A mirror surface light chasing angle control system and method based on temperature control for a photo-thermal power generation system

Abstract

This application discloses a mirror tracking angle control system and method for a temperature-controlled concentrated solar power (CSP) system. Existing CSP systems often rely on fixed mirror angle adjustment strategies, which cannot respond promptly to complex changes in illumination and temperature, resulting in low heat collection efficiency. This system, through the collaboration of multiple intelligent modules such as data acquisition, illumination prediction, temperature prediction, and mirror adjustment, can adjust the collector angle in real time and accurately, maximizing illumination reception efficiency and optimizing heat collection effect. In addition, the system also uses a heat collection efficiency detection and feedback module to ensure that angle adjustment does not affect heat collection efficiency and makes the operation more intelligent and automated. Overall, this temperature-sensing-based joint adjustment method improves the adaptive capability, heat collection efficiency, and system stability of the CSP system, overcoming the lag and instability of traditional technologies.

Description

Technical Field

[0001] This application relates to the field of molten salt thermal energy storage technology, and in particular to a mirror tracking angle control system and method for a temperature-controlled solar thermal power generation system. Background Technology

[0002] A solar thermal power generation system based on molten salt thermal storage achieves efficient heating and energy storage of high-temperature salt media by directional reflection of solar energy through a heat collection mirror field. This provides a stable high-temperature heat source for the thermal power generation process and is one of the core components in building a large-scale renewable energy thermal power system.

[0003] Although molten salt thermal energy storage technology has been widely used in some large-scale centralized solar power projects, its efficiency has not reached optimal levels due to the lag in the response of many systems to changes in light and temperature. In particular, in high-temperature molten salt thermal energy storage systems, temperature control often has a certain delay, which means that when light changes rapidly, the heat collection system cannot adjust the mirror angle in time, resulting in the light not being effectively focused into the molten salt, thus affecting the thermal energy storage efficiency.

[0004] Furthermore, most existing solar collector systems rely on fixed adjustment strategies and lack adaptive adjustment capabilities. This prevents the system from effectively self-adjusting in the face of complex weather conditions, temperature fluctuations, or changes in sunlight. Especially when the collector surface temperature is high, if the mirror angle is not adjusted in time, heat loss will further increase, affecting the stability and sustainability of thermal storage. Summary of the Invention

[0005] The main objective of this application is to provide a temperature-controlled solar thermal power generation system mirror tracking angle control system and method to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, this application provides the following technical solution:

[0007] A temperature-controlled solar thermal power generation system mirror tracking angle control system and method, including a data acquisition module, an illumination prediction module, a temperature prediction module, a mirror adjustment module, a heat collection efficiency detection module, and a feedback module;

[0008] The data acquisition module is used to acquire multi-source data from the device, preprocess the acquired parameters, and then reorganize them into a first data group, a second data group, and a third data group.

[0009] The illumination prediction module is used to couple the first data group and the second data group to generate illumination prediction coefficients (LCFs), analyze them, and determine whether angle adjustment is needed in advance based on the analysis results.

[0010] The temperature prediction module is used to couple the second and third data sets to generate the temperature change prediction coefficient (TCF), analyze it, and determine whether angle adjustment is needed in advance based on the analysis results.

[0011] The mirror adjustment module is used to couple the first data and the third data group to generate the mirror positioning coefficient MLC, and analyze it. Based on the analysis results, it is determined whether the angle adjustment needs to be performed in advance.

[0012] The heat collection efficiency detection module is used to couple the first data group, the second data group and the third data group to generate the heat collection efficiency coefficient CPF, and analyze it to determine whether the proposed angle adjustment will affect the heat collection efficiency.

[0013] The feedback module is used to send various data to the visualization terminal.

[0014] Preferably, the data acquisition module includes a data acquisition unit and a data preprocessing unit;

[0015] The data acquisition unit is used to acquire multi-source data from the device, including solar radiation intensity, solar altitude angle, solar azimuth angle, incident angle, mirror adjustment angle, ambient temperature, collector temperature, temperature change rate, target temperature, temperature feedback adjustment angle, heat collection efficiency, incident light power, heat loss rate, collector surface temperature, and gain adjustment angle.

[0016] The data preprocessing unit is used to preprocess and dimensionless the collected multi-source data, and to group the processed data to generate a first data group, a second data group, and a third data group.

[0017] The first data set includes solar radiation intensity (A), solar altitude angle (B), solar azimuth angle (C), angle of incidence (D), and mirror adjustment angle (E).

[0018] The second data set includes ambient temperature F, collector temperature G, temperature change rate H, target temperature I, and temperature feedback adjustment angle J.

[0019] The third data set includes the collector efficiency K, incident light power L, heat loss rate M, collector surface temperature N, and gain adjustment angle O.

[0020] Preferably, the illumination prediction module includes an illumination prediction data coupling unit and an illumination data analysis unit;

[0021] The illumination prediction data coupling unit is used to extract data from the first data group and the second data group, including solar radiation intensity A, incident angle D, mirror adjustment angle E, collector temperature G, and temperature feedback adjustment angle J. The extracted multiple data are input into a pre-trained deep learning framework, and feature fusion is performed through a multi-layer neural network to calculate and obtain the illumination prediction coefficient LCF.

[0022] The illumination data analysis unit is used to analyze the calculated illumination prediction coefficient (LCF) and, based on the analysis results, determine whether angle adjustment is needed in advance. The specific method is as follows:

[0023] when When this time, it means that the equipment needs to be adjusted in angle to avoid lag;

[0024] when When the time is right, it means that the device does not need to be adjusted at the current angle to avoid lag.

[0025] Preferably, the illumination prediction data coupling unit calculates the illumination prediction coefficient LCF using the following formula:

[0026] ;

[0027] In the formula: A is the solar radiation intensity, D is the incident angle, E is the surface adjustment angle, and G is the collector temperature.

[0028] A temperature-controlled solar thermal power generation system with a mirror tracking angle control system is characterized in that the temperature prediction module includes a temperature prediction data coupling unit and a temperature prediction analysis unit.

[0029] The temperature prediction data coupling unit is used to extract data from the second data group and the second data group, including collector temperature G, ambient temperature F, target temperature I, heat collection efficiency K, heat loss rate M, temperature feedback adjustment angle J, and temperature change rate H. The extracted multiple data are input into a pre-trained deep learning framework, and feature fusion is performed through a multi-layer neural network to calculate and obtain the temperature change prediction coefficient TCF.

[0030] The temperature prediction and analysis unit is used to analyze the calculated data and, based on the analysis results, determine whether angle adjustment is needed in advance. The specific method is as follows:

[0031] when When this time, it means that the equipment needs to be adjusted in angle to avoid lag;

[0032] when When the time is right, it means that the device does not need to be adjusted at the current angle to avoid lag.

[0033] Preferably, the temperature prediction data coupling unit calculates the temperature change prediction coefficient (TCF) using the following formula:

[0034] ;

[0035] In the formula: G is the collector temperature, F is the ambient temperature, I is the target temperature, K is the collector efficiency, M is the heat loss rate, J is the temperature feedback adjustment angle, and H is the temperature change rate.

[0036] Preferably, the mirror adjustment module includes a mirror adjustment data coupling unit and a mirror adjustment analysis unit;

[0037] The mirror adjustment data coupling unit is used to extract data from the first data group and the third data group, including solar radiation intensity A, incident angle D, solar altitude angle B, mirror adjustment angle E, heat collection efficiency K, heat loss rate M, gain adjustment angle O and incident light power L. The extracted multiple data are input into a pre-trained deep learning framework, and feature fusion is performed through a multi-layer neural network to calculate and obtain the temperature change prediction coefficient TCF.

[0038] The mirror adjustment analysis unit is used to analyze the calculated mirror positioning coefficient MLC and determine whether the mirror needs to be adjusted in advance based on the analysis results. The specific method is as follows:

[0039] when When this time, it means that the equipment needs to be adjusted in angle to avoid lag;

[0040] when When the time is right, it means that the device does not need to be adjusted at the current angle to avoid lag.

[0041] Preferably, the mirror adjustment data coupling unit calculates the mirror positioning coefficient MLC using the following formula:

[0042] ;

[0043] In the formula: A is the solar radiation intensity, D is the incident angle, B is the solar altitude angle, E is the mirror adjustment angle, K is the heat collection efficiency, M is the heat loss rate, O is the gain adjustment angle, and L is the incident light power.

[0044] Preferably, the heat collection efficiency detection module includes a heat collection efficiency data coupling unit and a heat collection efficiency analysis unit;

[0045] The heat collection efficiency data coupling unit is used to extract data from the first data group, the second data group and the third data group, and input the extracted multiple data into a pre-trained deep learning framework. Feature fusion is performed through a multi-layer neural network to calculate and obtain the heat collection efficiency coefficient CPF.

[0046] The heat collection efficiency analysis unit is used to analyze the calculated heat collection efficiency coefficient (CPF) and, based on the analysis results, determine whether angle adjustment will affect the heat collection efficiency. The specific method is as follows:

[0047] when When this condition is met, it means that the current adjustments to the equipment will not affect the heat collection performance, and the adjustments can be made.

[0048] when When this happens, it means that the current adjustment to the equipment will affect the heat collection performance, causing the first-stage heat collection to decay, and the operation needs to be delayed by 0.5 seconds.

[0049] when When this happens, it means that the current adjustment to the equipment will affect the heat collection performance and cause secondary heat collection attenuation, and the operation needs to be delayed by 1.5 seconds.

[0050] when This indicates that the current adjustments to the equipment will affect the heat collection performance, causing a three-stage heat collection attenuation, and therefore cannot be executed;

[0051] The heat collection efficiency data coupling unit calculates the heat collection efficiency coefficient (CPF) using the following formula:

[0052] ;

[0053] In the formula: A is the solar radiation intensity, B is the solar altitude angle, C is the solar azimuth angle, D is the incident angle, E is the mirror adjustment angle, F is the ambient temperature, G is the collector temperature, H is the temperature change rate, I is the target temperature, J is the temperature feedback adjustment angle, K is the heat collection efficiency, L is the incident light power, M is the heat loss rate, N is the collector surface temperature, and O is the gain adjustment angle.

[0054] A method for controlling the mirror tracking angle in a temperature-controlled concentrated solar power (CSP) system, comprising the following steps:

[0055] S1. The data acquisition module collects multi-source data from the device and preprocesses the collected parameters, then reorganizes them into the first data group, the second data group, and the third data group.

[0056] S2. The first data group and the second data group are coupled through the illumination prediction module to generate illumination prediction coefficients (LCFs), and the LCFs are analyzed. Based on the analysis results, it is determined whether angle adjustment is needed in advance.

[0057] S3. The temperature prediction module couples the second and third data sets to generate the temperature change prediction coefficient (TCF), analyzes it, and determines whether angle adjustment is needed in advance based on the analysis results.

[0058] S4. The mirror adjustment module couples the first and third data groups to generate the mirror positioning coefficient MLC, analyzes it, and determines whether angle adjustment needs to be performed in advance based on the analysis results.

[0059] S5. The first data group, the second data group and the third data group are coupled through the heat collection efficiency detection module to generate the heat collection efficiency coefficient CPF, and the CPF is analyzed to determine whether the angle adjustment to be performed will affect the heat collection efficiency.

[0060] S6. Feedback various data to the visualization terminal through the feedback module. Attached Figure Description

[0061] Figure 1 This is the system flowchart for this application.

[0062] Figure 2 This is a flowchart illustrating the steps of the method described in this application.

[0063] In the diagram: 1. Data acquisition module; 11. Data acquisition unit; 12. Data preprocessing unit; 2. Illumination prediction module; 21. Illumination prediction data coupling unit; 22. Illumination data analysis unit; 3. Temperature prediction module; 31. Temperature prediction data coupling unit; 32. Temperature prediction analysis unit; 4. Mirror adjustment module; 41. Mirror adjustment data coupling unit; 42. Mirror adjustment analysis unit; 5. Heat collection efficiency detection module; 51. Heat collection efficiency data coupling unit; 52. Heat collection efficiency analysis unit; 6. Feedback module. Detailed Implementation

[0064] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0065] The terms "first," "second," and "third" in this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0066] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0067] Example 1: Please refer to Figure 1 A temperature-controlled solar thermal power generation system mirror tracking angle control system and method, including a data acquisition module 1, an illumination prediction module 2, a temperature prediction module 3, a mirror adjustment module 4, a heat collection efficiency detection module 5, and a feedback module 6.

[0068] The data acquisition module 1 is used to acquire multi-source data from the device, preprocess the acquired parameters, and then reorganize them into the first data group, the second data group, and the third data group.

[0069] The illumination prediction module 2 is used to couple the data of the first data group and the second data group to generate the illumination prediction coefficient LCF, and analyze it. Based on the analysis results, it is determined whether the angle adjustment needs to be performed in advance.

[0070] Temperature prediction module 3 is used to couple the second and third data sets to generate temperature change prediction coefficient (TCF), analyze the TCF, and determine whether angle adjustment is needed in advance based on the analysis results.

[0071] The mirror adjustment module 4 is used to couple the first data and the third data group to generate the mirror positioning coefficient MLC, and analyze it. Based on the analysis results, it is determined whether the angle adjustment needs to be performed in advance.

[0072] The heat collection efficiency detection module 5 is used to couple the first data group, the second data group and the third data group to generate the heat collection efficiency coefficient CPF, and analyze it to determine whether the proposed angle adjustment will affect the heat collection efficiency.

[0073] Feedback module 6 is used to send various data to the visualization terminal.

[0074] In this embodiment, the data acquisition module 1 is the foundational module of the system, primarily used for real-time, multi-source data acquisition of various parameters of the solar thermal power generation system. This data includes solar radiation intensity, solar altitude angle, incident angle, collector temperature, temperature change rate, and target temperature. By monitoring these key parameters, the system ensures accurate input information. This module preprocesses and dimensionlessly transforms the acquired raw data, then organizes it into three data sets: a first data set, a second data set, and a third data set, providing precise input for subsequent data analysis and prediction modules. The core task of this module is to ensure the system can acquire data stably and in real-time, providing a reliable basis for subsequent operations such as prediction and adjustment.

[0075] The main function of the illumination prediction module 2 is to predict illumination conditions based on the combination of the first and second data sets and generate the illumination prediction coefficient (LCF). This module analyzes data such as solar radiation intensity, incident angle, and mirror adjustment angle, combined with collector temperature and temperature feedback adjustment angle, to assess current illumination conditions in real time and predict future illumination trends. Through data coupling and feature fusion within a deep learning framework, the system can derive the illumination prediction coefficient (LCF) and determine whether mirror angle adjustment is necessary in advance based on preset analysis criteria, thereby avoiding efficiency losses due to lag. This module significantly improves the solar thermal system's responsiveness to rapidly changing illumination conditions, ensuring maximum illumination reception by the collector system and enhancing the overall system efficiency.

[0076] The core task of temperature prediction module 3 is to generate the temperature change prediction coefficient (TCF) based on the coupling of the second and third data sets. This module analyzes data including collector temperature, ambient temperature, temperature change rate, collector efficiency, and heat loss rate to predict the collector's temperature change trend. Based on the difference between the current and target temperatures, it determines whether the mirror angle needs to be adjusted in advance. By analyzing the temperature change rate and feedback adjustment angle, the temperature prediction module effectively solves the system's lag in responding to temperature changes. It ensures that the system can adjust the collector angle promptly in the early stages of temperature changes, thereby avoiding the adverse effects of temperature fluctuations on system performance and improving collector efficiency and system stability.

[0077] The mirror adjustment module 4 is responsible for coupling data based on the first and third data sets to generate the mirror positioning coefficient MLC. This module combines information such as solar radiation intensity, incident angle, solar altitude angle, mirror adjustment angle, and heat collection efficiency to analyze and calculate in real time whether the mirror angle needs adjustment. Through feature fusion using a deep learning framework, the system can automatically evaluate whether the current mirror position is optimal and determine whether the mirror angle needs to be adjusted in advance to optimize light focusing and heat collection. This module addresses the shortcomings of traditional solar thermal systems that rely on fixed adjustment strategies, making mirror adjustment more intelligent and adaptive, thereby improving heat collection efficiency.

[0078] The solar collector efficiency detection module 5 couples the first, second, and third data sets to generate the solar collector efficiency coefficient (CPF). This module monitors the system's solar collector efficiency in real time and determines whether preset angle adjustments will affect the heat collection effect based on the CPF. When the CPF exceeds a specific value, the system will perform angle adjustment; if the CPF is too low, angle adjustment will be delayed to avoid wasting energy. This module ensures that the solar thermal system maintains optimal heat collection performance under different light and temperature conditions by accurately detecting the system's solar collector efficiency, effectively preventing efficiency loss due to improper adjustment.

[0079] Feedback module 6 transmits information such as light prediction, temperature prediction, and thermal efficiency to the visualization terminal based on real-time collected data and calculation results. This allows operators to view the system's operating status in real time, understand the changing trends of various parameters, and make timely adjustments. This module not only provides actionable feedback information but also offers intuitive decision-making support through data visualization, helping to optimize system management and maintenance, and improve system reliability and usability.

[0080] Existing concentrated solar power (CSP) systems often rely on fixed mirror angle adjustment strategies, which cannot respond promptly to complex changes in illumination and temperature, resulting in low heat collection efficiency. This system, however, utilizes the collaboration of multiple intelligent modules, including data acquisition, illumination prediction, temperature prediction, and mirror adjustment, to adjust the collector angle in real time and accurately, maximizing illumination reception efficiency and optimizing heat collection performance. Furthermore, the system employs a heat collection efficiency detection and feedback module to ensure that angle adjustment does not affect heat collection efficiency, making operation more intelligent and automated. Overall, this temperature-sensing-based joint adjustment method enhances the adaptive capability, heat collection efficiency, and system stability of CSP systems, overcoming the lag and instability inherent in traditional technologies.

[0081] Example 2: Please refer to Figure 1 The data acquisition module 1 includes a data acquisition unit 11 and a data preprocessing unit 12;

[0082] The data acquisition unit 11 is used to acquire multi-source data of the device, including solar radiation intensity, solar altitude angle, solar azimuth angle, incident angle, mirror adjustment angle, ambient temperature, collector temperature, temperature change rate, target temperature, temperature feedback adjustment angle, heat collection efficiency, incident light power, heat loss rate, collector surface temperature, and gain adjustment angle.

[0083] The data preprocessing unit 12 is used to preprocess and dimensionless the collected multi-source data, and to group the processed data to generate a first data group, a second data group, and a third data group.

[0084] The first data set includes solar radiation intensity (A), solar altitude angle (B), solar azimuth angle (C), angle of incidence (D), and mirror adjustment angle (E).

[0085] The second data set includes ambient temperature F, collector temperature G, temperature change rate H, target temperature I, and temperature feedback adjustment angle J.

[0086] The third data set includes the collector efficiency K, incident light power L, heat loss rate M, collector surface temperature N, and gain adjustment angle O.

[0087] In this embodiment, the data acquisition unit 11 performs multi-source data acquisition on the device, enabling the system to monitor and collect multiple key parameters in real time, including solar radiation intensity, solar altitude angle, collector temperature, temperature change rate, and heat collection efficiency. This comprehensive data acquisition allows the system to simultaneously obtain all important information related to factors such as illumination, temperature, and heat collection efficiency. In contrast, traditional systems often rely on relatively simple parameters and cannot comprehensively acquire all factors affecting system efficiency.

[0088] The data preprocessing unit 12 processes and dimensionlessly transforms the raw data after data acquisition. This helps eliminate dimensional differences between data points, enabling all data to be processed according to a unified standard. Dimensionless data improves the stability and accuracy of subsequent analysis, avoiding calculation errors caused by different dimensions.

[0089] Through the aforementioned improvements, the mirror tracking angle control system for temperature-controlled concentrated solar power (CSP) has been optimized across all stages of data acquisition, processing, and analysis. Compared to traditional CSP systems, this system, based on real-time multi-source data acquisition and intelligent analysis, significantly enhances its responsiveness to changes in illumination, temperature, and collector efficiency. Through modular data grouping, real-time feedback, and dimensionless processing, the system can more precisely adjust the mirror angle and collector settings, effectively improving collector efficiency and system stability. Furthermore, the system's adaptive capabilities enable it to maintain high-efficiency operation under diverse environmental conditions, overcoming the lag and instability inherent in traditional technologies.

[0090] Example 3: Please refer to Figure 1 The illumination prediction module 2 includes an illumination prediction data coupling unit 21 and an illumination data analysis unit 22;

[0091] The illumination prediction data coupling unit 21 is used to extract data from the first data group and the second data group, including solar radiation intensity A, incident angle D, mirror adjustment angle E, collector temperature G and temperature feedback adjustment angle J, and input the extracted multiple data into a pre-trained deep learning framework, perform feature fusion through a multi-layer neural network, and then calculate and obtain the illumination prediction coefficient LCF.

[0092] The illumination data analysis unit 22 is used to analyze the calculated illumination prediction coefficient (LCF) and determine whether angle adjustment is needed in advance based on the analysis results. The specific method is as follows:

[0093] when When this time, it means that the equipment needs to be adjusted in angle to avoid lag;

[0094] when When the time is right, it means that the device does not need to be adjusted at the current angle to avoid lag.

[0095] In this embodiment, the illumination prediction data coupling unit 21 extracts key data such as solar radiation intensity, incident angle, mirror adjustment angle, collector temperature, and temperature feedback adjustment angle from the first and second data sets, and inputs this data into a pre-trained deep learning framework for feature fusion using a multi-layer neural network. This data fusion method automatically learns the complex relationships between various factors through a deep learning model, providing more accurate and intelligent illumination prediction.

[0096] The illumination data analysis unit 22 analyzes the calculated illumination prediction coefficient (LCF) and determines whether the mirror angle needs to be adjusted in advance based on the analysis results. According to the set threshold, when the LCF value is greater than 0.25, the system automatically determines that the device angle needs to be adjusted to avoid lag; when the LCF value is less than or equal to 0.25, the system determines that no adjustment is needed.

[0097] By setting a critical value for the Light Prediction Factor (LCF), the system can accurately determine when angle adjustment is needed, thereby avoiding the impact of hysteresis on heat collection efficiency. When the LCF value is greater than 0.25, it indicates that the current light change is large and pre-adjustment is required; while when the LCF is less than or equal to 0.25, it indicates that the light change is small and the system can delay adjustment, thereby avoiding frequent and unnecessary adjustments.

[0098] The illumination prediction module 2 effectively solves the problems of lag and slow response in traditional concentrated solar power (CSP) systems by combining data coupling with a deep learning framework. Compared with traditional systems, this system significantly improves the accuracy of illumination prediction and the precision of adjustment decisions through multi-source data fusion and real-time analysis, ensuring that the mirror angle can be adjusted in real time according to illumination conditions to maximize heat collection efficiency. This intelligent and automated adjustment method not only improves the system's heat collection efficiency but also enables the system to better adapt to environmental changes, ensuring stable operating performance and overcoming the lag problem of traditional CSP systems.

[0099] Example 4: Please refer to Figure 1 The illumination prediction data coupling unit 21 calculates the illumination prediction coefficient LCF using the following formula:

[0100] ;

[0101] In the formula: A is the solar radiation intensity, D is the incident angle, E is the surface adjustment angle, and G is the collector temperature.

[0102] In this embodiment, the formula combines solar radiation intensity A, incident angle D, mirror adjustment angle E, and collector temperature G to form a comprehensive illumination prediction coefficient LCF that takes into account multiple factors. In particular, the introduction of incident angle and collector temperature can more accurately reflect the actual illumination reception and the working status of the collector.

[0103] By incorporating a mirror adjustment angle E, the system can adjust the mirror angle of the solar collector in real time to optimize the focusing effect of sunlight. A reasonable mirror adjustment angle ensures that the solar collector is always at the optimal angle, thereby maximizing the light absorption rate.

[0104] By adjusting the ratio of the temperature feedback adjustment angle J to the collector temperature G, the system can adjust the response of the solar collector system in real time when the collector temperature changes, avoiding unnecessary adjustments caused by temperature variations. This feedback mechanism makes the light prediction coefficient (LCF) more sensitive, enabling the system to respond quickly to environmental changes.

[0105] By integrating multiple key parameters into the calculation formula of the illumination prediction coefficient (LCF), the illumination prediction data coupling unit 21 provides the system with a more accurate and real-time illumination adjustment strategy. The core advantage of this method lies in its ability to flexibly adjust the mirror angle and collector status, maximizing heat collection efficiency and avoiding performance losses caused by hysteresis, over-adjustment, or under-adjustment in traditional systems. This ensures that the system can operate efficiently under different environmental conditions, greatly improving the overall efficiency of the solar thermal power generation system.

[0106] Example 5: Please refer to Figure 1 The temperature prediction module 3 includes a temperature prediction data coupling unit 31 and a temperature prediction analysis unit 32;

[0107] The temperature prediction data coupling unit 31 is used to extract data from the second data group and the second data group, including collector temperature G, ambient temperature F, target temperature I, heat collection efficiency K, heat loss rate M, temperature feedback adjustment angle J and temperature change rate H. The extracted multiple data are input into a pre-trained deep learning framework, and feature fusion is performed through a multi-layer neural network to calculate and obtain the temperature change prediction coefficient TCF.

[0108] The temperature prediction and analysis unit 32 is used to analyze the calculated data and determine whether angle adjustment is needed in advance based on the analysis results. The specific method is as follows:

[0109] when When this time, it means that the equipment needs to be adjusted in angle to avoid lag;

[0110] when When the time is right, it means that the device does not need to be adjusted at the current angle to avoid lag.

[0111] In this embodiment, the temperature prediction data coupling unit 31 extracts multiple key parameters from the second data set, including collector temperature G, ambient temperature F, target temperature I, heat collection efficiency K, heat loss rate M, temperature feedback adjustment angle J, and temperature change rate H, and inputs these data into a pre-trained deep learning framework. Through feature fusion using multi-layer neural networks, the system can process and analyze this complex temperature change data, accurately predicting future temperature change trends.

[0112] By calculating the Temperature Change Prediction Factor (TCF), this module can quantify the relationship between temperature changes and factors such as collector efficiency and heat loss. Through analysis of factors such as collector temperature, ambient temperature, target temperature, and collector efficiency, a real-time TCF value is calculated, indicating whether angle adjustment is needed. When the TCF value exceeds a specific threshold, such as 0.35, the system determines that angle adjustment is necessary in advance to avoid a decrease in collector efficiency due to lag in temperature changes.

[0113] The temperature prediction and analysis unit 32 analyzes the calculated temperature change prediction coefficient (TCF) and determines whether the collector angle needs to be adjusted in advance based on the TCF value. When the TCF value is greater than 0.35, it means that the system needs to adjust the angle in advance; when the TCF value is less than or equal to 0.35, the system determines that no adjustment is needed. In this way, the system can react in the early stages of temperature changes, avoiding a decrease in heat collection efficiency due to adjustment lag.

[0114] Through innovative design of the temperature prediction module, particularly the introduction of a deep learning framework and the calculation of the Temperature Change Prediction Factor (TCF), this system significantly improves the temperature regulation capability of the concentrated solar power (CSP) system. Compared to traditional CSP systems, which often rely on relatively simple temperature control strategies and have slow responses, this system, by analyzing multi-dimensional data such as collector temperature, ambient temperature, and collector efficiency, can quickly predict temperature changes and make adjustments in advance. This not only improves collector efficiency but also significantly enhances the system's stability and adaptability, overcoming the lag problem inherent in traditional technologies.

[0115] Example 6: Please refer to Figure 1 A temperature-controlled solar thermal power generation system mirror tracking angle control system and method, wherein the temperature prediction data coupling unit 31 calculates the temperature change prediction coefficient TCF using the following formula:

[0116] ;

[0117] In the formula: G is the collector temperature, F is the ambient temperature, I is the target temperature, K is the collector efficiency, M is the heat loss rate, J is the temperature feedback adjustment angle, and H is the temperature change rate.

[0118] In this embodiment, the formula represents the ratio of the difference between the collector temperature G and the ambient temperature F to the difference between the target temperature I and the ambient temperature F. This relationship helps the system assess in real time how close the current collector temperature is to the target temperature, thereby determining whether advance adjustment is necessary. The system can predict whether the collector has reached the predetermined temperature target based on temperature change trends, thus optimizing the heat collection efficiency.

[0119] The formula combines the heat collection efficiency K and the heat loss rate M. The system achieves optimal heat collection performance when both heat collection efficiency and heat loss rate are high. This allows the system to adjust its control strategy in real time under different light and temperature conditions to optimize heat collection efficiency, thereby avoiding performance degradation caused by excessive heat loss or low heat collection efficiency.

[0120] The formula combines the temperature feedback adjustment angle J and the temperature change rate H. The temperature feedback adjustment angle reflects the difference between the collector temperature and the target temperature, while the temperature change rate indicates how fast the temperature changes. This parameter can adjust the collector angle in a timely manner according to the rate of temperature change and the feedback adjustment angle, ensuring that the system can respond quickly to temperature changes and maximize heat collection efficiency.

[0121] Example 7: Please refer to Figure 1 The mirror adjustment module 4 includes a mirror adjustment data coupling unit 41 and a mirror adjustment analysis unit 42;

[0122] The mirror adjustment data coupling unit 41 is used to extract data from the first data group and the third data group, including solar radiation intensity A, incident angle D, solar altitude angle B, mirror adjustment angle E, heat collection efficiency K, heat loss rate M, gain adjustment angle O and incident light power L. The extracted multiple data are input into a pre-trained deep learning framework, and feature fusion is performed through a multi-layer neural network to calculate and obtain the temperature change prediction coefficient TCF.

[0123] The mirror adjustment analysis unit 42 is used to analyze the calculated mirror positioning coefficient MLC and determine whether the mirror needs to be adjusted in advance based on the analysis results. The specific method is as follows:

[0124] when When this time, it means that the equipment needs to be adjusted in angle to avoid lag;

[0125] when When the time is right, it means that the device does not need to be adjusted at the current angle to avoid lag.

[0126] In this embodiment, the mirror adjustment data coupling unit 41 extracts multiple key parameters from the first and third data sets, including solar radiation intensity A, incident angle D, solar altitude angle B, mirror adjustment angle E, heat collection efficiency K, heat loss rate M, gain adjustment angle O, and incident light power L. This data is then input into a pre-trained deep learning framework for multi-layer neural network feature fusion. Through this comprehensive analysis, the system can accurately calculate the temperature change prediction coefficient (TCF), providing intelligent decision support for subsequent mirror adjustment.

[0127] The mirror adjustment analysis unit 42 is responsible for analyzing the calculated mirror positioning coefficient MLC. When the mirror positioning coefficient MLC is greater than 0.45, it indicates that angle adjustment needs to be performed in advance to avoid lag; when MLC is less than or equal to 0.45, it indicates that no adjustment is needed in advance. Through this analysis, the system can determine in real time whether the mirror angle needs to be adjusted, thereby ensuring maximum heat collection efficiency.

[0128] The design of mirror adjustment module 4, through deep learning framework and intelligent data analysis, significantly improves the regulation capability and efficiency of the concentrated solar power (CSP) system. Compared with traditional systems, this module can not only adjust the mirror angle in real time, but also intelligently determine whether the angle needs to be adjusted in advance based on factors such as illumination, temperature, and heat collection efficiency, avoiding lag and energy loss.

[0129] Example 8: Please refer to Figure 1 The mirror adjustment data coupling unit 41 calculates the mirror positioning coefficient MLC using the following formula:

[0130] ;

[0131] In the formula: A is the solar radiation intensity, D is the incident angle, B is the solar altitude angle, E is the mirror adjustment angle, K is the heat collection efficiency, M is the heat loss rate, O is the gain adjustment angle, and L is the incident light power.

[0132] In this embodiment, the formula combines solar radiation intensity A, incident angle D, solar altitude angle B, and mirror adjustment angle E. This combination considers the effectiveness of solar radiation reaching the collector surface and the effect of collector mirror adjustment. By calculating the product of these factors, the system can accurately assess the angular relationship between the current illumination conditions and the collector surface, thereby optimizing light focusing and heat collection efficiency.

[0133] Combining the collector efficiency K and the heat loss rate M reflects the energy conversion capability of the solar collector under different operating environments. A high collector efficiency and a low heat loss rate allow the system to utilize the collected heat more effectively. This ratio enables the system to assess the collector's performance under current conditions and adjust the mirror angle accordingly to maintain optimal heat collection.

[0134] By comprehensively considering multiple key parameters, the mirror adjustment data coupling unit 41 enables the system to evaluate the mirror adjustment needs in real time, ensuring that the mirror angle is adjusted promptly and accurately under different lighting conditions. This module utilizes a deep learning framework for real-time calculation and adjustment, enabling the photothermal system to respond quickly to environmental changes and improving the system's adaptability.

[0135] Example 9: Please refer to Figure 1The heat collection efficiency detection module 5 includes a heat collection efficiency data coupling unit 51 and a heat collection efficiency analysis unit 52;

[0136] The heat collection efficiency data coupling unit 51 is used to extract data from the first data group, the second data group and the third data group, and input the extracted multiple data into a pre-trained deep learning framework. Feature fusion is performed through a multi-layer neural network to calculate and obtain the heat collection efficiency coefficient CPF.

[0137] The heat collection efficiency analysis unit 52 is used to analyze the calculated heat collection efficiency coefficient (CPF). Based on the analysis results, it determines whether angle adjustment will affect the heat collection efficiency. The specific method is as follows:

[0138] when When this condition is met, it means that the current adjustments to the equipment will not affect the heat collection performance, and the adjustments can be made.

[0139] when When this happens, it means that the current adjustment to the equipment will affect the heat collection performance, causing the first-stage heat collection to decay, and the operation needs to be delayed by 0.5 seconds.

[0140] when When this happens, it means that the current adjustment to the equipment will affect the heat collection performance and cause secondary heat collection attenuation, and the operation needs to be delayed by 1.5 seconds.

[0141] when This indicates that the current adjustment to the equipment will affect the heat collection performance, causing a three-stage heat collection decay, making it impossible to execute.

[0142] In this embodiment, the heat collection efficiency data coupling unit 51 extracts multiple data points from the first, second, and third data groups, and inputs the extracted parameters into a pre-trained deep learning framework. It then uses a multi-layer neural network to fuse features, thereby calculating the heat collection efficiency coefficient (CPF). This deep learning method not only automatically learns complex relationships from data but also accurately predicts heat collection efficiency under constantly changing environmental conditions.

[0143] The heat collection efficiency analysis unit 52 is responsible for analyzing the calculated heat collection efficiency coefficient (CPF) and determining whether angle adjustment should be performed based on the analysis results. Through hierarchical management of the CPF value, when the CPF value is greater than or equal to 1, it indicates that the current adjustment will not affect the heat collection efficiency and can be executed immediately; when the CPF value is between 0.9 and 1, the system will delay the adjustment for 0.5 seconds; when the CPF value is between 0.75 and 0.9, it will cause a first-level heat collection attenuation, and the adjustment will be delayed for 1.5 seconds; and when the CPF is less than 0.75, the system determines that the adjustment will have a significant impact on the heat collection efficiency and therefore will not perform angle adjustment.

[0144] Based on the different ranges of the coefficient of performance (CPF), the system effectively avoids over-adjustment or under-adjustment by delaying the execution of adjustments. For example, when the CPF value is low, below 0.75, the system decides not to perform adjustments to avoid unnecessary energy waste; when the CPF value is high, above 1, the system will immediately perform adjustments to ensure optimal heat collection performance.

[0145] By introducing the collector efficiency detection module 5, the system not only achieves real-time calculation and monitoring of collector efficiency, but also intelligently determines whether the collector angle needs adjustment through deep learning algorithms and collector efficiency factor (CPF) analysis. This mechanism significantly improves the response speed and adaptability of the solar thermal power generation system, enabling the system to dynamically adjust collector efficiency under complex environmental conditions and avoid energy waste caused by over-adjustment or under-adjustment. Compared with traditional solar thermal systems, this system can monitor collector efficiency in real time and make intelligent decisions, ensuring that the system maintains the highest collector efficiency under different light and temperature changes, thereby improving overall efficiency and system stability.

[0146] Example 10: Please refer to Figure 1 The collector efficiency data coupling unit 51 calculates the collector efficiency coefficient (CPF) using the following formula:

[0147] ;

[0148] In the formula: A is the solar radiation intensity, B is the solar altitude angle, C is the solar azimuth angle, D is the incident angle, E is the mirror adjustment angle, F is the ambient temperature, G is the collector temperature, H is the temperature change rate, I is the target temperature, J is the temperature feedback adjustment angle, K is the heat collection efficiency, L is the incident light power, M is the heat loss rate, N is the collector surface temperature, and O is the gain adjustment angle.

[0149] In this embodiment, the effects of solar radiation intensity A, incident angle D, solar altitude angle B, and mirror adjustment angle E on the solar collector efficiency are reflected. Through this comprehensive formula, the system can calculate the optimal combination of current light intensity and solar collector angle in real time, ensuring that the solar collector operates under optimal lighting conditions.

[0150] This system combines the temperature feedback adjustment angle J and the temperature change rate H, while also considering the difference between the collector temperature G and the ambient temperature F. Through this component, the system can adjust according to the difference between the collector temperature and the target temperature, and respond promptly to temperature changes.

[0151] By combining the heat collection efficiency K and the heat loss rate M, the system ensures that heat loss is minimized and heat collection efficiency is maximized during operation. A lower heat loss rate allows the system to convert photothermal energy into actual thermal energy more efficiently. The relationship between incident light power L and collector surface temperature N is considered, and a gain adjustment angle O is introduced. The gain adjustment angle affects the focusing effect of the mirror, while the incident light power determines the intensity of the actual illumination. By combining these two parameters, the system can adjust the collector angle and heat collection efficiency promptly when illumination conditions change.

[0152] A method for controlling the mirror tracking angle in a temperature-controlled concentrated solar power (CSP) system; please refer to [link / reference]. Figure 2 The specific steps are as follows:

[0153] S1. The device acquires multi-source data through the data acquisition module 1, and preprocesses the acquired parameters, and then reorganizes them into the first data group, the second data group and the third data group.

[0154] S2. The first data group and the second data group are coupled by the illumination prediction module 2 to generate the illumination prediction coefficient LCF, and the LCF is analyzed. Based on the analysis results, it is determined whether the angle adjustment needs to be performed in advance.

[0155] S3. The second and third data groups are coupled through the temperature prediction module 3 to generate the temperature change prediction coefficient TCF, and the analysis is performed. Based on the analysis results, it is determined whether the angle adjustment needs to be performed in advance.

[0156] S4. The mirror adjustment module 4 couples the first and third data groups to generate the mirror positioning coefficient MLC, analyzes it, and determines whether the angle adjustment needs to be performed in advance based on the analysis results.

[0157] S5. The first data group, the second data group and the third data group are coupled through the heat collection efficiency detection module 5 to generate the heat collection efficiency coefficient CPF, and the CPF is analyzed to determine whether the angle adjustment to be performed will affect the heat collection efficiency.

[0158] S6. Feedback modules 6 feed back various data to the visualization terminal.

[0159] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0160] The specific embodiments of the invention have been described in detail above, but they are only examples, and this application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this application. Therefore, all equivalent changes, modifications, and improvements made without departing from the spirit and principles of this application should be covered within the scope of this application.

Claims

1. A mirror tracking angle control system for a temperature-controlled solar thermal power generation system, characterized in that: It includes a data acquisition module (1), a light prediction module (2), a temperature prediction module (3), a mirror adjustment module (4), a heat collection efficiency detection module (5), and a feedback module (6); The data acquisition module (1) is used to acquire multi-source data from the device and preprocess the acquired parameters, and then reorganize them into a first data group, a second data group and a third data group. The illumination prediction module (2) is used to couple the data of the first data group and the second data group to generate the illumination prediction coefficient LCF, and analyze it. Based on the analysis results, it is determined whether the angle needs to be adjusted in advance. The temperature prediction module (3) is used to couple the second data group and the third data group to generate the temperature change prediction coefficient TCF, and analyze it. Based on the analysis results, it is determined whether the angle needs to be adjusted in advance. The mirror adjustment module (4) is used to couple the first data and the third data group to generate the mirror positioning coefficient MLC, and analyze it. Based on the analysis results, it is determined whether the angle adjustment needs to be performed in advance. The heat collection efficiency detection module (5) is used to couple the first data group, the second data group and the third data group to generate the heat collection efficiency coefficient CPF, and analyze it to determine whether the angle adjustment to be performed will affect the heat collection efficiency. The feedback module (6) is used to feed back various data to the visualization terminal.

2. The mirror tracking angle control system for a temperature-controlled solar thermal power generation system according to claim 1, characterized in that, The data acquisition module (1) includes a data acquisition unit (11) and a data preprocessing unit (12). The data acquisition unit (11) is used to acquire multi-source data of the device, including solar radiation intensity, solar altitude angle, solar azimuth angle, incident angle, mirror adjustment angle, ambient temperature, collector temperature, temperature change rate, target temperature, temperature feedback adjustment angle, heat collection efficiency, incident light power, heat loss rate, collector surface temperature and gain adjustment angle. The data preprocessing unit (12) is used to preprocess and dimensionless the collected multi-source data, and to group the processed data to generate a first data group, a second data group and a third data group. The first data set includes solar radiation intensity (A), solar altitude angle (B), solar azimuth angle (C), angle of incidence (D), and mirror adjustment angle (E). The second data set includes ambient temperature F, collector temperature G, temperature change rate H, target temperature I, and temperature feedback adjustment angle J. The third data set includes the collector efficiency K, incident light power L, heat loss rate M, collector surface temperature N, and gain adjustment angle O.

3. The mirror tracking angle control system for a temperature-controlled solar thermal power generation system according to claim 2, characterized in that, The illumination prediction module (2) includes an illumination prediction data coupling unit (21) and an illumination data analysis unit (22). The illumination prediction data coupling unit (21) is used to extract data from the first data group and the second data group, including solar radiation intensity A, incident angle D, mirror adjustment angle E, collector temperature G and temperature feedback adjustment angle J, and input the extracted multiple data into a pre-trained deep learning framework, perform feature fusion through a multi-layer neural network, and then calculate and obtain the illumination prediction coefficient LCF. The illumination data analysis unit (22) is used to perform data analysis on the calculated illumination prediction coefficient LCF, and determine whether angle adjustment is needed in advance based on the analysis results. The specific method is as follows: when When this time, it means that the equipment needs to be adjusted in angle to avoid lag; when When the time is right, it means that the device does not need to be adjusted at the current angle to avoid lag.

4. The mirror tracking angle control system for a temperature-controlled solar thermal power generation system according to claim 3, characterized in that, The illumination prediction data coupling unit (21) calculates the illumination prediction coefficient LCF using the following formula: ； In the formula: A is the solar radiation intensity, D is the incident angle, E is the surface adjustment angle, and G is the collector temperature.

5. The mirror tracking angle control system for a temperature-controlled solar thermal power generation system according to claim 4, characterized in that, The temperature prediction module (3) includes a temperature prediction data coupling unit (31) and a temperature prediction analysis unit (32). The temperature prediction data coupling unit (31) is used to extract data from the second data group and the second data group, including collector temperature G, ambient temperature F, target temperature I, heat collection efficiency K, heat loss rate M, temperature feedback adjustment angle J and temperature change rate H, and input the extracted multiple data into a pre-trained deep learning framework, perform feature fusion through a multi-layer neural network, and then calculate and obtain the temperature change prediction coefficient TCF. The temperature prediction and analysis unit (32) is used to analyze the calculated data and determine whether angle adjustment is needed in advance based on the analysis results. The specific method is as follows: when When this time, it means that the equipment needs to be adjusted in angle to avoid lag; when When the time is right, it means that the device does not need to be adjusted at the current angle to avoid lag.

6. The mirror tracking angle control system for a temperature-controlled solar thermal power generation system according to claim 5, characterized in that, The temperature prediction data coupling unit (31) calculates the temperature change prediction coefficient (TCF) using the following formula: ； In the formula: G is the collector temperature, F is the ambient temperature, I is the target temperature, K is the collector efficiency, M is the heat loss rate, J is the temperature feedback adjustment angle, and H is the temperature change rate.

7. The mirror tracking angle control system for a temperature-controlled solar thermal power generation system according to claim 6, characterized in that, The mirror adjustment module (4) includes a mirror adjustment data coupling unit (41) and a mirror adjustment analysis unit (42). The mirror adjustment data coupling unit (41) is used to extract data from the first data group and the third data, including solar radiation intensity A, incident angle D, solar altitude angle B, mirror adjustment angle E, heat collection efficiency K, heat loss rate M, gain adjustment angle O and incident light power L, and input the extracted multiple data into a pre-trained deep learning framework, perform feature fusion through a multi-layer neural network, and then calculate and obtain the temperature change prediction coefficient TCF. The mirror adjustment analysis unit (42) is used to perform data analysis on the calculated mirror positioning coefficient MLC, and to determine whether the mirror needs to be adjusted in advance based on the analysis results. The specific method is as follows: when When this time, it means that the equipment needs to be adjusted in angle to avoid lag; when When the time is right, it means that the device does not need to be adjusted at the current angle to avoid lag.

8. The mirror tracking angle control system for a temperature-controlled solar thermal power generation system according to claim 7, characterized in that, The mirror adjustment data coupling unit (41) calculates the mirror positioning coefficient MLC using the following formula: ； In the formula: A is the solar radiation intensity, D is the incident angle, B is the solar altitude angle, E is the mirror adjustment angle, K is the heat collection efficiency, M is the heat loss rate, O is the gain adjustment angle, and L is the incident light power.

9. The mirror tracking angle control system for a temperature-controlled solar thermal power generation system according to claim 8, characterized in that, The heat collection efficiency detection module (5) includes a heat collection efficiency data coupling unit (51) and a heat collection efficiency analysis unit (52). The heat collection efficiency data coupling unit (51) is used to extract data from the first data group, the second data group and the third data group, and input the extracted multiple data into a pre-trained deep learning framework, perform feature fusion through a multi-layer neural network, and then calculate and obtain the heat collection efficiency coefficient CPF. The heat collection efficiency analysis unit (52) is used to perform data analysis on the calculated heat collection efficiency coefficient CPF. Based on the analysis results, it determines whether angle adjustment will affect the heat collection efficiency. The specific method is as follows: when When this condition is met, it means that the current adjustments to the equipment will not affect the heat collection performance, and the adjustments can be made. when When this happens, it means that the current adjustment to the equipment will affect the heat collection performance, causing the first-stage heat collection to decay, and the operation needs to be delayed by 0.5 seconds. when When this happens, it means that the current adjustment to the equipment will affect the heat collection performance and cause secondary heat collection attenuation, and the operation needs to be delayed by 1.5 seconds. when This indicates that the current adjustments to the equipment will affect the heat collection performance, causing a three-stage heat collection attenuation, and therefore cannot be executed; The heat collection efficiency data coupling unit (51) calculates the heat collection efficiency coefficient (CPF) using the following formula: ； In the formula: A is the solar radiation intensity, B is the solar altitude angle, C is the solar azimuth angle, D is the incident angle, E is the mirror adjustment angle, F is the ambient temperature, G is the collector temperature, H is the temperature change rate, I is the target temperature, J is the temperature feedback adjustment angle, K is the heat collection efficiency, L is the incident light power, M is the heat loss rate, N is the collector surface temperature, and O is the gain adjustment angle.

10. A method for controlling the mirror tracking angle of a solar thermal power generation system based on temperature control, characterized in that: The temperature-controlled solar thermal power generation system mirror tracking angle control method is executed by the temperature-controlled solar thermal power generation system mirror tracking angle control system described in any one of claims 1 to 9, and the specific steps are as follows: S1. The device is subjected to multi-source data acquisition through the data acquisition module (1), and the acquired parameters are preprocessed and then reorganized into the first data group, the second data group and the third data group. S2. The first data group and the second data group are coupled by the illumination prediction module (2) to generate the illumination prediction coefficient LCF, and the LCF is analyzed. Based on the analysis results, it is determined whether the angle adjustment needs to be performed in advance. S3. The temperature prediction module (3) couples the second data group and the third data group to generate the temperature change prediction coefficient TCF and analyzes it. Based on the analysis results, it is determined whether the angle adjustment needs to be performed in advance. S4. The mirror adjustment module (4) couples the first data and the third data group to generate the mirror positioning coefficient MLC and analyzes it. Based on the analysis results, it is determined whether the angle adjustment needs to be performed in advance. S5. The first data group, the second data group and the third data group are coupled by the heat collection efficiency detection module (5) to generate the heat collection efficiency coefficient CPF, and analyze it to determine whether the angle adjustment to be performed will affect the heat collection efficiency. S6. Feedback various data to the visualization terminal through the feedback module (6).

Images