Temperature control method and system for vacuum heat treatment furnace

Abstract

The invention relates to the technical field of heat treatment heating control, in particular to a temperature control method and system for a vacuum heat treatment furnace. According to the embodiment of the invention, the resistance multiplying power sequence is obtained by comparing the preset reference resistance sequence with the working condition resistance sequence, the overall heat capacity scale characteristics of the load are identified according to the statistical characteristics of the resistance multiplying power sequence, the heat capacity scale factor is introduced, the compactness of the load loading structure is identified based on the change characteristics of the resistance multiplying power sequence, and the hysteresis retention coefficient is introduced. And determining a sliding start deviation according to the heat capacity scale factor, so that the large heat capacity load smoothly approaches the target temperature by self inertia, and temperature overshoot is eliminated. And constructing an excess integral calculation function by using the heat capacity scale factor and the hysteresis retention coefficient so as to decouple the kinetic energy component and the potential energy component in the accumulated integral term. And the problem that the process time is prolonged due to the false arrival phenomenon caused by the surface temperature drop of the compact load due to the cold source restraint of the core part is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of heating control of heat treatment, in particular to a temperature control method and system of a vacuum heat treatment furnace. BACKGROUND

[0002] The vacuum heat treatment furnace is widely used in annealing, tempering and quenching processes of metal materials, and its core heating mechanism is resistance radiation heating. Unlike convection heating, the radiation heat transfer rate is proportional to the fourth power of the absolute temperature, which leads to strong nonlinear characteristics of the system during the heating process. In actual production, the vacuum heat treatment furnace usually faces the working conditions of “multi-variety and variable load”. Different batches of workpieces have significant differences in total weight (thermal capacity scale) and loading structure (stacking density).

[0003] The inventor found in practice that the prior art has the following defects: The existing temperature control usually adopts PID algorithm. In the process of handling large thermal capacity load, in order to maintain the heating rate, the integral term of the PID controller will continue to accumulate with time, in order to overcome the huge thermal inertia. However, when the furnace temperature approaches the set value, due to the thermal lag characteristics of the controlled object, the controller often cannot unload the huge integral amount accumulated in time, resulting in that the heating power still remains high when approaching the target value, thereby causing temperature overshoot. The overshoot is unacceptable for some sensitive materials (such as aviation aluminum alloy and titanium alloy), which may cause grain coarsening or performance failure.

[0004] In order to suppress overshoot, the prior art usually adopts anti-integral saturation measures, such as setting a fixed integral limit or simply reducing the integral term when the deviation decreases. However, this processing method based on a single deviation dimension ignores the complex influence of workpiece loading structure on heat conduction. For “heavy and dense” load (such as tightly stacked coils or thick-walled forgings), its surface absorbs heat quickly but conducts slowly to the core, resulting in a large temperature difference between the surface and the core. At this time, if the integral term is greatly reduced only according to the temperature deviation, the surface heating power will be cut off too quickly, and the relatively low temperature core will quickly absorb the surface heat, causing the surface temperature to drop instantly, forming a “false arrival” phenomenon, which prolongs the process stabilization time. SUMMARY

[0005] In order to solve the technical problem that in the process of controlling the temperature of the vacuum heat treatment furnace using the traditional PID algorithm, when facing large thermal capacity load, using anti-integral saturation measures to suppress overshoot only according to temperature deviation may ignore the complex influence of load loading structure on heat conduction, which may cause false arrival of process target temperature, the purpose of the present application is to provide a temperature control method and system of a vacuum heat treatment furnace, and the technical scheme adopted is as follows: The application provides a temperature control method of a vacuum heat treatment furnace, the method comprising: continuously collecting furnace temperature data and heating output ratio data under a preset temperature feature identification interval; calculating a temperature rise rate based on the furnace temperature data, calculating a unit temperature rise resistance coefficient based on the furnace temperature data, the heating output ratio data and the temperature rise rate, and constructing a working condition resistance sequence; comparing a preset reference resistance sequence with the working condition resistance sequence to obtain a resistance multiple sequence, obtaining a heat capacity scale factor based on statistical characteristics of the resistance multiple sequence, analyzing the change trend of elements in the resistance multiple sequence to obtain a resistance drift feature, calculating a lag retention coefficient based on the resistance drift feature, and constructing an excess integral calculation function based on the heat capacity scale factor and the lag retention coefficient; calculating a sliding start deviation based on a preset basic sliding deviation and the heat capacity scale factor, calculating a deviation value based on the furnace temperature data and a preset process setting temperature, and performing integral term assignment correction on the excess integral calculation function based on the sliding start deviation and the deviation value.

[0006] Further, the collection of the furnace temperature data and the heating output ratio data further comprises: constructing a sliding window in the continuously collected furnace temperature data, and taking the average value of a plurality of data in the current window as the furnace temperature data at the current sampling time; constructing a sliding window in the continuously collected heating output ratio data, and calculating the average value of all heating output ratios in the current window as the heating output ratio at the current sampling time.

[0007] Further, the temperature rise rate is obtained in the following manner: a linear regression fitting method is used to fit a plurality of furnace temperature data points in the sliding window, and the slope of the fitted straight line is extracted as the temperature rise rate at the current sampling time.

[0008] Further, the unit temperature rise resistance coefficient is obtained in the following manner: the furnace temperature data is converted into thermodynamic absolute temperature, and the product of the fourth power value of the converted result and the temperature rise rate is taken as the denominator, and the heating output ratio is taken as the numerator to calculate the unit temperature rise resistance coefficient at the current sampling time.

[0009] Further, the working condition resistance sequence is obtained in the following manner: initializing the working condition resistance sequence; for each integer temperature index value in the preset temperature identification interval, obtaining all furnace temperature data existing in the index value in the preset temperature interval, taking the arithmetic mean of the unit temperature rise resistance coefficients corresponding to the furnace temperature data as the sequence filling value, and finally obtaining the working condition resistance sequence after numerical filling of the elements in the working condition resistance sequence. If the temperature index value does not exist corresponding furnace temperature data in the preset temperature interval, the nearest two furnace temperature data are searched forward and backward under the temperature dimension for the temperature index value, the arithmetic mean value of the unit temperature rise resistance coefficients corresponding to the two furnace temperature data is taken as a sequence filling value, numerical filling is performed on the elements in the working condition resistance sequence, and finally the working condition resistance sequence after numerical filling is obtained; Further, the obtaining method of the thermal capacity scale factor comprises: The elements in the preset reference resistance sequence are taken as denominators, and the elements in the working condition resistance sequence with the same index as the denominators are taken as numerators to calculate resistance multiples, and all the resistance multiples constitute a multiple resistance sequence; Median statistical operation is performed on all elements in the resistance multiple sequence, and the calculation result is taken as the thermal capacity scale factor.

[0010] Further, the obtaining method of the hysteresis retention coefficient comprises: A regression straight line fitting is performed on the resistance multiple sequence by using the least square method, the slope of the regression straight line is obtained, and is taken as a resistance drift characteristic; The resistance drift characteristic is mapped in a preset coefficient interval to obtain the hysteresis retention coefficient.

[0011] Further, the obtaining method of the excess integral calculation function comprises: An integral term accumulated by the PID controller in the temperature rising process is obtained, and the thermal capacity scale factor and the hysteresis retention coefficient are combined to construct an excess integral calculation function: Wherein, is an excess integral amount; is an integral term accumulated by the PID controller in the temperature rising process; is a thermal capacity scale factor; is a hysteresis retention coefficient.

[0012] Further, the integral term assignment correction comprises: A preset basic coasting deviation is obtained, a coasting starting deviation is calculated by combining the thermal capacity scale factor, a preset process setting temperature is obtained, a deviation value is calculated by combining the furnace temperature data, when the deviation value is less than or equal to the coasting starting deviation, the integral term of the PID is obtained, the calculation result of the excess integral calculation function is combined, the steady-state maintenance integral is calculated and used for the integral term assignment correction of the PID control; When the deviation value is greater than the coasting starting deviation, the integral term assignment correction is not performed.

[0013] Further, the present application also provides a temperature control system of a vacuum heat treatment furnace, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and all steps of the temperature control method of the vacuum heat treatment furnace are implemented when the processor executes the computer program.

[0014] The present application has the following beneficial effects: The embodiment of the present application obtains a resistance ratio sequence by analyzing and comparing a preset reference resistance sequence and a working condition resistance sequence, identifies the overall heat capacity scale heat absorption characteristics of the load according to the statistical characteristics of the resistance ratio sequence, introduces a heat capacity scale factor, simultaneously identifies the compactness of the load loading structure based on the change characteristics of the resistance ratio sequence, and introduces a hysteresis retention coefficient. The heat capacity scale factor is used to determine a coasting start deviation, so that the large heat capacity load can use its inertia to smoothly approach the target temperature, thereby eliminating temperature overshoot. The heat capacity scale factor and the hysteresis retention coefficient are used to construct an excess integral calculation function model, so as to decouple the kinetic energy component and the potential energy component existing in the cumulative integral term of the PID. Thus, the cumulative integral term is forced to retain the potential energy component required for heat penetration in the control process, effectively solves the problem of surface temperature drop caused by the core cold source of the compact load, and the false arrival phenomenon prolongs the process stabilization time, significantly shortens the process stabilization time and improves the robustness of the control algorithm to variable load working conditions. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0016] Figure 1 A flow chart of a temperature control method of a vacuum heat treatment furnace provided by an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the specific implementation, structure, features and effects of the temperature control method of a vacuum heat treatment furnace according to the present application are described in detail as follows by combining the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0018] 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 application belongs.

[0019] Specifically, the temperature control method and system of the vacuum heat treatment furnace are described below in combination with the drawings.

[0020] Embodiment 1 The present application provides a temperature control method and system of a vacuum heat treatment furnace, please refer to Figure 1 which shows a flowchart of a temperature control method of a vacuum heat treatment furnace according to an embodiment of the present application, the method comprises: Step S101: continuously collecting furnace temperature data and heating output ratio data in a preset temperature characteristic identification interval.

[0021] Since the radiation heat transfer mechanism of the vacuum heat treatment furnace is dominant only in a specific temperature zone, and the original signals collected on site are mixed with high-frequency thermal noise of the sensor and inherent output oscillation of the PID regulation, it is necessary to preset a temperature characteristic identification interval with significant radiation characteristics, and data collection is performed in the preset temperature characteristic identification interval, that is, furnace temperature data and heating output ratio data are collected.

[0022] In an implementation manner of the embodiment of the present application, the temperature characteristic identification interval is usually selected in the middle temperature section where the radiation heat transfer is dominant and the temperature rise is relatively stable, and is set to , wherein , .

[0023] Step S102: calculating a temperature rise rate based on the furnace temperature data; calculating a unit temperature rise resistance coefficient based on the furnace temperature data, the heating output ratio data and the temperature rise rate, and constructing a working condition resistance sequence.

[0024] The temperature rise rate is the change amount of the furnace temperature per unit time, which represents the speed of driving the load to rise in temperature by the current heating power.

[0025] The heating output ratio mainly represents the total energy input data of the system, and reflects the radiation heat transfer environment efficiency through the furnace temperature data, which can exclude the nonlinear interference of the environment on the heat transfer efficiency in the first step, and then, in combination with the temperature rise rate, indicates the difference between the theoretical load temperature rise and the actual load temperature rise caused by the current total energy input, so as to calculate the unit temperature rise resistance coefficient which represents only the inherent thermal resistance characteristics of the load and is stripped of the environmental factors. The unit temperature rise resistance coefficient is a equivalent resistance (i.e. the interference caused by the heat capacity scale and the loading structure) that needs to be overcome by the vacuum heat treatment furnace heating system to drive the load to produce a unit temperature rise rate at the current temperature, which is stripped of the influence of the environmental temperature.

[0026] Finally, the plurality of unit temperature rise resistance coefficients obtained in the above steps are sorted to construct a working condition resistance sequence. For example, based on temperature sorting, based on time sorting, etc., the specific manner is not limited by the present application.

[0027] Step S103: comparing the preset reference resistance sequence with the working condition resistance sequence to obtain a resistance ratio sequence; based on statistical characteristics of the resistance ratio sequence, obtaining a thermal capacity scale factor; analyzing the change trend of elements in the resistance ratio sequence to obtain a resistance drift characteristic; based on the resistance drift characteristic, calculating a hysteresis retention coefficient; based on the thermal capacity scale factor and the hysteresis retention coefficient, constructing an excess integral calculation function.

[0028] Since the total thermal capacity of the load in the vacuum heat treatment working condition differs significantly, the statistical characteristics of the radiation resistance ratio sequence are used in the embodiment of the present application to extract the thermal capacity scale factor, which realizes quantitative characterization of the relative thermal inertia order of the current load, so that the control system can construct an integral calculation function and a coasting start deviation in accordance with the current physical characteristics, effectively solving the temperature overshoot problem caused by the excess of the accumulated thermal kinetic energy of the large thermal capacity load.

[0029] In order to quantify the change of the current load relative to the empty furnace state, the elements in the preset reference resistance sequence and the working condition resistance sequence can be compared to reflect the resistance ratio relationship of different loads and empty load states. Among them, the resistance ratio sequence is used to reflect the ratio relationship characteristics of the resistance of the current load and the empty furnace reference at different temperature points.

[0030] The elements in the resistance ratio sequence directly reflect the multiple relationship of the total thermal resistance required by the system after the current load is introduced relative to the empty furnace state. Since the total thermal capacity of the load is the main factor that determines the amplitude of the heat absorption resistance reference, and the amplitude is relatively stable in the temperature zone dominated by radiation heat transfer, the overall scale can be extracted by statistical extraction of the whole sequence, which can stably quantify the total thermal capacity scale multiple of the current load relative to the empty furnace, that is, the thermal capacity scale factor. Among them, the thermal capacity scale factor represents the overall thermal capacity scale of the load and is used to determine the coasting start deviation in the subsequent steps.

[0031] Since the dense loading structure of the load in vacuum heat treatment shows that as the furnace temperature rises, the temperature difference between the workpiece surface and the core increases, the power growth rate required by the system to maintain the surface temperature is higher than that of the empty furnace, that is, the resistance ratio sequence shows a positive drift trend as the temperature rises. Therefore, the embodiment of the present application uses a trend characteristic analysis method based on the evolution of the resistance ratio sequence with temperature to extract the hysteresis retention coefficient, which characterizes the heat absorption characteristics of the dense loading structure, effectively eliminates the temperature drop and false arrival phenomenon in the dense load working condition, and significantly shortens the process thermal equilibrium time.

[0032] The resistance drift characteristic represents that the resistance ratio sequence presents a positive drift trend with the increase of temperature for the dense load heat absorption condition, and therefore the data change trend in the resistance ratio sequence can be analyzed.

[0033] The hysteresis retention coefficient is used to represent the dense load structure heat absorption characteristic of the load, and therefore can be quantified by the resistance drift characteristic.

[0034] The excess integral calculation function is mainly used to calculate the excess integral amount that should be removed from the PID controller.

[0035] Since the PID cumulative integral term in the vacuum heat treatment heating process mixes the acceleration kinetic energy component for overcoming thermal inertia and the steady potential energy component for maintaining core heat transfer, and the traditional anti-windup algorithm cannot distinguish between the two according to the load characteristics, making it difficult for the control strategy to be considered, the heat capacity scale factor representing the overall heat capacity scale characteristics of the load and the hysteresis retention coefficient representing the load loading structure characteristics are used to intervene and adjust the PID cumulative integral term, thereby constructing an excess integral calculation function model, realizing the decoupling of the excess kinetic energy that needs to be cut off and the maintenance potential energy that needs to be retained in the integral term, and thereby ensuring temperature without overshoot approximation while effectively maintaining the internal heat penetration driving force of the load, avoiding the surface temperature drop of the dense load, and significantly improving the adaptive robustness of the control system.

[0036] Step S104: Calculate the sliding start deviation according to the preset basic sliding deviation and the heat capacity scale factor, calculate the deviation value based on the furnace temperature data and the preset process set temperature, and perform integral term value correction based on the sliding start deviation and the deviation value and the excess integral calculation function.

[0037] Since different heat capacity loads have significant differences in thermal inertia sliding distance when approaching the set temperature, and the fixed deviation triggered feedforward strategy cannot adapt to the inertia difference under variable load conditions, the heat capacity scale factor is used to calculate the difference between the sliding start deviation and the actual deviation value, which realizes the precise matching of the control intervention timing and the current load physical inertia, thereby ensuring that large heat capacity loads can reserve sufficient sliding buffer space to completely consume the accumulated heat kinetic energy, effectively avoiding temperature overshoot caused by intervention lag.

[0038] For large thermal capacity load, its accumulated thermal kinetic energy is significantly higher than that of empty furnace, so it needs longer temperature glide distance to consume this part of energy. If a fixed temperature deviation is used as the starting point of intervention control, it will often lead to late intervention for heavy load conditions, which cannot avoid overshoot. Therefore, the system needs to dynamically set the intervention time according to the thermal capacity size of the load, so the glide starting deviation can be calculated by the thermal capacity size factor representing the overall thermal capacity size of the load. The glide starting deviation refers to the temperature difference threshold between the current real-time temperature and the set target temperature when the vacuum heat treatment furnace heating system triggers the intervention control in the heating stage of the vacuum heat treatment furnace.

[0039] The deviation value is the deviation between the real-time monitored current furnace temperature and the process set temperature.

[0040] When the deviation value is less than or equal to the glide starting deviation, the vacuum heat treatment furnace heating system triggers the intervention control, and the excess integral calculation function is used to modify the integral term value. When the deviation value is greater than the glide starting deviation, the intervention control is not triggered.

[0041] After the processes of steps S101 to S104, the integral term value modification of the PID controller in the control process can be realized. First, the furnace temperature data and the heating output ratio data are obtained in the preset temperature characteristic identification interval, and the temperature rise rate is calculated based on the furnace temperature data. The unit temperature rise resistance coefficient is calculated based on the relationship among the furnace temperature data, the heating output ratio data and the temperature rise rate, and the unit temperature rise resistance coefficient is sorted to construct a working condition resistance sequence. By comparing the element ratio relationship between the preset reference resistance sequence and the working condition resistance sequence, the resistance ratio is obtained, and the resistance ratio sequence is constructed. The thermal capacity size factor is obtained by analyzing the median statistical characteristics of the resistance ratio sequence, and the resistance drift characteristics are obtained by analyzing the data trend characteristics of the resistance ratio sequence, and the hysteresis retention coefficient is calculated. The excess integral function is constructed based on the thermal capacity size factor and the hysteresis retention coefficient. Further, the glide starting deviation is determined by using the thermal capacity size factor and the preset basic glide deviation. The current deviation value is calculated by using the current furnace temperature data and the preset process set temperature. Finally, the process integral is calculated by using the excess integral function according to the size relationship between the glide starting deviation and the deviation value, so as to modify the integral term value of the PID controller in the control process.

[0042] Preferably, in some possible implementation manners of the embodiments of the present application, the furnace temperature data and the heating output ratio data are collected, and then the following steps are further included: Since the radiation heat transfer mechanism of the vacuum heat treatment furnace only dominates in a specific temperature zone, and the original signals collected on site are mixed with high-frequency thermal noise of the sensor and inherent output oscillation of the PID regulation, it is necessary to preset a temperature feature recognition interval with a prominent radiation feature, and to construct a sliding window to perform arithmetic average filtering on the heating output ratio data, i.e., to calculate the arithmetic average value in each sliding window as the heating output ratio at the current sampling time. Further, multi-point average value calculation is performed on the furnace temperature data, i.e., to take the average value of the last multiple points in the current sliding window as the furnace temperature data at the current sampling time.

[0043] Thus, the problem of divergence in the calculation of the normalized unit temperature rise resistance coefficient due to random jitter of the signals, and thus the problem of inability to accurately extract the load thermal physical features, are effectively solved, and finally the smoothed heating output ratio data and the furnace temperature data are obtained.

[0044] In one specific embodiment of the present embodiment, the furnace temperature data and the heating output ratio data are collected, and then the following steps are further included: Constructing a sliding window: the system maintains a first-in-first-out data queue with a length of 60 seconds in the memory, which is used to store the furnace temperature and the heating output ratio at the last N sampling times, i.e., the size of the sliding window is N.

[0045] Calculating the smoothed furnace temperature and the power: The furnace temperature at the kth sampling time : the average value of the last 5 points in the current window is taken to filter out random peak noise.

[0046] The heating output ratio at the kth sampling time : the arithmetic average value of all the heating output ratios in the current window is calculated. This processing can smooth out the high-frequency output jitter in the PID control process and obtain a steady-state power value that can represent the average energy input in the current period.

[0047] Preferably, in some possible implementation manners of the present embodiment, the heating output ratio is calculated in the following manner: The heating output ratio is the ratio of the power to the temperature rise rate, which is the change amount of the furnace temperature per unit time and represents the speed of the current heating power driving the load to rise in temperature.

[0048] Since the radiation heat transfer efficiency of a single object is proportional to the fourth power of its absolute temperature, the original power and the temperature rise rate data in the heating process show strong nonlinearity and lack of horizontal comparability at different temperatures, so it is necessary to calculate the temperature rise rate in real time to quantify the dynamic response of the system.

[0049] In one specific embodiment of the present embodiment, the temperature rise rate is calculated in the following manner: This invention employs a linear regression fitting method. The processor performs a linear regression fitting within a sliding window. The temperature data points are linearly fitted using the least squares method in chronological order, and the slope of the fitted line is extracted as the temperature rise rate at the current k-th sampling time. This method can extract the true temperature change trend from noisy temperature data, ensuring... Numerical stability.

[0050] Preferably, in a specific implementation of an embodiment of the present invention, the system processor calculates... Perform validity gating. A data set is marked as valid and proceeds to the next step only if it simultaneously meets the following conditions: Located in the feature recognition region Inside; Vacuum level inside the furnace at the kth sampling time Better than the preset radiation threshold (e.g.) This ensures that the heat transfer mechanism is pure radiation.

[0051] Temperature rise rate Greater than the minimum effective rate threshold (For example This threshold is set to exclude data from periods of stagnant or fluctuating temperature, preventing the risk of divergence in subsequent calculations where the denominator approaches zero.

[0052] Among them, the vacuum degree inside the furnace at the kth sampling time The data is acquired in real time by a vacuum gauge installed on the furnace chamber of the vacuum heat treatment furnace.

[0053] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the unit temperature rise resistance coefficient includes: Since ambient temperature has a nonlinear effect on heat transfer efficiency, this embodiment uses a normalized model based on the Stephan-Boltzmann law to calculate the unit temperature rise resistance coefficient, thereby mathematically eliminating the nonlinear gain interference of ambient temperature on the heat transfer data. The furnace temperature data is converted to thermodynamic absolute temperature, and the product of the fourth power of the converted result and the temperature rise rate is used as the denominator, with the heating output ratio as the numerator, to calculate the unit temperature rise resistance at the current sampling time. That is, in the above process, the ratio of the heating output ratio to the fourth power of the converted result represents the theoretical heating output power provided by the heating system after eliminating ambient temperature interference. Dividing the theoretical heating output power by the temperature rise rate yields the unit temperature rise resistance coefficient.

[0054] In one specific implementation of the embodiment, in view of the zero-division exception in the unit temperature rise resistance coefficient calculation process and the dimensional inconsistency between the calculation parameters, the unit temperature rise resistance coefficient can be calculated in the following manner: Wherein: is the unit temperature rise resistance coefficient at the kth sampling time; Convert Celsius temperature to thermodynamic absolute temperature; is used to offset the nonlinear gain of the radiation environment, so that the calculation results are comparable at different temperatures; is the maximum value function, is a protective clamping of the denominator to avoid zero-division exception and enhance the robustness of the algorithm in extreme working conditions.

[0055] is a magnitude normalization constant. The selection principle is to make the calculated value fall within a conventional interval (e.g. between 1.0 and 100.0) for numerical processing. In this embodiment, according to the rated power density of the heating element and the size of the furnace, is calibrated to . The calibration method is: in the empty furnace full power temperature rise test, select the typical data at time and substitute it into the formula, adjust so that the result is about 1.0.

[0056] Preferably, in some possible implementations of the embodiment of the application, the working condition resistance sequence acquisition method comprises: Due to the large difference in temperature rise rate under different load conditions, the time consumption of feature recognition intervals varies, resulting in that the discrete data points collected based on the time axis cannot be directly aligned in quantity and distribution. In order to realize the subsequent point-by-point comparison with the reference data, the system needs to map the data in the "time domain" to a fixed-length sequence in the "temperature domain". It ensures that no matter how fast or slow the temperature rises, the system can finally output a fixed-length, continuous and smooth working condition resistance sequence, providing a standardized input for the subsequent module. That is, the working condition resistance sequence is the result of data interpolation and replacement of the unit temperature rise resistance coefficient at the preset temperature index, representing a data set of heat absorption resistance characteristics at different temperature points under the current furnace working condition (i.e. under specific load conditions).

[0057] In one specific implementation of the embodiment, the working condition resistance sequence acquisition method is: Initialize a working condition resistance sequence in the system, which is indexed by integer temperature , covering from arrive For each integer temperature point. The sequence filling process specifically includes the following two cases: 1. For each temperature index Retrieve the temperature index within the range All furnace temperature data within the range. Calculate the arithmetic mean of the resistance coefficient per unit temperature rise corresponding to the furnace temperature data as the fill value of the operating resistance sequence, and fill it under the corresponding temperature index of the operating resistance sequence.

[0058] 2. If, due to an extremely rapid heating rate, a certain temperature index... In the interval If no corresponding furnace temperature data exists within the range, then search for the two nearest empty furnace temperature data in that temperature range, calculate the arithmetic mean of the unit temperature rise resistance coefficient corresponding to these two furnace temperature data as the filling value of the working condition resistance sequence, and fill it into the temperature index corresponding to the working condition resistance sequence.

[0059] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the heat capacity scale factor includes: Since the operating condition resistance sequence itself only reflects the current absolute resistance level and cannot directly distinguish the load attributes (i.e., it cannot distinguish the heat capacity scale and the interference generated by the loading structure), the heating system after the load intervention will output more energy to overcome the increased heat capacity of the load in order to maintain the temperature rise rate. This results in the unit temperature rise resistance coefficient showing an upward characteristic compared with the unit temperature rise resistance coefficient under no-load conditions. By comparing and calculating, the overall heat capacity scale characteristics of the load are decoupled and extracted, thereby distinguishing the first load attribute characteristics.

[0060] Specifically, a preset reference resistance sequence is created to reflect the characteristics of the heating system under no-load conditions. This sequence is then compared with the elements of the operating condition resistance sequence. Specifically, the elements in the preset reference resistance sequence are used as the denominator, and the elements in the operating condition resistance sequence with the same index as the denominator are used as the numerator to calculate the resistance ratio. All resistance ratios form a ratio resistance sequence. This sequence reflects the heat absorption resistance of the current load relative to the baseline ratio of the empty furnace at different temperature points.

[0061] Further median statistical calculations were performed on the elements in the resistance ratio sequence, and the result was defined as the heat capacity scale factor λ. The median was chosen instead of the arithmetic mean because instantaneous numerical disturbances caused by dynamic control adjustments may occur at the beginning and end of the feature identification interval. Median statistics have strong noise resistance and can effectively suppress these edge noises, ensuring that λ truly reflects the average heat capacity ratio of the load.

[0062] Further, in one specific implementation of the embodiment of the present application, considering that the total thermal capacity of the system after adding the load must be greater than or equal to the empty furnace thermal capacity, theoretically should not be less than . In order to prevent unreasonable values (for example ) from being calculated due to sensor errors or outdated reference sequences, the system introduces lower limit constraint logic: In this way, it is ensured that the subsequent calculated coasting deviation and integral cut-off amount are always physically meaningful. If the calculated is close to , it indicates that the current working condition is empty furnace or extremely light load; if is significantly greater than (such as or ), it indicates a heavy load working condition. Among them, is a median calculation function, is a resistance ratio sequence.

[0063] Preferably, in some possible implementations of the embodiment of the present application, the acquisition method of the hysteresis retention coefficient comprises: Since the working condition resistance sequence itself only reflects the current absolute resistance level, it cannot directly distinguish the properties of the load (that is, it cannot distinguish the interference caused by the thermal capacity scale and the loading structure), therefore, based on the thermal response difference characteristics of different loading structures under the same weight, that is, for the same weight of the load, the thermal response characteristics of loose stacking (such as thin plates) and tight stacking (such as dense coils) are different: the former has small temperature difference between inside and outside, and the latter has serious core lag. Decouple the lag of the loading structure, thereby distinguishing the second load property characteristic.

[0064] Specifically, the resistance ratio sequence is first smoothed, a first-order linear regression analysis is performed on the temperature index of the resistance ratio sequence by using the least square method, the slope of the regression straight line is extracted as the resistance drift feature, and the hysteresis retention coefficient is calculated by using the resistance drift feature in combination with a preset coefficient interval.

[0065] In one specific implementation of the embodiment, the acquisition method of the hysteresis retention coefficient is: First, a sliding average filtering or polynomial smoothing is performed on the resistance ratio sequence. A first-order linear regression analysis is performed on the temperature index of the resistance ratio sequence by using the least square method, and the slope of the regression straight line is extracted as the resistance drift feature in combination with a preset coefficient interval (that is, limiting the minimum value of to , the maximum value is 1, the hysteresis retention coefficient is calculated , the calculation formula is as follows: Wherein: is a sensitivity gain coefficient, which is used to amplify the small slope characteristics to control interval. In this embodiment, The determination method of Value (for example ), adjust So that Approximately . The value range is 100.0 to 1000.0, and the experience value is .

[0066] is the lower threshold value (this embodiment takes ), to ensure that no matter how dense the load, the system at least retains Integral value to maintain the basic penetration driving force, prevent heating completely interrupted.

[0067] is a limiting function, which ensures that the output value is strictly limited in Closed interval.

[0068] When , it is judged as non-dense load, and .

[0069] When the load is loose, , , which means that there is no need to reserve special, can be cut off according to the conventional logic integral; when the load is dense, , Significantly smaller than , which means that the system identifies high hysteresis risk, will reduce the integral cut-off amount.

[0070] Preferably, in some possible implementation manners of the embodiment of the present application, the acquisition manner of the excess integral calculation function comprises: Since the integral term accumulated by the PID controller during the heating process contains multiple components, mainly the steady-state base value for compensating the inherent heat dissipation of the system, the kinetic energy component for overcoming the thermal inertia of the load, and the potential energy component for maintaining the thermal gradient inside and outside the workpiece, which are coupled together, when the heating process is close to the end and close to the set heating temperature, the kinetic energy component needs to be removed to suppress overshoot due to thermal inertia redundancy, and the potential energy component needs to be retained to resist the surface temperature collapse caused by core heat absorption. Therefore, the heat capacity scale factor representing the overall heat capacity scale characteristics of the load and the hysteresis retention coefficient representing the load loading structure characteristics are used to intervene and adjust the PID accumulated integral term, so as to build an excess integral calculation function model to realize the kinetic and potential energy separation of the integral term.

[0071] In one specific embodiment of the present embodiment, the specific acquisition method of the excess integral calculation function is as follows: Excess integral calculation function: In the function model: is the excess integral component; is the integral term accumulated by the PID controller during the heating process; term The theoretical maximum removal ratio is determined. For example, if the heat capacity of the load is 5 times that of the empty furnace ( ), about ( ) of the integral term is the "kinetic energy component" contributed by the load inertia.

[0072] term As a structure correction coefficient, the above removal ratio is attenuated. If the load is dense, resulting in , the system only removes of the theoretical value, and forcibly retains the remaining part as the "potential energy component".

[0073] Through the model, the system successfully realizes the kinetic and potential energy separation of the integral term. At this point, the heat capacity scale factor , the hysteresis retention coefficient and the excess integral model have been calculated and locked. These parameters, as fixed characteristics of the current batch of processes, will be directly transmitted to the subsequent execution module.

[0074] Preferably, in some possible implementations of the embodiments of the present application, the integral term assignment modification includes: Obtain a preset basic sliding deviation, calculate a sliding start deviation in combination with the heat capacity scale factor. Obtain a preset process set temperature, calculate a deviation value in combination with the furnace temperature data.

[0075] When the deviation value is less than or equal to the coasting start deviation, the integral term of the PID is obtained, the calculation result of the excess integral calculation function is combined, the steady-state maintenance integral is calculated, and the integral term assignment correction for the PID control is used.

[0076] When the deviation value is greater than the coasting start deviation, the integral term assignment correction is not performed.

[0077] In one specific embodiment of the present embodiment, the integral term assignment correction includes: Coasting start deviation calculation: System call heat capacity scale factor , combined with the preset basic coasting deviation . Wherein, is defined as the overshoot of the furnace temperature which continues to rise by its own inertia and finally stabilizes after the heating power is cut off under the condition of full power temperature rise of an empty furnace (in the present embodiment, it is set to ). The coasting start deviation is calculated: The square root term in the formula reflects the nonlinear relationship between thermal inertia and mass. The larger the value is, the larger the calculated is, which means that the system will start intervention in a position farther from the target temperature, reserving sufficient coasting space.

[0078] State monitoring and trigger latching: The system monitors the deviation between the current furnace temperature and the process set temperature in real time.

[0079] When , the subsequent integral term assignment correction process is performed; otherwise, it is not performed.

[0080] Further, in order to prevent repeated triggering of intervention near the critical point due to temperature measurement noise, one specific embodiment of the present embodiment can be: A Boolean type state flag Flag_Trig is set, and the initial value is False. A single trigger signal is generated only when the following conditions are met: The current deviation ; Flag_Trig is False.

[0081] After triggering, Flag_Trig is immediately set to True, ensuring that the intervention is performed only once in the entire temperature rise process.

[0082] ​During the control period of the single trigger signal, the system temporarily suspends the regular PID operation and performs direct assignment correction of the integral term.

[0083] The integral term assignment correction process includes: 1. Excess integral amount calculation: The system reads the current cumulative integral value of the PID controller . Calls the built excess integral function model, and substitutes the current locked and , calculates the excess integral amount that should be removed: Steady-state maintenance integral generation and bottom protection: The system performs subtraction operation to calculate the corrected steady-state maintenance integral . In order to prevent excessive removal of integral due to parameter estimation deviation (for example, removal to 0 causes complete interruption of heating, leading to temperature drop), the system introduces anti-drop bottom protection logic: This logic ensures that no matter how the calculation result is, at least of the original integral value is retained to maintain the most basic heating power output.

[0084] 2. Integral reset and closed-loop recovery: The system forces the calculated to be written into the integral storage unit of the PID controller as the reset initial value of the integral term.

[0085] Reset moment: the output power of the controller will instantaneously decrease due to the step-down of the integral term (for example, from to ), thus achieving the effect of emergency stop.

[0086] 3. Takeover operation: after the writing operation is completed, the PID controller immediately resumes the automatic operation mode. At this time, the controller takes as the basis and continues closed-loop adjustment according to the remaining deviation . Since most of the excess energy has been removed by feedforward, the controller only needs to fine-tune to maintain the temperature smoothly transition to , avoiding overshoot and ensuring continuous heat penetration to the core.

[0087] In summary, this invention obtains a resistance ratio sequence by comparing a preset benchmark resistance sequence with an operating resistance sequence. Based on the statistical characteristics of the resistance ratio sequence, it identifies the heat absorption characteristics of the overall heat capacity of the load and introduces a heat capacity factor. Simultaneously, based on the changing characteristics of the resistance ratio sequence, it identifies the compactness of the load structure and introduces a hysteresis retention coefficient. The gliding initiation deviation is determined based on the heat capacity factor, allowing the large heat capacity load to smoothly approach the target temperature using its own inertia, thereby eliminating temperature overshoot. Furthermore, the heat capacity factor and hysteresis retention coefficient are used to construct an excess integral calculation function model to decouple the kinetic and potential energy components in the PID cumulative integral term. This intervention in the control process forces the cumulative integral term to retain the potential energy component required for heat penetration, effectively solving the problem of surface temperature drop and false arrival phenomena caused by the core cold source constraint of dense loads, which prolongs the process stabilization time. This significantly shortens the process stabilization time and improves the robustness of the control algorithm to variable load conditions.

[0088] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0089] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A temperature control method for a vacuum heat treatment furnace, characterized in that, The method includes: Continuously collect furnace temperature data and heating output ratio data within a preset temperature feature identification range; Based on furnace temperature data, calculate the temperature rise rate; based on furnace temperature data, heating output ratio data, and temperature rise rate, calculate the unit temperature rise resistance coefficient and construct the operating condition resistance sequence. By comparing the preset benchmark resistance sequence with the operating condition resistance sequence, a resistance ratio sequence is obtained; based on the statistical characteristics of the resistance ratio sequence, the heat capacity scale factor is obtained; the changing trend of the elements in the resistance ratio sequence is analyzed to obtain the resistance drift characteristics; based on the resistance drift characteristics, the hysteresis retention coefficient is calculated; based on the heat capacity scale factor and the hysteresis retention coefficient, an excess integral calculation function is constructed. Based on the preset basic sliding deviation and combined with the heat capacity scale factor, the sliding initial deviation is calculated; based on the furnace temperature data and the preset process setting temperature, the deviation value is calculated; based on the sliding initial deviation and the deviation value, the integral term is assigned and corrected using the excess integral calculation function.

2. The temperature control method for a vacuum heat treatment furnace according to claim 1, characterized in that, The data collected includes furnace temperature and heating output ratio data, followed by: A sliding window is constructed from the continuously collected furnace temperature data, and the average value of multiple data within the current window is taken as the furnace temperature data at the current sampling time. A sliding window is constructed from the continuously collected heating output ratio data, and the average value of all heating output ratios within the current window is calculated as the heating output ratio at the current sampling time.

3. The temperature control method for a vacuum heat treatment furnace according to claim 1, characterized in that, The methods for obtaining the temperature rise rate include: A linear regression fitting method was used to fit multiple furnace temperature data points within a sliding window, and the slope of the fitted line was extracted as the temperature rise rate at the current sampling time.

4. The temperature control method for a vacuum heat treatment furnace according to claim 1, characterized in that, The methods for obtaining the unit temperature rise resistance coefficient include: The furnace temperature data is converted into thermodynamic absolute temperature, and the product of the fourth power of the converted result and the temperature rise rate is used as the denominator, while the heating output ratio is used as the numerator to calculate the unit temperature rise resistance coefficient at the current sampling time.

5. The temperature control method for a vacuum heat treatment furnace according to claim 1, characterized in that, The methods for obtaining the operating condition resistance sequence include: Initialize the operating condition resistance sequence; For each integer temperature index value within the preset temperature identification range, obtain all furnace temperature data that exist within the preset temperature range for that index value, use the arithmetic mean of the unit temperature rise resistance coefficient corresponding to the furnace temperature data as the sequence filling value, then fill the elements in the working condition resistance sequence with values, and finally obtain the working condition resistance sequence after numerical filling. If the temperature index value does not have corresponding furnace temperature data in the preset temperature range, then the two nearest furnace temperature data are searched forward and backward in the temperature dimension for the temperature index value. Then, the arithmetic mean of the unit temperature rise resistance coefficients corresponding to the two furnace temperature data is used as the sequence filling value. Then, the elements in the working condition resistance sequence are numerically filled to obtain the working condition resistance sequence after numerical filling.

6. The temperature control method for a vacuum heat treatment furnace according to claim 1, characterized in that, The heat capacity scale factor is obtained in the following ways: The elements in the preset benchmark resistance sequence are used as the denominator, and the elements in the working condition resistance sequence with the same index as the denominator are used as the numerator to calculate the resistance ratio. All resistance ratios are combined to form the ratio resistance sequence. Perform median statistical calculations on all elements in the drag ratio sequence and use the results as the heat capacity scale factor.

7. The temperature control method for a vacuum heat treatment furnace according to claim 1, characterized in that, The hysteresis retention coefficient is obtained in the following ways: The least squares method was used to fit a regression line to the drag ratio sequence, and the slope of the regression line was obtained as a drag drift feature. The drag drift characteristic is mapped onto a preset coefficient range to obtain the hysteresis retention coefficient.

8. The temperature control method for a vacuum heat treatment furnace according to claim 1, characterized in that, The methods for obtaining the excess integral calculation function include: Obtain the integral term accumulated by the PID controller during the heating process, and construct the excess integral calculation function by combining the heat capacity scale factor and the hysteresis retention coefficient: in, This is an excess integral. This is the integral term accumulated by the PID controller during the heating process; This is the heat capacity scale factor; This is the lag retention factor.

9. The temperature control method for a vacuum heat treatment furnace according to claim 1, characterized in that, The correction of the integral term assignment includes: Obtain the preset basic sliding deviation, and calculate the sliding start deviation by combining it with the heat capacity scale factor; obtain the preset process set temperature, and calculate the deviation value by combining it with the furnace temperature data; when the deviation value is less than or equal to the sliding start deviation, obtain the integral term of the PID, and calculate the steady-state maintenance integral by combining it with the calculation result of the excess integral calculation function, and use it to assign and correct the integral term of the PID control. When the deviation value is greater than the initial deviation of the gliding, the integral term will not be assigned a correction value.

10. A temperature control system for a vacuum heat treatment furnace, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the temperature control method for a vacuum heat treatment furnace as described in any one of claims 1 to 9.

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