An intelligent semiconductor temperature difference power generation control system

Abstract

The present invention discloses an intelligent semiconductor temperature difference power generation control system, which relates to the field of semiconductor temperature difference power generation control technology. The system includes the following components: a high-precision temperature difference monitoring module: a high-sensitivity temperature sensor array is used to monitor the temperature of a heat source and a cold source in real time with high frequency and high precision. The design of the sensor array fully considers the influence of environmental factors such as wind speed, humidity and solar radiation to ensure that accurate temperature data can be provided in various environments. In addition, the module also integrates a data preprocessing function, which can automatically filter abnormal values. The present invention can monitor the temperature of a heat source and a cold source in real time and accurately through a high-precision temperature difference monitoring module, and fully considers the influence of environmental factors to ensure that accurate temperature data can be provided in various environments. Combined with advanced PID control algorithms and machine learning models, the energy conversion efficiency is maximized through continuous iteration and optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of semiconductor temperature difference power generation control, and in particular to an intelligent semiconductor temperature difference power generation control system. Background Art

[0002] With the increasing global energy crisis and the continuous improvement of environmental awareness, semiconductor thermoelectric power generation technology, as a new and clean energy conversion method, has gradually attracted people's attention. The semiconductor thermoelectric power generation control system is designed based on this technology. It uses the thermoelectric effect of semiconductor materials to convert the temperature difference in the environment into electrical energy. This system can not only make full use of natural temperature difference resources and reduce dependence on traditional energy, but also achieve sustainable development of energy. Therefore, research and development of efficient and intelligent semiconductor thermoelectric power generation control systems has important strategic significance and application value.

[0003] Although semiconductor thermoelectric power generation technology has broad application prospects, the existing semiconductor thermoelectric power generation control system still has some obvious shortcomings. First, traditional control systems often lack the ability to accurately monitor and quickly respond to temperature changes, which makes it difficult for the system to fully utilize temperature difference resources and the energy conversion efficiency is low. Secondly, existing control systems often lack intelligent optimization mechanisms and cannot adjust control strategies in real time according to changes in ambient temperature and load demand, thereby limiting the system's power generation efficiency. Traditional control systems are also easily affected by external interference and noise, resulting in reduced system stability and reliability.

[0004] However, due to the lack of an effective intelligent adjustment mechanism, the existing semiconductor temperature difference power generation control system often finds it difficult to maintain efficient operation when faced with complex and changeable environmental conditions. When the temperature changes drastically and the load demand fluctuates greatly, the system cannot make timely adjustments, resulting in a significant decrease in energy conversion efficiency. Existing control systems often lack self-learning and adaptive capabilities and are unable to continuously optimize control strategies based on historical operating data, which affects the long-term stable operation and performance improvement of the system and limits its application and promotion in a wide range of fields. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and to provide an intelligent semiconductor temperature difference power generation control system, which can use advanced machine learning algorithms to train a logistic regression risk assessment model to evaluate the impact of different reagent characteristics on reagent risks, and use maximum likelihood estimation and multi-layer perceptrons to automatically process and analyze reagent data without human intervention, thereby greatly improving the efficiency and degree of automation of early warning, accurately assessing the risk level of reagents, and reducing false alarms and missed alarms.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent semiconductor temperature difference power generation control system, characterized in that the system includes the following components:

[0007] High-precision temperature difference monitoring module: It uses a high-sensitivity temperature sensor array to monitor the temperature of heat and cold sources in real time with high frequency and high precision. The design of the sensor array fully considers the influence of wind speed, humidity and solar radiation environmental factors to ensure that accurate temperature data can be provided in various environments. In addition, the module also integrates data preprocessing function, which can automatically filter out abnormal values;

[0008] Intelligent algorithm optimization module: This module collects the temperature data of historical heat sources and cold sources to train the multi-layer perceptron MLP neural network, and receives the temperature data from the high-precision temperature difference monitoring module in real time. It uses the neural network to predict the real-time temperature data, calculates the temperature error signal, and uses the PID control algorithm formula to calculate the control output to adjust the working state of the thermoelectric component to maximize the energy conversion efficiency. The specific steps of the intelligent algorithm optimization module are:

[0009] (1) Temperature difference data reception: Receive temperature data of heat sources and cold sources collected by the high-sensitivity temperature sensor array in the high-precision temperature difference monitoring module;

[0010] (2) Data analysis: Analyze real-time temperature data, identify trends and patterns of temperature changes, combine historical operating data, and use machine learning models for data mining and feature extraction;

[0011] (3) Strategy calculation: Based on the data analysis results, the PID control algorithm is used to calculate the control parameters of the thermoelectric components, and the machine learning model is used to predict the optimal control parameters to maximize the energy conversion efficiency;

[0012] (4) Execution and adjustment: Output the calculated control parameters to the thermoelectric component, adjust its working state, monitor the response and performance of the thermoelectric component in real time, and adjust the control strategy based on feedback;

[0013] Adaptive control strategy module: This module uses the multi-layer perceptron MLP neural network in the intelligent algorithm optimization module to predict future temperature change trends and operating conditions, and uses adaptive control logic to calculate the optimal thermal management strategy. The adaptive control logic is that when external environmental temperature fluctuations and internal load changes are detected, the module will re-evaluate the effectiveness of the existing strategy and guide the thermoelectric component and the heat dissipation device to work together based on the maximum energy conversion efficiency calculated by the intelligent algorithm optimization module. The specific steps of the adaptive control strategy module are:

[0014] (1) Temperature data collection: collect real-time temperature data, environmental parameters such as wind speed, humidity, solar radiation, equipment status information, and historical operation data;

[0015] (2) Operating condition assessment: Based on real-time temperature data, environmental parameters and equipment status, assess the current operating conditions and predict future temperature change trends and operating conditions;

[0016] (3) Strategy calculation: Based on the evaluation results, the optimal thermal management strategy is calculated using adaptive control logic. The working state of the thermoelectric component and the operating mode of the thermal management component are adjusted according to the optimal control parameters predicted by the machine learning model.

[0017] (4) Strategy execution: Output the calculated control strategy to the thermoelectric component and execute the corresponding control operation;

[0018] (5) Effect evaluation and feedback: Real-time monitoring of the system's operating status and power generation efficiency, evaluation of the execution effect of the control strategy, and adjustment of the control strategy based on the energy efficiency feedback mechanism to form a closed-loop control;

[0019] Heat source and cold source management module: It integrates efficient heat exchange and heat dissipation design, and realizes precise control of heat flow path and speed through active thermal management components of intelligent valves and fans. The module dynamically adjusts the operating status of thermal management components according to real-time temperature data and adaptive control strategies to further expand the temperature difference and improve power generation efficiency. At the same time, it also has fault self-diagnosis and self-recovery functions;

[0020] Energy efficiency feedback and adjustment module: responsible for real-time evaluation of the system's power generation efficiency, adjusting the control strategy based on the evaluation results, collecting and analyzing the real-time operating data of the thermoelectric components, calculating the current energy conversion efficiency, and comparing it with the preset target value, and automatically adjusting the control parameters.

[0021] Furthermore, the high-precision temperature difference monitoring module uses moving average filtering to reduce the influence of noise on the temperature difference signal, making the signal smoother. The algorithm formula is: , y[n] is the smoothed temperature difference data, x[n] is the original temperature difference data, and N is the length of the averaging window.

[0022] Furthermore, the high-precision temperature difference monitoring module filters the temperature difference data to smooth the temperature difference data, and maps the temperature difference data to the [0, 1] interval through the Sigmoid normalization algorithm, making the data more intuitive and easy to compare, that is, , F(x) is the normalized data, and x is the original temperature difference data.

[0023] Furthermore, the intelligent algorithm optimization module constructs a machine learning model through the neural network technology in the machine learning algorithm, and its operation mode is as follows: collect historical operation data, including temperature data of heat sources and cold sources, working status of thermoelectric components, and energy conversion efficiency, preprocess the data, including data cleaning and normalization, to facilitate the training of the neural network, design a multi-layer perceptron (MLP) neural network structure, use the preprocessed historical data as a training data set, train the neural network, and minimize the prediction error through the back propagation algorithm (BP) and the gradient descent optimization algorithm, that is, the back propagation algorithm calculates the error between the model prediction value and the actual value: , the gradient descent algorithm updates the weights and biases of the neural network to reduce the error: , x is the input temperature difference data, y is the predicted output of the model, θt represents the temperature parameter value at the tth iteration, and the trained neural network is evaluated using the validation data set to check its prediction performance. The structure and parameters of the neural network are adjusted according to the evaluation results to further optimize the performance of the model. The trained neural network is integrated into the intelligent algorithm optimization module, which receives data from the high-precision temperature difference monitoring module in real time, uses the neural network to predict the real-time data, and outputs the optimal control parameters to the thermoelectric component.

[0024] Furthermore, the PID control algorithm collects the temperature data of the heat source and the cold source in real time through the high-precision temperature difference monitoring module to obtain the real-time temperature value of the current system, compares the real-time temperature value with the target temperature value, and obtains the temperature error signal e(t), that is, e(t) = target temperature - real-time temperature, and uses the formula of the PID algorithm to calculate the control output: , , , To control the parameters, the control output obtained according to PID calculation is output to the thermoelectric component to adjust the working state of the system so that the system temperature gradually approaches the target temperature.

[0025] Furthermore, the heat source and cold source management module has the following specific steps:

[0026] (1) Real-time temperature monitoring: Using a high-precision temperature sensor array, the temperature of the heat source and the cold source is monitored in real time, taking into account the influence of environmental factors such as wind speed, humidity, and solar radiation to ensure the accuracy of temperature data;

[0027] (2) Data analysis and strategy generation: Combine real-time temperature data and adaptive control strategies to analyze the current temperature difference, predict future operating trends based on the analysis results, and generate thermal management strategies accordingly;

[0028] (3) Thermal management component control: Through the active thermal management components of intelligent valves and fans, the heat flow path and speed are precisely controlled according to the generated thermal management strategy, and the operating state of the thermal management components is dynamically adjusted to further expand the temperature difference and improve power generation efficiency;

[0029] (4) Fault self-diagnosis and recovery: Real-time monitoring of the operating status of thermal management components, detection of existing faults, automatic fault self-diagnosis, and self-recovery.

[0030] Furthermore, the heat source and cold source management module collects heat source data in real time through a high-precision temperature difference monitoring module. and cold source Temperature data, calculate the temperature difference between the hot source and the cold source: , based on the calculated temperature difference , ambient temperature changes, load requirements and equipment status, formulate thermal management strategies. The size of the temperature difference directly determines the efficiency of the heat exchange and the control measures that need to be taken. Output control instructions to the active thermal management components of the smart valves and fans, and perform corresponding operations such as adjusting the heat flow path and controlling the heat exchange rate according to the received control instructions to achieve effective management and utilization of the temperature difference.

[0031] Furthermore, the energy efficiency feedback and adjustment module collects the electric energy generated by the thermoelectric component in real time. and the amount of heat transferred from the heat source to the cold source , calculate the current energy conversion efficiency: , η is the energy conversion efficiency. The calculated real-time efficiency is compared with the preset target efficiency, and the control strategy is adjusted to optimize the operating state of the thermoelectric module and improve the energy conversion efficiency.

[0032] Compared with the prior art, this intelligent semiconductor temperature difference power generation control system has the following beneficial effects:

[0033] 1. The present invention uses a high-precision temperature difference monitoring module to monitor the temperature of the heat source and the cold source in real time and accurately, and fully considers the influence of environmental factors such as wind speed, humidity, and solar radiation, to ensure that accurate temperature data can be provided in various environments. Combined with advanced PID control algorithms and machine learning models, the energy conversion efficiency of the system is gradually improved through continuous iteration and optimization. The semiconductor temperature difference power generation technology has the characteristics of compact structure, no wear, no leakage, clean, no noise pollution, long life, and high reliability, which further improves the performance of the semiconductor temperature difference power generation control system and maximizes the energy conversion efficiency.

[0034] 2. The adaptive control strategy module and machine learning model of the present invention give the system strong adaptability and intelligence. The system can automatically adjust the thermal management strategy according to different operating conditions, predict future operating trends, and optimize the control parameters accordingly. This adaptive control strategy enables the system to maintain efficient operation in various complex environments. At the same time, the machine learning model can continuously learn and optimize the control strategy based on historical data, making the system more intelligent and adaptable.

[0035] 3. The heat source and cold source management module and the energy efficiency feedback and adjustment module of the present invention jointly ensure the stability and reliability of the system. The efficient heat exchange and heat dissipation design enables the system to maintain a large temperature difference and improve the power generation efficiency. At the same time, the fault self-diagnosis and self-recovery functions enable the system to automatically diagnose and attempt to recover when a fault occurs, reducing maintenance costs. The energy efficiency feedback and adjustment module can evaluate the power generation efficiency of the system in real time, and adjust the control strategy according to the evaluation results to ensure that the system is always in the optimal working state. This design makes the system have stronger stability and reliability, and is suitable for a variety of temperature difference power generation scenarios.

[0036] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0038] Figure 1 This is a process operation diagram of an intelligent semiconductor temperature difference power generation control system.

[0039] Figure 2 The figure is a flow chart of an intelligent semiconductor temperature difference power generation control system. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] An intelligent semiconductor temperature difference power generation control system, the system includes the following components:

[0042] High-precision temperature difference monitoring module: It uses a high-sensitivity temperature sensor array to monitor the temperature of heat and cold sources in real time with high frequency and high precision. The design of the sensor array fully considers the influence of wind speed, humidity and solar radiation environmental factors to ensure that accurate temperature data can be provided in various environments. In addition, the module also integrates data preprocessing function, which can automatically filter out abnormal values;

[0043] Intelligent algorithm optimization module: This module combines advanced PID control algorithms and machine learning models to intelligently optimize the working status of thermoelectric components. The PID control algorithm ensures the stability and response speed of the system, while the machine learning model learns and predicts the optimal control parameters based on historical operating data and real-time temperature data.

[0044] Adaptive control strategy module: Adaptive control logic is used to enable the system to automatically adjust the thermal management strategy according to different working conditions. By analyzing multiple factors such as ambient temperature changes, load requirements, and equipment status, the system predicts future operating trends and adjusts the working status of the thermoelectric components and the operating mode of the thermal management components accordingly, so that the system can maintain efficient operation in various complex environments.

[0045] Heat source and cold source management module: Integrates efficient heat exchange and heat dissipation design, and realizes precise control of heat flow path and speed through active thermal management components such as intelligent valves, fans, etc. The module dynamically adjusts the operating status of thermal management components according to real-time temperature data and adaptive control strategies to further expand the temperature difference and improve power generation efficiency. At the same time, it also has fault self-diagnosis and self-recovery functions.

[0046] Energy efficiency feedback and adjustment module: responsible for real-time evaluation of the system's power generation efficiency, adjusting the control strategy based on the evaluation results, collecting and analyzing the real-time operating data of the thermoelectric components, calculating the current energy conversion efficiency, and comparing it with the preset target value, and automatically adjusting the control parameters.

[0047] Embodiment 1

[0048] The present embodiment provides an intelligent semiconductor temperature difference power generation control system, which realizes the maximum energy conversion efficiency by real-time monitoring and analyzing the temperature difference between the heat source and the cold source, and automatically adjusting the working state of the thermoelectric module using advanced control logic. In addition, the present invention also incorporates a machine learning algorithm, so that the system can self-learn according to historical operating data, predict and adapt to different working conditions, so as to achieve the optimal temperature difference power generation efficiency.

[0049] In the specific implementation, the system uses a high-sensitivity temperature sensor array through a high-precision temperature difference monitoring module to monitor the temperature of the heat source and the cold source in real time. The design of the sensor array fully considers the influence of environmental factors such as wind speed, humidity and solar radiation to ensure that accurate temperature data can be provided in various environments. At the same time, the module integrates a moving average filter to reduce the impact of noise on the temperature difference signal and make the signal smoother. The algorithm formula is: , y[n] is the smoothed temperature difference data, x[n] is the original temperature difference data, N is the length of the average window, and the Sigmoid normalization algorithm is used to filter the temperature difference data to smooth the temperature difference data. The temperature difference data is mapped to the [0, 1] interval through the Sigmoid normalization algorithm to make the data more intuitive and easy to compare, that is, , F(x) is the normalized data, x is the original temperature difference data, which reduces the influence of noise and makes the data smoother and more intuitive.

[0050] The intelligent algorithm optimization module combines the PID control algorithm and the machine learning model based on neural network to intelligently optimize the working state of the thermoelectric component. By receiving the temperature data from the high-precision temperature difference monitoring module and combining it with the historical operation data, the machine learning model is used for data mining and feature extraction to predict the optimal control parameters. The PID control algorithm ensures the stability and response speed of the system.

[0051] Adaptive control strategy module This module adopts adaptive control logic to predict future operating trends based on multiple factors such as ambient temperature changes, load demand, and equipment status, and adjusts the working status of the thermoelectric components and the operating mode of the thermal management components accordingly, so that the system can maintain efficient operation in various complex environments.

[0052] The heat source and cold source management module integrates efficient heat exchange and heat dissipation design, and realizes precise control of heat flow path and speed through active thermal management components of intelligent valves and fans. It dynamically adjusts the operating status of thermal management components according to real-time temperature data and adaptive control strategies to further expand the temperature difference and improve power generation efficiency. At the same time, it also has fault self-diagnosis and self-recovery functions.

[0053] In addition, the energy efficiency feedback and adjustment module is responsible for real-time evaluation of the system's power generation efficiency, adjusting the control strategy based on the evaluation results, collecting and analyzing the real-time operating data of the thermoelectric components, calculating the current energy conversion efficiency, and comparing it with the preset target value. The control parameters are automatically adjusted based on the comparison results to improve the overall energy efficiency of the system.

[0054] Embodiment 2

[0055] The intelligent semiconductor temperature difference power generation control system in this embodiment places special emphasis on the multi-layer perceptron neural network structure of the machine learning model. It learns and predicts the optimal control parameters through historical operation data and real-time temperature data, and uses the back propagation algorithm and gradient descent optimization algorithm for training to minimize prediction errors, continuously iterate and optimize, and maximize energy conversion efficiency.

[0056] First, the temperature data of the heat source and the cold source collected by the high-sensitivity temperature sensor array in the high-precision temperature difference monitoring module is received, the real-time temperature data is analyzed, the trend and pattern of temperature change are identified, and the neural network technology in the machine learning model is used to build a machine learning model in combination with the historical operation data. The operation mode is as follows: collect historical operation data, including the temperature data of the heat source and the cold source, the working status of the thermoelectric component, and the energy conversion efficiency, pre-process the data, including data cleaning and normalization, to facilitate the training of the neural network, design a multi-layer perceptron (MLP) neural network structure, use the pre-processed historical data as the training data set, train the neural network, and minimize the prediction error through the back propagation algorithm (BP) and the gradient descent optimization algorithm, that is, the back propagation algorithm calculates the error between the model prediction value and the actual value: , the gradient descent algorithm updates the weights and biases of the neural network to reduce the error: , x is the input temperature difference data, y is the predicted output of the model, θt represents the temperature parameter value at the tth iteration, and the trained neural network is evaluated using the validation data set to check its prediction performance. The structure and parameters of the neural network are adjusted according to the evaluation results to further optimize the performance of the model. The trained neural network is integrated into the intelligent algorithm optimization module, which receives data from the high-precision temperature difference monitoring module in real time, uses the neural network to predict the real-time data, and outputs the optimal control parameters to the thermoelectric component.

[0057] Subsequently, the PID control algorithm is used to collect the temperature data of the heat source and the cold source in real time through the high-precision temperature difference monitoring module to obtain the real-time temperature value of the current system. The real-time temperature value is compared with the target temperature value to obtain the temperature error signal e(t), that is, e(t) = target temperature - real-time temperature. The control output is calculated using the formula of the PID algorithm: , , , To control the parameters, the control output obtained according to PID calculation is output to the thermoelectric component to adjust the working state of the system so that the system temperature gradually approaches the target temperature.

[0058] Finally, adaptive control logic is used to predict future operating trends based on multiple factors such as ambient temperature changes, load demand, and equipment status, and adjust the working status of thermoelectric components and the operating mode of thermal management components accordingly, so that the system can maintain efficient operation in various complex environments. By integrating high-efficiency heat exchange and heat dissipation design, and through active thermal management components such as smart valves and fans, precise control of heat flow path and speed is achieved. According to real-time temperature data and adaptive control strategies, the operating status of thermal management components is dynamically adjusted to further expand the temperature difference and improve power generation efficiency.

[0059] In addition, the system will collect and analyze the real-time operating data of the thermoelectric components and calculate the current energy conversion efficiency. The calculated real-time efficiency is compared with the preset target efficiency, and the control strategy is adjusted to optimize the operating state of the thermoelectric module and improve the overall energy efficiency of the system.

[0060] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An intelligent semiconductor temperature difference power generation control system, characterized in that: The system consists of the following components: High-precision temperature difference monitoring module: It uses a high-sensitivity temperature sensor array to monitor the temperature of heat and cold sources in real time with high frequency and high precision. The design of the sensor array fully considers the influence of wind speed, humidity and solar radiation environmental factors to ensure that accurate temperature data can be provided in various environments. In addition, the module also integrates data preprocessing function, which can automatically filter out abnormal values; Intelligent algorithm optimization module: This module collects the temperature data of historical heat sources and cold sources to train the multi-layer perceptron MLP neural network, and receives the temperature data from the high-precision temperature difference monitoring module in real time. It uses the neural network to predict the real-time temperature data, calculates the temperature error signal, and uses the PID control algorithm formula to calculate the control output to adjust the working state of the thermoelectric component to maximize the energy conversion efficiency. The specific steps of the intelligent algorithm optimization module are: (1) Temperature difference data reception: Receive temperature data of heat sources and cold sources collected by the high-sensitivity temperature sensor array in the high-precision temperature difference monitoring module; (2) Data analysis: Analyze real-time temperature data, identify trends and patterns of temperature changes, combine historical operating data, and use machine learning models for data mining and feature extraction; (3) Strategy calculation: Based on the data analysis results, the PID control algorithm is used to calculate the control parameters of the thermoelectric components, and the machine learning model is used to predict the optimal control parameters to maximize the energy conversion efficiency; (4) Execution and adjustment: Output the calculated control parameters to the thermoelectric component, adjust its working state, monitor the response and performance of the thermoelectric component in real time, and adjust the control strategy based on feedback; Adaptive control strategy module: This module uses the multi-layer perceptron MLP neural network in the intelligent algorithm optimization module to predict future temperature change trends and operating conditions, and uses adaptive control logic to calculate the optimal thermal management strategy. The adaptive control logic is that when external environmental temperature fluctuations and internal load changes are detected, the module will re-evaluate the effectiveness of the existing strategy and guide the thermoelectric component and the heat dissipation device to work together based on the maximum energy conversion efficiency calculated by the intelligent algorithm optimization module. The specific steps of the adaptive control strategy module are: (1) Temperature data collection: collect real-time temperature data, environmental parameters such as wind speed, humidity, solar radiation, equipment status information, and historical operation data; (2) Operating condition assessment: Based on real-time temperature data, environmental parameters and equipment status, assess the current operating conditions and predict future temperature change trends and operating conditions; (3) Strategy calculation: Based on the evaluation results, the optimal thermal management strategy is calculated using adaptive control logic. The working state of the thermoelectric component and the operating mode of the thermal management component are adjusted according to the optimal control parameters predicted by the machine learning model. (4) Strategy execution: Output the calculated control strategy to the thermoelectric component and execute the corresponding control operation; (5) Effect evaluation and feedback: Real-time monitoring of the system's operating status and power generation efficiency, evaluation of the execution effect of the control strategy, and adjustment of the control strategy based on the energy efficiency feedback mechanism to form a closed-loop control; Heat source and cold source management module: It integrates efficient heat exchange and heat dissipation design, and realizes precise control of heat flow path and speed through active thermal management components of intelligent valves and fans. The module dynamically adjusts the operating status of thermal management components according to real-time temperature data and adaptive control strategies to further expand the temperature difference and improve power generation efficiency. At the same time, it also has fault self-diagnosis and self-recovery functions; Energy efficiency feedback and adjustment module: responsible for real-time evaluation of the system's power generation efficiency, adjusting the control strategy based on the evaluation results, collecting and analyzing the real-time operating data of the thermoelectric components, calculating the current energy conversion efficiency, and comparing it with the preset target value, and automatically adjusting the control parameters.

2. The intelligent semiconductor temperature difference power generation control system according to claim 1, characterized in that: The high-precision temperature difference monitoring module uses moving average filtering to reduce the impact of noise on the temperature difference signal and make the signal smoother. The algorithm formula is: , y[n] is the smoothed temperature difference data, x[n] is the original temperature difference data, and N is the length of the averaging window.

3. The intelligent semiconductor temperature difference power generation control system according to claim 2 is characterized in that: The high-precision temperature difference monitoring module filters the temperature difference data to smooth the temperature difference data, and maps the temperature difference data to the [0, 1] interval through the Sigmoid normalization algorithm, making the data more intuitive and easy to compare. , F(x) is the normalized data, and x is the original temperature difference data.

4. The intelligent semiconductor temperature difference power generation control system according to claim 1, characterized in that: The intelligent algorithm optimization module constructs a machine learning model through the neural network technology in the machine learning algorithm. Its operation mode is as follows: collect historical operation data, including temperature data of heat source and cold source, working status of thermoelectric components, energy conversion efficiency, preprocess the data, including data cleaning and normalization, to facilitate the training of neural network, design a multi-layer perceptron (MLP) neural network structure, use the preprocessed historical data as the training data set, train the neural network, and minimize the prediction error through the back propagation algorithm (BP) and gradient descent optimization algorithm, that is, the back propagation algorithm calculates the error between the model prediction value and the actual value: , the gradient descent algorithm updates the weights and biases of the neural network to reduce the error: , x is the input temperature difference data, y is the predicted output of the model, θt represents the temperature parameter value at the tth iteration, and the trained neural network is evaluated using the validation data set to check its prediction performance. The structure and parameters of the neural network are adjusted according to the evaluation results to further optimize the performance of the model. The trained neural network is integrated into the intelligent algorithm optimization module, which receives data from the high-precision temperature difference monitoring module in real time, uses the neural network to predict the real-time data, and outputs the optimal control parameters to the thermoelectric component.

5. The intelligent semiconductor temperature difference power generation control system according to claim 4, characterized in that: The PID control algorithm collects the temperature data of the heat source and the cold source in real time through the high-precision temperature difference monitoring module to obtain the real-time temperature value of the current system, compares the real-time temperature value with the target temperature value, and obtains the temperature error signal e(t), that is, e(t) = target temperature - real-time temperature. The control output is calculated using the formula of the PID algorithm: , , , To control the parameters, the control output obtained according to PID calculation is output to the thermoelectric component to adjust the working state of the system so that the system temperature gradually approaches the target temperature.

6. The intelligent semiconductor temperature difference power generation control system according to claim 1, characterized in that: The specific steps of the heat source and cold source management module are as follows: (1) Real-time temperature monitoring: Using a high-precision temperature sensor array, the temperature of the heat source and the cold source is monitored in real time, taking into account the influence of environmental factors such as wind speed, humidity, and solar radiation to ensure the accuracy of temperature data; (2) Data analysis and strategy generation: Combine real-time temperature data and adaptive control strategies to analyze the current temperature difference, predict future operating trends based on the analysis results, and generate thermal management strategies accordingly; (3) Thermal management component control: Through the active thermal management components of intelligent valves and fans, the heat flow path and speed are precisely controlled according to the generated thermal management strategy, and the operating state of the thermal management components is dynamically adjusted to further expand the temperature difference and improve power generation efficiency; (4) Fault self-diagnosis and recovery: Real-time monitoring of the operating status of thermal management components, detection of existing faults, automatic fault self-diagnosis, and self-recovery.

7. The intelligent semiconductor temperature difference power generation control system according to claim 6, characterized in that: The heat source and cold source management module collects heat sources in real time through the high-precision temperature difference monitoring module and cold source Temperature data, calculate the temperature difference between the hot source and the cold source: , based on the calculated temperature difference , ambient temperature changes, load requirements and equipment status, formulate thermal management strategies. The size of the temperature difference directly determines the efficiency of the heat exchange and the control measures that need to be taken. Output control instructions to the active thermal management components of the smart valves and fans, and perform corresponding operations such as adjusting the heat flow path and controlling the heat exchange rate according to the received control instructions to achieve effective management and utilization of the temperature difference.

8. The intelligent semiconductor temperature difference power generation control system according to claim 1, characterized in that: The energy efficiency feedback and adjustment module collects the electric energy generated by the thermoelectric component in real time and the amount of heat transferred from the heat source to the cold source , calculate the current energy conversion efficiency: , η is the energy conversion efficiency. The calculated real-time efficiency is compared with the preset target efficiency, and the control strategy is adjusted to optimize the operating state of the thermoelectric module and improve the energy conversion efficiency.

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