A fast temperature response system and method in the heating process of aluminum mold

Abstract

The present invention discloses a rapid temperature response system and method in the heating process of an aluminum mold, and relates to the technical field of aluminum molds. The system is capable of collecting temperature response data in the heating process of the mold in real time by setting a number of detection points on an infrared mold heating furnace and installing an integrated sensor group. These data are recorded in a work log through a temperature response data acquisition module, and are preprocessed and integrated by a data integration module to obtain a temperature response data set and store it in a time series database. The temperature prediction module uses these data to dynamically calculate an adaptive coefficient Kadapt, thereby optimizing the heating power Poptimal of the heating equipment. This real-time calculation and adjustment mechanism enables the system to respond quickly to temperature changes, significantly shortens the time for the mold to reach the target temperature, and ensures high precision of temperature control. This improvement improves production efficiency and reduces the scrap rate caused by inaccurate temperature control.

Description

Technical Field

[0001] The invention relates to the technical field of aluminum molds, and in particular to a rapid temperature response system and method during the heating process of an aluminum mold. Background Art

[0002] With the development of intelligent manufacturing, the importance of temperature control in various industrial processing processes continues to increase, especially in the heating process of aluminum molds in infrared mold heating furnaces, where precise temperature control directly affects the quality of the final product; in industrial processing, aluminum molds are widely used in various molding and manufacturing processes, and these processes have extremely strict requirements on mold temperature. In order to achieve more efficient and accurate temperature control, an adaptive temperature control algorithm is used to build a fast temperature response system in the heating process of aluminum molds, which can help the system respond quickly to temperature changes and ensure that the mold temperature remains within the ideal range, thereby improving production efficiency and product quality.

[0003] At present, during the heating process of aluminum molds in infrared mold heating furnaces, traditional temperature control systems often find it difficult to cope with complex heat conduction environments and dynamic production needs. These systems usually use fixed heating power settings and cannot adjust the heating strategy in real time, resulting in slow temperature response or insufficient temperature control accuracy. In addition, factors such as changes in the ambient temperature in the furnace, the thermal conductivity characteristics of the aluminum mold material, and the influence of the surface oxide layer will cause the performance of the temperature control system to deteriorate, thereby affecting the uniformity and efficiency of mold heating. In this case, uneven temperature distribution may occur inside the aluminum mold, affecting the quality and consistency of the product. First, the temperature response time is too long, so that the aluminum mold cannot reach the set temperature within the expected time, which prolongs the production cycle and reduces production efficiency. Second, inaccurate temperature control of the infrared mold heating furnace may cause local overheating or insufficient temperature of the aluminum mold, which directly affects the quality of the finished product and even leads to an increase in the scrap rate during the production process. In addition, the low energy efficiency of the infrared mold heating furnace may lead to unnecessary energy waste and increase production costs. To this end, improving the response speed and accuracy of the temperature control system, especially introducing an adaptive temperature control algorithm, has become the key to improving the control level of the aluminum mold heating process.

[0004] Therefore, how to intelligently control the temperature during the heating process of aluminum molds is currently a technical problem that technicians need to solve. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a system and method for rapid temperature response in the heating process of an aluminum mold, which solves the problems mentioned in the background technology.

[0006] To achieve the above objectives, the first aspect of the present invention provides a rapid temperature response system in the heating process of an aluminum mold, which is implemented by the following technical scheme: comprising a temperature response data acquisition module, a data integration module, a temperature prediction module, a temperature response analysis module and an optimization evaluation module;

[0007] The temperature response data acquisition module collects the temperature response data of the aluminum mold heating process in real time by setting several detection points and installing an integrated sensor group on the infrared mold heating furnace, and records the temperature response data in the work log of the heating equipment;

[0008] The data integration module is used to build an API log interface to connect with the work log, extract the temperature response data recorded in the work log in real time, pre-process the temperature response data, and summarize the data after pre-processing to obtain a temperature response data set, and then mark the obtained temperature response data set and store it in a time series database;

[0009] The temperature prediction module is used to extract the temperature response data set in the time series database in real time, dynamically calculate and output the adaptive coefficient Kadapt, and then calculate and adjust the heating power of the heating equipment according to the output adaptive coefficient Kadapt, output the optimized heating power Poptimal, and then construct a temperature prediction algorithm model, input the optimized heating power Poptimal into the temperature prediction algorithm model, and calculate and output the predicted furnace temperature T(t+1);

[0010] The temperature response analysis module is used to construct a temperature response prediction model, extract the optimized heating power Poptimal and the predicted furnace temperature T(t+1), input them into the temperature response time algorithm formula, output the temperature response time Wdxy, and then pre-set the response threshold X and the output temperature response time Wdxy for preliminary comparison and evaluation;

[0011] The optimization evaluation module is used to further adjust the calculation method of the adaptive coefficient Kadapt by triggering the adjustment mechanism after the preliminary evaluation, output the adjustment adaptation coefficient Kadapt2, and then input the obtained adjustment adaptation coefficient Kadapt2 into the system for re-execution, output the optimized temperature response time Wdxy2, and perform a secondary comparative evaluation with the response threshold X.

[0012] Preferably, the temperature response data acquisition module includes a sensor acquisition unit and a data recording unit;

[0013] The sensor acquisition unit collects the temperature response data of the aluminum mold heating process in real time by installing an integrated sensor group on the infrared mold heating furnace and reading the data of the integrated sensor group in real time;

[0014] The integrated sensor group includes a thermocouple, an infrared temperature sensor, an ambient temperature sensor, a heat flow meter, a laser interferometer, a power meter and a multi-point temperature sensor network;

[0015] The temperature response data includes mold heat flow Q, aluminum mold temperature difference △Tm, heating temperature Tj, ambient temperature Th, surface oxide layer thickness Ssurface, power output value Pprev, input total heating power Pin, output total heating power Pon, current aluminum mold temperature Tcurr, aluminum mold temperature upper limit value Tmax and aluminum mold temperature lower limit value Tmin;

[0016] The mold heat flow Q, the temperature difference ΔTm in the aluminum mold, the heating temperature Tj and the ambient temperature Th are collected and obtained by a heat flow meter, a thermocouple, an infrared temperature sensor and an ambient temperature sensor respectively;

[0017] The surface oxide layer thickness Ssurface is acquired by laser interferometer;

[0018] The power output value Pprev, the input total heating power Pin, and the output total heating power Pon are acquired through a power meter;

[0019] The upper temperature limit Tmax in the aluminum mold and the lower temperature limit Tmin in the mold are acquired through a multi-point temperature sensor network;

[0020] The data recording unit is used to adopt the industrial standard Ethernet / IP communication protocol to record the collected temperature response data in the work log according to the log structure, and the log structure includes the sensor ID, collection time, parameter name and parameter value.

[0021] Preferably, the data integration module includes a data processing module and an integrated storage module;

[0022] The data processing module is used to set an API application program interface to connect with the operating system work log of the infrared mold heating furnace, extract the temperature response data in the work log in real time, pre-process the temperature response data, and then integrate it to obtain a temperature response data set;

[0023] The temperature response data set includes mold thermal conductivity Hcond(t), thermal resistance Rther, ambient temperature change rate △Tam, mold heat capacity Cthe(t), surface oxide layer thickness Ssurface, power output value Pprev(t-1) in the previous time period, energy loss Eloss and mold temperature gradient △Tgrad;

[0024] The heat transfer coefficient Hcond(t) is obtained by calculating the mold heat flow Q and the temperature difference △Tm in the aluminum mold. The calculation formula is: Where A represents the cross-sectional area of ​​the aluminum mold;

[0025] The thermal resistance coefficient Rther is obtained by calculating the temperature difference △Tm in the aluminum mold and the heating temperature Tj. The calculation formula is:

[0026] The ambient temperature change rate ΔTam is obtained by calculating the ambient temperature Th, and the calculation formula is: In the formula, d represents microvariable, t represents time;

[0027] The mold heat capacity Cthe(t) is obtained by calculating the mold heat flow Q and the temperature difference △Tm in the aluminum mold. The calculation formula is: Where, dt represents the time increment, d△Tm represents the temperature difference increment in the aluminum mold;

[0028] The power output value Pprev(t-1) of the previous time period is obtained by extracting the power output value of the previous time period with the current power output value Pprev;

[0029] The energy loss Eloss is obtained by calculating the input total heating power Pin and the output total heating power Pon, and the calculation formula is Eloss=Pin-Pon;

[0030] The mold temperature gradient △Tgrad is obtained by calculating the upper temperature limit Tmax in the aluminum mold and the lower temperature limit Tmin in the mold. The calculation formula is: Where L represents the measurement distance inside the mold;

[0031] The integrated storage module is used to construct a time series database, mark the preprocessed temperature response data set with a timestamp, and set a write interface to store the temperature response data set in the time series database in the order of the timestamps.

[0032] Preferably, the temperature prediction module includes a heating adaptive adjustment unit, a heating power optimization unit and a temperature prediction module;

[0033] The adaptive adjustment unit is used to construct an adaptive adjustment algorithm formula, and at the same time, a write interface is set to extract the temperature response data set in the database in real time, input it into the adaptive adjustment algorithm formula, and output the adaptive coefficient Kadapt;

[0034] The adaptive coefficient Kadapt is calculated and obtained by the following adaptive adjustment algorithm formula;

[0035]

[0036] In the formula, Tset represents the target heating temperature, Tset-Tcurr represents the temperature difference that the mold needs to reach, γ represents the weight value of the ambient temperature change rate, which is used to control the influence of ambient temperature change in the calculation, δ represents the energy loss influence coefficient, which is used to control the influence of energy loss in the calculation, and θ represents the mold temperature gradient influence coefficient, which is used to control the influence of temperature gradient in the calculation.

[0037] Preferably, the heating power optimization unit is used to construct a heating power optimization algorithm formula, and then input the adaptive coefficient Kadapt and the temperature response data set into the heating power optimization algorithm formula, output the optimized heating power Poptimal, and optimize the heating power of the system;

[0038] The optimized heating power Poptimal is calculated and obtained by the following heating power optimization algorithm formula;

[0039]

[0040] In the formula, α represents the power allocation adjustment coefficient, ε represents a small constant, which prevents the denominator from being zero when the current aluminum mold temperature Tcurr is zero, ensures the stability of the calculation, and avoids mathematical uncertainty. Log represents the logarithmic function. It represents a smoothing function. Through a logarithmic function, the adjustment of the heating power is smoothed to prevent drastic fluctuations in the heating power and ensure a smooth heating process.

[0041] Preferably, the temperature prediction module is used to construct a temperature prediction algorithm model, input the optimized heating power Poptimal and the temperature response data set into the temperature prediction algorithm model, and output the predicted furnace temperature T(t+1);

[0042] The predicted furnace temperature T(t+1) is calculated and obtained by the following temperature prediction algorithm model;

[0043]

[0044] Wherein, T(t) represents the mold temperature at the current moment. The higher the demand for temperature adjustment, the longer the response time may be. Poptimal×Hcond(t)-Eloss represents the net heat transferred to the aluminum mold. The thermal resistance coefficient Rther is used as part of the denominator in the formula to correct the thermal conduction efficiency of the system at different powers. Cthe(t)×(1+Rther×Ssurface) represents the comprehensive resistance term of heating efficiency. Poptimal represents the optimized heating power, Hcond(t) represents the mold thermal conductivity coefficient, Eloss represents energy loss, Cthe(t) represents the mold heat capacity at time t, Rther represents the thermal resistance coefficient, and Ssurface represents the thickness of the surface oxide layer.

[0045] Preferably, the temperature response analysis module includes a temperature response calculation unit and a temperature response evaluation unit;

[0046] The temperature response calculation unit constructs a temperature response time algorithm formula, and then inputs the acquired predicted furnace temperature T(t+1), optimized heating power Poptimal and temperature response data set into the temperature response time algorithm formula to calculate and output the temperature response time Wdxy;

[0047] The temperature response time Wdxy is calculated and obtained by the following temperature response time algorithm formula;

[0048]

[0049] Wherein, Tset-Tcurr represents the temperature difference of the heating equipment, which is used to adjust the relationship between the heating temperature requirement and the temperature response time, Tset represents the target heating temperature, and Tcurr represents the current aluminum mold temperature;

[0050] The temperature response evaluation unit sets the response threshold value X through the user, and then performs a preliminary comparative evaluation with the obtained temperature response time Wdxy to analyze the temperature response state during the heating process of the aluminum mold. The specific evaluation contents are as follows;

[0051] When the temperature response time Wdxy>response threshold X, it means that the temperature response of the infrared mold heating furnace is abnormal. At this time, adaptive adjustment optimization is performed. The system response is too slow and the heating efficiency needs to be improved. It may be necessary to increase the power or reduce the energy loss.

[0052] When the temperature response time Wdxy = response threshold X, it means that the temperature response of the infrared mold heating furnace is normal, and no optimization is required at this time;

[0053] When the temperature response time Wdxy is less than the response threshold X, it indicates that the temperature response of the infrared mold heating furnace is abnormal. At this time, adaptive adjustment optimization is performed. The system responds too fast and there may be a risk of overheating. It is necessary to reduce the heating power or introduce a buffer mechanism.

[0054] Preferably, the optimization evaluation module includes an adaptive optimization module;

[0055] The adaptive optimization module is used to be triggered when the temperature response is abnormal during the preliminary comparison and evaluation, and calculates and outputs the adjustment adaptation coefficient Kadapt2 by constructing an adaptive adjustment algorithm formula;

[0056] The adjustment adaptation coefficient Kadapt2 is calculated and obtained by the following adaptive adjustment algorithm formula;

[0057]

[0058] Wherein, β represents the overall adjustment coefficient of the adaptive coefficient, λ represents the temperature gradient adjustment coefficient, Hcond(t) represents the mold thermal conductivity coefficient, Cthe(t) represents the mold heat capacity at time t, Ssurface represents the thickness of the surface oxide layer, Rther represents the thermal resistance coefficient, △Tam represents the ambient temperature change rate, Eloss represents the energy loss, Pprev represents the power output value, and △Tgrad represents the mold temperature gradient.

[0059] Preferably, the optimization evaluation module is used to substitute the output adjustment adaptation coefficient Kadapt2 into the heating power optimization algorithm formula, and re-execute the system until the output optimized temperature response time Wdxy2 is obtained, and then perform a secondary comparison evaluation with the response threshold value X, and generate an adjustment strategy and an iteration strategy;

[0060] When the optimized temperature response time Wdxy2>response threshold X, it means that the temperature response of the infrared mold heating furnace is abnormal after optimization and adjustment. At this time, the first adjustment strategy is executed to increase the overall adjustment coefficient β of the adaptive coefficient and the temperature gradient adjustment coefficient λ by 10%. After adjustment, the optimization evaluation module is executed again to perform iterative optimization until the temperature response is normal and the iteration is stopped;

[0061] When the optimized temperature response time Wdxy2 = response threshold X, it means that the temperature response of the infrared mold heating furnace is normal after optimization and adjustment, and no optimization is required at this time;

[0062] When the optimized temperature response time Wdxy2 is less than the response threshold X, it means that the temperature response of the infrared mold heating furnace is abnormal after optimization and adjustment. At this time, the second adjustment strategy is executed to reduce the overall adjustment coefficient β of the adaptive coefficient and the temperature gradient adjustment coefficient λ by 10%. After adjustment, the optimization evaluation module is executed again to perform iterative optimization until the temperature response is normal and the iteration is stopped.

[0063] A second aspect of the present application provides a method for rapid temperature response during heating of an aluminum mold, comprising the following steps:

[0064] S1. Set several detection points and install an integrated sensor group on the infrared mold heating furnace to collect temperature response data during the heating process of the aluminum mold in real time, and record the temperature response data in the work log of the heating equipment;

[0065] S2. Build an API log interface to connect with the work log, extract the temperature response data recorded in the work log in real time, pre-process the temperature response data, and summarize the data after pre-processing to obtain a temperature response data set, and then mark the obtained temperature response data set and store it in a time series database;

[0066] S3, extract the temperature response data set in the time series database, dynamically calculate and output the adaptive coefficient Kadapt, and then calculate and adjust the heating power of the heating equipment according to the output adaptive coefficient Kadapt, output the optimized heating power Poptimal, and then build a temperature prediction algorithm model, input the optimized heating power Poptimal into the temperature prediction algorithm model, and calculate and output the predicted furnace temperature T(t+1);

[0067] S4, construct a temperature response prediction model, extract the optimized heating power Poptimal and the predicted furnace temperature T(t+1), input them into the temperature response time algorithm formula, output the temperature response time Wdxy, and then pre-set the response threshold X and the output temperature response time Wdxy for preliminary comparison and evaluation;

[0068] S5. After the preliminary evaluation, the calculation method of the adaptive coefficient Kadapt is further adjusted by triggering the adjustment mechanism, and the adjustment adaptation coefficient Kadapt2 is output. The obtained adjustment adaptation coefficient Kadapt2 is then input into the system for re-execution, and the optimized temperature response time Wdxy2 is output, and a secondary comparative evaluation is performed with the response threshold value X.

[0069] The present invention provides a rapid temperature response system and method during the heating process of an aluminum mold, which has the following beneficial effects:

[0070] (1) The system sets several detection points on the infrared mold heating furnace and installs an integrated sensor group. The system can collect temperature response data during the mold heating process in real time. These data are recorded in the work log through the temperature response data acquisition module, and are preprocessed and integrated by the data integration module to obtain the temperature response data set and store it in the time series database. The temperature prediction module uses this data to dynamically calculate the adaptive coefficient Kadapt to optimize the heating power Poptimal of the heating equipment. This real-time calculation and adjustment mechanism enables the system to respond quickly to temperature changes, significantly shortens the time for the mold to reach the target temperature, and ensures high precision of temperature control. This improvement improves production efficiency and reduces the scrap rate caused by inaccurate temperature control.

[0071] (2) The system preprocesses and integrates the real-time collected temperature response data through an integrated data processing module to extract the temperature response data set. Based on the temperature response data set, the system can accurately calculate the optimal distribution of heating power, avoiding the energy waste caused by fixed heating power settings in traditional systems. The heating power optimization unit outputs the optimized heating power Poptimal by analyzing the adaptive coefficient Kadapt and the temperature response data set, thereby achieving the best heating effect while reducing energy consumption. This energy management optimization not only reduces production costs, but also makes the system more environmentally friendly and efficient in terms of resource utilization.

[0072] (3) The system accurately calculates the temperature response time Wdxy by constructing a temperature response time algorithm, combining the optimized heating power Poptimal and the predicted temperature T(t+1). The system can quickly identify abnormalities in the temperature response by performing a preliminary comparative evaluation with the preset response threshold X. When an abnormal response time is detected, the optimization evaluation module triggers the adjustment mechanism, outputs a new adjustment adaptation coefficient Kadapt2 by adjusting the calculation method of the adaptive coefficient Kadapt, and re-optimizes the system's heating power and temperature response time Wdxy2, and performs a secondary comparative evaluation with the response threshold X, and generates an adjustment strategy and an iteration strategy. This dynamic adjustment mechanism ensures the efficient operation of the system under various working conditions, avoids overheating or lag problems caused by too fast or too slow temperature response, further improves the stability and adaptability of the system, and ensures the continuity of the production process and the stability of product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 This is a schematic diagram of a rapid temperature response system flow in the heating process of an aluminum mold according to the present invention;

[0074] Figure 2 The present invention is a schematic diagram of the steps of a method for rapid temperature response during heating of an aluminum mold. DETAILED DESCRIPTION

[0075] 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.

[0076] Example 1

[0077] See also Figure 1, the present invention provides a rapid temperature response system in the heating process of an aluminum mold. To achieve the above purpose, the present invention is implemented through the following technical scheme: including a temperature response data acquisition module, a data integration module, a temperature prediction module, a temperature response analysis module and an optimization evaluation module;

[0078] The temperature response data acquisition module sets several detection points and installs an integrated sensor group on the infrared mold heating furnace to collect the temperature response data of the aluminum mold heating process in real time, and records the temperature response data in the work log of the heating equipment;

[0079] The data integration module is used to build an API log interface to connect with the work log, extract the temperature response data recorded in the work log in real time, pre-process the temperature response data, and summarize the data after pre-processing to obtain the temperature response data set, and then mark the obtained temperature response data set and store it in the time series database;

[0080] The temperature prediction module is used to extract the temperature response data set in the time series database in real time, dynamically calculate and output the adaptive coefficient Kadapt, and then calculate and adjust the heating power of the heating equipment according to the output adaptive coefficient Kadapt, output the optimized heating power Poptimal, and then build a temperature prediction algorithm model, input the optimized heating power Poptimal into the temperature prediction algorithm model, and calculate and output the predicted furnace temperature T(t+1);

[0081] The temperature response analysis module is used to build a temperature response prediction model, extract the optimized heating power Poptimal and the predicted furnace temperature T(t+1), input them into the temperature response time algorithm formula, output the temperature response time Wdxy, and then pre-set the response threshold X and the output temperature response time Wdxy for preliminary comparison and evaluation;

[0082] The optimization evaluation module is used to further adjust the calculation method of the adaptive coefficient Kadapt by triggering the adjustment mechanism after the preliminary evaluation, output the adjustment adaptation coefficient Kadapt2, and then input the obtained adjustment adaptation coefficient Kadapt2 into the system for re-execution, output the optimized temperature response time Wdxy2, and perform a secondary comparative evaluation with the response threshold X.

[0083] In this embodiment, the system monitors and records the key temperature data in the mold heating process in real time through the integrated sensor group installed on the heating equipment through the temperature response data acquisition module, providing accurate basic data for subsequent processing. Secondly, the data integration module extracts and preprocesses these data in real time through the API interface to ensure the integrity and real-time nature of the data, and stores them in the time series database after aggregation. This modular data management not only improves the data processing capability of the system, but also ensures the response speed of the system, can quickly adapt to temperature changes, and optimize heating control. After extracting the data in the time series database in real time, the temperature prediction module uses the adaptive coefficient Kadapt generated by dynamic calculation to optimize the power output Poptimal of the heating equipment. Subsequently, the system calculates the predicted temperature T(t+1) of future temperature changes through the temperature prediction algorithm model to ensure accurate control of the heating process. The temperature response analysis module further constructs a temperature response time algorithm, comprehensively analyzes the predicted temperature and the optimized heating power, outputs the temperature response time Wdxy, and compares and evaluates it with the set response threshold X. Compared with the traditional fixed power control method, this system can dynamically adjust the heating power according to real-time data, making the temperature response faster and more accurate, significantly improving the efficiency and stability of the heating process. After the temperature response analysis, the optimization and evaluation module further improves the calculation method of the adaptive coefficient Kadapt by triggering the adjustment mechanism, and outputs a new adjustment adaptation coefficient Kadapt2. The obtained adjustment adaptation coefficient Kadapt2 is then input into the system for re-execution, and the optimized temperature response time Wdxy2 is output, and a secondary comparison and evaluation is performed with the response threshold X. Through this optimization cycle, the system can quickly adjust the heating strategy when an abnormal temperature response is detected to avoid the risk of overheating or insufficient heating.

[0084] Example 2

[0085] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the temperature response data acquisition module includes a sensor acquisition unit and a data recording unit;

[0086] The sensor acquisition unit collects the temperature response data of the aluminum mold heating process in real time by installing an integrated sensor group on the infrared mold heating furnace and reading the data of the integrated sensor group in real time;

[0087] The integrated sensor set includes thermocouples, infrared temperature sensors, ambient temperature sensors, heat flow meters, laser interferometers, power meters, and a multi-point temperature sensor network;

[0088] The temperature response data includes mold heat flow Q, temperature difference △Tm inside the aluminum mold, heating temperature Tj, ambient temperature Th, surface oxide layer thickness Ssurface, power output value Pprev, input total heating power Pin, output total heating power Pon, current aluminum mold temperature Tcurr, upper limit value Tmax inside the aluminum mold and lower limit value Tmin inside the aluminum mold;

[0089] The mold heat flow Q, the temperature difference △Tm inside the aluminum mold, the heating temperature Tj and the ambient temperature Th are collected and obtained by the heat flow meter, thermocouple, infrared temperature sensor and ambient temperature sensor respectively;

[0090] The surface oxide layer thickness Ssurface is acquired by laser interferometer;

[0091] The power output value Pprev, the input total heating power Pin, and the output total heating power Pon are acquired through the power meter;

[0092] The upper temperature limit Tmax in the aluminum mold and the lower temperature limit Tmin in the mold are acquired through a multi-point temperature sensor network;

[0093] The data recording unit is used to adopt the industrial standard Ethernet / IP communication protocol to record the collected temperature response data in the work log according to the log structure. The log structure includes the sensor ID, collection time, parameter name and parameter value.

[0094] In this embodiment, the system uses the sensor acquisition unit and data recording unit in the temperature response data acquisition module. The system is equipped with a variety of integrated sensor groups on the infrared mold heating furnace. These sensors can monitor the temperature response data during the heating process of the aluminum mold in real time. The data recording unit uses the industrial standard Ethernet / IP communication protocol to accurately record the collected data in the work log, ensuring the integrity and traceability of the data.

[0095] Example 3

[0096] This embodiment is explained in Example 2. Please refer to Figure 1 ,Specifically: the data integration module includes a data processing module and an ,integrated storage module;

[0097] The data processing module is used to set up an API application program interface to connect with the work log, extract the temperature response data in the work log in real time, pre-process the temperature response data, and then integrate it to obtain the temperature response data set;

[0098] The temperature response data set includes the mold thermal conductivity coefficient Hcond(t), thermal resistance coefficient Rther, ambient temperature change rate △Tam, mold heat capacity Cthe(t), surface oxide layer thickness Ssurface, power output value Pprev(t-1) in the previous time period, energy loss Eloss and mold temperature gradient △Tgrad;

[0099] The heat transfer coefficient Hcond(t) is obtained by calculating the mold heat flow Q and the temperature difference △Tm in the aluminum mold. The calculation formula is: Where A represents the cross-sectional area of ​​the aluminum mold;

[0100] The thermal resistance coefficient Rther is obtained by calculating the temperature difference △Tm and heating temperature Tj in the aluminum mold. The calculation formula is:

[0101] The ambient temperature change rate △Tam is obtained by calculating the ambient temperature Th, and the calculation formula is: In the formula, d represents microvariable, t represents time;

[0102] The mold heat capacity Cthe(t) is obtained by calculating the mold heat flow Q and the temperature difference △Tm in the aluminum mold. The calculation formula is: Where, dt represents the time increment, d△Tm represents the temperature difference increment in the aluminum mold;

[0103] The power output value Pprev(t-1) of the previous time period is obtained by extracting the power output value of the previous time period with the current power output value Pprev;

[0104] The energy loss Eloss is obtained by calculating the input total heating power Pin and the output total heating power Pon, and the calculation formula is Eloss = Pin-Pon;

[0105] The mold temperature gradient △Tgrad is obtained by calculating the upper temperature limit Tmax in the aluminum mold and the lower temperature limit Tmin in the mold. The calculation formula is: Where L represents the measurement distance inside the mold;

[0106] The integrated storage module is used to build a time series database, mark the preprocessed temperature response data set with a timestamp, and set a write interface to store the temperature response data set in the time series database in the order of timestamps.

[0107] In this embodiment, the system comprehensively processes and accurately manages the temperature response data through the data integration module. The data processing module extracts the temperature response data in the work log in real time by setting the API application program interface, and preprocesses and integrates it to generate a temperature response data set. These parameters are calculated and processed through data collected by various sensors to ensure the accuracy and real-time nature of the data. Subsequently, the integrated storage module stores these preprocessed data sets in a time series database and marks the timestamp to ensure the traceability and sequentiality of the data. Through the refined processing and accurate storage of real-time data, the system not only achieves more efficient temperature prediction and control, but also brings significant improvements in energy utilization, temperature response time, and product quality control, greatly enhancing the stability and accuracy of the entire heating process.

[0108] Example 4

[0109] This embodiment is explained in Example 3, please refer to Figure 1 ,Specifically: the temperature prediction module includes a heating adaptive adjustment unit, a heating power optimization unit and a temperature prediction module;

[0110] The adaptive adjustment unit is used to construct an adaptive adjustment algorithm formula, and at the same time set a write interface to extract the temperature response data set in the database in real time, input it into the adaptive adjustment algorithm formula, and output the adaptive coefficient Kadapt;

[0111] The adaptive coefficient Kadapt is calculated by the following adaptive adjustment algorithm formula;

[0112]

[0113] In the formula, Tset represents the target heating temperature, Tset-Tcurr represents the temperature difference that the mold needs to reach, γ represents the weight value of the ambient temperature change rate, which is used to control the influence of ambient temperature change in the calculation, δ represents the energy loss influence coefficient, which is used to control the influence of energy loss in the calculation, and θ represents the mold temperature gradient influence coefficient, which is used to control the influence of temperature gradient in the calculation.

[0114] The heating power optimization unit is used to construct a heating power optimization algorithm formula, and then input the adaptive coefficient Kadapt and the temperature response data set into the heating power optimization algorithm formula, output the optimized heating power Poptimal, and optimize the heating power of the system;

[0115] The optimized heating power Poptimal is calculated and obtained by the following heating power optimization algorithm formula;

[0116]

[0117] In the formula, α represents the power allocation adjustment coefficient, ε represents a small constant, which prevents the denominator from being zero when the current aluminum mold temperature Tcurr is zero, ensures the stability of the calculation, and avoids mathematical uncertainty. Log represents the logarithmic function. It represents a smoothing function. Through a logarithmic function, the adjustment of the heating power is smoothed to prevent drastic fluctuations in the heating power and ensure a smooth heating process.

[0118] The temperature prediction module is used to build a temperature prediction algorithm model, input the optimized heating power Poptimal and the temperature response data set into the temperature prediction algorithm model, and output the predicted furnace temperature T(t+1);

[0119] The predicted furnace temperature T(t+1) is calculated and obtained by the following temperature prediction algorithm model;

[0120]

[0121] Wherein, T(t) represents the mold temperature at the current moment. The higher the demand for temperature adjustment, the longer the response time may be. Poptimal×Hcond(t)-Eloss represents the net heat transferred to the aluminum mold. The thermal resistance coefficient Rther is used as part of the denominator in the formula to correct the thermal conduction efficiency of the system at different powers. Cthe(t)×(1+Rther×Ssurface) represents the comprehensive resistance term of heating efficiency.

[0122] In this embodiment, the system realizes precise control and intelligent prediction of the heating process of the aluminum mold through the coordinated work of the heating adaptive adjustment unit, the heating power optimization unit and the temperature prediction unit. The adaptive adjustment unit uses the temperature response data extracted in real time to build and execute the adaptive adjustment algorithm, and dynamically calculates the adaptive coefficient Kadapt to ensure that the system can flexibly respond to temperature changes. The heating power optimization unit combines Kadapt with the temperature response data set to generate the optimized heating power Poptimal, and smoothes the adjustment of the heating power through a logarithmic function to avoid drastic fluctuations in the heating power and ensure a smooth and efficient heating process. The temperature prediction module further uses these data to construct a temperature prediction algorithm model and output the predicted temperature T(t+1), so that the system can respond to future temperature changes in advance.

[0123] Example 5

[0124] This embodiment is explained in Example 4. Please refer to Figure 1 ,Specifically: the temperature response analysis module includes a temperature response ,calculation unit and a temperature response evaluation unit;

[0125] The temperature response calculation unit constructs a temperature response time algorithm formula, and then inputs the obtained predicted furnace temperature T(t+1), optimized heating power Poptimal and temperature response data set into the temperature response time algorithm formula to calculate and output the temperature response time Wdxy;

[0126] The temperature response time Wdxy is calculated and obtained by the following temperature response time algorithm formula;

[0127]

[0128] In the formula, Tset-Tcurr represents the temperature difference of the heating equipment, which is used to adjust the relationship between the heating temperature demand and the temperature response time.

[0129] The temperature response evaluation unit sets the response threshold X through the user, and then makes a preliminary comparative evaluation with the obtained temperature response time Wdxy to analyze the temperature response state during the heating process of the aluminum mold. The specific evaluation contents are as follows;

[0130] When the temperature response time Wdxy>response threshold X, it means that the temperature response of the infrared mold heating furnace is abnormal. At this time, adaptive adjustment optimization is performed. The system response is too slow and the heating efficiency needs to be improved. It may be necessary to increase the power or reduce the energy loss.

[0131] When the temperature response time Wdxy = response threshold X, it means that the temperature response of the infrared mold heating furnace is normal, and no optimization is required at this time;

[0132] When the temperature response time Wdxy is less than the response threshold X, it indicates that the temperature response of the infrared mold heating furnace is abnormal. At this time, adaptive adjustment optimization is performed. The system responds too fast and there may be a risk of overheating. It is necessary to reduce the heating power or introduce a buffer mechanism.

[0133] In this embodiment, the system realizes accurate monitoring and intelligent optimization of the heating process of the aluminum mold through the cooperation of the temperature response calculation unit and the temperature response evaluation unit. The temperature response calculation unit constructs a temperature response time algorithm, inputs the predicted temperature T(t+1), the optimized heating power Poptimal and the temperature response data set into the formula, and calculates the temperature response time Wdxy of the system. Subsequently, the temperature response evaluation unit compares and evaluates Wdxy with the preset response threshold X to analyze the state of the temperature response. If the temperature response is found to be abnormal, whether the response is too slow or too fast, the system will automatically trigger adaptive adjustment optimization, and further adjust the heating power to ensure that the system can respond to temperature changes in a timely manner and avoid overheating or insufficient heating.

[0134] Example 6

[0135] This embodiment is explained in Example 5. Please refer to Figure 1 ,Specifically: the optimization and evaluation module includes an adaptive optimization ,module and an optimization evaluation module;

[0136] The adaptive optimization module is used to be triggered when the temperature response abnormality is identified in the preliminary comparative evaluation. By constructing the adaptive adjustment algorithm formula, it calculates the output adjustment adaptation coefficient Kadapt2;

[0137] The adjustment adaptation coefficient Kadapt2 is calculated and obtained by the following adaptive adjustment algorithm formula;

[0138]

[0139] Where β represents the overall adjustment coefficient of the adaptive coefficient, and λ represents the temperature gradient adjustment coefficient.

[0140] The optimization evaluation module is used to substitute the output adjustment adaptation coefficient Kadapt2 into the heating power optimization algorithm formula, and re-execute the system until the output optimized temperature response time Wdxy2 is obtained, and then conduct a secondary comparison evaluation with the response threshold value X, and generate an adjustment strategy and an iteration strategy;

[0141] When the optimized temperature response time Wdxy2>response threshold X, it means that the temperature response of the infrared mold heating furnace is abnormal after optimization and adjustment. At this time, the first adjustment strategy is executed to increase the overall adjustment coefficient β of the adaptive coefficient and the temperature gradient adjustment coefficient λ by 10%. After adjustment, the optimization evaluation module is executed again to perform iterative optimization until the temperature response is normal and the iteration is stopped;

[0142] When the optimized temperature response time Wdxy2 = response threshold X, it means that the temperature response of the infrared mold heating furnace is normal after optimization and adjustment, and no optimization is required at this time;

[0143] When the optimized temperature response time Wdxy2 is less than the response threshold X, it means that the temperature response of the infrared mold heating furnace is abnormal after optimization and adjustment. At this time, the second adjustment strategy is executed to reduce the overall adjustment coefficient β of the adaptive coefficient and the temperature gradient adjustment coefficient λ by 10%. After adjustment, the optimization evaluation module is executed again to perform iterative optimization until the temperature response is normal and the iteration is stopped.

[0144] In this embodiment, the system realizes continuous optimization and intelligent adjustment of the aluminum mold heating process through the close collaboration of the adaptive optimization module and the optimization evaluation module. When the adaptive optimization module identifies the abnormal temperature response in the preliminary comparative evaluation, it automatically triggers the adaptive adjustment algorithm and calculates the new adjustment adaptation coefficient Kadapt2 to further optimize the heating strategy. The optimization evaluation module substitutes Kadapt2 into the heating power optimization algorithm formula, re-executes the system, calculates and outputs the optimized temperature response time Wdxy2, and performs a secondary comparative evaluation with the preset response threshold X. According to the comparison results, the system will execute the corresponding adjustment strategy to further improve the accuracy and stability of temperature control through iterative optimization.

[0145] Example 7

[0146] See also Figure 1 and Figure 2 , a rapid temperature response method in the heating process of an aluminum mold, comprising the following steps:

[0147] S1. First, receive and send communication signals through 5G base stations, deploy sensors and monitoring equipment, collect communication data in real time, and then summarize and obtain communication data sets to store in the database;

[0148] S2. Use filters and smoothing algorithms to remove outliers, missing data, and noise data, unify the data to the same dimension and range, and perform feature extraction on the communication data set to obtain the average delay D. avg , average signal strength S avg and the average signal-to-noise ratio SNR avg ;

[0149] S3, calculating the signal jitter coefficient J according to the delay D, and comparing and evaluating the signal jitter coefficient J with the preset jitter threshold F1;

[0150] S4. When the signal jitter is abnormal, further calculation is required to obtain the signal strength fluctuation coefficient S var SNR var , and then the average delay D avg Correlate with the signal jitter coefficient J to obtain the comprehensive evaluation index Q;

[0151] S5. Finally, the comprehensive evaluation index Q is compared with the preset comprehensive evaluation threshold F2 to comprehensively evaluate the performance and stability of the 5G communication network, and optimization measures are implemented based on the evaluation results.

[0152] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A rapid temperature response system during the heating process of an aluminum mold, characterized in that: It includes temperature response data acquisition module, data integration module, temperature prediction module, temperature response analysis module and optimization evaluation module; The temperature response data acquisition module collects data in real time during the heating process of the aluminum mold by setting several detection points and installing an integrated sensor group on the infrared mold heating furnace, and records the temperature response data in the work log of the heating equipment; The data integration module is used to build an API log interface to connect with the work log, extract the temperature response data recorded in the work log in real time, pre-process the temperature response data, and summarize the data after pre-processing to obtain a temperature response data set, and then mark the obtained temperature response data set and store it in a time series database; The temperature prediction module is used to extract the temperature response data set in the time series database in real time, dynamically calculate and output the adaptive coefficient Kadapt, and then calculate and adjust the heating power of the heating equipment according to the output adaptive coefficient Kadapt, output the optimized heating power Poptimal, and then construct a temperature prediction algorithm model, input the optimized heating power Poptimal into the temperature prediction algorithm model, and output the predicted furnace temperature T(t+1), which is the predicted furnace temperature corresponding to the time t+1. The temperature response analysis module is used to construct a temperature response prediction model, and extract the optimized heating power Poptimal and the predicted furnace temperature T(t+1), input them into the temperature response time algorithm formula, output the temperature response time Wdxy, and then pre-set the response threshold X and the output temperature response time Wdxy for preliminary comparison and evaluation; The optimization evaluation module is used to adjust the calculation method of the adaptive coefficient Kadapt by triggering the adjustment mechanism after the preliminary evaluation, obtain the adjustment adaptation coefficient Kadapt2, then input the obtained adjustment adaptation coefficient Kadapt2 into the system for re-execution, output the optimized temperature response time Wdxy2, and perform a secondary comparative evaluation with the response threshold X.

2. The rapid temperature response system in the aluminum mold heating process according to claim 1, characterized in that: The temperature response data acquisition module includes a sensor acquisition unit and a data recording unit; The sensor acquisition unit collects the temperature response data of the aluminum mold heating process in real time by installing an integrated sensor group on the infrared mold heating furnace and reading the data of the integrated sensor group in real time; The integrated sensor group includes a thermocouple, an infrared temperature sensor, an ambient temperature sensor, a heat flow meter, a laser interferometer, a power meter and a multi-point temperature sensor network; The temperature response data includes mold heat flow Q, aluminum mold temperature difference △Tm, heating temperature Tj, ambient temperature Th, surface oxide layer thickness Ssurface, power output value Pprev, input total heating power Pin, output total heating power Pon, current aluminum mold temperature Tcurr, aluminum mold temperature upper limit value Tmax and aluminum mold temperature lower limit value Tmin; The data recording unit is used to adopt the industrial standard Ethernet / IP communication protocol to record the collected temperature response data in the work log according to the log structure, and the log structure includes the sensor ID, collection time, parameter name and parameter value.

3. The rapid temperature response system in the aluminum mold heating process according to claim 1, characterized in that: The data integration module includes a data processing module and an integrated storage module; The data processing module is used to set an API application program interface to connect with the operating system work log of the infrared mold heating furnace, extract the temperature response data in the work log in real time, pre-process the temperature response data, and then integrate it to obtain a temperature response data set; The temperature response data set includes the mold thermal conductivity coefficient Hcond(t), thermal resistance coefficient Rther, ambient temperature change rate △Tam, mold heat capacity Cthe(t), surface oxide layer thickness Ssurface, power output value Pprev(t-1) of the previous time period, energy loss Eloss and mold temperature gradient △Tgrad at time t, and the power output value Pprev(t-1) is the power output value at time t-1; The integrated storage module is used to construct a time series database, mark the preprocessed temperature response data set with a timestamp, and set a write interface to store the temperature response data set in the time series database in the order of the timestamps.

4. The rapid temperature response system in the aluminum mold heating process according to claim 1, characterized in that: The temperature prediction module includes a heating adaptive adjustment unit, a heating power optimization unit and a temperature prediction module; The adaptive adjustment unit is used to construct an adaptive adjustment algorithm formula, and at the same time, a write interface is set to extract the temperature response data set in the database in real time, input it into the adaptive adjustment algorithm formula, and output the adaptive coefficient Kadapt; The adaptive coefficient Kadapt is calculated and obtained by the following adaptive adjustment algorithm formula; In the formula, Tset represents the target heating temperature, Tcurr represents the current aluminum mold temperature, Tset-Tcurr represents the temperature difference that the mold needs to reach, γ represents the weight value of the ambient temperature change rate, δ represents the energy loss influence coefficient, θ represents the mold temperature gradient influence coefficient, Kadapt represents the adaptive coefficient, Hcond(t) represents the mold thermal conductivity coefficient at time t, Rther represents the thermal resistance coefficient, △Tam represents the ambient temperature change rate, Cthe(t) represents the mold heat capacity at time t, Ssurface represents the thickness of the surface oxide layer, Eloss represents the energy loss, Pprev represents the power output value, and △Tgrad represents the mold temperature gradient.

5. The rapid temperature response system in the aluminum mold heating process according to claim 4, characterized in that: The heating power optimization unit is used to construct a heating power optimization algorithm formula, then input the adaptive coefficient Kadapt and the temperature response data set into the heating power optimization algorithm formula, output the optimized heating power Poptimal, and optimize the heating power of the system; The optimized heating power Poptimal is calculated and obtained by the following heating power optimization algorithm formula; In the formula, α represents the power allocation adjustment coefficient, ε represents a small constant, and log represents a logarithmic function. Represents a smoothing function that smoothes the adjustment of the heating power through a logarithmic function.

6. The rapid temperature response system in the aluminum mold heating process according to claim 1, characterized in that: The temperature prediction module is used to construct a temperature prediction algorithm model, input the optimized heating power Poptimal and the temperature response data set into the temperature prediction algorithm model, and output the predicted furnace temperature T(t+1); The predicted furnace temperature T(t+1) is calculated and obtained by the following temperature prediction algorithm model; Wherein, T(t) represents the mold temperature at the current moment, Poptimal×Hcond(t)-Eloss represents the net heat transferred to the aluminum mold, Cthe(t)×(1+Rther×Ssurface) represents the comprehensive resistance term of heating efficiency, Poptimal represents the optimized heating power, Hcond(t) represents the mold thermal conductivity coefficient, Eloss represents the energy loss, Cthe(t) represents the mold heat capacity at time t, Rther represents the thermal resistance coefficient, and Ssurface represents the thickness of the surface oxide layer.

7. The rapid temperature response system in the aluminum mold heating process according to claim 1, characterized in that: The temperature response analysis module includes a temperature response calculation unit and a temperature response evaluation unit; The temperature response calculation unit constructs a temperature response time algorithm formula, and then inputs the acquired predicted furnace temperature T(t+1), optimized heating power Poptimal and temperature response data set into the temperature response time algorithm formula to calculate and output the temperature response time Wdxy; The temperature response time Wdxy is calculated and obtained by the following temperature response time algorithm formula; Wherein, Tset-Tcurr represents the temperature difference of the heating equipment, which is used to adjust the relationship between the heating temperature requirement and the temperature response time, Tset represents the target heating temperature, and Tcurr represents the current aluminum mold temperature; The temperature response evaluation unit sets the response threshold value X through the user, and then performs a preliminary comparative evaluation with the obtained temperature response time Wdxy to analyze the temperature response state during the heating process of the aluminum mold. The specific evaluation contents are as follows; When the temperature response time Wdxy>response threshold X, it means that the temperature response of the infrared mold heating furnace is abnormal, and adaptive adjustment optimization is performed at this time; When the temperature response time Wdxy = response threshold X, it means that the temperature response of the infrared mold heating furnace is normal, and no optimization is required at this time; When the temperature response time Wdxy is less than the response threshold X, it indicates that the temperature response of the infrared mold heating furnace is abnormal, and adaptive adjustment optimization is performed at this time.

8. The rapid temperature response system in the aluminum mold heating process according to claim 1, characterized in that: The optimization evaluation module includes an adaptive optimization module; The adaptive optimization module is used to be triggered when the temperature response is abnormal during the preliminary comparison and evaluation, and calculates and outputs the adjustment adaptation coefficient Kadapt2 by constructing an adaptive adjustment algorithm formula; The adjustment adaptation coefficient Kadapt2 is calculated and obtained by the following adaptive adjustment algorithm formula; Wherein, β represents the overall adjustment coefficient of the adaptive coefficient, λ represents the temperature gradient adjustment coefficient, Hcond(t) represents the mold thermal conductivity coefficient, Cthe(t) represents the mold heat capacity at time t, Ssurface represents the thickness of the surface oxide layer, Rther represents the thermal resistance coefficient, △Tam represents the ambient temperature change rate, Eloss represents the energy loss, Pprev represents the power output value, and △Tgrad represents the mold temperature gradient.

9. The rapid temperature response system in the aluminum mold heating process according to claim 8, characterized in that: The optimization evaluation module is used to substitute the output adjustment adaptation coefficient Kadapt2 into the heating power optimization algorithm formula, and re-execute the system until the output optimized temperature response time Wdxy2 is obtained, and then perform a secondary comparison evaluation with the response threshold value X, and generate an adjustment strategy and an iteration strategy; When the optimized temperature response time Wdxy2>response threshold X, it means that the temperature response of the infrared mold heating furnace is abnormal after optimization and adjustment, then the first adjustment strategy is executed, and the overall adjustment coefficient β of the adaptive coefficient and the temperature gradient adjustment coefficient λ are increased by 10%. After adjustment, the optimization evaluation module is executed again, and iterative optimization is performed until the temperature response is normal, and the iteration is stopped; When the optimized temperature response time Wdxy2 = response threshold X, it means that the temperature response of the infrared mold heating furnace is normal after optimization and adjustment, and no optimization is required; When the optimized temperature response time Wdxy2 is less than the response threshold X, it means that the temperature response of the infrared mold heating furnace is abnormal after optimization and adjustment. Then the second adjustment strategy is executed to reduce the overall adjustment coefficient β of the adaptive coefficient and the temperature gradient adjustment coefficient λ by 10%. After adjustment, the optimization evaluation module is executed again to perform iterative optimization until the temperature response is normal and the iteration is stopped.

10. A method for rapid temperature response during heating of an aluminum mold, applied to a system for rapid temperature response during heating of an aluminum mold as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: S1. Set several detection points and install an integrated sensor group on the infrared mold heating furnace to collect temperature response data during the heating process of the aluminum mold in real time, and record the temperature response data in the work log of the heating equipment; S2. Build an API log interface to connect with the work log, extract the temperature response data recorded in the work log in real time, pre-process the temperature response data, and summarize the data after pre-processing to obtain a temperature response data set, and then mark the obtained temperature response data set and store it in a time series database; S3, extract the temperature response data set in the time series database, dynamically calculate and output the adaptive coefficient Kadapt, and then calculate and adjust the heating power of the heating equipment according to the output adaptive coefficient Kadapt, output the optimized heating power Poptimal, and then build a temperature prediction algorithm model, input the optimized heating power Poptimal into the temperature prediction algorithm model, and calculate and output the predicted furnace temperature T(t+1); S4, construct a temperature response prediction model, extract the optimized heating power Poptimal and the predicted furnace temperature T(t+1), input them into the temperature response time algorithm formula, output the temperature response time Wdxy, and then pre-set the response threshold X and the output temperature response time Wdxy for preliminary comparison and evaluation; S5. After the preliminary evaluation, the calculation method of the adaptive coefficient Kadapt is further adjusted by triggering the adjustment mechanism, and the adjustment adaptation coefficient Kadapt2 is output. The obtained adjustment adaptation coefficient Kadapt2 is then input into the system for re-execution, and the optimized temperature response time Wdxy2 is output, and a secondary comparative evaluation is performed with the response threshold value X.

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