Mold temperature control method, electronic device, and storage medium

Abstract

The application discloses a die temperature control method, an electronic device and a storage medium. The die temperature control method comprises the following steps: acquiring an infrared image of a die casting die and real-time temperature data of an internally embedded thermocouple, fusing the infrared image and the thermocouple data, generating a three-dimensional corrected temperature field containing the surface and internal temperature of the die, when the three-dimensional corrected temperature field is abnormal, outputting an adjustment value of a target adjustment parameter through a parameter prediction model, wherein the parameter prediction model introduces a thermodynamic equation residual constraint in a loss function during training, sending the adjustment value to a die temperature control system for execution, collecting a new temperature field in the next production cycle, and if the deviation from the target temperature field exceeds a threshold value, incrementally adjusting the parameter through a PID algorithm. Through the die temperature control method, the die temperature of the die casting model in die casting can be controlled, and the speed and accuracy of die temperature control can be improved.

Description

Technical Field

[0001] This invention relates to the field of die casting, and more particularly to a mold temperature control method, electronic device, and storage medium. Background Technology

[0002] Due to requirements for vehicle body strength and weight reduction, the application of integrated die casting technology for automobiles is becoming increasingly widespread. Among these requirements, precise mold temperature control plays a crucial role in reducing die casting defects such as porosity and cold shuts. The large die casting molds used in integrated die casting technology often have hundreds of water and oil channels. How to reduce casting defects through accurate mold temperature control, thereby improving the economy and product quality of integrated die casting technology, has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0003] In a first aspect, embodiments of the present invention provide a mold temperature control method, wherein the mold temperature control method includes:

[0004] Acquire infrared images of the die-casting mold and real-time temperature data of the internally embedded thermocouples;

[0005] By fusing infrared images and thermocouple data, a three-dimensional corrected temperature field containing the surface and internal temperatures of the mold is generated;

[0006] When correcting the anomaly in the three-dimensional temperature field, the adjustment value of the target adjustment parameter is output through the parameter prediction model. The residual constraint of the thermodynamic equation is introduced into the loss function during the training of the parameter prediction model.

[0007] The adjustment value is sent to the mold temperature control system for execution.

[0008] In the next production cycle, a new temperature field is collected. If the deviation from the target temperature field exceeds the threshold, the parameters are adjusted incrementally using a PID algorithm.

[0009] Therefore, compared to related technologies, the mold temperature control method provided in this invention achieves accurate mold temperature judgment by fusing infrared images of the mold interior with temperature data from internal thermocouple point sensors to form a three-dimensional corrected temperature field. This allows for the output of adjustment parameters through a parameter prediction model. Furthermore, the parameter prediction model incorporates thermodynamic equation residual constraints during training to prevent it from outputting solutions that violate physical laws, thus improving its accuracy. Simultaneously, by judging the deviation between the temperature field adjusted by the parameter prediction model's output parameters and the predicted temperature field, a PID algorithm is used for fine-tuning, achieving closed-loop control. Thus, through the above methods and their combination, the accuracy of mold temperature control is improved, decision-making efficiency is enhanced, and the reliability of the entire system is strengthened.

[0010] Secondly, embodiments of the present invention provide an electronic device, including a processor, wherein...

[0011] The processor executes the program to implement the mold temperature control method as described in the first aspect;

[0012] The electronic device is connected to a temperature compensator, which is used to dynamically calibrate thermocouple readings according to the ambient temperature.

[0013] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, it controls the operation of the electronic device of the third aspect.

[0014] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and are not intended to limit the technical solutions of the present invention. Attached Figure Description

[0015] Figure 1 A flowchart of a mold temperature control method provided in an embodiment of the present invention;

[0016] Figure 2 A flowchart of a method for controlling mold temperature that includes a trained parameter prediction model and a lightweight parameter prediction model, provided as an embodiment of the present invention;

[0017] Figure 3 A flowchart illustrating the process of introducing thermodynamic residual constraints into the loss function during training of a parameter prediction model, as provided in an embodiment of the present invention.

[0018] Figure 4 A flowchart of PID incremental adjustment provided in an embodiment of the present invention;

[0019] Figure 5 A flowchart for generating a three-dimensional corrected temperature field including the surface and internal temperatures of a mold, provided for embodiments of the present invention;

[0020] Figure 6 A flowchart of a mold temperature control method including manual correction instructions provided in an embodiment of the present invention;

[0021] Figure 7 This is a flowchart illustrating reinforcement learning updates of a parameter prediction model based on manually corrected instructions, provided as an embodiment of the present invention. Detailed Implementation

[0022] To gain a more detailed understanding of the features and technical content of the embodiments of the present invention, the implementation of the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for reference and illustration only and are not intended to limit the embodiments of the present invention.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0024] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0025] It should also be noted that the terms "first, second, third" used in the embodiments of the present invention are only used to distinguish similar objects and do not represent a specific order of objects. It is understood that "first, second, third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present invention described herein can be implemented in an order other than that illustrated or described herein.

[0026] Furthermore, the reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0027] This invention provides a mold temperature control method, such as... Figure 1 As shown, the mold temperature control method includes the following steps:

[0028] 100: Acquire infrared images of the die-casting mold and real-time temperature data of the internally embedded thermocouples;

[0029] 200: Fusion of infrared images and thermocouple data to generate a three-dimensional corrected temperature field that includes the surface and internal temperatures of the mold;

[0030] 300: When correcting the anomaly in the three-dimensional temperature field, the adjustment value of the target adjustment parameter is output through the parameter prediction model. The residual constraint of the thermodynamic equation is introduced into the loss function during the training of the parameter prediction model.

[0031] 400: The adjustment value is sent to the mold temperature control system for execution;

[0032] 500: In the next production cycle, a new temperature field is collected. If the deviation from the target temperature field exceeds the threshold, the parameters are adjusted incrementally using a PID algorithm.

[0033] The mold temperature control method provided in this embodiment of the invention achieves accurate determination of mold temperature by fusing infrared images of the mold interior with temperature data from internal thermocouple point sensors to form a three-dimensional corrected temperature field. The method then outputs adjustment values ​​for regulating parameters through a parameter prediction model. Furthermore, the parameter prediction model incorporates thermodynamic equation residual constraints during training to prevent it from outputting solutions that violate physical laws, thus improving its accuracy. Simultaneously, by judging the deviation between the temperature field adjusted by the parameter prediction model's output and the predicted temperature field, a PID algorithm is used for fine-tuning, achieving closed-loop control. Therefore, through the above steps and their combination, the accuracy of mold temperature control is improved, decision-making efficiency is enhanced, and the reliability of the entire system is strengthened.

[0034] In some embodiments, to obtain more accurate and valuable data through pre-embedded thermocouples inside the mold, thermocouples can be embedded in the deep cavity, undercut structure, and outlet of the cooling pipe of the die-casting mold, in concealed areas ≤5mm from the cooling pipe, within ±3mm of areas where the wall thickness changes abruptly, and in the mold cavity corresponding to castings with a historical defect rate >10%. This allows the temperature values ​​of points within the mold prone to temperature anomalies, internal areas identified by infrared images of the mold surface, or areas where die-casting defects frequently occur to be obtained by thermocouples, thereby improving the accuracy of the three-dimensional corrected temperature field in reflecting the actual temperature of the mold.

[0035] In some embodiments, such as Figure 2 As shown, the mold temperature control method also includes:

[0036] 010: Based on a preset training set, train a parameter mapping model to establish the mapping relationship between pipeline parameters and mold temperature;

[0037] 020: Train the parameter prediction model based on the parameter mapping model, and then perform lightweight processing on the parameter prediction model;

[0038] Specifically, in step 010, the preset training set can consist of CAE (computer-aided engineering) simulation data. The parameter mapping model is pre-trained using the CAE simulation data to establish the mapping relationship between pipeline parameters and mold temperature. In step 020, for the trained parameter mapping model, reinforcement learning is performed on the training set composed of the parameter mapping model's output and real-time temperature field data to obtain a parameter prediction model.

[0039] The parameter prediction model training in step 020 also includes:

[0040] 021: Perform channel pruning on the neural network of the parameter prediction model, removing nodes with absolute weight values ​​< 0.001;

[0041] 022: Compress the parameter prediction model;

[0042] 023: Deploy the parameter prediction model on edge computing devices.

[0043] Specifically, in step 021, the L1 norm (L1_morm) of the neurons in the parameter prediction model can be calculated, and nodes with absolute weight values ​​< 0.001 can be selected based on the importance of the neurons. Empirically, this part accounts for 12% to 18% of the total parameters of the model, which can reduce the parameters and reduce the response latency of the parameter prediction model. In step 022, for example, the FP32 parameters of the parameter prediction model can be converted to INT8, and the size of the parameter prediction model can be reduced to less than 30% of the original size through compression. In step 023, the parameter prediction model is deployed on an edge computing device, and the parameters are optimized for the edge computing device to reduce the end-to-end response latency.

[0044] In some embodiments, such as Figure 3 As shown, during the parameter prediction model training in step 300, a thermodynamic equation residual constraint is introduced into the loss function, specifically:

[0045] 310: Add a residual term from the heat conduction equation to the loss function:

[0046] ;

[0047] in =0.7 to 1.2;

[0048] 320: The search space is limited based on the historical qualified parameter set, and the constraints include: the point cooling time and flow rate must meet the following conditions. , This is the pipe diameter coefficient.

[0049] Specifically, in step 310, and These are the temperature field matrix predicted by the parameter prediction model and the actual simulated temperature field matrix, respectively. The predicted temperature field matrix corresponds to the number of nodes in the 3D mesh of the mold, representing the predicted temperature distribution corresponding to the parameter combination calculated by the parameter prediction model based on the input temperature field. The actual simulated temperature field matrix is ​​generated by high-precision thermodynamic simulation of the training parameters through CAE, so that it can be used as labeled data for supervised learning to guide the direction of model optimization. The weighting coefficient used to balance prediction accuracy and conformity to physical laws ranges from 0.7 to 1.2. Experiments show that... When the value is 0.9, the overall effect is optimal in automotive structural component molds; Let T be the Laplace operator, and T be the three-dimensional transient temperature distribution. Used to characterize the second-order rate of change of the temperature field in space, reflecting the diffusion intensity of heat transfer; α is the thermal diffusivity of the mold material; Here, T is a partial differential operator, T is the three-dimensional transient temperature distribution, and t is the time variable. The time derivative of temperature reflects the rate of temperature change per unit time, indicating the heating / cooling rate of the mold. This data is obtained through differential calculation of time-series data from an infrared camera and thermocouples. By adding a residual term from the heat conduction equation to the loss function during the parameter prediction model training process in step 310, the parameter prediction model can autonomously avoid non-physical interpretations, thus preventing it from outputting data that does not conform to real-world physical laws.

[0050] In step 320, The minimum volumetric flow rate (m³ / s) required to maintain turbulent flow. The maximum safe flow rate (m³ / s) to prevent water hammer effect is, where,

[0051] ;

[0052] ;

[0053] =4000, The kinematic viscosity of cooling water, Here, A represents the pipe's pressure limit, and k is the pipe's cross-sectional area; k is a correction factor characterizing the pipe diameter's effect on the flow-time relationship, which is related to the pipe diameter. For the on / off time of the cold circuit, This can be equivalent to the total flow rate of the cooling medium. Step 320 ensures that the parameters output by the parameter prediction model are within a preset range, preventing insufficient flow or overpressure in the mold cooling pipes.

[0054] In some embodiments, such as Figure 4 As shown, the PID incremental adjustment in step 500 is specifically as follows:

[0055] 510: Calculate the temperature field deviation matrix ;

[0056] 520: Generate adjustment amount by region:

[0057] ;

[0058] in =0.8, =0.05, =0.15;

[0059] Among them, for the generated adjustment amount, the single adjustment amplitude is limited to no more than ±15% of the parameter range.

[0060] Specifically, the temperature field deviation matrix in step 510 The difference between the actual temperature field and the target temperature field of the mold is used to determine the scaling factor in step 520. =0.8, The regional average temperature deviation reflects the abnormal area. Mean, integral coefficient =0.05, The differential coefficient is the sum of historical deviations. =0.15, This is the current deviation matrix, reflecting the latest... value, This is the deviation matrix for the previous cycle, reflecting the previous cycle. The value; for the generated adjustment amount, the adjustment range does not exceed ±15% of the parameter range. This is to take into account the safety limit of pipeline pressure bearing. By setting an upper limit for the adjustment range, the water hammer effect caused by a sudden increase in flow in the pipeline can be prevented. Through steps 510 and 520 and the limitation of the adjustment amount, the adjustment amount can be generated in real time according to the deviation of the three-dimensional corrected temperature field. This serves as a supplementary fine-tuning amount to the target adjustment parameters output by the parameter prediction model. By setting the values ​​of the proportional coefficient, integral coefficient, and derivative coefficient respectively, the output of the adjustment amount is optimized, which can adjust the temperature of the mold more quickly, reduce the power consumption of the die-casting system, and reduce the scrap rate.

[0061] Furthermore, in step 520, when the adjustment amount output by the parameter prediction model for the first time is greater than or equal to 15%, a coarse adjustment operation is performed, setting the parameters of the mold temperature control system to the adjustment value output by the parameter prediction model. In the next production cycle after the coarse adjustment, a new temperature field is collected and the difference between the new temperature field and the target temperature field is calculated, i.e., the temperature field deviation matrix. When satisfied At that time, activate the PID fine-tuning layer:

[0062] ;

[0063] in, This is the fine-tuning output of the PID controller. To compensate for local temperature gain, For historical cumulative error compensation, The average temperature difference in the key area. For the temperature deviation over time integral, the preferred method is... =0.3, =0.02, and at the same time, when At this time, the emergency shutdown protocol is triggered, and production is stopped. Specifically, in the coarse adjustment stage, the adjustment value output by the parameter prediction model is directly used to fully adjust the mold temperature control system to minimize temperature field deviation matrix. For conditions exceeding 10℃, and to reduce the risk of PID overshoot, while when the temperature field deviation matrix... Between 5℃ and 10℃, it can be assumed that the PID fine-tuning layer can ensure that the mold temperature is within the expected range, thus guaranteeing stable production. However, when the temperature field deviation matrix... At this point, the temperature inside the mold can be considered to have reached the critical temperature for solidification defects in the aluminum alloy melt. The probability of defects in the casting is close to 100%, and die-casting production should be stopped immediately to protect the mold and equipment. Therefore, by separating the coarse and fine adjustments of the PID controller, adjustment time can be saved, overshoot reduced, and steady-state error decreased, thereby improving production efficiency, reducing scrap rates, and lowering the risk of damage to die-casting equipment, mold temperature control systems, and molds.

[0064] In some embodiments, such as Figure 5 As shown, step 200, generating a three-dimensional corrected temperature field that includes the surface and internal temperatures of the mold, specifically includes:

[0065] 210: Map thermocouple data to temperature nodes in the internal volume mesh of the mold;

[0066] 220: Surface infrared temperature and internal node data are fused using the Laplace smoothing algorithm;

[0067] 230: Radial basis function interpolation is used to fill the blind zone in the inverted region.

[0068] Specifically, in step 210, the thermocouple positions and spatial coordinates are matched to a three-dimensional volume network through thermocouple data and spatial coordinate analysis, thereby generating temperature nodes inside the mold. The spatial coordinates can be obtained from the mold's CAD model, and a direct attenuation factor can be introduced when mapping thermocouples to spatial coordinates. Where d is the depth, thus solving the problem of heat transfer distortion in the deep cavity area of ​​the mold; in step 220, the Laplace regularization fusion algorithm is used to fuse the infrared temperature of the mold surface collected by the infrared camera with the internal node data of the mold collected by the thermocouple, through the following formula:

[0069] ;

[0070] In the discrete solution formula, The matrix represents the infrared temperature field on the surface of the mold. To smooth out the weights, based on experimental results, the optimal weighting is selected for general-purpose molds. =0.8, For Laplace matrix, Using an identity matrix, the infrared accuracy of the mold surface and the smoothness of the internal thermocouple data are balanced by an optimized smoothing weight. In step 230, based on known temperature points, namely the infrared points on the mold surface and the thermocouple points inside the mold, the temperature of the undercut blind zone inside the mold is predicted by radial basis function (RBF) interpolation to improve the accuracy of the three-dimensional corrected temperature field.

[0071] In some embodiments, such as Figure 6 As shown, the mold temperature control method also includes:

[0072] 600: Calculate the confidence level of the parameter prediction model;

[0073] 700: If the confidence level of the parameter prediction model is less than 90%, accept the manual correction instruction and store it in the historical experience database.

[0074] Specifically, in step 600, by

[0075] ;

[0076] Calculate the confidence score conf, where, The temperature field predicted by the parameter prediction model. For the target temperature field, The maximum permissible deviation threshold can be set to 10℃. In step 700, when the confidence level of the parameter prediction model is less than 90%, the operator can be alerted that the parameters output by the parameter prediction model have a deviation by overlaying a deviation heatmap on the CAD model or by describing the deviation value and deviation coordinates in text. The operator can then issue corresponding instructions to adjust the temperature of the mold. Furthermore, the operator's correction records are automatically clustered and stored in the historical experience database. Through the confidence quantification mechanism, the reliability of the parameter prediction model is objectively measured. The confidence level is then used to determine whether the parameter prediction model is fully automated or requires manual intervention, and the data from manual corrections is collected to facilitate subsequent model improvements.

[0077] In some embodiments, such as Figure 7 As shown, the mold temperature control method also includes:

[0078] 800: When the cumulative number of manual corrections exceeds the threshold, update the parameters and predict the model weights based on reinforcement learning.

[0079] Specifically, in step 800, when the number of manual corrections exceeds a threshold, such as 5 or 10 times, an experience pool is constructed based on the most recent correction records. Reinforcement learning is then applied to the parameter prediction model, and a reward function can be constructed, for example:

[0080] ;

[0081] in, The deviation matrix between the actual temperature field measured in a new die-casting production cycle and the target temperature field after manual parameter correction enables the parameter prediction model to have continuous learning capabilities. Integrating the experience of manual correction into the parameter prediction model can reduce the need for manual intervention, improve the confidence of the parameter prediction model, and thus improve the accuracy of mold temperature control.

[0082] This invention provides an electronic device, which includes a processor, wherein the processor executes a program that is the method provided in any of the above embodiments; the electronic device is connected to a temperature compensator, which is used to dynamically calibrate thermocouple readings according to the ambient temperature.

[0083] This invention provides a computer-readable storage medium storing a computer program thereon, wherein when the program is executed by a processor, it controls the operation of the electronic device provided in the above embodiments.

[0084] It should be noted that, in this invention, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, product, or apparatus that includes that element.

[0085] In the several embodiments provided by this invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0086] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A mold temperature control method, characterized in that, include: Acquire infrared images of the die-casting mold and real-time temperature data of the internally embedded thermocouples; By fusing infrared images and thermocouple data, a three-dimensional corrected temperature field containing the surface and internal temperatures of the mold is generated; When correcting the anomaly in the three-dimensional temperature field, the adjustment value of the target adjustment parameter is output through the parameter prediction model. The residual constraint of the thermodynamic equation is introduced into the loss function during the training of the parameter prediction model. The adjustment value is sent to the mold temperature control system for execution. In the next production cycle, a new temperature field is collected. If the deviation from the target temperature field exceeds the threshold, the parameters are adjusted incrementally using a PID algorithm.

2. The mold temperature control method as described in claim 1, characterized in that, Acquiring infrared images of die-casting molds and real-time temperature data of internally embedded thermocouples also includes embedding thermocouples in the deep cavity, undercut structure, and outlet of the cooling pipe of the die-casting mold, in the concealed area ≤5mm away from the cooling pipe, within ±3mm of the wall thickness change point, and in the mold cavity corresponding to castings with a historical defect rate >10%.

3. The mold temperature control method as described in claim 1, characterized in that, The method further includes: Based on a pre-set training set, a parameter mapping model is trained to establish the mapping relationship between pipeline parameters and mold temperature. A parameter prediction model is obtained by training a parameter mapping model, and then the parameter prediction model is simplified. The parameter prediction model training also includes: Channel pruning is performed on the neural network of the parameter prediction model, removing nodes with absolute weight values ​​< 0.001; Compress the parameter prediction model; Deploy the parameter prediction model on edge computing devices.

4. The mold temperature control method as described in claim 1, characterized in that, During the training of the parameter prediction model, a residual constraint based on the thermodynamic equation is introduced into the loss function, specifically: Add a residual term from the heat conduction equation to the loss function: ,in =0.7-1.2, The temperature field matrix predicted by the parameter prediction model. This is the actual temperature field matrix for simulation. These are weighting coefficients used to balance the accuracy of predictions with their conformity to physical laws. Let T be the Laplace operator, and T be the three-dimensional transient temperature distribution. The second-order rate of change of the temperature field in space is used to characterize the thermal diffusivity of the mold material. For partial differential operators, t is the time variable. This is the time derivative of temperature; The search space is limited by a set of historical qualified parameters, and the constraints include: the point cooling time and flow rate must meet certain conditions. , This is the pipe diameter coefficient. For the on / off time of the cold circuit, To maintain the minimum volumetric flow rate in turbulent conditions, The maximum safe flow rate to prevent water hammer effect.

5. The mold temperature control method as described in claim 1, characterized in that, The specific incremental adjustment parameters of the PID algorithm are as follows: Calculate the temperature field deviation matrix ΔT; Generate adjustment amounts by region: ,in =0.8, =0.05, =0.15, The regional average temperature deviation This is the sum of historical deviations. This is the current deviation matrix. This is the deviation matrix for the previous period; Among them, for the generated adjustment amount, the single adjustment amplitude is limited to no more than ±15% of the parameter range.

6. The mold temperature control method as described in claim 1, characterized in that, Generating a three-dimensional corrected temperature field that includes both the surface and internal temperatures of the mold specifically includes: Map thermocouple data to temperature nodes of the internal volume grid of the mold; The surface infrared temperature and internal node data are fused using the Laplace smoothing algorithm; Radial basis function interpolation is used to fill the blind zone in the inverted region.

7. The mold temperature control method as described in claim 1, characterized in that, The method further includes: Calculate the confidence level of the parameter prediction model; If the confidence level of the parameter prediction model is less than 90%, a manual correction instruction will be accepted and the data will be stored in the historical experience database.

8. The mold temperature control method as described in claim 7, characterized in that, The method further includes: When the cumulative number of manual corrections exceeds the threshold, the model weights are predicted by updating the parameters based on reinforcement learning.

9. An electronic device, comprising a processor, characterized in that: When the processor executes the program, it implements the mold temperature control method according to any one of claims 1-6; The electronic device is connected to a temperature compensator, which is used to dynamically calibrate thermocouple readings according to the ambient temperature.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, it controls the operation of the electronic device as described in claim 9.

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