Temperature control system and method for heat dissipation of oven

Abstract

The invention discloses a temperature control system and method for heat dissipation of an oven, and particularly relates to the field of temperature control, and the system comprises a system central processor 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. A plurality of key coefficient mathematical models are constructed according to key features through the artificial intelligence prediction module, comprehensive evaluation and prediction of the heat dissipation performance of the oven are achieved, meanwhile, execution results are collected in real time through the feedback and optimization module, the performance comprehensive evaluation coefficient is recalculated, and if the performance comprehensive evaluation coefficient does not reach a preset value, the heat dissipation performance of the oven is evaluated. The heat dissipation strategy is automatically adjusted and continuously optimized, meanwhile, coefficients are output to a user in time, an alarm is given, the user is informed of the state of the oven, the user can maintain the oven in time conveniently, the use convenience and the intelligent level of the oven are improved, and the user experience is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control, and more particularly to a temperature control system and method for heat dissipation of an oven. Background Art

[0002] As an indispensable electrical appliance in the modern kitchen, the stability and accuracy of the temperature control system of the oven is crucial to the quality and efficiency of baking food. With the advancement of technology and the increase in consumer demand, the oven temperature control system has gradually evolved from the traditional relay temperature control method to an intelligent temperature control system based on advanced technologies such as single-chip microcomputers and PID control algorithms.

[0003] However, it still has some shortcomings in actual use. For example, the existing technology has a single heat dissipation method in terms of heat dissipation efficiency, mainly relying on natural heat dissipation or simple fan heat dissipation, and cannot be dynamically adjusted according to the actual temperature and working status inside the oven, resulting in slow heat dissipation and heat accumulation, affecting the service life of the oven, and may cause safety hazards due to overheating. The existing technology lacks accurate temperature monitoring and regulation mechanism in temperature control accuracy, and is prone to large temperature fluctuations. It is difficult to meet the precise requirements of different ingredients for baking temperature, resulting in unstable quality of baked food. The existing technology has poor adaptability to the environment. When the ambient temperature and humidity change, the heat dissipation and temperature control performance decreases significantly, and stable operation cannot be guaranteed 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 are difficult to detect potential problems and optimize in time. As the use time increases, the performance decreases and the user is difficult to detect. Summary of the invention

[0004] In order to overcome the above defects of the prior art, an embodiment of the present invention provides a temperature control system and method for heat dissipation of an oven, and solves the problems raised in the above background technology through the following scheme.

[0005] To achieve the above object, the present invention provides the following technical solutions: a temperature control system and method for heat dissipation of an oven, 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 operation database includes all data texts of a temperature control system for heat dissipation of an oven, and collects information texts output by each module in real time. The system central processor module is used to centrally control the information text instructions output by each module, and the user information terminal is a device for receiving information output by a temperature control system for heat dissipation of an oven;

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

[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. The feature extraction unit is used to extract key features from the preprocessed data.

[0009] The artificial intelligence prediction module is used to construct a mathematical model of temperature control accuracy coefficient, a mathematical model of heat dissipation efficiency coefficient, a mathematical model of temperature uniformity coefficient, a mathematical model of energy consumption coefficient, a mathematical model of reliability coefficient, a mathematical model of environmental adaptability coefficient and a mathematical model of comprehensive performance evaluation coefficient according to 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, and when the comprehensive performance evaluation coefficient is less than the preset value, sends an instruction to the execution module to adjust the speed and power of the cooling fan;

[0011] The execution module is used to execute corresponding heat dissipation operations 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 comprehensive performance evaluation coefficient is output to the user information terminal and an alarm is issued to remind the user to perform maintenance.

[0013] Preferably, the key features include: average temperature deviation parameter Temperature fluctuation range parameter ΔT range , overshoot parameter σ, heat loss rate parameter Q loss , time parameter to reach stable temperature t stable , heat dissipation power ratio parameter α, temperature standard deviation parameter σ T , Maximum temperature difference parameter ΔT max-min , Energy consumption per unit time parameter E unit , energy consumption fluctuation coefficient β, failure rate parameter λ, mean time between failures parameter MTBF, temperature sensor stability parameter γ, temperature control deviation parameter ΔT at different ambient temperatures env，i , Humidity influence coefficient μ j .

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

[0015] After the oven has been running for 5 minutes, the actual temperature T at the center of the oven is collected every 30 seconds. i , and the preset temperature T set Difference, get the temperature deviation ΔT i =T i -T set , and then calculate the average of these deviations, which is Where n is the number of acquisitions;

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

[0017] The specific collection method of the overshoot parameter is as follows:

[0018] When the oven starts to heat up from the initial temperature to the preset temperature T set When the temperature rises, record the highest temperature T peak , overshoot

[0019] The specific collection method of the heat loss rate parameter is as follows:

[0020] According to Newton's law of cooling, Q = hA (TT env ), arrange temperature sensors on the top, side and bottom of the oven shell and label them as 1, 2, 3, ...i..., m, and measure the sensor temperature T surf，i And the ambient temperature T env The heat dissipation surface area A and the comprehensive heat dissipation coefficient h of the oven shell are determined experimentally, and the heat loss rate Q at each position is calculated. i =hA(T surf，i -T env ), and then take the average value to get the total heat loss rate Where m is the number of measurement locations;

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

[0022] During the operation of the oven, use a power analyzer to measure the total power P of the oven. total And the power consumed by the cooling system P loss , heat dissipation power ratio

[0023] The specific collection method of the temperature standard deviation parameter is as follows:

[0024] Distribute k temperature sensors evenly inside the oven and collect the temperature T of these locations simultaneously during the stable operation of the oven. j , first calculate the average temperature Then calculate the temperature standard deviation according to 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 value and the minimum value of the temperature sensor during the temperature standard deviation parameter acquisition process;

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

[0027] Use an energy meter or power analyzer to measure the total energy E consumed by the oven during a complete baking process, including preheating, heating and keeping warm stages. total , and the total duration of the working cycle t total , energy consumption per unit time

[0028] The specific collection method of the energy consumption fluctuation coefficient is as follows:

[0029] During the operation of the oven, the power consumption P is measured continuously at 1 minute intervals. i , calculate the average power Then calculate the energy consumption fluctuation coefficient according to the formula Where l is the number of measurements;

[0030] The specific collection method of the temperature sensor stability parameter is as follows:

[0031] During the operation of the oven, the temperature sensor is calibrated every week, and the temperature deviation ΔT before and after each calibration is recorded. cal，i , calculate the temperature sensor stability index Where s is the number of calibrations;

[0032] The specific collection method of the temperature control deviation parameters at different ambient temperatures is as follows:

[0033] The oven is placed in an environmental chamber with m gradient ambient temperatures. At each ambient temperature, the average temperature deviation ΔT inside the oven during stable operation is measured and calculated according to the acquisition method of the average temperature deviation parameter. env，i ;

[0034] The specific collection method of the humidity influence coefficient is as follows:

[0035] Under n gradient humidity environments, measure the heat loss rate of the oven in the same working mode. Assume that the humidity is H j The heat loss rate is Q loss，j , with reference humidity H ref Heat loss rate Q loss，ref As a benchmark, the humidity influence coefficient is calculated as: Among them, H ref For reference humidity.

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

[0037]

[0038] Where f1 refers to the temperature control accuracy coefficient, Refers to the average temperature deviation parameter, ΔT range refers to the temperature fluctuation range parameter, and σ refers to the overshoot parameter.

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

[0040]

[0041] Where f2 refers to the heat dissipation efficiency coefficient, Q loss , max Q is the maximum heat loss rate in theory or in the same type of oven, loss Refers to the heat loss rate parameter, t stable It refers to the time parameter to reach a stable temperature, and α refers to the heat dissipation power ratio parameter.

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

[0043]

[0044] Where f3 refers to the temperature uniformity coefficient, σ T Refers to the temperature standard deviation parameter, ΔT max-min Refers to the maximum temperature difference parameter.

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

[0046]

[0047] Where f4 refers to the energy consumption coefficient, E unit Refers to the energy consumption parameter per unit time, and β refers to the energy consumption fluctuation coefficient.

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

[0049]

[0050] Where f5 refers to the reliability factor, MTBF min It refers to the minimum mean time between failures among ovens of the same type, λ refers to the failure rate parameter, MTBF refers to the mean time between failures parameter, and γ refers to the temperature sensor stability parameter.

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

[0052]

[0053] Where f6 refers to the environmental adaptability coefficient, m is the number of test environment temperature, n is the number of test environment humidity, ΔT env，i Refers to the temperature control deviation parameter ΔT at the i-th ambient temperature env，i , μ j Refers to the i-th humidity influence coefficient;

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

[0055] ω=0.2f1+0.1f2+0.15f3+0.15f4+0.2f5+0.2f6, where ω refers to the comprehensive performance evaluation coefficient, f1 refers to the temperature control accuracy coefficient, f2 refers to the heat dissipation efficiency coefficient, f3 refers to the temperature uniformity coefficient, f4 refers to the energy consumption coefficient, f5 refers to the reliability coefficient, and f6 refers to the environmental adaptability coefficient.

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

[0057] Preferably, a temperature control method for heat dissipation of an oven comprises:

[0058] S1: Data collection: 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: Clean, denoise and normalize the collected raw data, and extract key features from the pre-processed data;

[0060] S3: Artificial intelligence prediction: used to construct mathematical models of 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 extracted features;

[0061] S4: control strategy judgment: compare the comprehensive performance evaluation coefficient with a preset value, and when the comprehensive 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: according to the instruction, execute the corresponding heat dissipation operation;

[0063] S6: Feedback and optimization: used to collect the results of the execution operation and recalculate the comprehensive performance evaluation coefficient. If the recalculated comprehensive performance evaluation coefficient is still less than the preset value, the comprehensive performance evaluation coefficient is output to the user information terminal and an alarm is issued to remind the user to perform maintenance.

[0064] Technical effects and advantages of the present invention:

[0065] 1. The data acquisition module of the present invention acquires multi-dimensional information such as the operating data of the temperature control system, the internal and external temperatures of the oven, and the speed of the cooling fan in real time, providing comprehensive data support for subsequent precise control. The data processing module cleans, denoises, and normalizes the original 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 regulation. The control strategy judgment module compares the comprehensive performance evaluation coefficient with the preset value, and instructs the execution module to adjust the speed and power of the cooling fan according to the comparison results, achieving precise dynamic regulation to ensure that the oven is always in the best working state;

[0066] 2. The temperature control accuracy coefficient model of the present invention integrates the average temperature deviation, fluctuation range and overshoot parameters, and effectively reduces temperature fluctuations through precise calculation and regulation, ensuring that the temperature inside the oven is always accurate and stable, greatly improving the quality and taste of baked food. The temperature uniformity coefficient model optimizes heat distribution with the help of temperature standard deviation and maximum temperature difference parameters, eliminates local overheating or overcooling areas, and makes the food evenly heated in the oven to avoid uneven baking. The reliability coefficient model covers the 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, and reducing maintenance costs and use risks. The environmental adaptability coefficient model incorporates the temperature control deviation and humidity influence coefficient at different ambient temperatures, so that the oven can maintain good performance under various environmental conditions without being disturbed by changes in the external environment.

[0067] 3. The present invention collects execution results in real time through the feedback and optimization module, recalculates the comprehensive performance evaluation coefficient, and automatically adjusts the heat dissipation strategy and continuously optimizes if the preset value is not reached. At the same time, the coefficient is output to the user in time and an alarm is issued to inform the user of the oven status, so as to facilitate the user to repair it in time, improve the convenience and intelligence level of the oven, and enhance the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0069] Figure 2 It is a schematic diagram of the structure of the method of the present invention. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0071] As attached Figure 1 The temperature control system for heat dissipation of an oven 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 operation database includes all data texts of a temperature control system for heat dissipation of an oven, and collects information texts output by each module in real time. The system central processor module is used to centrally control the information text instructions output by each module, and the user information terminal is a device for receiving information output by a temperature control system for heat dissipation of an oven;

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

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

[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. The feature extraction unit is used to extract key features from the preprocessed data.

[0076] It should be specifically explained in this embodiment that the key features include: average temperature deviation parameter Temperature fluctuation range parameter ΔT range , overshoot parameter σ, heat loss rate parameter Q loss , time parameter to reach stable temperature t stable , heat dissipation power ratio parameter α, temperature standard deviation parameter σ T , Maximum temperature difference parameter ΔT max-min , Energy consumption per unit time parameter E unit, energy consumption fluctuation coefficient β, failure rate parameter λ, mean time between failures parameter MTBF, temperature sensor stability parameter γ, temperature control deviation parameter ΔT at different ambient temperatures env，i , Humidity influence coefficient μ j ;

[0077] The specific collection method of the average temperature deviation parameter is as follows:

[0078] After the oven has been running for 5 minutes, the actual temperature T at the center of the oven is collected every 30 seconds. i , and the preset temperature T set Difference, get the temperature deviation ΔT i =T i -T set , and then calculate the average of these deviations, which is Where n is the number of acquisitions;

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

[0080] The specific collection method of the overshoot parameter is as follows:

[0081] When the oven starts to heat up from the initial temperature to the preset temperature T set When the temperature rises, record the highest temperature T peak , overshoot

[0082] The specific collection method of the heat loss rate parameter is as follows:

[0083] According to Newton's law of cooling, Q = hA (TT env ), arrange temperature sensors on the top, side and bottom of the oven shell and label them as 1, 2, 3, ...i..., m, and measure the sensor temperature T surf，i And the ambient temperature T env The heat dissipation surface area A and the comprehensive heat dissipation coefficient h of the oven shell are determined experimentally, and the heat loss rate Q at each position is calculated. i =hA(T surf，i -T env ), and then take the average value to get the total heat loss rate Where m is the number of measurement locations;

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

[0085] During the operation of the oven, use a power analyzer to measure the total power P of the oven. total And the power consumed by the cooling system P loss, heat dissipation power ratio

[0086] The specific collection method of the temperature standard deviation parameter is as follows:

[0087] Distribute k temperature sensors evenly inside the oven and collect the temperature T of these locations simultaneously during the stable operation of the oven. j , first calculate the average temperature Then calculate the temperature standard deviation according to 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 value and the minimum value of the temperature sensor during the temperature standard deviation parameter acquisition process;

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

[0090] Use an energy meter or power analyzer to measure the total energy E consumed by the oven during a complete baking process, including preheating, heating and keeping warm stages. total , and the total duration of the working cycle t total , energy consumption per unit time

[0091] The specific collection method of the energy consumption fluctuation coefficient is as follows:

[0092] During the operation of the oven, the power consumption P is measured continuously at 1 minute intervals. i , calculate the average power Then calculate the energy consumption fluctuation coefficient according to the formula Where l is the number of measurements;

[0093] The specific collection method of the temperature sensor stability parameter is as follows:

[0094] During the operation of the oven, the temperature sensor is calibrated every week, and the temperature deviation ΔT before and after each calibration is recorded. cal，i , calculate the temperature sensor stability index Where s is the number of calibrations;

[0095] The specific collection method of the temperature control deviation parameters at different ambient temperatures is as follows:

[0096] The oven is placed in an environmental chamber with m gradient ambient temperatures. At each ambient temperature, the average temperature deviation ΔT inside the oven during stable operation is measured and calculated according to the acquisition method of the average temperature deviation parameter. env，i ;

[0097] The specific collection method of the humidity influence coefficient is as follows:

[0098] Under n gradient humidity environments, measure the heat loss rate of the oven in the same working mode. Assume that the humidity is H j The heat loss rate is Q loss，j , with reference humidity H ref Heat loss rate Q loss，ref As a benchmark, the humidity influence coefficient is calculated as: Among them, H ref is the reference humidity;

[0099] The artificial intelligence prediction module is used to construct a mathematical model of temperature control accuracy coefficient, a mathematical model of heat dissipation efficiency coefficient, a mathematical model of temperature uniformity coefficient, a mathematical model of energy consumption coefficient, a mathematical model of reliability coefficient, a mathematical model of environmental adaptability coefficient and a mathematical model of comprehensive performance evaluation coefficient according to the features extracted by the data processing module;

[0100] It should be specifically explained in this embodiment that the mathematical model of the temperature control accuracy coefficient is as follows:

[0101]

[0102] Where f1 refers to the temperature control accuracy coefficient, Refers to the average temperature deviation parameter, ΔT range refers to the temperature fluctuation range parameter, σ refers to the overshoot parameter;

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

[0104]

[0105] Where f2 refers to the heat dissipation efficiency coefficient, Q loss , max Q is the maximum heat loss rate in theory or in the same type of oven, loss Refers to the heat loss rate parameter, t stable It refers to the time parameter to reach stable temperature, and α refers to the heat dissipation power ratio parameter;

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

[0107]

[0108] Where f3 refers to the temperature uniformity coefficient, σ T Refers to the temperature standard deviation parameter, ΔT max-min Refers to the maximum temperature difference parameter;

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

[0110]

[0111] Where f4 refers to the energy consumption coefficient, E unit Refers to the energy consumption parameter per unit time, β refers to the energy consumption fluctuation coefficient;

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

[0113]

[0114] Where f5 refers to the reliability factor, MTBF min It refers to the minimum mean time between failures among ovens of the same type, λ refers to the failure rate parameter, MTBF refers to the mean time between failures parameter, and γ refers to the temperature sensor stability parameter;

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

[0116]

[0117] Where f6 refers to the environmental adaptability coefficient, m is the number of test environment temperature, n is the number of test environment humidity, ΔT env，i Refers to the temperature control deviation parameter ΔT at the i-th ambient temperature env，i , μ j Refers to the i-th humidity influence coefficient;

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

[0119] ω=0.2f1+0.1f2+0.15f3+0.15f4+0.2f5+0.2f6, where ω refers to the comprehensive performance evaluation coefficient, f1 refers to the temperature control accuracy coefficient, f2 refers to the heat dissipation efficiency coefficient, f3 refers to the temperature uniformity coefficient, f4 refers to the energy consumption coefficient, f5 refers to the reliability coefficient, and f6 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, and when the comprehensive performance evaluation coefficient is less than the preset value, sends an instruction to the execution module to adjust the speed and power of the cooling fan;

[0121] What needs to be specifically explained in this embodiment is 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 performance of the current oven heat dissipation system has reached an acceptable level or whether adjustment and optimization are required. The preset value provides a basis for the control strategy judgment module to adjust 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 gap between the preset value and the actual evaluation coefficient. The larger the gap, the larger the adjustment range may be, so as to quickly improve the system performance; when the gap is small, the adjustment range is relatively small to avoid excessive adjustment leading to system instability.

[0122] The execution module is used to execute corresponding heat dissipation operations 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 comprehensive performance evaluation coefficient is output to the user information terminal and an alarm is issued to remind the user to perform maintenance;

[0124] As attached Figure 2 A temperature control method for heat dissipation of an oven is shown, comprising:

[0125] S1: Data collection: 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: Clean, denoise and normalize the collected raw data, and extract key features from the pre-processed data;

[0127] S3: Artificial intelligence prediction: used to construct mathematical models of 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 extracted features;

[0128] S4: control strategy judgment: compare the comprehensive performance evaluation coefficient with a preset value, and when the comprehensive 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: according to the instruction, execute the corresponding heat dissipation operation;

[0130] S6: Feedback and optimization: used to collect the results of the execution operation and recalculate the comprehensive performance evaluation coefficient. If the recalculated comprehensive performance evaluation coefficient is still less than the preset value, the comprehensive performance evaluation coefficient is output to the user information terminal and an alarm is issued to remind the user to perform maintenance;

[0131] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0132] Finally: 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 in the protection scope of the present invention.

Claims

1. A temperature control system for heat dissipation of an oven, characterized in that: include: System central processing unit module, system operation database, user information terminal, data acquisition module, data processing module, artificial intelligence prediction module, control strategy judgment module, execution module and feedback and optimization module; The system operation database includes all data texts of a temperature control system for heat dissipation of an oven, and collects information texts output by each module in real time. The system central processor module is used to centrally control the information text instructions output by each module, and the user information terminal is a device for receiving information output by a temperature control system for heat dissipation of an oven; The data acquisition module is used to collect the operating data of the temperature control system, the temperature data inside the oven, the speed data of the cooling fan and the ambient temperature data outside the oven in real time; 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. The feature extraction unit is used to extract key features from the preprocessed data. The artificial intelligence prediction module is used to construct a mathematical model of temperature control accuracy coefficient, a mathematical model of heat dissipation efficiency coefficient, a mathematical model of temperature uniformity coefficient, a mathematical model of energy consumption coefficient, a mathematical model of reliability coefficient, a mathematical model of environmental adaptability coefficient and a mathematical model of comprehensive performance evaluation coefficient according to the features extracted by the data processing module; The control strategy judgment module compares the comprehensive performance evaluation coefficient output by the artificial intelligence prediction module with a preset value, and when the comprehensive performance evaluation coefficient is less than the preset value, sends an instruction to the execution module to adjust the speed and power of the cooling fan; The execution module is used to execute corresponding heat dissipation operations 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 comprehensive performance evaluation coefficient is output to the user information terminal and an alarm is issued to remind the user to perform maintenance.

2. The temperature control system for heat dissipation of an oven according to claim 1, characterized in that: The key features include: average temperature deviation parameters Temperature fluctuation range parameter ΔT range , overshoot parameter σ, heat loss rate parameter Q loss , time parameter to reach stable temperature t stable , heat dissipation power ratio parameter α, temperature standard deviation parameter σ T , Maximum temperature difference parameter ΔT nax-min , Energy consumption per unit time parameter E unit , energy consumption fluctuation coefficient β, failure rate parameter λ, mean time between failures parameter MTBF, temperature sensor stability parameter γ, temperature control deviation parameter ΔT at different ambient temperatures env，i , Humidity influence coefficient μ j The mathematical model of the temperature control accuracy coefficient is as follows: Where f1 refers to the temperature control accuracy coefficient, Refers to the average temperature deviation parameter, ΔT range refers to the temperature fluctuation range parameter, and σ refers to the overshoot parameter.

3. The temperature control system for heat dissipation of an oven according to claim 1, characterized in that: The mathematical model of the heat dissipation efficiency coefficient is specifically as follows: Where f2 refers to the heat dissipation efficiency coefficient, Q loss , max Q is the maximum heat loss rate in theory or in the same type of oven. loss Refers to the heat loss rate parameter, t stable It refers to the time parameter to reach a stable temperature, and α refers to the heat dissipation power ratio parameter.

4. The temperature control system for heat dissipation of an oven according to claim 1, characterized in that: The mathematical model of the temperature uniformity coefficient is as follows: Where f3 refers to the temperature uniformity coefficient, σ T Refers to the temperature standard deviation parameter, ΔT max-min Refers to the maximum temperature difference parameter.

5. The temperature control system for heat dissipation of an oven according to claim 1, characterized in that: The mathematical model of the energy consumption coefficient is as follows: Where f4 refers to the energy consumption coefficient, E unit Refers to the energy consumption parameter per unit time, and β refers to the energy consumption fluctuation coefficient.

6. The temperature control system for heat dissipation of an oven according to claim 1, characterized in that: The mathematical model of the reliability coefficient is as follows: Where f5 refers to the reliability factor, MTBF min It refers to the minimum mean time between failures among ovens of the same type, λ refers to the failure rate parameter, MTBF refers to the mean time between failures parameter, and γ refers to the temperature sensor stability parameter.

7. The temperature control system for heat dissipation of an oven according to claim 1, characterized in that: The mathematical model of the environmental adaptability coefficient is as follows: Where f6 refers to the environmental adaptability coefficient, m is the number of test environment temperature, n is the number of test environment humidity, ΔT env，i Refers to the temperature control deviation parameter ΔT at the i-th ambient temperature env，i , μ j Refers to the i-th humidity influence coefficient.

8. The temperature control system for heat dissipation of an oven according to claim 1, characterized in that: The mathematical model of the comprehensive performance evaluation coefficient is as follows: ω=0.2f1+0.1f2+0.15f3+0.15f4+0.2f5+0.2f6, where ω refers to the comprehensive performance evaluation coefficient, f1 refers to the temperature control accuracy coefficient, f2 refers to the heat dissipation efficiency coefficient, f3 refers to the temperature uniformity coefficient, f4 refers to the energy consumption coefficient, f5 refers to the reliability coefficient, and f6 refers to the environmental adaptability coefficient.

9. A method for controlling the heat dissipation temperature of an oven, which is implemented based on the temperature control system for heat dissipation of an oven according to any one of claims 1 to 8, characterized in that: include: S1: Data collection: 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: Clean, denoise and normalize the collected raw data, and extract key features from the pre-processed data; S3: Artificial intelligence prediction: used to construct mathematical models of 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 extracted features; S4: control strategy judgment: compare the comprehensive performance evaluation coefficient with a preset value, and when the comprehensive 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: according to the instruction, execute the corresponding heat dissipation operation; S6: Feedback and optimization: used to collect the results of the execution operation and recalculate the comprehensive performance evaluation coefficient. If the recalculated comprehensive performance evaluation coefficient is still less than the preset value, the comprehensive performance evaluation coefficient is output to the user information terminal and an alarm is issued to remind the user to perform maintenance.

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