A temperature continuous control method suitable for high-temperature heating furnace

Abstract

The application relates to the technical field of high-temperature heating furnace temperature control, in particular to a temperature continuous control method suitable for a high-temperature heating furnace. The method comprises the following steps: integrating temperature data by analyzing the temperature data collected by different instruments, calculating temperature change coincidence degrees in combination with neighborhood difference, selecting a characteristic curve through historical temperature curve fitting, and determining a temperature rising section and a temperature maintaining section; determining initial PID parameters based on the coincidence degrees of current temperature data and historical data and PID parameter distribution; and finally, adjusting proportional gain parameters through temperature fluctuation characteristics of a control period and differences from the characteristic curve, so that temperature oscillation and deviation elimination in the high-temperature furnace heating process can be accurately controlled.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of high-temperature heating furnace temperature control, in particular to a temperature continuous control method suitable for a high-temperature heating furnace. BACKGROUND

[0002] The high-temperature heating furnace is a general term of some high-temperature heating devices, is applied to various industries, and is a common industrial equipment. The high-temperature heating furnace can be applied to the metal processing industry as an energy-saving electric furnace, and is also used in the construction and pharmaceutical industries. With the continuous improvement of industrial technology, the application field of the high-temperature heating furnace gradually increases the temperature control requirements.

[0003] In the tungsten-molybdenum powder metallurgy sintering process of the high-temperature heating furnace, the PID algorithm is generally used for temperature continuous control. However, in the PID parameter setting aspect, the traditional method depends on experience or static setting, and it is difficult to adapt to the dynamic characteristic changes in different stages of the sintering process, which easily leads to temperature overshoot, oscillation or slow response, affects the control precision, efficiency and increases the energy consumption. SUMMARY

[0004] In view of the above, it is necessary to provide a temperature continuous control method suitable for a high-temperature heating furnace to solve the above problems.

[0005] One embodiment of the application provides a temperature continuous control method suitable for a high-temperature heating furnace, which comprises the following steps:

[0006] In the heating process of the high-temperature heating furnace, a sequence composed of temperature data collected by two different instruments at all moments is respectively recorded as a first temperature data sequence and a second temperature data sequence;

[0007] A temperature switching range and a neighborhood of the temperature data are preset, temperature data collected by the two instruments at each moment which simultaneously meet the temperature switching range are screened out, a temperature change coincidence degree between the temperature data collected at the corresponding moment is obtained in combination with the difference distribution between the neighborhood data, a moment with the maximum temperature change coincidence degree is taken as a switching point, and the first temperature data sequence and the second temperature data sequence are integrated; based on the integrated temperature data in the historical multiple heating processes, a reference heating process of the current heating is screened out, and the high-temperature furnace heating process is segmented to obtain a heating-up section and a holding section;

[0008] The PID parameters of the PID controller in each heating process of the high-temperature heating furnace are acquired, the distribution of the temperature change coincidence degree between the temperature data of the same order in the integrated temperature data of the current heating process and the historical heating process is analyzed, the initial proportional gain parameters of the current temperature rising section and the temperature holding section are determined in combination with the proportional gain parameters in the PID parameters of the historical temperature rising section and the temperature holding section; a preset control period is set, the temperature oscillation characteristic value after constraint is obtained based on the fluctuation characteristics of the integrated temperature data of each control period and the intersection characteristics between the temperature data and the reference heating process temperature data;

[0009] For each control period, the deviation elimination degree of each control period is determined according to the difference distribution characteristics between the integrated temperature data and the temperature data of the current heating reference heating process; the proportional gain parameters of the PID algorithm of the next control period are adjusted in combination with the initial proportional gain parameters by comparing the temperature oscillation characteristic value after constraint of each control period with the deviation elimination degree.

[0010] The temperature change coincidence degree between the temperature data collected at the corresponding moment is obtained, and the temperature change coincidence degree between the temperature data collected at the corresponding moment is obtained.

[0011] For the two temperature data of the same sampling moment within the temperature switching range in the first temperature data sequence and the second temperature data sequence, the absolute value of the difference between the temperature data of the same moment in the neighborhood temperature sequence of the two temperature data is calculated, and the reciprocal of the sum of all difference absolute values obtained by the two temperature data is calculated to obtain the temperature change coincidence degree between the two temperature data of the same sampling moment.

[0012] The first temperature data sequence and the second temperature data sequence are integrated, and the process is specifically: the switching point and the temperature data before the switching point in the first temperature data sequence are combined with the temperature data after the switching point in the second temperature sequence to form a temperature sequence, and the temperature data in the temperature sequence is taken as the integrated temperature data, wherein the temperature data of the first sequence is collected by a thermocouple, and the temperature data of the second sequence is collected by an infrared thermometer.

[0013] The sequence of temperature data of the current heating reference heating process satisfies the minimum sum of the DTW distance between the sequence and the integrated temperature data sequence in the remaining heating processes.

[0014] The heating process of the high-temperature furnace is segmented to obtain the temperature rising section and the temperature holding section, and the acquisition process is:

[0015] The inflection point of the fitting curve of the temperature data in the current heating reference heating process is obtained, the first inflection point and the second inflection point are obtained, and the time period before the first inflection point is taken as the temperature rising section, and the time period after the first inflection point and before the second inflection point is taken as the temperature holding section.

[0016] The process of determining the initial proportional gain parameter of the current heating-up section and the holding section is specifically:

[0017] The proportional gain parameter with the highest occurrence frequency in the historical heating-up section is taken as the initial proportional gain parameter of the PID control algorithm of the heating-up section.

[0018] The sum of the temperature change coincidence degrees between the integrated temperature data of the holding section in the current heating process and the integrated temperature data of the holding section in the historical heating process is obtained, the proportional gain parameter of the PID parameter of the holding section in the historical heating process with the largest sum of temperature change coincidence degrees is selected as the initial proportional gain parameter of the PID control algorithm of the holding section in the current heating process.

[0019] The constrained temperature oscillation characteristic value is obtained, and the constrained temperature oscillation characteristic value is obtained.

[0020] Based on the fluctuation characteristics of the integrated temperature data of each control cycle, the intersection characteristics between the temperature data in the current heating and the reference heating process are combined to obtain the temperature oscillation characteristic value of each control cycle.

[0021] The sequence composed of the integrated temperature data in each control cycle is recorded as a local temperature data sequence, the first-order difference sequence is obtained, and the number of adjacent elements with different positive and negative signs in the first-order difference sequence is obtained; the difference absolute value of each adjacent temperature data in the local temperature data sequence is calculated, and the difference absolute value is divided by the time interval between the adjacent temperature data to obtain the temperature change rate; all the obtained temperature change rates are accumulated, and then multiplied by the number to obtain the constraint factor of each control cycle.

[0022] The temperature oscillation characteristic value is divided by the normalized constraint factor, and the obtained ratio is normalized to obtain the constrained temperature oscillation characteristic value.

[0023] The temperature oscillation characteristic value of each control cycle is obtained, and the temperature oscillation characteristic value of each control cycle is obtained.

[0024] The proportion of the number of extreme points of the local temperature data sequence of each control cycle is obtained; the sum of the difference absolute values of all temperature data in the control cycle and the temperature data at the corresponding time in the reference heating process of the current heating is calculated.

[0025] In the current heating process, for the temperature data at the i th time, if it satisfies that the temperature data at the i-1 th time is greater than the temperature data at the i-1 th time in the reference heating process of the current heating, and the temperature of the temperature data at the i+1 th time is less than the temperature data at the i+1 th time in the reference heating process of the current heating; the temperature data at the i th time is a temperature change characteristic point; the number of temperature change characteristic points in each control cycle is counted.

[0026] For each control cycle, the result of positively fusing the percentage of the number, the sum, and the number of temperature change feature points is used as the temperature oscillation feature value for each control cycle.

[0027] Specifically, determining the deviation elimination degree for each control cycle involves:

[0028] For the integrated temperature data in the control cycle, calculate the absolute value of the difference between each temperature data and the temperature data at the corresponding moment in the current heating reference process, and record it as the deviation value of each temperature data.

[0029] Calculate the absolute value and minimum value of the difference between the deviation value of the temperature data and the deviation value of the adjacent previous temperature data. The ratio of the absolute value of the difference to the minimum value is used as the degree of change of the deviation value of each temperature data.

[0030] The ratio of the deviation value to the degree of change of the deviation value is calculated, and the negative correlation mapping result obtained by accumulating the ratios obtained from all temperature data is used as the degree of deviation elimination for each control cycle.

[0031] Specifically, adjusting the proportional gain parameter of the PID algorithm for the next control cycle involves:

[0032] The initial proportional gain parameters obtained in the heating and holding stages are used as the proportional gain parameters for the first control cycle of the heating and holding stages.

[0033] The formula for adjusting the proportional gain parameter is:

[0034] In the formula: The proportional gain parameter represents the control period; This indicates the preset adjustment factor; This represents the temperature oscillation characteristic value after the control period constraint; Indicates the degree of deviation elimination in the control cycle; This represents the cumulative sum of the deviations of temperature data within the control cycle; This represents the sum of all temperature change rates within the control cycle; This represents the proportional gain parameter for the next control cycle. , All are preset thresholds, among which and ; This represents the normalization function.

[0035] This application has at least the following beneficial effects:

[0036] The application cooperates with the thermocouple and the infrared thermometer, dynamically determines the optimal switching point with the maximum coincidence degree of temperature change based on the temperature data of the same sampling time point and its neighborhood in the preset switching range, realizes the smooth and seamless integration of sensor data, effectively solves the problems of insufficient temperature measurement accuracy and switching impact in the high-temperature section, and lays a foundation for accurate control. Secondly, by using the integrated temperature data sequence of the historical sintering process, the temperature data of the reference heating process of the current heating are obtained as an ideal reference to accurately divide the heating section and the holding section, better support the temperature control strategy, ensure the heating and holding processes more refined, and further improve the reliability and efficiency of the whole process. Finally, based on the characteristic curve and historical experience, the initial PID parameters are set, and in each control period, the temperature oscillation characteristic value is constructed and constrained to reflect the oscillation intensity and deviation elimination degree, and the proportional gain parameters of the PID are adaptively and dynamically adjusted according to the oscillation intensity and deviation elimination degree, so as to effectively suppress temperature oscillation, reduce overshoot, and accelerate deviation elimination. The temperature control accuracy, response speed and stability of the high-temperature heating furnace in the complex sintering process are significantly improved, and the energy consumption is finally reduced. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 A flowchart of a temperature continuous control method suitable for a high-temperature heating furnace is provided in the application.

[0038] Figure 2 A flowchart of the acquisition of the heating section and the holding section is provided in the application. DETAILED DESCRIPTION

[0039] In the description of the embodiments of the application, the words "exemplary", "or", "for example" are used to mean as an example, instance, or illustration. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the application should not be interpreted as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of "exemplary", "or", "for example" is intended to present the relevant concept in a specific manner.

[0040] 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 the application belongs. The terms used in the specification of the application are only for the purpose of describing specific embodiments and are not intended to limit the application.

[0041] In addition, it should be noted that the terms "first", "second" in the application and its drawings are used to distinguish similar objects, and are not intended to describe a specific order or sequence. The method disclosed in the embodiments of the application or the method shown in the flowchart includes one or more steps for implementing the method, and the execution order of the steps can be interchanged with each other without departing from the scope of the application, and some steps can also be deleted.

[0042] 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.

[0043] The application provides a continuous temperature control method for a high-temperature heating furnace, and belongs to the technical field of high-temperature heating furnace temperature control. Figure 1 The method comprises the following steps:

[0044] S1: during the heating process of the high-temperature heating furnace, a sequence of temperature data collected by two different instruments at all times is respectively recorded as a first temperature data sequence and a second temperature data sequence.

[0045] A thermocouple is installed at the center of the furnace chamber of the high-temperature heating furnace to collect the temperature at each time in the high-temperature heating furnace, and the temperature data collected at all times is used to form a first temperature data sequence of the high-temperature heating furnace.

[0046] Since the temperature detection temperature of the thermocouple is usually 2300 DEG C, which is lower than the maximum temperature 2500 DEG C in the sintering process of the high-temperature heating furnace, an infrared temperature detector is used to collect the temperature data of the high-temperature heating furnace simultaneously, the sensor of the infrared temperature detector is installed outside the high-temperature heating furnace and points to the area in the furnace that needs to be monitored. By accurately aiming at the heating area in the furnace, the infrared temperature detector can obtain the temperature in the furnace in real time, and obtain a second temperature data sequence.

[0047] At this point, the collection of the temperature data related to the high-temperature heating furnace is completed.

[0048] S2: a temperature switching range and a neighborhood of temperature data are preset, temperature data collected by the two instruments at each time that simultaneously meet the temperature switching range are screened out, the difference distribution between the neighborhood data is combined, the temperature change coincidence degree between the temperature data collected at the corresponding time is obtained, the time with the maximum temperature change coincidence degree is taken as a switching point, and the first temperature data sequence and the second temperature data sequence are integrated; based on the integrated temperature data in the historical multiple heating processes, a reference heating process of the current heating is screened out, and the high-temperature furnace heating process is segmented to obtain a temperature rising section and a temperature holding section.

[0049] In the continuous temperature control of the high-temperature heating furnace, PID control is the most widely used algorithm, and the parameter setting directly determines the stability, response speed and precision of the control system. The application controls the temperature of the high-temperature heating furnace in the sintering process by self-adaptive adjustment of the parameters of the PID algorithm.

[0050] This application addresses the sintering process of tungsten-molybdenum powder metallurgy materials after pressing in a high-temperature heating furnace. During this process, the temperature changes from room temperature to high temperature. While thermocouples are typically used for temperature measurement in the low-temperature range, their accuracy decreases sharply with increasing temperature in the high-temperature range. Therefore, an infrared thermometer with a temperature measurement range of 1000℃ to 3200℃ should be used for temperature measurement in the high-temperature range. When thermocouples and infrared thermometers work together, a switching point needs to be set. Before the switching point, temperature control is performed using temperature data collected by the thermocouples; after the switching point, control is performed using temperature data collected by the infrared thermometer. This application uses 1400℃ to 1600℃ as the temperature switching range and analyzes the temperature data when the temperature reaches this range. The analysis here uses temperature data collected by the thermocouples at the same time. Temperature data collected by infrared thermometer For example, obtain respectively Point and The 20 nearest neighboring data points of a given temperature data point in its sampling time sequence are denoted as the neighboring data points of that data point. This number of 20 is arbitrarily set; implementers can adjust the number of neighboring data points according to actual circumstances, and this application does not impose any restrictions on this. The sequence of all neighboring data points for each data point is denoted as the neighborhood temperature sequence.

[0051] For the temperature data at the same sampling time in the first temperature data sequence and the second temperature data sequence that are within the temperature switching range, calculate the absolute value of the difference between the temperature data at the same time in the neighborhood temperature sequences of the two temperature data. Then, sum all the absolute values ​​of the differences obtained from the two temperature data and take the reciprocal to obtain the degree of overlap of temperature changes between the two temperature data at the same sampling time.

[0052] It should be understood that the greater the overlap of temperature changes, the closer the temperatures collected by the thermocouple and the infrared thermometer are. In this case, the temperature sampling accuracy is higher, and the impact on the temperature control system is smaller when switching temperature data. Conversely, the smaller the overlap of temperature changes, the greater the temperature difference between the two types of sensors, the lower the temperature sampling accuracy, and the greater the impact on the temperature control system when switching temperature data.

[0053] The temperature data with the highest degree of overlap in temperature changes is selected as the switching point. If there are multiple temperature data with the highest degree of overlap, the temperature data closest to the center of the temperature switching range is selected as the switching point. The two temperature data sequences can then be integrated to obtain a temperature sequence. The temperature data at and before the switching point in the temperature sequence are temperature data collected by thermocouples, while the temperature data after the switching point are temperature data collected by infrared thermometers. It should be understood that the temperature sequence is composed of the integrated temperature data corresponding to the heating process. All temperature data mentioned thereafter are integrated temperature data.

[0054] In each historical sintering work using a high-temperature heating furnace for tungsten-molybdenum powder metallurgy, a corresponding integrated historical temperature sequence can be obtained, and a fitting curve of the historical temperature sequence is recorded as a temperature change curve, wherein the ordinate of the temperature change curve is temperature data, and the abscissa is time. It should be understood that each historical sintering can obtain a temperature change curve from room temperature to the end; the obtained historical temperature sequence is counted to generate temperature data of a current heating reference heating process; the temperature data in the current heating reference heating process form a sequence that meets the minimum sum of DTW distances between all obtained historical temperature sequences, and the PID parameters are adaptively regulated according to the deviation of the temperature data of the current heating reference heating process from real-time temperature values, effectively improving the temperature control precision and efficiency of the high-temperature heating furnace.

[0055] The temperature change of the high-temperature heating furnace during the sintering process is usually divided into a heating-up stage, a holding stage, and a cooling-down stage. Therefore, a first derivative method is used to detect the inflection points of the sequence of the temperature data of the current heating reference heating process, to obtain two inflection points, which are recorded as a first inflection point and a second inflection point in the order of time acquisition, and the time period before the first inflection point is the heating-up segment, and the time period after the first inflection point and before the second inflection point is the holding segment. In this application, PID control is mainly performed on the heating-up segment and the holding segment. It should be noted that if there are multiple inflection points, the two inflection points closest to the tungsten-molybdenum powder metallurgy sintering temperature of 2000℃ are taken as the first inflection point and the second inflection point in the order of time.

[0056] The flowchart for obtaining the heating-up segment and the holding segment is shown in Figure 2 .

[0057] S3: Obtain the PID parameters of the PID controller in each heating process of the high-temperature heating furnace, analyze the distribution of the temperature change coincidence degree between the same sequence temperature data in the integrated temperature data of the current heating process and the historical heating process, determine the initial proportional gain parameters of the current heating-up segment and the holding segment in combination with the proportional gain parameters in the historical heating-up segment and the holding segment PID parameters; preset a control period, and based on the fluctuation characteristics of the integrated temperature data of each control period, the intersection characteristics between the reference heating process temperature data are combined to obtain the constrained temperature oscillation characteristic value.

[0058] In the tungsten-molybdenum powder metallurgy sintering process, if it is in the heating-up stage, a large power is needed for rapid heating, and accurate and stable temperature control is needed in the holding stage, and the corresponding PID parameters are different.

[0059] First, according to the initial parameters of the PID control algorithm of the temperature control of the historical tungsten-molybdenum powder metallurgy sintering, the initial proportional gain parameter of the PID control algorithm of the temperature control of the current sintering process is obtained. For the temperature control of the temperature control of the historical tungsten-molybdenum powder metallurgy sintering, the frequency of the PID parameter is the highest, and the initial proportional gain parameter of the PID control algorithm of the temperature control of the temperature control of the current sintering process is obtained. The temperature change coincidence degree between the integrated temperature data of the current heating process and the integrated temperature data of the historical heating process is obtained, and the proportional gain parameter of the PID parameter of the temperature control of the historical heating process with the maximum temperature change coincidence degree is selected as the initial proportional gain parameter of the PID control algorithm of the temperature control of the current heating process.

[0060] Further, the initial PID parameters are adaptively regulated, and the control period of the PID parameters in the real-time sintering process (from room temperature to sintering end) under the control of the initial PID parameters is obtained. The implementer can adjust the acquisition of the local temperature data sequence, which is assumed to be Q here. The local temperature data sequence collected in the control period is analyzed, and the temperature oscillation characteristic value Z in the control period is constructed, which has the following formula form: ; In the formula, A represents the number of extreme points of the integrated temperature data in the control period, the extreme point extraction algorithm is a known means, and will not be described here, N represents the number of integrated temperature data in the control period, The greater the number of temperature data wave peaks and wave troughs in the control period, the greater the oscillation characteristic value; m represents the number of temperature change characteristic points in the control period; The greater the difference between the a-th extreme point and the ideal temperature, the greater the overshoot, and the greater the temperature oscillation characteristic value.

[0061] Wherein, the method for obtaining the temperature change characteristic point is: in the heating process, for the temperature data of the i-th moment, if it satisfies that the temperature data of the i-1-th moment is greater than the temperature data of the i-1-th moment in the reference heating process of the current heating, and the temperature data of the i+1-th moment is less than the temperature data of the i+1-th moment in the reference heating process of the current heating; the temperature data of the i-th moment is the temperature change characteristic point.

[0062] It should be understood that the greater the temperature oscillation characteristic value, the more obvious the temperature sustained oscillation phenomenon under the control of the PID parameter, indicating that the proportional gain is too large or the integral time is too small; the smaller the temperature oscillation characteristic value, the weaker the temperature sustained oscillation phenomenon under the control of the PID parameter, indicating that the proportional gain is too small or the integral time is too large.

[0063] The temperature oscillation characteristic value is larger due to frequent output jitter in the PID parameter control, but the frequent output jitter phenomenon is not caused by too large proportional gain or too small integral time, but too large differential time, so the application constructs a constraint factor according to the frequent output jitter phenomenon to constrain the temperature oscillation characteristic value, so that the constrained temperature oscillation characteristic value can accurately reflect the temperature oscillation phenomenon and reduce the interference of frequent jitter caused by too large differential time, wherein the constraint factor is denoted as Y, and the formula is: , wherein: u represents the number of changes in the temperature change direction of the temperature data in the control period, for example, the initial number of changes is 0, when any one data point satisfies that the temperature change direction thereof is inconsistent with the temperature change direction of the adjacent temperature data, the number of changes is increased by 1; N represents the number of integrated temperature data in the control period; represents the temperature change rate of the n th integrated temperature data in the control period, and the temperature change rate is specifically the absolute value of the temperature difference between one temperature data and the previous temperature data, divided by the interval time between the two data points; the constrained temperature oscillation characteristic value is , wherein: Z represents the initial temperature oscillation characteristic value, Y represents the normalized constraint factor, is the constrained temperature oscillation characteristic value, represents a normalization function.

[0064] It should be noted that the temperature change direction of the data point a can be obtained according to the temperature value of the next data point b, if the temperature of the b point is greater than or equal to the temperature of the a point, the temperature of the a point changes in the positive direction, if the temperature of the b point is less than the temperature of the a point, the temperature of the a point changes in the negative direction; the specific method for obtaining the number of changes in the temperature change direction is to obtain the first order difference sequence of the local temperature data sequence in the control period, and the number of changes in the temperature change direction of the corresponding control period is the number of adjacent elements with different signs in the first order difference sequence.

[0065] It should be understood that the larger the constraint factor is, the stronger the correlation between the temperature oscillation characteristic value and the frequent output jitter phenomenon is, and the weaker the actual temperature oscillation phenomenon is, and the smaller the constraint factor is, the weaker the correlation between the temperature oscillation characteristic value and the frequent output jitter phenomenon is, and the stronger the actual temperature oscillation phenomenon is.

[0066] S4: For each control period, according to the difference distribution characteristics between the integrated temperature data and the temperature data of the current heating reference heating process, the deviation elimination degree of each control period is determined; the constrained temperature oscillation characteristic value of each control period is compared with the deviation elimination degree, and the initial proportional gain parameter is combined to adjust the proportional gain parameter of the PID algorithm in the next control period.

[0067] When judging the proportional gain parameter in the PID algorithm according to the temperature oscillation characteristic value, the integral time parameter still has an influence. In order to further judge whether the integral time parameter or the proportional gain parameter is set unreasonably, a deviation elimination degree is constructed by eliminating the cumulative deviation, and the PID parameter is further judged and adjusted according to the deviation elimination degree. The integral time eliminates the historical cumulative deviation. When the integral time is too large, the integral effect is weak, the static deviation elimination speed is slow, and the system response is sluggish. When the integral time is too small, the integral effect is too strong, and overshoot and oscillation are easily caused. Therefore, for the temperature data in a control period, a deviation elimination degree F is constructed, and the formula is: ; in the formula, N represents the number of integrated temperature data in a control period; the absolute value of the difference between the nth temperature data and the temperature data at the corresponding time in the reference heating process of the current heating is denoted as a deviation value; the deviation value change degree of the nth temperature data and the adjacent previous temperature data is denoted as a deviation value change degree, and the deviation value change degree is obtained by calculating the absolute value of the difference between the deviation value of the temperature data and the deviation value of the adjacent previous temperature data, and the minimum value; and the ratio of the absolute value to the minimum value is taken as the deviation value change degree.

[0068] It should be understood that, the greater the deviation value of the temperature data and the smaller the deviation value change degree, the greater the static deviation of the temperature data, that is, the greater the historical cumulative deviation, and the smaller the deviation elimination degree. The greater the deviation elimination degree, the faster the temperature deviation is eliminated under the control of the current PID parameter, the better the effect of the integral time, and the more likely the oscillation phenomenon is caused by the too small integral time. The smaller the deviation elimination degree, the slower the temperature deviation is eliminated under the control of the current PID parameter, the worse the effect of the integral time, and the more likely the oscillation phenomenon is caused by the too large proportional gain.

[0069] Further, the proportional gain parameter of the PID algorithm in the temperature continuous control of the high-temperature heating furnace is adjusted adaptively, and the formula of the adjusted proportional gain parameter is:

[0070] in the formula, the proportional gain parameter of the control period is denoted as Kp; a preset adjustment factor is denoted as a; the temperature oscillation characteristic value of the control period after constraint is denoted as T; the deviation elimination degree of the control period is denoted as F; the cumulative sum of the deviation values of the temperature data in the control period is denoted as S; the sum of all temperature change rates in the control period is denoted as V; the proportional gain parameter of the next control period is denoted as Kp1; , are all preset threshold values, wherein and ; denotes a normalization function; The greater the value of f, the greater the possibility that the current temperature data is oscillating, and the more likely that the oscillation is caused by an excessively large proportional gain parameter. The greater the value of f, the faster the current temperature deviation is eliminated, and the lower the temperature change rate, indicating a greater possibility that the proportional gain coefficient is too small. In the present application, f = 0.1 is set. In addition, and are preset threshold values, with values of 0.7 and 0.3, respectively, which can be adjusted by the implementer.

[0071] The adjusted proportional gain parameter is used as the proportional gain parameter of the PID algorithm in the next control cycle of the high-temperature heating furnace device to control the temperature of the high-temperature heating furnace, improve the temperature control precision and efficiency, and reduce energy consumption. The high-temperature heating furnace device mainly consists of three parts: a furnace body structure, a temperature measurement system, and a control system. The furnace body structure includes a hearth and heating elements. The hearth is insulated by multiple layers of refractory materials, with a maximum tolerance temperature of ≥2500°C. The heating elements are ring-shaped silicon-molybdenum rods or graphite heating bodies, with a power adjustable range of 0-100 kW. The temperature measurement system includes a thermocouple array and an infrared temperature meter. The thermocouple array is an S-type thermocouple distributed on the inner wall of the hearth and the material support platform, which monitors the temperature of multiple low-temperature zones in real time. The infrared temperature meter is installed on the optical window of the side wall of the furnace body, with a focal length aligned with the central heating area. The control system includes a core controller, an execution mechanism, and data interaction. The core controller is an embedded industrial PLC running an adaptive PID algorithm. The execution mechanism is a solid-state relay that adjusts the current of the heating elements with a response time of ≤20 ms. Data interaction refers to the real-time uploading of temperature data to the upper computer to generate sintering process monitoring curves and parameter adjustment logs.

[0072] By continuously controlling the temperature during the sintering process of the high-temperature heating furnace through adaptive adjustment of the PID parameters, a controllable high-temperature thermal environment is provided, which promotes the metallurgical bonding and densification of tungsten-molybdenum powder particles through the diffusion mechanism, forms and maintains a uniform and stable thermal field, ensures consistent heating of all parts of the material, and prevents deformation or uneven performance. Precise execution of the preset temperature rising-holding-cooling process curve, especially the stringent requirement for temperature stability during the holding phase, directly determines the key performance of the final sintered body, such as grain size, density, strength, and electrical conductivity. The precision, efficiency, and stability of the high-temperature heating furnace in performing its core thermal work during complex sintering processes are significantly improved, ultimately reducing energy consumption.

[0073] The computer program product of the present application can be a computer program implemented on one or more computers. The program instructions can be stored on a computer readable medium, such as a hard disk, CD-ROM, optical storage, or any other tangible medium. The program instructions can be downloaded from the Internet or another network. The program instructions can be embodied in a carrier wave traveling over the Internet or other network. The computer readable medium can be a machine readable storage device, a machine readable transmission device, or a combination of both. The computer readable medium can be a computer readable storage device, a computer readable transmission device, or a combination of both.

[0074] The above embodiments are only used to illustrate the technical solutions of the present application, not limit the technical solutions of the present application; although the technical solutions of the present application are described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A temperature continuous control method suitable for a high-temperature heating furnace, characterized by, The method comprises the following steps: During the heating process in the high-temperature heating furnace, a sequence of temperature data at all moments collected by two different instruments is respectively recorded as a first temperature data sequence and a second temperature data sequence; For two temperature data at the same sampling moment within the temperature switching range in the first temperature data sequence and the second temperature data sequence, the absolute value of the difference between the temperature data at the same moment in the neighborhood temperature sequence of the two temperature data is calculated, all the absolute values of the difference obtained by the two temperature data are accumulated and then inverted to obtain the temperature change coincidence degree between the two temperature data at the same sampling moment; the moment with the maximum temperature change coincidence degree is taken as the switching point, the temperature data before the switching point in the first temperature data sequence and the temperature data after the switching point in the second temperature data sequence are combined to form a temperature sequence, and the temperature data in the temperature sequence is taken as the integrated temperature data, wherein the temperature data in the first sequence is collected by a thermocouple, and the temperature data in the second sequence is collected by an infrared thermometer; based on the integrated temperature data in the historical multiple heating processes, a reference heating process for the current heating is screened out, and the high-temperature furnace heating process is segmented to obtain a heating-up section and a holding section; PID parameters of the PID controller in each heating process of the high-temperature heating furnace are obtained, the distribution of the temperature change coincidence degree between the temperature data in the same order in the integrated temperature data of the current heating process and the historical heating process is analyzed, the initial proportional gain parameters of the current heating-up section and the holding section are determined in combination with the proportional gain parameters in the PID parameters of the historical heating-up section and the holding section; a control period is preset, the temperature fluctuation characteristic value of each control period is obtained based on the fluctuation characteristics of the integrated temperature data in each control period and in combination with the intersection characteristics between the temperature data in the reference heating process for the current heating; a sequence of the integrated temperature data in each control period is recorded as a local temperature data sequence, a first-order difference sequence is obtained, and the number of adjacent elements with different positive and negative signs in the first-order difference sequence is obtained; the absolute value of the difference between each adjacent temperature data in the local temperature data sequence is calculated, and the temperature change rate is obtained by dividing the time interval between the adjacent temperature data; all the temperature change rates obtained are accumulated, and then multiplied by the number to obtain a constraint factor of each control period; the temperature fluctuation characteristic value is divided by the normalized constraint factor, and the ratio obtained is normalized to obtain a constrained temperature fluctuation characteristic value; For the integrated temperature data in the control period, the absolute value of the difference between each temperature data and the temperature data at the corresponding time in the reference heating process of the current heating is calculated, denoted as the deviation value of each temperature data; the absolute value of the difference between the deviation value of the temperature data and the deviation value of the adjacent previous temperature data is calculated, and the minimum value is calculated, and the ratio of the absolute value to the minimum value is taken as the deviation value change degree of each temperature data; the ratio of the deviation value to the deviation value change degree is calculated, and the cumulative negative correlation mapping result of the ratio of all temperature data is taken as the deviation elimination degree of each control period; the constrained temperature oscillation characteristic value of each control period is compared with the deviation elimination degree, and the initial proportional gain parameter is combined to adjust the proportional gain parameter of the PID algorithm of the next control period.

2. A method for continuous temperature control suitable for use in a high temperature furnace as claimed in claim 1, wherein, The sequence composed of the temperature data of the reference heating process of the current heating satisfies the minimum sum of the DTW distance between the sequence composed of the integrated temperature data in the remaining heating processes.

3. The method of continuously controlling the temperature of a high-temperature heating furnace according to claim 1, wherein The acquisition process of segmenting the high-temperature furnace heating process to obtain the temperature rising segment and the temperature holding segment is: Obtain the inflection point of the fitting curve of the temperature data in the reference heating process of the current heating, obtain the first inflection point and the second inflection point, and the time period before the first inflection point is taken as the temperature rising segment, and the time period after the first inflection point and before the second inflection point is taken as the temperature holding segment.

4. The method of continuously controlling the temperature of a high-temperature heating furnace according to claim 1, wherein The process of determining the initial proportional gain parameter of the current temperature rising segment and the temperature holding segment is specifically: The proportional gain parameter with the highest occurrence frequency in the historical temperature rising segment is taken as the initial proportional gain parameter of the PID control algorithm of the temperature rising segment; The sum of the temperature change coincidence degrees between the integrated temperature data of the temperature holding segment in the current heating process and the integrated temperature data of the temperature holding segment in the historical heating process is obtained, and the proportional gain parameter of the PID parameter of the temperature holding segment in the historical heating process with the maximum sum of the temperature change coincidence degrees is selected as the initial proportional gain parameter of the PID control algorithm of the temperature holding segment in the current heating process.

5. The method for continuous temperature control suitable for high temperature heating furnace as claimed in claim 1, wherein, The temperature oscillation characteristic value of each control period is obtained, specifically: Obtain the number proportion of the extreme points of the local temperature data sequence of each control period; calculate the sum of the absolute values of the differences between all temperature data in the control period and the temperature data at the corresponding time in the reference heating process of the current heating; In the current heating process, for the temperature data at the i-th time, if it satisfies that the temperature data at the i-1-th time is greater than the temperature data at the i-1-th time in the reference heating process of the current heating, and the temperature of the temperature data at the i+1-th time is less than the temperature data at the i+1-th time in the reference heating process of the current heating, then the temperature data at the i-th time is a temperature change feature point; the number of temperature change feature points in each control period is counted; For each control period, the result of forward fusion of the number proportion, the sum and the number of temperature change feature points is taken as the temperature oscillation characteristic value of each control period.

6. The method of continuously controlling the temperature of a high-temperature heating furnace according to Claim 1, wherein The adjustment of the proportional gain parameter of the PID algorithm of the next control period is specifically: The initial proportional gain parameters obtained from the temperature rising segment and the temperature holding segment are taken as the proportional gain parameters of the first control period of the temperature rising segment and the temperature holding segment. The proportional gain parameter adjustment formula is: wherein: represents a proportional gain parameter of the control period; represents a preset adjustment factor; represents a temperature oscillation eigenvalue after control period constraint; represents a deviation elimination degree of the control period; represents a cumulative sum of deviation values of temperature data in the control period; represents a sum value of all temperature change rates in the control period; represents a proportional gain parameter of the next control period; , are both preset threshold values, wherein and ; represents a normalization function.

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