data volume be D and the backup frequency be F. b , the storage capacity of a single storage node is C n , then the number of storage nodes Each storage node is connected by a redundant link, and the link redundancy is R. R≥0.

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7. The integrated method for rainwater resource utilization and sand fixation in desert photovoltaic areas according to claim 1 is characterized in that: In the optimization and configuration of the sand-fixing vegetation, a microbial community regulation system is constructed in the sand-fixing vegetation community, by adding a specific microbial agent combination to the soil, including phosphate-solubilizing bacteria, potassium-solubilizing bacteria and growth-promoting bacteria, and the amount of the agent added is 10 per gram of soil. 5 -10 7 The active bacteria regulate the soil microbial community structure and promote the circulation and release of soil nutrients. The ratio of the microbial agent combination is determined according to the soil nutrient status. Assuming the phosphorus content in the soil is P s , potassium content is K s , the organic matter content is O s , the ratio of phosphate-solubilizing bacteria, potassium-solubilizing bacteria and growth-promoting bacteria is When adding bacterial agents, adopt a layered application method and divide the bacterial agents into three layers and mix them evenly into the soil. The surface layer depth is 0-5 cm, the middle layer depth is 5-15 cm, and the bottom layer depth is 15-30 cm. The application ratio of each layer is 3:5:

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8. The integrated method for rainwater resource utilization and sand fixation in desert photovoltaic areas according to claim 1 is characterized in that: In the photovoltaic panel array layout and rainwater collection system design, the top of the rainwater storage pool adopts an openable awning structure. The awning is made of reflective and heat-insulating materials. The awning can be closed during the high temperature period in summer to reduce rainwater evaporation and the rise in water temperature in the storage pool. The opening and closing of the awning is automatically controlled by an intelligent control system according to the temperature and solar radiation intensity. When the temperature is higher than 35°C and the solar radiation intensity is greater than 800W / m 2 It will automatically shut down when the temperature is below 30℃ and the solar radiation intensity is less than 500W / m 2 The awning opens automatically when the sunshade is opened. The opening and closing mechanism of the awning is driven by an electric push rod. The thrust of the electric push rod is determined according to the area and weight of the awning. Suppose the area of ​​the awning is S, the weight per unit area is W, and the wind resistance coefficient is C. w , then the electric push rod thrust F = S × W × C w ×1.

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9. The integrated method for rainwater resource utilization and sand fixation in desert photovoltaic areas according to claim 1 is characterized in that: In the above-mentioned sand ecological monitoring and optimization management, the data processing center adopts the architecture of edge computing and cloud computing collaboration. The edge computing equipment is deployed near the photovoltaic field and is responsible for the preliminary processing of data with high real-time requirements, the cleaning and simple analysis of sensor data. The cloud computing platform is used for large-scale data storage, complex model calculation and overall system optimization decision-making. The edge computing equipment and the cloud computing platform are connected through a dedicated network, and the data transmission delay is less than 100ms. Assume that the amount of data processed in real time is D r , the processing time requirement is T r , the data processing complexity is C p , then the computing power of the edge computing device In addition, the cloud computing platform adopts an elastic computing resource allocation strategy to dynamically allocate computing resources according to the overall data processing load of the system.

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