A temperature control system and method for oven heat dissipation

Abstract

The application discloses a kind of oven heat dissipation temperature control system and method, specifically related to temperature control field, including system central processing unit module, system operation database, user information end, data acquisition module, data processing module, artificial intelligence prediction module, control strategy judging module, execution module and feedback and optimization module.The application is constructed multiple key coefficient mathematical models according to key features by artificial intelligence prediction module, realize all-around evaluation and prediction to oven heat dissipation performance, simultaneously, the execution result is collected in real time by feedback and optimization module, recalculate performance comprehensive evaluation coefficient, if not reach preset value, automatically adjust heat dissipation strategy and continue optimization, simultaneously, timely to user output coefficient and issue alarm, inform user oven state, facilitate user timely maintenance, improve oven use convenience and intelligent level, enhance user experience.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology, and more specifically, to a temperature control system and method for oven heat dissipation. Background Technology

[0002] As an indispensable appliance in modern kitchens, the stability and accuracy of the oven's temperature control system are crucial to the quality and efficiency of baking food. With the advancement of technology and the improvement of consumer demand, the oven temperature control system has gradually evolved from the traditional relay temperature adjustment method to an intelligent temperature control system based on advanced technologies such as microcontrollers and PID control algorithms.

[0003] However, in actual use, it still has some shortcomings. For example, the existing technology has a single heat dissipation method, mainly relying on natural heat dissipation or simple fan heat dissipation. It cannot dynamically adjust according to the actual temperature and working status inside the oven, resulting in slow heat dissipation, heat accumulation, affecting the lifespan of the oven, and potentially causing safety hazards due to overheating. In terms of temperature control accuracy, the existing technology lacks a precise temperature monitoring and control mechanism, which is prone to large temperature fluctuations and cannot meet the precise requirements of different ingredients for baking temperature, resulting in unstable quality of baked food. The existing technology has poor environmental adaptability. When the ambient temperature and humidity change, the heat dissipation and temperature control performance will significantly decrease, and it cannot guarantee stable operation under various conditions. At the same time, traditional ovens lack an effective performance evaluation system, cannot monitor the operating status of the heat dissipation system in real time, and cannot detect potential problems and optimize them in time. As the usage time increases, the performance declines without the user's notice. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a temperature control system and method for oven heat dissipation, which solves the problems mentioned in the background art through the following solutions.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a temperature control system and method for oven heat dissipation, comprising a system central processing unit module, a system operation database, a user information terminal, a data acquisition module, a data processing module, an artificial intelligence prediction module, a control strategy judgment module, an execution module, and a feedback and optimization module;

[0006] The system's operating database includes all data text of a temperature control system for oven heat dissipation, and collects information text output by each module in real time. The system's central processing unit module is used to centrally control the information text instructions output by each module, and the user information terminal is a device that receives information output from the temperature control system for oven heat dissipation.

[0007] The data acquisition module is used to collect real-time operating data of the temperature control system, temperature data inside the oven, speed data of the cooling fan, and ambient temperature data outside the oven.

[0008] The data processing module includes a data preprocessing unit and a feature extraction unit. The data preprocessing unit is used to clean, denoise, and normalize the collected raw data, and the feature extraction unit is used to extract key features from the preprocessed data.

[0009] The artificial intelligence prediction module is used to construct mathematical models for temperature control accuracy coefficient, heat dissipation efficiency coefficient, temperature uniformity coefficient, energy consumption coefficient, reliability coefficient, environmental adaptability coefficient, and comprehensive performance evaluation coefficient based on the features extracted by the data processing module.

[0010] The control strategy judgment module compares the comprehensive performance evaluation coefficient output by the artificial intelligence prediction module with a preset value. When the comprehensive performance evaluation coefficient is less than the preset value, it sends an instruction to the execution module to adjust the speed and power of the cooling fan.

[0011] The execution module: executes the corresponding heat dissipation operation according to the instructions issued by the control strategy formulation module;

[0012] The feedback and optimization module is used to collect the operation results of the execution module and recalculate the comprehensive performance evaluation coefficient. If the recalculated comprehensive performance evaluation coefficient is still less than the preset value, the module outputs the comprehensive performance evaluation coefficient to the user information terminal and issues an alarm to remind the user to carry out maintenance.

[0013] Preferably, the key feature includes: average temperature deviation parameter. Temperature fluctuation range parameter Overshoot parameters Heat loss rate parameter Time to reach stable temperature parameter Heat dissipation power ratio parameter Temperature standard deviation parameter Maximum temperature difference parameter Energy consumption parameters per unit time Energy consumption fluctuation coefficient Failure rate parameters Mean time between failures (MTBF) parameter Temperature sensor stability parameters Temperature control deviation parameters under different ambient temperatures Humidity Influence Coefficient .

[0014] Preferably, the specific method for collecting the average temperature deviation parameter is as follows:

[0015] After the oven has been running continuously for 5 minutes, the actual temperature of the center point inside the oven is collected every 30 seconds. , and preset temperature The difference is calculated to obtain the temperature deviation. = Then calculate the average of these deviations, i.e. = where n is the number of collections;

[0016] The temperature fluctuation range parameter refers to the difference between the maximum and minimum temperature values ​​in the temperature data collected after the chamber has been running continuously for 5 minutes.

[0017] The specific method for acquiring the overshoot parameter is as follows:

[0018] When the oven starts to heat up from the initial temperature to the preset temperature Record the highest temperature during the temperature rise process. overshoot 00%;

[0019] The specific method for collecting the heat loss rate parameter is as follows:

[0020] According to Newton's law of cooling Temperature sensors are arranged on the top, sides, and bottom of the oven shell and labeled 1, 2, 3, ... i, ... m to measure the temperature of the sensors. and ambient temperature And the heat dissipation surface area of ​​the oven shell was determined through experiments. and overall heat dissipation coefficient The heat loss rate at each location was calculated. Then, the average value is calculated to obtain the total heat loss rate. , where m is the number of measurement locations;

[0021] The specific method for collecting the heat dissipation power ratio parameter is as follows:

[0022] During oven operation, a power analyzer is used to measure the oven's total power. and the power consumed by the cooling system heat dissipation power ratio ;

[0023] The specific method for collecting the temperature standard deviation parameter is as follows:

[0024] k temperature sensors are evenly distributed inside the oven, and the temperature at these locations is collected simultaneously during the oven's stable operation phase. First calculate the average temperature Then calculate the temperature standard deviation using the standard deviation formula. = , where k is the number of temperature sensors.

[0025] The maximum temperature difference parameter refers to the difference between the maximum and minimum values ​​of the temperature sensor during the temperature standard deviation parameter acquisition process;

[0026] The specific method for collecting the energy consumption parameter per unit time is as follows:

[0027] Use an electricity meter or power analyzer to measure the total electrical energy consumed by the oven during a complete baking process, including the preheating, heating, and holding stages. and the total duration of the work cycle. Energy consumption per unit time = ;

[0028] The specific method for collecting the energy consumption fluctuation coefficient is as follows:

[0029] Power consumption was measured continuously at 1-minute intervals during oven operation. Calculate the average power Then, calculate the energy consumption fluctuation coefficient according to the formula. = ,in For the number of measurements;

[0030] The specific method for acquiring the stability parameters of the temperature sensor is as follows:

[0031] During oven operation, the temperature sensor is calibrated weekly, and the temperature deviation before and after each calibration is recorded. Calculate the stability index of the temperature sensor , where s is the number of calibrations;

[0032] The specific methods for collecting the temperature control deviation parameters under different ambient temperatures are as follows:

[0033] The oven was placed in an environmental chamber with m gradient temperatures. At each environmental temperature, the average temperature deviation inside the oven during stable operation was measured and calculated according to the method for collecting the average temperature deviation parameter. ;

[0034] The specific method for collecting the humidity influence coefficient is as follows:

[0035] Under n humidity gradients, the heat loss rate of an oven under the same operating mode is measured. Let the humidity be... The rate of heat loss at that time is Reference humidity The rate of heat loss Based on this, the humidity influence coefficient is calculated as follows: = ,in This is for reference humidity.

[0036] Preferably, the mathematical model for the temperature control accuracy coefficient is as follows:

[0037] ,

[0038] in This refers to the temperature control accuracy coefficient. The average temperature deviation parameter The parameter refers to the temperature fluctuation range. This refers to the overshoot parameter.

[0039] Preferably, the mathematical model for the heat dissipation efficiency coefficient is as follows:

[0040] ,

[0041] in The coefficient of performance refers to the heat dissipation efficiency. This is theoretically the highest rate of heat loss among ovens of the same type. The parameter refers to the rate of heat loss. The parameter indicating the time to reach a stable temperature. This refers to the percentage of heat dissipation power.

[0042] Preferably, the mathematical model for the temperature uniformity coefficient is as follows:

[0043] ,

[0044] in This refers to the temperature uniformity coefficient. The standard deviation of temperature parameter This refers to the maximum temperature difference parameter.

[0045] Preferably, the mathematical model for the energy consumption coefficient is as follows:

[0046] ,

[0047] in Energy consumption coefficient Energy consumption per unit time This refers to the energy consumption fluctuation coefficient.

[0048] Preferably, the mathematical model for the reliability coefficient is as follows:

[0049] ,

[0050] in Reliability coefficient This refers to the minimum mean time between failures (MTBF) among ovens of the same type. Failure rate parameter Mean time between failures (MTBF) parameter This refers to the stability parameters of the temperature sensor.

[0051] Preferably, the mathematical model for the environmental adaptability coefficient is as follows:

[0052] ,

[0053] in This refers to the environmental adaptability coefficient. To test the number of ambient temperatures, To test the amount of ambient humidity, The temperature control deviation parameter refers to the temperature control deviation parameter under the i-th ambient temperature. Refers to the first Humidity influence coefficient;

[0054] The mathematical model for the comprehensive performance evaluation coefficient is as follows:

[0055] + +0.15 + + +0.2 ,in This refers to the overall performance evaluation coefficient. This refers to the temperature control accuracy coefficient. The coefficient of performance refers to the heat dissipation efficiency. This refers to the temperature uniformity coefficient. Energy consumption coefficient Reliability coefficient This refers to the environmental adaptability coefficient.

[0056] Preferably, the preset value is a critical value determined based on the design requirements, performance standards, and actual application scenarios of the oven heat dissipation temperature control system.

[0057] Preferably, a temperature control method for oven heat dissipation includes:

[0058] S1: Data Acquisition: Used to collect real-time operating data of the temperature control system, temperature data inside the oven, speed data of the cooling fan, and ambient temperature data outside the oven.

[0059] S2: Data processing: Cleaning, denoising, and normalizing the collected raw data, and extracting key features from the preprocessed data;

[0060] S3: Artificial Intelligence Prediction: Used to construct mathematical models for temperature control accuracy coefficient, heat dissipation efficiency coefficient, temperature uniformity coefficient, energy consumption coefficient, reliability coefficient, environmental adaptability coefficient, and comprehensive performance evaluation coefficient based on extracted features.

[0061] S4: Control strategy judgment: Compare the overall performance evaluation coefficient with the preset value. When the overall performance evaluation coefficient is less than the preset value, issue a command to adjust the speed and power of the cooling fan.

[0062] S5: Heat dissipation execution: Execute the corresponding heat dissipation operation according to the instruction;

[0063] S6: Feedback and Optimization: Used to collect the results of the operation and recalculate the overall performance evaluation coefficient. If the recalculated overall performance evaluation coefficient is still less than the preset value, the overall performance evaluation coefficient is output to the user information terminal and an alarm is issued to remind the user to carry out maintenance.

[0064] The technical effects and advantages of this invention are as follows:

[0065] 1. The data acquisition module of this invention acquires real-time operating data of the temperature control system, internal and external oven temperatures, and cooling fan speed, providing comprehensive data support for subsequent precise control. The data processing module cleans, denoises, and normalizes the raw data, and extracts key features to ensure data accuracy and effectiveness. The artificial intelligence prediction module constructs multiple key coefficient mathematical models based on these features to achieve a comprehensive evaluation and prediction of the oven's heat dissipation performance, laying the foundation for intelligent control. The control strategy judgment module compares the comprehensive performance evaluation coefficient with preset values ​​and, based on the comparison results, instructs the execution module to adjust the cooling fan speed and power, achieving precise dynamic control and ensuring that the oven is always in optimal working condition.

[0066] 2. The temperature control accuracy coefficient model of this invention integrates the average temperature deviation, fluctuation range, and overshoot parameters. Through precise calculation and control, it effectively reduces temperature fluctuations, ensuring that the internal temperature of the oven remains accurate and stable, greatly improving the quality and taste of baked food. The temperature uniformity coefficient model optimizes heat distribution by using temperature standard deviation and maximum temperature difference parameters, eliminating local overheated or undercooled areas, ensuring that food is heated evenly in the oven and avoiding uneven baking. The reliability coefficient model covers failure rate, mean time between failures, and temperature sensor stability parameters, comprehensively ensuring the stable and reliable operation of the heat dissipation system, reducing the failure rate, maintenance costs, and usage risks. The environmental adaptability coefficient model incorporates the temperature control deviation and humidity influence coefficients under different ambient temperatures, enabling the oven to maintain good performance under various environmental conditions and unaffected by changes in the external environment.

[0067] 3. This invention collects execution results in real time through a feedback and optimization module, recalculates the comprehensive performance evaluation coefficient, and automatically adjusts the heat dissipation strategy and continuously optimizes it if the preset value is not reached. At the same time, it outputs the coefficient to the user in a timely manner and issues an alarm to inform the user of the oven status, so as to facilitate timely maintenance by the user, improve the convenience and intelligence level of the oven, and enhance the user experience. Attached Figure Description

[0068] Figure 1 This is a schematic diagram of the overall structure of the present invention;

[0069] Figure 2 This is a schematic diagram of the method structure of the present invention. Detailed Implementation

[0070] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] As attached Figure 1 The temperature control system for oven heat dissipation shown includes: a system central processing unit module, a system operation database, a user information terminal, a data acquisition module, a data processing module, an artificial intelligence prediction module, a control strategy judgment module, an execution module, and a feedback and optimization module;

[0072] The system's operating database includes all data text of a temperature control system for oven heat dissipation, and collects information text output by each module in real time. The system's central processing unit module is used to centrally control the information text instructions output by each module, and the user information terminal is a device that receives information output from the temperature control system for oven heat dissipation.

[0073] The output of the data acquisition module is electrically connected to the input of the data processing module, the output of the data processing module is electrically connected to the input of the artificial intelligence prediction module, the output of the artificial intelligence prediction module is electrically connected to the input of the control strategy judgment module, the output of the control strategy judgment module is electrically connected to the input of the execution module, and the output of the execution module is electrically connected to the input of the feedback and optimization module.

[0074] The data acquisition module is used to collect real-time operating data of the temperature control system, temperature data inside the oven, speed data of the cooling fan, and ambient temperature data outside the oven.

[0075] The data processing module includes a data preprocessing unit and a feature extraction unit. The data preprocessing unit is used to clean, denoise, and normalize the collected raw data, and the feature extraction unit is used to extract key features from the preprocessed data.

[0076] In this embodiment, it should be specifically noted that the key feature includes: average temperature deviation parameter. Temperature fluctuation range parameter Overshoot parameters Heat loss rate parameter Time to reach stable temperature parameter Heat dissipation power ratio parameter Temperature standard deviation parameter Maximum temperature difference parameter Energy consumption parameters per unit time Energy consumption fluctuation coefficient Failure rate parameters Mean time between failures (MTBF) parameter Temperature sensor stability parameters Temperature control deviation parameters under different ambient temperatures Humidity Influence Coefficient ;

[0077] The specific method for collecting the average temperature deviation parameter is as follows:

[0078] After the oven has been running continuously for 5 minutes, the actual temperature of the center point inside the oven is collected every 30 seconds. , and preset temperature The difference is calculated to obtain the temperature deviation. = Then calculate the average of these deviations, i.e. = where n is the number of collections;

[0079] The temperature fluctuation range parameter refers to the difference between the maximum and minimum temperature values ​​in the temperature data collected after the chamber has been running continuously for 5 minutes.

[0080] The specific method for acquiring the overshoot parameter is as follows:

[0081] When the oven starts to heat up from the initial temperature to the preset temperature Record the highest temperature during the temperature rise process. overshoot 00%;

[0082] The specific method for collecting the heat loss rate parameter is as follows:

[0083] According to Newton's law of cooling Temperature sensors are arranged on the top, sides, and bottom of the oven shell and labeled 1, 2, 3, ... i, ... m to measure the temperature of the sensors. and ambient temperature And the heat dissipation surface area of ​​the oven shell was determined through experiments. and overall heat dissipation coefficient The heat loss rate at each location was calculated. Then, the average value is calculated to obtain the total heat loss rate. , where m is the number of measurement locations;

[0084] The specific method for collecting the heat dissipation power ratio parameter is as follows:

[0085] During oven operation, a power analyzer is used to measure the oven's total power. and the power consumed by the cooling system heat dissipation power ratio ;

[0086] The specific method for collecting the temperature standard deviation parameter is as follows:

[0087] k temperature sensors are evenly distributed inside the oven, and the temperature at these locations is collected simultaneously during the oven's stable operation phase. First calculate the average temperature Then calculate the temperature standard deviation using the standard deviation formula. = , where k is the number of temperature sensors.

[0088] The maximum temperature difference parameter refers to the difference between the maximum and minimum values ​​of the temperature sensor during the temperature standard deviation parameter acquisition process;

[0089] The specific method for collecting the energy consumption parameter per unit time is as follows:

[0090] Use an electricity meter or power analyzer to measure the total electrical energy consumed by the oven during a complete baking process, including the preheating, heating, and holding stages. and the total duration of the work cycle. Energy consumption per unit time = ;

[0091] The specific method for collecting the energy consumption fluctuation coefficient is as follows:

[0092] Power consumption was measured continuously at 1-minute intervals during oven operation. Calculate the average power Then, calculate the energy consumption fluctuation coefficient according to the formula. = ,in For the number of measurements;

[0093] The specific method for acquiring the stability parameters of the temperature sensor is as follows:

[0094] During oven operation, the temperature sensor is calibrated weekly, and the temperature deviation before and after each calibration is recorded. Calculate the stability index of the temperature sensor , where s is the number of calibrations;

[0095] The specific methods for collecting the temperature control deviation parameters under different ambient temperatures are as follows:

[0096] The oven was placed in an environmental chamber with m gradient temperatures. At each environmental temperature, the average temperature deviation inside the oven during stable operation was measured and calculated according to the method for collecting the average temperature deviation parameter. ;

[0097] The specific method for collecting the humidity influence coefficient is as follows:

[0098] Under n humidity gradients, the heat loss rate of an oven under the same operating mode is measured. Let the humidity be... The rate of heat loss at that time is Reference humidity The rate of heat loss Based on this, the humidity influence coefficient is calculated as follows: = ,in For reference humidity;

[0099] The artificial intelligence prediction module is used to construct mathematical models for temperature control accuracy coefficient, heat dissipation efficiency coefficient, temperature uniformity coefficient, energy consumption coefficient, reliability coefficient, environmental adaptability coefficient, and comprehensive performance evaluation coefficient based on the features extracted by the data processing module.

[0100] In this embodiment, it should be specifically explained that the mathematical model for the temperature control accuracy coefficient is as follows:

[0101] ,

[0102] in This refers to the temperature control accuracy coefficient. The average temperature deviation parameter The parameter refers to the temperature fluctuation range. Overshoot parameter;

[0103] The mathematical model for the heat dissipation efficiency coefficient is as follows:

[0104] ,

[0105] in The coefficient of performance refers to the heat dissipation efficiency. This is theoretically the highest rate of heat loss among ovens of the same type. The parameter refers to the rate of heat loss. The parameter indicating the time to reach a stable temperature. This refers to the percentage of heat dissipation power.

[0106] The mathematical model for the temperature uniformity coefficient is as follows:

[0107] ,

[0108] in This refers to the temperature uniformity coefficient. The standard deviation of temperature parameter This refers to the maximum temperature difference parameter;

[0109] The mathematical model for the energy consumption coefficient is as follows:

[0110] ,

[0111] in Energy consumption coefficient Energy consumption per unit time This refers to the energy consumption fluctuation coefficient;

[0112] The mathematical model for the reliability coefficient is as follows:

[0113] ,

[0114] in Reliability coefficient This refers to the minimum mean time between failures (MTBF) among ovens of the same type. Failure rate parameter Mean time between failures (MTBF) parameter This refers to the stability parameters of the temperature sensor;

[0115] The mathematical model for the environmental adaptability coefficient is as follows:

[0116] ,

[0117] in This refers to the environmental adaptability coefficient. To test the number of ambient temperatures, To test the amount of ambient humidity, The temperature control deviation parameter refers to the temperature control deviation parameter under the i-th ambient temperature. Refers to the humidity influence coefficient of the i-th type;

[0118] The mathematical model for the comprehensive performance evaluation coefficient is as follows:

[0119] + +0.15 + + +0.2 ,in This refers to the overall performance evaluation coefficient. This refers to the temperature control accuracy coefficient. The coefficient of performance refers to the heat dissipation efficiency. This refers to the temperature uniformity coefficient. Energy consumption coefficient Reliability coefficient This refers to the environmental adaptability coefficient.

[0120] The control strategy judgment module compares the comprehensive performance evaluation coefficient output by the artificial intelligence prediction module with a preset value. When the comprehensive performance evaluation coefficient is less than the preset value, it sends an instruction to the execution module to adjust the speed and power of the cooling fan.

[0121] In this embodiment, it should be specifically noted that the preset value is a critical value determined based on the design requirements, performance standards, and actual application scenarios of the oven heat dissipation temperature control system. This value is used to determine whether the current performance of the oven heat dissipation system has reached an acceptable level or whether it needs to be adjusted and optimized. This preset value provides the control strategy judgment module with a basis for adjusting the speed and power of the cooling fan. When the system performance does not meet the preset requirements, the execution module determines the adjustment range of the cooling fan speed and power based on the difference between the preset value and the actual evaluation coefficient. The larger the difference, the larger the adjustment range may be to quickly improve the system performance; when the difference is small, the adjustment range is relatively small to avoid over-adjustment leading to system instability.

[0122] The execution module: executes the corresponding heat dissipation operation according to the instructions issued by the control strategy formulation module;

[0123] The feedback and optimization module is used to collect the operation results of the execution module and recalculate the comprehensive performance evaluation coefficient. If the recalculated comprehensive performance evaluation coefficient is still less than the preset value, the module outputs the comprehensive performance evaluation coefficient to the user information terminal and issues an alarm to remind the user to perform maintenance.

[0124] As attached Figure 2 The oven heat dissipation temperature control method shown includes:

[0125] S1: Data Acquisition: Used to collect real-time operating data of the temperature control system, temperature data inside the oven, speed data of the cooling fan, and ambient temperature data outside the oven.

[0126] S2: Data processing: Cleaning, denoising, and normalizing the collected raw data, and extracting key features from the preprocessed data;

[0127] S3: Artificial Intelligence Prediction: Used to construct mathematical models for temperature control accuracy coefficient, heat dissipation efficiency coefficient, temperature uniformity coefficient, energy consumption coefficient, reliability coefficient, environmental adaptability coefficient, and comprehensive performance evaluation coefficient based on extracted features.

[0128] S4: Control strategy judgment: Compare the overall performance evaluation coefficient with the preset value. When the overall performance evaluation coefficient is less than the preset value, issue a command to adjust the speed and power of the cooling fan.

[0129] S5: Heat dissipation execution: Execute the corresponding heat dissipation operation according to the instruction;

[0130] S6: Feedback and Optimization: Used to collect the results of the operation and recalculate the overall performance evaluation coefficient. If the recalculated overall performance evaluation coefficient is still less than the preset value, the overall performance evaluation coefficient is output to the user information terminal and an alarm is issued to remind the user to carry out maintenance.

[0131] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0132] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A temperature control system for oven heat dissipation, characterized in that, include: The system comprises a central processing unit module, a system operating database, a user information terminal, a data acquisition module, a data processing module, an artificial intelligence prediction module, a control strategy judgment module, an execution module, and a feedback and optimization module. The system's operating database includes all data text of a temperature control system for oven heat dissipation, and collects information text output by each module in real time. The system's central processing unit module is used to centrally control the information text instructions output by each module, and the user information terminal is a device that receives information output from the temperature control system for oven heat dissipation. The data acquisition module is used to collect real-time operating data of the temperature control system, temperature data inside the oven, speed data of the cooling fan, and ambient temperature data outside the oven. The data processing module includes a data preprocessing unit and a feature extraction unit. The data preprocessing unit is used to clean, denoise, and normalize the collected raw data, and the feature extraction unit is used to extract key features from the preprocessed data. The artificial intelligence prediction module is used to construct mathematical models for temperature control accuracy, heat dissipation efficiency, temperature uniformity, energy consumption, reliability, environmental adaptability, and comprehensive performance evaluation based on the features extracted by the data processing module. The specific mathematical model for the comprehensive performance evaluation coefficient is as follows: + +0.15 + + +0.2 ,in This refers to the overall performance evaluation coefficient. This refers to the temperature control accuracy coefficient. The coefficient of performance refers to the heat dissipation efficiency. This refers to the temperature uniformity coefficient. Energy consumption coefficient Reliability coefficient This refers to the environmental adaptability coefficient; The control strategy judgment module compares the comprehensive performance evaluation coefficient output by the artificial intelligence prediction module with a preset value. When the comprehensive performance evaluation coefficient is less than the preset value, it sends an instruction to the execution module to adjust the speed and power of the cooling fan. The execution module: executes the corresponding heat dissipation operation according to the instructions issued by the control strategy formulation module; The feedback and optimization module is used to collect the operation results of the execution module and recalculate the comprehensive performance evaluation coefficient. If the recalculated comprehensive performance evaluation coefficient is still less than the preset value, the module outputs the comprehensive performance evaluation coefficient to the user information terminal and issues an alarm to remind the user to carry out maintenance.

2. The oven heat dissipation temperature control system according to claim 1, characterized in that: The key features include: average temperature deviation parameters. Temperature fluctuation range parameter Overshoot parameters Heat loss rate parameter Time to reach stable temperature parameter Heat dissipation power ratio parameter Temperature standard deviation parameter Maximum temperature difference parameter Energy consumption parameters per unit time Energy consumption fluctuation coefficient Failure rate parameters Mean time between failures (MTBF) parameter Temperature sensor stability parameters Temperature control deviation parameters under different ambient temperatures Humidity Influence Coefficient The mathematical model for the temperature control accuracy coefficient is as follows: ， in This refers to the temperature control accuracy coefficient. The average temperature deviation parameter The parameter refers to the temperature fluctuation range. This refers to the overshoot parameter.

3. The oven heat dissipation temperature control system according to claim 1, characterized in that: The mathematical model for the heat dissipation efficiency coefficient is as follows: ， in The coefficient of performance refers to the heat dissipation efficiency. This is theoretically the highest rate of heat loss among ovens of the same type. The parameter refers to the rate of heat loss. The parameter indicating the time to reach a stable temperature. This refers to the percentage of heat dissipation power.

4. The oven heat dissipation temperature control system according to claim 1, characterized in that: The mathematical model for the temperature uniformity coefficient is as follows: ， in This refers to the temperature uniformity coefficient. The standard deviation of temperature parameter This refers to the maximum temperature difference parameter.

5. The temperature control system for oven heat dissipation according to claim 1, characterized in that: The mathematical model for the energy consumption coefficient is as follows: ， in Energy consumption coefficient Energy consumption per unit time This refers to the energy consumption fluctuation coefficient.

6. The temperature control system for oven heat dissipation according to claim 1, characterized in that: The mathematical model for the reliability coefficient is as follows: ， in Reliability coefficient This refers to the minimum mean time between failures (MTBF) among ovens of the same type. Failure rate parameter Mean time between failures (MTBF) parameter This refers to the stability parameters of the temperature sensor.

7. The oven heat dissipation temperature control system according to claim 1, characterized in that: The mathematical model for the environmental adaptability coefficient is as follows: ， in This refers to the environmental adaptability coefficient. To test the number of ambient temperatures, To test the amount of ambient humidity, The temperature control deviation parameter refers to the temperature control deviation parameter under the i-th ambient temperature. Refers to the first Humidity influence coefficient.

8. A temperature control method for oven heat dissipation, implemented based on the oven heat dissipation temperature control system according to any one of claims 1-7, characterized in that, include: S1: Data Acquisition: Used to collect real-time operating data of the temperature control system, temperature data inside the oven, speed data of the cooling fan, and ambient temperature data outside the oven. S2: Data processing: Cleaning, denoising, and normalizing the collected raw data, and extracting key features from the preprocessed data; S3: Artificial Intelligence Prediction: Used to construct mathematical models for temperature control accuracy coefficient, heat dissipation efficiency coefficient, temperature uniformity coefficient, energy consumption coefficient, reliability coefficient, environmental adaptability coefficient, and comprehensive performance evaluation coefficient based on extracted features. S4: Control strategy judgment: Compare the overall performance evaluation coefficient with the preset value. When the overall performance evaluation coefficient is less than the preset value, issue a command to adjust the speed and power of the cooling fan. S5: Heat dissipation execution: Execute the corresponding heat dissipation operation according to the instruction; S6: Feedback and Optimization: Used to collect the results of the operation and recalculate the overall performance evaluation coefficient. If the recalculated overall performance evaluation coefficient is still less than the preset value, the overall performance evaluation coefficient is output to the user information terminal and an alarm is issued to remind the user to carry out maintenance.

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