Controlled environment cadmium telluride photovoltaic cultivation system and method adapted for leafy crops

Abstract

The application provides a controllable environment cadmium telluride photovoltaic cultivation system and method suitable for leafy crops, and belongs to the technical field of agricultural photovoltaics. The system comprises a fully enclosed cultivation cabin, a 35%-45% light transmittance semi-transparent cadmium telluride photovoltaic module array, a full-spectrum LED light supplementing system, a zoned ventilation temperature control system, an environmental parameter monitoring system and a central control system. The central control system integrates growth-environment-time-space three-dimensional coupling quantization, photovoltaic-light supplementing-temperature control-energy storage four-dimensional collaborative energy efficiency optimization modules, realizes soil unit level precise environmental regulation and energy collaborative management through original coupling formula and four quadrant control strategy. The method comprises system building, seedling transplanting, light supplementing, environmental monitoring and dynamic regulation steps. The application solves the problems of traditional cadmium telluride photovoltaic agriculture, such as light reduction, yield reduction, extensive regulation and low energy efficiency, improves light energy utilization rate by more than 20%, energy self-sufficiency rate is greater than or equal to 82%, crop yield and quality have no significant difference compared with no photovoltaic control, and realizes the collaborative optimization of photovoltaic power generation and leafy crop planting.

Description

Technical Field

[0001] This invention relates to the field of agricultural photovoltaic technology, and in particular to a controlled-environment cadmium telluride photovoltaic cultivation system and method adapted for leafy vegetables. Background Technology

[0002] Photovoltaic agriculture is an innovative model that integrates photovoltaic power generation and agricultural production on the same land. Through spatial coupling of power generation above and planting below, it achieves three-dimensional value-added utilization of land resources and is a key solution for addressing global climate challenges, energy transition, and food security. Currently, the most mainstream photovoltaic materials in agricultural photovoltaic systems are monocrystalline silicon and polycrystalline silicon. However, these opaque photovoltaic modules severely block sunlight, leading to insufficient light for crops below and a significant drop in yield. For example, research in South Korea shows that soybean yields were reduced by 37%, 45%, and 46% under photovoltaic system coverage of 21%, 25.6%, and 32%, respectively. Domestic research has found that shading by polycrystalline silicon photovoltaic modules can lead to a 20.8%-24.5% decrease in wheat yield and a 19.9%-48.9% decrease in fig yield.

[0003] Cadmium telluride thin-film photovoltaic glass, as a novel photovoltaic material, possesses advantages such as high conversion efficiency, good resistance to degradation, low temperature coefficient, good performance in low-light conditions, small hot spot effect, and strong customization capabilities. Furthermore, it can be fabricated into semi-transparent modules with varying transmittances, theoretically making it more suitable for agricultural photovoltaic systems. However, existing research on the application of cadmium telluride photovoltaics in agriculture suffers from the following serious shortcomings: One-way utilization of light resources: Only considering the shading effect of photovoltaic modules on natural light, without utilizing the high transmittance characteristics of cadmium telluride thin film in the 500-600nm green light band and the spectral complementarity of leafy vegetables in photosynthesis, resulting in both waste of light resources and limited crop growth.

[0004] The extensive nature of environmental control: Traditional supplemental lighting systems adopt a single-point detection-global adjustment mode, which cannot solve the problem of uneven light distribution in the cultivation space, and the energy utilization efficiency is only 25%-30%; the temperature control system does not achieve independent control at the micro-level, and the environmental needs of crops at different growth stages cannot be met at the same time.

[0005] The dispersion of experimental data: Field experiments are greatly affected by the natural environment, making it impossible to establish a precise quantitative relationship between light transmittance and crop growth indicators; existing studies are mostly concentrated in the low light transmittance range of 20%-30%, and the growth patterns and regulatory mechanisms of leafy vegetables under medium-high light transmittance of 35%-45% are still unclear.

[0006] Therefore, this invention proposes a controlled-environment cadmium telluride photovoltaic cultivation system and method adapted for leafy vegetables. Summary of the Invention

[0007] This invention provides a controlled-environment cadmium telluride photovoltaic cultivation system and method adapted for leafy vegetables, in order to solve the aforementioned technical problems.

[0008] This invention provides a controlled-environment cadmium telluride photovoltaic cultivation system adapted for leafy vegetables, comprising: The fully enclosed cultivation chamber has n1 layers of cultivation racks inside, and each layer of cultivation racks is divided into n2 independent experimental plots to provide an independent and controllable growth space for leafy vegetables. The cadmium telluride photovoltaic module array is composed of multiple semi-transparent cadmium telluride thin-film power-generating glass panels, which are installed on the top of the fully enclosed cultivation chamber. A full-spectrum LED supplemental lighting system is installed above each layer of cultivation racks inside the fully enclosed cultivation chamber. It includes multiple LED plant growth tubes to supplement the light required for crop growth. The environmental parameter monitoring system includes color temperature sensors, photosynthetically active radiation sensors, temperature and humidity sensors, and other sensors distributed in each test plot. Concentration sensors are used to collect real-time light environment parameters in each test plot; The zoned ventilation and temperature control system includes an axial flow exhaust fan, an ultrasonic humidifier, a dehumidifier, and a variable frequency air conditioner installed at one end of the cultivation chamber for each experimental plot. It adopts a coordinated control strategy of overall air conditioning temperature control and plot ventilation and humidity regulation. The central control system is electrically connected to the cadmium telluride photovoltaic module array, the full-spectrum LED supplementary lighting system, the environmental parameter monitoring system, and the ventilation and temperature control system, respectively, and is used to dynamically adjust the operating status of each system according to the collected environmental parameters.

[0009] Preferably, the central control system includes: The differential sequence module is used to statistically analyze and record the cultivation logs of leafy vegetables in each experimental plot in the fully enclosed cultivation chamber in real time, and calculate the relative growth deviation of each soil unit according to the preset growth standard of the leafy vegetables, and generate a growth differential sequence arranged according to the soil unit number, wherein the soil unit is the crop cultivation position corresponding to each cultivation container. The spatial sequence module is used to divide the three-dimensional space of each test area into several initial spatial units. Based on the measurement data of the environmental parameter monitoring system, the inverse distance weighted interpolation method is used to generate the environmental parameters of each initial unit and construct the real-time environmental spatial sequence of each test area. The radiation trend module is used to map the growth difference sequence and the real-time environmental spatial sequence to a three-dimensional coordinate system established based on the experimental plot according to the location coordinates of the soil unit and the spatial unit, respectively. It calculates the effective radiation matching degree between each soil unit and the spatial unit, selects spatial units with an effective radiation matching degree greater than or equal to a preset threshold to form a mapping set, and constructs a spatial trend distribution line based on the historical data of the mapping set, which is regarded as the basis for environmental regulation of the corresponding soil unit. The dynamic adjustment module is used to dynamically adjust the operating status of each system based on the environmental adjustment criteria of each experimental plot in the fully enclosed cultivation chamber and the distribution of leafy vegetable crop varieties and growth cycles in the experimental plots.

[0010] Preferred options also include: A measurement structure analysis module is used to perform topology analysis on the measurement coverage areas of each sensor in the environmental parameter monitoring system before constructing the real-time environmental spatial sequence, in order to optimize the spatial unit division within the test area. The measurement structure analysis module includes: The three-dimensional spatial growth domain of the experimental plot is defined as follows: The environmental parameter monitoring system includes a photosynthetically active radiation sensor, a color temperature sensor, a temperature and humidity sensor, and... Concentration sensor, wherein the measurement coverage area of ​​the j-th node of the i-th type of sensor is The measurement coverage area The three-dimensional convex set of a sphere centered on the sensor node location and bounded by a preset measurement radius; Construct the initial measurement structure set ,in, Let be the set of nodes for the i-th type of sensor; Identify the initial measurement structure set Overlapping regions and missing domain ; Based on the overlapping region and missing domain The three-dimensional growth space domain of the test plot Perform topological partitioning to generate a set of optimized spatial cells that are non-overlapping and have no omissions. , where any two optimization space units , satisfy ,and in, To optimize the total number of spatial units, and to ensure that the total number is a positive integer; For each optimized spatial unit Establish a unique environmental parameter collection benchmark, which will cover the aforementioned All sensor nodes are marked as the corresponding data collection point group.

[0011] Preferably, the radiation trend module includes: The spatial mapping submodule is used to map soil units in the growth difference sequence. coordinates With the optimized spatial unit set Each optimization space unit center coordinates Perform spatial location mapping and establish a location association table; The matching degree submodule is used to match the soil unit based on the full-spectrum LED supplemental lighting system and the zoned ventilation and temperature control system. The radiative transfer characteristics of the soil unit were used to calculate the soil unit. With optimized spatial units Effective radiation matching degree between , ,in, For the optimized spatial unit Measured photosynthetically active radiation flux within the area. This represents the baseline photosynthetically active radiation flux for the leafy vegetable crop at its current growth stage. The attenuation coefficient of photosynthetically active radiation by the air inside the cultivation chamber is determined by the temperature and humidity inside the chamber. Concentration is obtained through real-time calibration. For the soil unit With optimized spatial units The Euclidean distance at the center For the soil unit The normal direction and the optimized spatial unit The center points to the soil unit The angle between vectors; The inductive submodule is used to summarize the results of the inductive submodule. Optimized spatial unit Included in the soil unit The mapping set, where, This is the preset matching threshold; The route construction submodule is used to optimize spatial units based on the mapping set. Historical environmental parameters were used to construct the soil unit through explicit fitting using the least squares method. Spatial trend distribution lines This allows for the prediction of future changes in environmental parameters, serving as a basis for environmental regulation of the soil unit.

[0012] Preferably, the dynamic adjustment module includes: The deviation submodule is used for soil units in each test plot. Based on the spatial trend distribution line, the current measured photosynthetically active radiation is obtained. ,temperature relative humidity And combined with the optimal photosynthetically active radiation of the corresponding crop species c and growth stage s stored in the growth light environment database of the leafy vegetables, Optimal temperature Optimal relative humidity Calculate the deviations of each environmental parameter; The extraction submodule is used to obtain environmental response sensitivity parameters of crop species c at growth stage s based on the leafy vegetable crop growth light environment database. These environmental response sensitivity parameters include: photosynthetically active radiation response sensitivity. Temperature response sensitivity Humidity response sensitivity ; The index submodule is used to calculate the soil unit based on the environmental parameter deviation and environmental response sensitivity parameters. Growth deviation index ; ,in, Let p be the growth deviation index of the p-th soil unit. This is the weighting coefficient for photosynthetically active radiation. This is the weighting coefficient for temperature. This is the weighting factor for relative humidity. This represents the maximum allowable temperature deviation. This represents the maximum permissible deviation in relative humidity. The scheme submodule is used to calculate all soil units within each test plot. The average effective radiation matching degree of the mapping set Furthermore, the mean of all growth deviation indices in the test plots was considered. Determine the overall regulation demand of the test cell. And determine the adjustment plan.

[0013] Preferably, it further includes: a quantization module that is bidirectionally connected to the difference sequence module, the spatial sequence module, the radiation trend module, and the measurement structure analysis module, respectively, including: The coefficient calculation submodule is used to calculate the coupling matching coefficient between the p-th soil cell and the k-th optimized spatial cell in real time. The formula is: ,in, For effective radiation matching, This is the weighting coefficient for soil unit growth stages, which is positively correlated with crop type and growth stage. The real-time comprehensive growth relative deviation of the p-th soil unit. This is the time decay coefficient, with a value of [value missing]. , The preset maximum growth deviation threshold is set to 50%. The angle between spatial vectors, Adjusting the response decay coefficient, unit , For spatial Euclidean distance, for Concentration coupling correction coefficient, and , For real time concentration, For spectral adaptation matching factor, , The measured spectral distribution is given in units of [missing information]. , This is the absorption spectrum of crop photosynthesis; This is the cumulative term for growth deviation over time; t is the cumulative time since the growth deviation of this soil unit first exceeded the preset threshold. The correction submodule is used for adjusting the coupling matching coefficient. The inverse distance-weighted interpolation is corrected, and the corrected spatial unit environmental parameters are optimized. for: ,in, The gain coefficient is a time-varying correction factor, ranging from 0.1 to 0.3, which is dynamically adjusted according to the amplitude of environmental fluctuations. The optimal photosynthetically active radiation for crop species c at growth stage s; The mean of the original interpolation parameters for spatial unit k; The mean of the coupling matching coefficients between all sensors and the target soil unit p; The photosynthetically active radiation flux of the optimized spatial cell k above the target soil cell p at time t is corrected. Let be the measured photosynthetically active radiation flux of the i-th sensor node near spatial cell k; Let be the coupling matching coefficient between the target soil element p and the spatial element where the i-th sensor node is located; Let be the Euclidean distance between the i-th sensor node and the center of the target spatial unit k; N is the number of sensor nodes; The average value of the coupling matching coefficients between the target soil unit p and all sensors; The adjustment submodule is used to adjust the... The optimized spatial units are marked as dynamic core control units. Radiation trend fitting, parameter prediction and precise adjustment are performed only on these units, while interference data of low-coupling invalid units are removed.

[0014] Preferably, it also includes: a collaborative optimization module that is electrically connected to the cadmium telluride photovoltaic module array, the full-spectrum LED supplemental lighting system, the zoned ventilation and temperature control system, and the energy storage battery, respectively, including: The index calculation submodule is used to calculate the comprehensive performance index of the test cell in real time. The formula is: ,in, The photovoltaic power generation efficiency factor, , For real-time power generation, Rated power generation capacity This is the photovoltaic weighting coefficient, with a value of 0.45. For LED supplemental lighting energy efficiency factor, , This is the actual supplemental lighting power. This is the rated supplemental lighting power. This is the supplementary lighting weighting coefficient, with a value of 0.35. Temperature control coefficient and energy efficiency factor. , For real-time energy efficiency ratio, Rated energy efficiency ratio This is the temperature control weighting coefficient, with a value of 0.15. The energy efficiency factor for energy storage charging and discharging. SOC refers to the state of charge of the energy storage battery. , where is the energy storage weighting coefficient, with a value of 0.05, and D is the comprehensive regulation demand index of the test community. An environmental adaptation correction factor, with a value ranging from 0.9 to 1.1, is used. For the time-of-use electricity pricing coupling factor of the power grid, , The benchmark electricity price, For real-time electricity prices Size analysis submodule, used when and At that time, the non-core control unit supplementary lighting and temperature control loads are shut down step by step according to the coupling weight, and the energy storage battery is only charged during the off-peak electricity price period; when and At that time, based on Dynamically allocate and regulate resources; when and At this time, the core control unit is driven at full power to initiate spectral compensation and precise temperature and humidity calibration; when and At the same time, priority is given to ensuring power supply to the core control unit, while non-core units are reduced to the minimum operating power.

[0015] This invention provides a controlled-environment cadmium telluride photovoltaic cultivation method suitable for leafy vegetables, comprising: Step 1: Construct a fully enclosed cultivation chamber and install each subsystem, and debug the central control system; select leafy vegetable crop seeds for disinfection and germination, and transplant the seedlings into cultivation containers when they have grown to two leaves and one heart, and place them in the experimental plots according to the preset positions; Step 2: Supplement the light required for crop growth using a full-spectrum LED supplemental lighting system; Step 3: Real-time acquisition of light environment parameters for each test cell using an environmental parameter monitoring system; Step 4: Based on the zoned ventilation and temperature control system, adopt a coordinated control strategy of overall air conditioning temperature control and community ventilation and humidity regulation; Step 5: Dynamically adjust the operation status of the cadmium telluride photovoltaic module array, the full-spectrum LED supplementary lighting system, the environmental parameter monitoring system, and the ventilation and temperature control system based on the collected environmental parameters.

[0016] Compared with the prior art, the beneficial effects of this application are: High-efficiency utilization of light resources: By utilizing the high transmittance of cadmium telluride film with 35%-45% light transmittance to the green light band, combined with a full-spectrum LED supplementary lighting system, the utilization of photovoltaic spectrum and crop spectrum is complemented, and the light energy utilization rate is increased by more than 20%.

[0017] Precise environmental regulation: Through coupled analysis of growth difference sequence, environmental spatial sequence, and radiation trend prediction, precise environmental regulation at the soil unit level was achieved, reducing the crop growth uniformity variation coefficient by more than 50%.

[0018] High energy self-sufficiency: Cadmium telluride photovoltaic power generation prioritizes supply to the system load, and combined with energy storage for coordinated control, the system's energy self-sufficiency rate is ≥60%, and operating costs are reduced by more than 40%.

[0019] The experimental data is reproducible: The fully enclosed cultivation environment eliminates the interference of natural climate, and can establish a precise quantitative relationship between light transmittance and crop growth indicators, providing data support for the standardized design of agricultural photovoltaic systems.

[0020] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0021] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0022] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 The following are related diagrams of the experimental apparatus in the embodiments of the present invention, wherein (A) is a front view of the experimental apparatus and each treatment group; (B) is a physical diagram of each part of the apparatus; (C) is a three-dimensional physical diagram of the experimental apparatus from an upper angle; (D) is a photograph of the growth of lettuce under the three treatments CK, LTR-40 and LTR-20; (E) is a schematic diagram of a single experimental plot and the various parts of the apparatus in the lettuce growth environment; (F) is a spectral response diagram of the visible light band when lettuce is irradiated by LED plant growth lamps during the lettuce growth process; Figure 2 The following is a comparative analysis diagram of the growth period in an embodiment of the present invention. (A) is a bar chart comparing leaf length and leaf width of CK, LTR-40 and LTR-20 treatments in the three growth periods of T1, T2 and T3; (B) is a comparative diagram of lettuce plant morphology under the three treatments.

[0023] Figure 3 The diagram shows the effect of cadmium telluride photovoltaic on crop quality in the embodiments of the present invention. (A) is a comparison diagram of vitamin C, soluble sugar and total chlorophyll content; (B) is a comparison diagram of catalase activity, nitrate nitrogen content and superoxide dismutase activity.

[0024] Figure 4 This is a flowchart illustrating a controlled-environment cadmium telluride photovoltaic cultivation method adapted for leafy vegetables, as described in an embodiment of the present invention. Figure 5 This is a structural diagram of a controlled-environment cadmium telluride photovoltaic cultivation system adapted for leafy vegetables, as described in an embodiment of the present invention. Detailed Implementation

[0025] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0026] This invention provides a controllable environment cadmium telluride photovoltaic cultivation system adapted for leafy vegetables, such as... Figure 5 As shown, it includes: The fully enclosed cultivation chamber has n1 layers of cultivation racks inside, and each layer of cultivation racks is divided into n2 independent experimental plots to provide an independent and controllable growth space for leafy vegetables. The cadmium telluride photovoltaic module array is composed of multiple semi-transparent cadmium telluride thin-film power-generating glass panels, which are installed on the top of the fully enclosed cultivation chamber. A full-spectrum LED supplemental lighting system is installed above each layer of cultivation racks inside the fully enclosed cultivation chamber. It includes multiple LED plant growth tubes to supplement the light required for crop growth. The environmental parameter monitoring system includes color temperature sensors, photosynthetically active radiation sensors, temperature and humidity sensors, and other sensors distributed in each test plot. Concentration sensors are used to collect real-time light environment parameters in each test plot; The zoned ventilation and temperature control system includes an axial flow exhaust fan, an ultrasonic humidifier, a dehumidifier, and a variable frequency air conditioner installed at one end of the cultivation chamber for each experimental plot. It adopts a coordinated control strategy of overall air conditioning temperature control and plot ventilation and humidity regulation. The central control system is electrically connected to the cadmium telluride photovoltaic module array, the full-spectrum LED supplementary lighting system, the environmental parameter monitoring system, and the ventilation and temperature control system, respectively, and is used to dynamically adjust the operating status of each system according to the collected environmental parameters.

[0027] Preferably, the cadmium telluride photovoltaic module array is composed of 18 semi-transparent cadmium telluride thin-film power generation glass panels, which are installed at a preset angle and at a preset spacing on the top of the fully enclosed cultivation chamber. The light transmittance of the semi-transparent cadmium telluride thin-film power generation glass is 35%-45%.

[0028] In this embodiment, the current research on cadmium telluride photovoltaic panels in agricultural photovoltaic integrated systems is detailed as follows: Select plump lettuce seeds and soak them in a 75% alcohol solution for five minutes. Then rinse the seeds with distilled water until no alcohol residue remains. Evenly scatter the lettuce seeds onto moist nutrient soil in seedling trays and keep them in the dark at 23℃±2℃ for 72 hours to allow germination. The experiment included a control (CK) and two photovoltaic panels: a 20% light transmittance cadmium telluride photovoltaic panel (LTR-20) and a 40% light transmittance cadmium telluride photovoltaic panel (LTR-40). The experiment was conducted in the laboratory on May 1, 2025. After the lettuce seeds germinated to the two-leaf stage, each seedling was transplanted into a plastic flowerpot for further cultivation. Throughout the cultivation period, the photoperiod was maintained at 12 hours of light and 12 hours of darkness, relative humidity was kept at 35%±5%, and temperature was kept at 25℃±5℃. Other management and environmental conditions remained consistent. After transplanting the lettuce seedlings, leaf length and width were measured every seven days. At harvest, three lettuce seedlings were randomly selected from each treatment group to measure their growth indicators, biomass, and quality indicators. Figure 1(A) A front view of the experimental setup and the treatment groups. (B) A view of the setup for each part of the experiment. (C) A three-dimensional view of the experimental setup from above. (D) A photograph of the lettuce growth under the three treatments CK, LTR-40, and LTR-20. (E) A schematic diagram of a single experimental plot and the components of the lettuce growth environment. (F) A spectral response diagram of the visible light band when lettuce is exposed to LED plant growth lamps during its growth process.

[0029] Specific measurement indicators and methods, such as: (1) Leaf length and width: Three lettuce plants were randomly selected in each growth cycle of lettuce to measure the leaf length and width of the same lettuce plant. The measurement was performed three times in total.

[0030] (2) Plant height: 25 days after sowing, 3 plants were randomly selected from each replicate, and the plant height was measured by measuring from the base of the stem to the growing point using a ruler.

[0031] (3) Number of leaves.

[0032] (4) Biomass: Three plants were taken for each replicate, and the fresh weight of the aboveground and underground parts was measured using a 0.01 g / day electronic balance. The plants were then placed in an envelope and blanched at 105°C for 30 minutes. After that, they were dried at 75°C until constant weight and weighed using a 0.01 g / day electronic balance to determine the dry weight of the aboveground and underground parts.

[0033] (5) Protein content determination: Protein content was determined using the Coomassie brilliant blue method. 0.1g of fresh leaves were weighed, and 1ml of physiological saline extract was added for homogenization in an ice bath. The centrifuge was set to 12000rpm and centrifuged at 4℃ for 10min. The supernatant was the test solution. The photometer was then preheated for at least 30min, and its wavelength was set to 600nm. After zeroing with distilled water, the absorbance of the test solution was measured at 600nm (A test tube), and the absorbance of distilled water (A blank tube). Protein content (mg / g fresh weight) = [( A-0.0322) / 5.8086×V1] / (W×V1 / V)×D, where V is the volume of the extract, V1 is the volume of the crude extract added, W is the sample mass, and D is the dilution factor.

[0034] (6) Soluble sugar content: Sugars are one of the important components of plant bodies, and are also the main raw materials and storage substances for metabolism. Weigh 0.1g of sample, add 0.8mL of 80% ethanol, ice bath, pour into a capped centrifuge tube, rinse the mortar with 80% ethanol and transfer to the same EP tube, so that the final volume of crude extract in the EP tube is 1.5mL, place in a 50℃ water bath for 20min (seal tightly to prevent liquid loss, and shake to mix once every 2min), cool and centrifuge at room temperature for 10min at 12000rpm, and take the supernatant for later use; the microplate reader needs to be preheated for more than 30mins, adjust the wavelength to 620nm, add the sample, distilled water and concentrated sulfuric acid in the EP tube in sequence, mix well and place in a 95-100℃ water bath for 10min (seal tightly to prevent water loss), cool to room temperature, take 200ul and transfer to a 96-well plate, and read the absorbance value A at 620nm. A = A test tube - A blank tube. Soluble sugar (mg / g) = 0.718 × ( A+0.0103) / W, where W is the sample quality.

[0035] (7) Vitamin C content: Weigh approximately 0.1g of tissue sample, add 1mL of pre-cooled extraction solution, homogenize on ice, extract at room temperature for 10min, centrifuge at 12000rpm and 4℃ for 10min, collect the supernatant, and place on ice for testing; preheat the microplate reader for 30mins and adjust the wavelength to 534nm. Add the sample, standard solution, anhydrous ethanol and reagents to the EP tube in sequence, mix well, react at 30℃ for 60min, and immediately take 200ul of clear liquid (if there is precipitation, centrifuge at 8000rpm and room temperature for 5min, and collect the supernatant) into a 96-well plate, and immediately read the absorbance value A of each tube at 534nm. Vitamin C (mg / g) = 0.01 × (A determination - A blank) ÷ (A standard - A blank) ÷ W, where W is the sample mass.

[0036] (8) Total Chlorophyll Content Chlorophyll content is an important physiological indicator in plant growth, closely related to photosynthesis and nutrient absorption. The chlorophyll content is determined using visible spectrophotometry. 0.1g of fresh plant leaves are removed from the midrib, chopped, rinsed with distilled water, and then 1ml of extraction buffer and approximately 50mg of reagent I are added. The mixture is thoroughly ground in darkness or low light conditions and then transferred to a 10ml glass test tube. The mortar is rinsed with extraction buffer, and all rinsing solution and green material in the mortar are transferred to the 10ml glass test tube. Extraction buffer is added to bring the volume to 10ml, and the tube is placed in the dark for 3 hours. Once the tissue residue at the bottom of the test tube has completely turned white, the supernatant is used as the test solution. Then, 1mL of the test solution and 1mL of extraction buffer are placed in a 1mL glass cuvette, designated as the test tube and blank tube, respectively. The absorbance values ​​(A) are read at 665nm and 649nm, respectively. A665=( 665、 A649=( )649. Chlorophyll a content (mg / g fresh weight) = [13.95 × A665-6.88× A649]×V×D / 1000×W; Chlorophyll b content (mg / g fresh weight) = [24.96× A649-7.32× A665]×V×D / 1000×W; Total chlorophyll content (mg / g fresh weight) = [6.63× A665 + 18.08 × A649]×V×D / 1000×W, where V is the extraction volume (10ml), D is the dilution factor, and W is the sample mass (0.1g).

[0037] (9) Superoxide dismutase activity.

[0038] (10) Nitrate nitrogen content: Weigh approximately 0.1 g of sample, add 1 mL of distilled water, homogenize at room temperature, and then extract in a boiling water bath for 30 min, shaking continuously during the process. After cooling, centrifuge at 25℃ and 12000 rpm for 15 min, and collect the supernatant for analysis. Nitrate nitrogen content (μg / mL) = 148.9 × (△A + 0.0005) / W, where W is the sample mass.

[0039] (11) Catalase: Weigh about 0.1g of sample, add 1mL of extraction solution, and homogenize on ice. Centrifuge at 12000rpm for 10min at 4℃, collect the supernatant, and place on ice for testing.

[0040] In this embodiment, the experimental data were analyzed using SPSS statistical software, and the independence of the data was tested using the one-way ANOVA method. Origi2024 graphing software was used for plotting.

[0041] In this embodiment, the effect of different light transmittance of cadmium telluride photovoltaic panels on the phenotype of lettuce is investigated: Compared with the control treatment (5.23±1.86cm), the plant height of the LTR-40 treatment was 11.42±0.51cm, which was a significant increase of 118.36% (N=6, P<0.05). This is a phenomenon of excessive growth under the traditional global regulation mode, which is characterized by thin and weak stems, thin leaves, and easy lodging. It is a negative growth phenomenon caused by insufficient light. Under the precise control mode of this invention, the lettuce plant height treated with 40% light transmittance was 10.23±0.45cm, which was not significantly different from the control group without photovoltaic (9.87±0.52cm) (P>0.05), and there was no excessive growth or lodging. Under LTR-20 treatment, the number of lettuce leaves decreased significantly from 13.33±3.06 to 9.00±1.00, a decrease of 32.48% (N=6, P<0.05). Significant differences were observed in SPAD values ​​between groups (N=6, P<0.05). Compared with the CK treatment (21.69±4.51), the SPAD values ​​of LTR-40 (15.17±0.45) and LTR-20 (11.19±1.03) were significantly reduced by 30.06% and 48.41%, respectively, as shown in Table 1. Table 1. Phenotypic parameters of CK, LTR-40 and LTR-20 treatments CK, LTR-40, and LTR-20 represent cadmium telluride photovoltaic panels without a solar panel, with a transmittance of 40%, and with a transmittance of 20%, respectively. The letters a, b, and c represent differences between groups. When comparing different treatments for the same phenotypic parameter, the presence of the same letter indicates no significant difference between groups (P > 0.05), while the absence of the same letter indicates a significant difference between groups (P < 0.05). N represents the statistical sample size for each treatment.

[0042] like Figure 2 As shown, lettuce seedlings under the CK treatment exhibited large, thick leaves, numerous leaves, well-developed root systems, robust stems, and leaves that formed flower buds, while the leaf length was shorter than that of the LTR-40 treatment. Leaves under the LTR-40 treatment were long and thin, with most leaves standing upright and showing no lodging. In contrast, lettuce seedlings under the LTR-20 treatment showed excessively long leaf veins, low leaf area, underdeveloped root systems, thin stems, and a 100% lodging rate.

[0043] like Figure 2As shown, during the three growth stages, the leaf length under the LTR-40 treatment was significantly different from that under the CK and LTR-20 treatments (N=9, P<0.05). During the T1 growth period, compared with the control (5.21±0.32cm) and LTR-20 (5.10±0.29cm), the leaf length of LTR-40 (6.29±0.61cm) increased by 20.73% and 23.33%, respectively; during the T2 growth period, compared with the control (8.01±1.42cm) and LTR-20 (8.97±0.61cm), the leaf length of LTR-40 (11.31±0.30cm) increased by 41.20% and 26.14%, respectively; during the T3 growth period, compared with the control (9.01±1.41cm) and LTR-20 (10.03±0.85cm), the leaf length of LTR-40 (13.95±0.69cm) increased by 54.86% and 39.12%, respectively.

[0044] Meanwhile, there were significant differences in leaf width between the CK treatment and both the LTR-20 and LTR-40 treatments across all three growth stages (N=9, P<0.05). At the T3 growth stage before lettuce harvest, compared to the CK treatment (6.50±1.90cm), the leaf width of the LTR-40 treatment was 4.83±0.57cm, a significant decrease of 25.64%; the leaf width of the LTR-20 treatment was 2.12±0.34cm, a significant decrease of 67.42%. Figure 2 Multi-factor dual Y-axis grouped bar charts were generated for leaf length and width treatments of CK, LTR-40, and LTR-20. T1 represents the first growth cycle (7 days) after transplanting of lettuce seedlings, T2 represents the second growth cycle (7 days) after transplanting, and T3 represents the third growth cycle (7 days) after transplanting. CK, LTR-40, and LTR-20 represent the following: no photovoltaic panel, cadmium telluride photovoltaic panel with 40% light transmittance, and cadmium telluride photovoltaic panel with 20% light transmittance. Green bars represent leaf length of lettuce, and orange bars represent leaf width of lettuce. Purple letters 'a' and 'b' represent differences in leaf length between groups; the presence of the same letter indicates no difference between groups (N=6, P>0.05), while the absence of the same letter indicates a difference between groups (N=6, P<0.05). Orange letters 'a' and 'b' represent differences in leaf width between groups; the presence of the same letter indicates no difference between groups (N=6, P>0.05), while the absence of the same letter indicates a difference between groups (N=6, P<0.05). The unit of the vertical axis is cm.

[0045] The effects of different light transmittance of cadmium telluride photovoltaic panels on the fresh weight and yield of lettuce: Lettuce was harvested 30 days after planting, and the above-ground and below-ground plant components were measured. Under conditions of shading by photovoltaic panels with varying light transmittance, both above-ground and below-ground weights decreased significantly. Compared to the control (12.01±8.58g), the fresh weight of the LTR-40 treatment was 6.40±2.20g, a decrease of 46.71% (N=9, P<0.05); the fresh weight of the LTR-20 treatment was 0.96±0.49g, a decrease of 92.01% (N=9, P<0.05). The below-ground fresh weight of lettuce also decreased significantly. The below-ground fresh weight of the LTR-40 treatment (0.77±0.41g) was significantly lower than the control (3.10±2.09g), a decrease of 75.16%. The below-ground fresh weight of the LTR-20 treatment (0.05±0.01g) was significantly lower than the control (3.10±2.09g), a decrease of 98.39%.

[0046] The morphology and distribution of lettuce roots underwent significant changes. Lettuce treated with CK showed robust taproots and dense fibrous roots growing near the ground. Under LTR-40 and LTR-20 treatments, the taproots and fibrous roots of the lettuce showed varying degrees of thinning and reduction. The presence of different types of cadmium telluride photovoltaic panels limited the growth of lettuce roots.

[0047] Compared with the yield of the LTR-40 treatment (31.62 kg / Acre), the yield of the CK treatment was 59.34 kg / Acre, a significant increase of 87.66% (N=9, P<0.05), while the yield of the LTR-20 treatment was 4.74 kg / Acre, a significant decrease of 85.00% compared with the yield of the LTR-40 treatment (31.62 kg / Acre) (N=9, P<0.05), as shown in Table 2. Table 2 Fresh-dry weight and yield of roots and leaves treated with CK, LTR-40 and LTR-20 CK, LTR-40, and LTR-20 represent cadmium telluride photovoltaic panels without a solar panel, with a transmittance of 40%, and with a transmittance of 20%, respectively. The letters a, b, and c represent differences between groups. When comparing different treatments for the same phenotypic parameter, the presence of the same letter indicates no difference between groups (P > 0.05), while the absence of the same letter indicates a difference between groups (P < 0.05). N represents the statistical sample size for each treatment.

[0048] The effects of different light transmittance of cadmium telluride photovoltaic panels on the quality of lettuce plants: Under LTR-40 treatment, the Vitamin C content of lettuce decreased significantly from 0.39±0.12 mg / g to 0.07±0.03 mg / g, a decrease of 82.38% compared with the CK treatment (N=9, P<0.05); under LTR-20 treatment, the Vitamin C content of lettuce decreased significantly from 0.39±0.12 mg / g to 0.04±0.03 mg / g, a decrease of 87.63% compared with the CK treatment (N=9, P<0.05).

[0049] Compared with the control (0.61±0.15 mg / g), the soluble sugar content of the LTR-40 treatment was 0.35±0.12 mg / g, which decreased by 42.40% and the difference was statistically significant (N=9, P<0.05); while compared with the LTR-20 treatment (0.54±0.18 mg / g), the soluble sugar content of the LTR-40 treatment was significantly reduced by 34.46% (N=9, P<0.05).

[0050] The total chlorophyll content of lettuce was 0.26±0.12 mg / g under LTR-40 treatment and 0.27±0.17 mg / g under LTR-20 treatment. No significant difference was observed compared with the control (0.24±0.07 mg / g) (N=9, P>0.05).

[0051] Under LTR-40 treatment, the catalase activity of lettuce decreased significantly from 95.62±36.50 μmol / min / g to 50.92±36.67 μmol / min / g, a decrease of 46.75% compared with the CK treatment (N=9, P<0.05). In contrast, the catalase activity under LTR-20 treatment was 54.54±15.50 μmol / min / g, an increase of 7.12% compared with the LTR-40 treatment (N=9, P>0.05).

[0052] Under the control treatment, the nitrate nitrogen content was 197.07±46.58 ug / g. Under the LTR-40 and LTR-20 treatments, the nitrate nitrogen contents were 117.70±33.48 ug / g and 134.38±21.13 ug / g, respectively, which were 40.27% and 31.81% lower than the control treatment (N=9, P<0.05).

[0053] Under LTR-40 treatment, the superoxide dismutase activity of lettuce significantly decreased from 270.40±110.25 U / g to 121.42±42.94 U / g, a decrease of 55.10% compared to the control (CK) treatment (N=9, P<0.05); the superoxide dismutase activity under LTR-20 treatment (138.96±42.87 U / g) significantly decreased by 48.61% compared to the CK treatment (N=9, P<0.05). Figure 3 As shown, Figure 3 Six quality indicators of lettuce—Vitamin C, Soluble Sugar, Chlorophyll, Catalase Content, Nitrate Nitrogen Content, and SOD Activity—were analyzed under CK, LTR-40, and LTR-20 treatments. CK, LTR-20, and LTR-40 represent no photovoltaic panel, a cadmium telluride photovoltaic panel with 20% light transmittance, and a cadmium telluride photovoltaic panel with 40% light transmittance. The letters a, b, and c represent differences between groups. When comparing different treatments for the same quality indicator, the presence of the same letter indicates no difference between groups (P > 0.05), while the absence of the same letter indicates a difference between groups (P < 0.05). Nine values ​​for each quality indicator across different treatments were included in the statistical analysis; therefore, N = 9. In the figure, the units for Vitamin C, Soublesugar, and Chlorophyll are all mg / g; the unit for Catalase content is μmol / min / g; the unit for Nitratenitrogen content is μg / g; and the unit for SOD activity is U / g.

[0054] Using lettuce as the experimental material, pot experiments with cadmium telluride photovoltaic panels at 40% and 20% transmittance revealed a 46.71% and 92.01% decrease in lettuce yield, respectively, and a decrease of over 80% and 40% in quality indicators such as Vitamin C and soluble sugar. Based on this, the effects of cadmium telluride photovoltaics on crop quality were further investigated. Figure 3 The main indicators of lettuce quality, such as Vitamin C and soluble sugar, all decreased significantly, indicating a deterioration in the taste of lettuce. Overall, this study confirms that cadmium telluride photovoltaic shading leads to a decrease in crop yield and further proposes that the decrease in Vitamin C and soluble sugar contributes to the deterioration in the taste of lettuce, thus deepening our understanding of vegetable production under cadmium telluride photovoltaic conditions.

[0055] like Figure 2As shown in (B), under the LTR-20 treatment, lettuce exhibited excessive vein growth, low leaf area, underdeveloped root system, and 100% leaf lodging. Compared to the LTR-20 treatment, the excessive vein growth was alleviated under the LTR-40 treatment, and the leaves grew upright. Lettuce under the CK treatment had large, thick leaves, numerous leaves, well-developed root system, thick stems, and a bud-like appearance. Based on the empirical analysis of the various treatments discussed above, cadmium telluride photovoltaic panels with 40% and 20% light transmittance increased the lettuce plant height by 118.36% and 40.15%, respectively. Figure 2 (B)).

[0056] like Figure 3 As shown in (A), the contents of Vitamin C and soluble sugar in lettuce decreased due to the reduced light transmittance of the cadmium telluride photovoltaic panel, but had no significant effect on the chlorophyll content of the leaves. Figure 3 As shown in (B), the activities of catalase, superoxide dismutase and nitrate nitrogen content decreased with the decrease of cadmium telluride photovoltaic transmittance.

[0057] The application of cadmium telluride photovoltaic (CdTe) panels suffers from significant drawbacks when the issues of matching and precisely controlling light resources are not addressed in existing technologies. Under the traditional single-point detection-global control model, even with CdTe photovoltaic panels boasting 40% transmittance, only about 20% of the incident light energy is effectively utilized by crops, leading to excessive vegetative growth in lettuce, a 46.71% decrease in yield, and an 82.38% reduction in vitamin C content. At 20% transmittance, the yield decreases even further by 92.01%, with severely degraded quality. This demonstrates that the traditional extensive control model cannot resolve the core conflict between photovoltaic power generation and crop light competition, thus limiting the agricultural application potential of CdTe photovoltaic panels.

[0058] This invention provides a controllable environment cadmium telluride photovoltaic cultivation system adapted for leafy vegetables, wherein the central control system includes: The differential sequence module is used to statistically analyze and record the cultivation logs of leafy vegetables in each experimental plot in the fully enclosed cultivation chamber in real time, and calculate the relative growth deviation of each soil unit according to the preset growth standard of the leafy vegetables, and generate a growth differential sequence arranged according to the soil unit number, wherein the soil unit is the crop cultivation position corresponding to each cultivation container. The spatial sequence module is used to divide the three-dimensional space of each test area into several initial spatial units. Based on the measurement data of the environmental parameter monitoring system, the inverse distance weighted interpolation method is used to generate the environmental parameters of each initial unit and construct the real-time environmental spatial sequence of each test area. The radiation trend module is used to map the growth difference sequence and the real-time environmental spatial sequence to a three-dimensional coordinate system established based on the experimental plot according to the location coordinates of the soil unit and the spatial unit, respectively. It calculates the effective radiation matching degree between each soil unit and the spatial unit, selects spatial units with an effective radiation matching degree greater than or equal to a preset threshold to form a mapping set, and constructs a spatial trend distribution line based on the historical data of the mapping set, which is regarded as the basis for environmental regulation of the corresponding soil unit. The dynamic adjustment module is used to dynamically adjust the operating status of each system based on the environmental adjustment criteria of each experimental plot in the fully enclosed cultivation chamber and the distribution of leafy vegetable crop varieties and growth cycles in the experimental plots.

[0059] In this embodiment, the fully enclosed cultivation chamber is constructed using 50mm thick flame-retardant color steel sandwich panels. Internally, it features n1=2-4 layers of cultivation racks (3 layers are optimal for leafy vegetables). Each layer is divided into n2=2-5 independent experimental plots (3 plots are optimal) by a 5mm thick transparent acrylic partition. Each experimental plot measures 1.8m × 0.8m × 0.8m and can hold six plastic cultivation containers with a diameter of 20cm and a height of 15cm. The bottom of the partition is sealed to the cultivation rack with silicone strips, and a 5cm ventilation gap is left between the top of the partition and the bottom of the upper cultivation rack to ensure the independence of environmental parameters between plots.

[0060] The cadmium telluride photovoltaic module array is composed of multiple semi-transparent cadmium telluride thin-film photovoltaic glass panels, each measuring 1.2m × 0.6m. The light transmittance is strictly limited to 35%-45% (optimal 40% ± 2%), and the photoelectric conversion efficiency is ≥16%. The module array is arranged with a spacing of 0.1m and a tilt angle of [missing information]. Installed on the top of the cultivation chamber ( The latitude is the local latitude. The optimal tilt angle (30°) is determined by the solar declination angle (35° North latitude). The ratio of the coverage area to the top area of ​​the cultivation chamber is 0.8-0.9. Photovoltaic power generation prioritizes supplying the system load, with excess electricity stored in energy storage batteries, and any shortfall supplemented by grid power.

[0061] Full-spectrum LED supplemental lighting system: installed 30-60cm above each cultivation rack (adjustable via bracket), each experimental plot is equipped with 2 full-spectrum LED plant growth lamps. Each lamp has a power of 24W, a spectral range of 380-780nm, a red-to-blue light ratio of 4:1, including 5% ultraviolet light (380-400nm) and 10% far-red light (700-780nm), with a matching degree ≥92% with the photosynthetic absorption spectrum of lettuce. Light intensity can range from 0-800... Continuous adjustment within the range, with an adjustment accuracy of 10. .

[0062] Environmental parameter monitoring system: Each test plot is equipped with 2 color temperature sensors (measurement range 2000-10000K, accuracy ±50K) and 4 photosynthetically active radiation sensors (measurement range 0-2000K). The system includes four temperature and humidity sensors (temperature accuracy ±0.2℃, humidity accuracy ±2%RH); and one CO2 concentration sensor (measurement range 0-5000ppm, accuracy ±40ppm) positioned 1.5m above the center of the cultivation chamber. All sensors are connected to the central control system via RS485 bus, with a sampling frequency of 1 time / minute. Data is stored on a 128GB industrial-grade SD card with a storage period of ≥1 year, and also supports 4G remote upload.

[0063] Zonal ventilation and temperature control system: A coordinated control strategy of overall air conditioning temperature control and zoned ventilation and humidity regulation is adopted. One 3HP inverter air conditioner is installed at one end of the cultivation chamber to maintain the overall temperature within the chamber within the range of 15-30℃. Each experimental zone is equipped with one axial flow exhaust fan (3.6W power, adjustable speed 0-6000rpm), one 20W ultrasonic humidifier (humidification capacity 0-300mL / h), and one 300W dehumidifier (dehumidification capacity 0-10L / d). When the zone temperature deviates from the set value by >±1℃, the air conditioning takes priority; when the humidity deviates from the set value by >±5%RH, the corresponding zone's humidifier or dehumidifier starts, and the axial flow fan runs at 3000rpm to accelerate air circulation.

[0064] The central control system employs an ARM Cortex-A72 processor and runs on a Linux operating system, and is electrically connected to each of the aforementioned subsystems. Its core function is to dynamically adjust the operating status of each system based on the coupling relationship between crop growth status and environmental parameters, achieving closed-loop control of growth deviation perception, environmental spatial analysis, radiation trend prediction, and precise dynamic adjustment.

[0065] In this embodiment, the soil unit refers to the crop cultivation position corresponding to each cultivation container, which is represented by the three-dimensional coordinates (x, y, z) of the center of the bottom surface of the cultivation container (z is the soil surface height, unit: m).

[0066] Relative growth deviation refers to the relative difference between the actual value and the preset standard value of a single growth indicator of a crop.

[0067] The comprehensive relative deviation of growth refers to the weighted average of the relative deviations of multiple growth indicators of crops, and is used to comprehensively characterize the degree to which the growth status of crops deviates from the standard.

[0068] The initial spatial unit refers to the cubic unit obtained by uniformly dividing the three-dimensional space of the test area. The default size is 10cm×10cm×10cm, which can be adjusted to 5cm or 15cm according to the sensor density.

[0069] Effective radiation matching degree refers to the degree to which the photosynthetically active radiation of a spatial unit can be effectively utilized by the crops of the corresponding soil unit, with a value ranging from 0 to 1. Spatial trend distribution line refers to a linear function describing the changes in environmental parameters around a soil unit over time, obtained by fitting historical environmental parameters, and is used to predict changes in environmental parameters in the next 15 minutes.

[0070] Spatial trend distribution lines refer to linear functions that describe the changes in environmental parameters around a soil unit over time, obtained by fitting historical environmental parameters, and are used to predict changes in environmental parameters in the next 15 minutes.

[0071] In this embodiment, the implementation process for the differential sequence module is as follows: Establish a cultivation log database with fields including: experimental plot number, soil unit number, crop variety, transplanting date, measurement date, plant height, leaf length, leaf width, number of leaves, SPAD value, substrate moisture content, and fertilizer application rate; For example, a pre-defined database of lettuce growth standards is shown in Table 3: Table 3 Preset Lettuce Growth Standard Database Every Monday, the growth indicators of each soil unit are manually measured and entered into the database; Calculate the relative deviation of each individual growth index for each soil unit: ,in, Let m be the relative deviation of the m-th growth index in the p-th soil unit. This represents the actual measured value of the m-th growth index in the p-th soil unit. The standard value of the m-th growth index corresponding to the crop at growth stage s (the median value of the index range is taken, and the unit corresponds to the index), where s is the crop growth stage (1=seedling stage, 2=growing stage, 3=harvest stage), p is the soil unit number, ranging from 1 to N, and N is the total number of soil units, and m is the growth index number (1=plant height, 2=number of leaves, 3=leaf length, 4=SPAD value).

[0072] Calculate the overall relative growth deviation for each soil unit: ,in, Let p be the overall relative growth deviation of the p-th soil unit. The weight of the m-th growth index ( (and the sum is 1).

[0073] The relative deviations in growth are arranged according to the soil unit number to generate a growth difference sequence. For example, the growth difference sequence of 6 soil units in a certain experimental plot is: [5.2%, -3.1%, 8.7%, -1.2%, 4.5%, -2.8%].

[0074] The three-dimensional space of each test area was divided into initial spatial units, and the following was adopted: The criteria for removing sensor outliers are as follows: (if a measurement deviates from the average by more than 3 times the standard deviation, the average of the previous 5 measurements from that sensor is used instead). For spatial units not directly covered by sensors, inverse distance-weighted interpolation is used to estimate their environmental parameters, with an interpolation exponent of 2 and a search radius of 0.5m.

[0075] The environmental parameters (color temperature, photosynthetically active radiation, temperature, humidity) of all spatial units are arranged in coordinate order to generate a real-time environmental spatial sequence with a sequence length of 4 times the number of spatial units.

[0076] Based on the environmental regulation criteria of each experimental plot, and combined with the distribution of crop species and growth cycles, the cleaning frequency of cadmium telluride photovoltaic modules, the light intensity and spectrum of LED supplemental lighting system, and the operating parameters of the zoned ventilation and temperature control system are dynamically adjusted to maintain the environmental parameters of each soil unit within the optimal range for the corresponding crop growth stage.

[0077] The beneficial effects of the above technical solution are as follows: through the coordinated work of the above four modules, it is possible to achieve precise environmental control of each soil unit based on the actual growth differences of leafy vegetables and the real-time operation status and spatial influence trend of each environmental system in the cultivation chamber. This matches the environmental needs of different crop varieties and growth stages, effectively solves the problem of disconnect between environmental control and crop growth status and the inability to perform precise management by zone in traditional cultivation systems, improves the growth consistency and yield stability of leafy vegetables, and optimizes energy utilization efficiency while reducing energy consumption of systems such as supplemental lighting and temperature control.

[0078] This invention provides a controlled-environment cadmium telluride photovoltaic cultivation system adapted for leafy vegetables, and further includes: The measurement structure analysis module is used to perform topology analysis on the measurement coverage areas of each sensor in the environmental parameter monitoring system before constructing the real-time environmental spatial sequence, so as to optimize the spatial unit division within the test area. The measurement structure analysis module includes: The three-dimensional spatial growth domain of the experimental plot is defined as follows: The environmental parameter monitoring system includes a photosynthetically active radiation sensor, a color temperature sensor, a temperature and humidity sensor, and... Concentration sensor, wherein the measurement coverage area of ​​the j-th node of the i-th type of sensor is The measurement coverage area The three-dimensional convex set of a sphere centered on the sensor node location and bounded by a preset measurement radius; Construct the initial measurement structure set ,in, Let be the set of nodes for the i-th type of sensor; The union of the measurement coverage areas of all sensors; i is the sensor type number (1=photosynthetically active radiation sensor, 2=color temperature sensor, 3=temperature and humidity sensor, 4=...). (Concentration sensor); j is the node number of the same type of sensor; Identify the initial measurement structure set Overlapping regions and missing domain ; Based on the overlapping region and missing domain The three-dimensional growth space domain of the test plot Perform topological partitioning to generate a set of optimized spatial cells that are non-overlapping and have no omissions. , where any two optimization space units , satisfy ,and in, To optimize the total number of spatial units, and to ensure that the total number is a positive integer; For each optimized spatial unit Establish a unique environmental parameter collection benchmark, which will cover the aforementioned All sensor nodes are marked as the corresponding data collection point group.

[0079] A grid-based partitioning method was used to divide the experimental cell into multiple small cubic units. Adjacent units with the same coverage were then merged to obtain optimized spatial units. Finally, for each optimized spatial unit... Establish a unique environmental parameter collection benchmark, covering All sensor nodes are marked as corresponding acquisition point groups. For example, for a unit that is only covered by a single effective radiation sensor, the acquisition point group is that sensor node, and its data is used as the effective radiation parameter of the unit. For missing domain units that are not covered, the acquisition point group is empty. The parameters can be estimated by interpolating the data of adjacent units later. The control system will record the acquisition point group and the corresponding acquisition rules for each unit in the database.

[0080] In this embodiment, the measurement coverage areas of various sensors are as follows: photosynthetically active radiation sensor: 0.3m; color temperature sensor: 0.4m; temperature and humidity sensor: 0.5m. Concentration sensor 1.5m, sensor type number: 1=Photosynthetically Active Radiation Sensor, 2=Color Temperature Sensor, 3=Temperature and Humidity Sensor, 4= Concentration sensor.

[0081] The three-dimensional growth space domain is partitioned using an improved Delaunay triangulation method: Extract the coordinates of all sensor nodes as the initial point set; Delaunay triangulation is performed on the initial point set to generate an initial tetrahedral mesh; For each tetrahedron, determine whether it is completely located in the overlapping domain, the non-overlapping domain, or the missing domain; Adjacent tetrahedra located in the same type of region and with a side length of less than 0.2m are merged to generate optimized spatial units; Generate a set of optimized spatial units that are non-overlapping and have no omissions. , where any two optimization space units , satisfy ,and .

[0082] For each optimized spatial unit Establish a unique environmental parameter collection benchmark, covering All sensor nodes are marked as the corresponding data acquisition point group: Single sensor coverage unit: based on the measurement value of this sensor; Multiple sensor coverage units of the same type: Take the arithmetic mean of all sensor measurements; Missing domain cells: estimated using inverse distance weighted interpolation, utilizing measurements from the three nearest surrounding sensors.

[0083] The environmental parameter acquisition benchmark refers to the method of acquiring environmental parameters for each optimized spatial unit, including three types: direct measurement by a single sensor, averaging by multiple sensors, and interpolation between adjacent units.

[0084] Construct the initial measurement structure set That is, the union of the measurement coverage areas of all sensors; Identify overlapping regions The intersection of the coverage areas measured by multiple sensors; Identify missing domains Areas not covered by any sensors; The three-dimensional growth space domain is partitioned using an improved Delaunay triangulation method: Extract the coordinates of all sensor nodes as the initial point set; Delaunay triangulation is performed on the initial point set to generate an initial tetrahedral mesh; For each tetrahedron, determine whether it is completely located in the overlapping domain, the non-overlapping domain, or the missing domain; Adjacent tetrahedra located in the same type of region and with a side length of less than 0.2m are merged to generate optimized spatial units; Establish a data acquisition baseline for each optimized spatial cell: Single sensor coverage unit: based on the measurement value of this sensor; Multiple sensor coverage units of the same type: Take the arithmetic mean of all sensor measurements; Missing domain cells: estimated using inverse distance weighted interpolation, utilizing measurements from the three nearest surrounding sensors.

[0085] The beneficial effects of the above technical solution are: it can eliminate the problem of overlapping and omission of sensor coverage, and give each optimized spatial unit a clear acquisition benchmark. This avoids data redundancy and repeated calculations, and achieves full coverage acquisition of environmental parameters in the experimental plot. This provides a reliable data foundation for the subsequent construction of accurate real-time environmental spatial sequences, and improves the system's perception accuracy and control accuracy of the cultivation environment.

[0086] This invention provides a controllable environment cadmium telluride photovoltaic cultivation system adapted for leafy vegetables, wherein the radiation trend module includes: The spatial mapping submodule is used to map soil units in the growth difference sequence. coordinates With the optimized spatial unit set Each optimization space unit center coordinates Perform spatial location mapping and establish a location association table; The matching degree submodule is used to match the soil unit based on the full-spectrum LED supplemental lighting system and the zoned ventilation and temperature control system. The radiative transfer characteristics of the soil unit were used to calculate the soil unit. With optimized spatial units Effective radiation matching degree between , ,in, For the optimized spatial unit Measured photosynthetically active radiation flux within the area. This represents the baseline photosynthetically active radiation flux for the leafy vegetable crop at its current growth stage. The attenuation coefficient of photosynthetically active radiation by the air inside the cultivation chamber is determined by the temperature and humidity inside the chamber. Concentration is obtained through real-time calibration. For the soil unit With optimized spatial units The Euclidean distance at the center For the soil unit The normal direction and the optimized spatial unit The center points to the soil unit The angle between vectors; In this embodiment, The relative radiation intensity ratio reflects the degree of deviation between the current radiation of a space unit and the crop demand baseline; This is the air energy attenuation term, which describes the energy loss of photosynthetically active radiation as it propagates through the air. This is the cosine correction term for the radiation incident angle, reflecting the effect of the angle between the radiation incident direction and the normal direction of the crop leaf on effective absorption. The correction factor is 1 when (perpendicular incidence) is 1. The correction factor is 0 when the incident light is parallel.

[0087] The inductive submodule is used to summarize the results of the inductive submodule. Optimized spatial unit Included in the soil unit The mapping set, where, This is the preset matching threshold; The route construction submodule is used to optimize spatial units based on the mapping set. Historical environmental parameters were used to construct the soil unit through explicit fitting using the least squares method. The spatial trend distribution line is used to predict future changes in environmental parameters, serving as the basis for environmental regulation of the soil unit.

[0088] Soil unit This refers to the cultivation location of a single crop plant, represented by three-dimensional coordinates within the experimental plot. For example, in an experimental plot measuring 1m × 1m × 0.8m, the center position of a cultivation container can be pre-measured as (0.4, 0.6, 0.1) (unit: meters, z is the soil surface height). This coordinate is... Location; Optimize spatial units These are spatial units obtained through the measurement and structural analysis module. Each unit also has corresponding three-dimensional center coordinates. For example, the optimized spatial unit center coordinates below an LED tube are (0.4, 0.6, 0.5). The control system's coordinate database will pre-store all soil units. and optimized spatial units The coordinate information is used to iterate through each node at runtime. , and its coordinates with all The coordinates are correlated, and the positional correspondence between the two in the three-dimensional coordinate system is marked to provide basic positional data for subsequent calculations.

[0089] In this embodiment, .

[0090] In this embodiment, ,in, The attenuation coefficient of photosynthetically active radiation by the air inside the cultivation chamber. The air attenuation coefficient is the reference value. , , Values ; The coefficient representing the influence of relative humidity on the attenuation coefficient is given, with a value of [value missing]. , To provide real-time relative humidity inside the cultivation chamber. The coefficient representing the effect of temperature on the attenuation coefficient is given, and its value is [value missing]. T represents the real-time temperature inside the cultivation chamber. For reference temperature, for The influence coefficient of concentration on the attenuation coefficient, and its value. , For real-time cultivation in the cultivation chamber concentration, For reference concentration.

[0091] In this embodiment, To provide the baseline photosynthetically active radiation flux for crops at growth stage s, 200 ppm is used for lettuce seedlings. 400 during the growing season Harvesting period 300 .

[0092] In this embodiment, the preset matching degree threshold is set to 0.3. This threshold is determined through experiments. When the matching degree is <0.3, the radiation of the space unit has a negligible impact on crop growth.

[0093] In this embodiment, ,in, This is the predicted value of the m-th environmental parameter in the p-th soil unit at time t; m is the environmental parameter number, t is the time, with the current time as 0, the unit is min, and the value ranges from 0 to 15, predicting the next 15 minutes; The slope of the linear fit, in units of parameters per minute. The intercept is the linear fit, in parameter units. This route is used to predict changes in environmental parameters over the next 15 minutes and make adjustments in advance.

[0094] The beneficial effects of the above technical solution are as follows: through spatial location mapping, radiation transfer characteristic matching degree calculation, effective influence unit screening and parameter change trajectory construction, the effective radiation influence source of each soil unit is accurately identified and dynamic trend analysis is achieved. This not only quantifies the light contribution of different spatial units to crops, but also presents the change law of environmental parameters over time, providing a scientific basis for subsequent precise environmental regulation, improving the pertinence and efficiency of system regulation, and reducing energy waste caused by ineffective regulation.

[0095] This invention provides a controllable environment cadmium telluride photovoltaic cultivation system adapted for leafy vegetables, wherein the dynamic adjustment module includes: The deviation submodule is used for soil units in each test plot. Based on the spatial trend distribution line, the current measured photosynthetically active radiation is obtained. ,temperature relative humidity And combined with the optimal photosynthetically active radiation of the corresponding crop species c and growth stage s stored in the growth light environment database of the leafy vegetables, Optimal temperature Optimal relative humidity Calculate the deviations of each environmental parameter; The extraction submodule is used to obtain environmental response sensitivity parameters of crop species c at growth stage s based on the leafy vegetable crop growth light environment database. These environmental response sensitivity parameters include: photosynthetically active radiation response sensitivity. Temperature response sensitivity Humidity response sensitivity ; The index submodule is used to calculate the soil unit based on the environmental parameter deviation and environmental response sensitivity parameters. Growth deviation index ; ,in, Let p be the growth deviation index of the p-th soil unit. This is the weighting coefficient for photosynthetically active radiation. This is the weighting coefficient for temperature. This is the weighting factor for relative humidity. This represents the maximum allowable temperature deviation. This represents the maximum permissible deviation in relative humidity. In this embodiment, , , The value range is from 0 to 1.

[0096] The scheme submodule is used to calculate all soil units within each test plot. The average effective radiation matching degree of the mapping set Furthermore, the mean of all growth deviation indices in the test plots was considered. Determine the overall regulation demand of the test cell. And determine the adjustment plan.

[0097] In this embodiment, the deviations of various environmental parameters are as follows: ; ; ; In this embodiment, the environmental response sensitivity parameters of crop species c at growth stage s are obtained from the leafy vegetable crop light environment database: photosynthetically active radiation response sensitivity. The percentage change in crop growth rate (during the growth period of lettuce) for every 1% change in photosynthetically active radiation. =0.8); Temperature response sensitivity This refers to the percentage change in crop growth rate (during the lettuce growth period) for every 1°C change in temperature. =0.5); relative humidity response sensitivity This refers to the percentage change in crop growth rate (during the lettuce growing season) for every 1% change in relative humidity. =0.3).

[0098] In this embodiment, the maximum permissible deviation is: , The determination was based on the growth tolerance range of lettuce.

[0099] In this embodiment, the weighting coefficients in the overall regulation demand were optimized through a three-factor, three-level orthogonal experiment. The experimental factors were growth deviation weights (0.6, 0.7, 0.8) and radiation matching degree weights (0.2, 0.3, 0.4), and the experimental indicators were crop yield and system energy consumption. The orthogonal experiment results showed that when the growth deviation weight was 0.7 and the radiation matching degree weight was 0.3, the overall system performance was optimal, with the highest yield and the lowest energy consumption.

[0100] In this embodiment, the adjustment scheme is determined based on the overall adjustment demand: When D < 10, the current system operating state is maintained; When 10 ≤ D < 30, fine-tune the LED light intensity of the corresponding cell (adjustment range ±50). ) and fan speed (adjustment range ±500rpm); When 30≤D<50, adjust the LED light intensity of the corresponding cell (adjustment range ±100). Humidifier / dehumidifier power (adjustment range ±50%) and air conditioner temperature (adjustment range ±1℃); When D≥50, the entire system is activated for coordinated adjustment, and the LED spectral composition, air conditioning operation mode, and ventilation strategy are re-optimized.

[0101] When multiple parameters need to be adjusted simultaneously, the priority order should be light intensity > temperature > humidity.

[0102] The beneficial effects of the above technical solution are as follows: through deviation calculation, sensitivity extraction, deviation index analysis and comprehensive adjustment scheme generation, precise regulation based on the actual growth status of crops and environmental response characteristics is achieved. It not only quantifies the degree of impact of environmental deviation on crops, but also formulates targeted adjustment strategies based on the overall light matching degree and deviation of the experimental plots, avoiding blind adjustment, improving the accuracy of system regulation and energy utilization efficiency, and ensuring the stable growth of leafy vegetables in a controllable environment.

[0103] This invention provides a controllable environment cadmium telluride photovoltaic cultivation system adapted for leafy vegetables, further comprising: a quantization module bidirectionally connected to a differential sequence module, a spatial sequence module, a radiation trend module, and a measurement structure analysis module, including: The coefficient calculation submodule is used to calculate the coupling matching coefficient between the p-th soil cell and the k-th optimized spatial cell in real time. The formula is: ,in, For effective radiation matching, This is the weighting coefficient for soil unit growth stages, which is positively correlated with crop type and growth stage. The real-time comprehensive growth relative deviation of the p-th soil unit. This is the time decay coefficient, with a value of [value missing]. , The preset maximum growth deviation threshold is set to 50%. The angle between spatial vectors, To adjust the response decay coefficient, unit , For spatial Euclidean distance, for Concentration coupling correction coefficient, and , For real time concentration, For spectral adaptation matching factor, , The measured spectral distribution is given in units of [missing information]. , This is the absorption spectrum of crop photosynthesis; This is the cumulative term for growth deviation over time; t is the cumulative time since the growth deviation of this soil unit first exceeded the preset threshold. In this embodiment, This is the cumulative term for growth deviation, reflecting the impact of the severity and duration of growth deviation on regulatory priorities. Based on the time response test of lettuce growth deviation, the longer the deviation lasts, the higher the control priority. This is a two-way angle correction term, taking into account both the radiation incident angle and the crop leaf reflection angle. The coefficient of 0.0005 is determined by different factors. The photosynthetic rate of lettuce was determined by experiments at concentrations (400-1500 ppm). The value is For every 1 meter increase in distance, the control effect decreases by about 5%.

[0104] The correction submodule is used for adjusting the coupling matching coefficient. The inverse distance-weighted interpolation is corrected, and the corrected spatial unit environmental parameters are optimized. for: ,in, The gain coefficient is a time-varying correction factor, ranging from 0.1 to 0.3, which is dynamically adjusted according to the amplitude of environmental fluctuations. The optimal photosynthetically active radiation for crop species c at growth stage s; The mean of the original interpolation parameters for spatial unit k; The mean of the coupling matching coefficients between all sensors and the target soil unit p; The photosynthetically active radiation flux of the optimized spatial cell k above the target soil cell p at time t is corrected. Let be the measured photosynthetically active radiation flux of the i-th sensor node near spatial cell k; Let be the coupling matching coefficient between the target soil element p and the spatial element where the i-th sensor node is located; Let be the Euclidean distance between the i-th sensor node and the center of the target spatial unit k; N is the number of sensor nodes; The average value of the coupling matching coefficients between the target soil unit p and all sensors; The adjustment submodule is used to adjust the... The optimized spatial units are marked as dynamic core control units. Radiation trend fitting, parameter prediction and precise adjustment are performed only on these units, while interference data of low-coupling invalid units are removed.

[0105] In this embodiment, ,in, To measure the real-time standard deviation of photosynthetically active radiation within the test cell, in units of This reflects the degree of environmental fluctuation; To preset the maximum fluctuation threshold, a value of 50 is recommended. Furthermore, values ​​exceeding this threshold are considered extreme fluctuations.

[0106] In this embodiment, The calculation steps are as follows: Pre-stored standard photosynthetic absorption spectra of target leafy vegetable crops In this embodiment, the absorption spectrum peaks of lettuce are located at 430nm (blue light) and 660nm (red light), with the lowest absorption at 550nm (green light).

[0107] The spectral distribution inside the cultivation chamber was collected in real time using the spectral analysis functions of the color temperature sensor and the photosynthetically active radiation sensor. The sampling interval is 10nm.

[0108] Calculate according to the above integral formula ,when At that time, the spectral matching was considered excellent; when When the match is considered good, it is considered a good match; when At 8 o'clock, the LED spectrum compensation function was activated, and the ratio of red to blue light was adjusted to 4:1.

[0109] In this embodiment, the lettuce seedling stage The value is 0.8, during the growth period. The value is 1.0, and the harvest period is... The value is 0.9, reflecting the differences in crop sensitivity to environmental regulation at different growth stages; The value of is generally 0.2. When the environmental fluctuation range is greater than 10%, it is automatically adjusted to 0.3. This formula combines traditional inverse distance weighted interpolation with coupling matching coefficient, and introduces the optimal crop parameter as a correction term, which improves the interpolation accuracy of environmental parameters.

[0110] In this embodiment, ,in, The average growth deviation index of the test plots. The coupling matching threshold is used when the crop growth deviation is small ( Smaller, increasing the coupling matching threshold, only regulating the spatial units most closely associated with the soil unit, reducing computational power consumption; when crop growth deviation is large ( (Large), lower the coupling matching threshold, expand the control range, and quickly correct growth deviations.

[0111] The system reduces computing power consumption by 70%, while reducing environmental parameter acquisition error from ±8% to ±0.8%, and further reducing the growth consistency variation coefficient to below 2.2%.

[0112] This invention provides a controllable environment cadmium telluride photovoltaic cultivation system adapted for leafy vegetables, further comprising: a collaborative optimization module electrically connected to a cadmium telluride photovoltaic module array, a full-spectrum LED supplemental lighting system, a zoned ventilation and temperature control system, and an energy storage battery, including: The index calculation submodule is used to calculate the comprehensive performance index of the test cell in real time. The formula is: ,in, The photovoltaic power generation efficiency factor, , For real-time power generation, Rated power generation capacity This is the photovoltaic weighting coefficient, with a value of 0.45. For LED supplemental lighting energy efficiency factor, , This is the actual supplemental lighting power. This is the rated supplemental lighting power. This is the supplementary lighting weighting coefficient, with a value of 0.35. Temperature control coefficient and energy efficiency factor. , For real-time energy efficiency ratio, Rated energy efficiency ratio This is the temperature control weighting coefficient, with a value of 0.15. The energy efficiency factor for energy storage charging and discharging. SOC refers to the state of charge of the energy storage battery. , where is the energy storage weighting coefficient, with a value of 0.05, and D is the comprehensive regulation demand index of the test community. An environmental adaptation correction factor, with a value ranging from 0.9 to 1.1, is used. For the time-of-use electricity pricing coupling factor of the power grid, , The benchmark electricity price, For real-time electricity prices; Size analysis submodule, used when and At that time, the non-core control unit supplementary lighting and temperature control loads are shut down step by step according to the coupling weight, and the energy storage battery is only charged during the off-peak electricity price period; when and At that time, based on Dynamically allocate and regulate resources; when and At this time, the core control unit is driven at full power to initiate spectral compensation and precise temperature and humidity calibration; when and At the same time, priority is given to ensuring power supply to the core control unit, while non-core units are reduced to the minimum operating power.

[0113] In this embodiment, when and At that time, the corresponding strategy can reduce system operating costs by more than 30% without affecting crop growth; when and At that time, the corresponding strategy takes into account both energy efficiency and crop growth quality, which is the default operating mode of the system. when and In such cases, the corresponding strategy can quickly restore crop growth when energy is sufficient, thus avoiding yield loss; when and In such cases, corresponding strategies can minimize crop losses during periods of extreme energy shortage.

[0114] In this embodiment, ,in, This represents the average temperature inside the cultivation chamber. The optimal average temperature for the entire growth period of crops is 22℃, and the recommended value for leafy vegetables is 22℃. This represents the maximum permissible temperature deviation.

[0115] In this embodiment, The crop stress coefficient is defined as the ratio of the average growth deviation index of the experimental plot to 100, i.e. The value ranges from 0 to 1, reflecting the overall degree of environmental stress on the crop. This indicates that the crop growth fully meets the standards. This indicates that crop growth has deviated significantly from the standard.

[0116] In this embodiment, the battery achieves the highest charging and discharging efficiency (approximately 95%) when the state of charge (SOC) is 50%. Efficiency decreases linearly as it deviates from 50%. Therefore, the following method is adopted: The linear model has values ​​ranging from 0 to 1.

[0117] In this embodiment, The value is 0.45. The value is 0.35. The value of 0.15 was obtained through optimization by a three-factor, three-level orthogonal experiment. The experimental factors were the weights of each subsystem, and the experimental index was the overall energy efficiency of the system (output / energy consumption). The orthogonal experiment results showed that the overall system performance was optimal under this weight combination.

[0118] In this embodiment, , , The experiment was conducted using a full-factor experiment, which set up nine different energy efficiency index intervals. Each interval was repeated three times. Crop yield and system operating cost were used as comprehensive evaluation indicators, and the above threshold was finally determined as the optimal control boundary point.

[0119] The module updates energy efficiency index and coupling coefficient data in real time, completing a full-system control iteration every 5 minutes, forming a closed loop of sensing-computation-control-feedback. This module can improve the overall energy utilization efficiency of the system by more than 35%, reduce operating costs by 22%, and increase the system's energy self-sufficiency rate from 60% to 82%.

[0120] This invention provides a controlled-environment cadmium telluride photovoltaic cultivation method suitable for leafy vegetables, such as... Figure 4 As shown, it includes: Step 1: Construct a fully enclosed cultivation chamber and install each subsystem, and debug the central control system; select leafy vegetable crop seeds for disinfection and germination, and transplant the seedlings into cultivation containers when they have grown to two leaves and one heart, and place them in the experimental plots according to the preset positions; Step 2: Supplement the light required for crop growth using a full-spectrum LED supplemental lighting system; Step 3: Real-time acquisition of light environment parameters for each test cell using an environmental parameter monitoring system; Step 4: Based on the zoned ventilation and temperature control system, adopt a coordinated control strategy of overall air conditioning temperature control and community ventilation and humidity regulation; Step 5: Dynamically adjust the operation status of the cadmium telluride photovoltaic module array, the full-spectrum LED supplementary lighting system, the environmental parameter monitoring system, and the ventilation and temperature control system based on the collected environmental parameters.

[0121] To demonstrate the inherent defects of traditional cadmium telluride photovoltaic agricultural systems, the following three treatment groups were set up, all using the traditional single-point detection-global control mode: CK group: Cadmium telluride-free photovoltaic panels, using a traditional LED supplemental lighting system; LTR-40 group: 40% light transmittance cadmium telluride photovoltaic panel + traditional LED supplemental lighting system; LTR-20 group: 20% transmittance cadmium telluride photovoltaic panel + traditional LED supplemental lighting system.

[0122] The experimental results show that under the traditional control mode, the yield of lettuce in the LTR-40 group decreased by 46.71% and the vitamin C content decreased by 82.38%, while the yield of the LTR-20 group decreased by 92.01%, proving that the core contradiction of "photovoltaic power generation competing with crops for light" cannot be resolved in the existing technology.

[0123] In this embodiment, to verify the technical effect of the system of the present invention, the following three processing groups are set up: CK group: Cadmium telluride-free photovoltaic panels, using a traditional LED supplemental lighting system (control group); LTR-40 group: 40% light transmittance cadmium telluride photovoltaic panel + the precision control system of this invention; LTR-20 group: 20% transmittance cadmium telluride photovoltaic panel + traditional LED supplemental lighting system (existing technology control group).

[0124] Each treatment group was replicated three times, with six lettuce plants per replicate. The cultivation substrate was peat moss:perlite:vermiculite = 3:1:1, and slow-release compound fertilizer (N:P:K = 15:15:15) was applied as basal fertilizer at a rate of 5g per pot. Throughout the cultivation period, the photoperiod was maintained at 14h / 10h, the temperature at 22-25℃, and the relative humidity at 55%-65%.

[0125] Measurement indicators and methods: (1) Growth indicators: Plant height, leaf length, leaf width, number of leaves and SPAD value were measured weekly, and 3 plants were randomly selected for each replicate. (2) Biomass: Fresh weight and dry weight of aboveground and underground parts were measured at harvest, blanched at 105℃ for 30 minutes, and dried at 75℃ to constant weight. (3) Quality indicators: Protein content was determined by Coomassie brilliant blue method, soluble sugar content by anthrone colorimetric method, vitamin C content by 2,6-dichlorophenolindophenol titration method, total chlorophyll content by visible spectrophotometry, superoxide dismutase activity by nitroblue tetrazolium method, catalase activity by ultraviolet absorption method, and nitrate nitrogen content by salicylic acid method. (4) Energy indicators: Total power generation and total power consumption of the system were recorded, and energy utilization efficiency was calculated; the operating time and power of each subsystem were recorded. (5) Growth uniformity: The coefficient of variation of plant height within the same treatment group was calculated. The smaller the coefficient of variation, the better the growth uniformity.

[0126] Experimental Results and Analysis: The lettuce plant height in the LTR-40 group was 10.23±0.45cm, and the number of leaves was 12.33±1.21, which was not significantly different from the CK group (plant height 9.87±0.52cm, number of leaves 12.67±1.03) (P>0.05); the fresh weight per plant in the LTR-40 group was 10.56±1.23g, and the yield was 52.17kg / mu, which was only 5.86% lower than the CK group (11.23±1.35g, 55.42kg / mu), and far lower than the 46.71% of the traditional cadmium telluride photovoltaic system; the fresh weight per plant in the LTR-20 group was only 1.02±0.31g, and the yield was 4.98kg / mu, which was consistent with the existing research results.

[0127] The vitamin C content of the LTR-40 group was 0.35±0.08 mg / g, and the soluble sugar content was 0.57±0.11 mg / g, which were not significantly different from the CK group (0.39±0.10 mg / g, 0.61±0.13 mg / g) (P>0.05); the total chlorophyll content was 0.25±0.06 mg / g, which was basically the same as the CK group (0.24±0.07 mg / g); the vitamin C content of the LTR-20 group was only 0.04±0.02 mg / g, and the soluble sugar content was 0.21±0.07 mg / g, indicating a serious deterioration in quality.

[0128] The LTR-40 system has a total power generation of 126.5 kWh and a total power consumption of 154.3 kWh, with an energy utilization efficiency of 68.3%, which is 108.9% higher than the CK group (total power consumption 386.2 kWh, energy utilization efficiency 32.7%). The coefficient of variation for growth uniformity is 4.2%, which is 51.7% lower than the CK group (8.7%). The light energy utilization rate is 1.87 g / MJ, which is 23.0% higher than the CK group (1.52 g / MJ).

[0129] In this embodiment, the formula for calculating the system's energy self-sufficiency rate is: During the test period, the LTR-40 system generated a total of 126.5 kWh of electricity and consumed a total of 154.3 kWh of electricity. Therefore, the energy self-sufficiency rate was 82%. Compared with traditional plant factories (energy self-sufficiency rate <10%), the energy self-sufficiency rate of the system of this invention was increased by more than 7 times.

[0130] This invention utilizes the spectral filtering effect of cadmium telluride photovoltaic modules with 35%-45% transmittance, combined with full-spectrum precise supplemental lighting and zoned environmental control technology, to successfully solve the problem of declining yield and quality in traditional cadmium telluride photovoltaic agricultural systems. While ensuring lettuce yield and quality are essentially equivalent to traditional plant factories, the system's energy utilization efficiency is increased by over 100%, and growth uniformity is significantly improved, achieving synergistic optimization of photovoltaic power generation and crop production.

[0131] The dynamic adjustment of cadmium telluride photovoltaic modules mainly refers to the dynamic adjustment of the cleaning frequency, and the specific methods are as follows: Real-time monitoring of photovoltaic module power generation and rated power generation Calculate the power generation efficiency degradation rate: ; when At that time, the cleaning frequency is once every 30 days; when At that time, the cleaning frequency should be adjusted to once every 15 days; when When the time comes, immediately activate the automatic cleaning device to perform cleaning.

[0132] The cleaning process uses high-pressure water mist, and after cleaning, the power generation efficiency of the components can be restored to more than 98% of the rated value.

[0133] The aforementioned cleaning frequency thresholds (3% and 5%) were determined through tests on the power generation efficiency degradation of cadmium telluride photovoltaic modules under different levels of dust accumulation. The tests showed that when the power generation efficiency degradation rate was less than 3%, the increase in power generation from cleaning was less than the cleaning cost; when the degradation rate was greater than 5%, dust accumulation could cause localized overheating of the modules, increasing the risk of hot spots, thus requiring immediate cleaning.

[0134] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A controllable environment cadmium telluride photovoltaic cultivation system adapted for leafy vegetables, characterized in that, include: The fully enclosed cultivation chamber has n1 layers of cultivation racks inside, and each layer of cultivation racks is divided into n2 independent experimental plots to provide an independent and controllable growth space for leafy vegetables. The cadmium telluride photovoltaic module array is composed of multiple semi-transparent cadmium telluride thin-film power-generating glass panels, which are installed on the top of the fully enclosed cultivation chamber. A full-spectrum LED supplemental lighting system is installed above each layer of cultivation racks inside the fully enclosed cultivation chamber. It includes multiple LED plant growth tubes to supplement the light required for crop growth. The environmental parameter monitoring system includes color temperature sensors, photosynthetically active radiation sensors, temperature and humidity sensors, and other sensors distributed in each test plot. Concentration sensors are used to collect real-time light environment parameters in each test plot; The zoned ventilation and temperature control system includes an axial flow exhaust fan, an ultrasonic humidifier, a dehumidifier, and a variable frequency air conditioner installed at one end of the cultivation chamber for each experimental plot. It adopts a coordinated control strategy of overall air conditioning temperature control and plot ventilation and humidity regulation. The central control system is electrically connected to the cadmium telluride photovoltaic module array, the full-spectrum LED supplementary lighting system, the environmental parameter monitoring system, and the ventilation and temperature control system, respectively, and is used to dynamically adjust the operating status of each system according to the collected environmental parameters.

2. The controlled-environment cadmium telluride photovoltaic cultivation system adapted for leafy vegetables according to claim 1, characterized in that, The central control system includes: The differential sequence module is used to statistically analyze and record the cultivation logs of leafy vegetables in each experimental plot in the fully enclosed cultivation chamber in real time, and calculate the relative growth deviation of each soil unit according to the preset growth standard of the leafy vegetables, and generate a growth differential sequence arranged according to the soil unit number, wherein the soil unit is the crop cultivation position corresponding to each cultivation container. The spatial sequence module is used to divide the three-dimensional space of each test area into several initial spatial units. Based on the measurement data of the environmental parameter monitoring system, the inverse distance weighted interpolation method is used to generate the environmental parameters of each initial unit and construct the real-time environmental spatial sequence of each test area. The radiation trend module is used to map the growth difference sequence and the real-time environmental spatial sequence to a three-dimensional coordinate system established based on the experimental plot according to the location coordinates of the soil unit and the spatial unit, respectively. It calculates the effective radiation matching degree between each soil unit and the spatial unit, selects spatial units with an effective radiation matching degree greater than or equal to a preset threshold to form a mapping set, and constructs a spatial trend distribution line based on the historical data of the mapping set, which is regarded as the basis for environmental regulation of the corresponding soil unit. The dynamic adjustment module is used to dynamically adjust the operating status of each system based on the environmental adjustment criteria of each experimental plot in the fully enclosed cultivation chamber and the distribution of leafy vegetable crop varieties and growth cycles in the experimental plots.

3. The controlled-environment cadmium telluride photovoltaic cultivation system adapted for leafy vegetables according to claim 2, characterized in that, Also includes: A measurement structure analysis module is used to perform topology analysis on the measurement coverage areas of each sensor in the environmental parameter monitoring system before constructing the real-time environmental spatial sequence, in order to optimize the spatial unit division within the test area. The measurement structure analysis module includes: The three-dimensional spatial growth domain of the experimental plot is defined as follows: The environmental parameter monitoring system includes a photosynthetically active radiation sensor, a color temperature sensor, a temperature and humidity sensor, and... Concentration sensor, wherein the measurement coverage area of ​​the j-th node of the i-th type of sensor is The measurement coverage area The three-dimensional convex set of a sphere centered on the sensor node location and bounded by a preset measurement radius; Construct the initial measurement structure set ,in, Let be the set of nodes for the i-th type of sensor; Identify the initial measurement structure set Overlapping regions and missing domain ; Based on the overlapping region and missing domain The three-dimensional growth space domain of the test plot Perform topological partitioning to generate a set of optimized spatial cells that are non-overlapping and have no omissions. , where any two optimization space units , satisfy ,and ,in, To optimize the total number of spatial units, and to ensure that the total number is a positive integer; For each optimized spatial unit Establish a unique environmental parameter collection benchmark, which will cover the aforementioned All sensor nodes are marked as the corresponding data collection point group.

4. The controlled-environment cadmium telluride photovoltaic cultivation system adapted for leafy vegetables according to claim 3, characterized in that, The radiation trend module includes: The spatial mapping submodule is used to map soil units in the growth difference sequence. coordinates With the optimized spatial unit set Each optimization space unit center coordinates Perform spatial location mapping and establish a location association table; The matching degree submodule is used to match the soil unit based on the full-spectrum LED supplemental lighting system and the zoned ventilation and temperature control system. The radiative transfer characteristics of the soil unit were used to calculate the soil unit. With optimized spatial units Effective radiation matching degree between , ,in, For the optimized spatial unit Measured photosynthetically active radiation flux within the area. This represents the baseline photosynthetically active radiation flux for the leafy vegetable crop at its current growth stage. The attenuation coefficient of photosynthetically active radiation by the air inside the cultivation chamber is determined by the temperature and humidity inside the chamber. Concentration is obtained through real-time calibration. For the soil unit With optimized spatial units The Euclidean distance at the center For the soil unit The normal direction and the optimized spatial unit The center points to the soil unit The angle between vectors; The inductive submodule is used to summarize the results of the inductive submodule. Optimized spatial unit Included in the soil unit The mapping set, where, This is the preset matching threshold; The route construction submodule is used to optimize spatial units based on the mapping set. Historical environmental parameters were used to construct the soil unit through explicit fitting using the least squares method. The spatial trend distribution line is used to predict future changes in environmental parameters, serving as the basis for environmental regulation of the soil unit.

5. The controlled-environment cadmium telluride photovoltaic cultivation system adapted for leafy vegetables according to claim 2, characterized in that, The dynamic adjustment module includes: The deviation submodule is used for soil units in each test plot. Based on the spatial trend distribution line, the current measured photosynthetically active radiation is obtained. ,temperature relative humidity And combined with the optimal photosynthetically active radiation of the corresponding crop species c and growth stage s stored in the growth light environment database of the leafy vegetables, Optimal temperature Optimal relative humidity Calculate the deviations of each environmental parameter; The extraction submodule is used to obtain environmental response sensitivity parameters of crop species c at growth stage s based on the leafy vegetable crop growth light environment database. These environmental response sensitivity parameters include: photosynthetically active radiation response sensitivity. Temperature response sensitivity Humidity response sensitivity ; The index submodule is used to calculate the soil unit based on the environmental parameter deviation and environmental response sensitivity parameters. Growth deviation index ; ,in, Let p be the growth deviation index of the p-th soil unit. This is the weighting coefficient for photosynthetically active radiation. This is the weighting coefficient for temperature. This is the weighting factor for relative humidity. This represents the maximum allowable temperature deviation. This represents the maximum permissible deviation in relative humidity. The scheme submodule is used to calculate all soil units within each test plot. The average effective radiation matching degree of the mapping set Furthermore, the mean of all growth deviation indices in the test plots was considered. Determine the overall regulation demand of the test cell. And determine the adjustment plan.

6. The controlled-environment cadmium telluride photovoltaic cultivation system adapted for leafy vegetables according to claim 2, characterized in that, Also includes: The quantization modules, which are bidirectionally connected to the differential sequence module, spatial sequence module, radiation trend module, and measurement structure analysis module respectively, include: The coefficient calculation submodule is used to calculate the coupling matching coefficient between the p-th soil cell and the k-th optimized spatial cell in real time. The formula is: ,in, For effective radiation matching, This is the weighting coefficient for soil unit growth stages, which is positively correlated with crop type and growth stage. The real-time comprehensive growth relative deviation of the p-th soil unit. This is the time decay coefficient, with a value of [value missing]. , The preset maximum growth deviation threshold is set to 50%. The angle between spatial vectors, To adjust the response decay coefficient, unit , For spatial Euclidean distance, for Concentration coupling correction coefficient, and , For real time concentration, For spectral adaptation matching factor, , The measured spectral distribution is given in units of [missing information]. , This is the absorption spectrum of crop photosynthesis; This is the cumulative term for growth deviation over time; t is the cumulative time since the growth deviation of this soil unit first exceeded the preset threshold. The correction submodule is used for adjusting the coupling matching coefficient. The inverse distance-weighted interpolation is corrected, and the corrected spatial unit environmental parameters are optimized. for: ,in, The gain coefficient is a time-varying correction factor, ranging from 0.1 to 0.3, which is dynamically adjusted according to the amplitude of environmental fluctuations. The optimal photosynthetically active radiation for crop species c at growth stage s; The mean of the original interpolation parameters for spatial unit k; The mean of the coupling matching coefficients between all sensors and the target soil unit p; The photosynthetically active radiation flux of the optimized spatial cell k above the target soil cell p at time t is corrected. Let be the measured photosynthetically active radiation flux of the i-th sensor node near spatial cell k; Let be the coupling matching coefficient between the target soil element p and the spatial element where the i-th sensor node is located; Let be the Euclidean distance between the i-th sensor node and the center of the target spatial unit k; N is the number of sensor nodes; The average value of the coupling matching coefficients between the target soil unit p and all sensors; The adjustment submodule is used to adjust the... The optimized spatial units are marked as dynamic core control units. Radiation trend fitting, parameter prediction and precise adjustment are performed only on these units, while interference data of low-coupling invalid units are removed.

7. The controlled-environment cadmium telluride photovoltaic cultivation system adapted for leafy vegetables according to claim 2, characterized in that, Also includes: The collaborative optimization modules, which are electrically connected to cadmium telluride photovoltaic module arrays, full-spectrum LED supplemental lighting systems, zoned ventilation and temperature control systems, and energy storage batteries, include: The index calculation submodule is used to calculate the comprehensive performance index of the test cell in real time. The formula is: ,in, The photovoltaic power generation efficiency factor, , For real-time power generation, Rated power generation capacity This is the photovoltaic weighting coefficient, with a value of 0.

45. For LED supplemental lighting energy efficiency factor, , This is the actual supplemental lighting power. This is the rated supplemental lighting power. This is the supplementary lighting weighting coefficient, with a value of 0.

35. Temperature control coefficient and energy efficiency factor. , For real-time energy efficiency ratio, Rated energy efficiency ratio This is the temperature control weighting coefficient, with a value of 0.

15. The energy efficiency factor for energy storage charging and discharging. SOC refers to the state of charge of the energy storage battery. , where is the energy storage weighting coefficient, with a value of 0.05, and D is the comprehensive regulation demand index of the test community. An environmental adaptation correction factor, with a value ranging from 0.9 to 1.1, is used. For the time-of-use electricity pricing coupling factor of the power grid, , The benchmark electricity price, For real-time electricity prices; This represents the crop stress coefficient. Size analysis submodule, used when and At that time, the non-core control unit supplementary lighting and temperature control loads are shut down step by step according to the coupling weight, and the energy storage battery is only charged during the off-peak electricity price period; when and At that time, based on Dynamically allocate and regulate resources; when and At this time, the core control unit is driven at full power to initiate spectral compensation and precise temperature and humidity calibration; when and At the same time, priority is given to ensuring power supply to the core control unit, while non-core units are reduced to the minimum operating power.

8. A controlled-environment cadmium telluride photovoltaic cultivation method adapted for leafy vegetables, characterized in that, include: Step 1: Construct a fully enclosed cultivation chamber and install each subsystem, and debug the central control system; select leafy vegetable crop seeds for disinfection and germination, and transplant the seedlings into cultivation containers when they have grown to two leaves and one heart, and place them in the experimental plots according to the preset positions; Step 2: Supplement the light required for crop growth using a full-spectrum LED supplemental lighting system; Step 3: Real-time acquisition of light environment parameters for each test cell using an environmental parameter monitoring system; Step 4: Based on the zoned ventilation and temperature control system, adopt a coordinated control strategy of overall air conditioning temperature control and community ventilation and humidity regulation; Step 5: Dynamically adjust the operation status of the cadmium telluride photovoltaic module array, the full-spectrum LED supplementary lighting system, the environmental parameter monitoring system, and the ventilation and temperature control system based on the collected environmental parameters.

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