Automatic hot-pressing control method and system, storage medium and electronic equipment

Abstract

The invention provides a hot-pressing automatic control method and system, a storage medium and electronic equipment, and relates to the technical field of advanced ceramic sintering. According to the technical scheme provided by the invention, the technological process is divided into a full-dimensional key stage, a single-factor master control stage and a transition stage. In the full-dimensional key stage, the temperature and pressure change curve is predicted, so that the first compensation control instruction has perspectiveness. In a single-factor master control stage, a double-layer control framework of an outer-layer tolerance zone and an inner-layer tolerance zone is constructed, and a second compensation control instruction can reduce excessive control on a secondary observation attribute and reduce unnecessary control action frequency while ensuring the control precision of the primary observation attribute. In the transition stage, the system dynamically adjusts the sampling frequency of temperature and pressure feedback data according to the temperature and pressure parameter change rate. High-precision control is maintained in the key stage to ensure the precision, and an efficiency priority strategy is adopted in the transition stage to shorten the process time, so that the firing efficiency can be improved on the premise of ensuring the firing precision of advanced ceramic materials.

Description

Technical Field

[0001] This application relates to the field of advanced ceramic sintering technology, specifically to an automatic hot pressing control method, system, storage medium, and electronic equipment. Background Technology

[0002] Hot pressing is a material thermal processing technique that uses heating, holding, and pressurization to achieve desired properties while the material is in a solid state. Hot pressing equipment typically employs a vacuum pump unit to evacuate the furnace, followed by heating with a heater, then gas filling and pressurization, ultimately completing the unloading process. In the semiconductor equipment manufacturing field, hot pressing is widely used in the sintering process of advanced ceramic components, such as aluminum nitride heaters, silicon carbide chucks, and structural parts in key equipment like CVD equipment and lithography machines.

[0003] In related technologies, hot pressing control methods typically determine corresponding control parameters based on a preset process flow, including key parameters such as temperature curves, pressure curves, and holding time. Then, a real-time feedback adjustment mechanism monitors the actual temperature and pressure values ​​within the hot pressing equipment and compares them with target parameters to ensure that the temperature and pressure within the equipment remain within the range required by the process flow. To ensure product quality meets standards, existing control systems usually employ a strategy of maintaining high-precision temperature and pressure control throughout the entire process, meaning that the most stringent control precision requirements and the highest control response frequency are maintained throughout the sintering process. This results in relatively low sintering efficiency. Summary of the Invention

[0004] This application provides an automatic hot pressing control method, system, storage medium, and electronic device that can improve firing efficiency while ensuring the firing precision of advanced ceramic materials.

[0005] In a first aspect, this application provides an automatic control method for hot pressing, the method comprising: Obtain the process flow data of the material to be sintered, and divide the process flow of the material to be sintered into multiple hot pressing stages based on the process flow data. The hot pressing stages include all-dimensional key stages, single-factor main control stages, or transition stages. Real-time monitoring of temperature and pressure feedback data and temperature and pressure parameter change rate of the material to be sintered during hot pressing; When the hot pressing process is in the critical stage of the entire dimension, the preset first process parameter curve of the critical stage, the temperature and pressure feedback data, and the temperature and pressure parameter change rate are input into the temperature and pressure parameter prediction model to obtain the predicted temperature and pressure change curve; a first compensation control command is generated based on the predicted temperature and pressure change curve and the preset process parameter curve to constrain the actual change trajectory of the temperature and pressure feedback parameter within the range limited by the temperature and pressure adjustment threshold of the first process parameter curve. When the hot pressing stage is in the single-factor master control stage, the primary and secondary observation attributes under the single-factor master control stage are determined; the outer tolerance zone corresponding to the primary observation attribute is generated according to the preset second process parameter curve of the single-factor master control stage; and the influence value of the secondary observation attribute on the primary observation data is mapped to the second process parameter curve to obtain the inner tolerance zone; a second compensation control command is generated according to the tolerance zone where the primary feedback data corresponding to the primary observation attribute in the temperature and pressure feedback data is located; the threshold range of the outer tolerance zone is greater than the threshold range of the inner tolerance zone; When the hot pressing process is in the transition stage, the sampling frequency of the temperature and pressure feedback data is reduced according to the rate of change of the temperature and pressure parameters, and a third compensation control command is generated when the rate of change of the temperature and pressure parameters is greater than a preset change threshold. When the corresponding hot pressing stage is reached, the first compensation control command, the second compensation control command, or the third compensation control command is sent to the hot pressing equipment.

[0006] By adopting the above technical solution, the process flow is divided into a full-dimensional critical stage, a single-factor master control stage, and a transition stage, realizing a differentiated precision control strategy and changing the traditional mode of uniform high-precision control throughout the entire process. In the full-dimensional critical stage, the predicted temperature and pressure change curves generated by the temperature and pressure parameter prediction model enable the first compensation control command to have foresight, precisely constraining the temperature and pressure feedback parameters within the temperature and pressure adjustment threshold range, effectively solving the lag problem of traditional feedback control. In the single-factor master control stage, by constructing a two-layer control architecture with outer and inner tolerance zones, the second compensation control command can ensure the control accuracy of the main observed attributes while reducing over-control of secondary observed attributes and lowering the frequency of unnecessary control actions. In the transition stage, the system dynamically adjusts the sampling frequency of temperature and pressure feedback data according to the rate of change of temperature and pressure parameters. When the rate of change exceeds a preset change threshold, a third compensation control command is generated, achieving a dynamic balance between control accuracy and system efficiency. By organically combining predictive control, hierarchical control, and adaptive control, this scheme maintains the necessary high-precision control to ensure product quality in critical stages, and adopts an efficiency-first strategy to shorten process time in transition stages, thereby improving firing efficiency while ensuring the firing precision of advanced ceramic materials.

[0007] Secondly, this application provides an automatic hot-pressing control system, the system comprising: The stage division module is used to acquire the process flow data of the material to be sintered, and divide the process flow of the material to be sintered into multiple hot pressing stages based on the process flow data. The hot pressing stages include all-dimensional key stages, single-factor main control stages, or transition stages. The monitoring module is used to monitor the temperature and pressure feedback data and the rate of change of temperature and pressure parameters of the material to be sintered during hot pressing in real time. The first control module is used to input the preset first process parameter curve of the all-dimensional critical stage, the temperature and pressure feedback data, and the temperature and pressure parameter change rate into the temperature and pressure parameter prediction model when the hot pressing treatment stage is in the all-dimensional critical stage, to obtain the predicted temperature and pressure change curve; and to generate a first compensation control command based on the predicted temperature and pressure change curve and the preset process parameter curve, so that the actual change trajectory of the temperature and pressure feedback parameter is constrained within the range limited by the temperature and pressure adjustment threshold of the first process parameter curve. The second control module is used to determine the primary and secondary observation attributes under the single-factor master control stage when the hot pressing stage is in the single-factor master control stage; generate the outer tolerance zone corresponding to the primary observation attribute according to the preset second process parameter curve of the single-factor master control stage; map the influence value of the secondary observation attribute on the primary observation data to the second process parameter curve to obtain the inner tolerance zone; generate a second compensation control command according to the tolerance zone where the primary feedback data corresponding to the primary observation attribute is located in the temperature and pressure feedback data; the threshold range of the outer tolerance zone is greater than the threshold range of the inner tolerance zone; The third control module is used to reduce the sampling frequency of the temperature and pressure feedback data according to the rate of change of the temperature and pressure parameters when the hot pressing process is in the transition stage, and to generate a third compensation control command when the rate of change of the temperature and pressure parameters is greater than a preset change threshold. The execution module is used to send the first compensation control command, the second compensation control command, or the third compensation control command to the hot pressing equipment when the corresponding hot pressing stage is reached.

[0008] Thirdly, this application provides a computer storage medium storing a plurality of instructions adapted for loading by a processor and executing any of the methods described above.

[0009] Fourthly, this application provides an electronic device including a processor, a memory, and a transceiver, wherein the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform any of the methods described above.

[0010] In summary, the beneficial effects of the technical solution of this application include: By adopting the above technical solution, the process flow is divided into a full-dimensional critical stage, a single-factor master control stage, and a transition stage, realizing a differentiated precision control strategy and changing the traditional mode of uniform high-precision control throughout the entire process. In the full-dimensional critical stage, the predicted temperature and pressure change curves generated by the temperature and pressure parameter prediction model enable the first compensation control command to have foresight, precisely constraining the temperature and pressure feedback parameters within the temperature and pressure adjustment threshold range, effectively solving the lag problem of traditional feedback control. In the single-factor master control stage, by constructing a two-layer control architecture with outer and inner tolerance zones, the second compensation control command can ensure the control accuracy of the main observed attributes while reducing over-control of secondary observed attributes and lowering the frequency of unnecessary control actions. In the transition stage, the system dynamically adjusts the sampling frequency of temperature and pressure feedback data according to the rate of change of temperature and pressure parameters. When the rate of change exceeds a preset change threshold, a third compensation control command is generated, achieving a dynamic balance between control accuracy and system efficiency. By organically combining predictive control, hierarchical control, and adaptive control, this scheme maintains the necessary high-precision control to ensure product quality in critical stages, and adopts an efficiency-first strategy to shorten process time in transition stages, thereby improving firing efficiency while ensuring the firing precision of advanced ceramic materials. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating an automatic hot-press control method according to an embodiment of this application; Figure 2 This is a schematic diagram of the structure of an automatic hot-pressing control system according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0012] Explanation of reference numerals in the attached drawings: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Implementation

[0013] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0014] In the description of the embodiments of this application, words such as "illustrative," "for example," or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "illustrative," "for example," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Rather, the use of words such as "illustrative," "for example," or "for example" is intended to present the relevant concepts in a specific manner.

[0015] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0016] First, let me briefly introduce the application scenarios of this application. Advanced ceramic materials, especially non-oxide ceramics such as aluminum nitride (AlN) and silicon carbide (SiC), have become core strategic materials supporting the development of cutting-edge technology industries due to their high thermal conductivity, high electrical insulation, excellent mechanical strength, resistance to plasma etching and chemical stability. They play an indispensable role in fields such as semiconductor manufacturing, aerospace and precision optics.

[0017] Taking the semiconductor industry, currently at the forefront of technology, as an example, these advanced ceramics are widely used in the manufacture of key components in core equipment such as chemical vapor deposition (CVD) equipment, plasma etching machines, and lithography machines. For instance, aluminum nitride ceramic heaters (AlN heaters) are responsible for providing a uniform and precise heating environment for wafers, and their temperature uniformity directly determines the quality of thin film deposition; while silicon carbide ceramic chucks (SiC chucks) need to maintain extremely high surface flatness and dimensional stability in high-temperature and plasma environments to ensure that wafers are firmly and contaminated-free during processing.

[0018] These stringent end-application requirements place near-limit demands on the final performance of advanced ceramic components, including but not limited to: ultra-high density exceeding 99% of theoretical values, near-zero internal porosity, submicron-level dimensional tolerances and surface smoothness, and highly consistent performance across batches. Any minute dimensional deviation, internal porosity, or performance inhomogeneity can lead to a decrease in wafer processing yield or even complete wafer scrap, resulting in significant economic losses. Achieving these performance indicators is highly dependent on the core process in its fabrication—hot pressing sintering. During hot pressing, the synergistic effect and evolution path of temperature and pressure play a decisive role in the material's microstructure (such as grain size and pore distribution) and macroscopic properties.

[0019] Therefore, in order to ensure the successful production of high-quality, highly reliable advanced ceramic products, existing technologies tend to adopt an extremely conservative control strategy when performing hot pressing processes.

[0020] Hot pressing is a material thermal processing technology that uses heating, heat preservation and pressure to obtain the desired properties of materials in a solid state. In order to ensure that the product quality meets the standards, the existing control system usually adopts a strategy of maintaining high-precision temperature and pressure control throughout the process. That is, the most stringent control precision requirements and the highest control response frequency are maintained throughout the sintering process, which results in low sintering efficiency.

[0021] Please see Figure 1 This is a flowchart illustrating an automatic hot-press control method provided in an embodiment of this application. This method can be implemented using a computer program, a microcontroller, or run on a hot-press automatic control system based on the von Neumann architecture. The computer program can be integrated into the application or run as a standalone utility application. The specific steps of the automatic hot-press control method are described in detail below.

[0022] S101: Obtain the process flow data of the material to be sintered, and divide the process flow of the material to be sintered into multiple hot pressing stages based on the process flow data. The hot pressing stages include all-dimensional key stages, single-factor main control stages, or transition stages. The material to be sintered refers to powdered or granular raw materials that require hot pressing for shaping and densification, such as aluminum nitride and silicon carbide. Process flow data represents historical and real-time data describing the changes in various parameters of the material throughout the hot pressing process, including temperature curves, pressure curves, time points, and material performance parameters. The hot pressing stage refers to several time periods with different control strategies, divided according to the different characteristics of the material's sensitivity to temperature and pressure. The all-dimensional critical stage represents the process period where the material is highly sensitive to both temperature and pressure. The single-factor master control stage represents the process period where the material is highly sensitive to only one parameter (temperature or pressure) while being relatively insensitive to the other. The transition stage refers to a relatively stable process period where the material is insensitive to or has low sensitivity to both temperature and pressure.

[0023] Specifically, the system first acquires complete process flow data of the material to be sintered through a data acquisition module. This data includes the material's physicochemical properties, historical production records, and standard process parameters. Then, it performs in-depth analysis on this data, calculating the material's sensitivity coefficients to temperature and pressure at different time points, and establishing a mathematical model between sensitivity and process parameters through correlation analysis. Next, based on preset sensitivity thresholds, the continuous process flow timeline is divided into discrete stages with different characteristics.

[0024] In some embodiments, intelligent stage division of the process flow can be achieved in several ways: Optionally, firstly, historical production data and standard process curves of materials are collected to establish a database containing multi-dimensional parameters such as temperature, pressure, time, and material density changes; then, machine learning algorithms are used to perform correlation analysis on these data to calculate the temperature sensitivity and pressure sensitivity values ​​at each time point; finally, sensitivity thresholds are set, and the process flow is automatically divided into three different processing stages based on the combination of high and low sensitivity. Optionally, through a combination of expert experience and theoretical analysis, process experts are first invited to manually mark key control points and sensitive periods based on material properties and process principles; then, a physical model of the hot pressing process of the material is established using numerical simulation software, and the influence weight of each parameter is calculated through sensitivity analysis; finally, stage division criteria are formulated based on expert knowledge and simulation results and applied to the actual process flow. It is understood that other methods based on statistical analysis, artificial intelligence, or hybrid modeling can also be used to achieve the scientific division of process stages, which is not limited here.

[0025] S102: Real-time monitoring of temperature and pressure feedback data and temperature and pressure parameter change rate of the material to be sintered during hot pressing process; Among them, temperature and pressure feedback data represent real-time numerical information reflecting the actual temperature and pressure environment status within the current processing environment, obtained through a distributed sensor network. Pressure data includes multi-dimensional information such as positive pressure, negative pressure (vacuum), and pressure change rate. Temperature and pressure parameter change rate is used to represent the magnitude and direction of change of temperature and pressure environmental parameters per unit time, including the rate of temperature rise and fall, the rate of pressure increase and decrease, and the rate of change of vacuum, which is usually quantified in degrees Celsius per minute, kilopascals per minute, or vacuum change per minute.

[0026] Specifically, the system continuously collects real-time temperature and pressure values ​​at preset sampling frequencies using high-precision temperature and pressure sensors deployed at key locations within the hot-pressing equipment cavity. The temperature sensors accurately measure the actual temperature within the cavity, while the pressure sensors precisely detect the actual pressure values, including positive and negative pressure (vacuum). Simultaneously, the system uses a numerical differential algorithm to calculate the first derivatives (rates of change) of these parameters in real time, obtaining the temperature and pressure change rates through time-difference calculations on continuous sampling points. The monitoring system also integrates data filtering and anomaly detection functions, eliminating sensor noise interference, identifying data anomalies and equipment malfunctions, and ensuring accurate and reliable temperature and pressure feedback information for subsequent control decisions.

[0027] In some embodiments, high-precision real-time monitoring of temperature and pressure of hot pressing equipment can be achieved in a variety of ways: Optionally, a multi-point distributed sensor monitoring network can be deployed. First, multiple high-temperature and high-pressure resistant temperature and pressure sensors are installed at different spatial locations in the cavity of the hot pressing equipment to monitor the spatial distribution of the temperature and pressure fields. Then, a sensor data fusion algorithm is established to perform weighted averaging and consistency verification on the multi-point measurement data to improve measurement accuracy and reliability.

[0028] Optionally, a non-contact monitoring scheme based on advanced sensing technology can be adopted. First, infrared thermometry is used to achieve non-contact measurement of the material surface temperature, avoiding the influence of contact sensors on high-temperature environments. Then, capacitive or inductive pressure sensors are used to achieve high-precision measurement of cavity pressure. S103: When the hot pressing process is in the critical stage of all dimensions, the preset first process parameter curve of the critical stage, the temperature and pressure feedback data, and the temperature and pressure parameter change rate are input into the temperature and pressure parameter prediction model to obtain the predicted temperature and pressure change curve; the first compensation control command is generated according to the predicted temperature and pressure change curve and the preset process parameter curve so that the actual change trajectory of the temperature and pressure feedback parameters is constrained within the range of the temperature and pressure adjustment threshold limit of the first process parameter curve. Among them, the preset first process parameter curve refers to the combination of ideal temperature curve and ideal pressure curve formulated for the full-dimensional critical stage, representing the standard trajectory of temperature and pressure changes over time; the temperature and pressure parameter prediction model is used to represent the algorithm model that mathematically predicts the coordinated changes of temperature and pressure over a future period of time based on the current temperature and pressure status and historical change trends; the predicted temperature and pressure change curve represents the expected change trajectory of temperature and pressure within the future time window output by the model; the first compensation control command refers to the high-precision equipment control command generated in the full-dimensional critical stage to simultaneously correct the temperature and pressure prediction deviations; the temperature and pressure adjustment threshold represents the minimum tolerance range that the actual temperature and pressure parameters are allowed to deviate from the ideal curve in the critical stage.

[0029] Specifically, the system first acquires the first process parameter curve corresponding to the current key stage as a dual-objective control benchmark. This curve includes the temperature target trajectory and the pressure target trajectory. Then, the system inputs the real-time acquired temperature and pressure feedback data, the calculated temperature change rate and pressure change rate, and the standard process curve into a pre-trained temperature and pressure parameter prediction model. This prediction model, based on deep learning or multivariate time series analysis algorithms, can consider the coupling relationship between temperature and pressure and their respective dynamic characteristics, predicting the coordinated temperature and pressure change trajectories in the short term. The system compares and analyzes the predicted temperature and pressure curves with the standard process curve, calculating the temperature prediction deviation and the pressure prediction deviation. Then, using a multi-input multi-output feedforward control algorithm, considering the coupling effect of temperature and pressure, it calculates the heating power adjustment and pressure adjustment required to simultaneously offset both types of deviations, ultimately generating a coordinated first compensation control command.

[0030] In some embodiments, high-precision collaborative predictive control for hot-pressing equipment can be achieved in several ways: Optionally, a temperature-pressure coupling prediction model based on a deep neural network is constructed. First, a large amount of historical temperature and pressure data of the hot-pressing equipment under different process conditions is collected for model training to learn the nonlinear coupling relationship between temperature and pressure. Then, a multi-step prediction algorithm is designed to simultaneously predict the temperature and pressure change trends of multiple future time steps. Finally, a model predictive control strategy is adopted to obtain the optimal compensation scheme that balances temperature control accuracy and pressure control accuracy by solving a multi-constraint optimization problem, and the optimization results are converted into temperature control commands and pressure control system commands for the hot-pressing equipment.

[0031] S104: When the hot pressing stage is in the single-factor master control stage, determine the main and secondary observation attributes under the single-factor master control stage; generate the outer tolerance zone corresponding to the main observation attribute based on the preset second process parameter curve of the single-factor master control stage; and map the influence value of the secondary observation attribute on the main observation data to the second process parameter curve to obtain the inner tolerance zone; generate the second compensation control command based on the tolerance zone where the main feedback data corresponding to the main observation attribute in the temperature and pressure feedback data is located; the threshold range of the outer tolerance zone is greater than the threshold range of the inner tolerance zone; Among them, the primary observed attribute represents the control parameter that has the most significant impact on material properties and process results at the current stage, which may be temperature or pressure; the secondary observed attribute represents the control parameter that has a relatively small impact on material properties at the current stage but still has a certain disturbance effect; the preset second process parameter curve represents the standard process trajectory based on the primary observed attribute for the single-factor master control stage; the outer tolerance zone refers to the relatively loose control boundary based on the primary observed attribute, representing the maximum deviation range that the system can tolerate under single-factor master control conditions; the inner tolerance zone represents the more stringent ideal control area formed after considering the disturbance effect of the secondary observed attribute; and the second compensation control command represents the control command generated in the single-factor master control stage based on the hierarchical response strategy.

[0032] Specifically, the system first automatically identifies and determines the primary and secondary control variables based on pre-established sensitivity analysis results and the characteristics of the current process stage. For example, in the preheating stage, temperature is the primary control factor while pressure is the secondary factor; in the isothermal pressurization stage, pressure is the primary control factor while temperature is the secondary factor. Then, the system sets an outer tolerance zone for the primary observed attribute based on the second process parameter curve. This is a relatively lenient control boundary that considers process safety and product quality requirements. Simultaneously, the system calculates the specific influence coefficients of the secondary observed attributes on the primary attribute through mathematical modeling and historical data analysis. For example, it calculates the degree of influence of pressure fluctuations on temperature control accuracy, or the interference effect of temperature changes on pressure stability. By mapping these quantified influence values ​​to the standard process curve, the system constructs a more compact and precise inner tolerance zone within the outer tolerance zone. During actual control, the system implements hierarchical control based on the location of the primary feedback data in different tolerance zone regions.

[0033] S105: When the hot pressing process is in the transition stage, the sampling frequency of the temperature and pressure feedback data is reduced according to the rate of change of temperature and pressure parameters, and a third compensation control command is generated when the rate of change of temperature and pressure parameters is greater than the preset change threshold. Among them, the sampling frequency represents the number of times the temperature and pressure data of the hot pressing equipment are collected per unit time; reducing the sampling frequency refers to the intelligent optimization operation of reducing the frequency of data collection when the system is stable in order to save computing resources, reduce system load and reduce equipment energy consumption; the preset change threshold is used to represent the critical change rate value for judging whether the hot pressing equipment process system has entered a dynamic change state from a stable state. This threshold needs to take into account the change characteristics of temperature and pressure; the third compensation control command represents the fast response control command generated when abnormal changes are detected in the transition stage or when a new process stage is about to be entered.

[0034] Specifically, the system first establishes a comprehensive stability assessment mechanism for temperature and pressure parameters, continuously monitoring the rate of change of temperature and pressure, and calculating the comprehensive rate of change index using mathematical methods such as Euclidean distance or weighted average. When the detected comprehensive rate of change is lower than a preset stability judgment threshold, the system considers the hot-pressing equipment to be in a stable operating state, at which point an intelligent frequency reduction sampling strategy is activated. The specific magnitude of the frequency reduction is dynamically determined based on the magnitude of the comprehensive rate of change and the duration of system stability; the smaller the rate of change and the longer the stability time, the lower the sampling frequency, but the system always maintains a sampling frequency no lower than the minimum required for safety monitoring. While performing frequency reduction sampling, the system maintains continuous monitoring of the comprehensive rate of change, using a sliding window statistical method to evaluate system stability in real time.

[0035] S106: When the corresponding hot pressing stage is reached, the first compensation control command, the second compensation control command, or the third compensation control command will be sent to the hot pressing equipment.

[0036] Specifically, the system first parses and converts the generated control commands, transforming the abstract temperature and pressure control quantities into concrete command formats that the heating and pressure control systems of the hot press equipment can understand and execute. Then, it sends the temperature and pressure control commands to the corresponding control modules of the hot press equipment via a pre-established industrial communication network. The communication process includes command packaging, transmission, reception confirmation, and execution status feedback. Upon receiving the commands, the hot press equipment control system coordinates and adjusts the corresponding actuators according to the command content, such as synchronously controlling the heater power and pressure regulating valve opening, and coordinating the actions of the temperature and pressure controllers.

[0037] Based on the above embodiments, as an optional implementation method, the method of obtaining the process flow data of the material to be sintered in step S101 and dividing the process flow of the material to be sintered into multiple hot pressing treatment stages according to the process flow data can be specifically implemented through the following steps S201-S204.

[0038] S201: Obtain the process flow data of the material to be sintered, which includes the production process parameters and material performance data of the material to be sintered. Among them, process flow data refers to the complete set of data describing each stage of the entire processing of materials from their original state to the final product; production process parameters represent the process variables that are directly controlled and monitored during hot pressing, including operating parameters such as temperature setpoint, pressure setpoint, heating rate, pressurization rate, holding time, and holding time; material performance data refers to the physicochemical properties of materials under different process conditions, including characteristic parameters such as density change, strength change, hardness change, microstructure evolution, and chemical composition change.

[0039] Specifically, production process parameters are obtained by accessing the historical operating records of the hot pressing equipment, extracting control parameter information such as temperature curves, pressure curves, and time points for different batches of products. Material performance data is acquired by conducting physical tests, chemical analyses, and metallographic examinations on samples produced under different process conditions to establish the correspondence between material properties and process parameters. The data acquisition process also includes cleaning and preprocessing the raw data, removing outliers and noisy data, supplementing missing data, and standardizing data formats and units. To ensure data completeness and representativeness, data samples need to cover different material batches, different process conditions, and different equipment states.

[0040] S202: Conduct correlation analysis on production process parameters and material performance parameters to obtain temperature sensitivity and pressure sensitivity at different time points; Correlation analysis refers to the analytical process of quantifying the mutual influence between production process parameters and material performance data through statistical and mathematical methods; temperature sensitivity indicates the degree to which material performance responds to temperature changes, and the larger the value, the more significant the impact of temperature changes on material performance; pressure sensitivity indicates the degree to which material performance responds to pressure changes, and the larger the value, the more significant the impact of pressure changes on material performance.

[0041] This step employs mathematical modeling to analyze the correlation between production process parameters and material performance data. The analysis first establishes a multiple regression model with temperature and pressure as independent variables and material performance indicators as dependent variables, solving for the regression coefficients using the least squares method. During the calculation, partial derivatives are calculated for temperature and pressure data at each time point to obtain the sensitivity values ​​of material performance to temperature and pressure. Temperature sensitivity is calculated by fixing the pressure parameter and calculating the partial derivative of the material performance indicator with respect to temperature, reflecting the impact of temperature changes alone on material performance. Similarly, pressure sensitivity is calculated by fixing the temperature parameter and calculating the partial derivative of the material performance indicator with respect to pressure, reflecting the impact of pressure changes alone on material performance. The correlation analysis also includes correlation testing, assessing the linear correlation between variables by calculating the Pearson correlation coefficient and testing the significance of the regression model using analysis of variance. To improve analytical accuracy, a sliding time window method is used to locally model the data within each time period, obtaining time-varying sensitivity parameters. The analysis results are output in time series format, forming temperature sensitivity values ​​and pressure sensitivity values ​​for different time points.

[0042] S203: Fit the temperature sensitivity and pressure sensitivity at different time points into temperature sensitivity curves and pressure sensitivity curves, respectively; Among them, the temperature sensitivity curve represents a mathematical function expression of the continuous change of temperature sensitivity over time; the pressure sensitivity curve represents a mathematical function expression of the continuous change of pressure sensitivity over time.

[0043] This step transforms discrete sensitivity data points into continuous sensitivity curves using a curve fitting algorithm. The fitting process first selects a suitable function model; commonly used models include polynomial, exponential, logarithmic, and trigonometric functions. Model selection involves comparing the goodness of fit of different function forms and choosing the function with the smallest mean square error as the optimal model. For the temperature sensitivity curve, the fitting process uses time as the x-axis and temperature sensitivity as the y-axis, determining the function parameters using the least squares method to minimize the sum of squared deviations between the fitted curve and the data points. The pressure sensitivity curve is similarly fitted, using the same method to determine the function parameters. The fitting algorithm also includes outlier detection and removal, identifying and removing outliers with excessive deviations by calculating the residuals between the data points and the fitted curve. To improve fitting accuracy, a piecewise fitting method is used, dividing the entire time series into several segments, fitting each segment separately, and then using a smoothing technique to ensure the continuity of the overall curve.

[0044] S204: Based on the temperature sensitivity curve and the pressure sensitivity curve, the process flow of the material to be sintered is divided into multiple hot pressing treatment stages.

[0045] The segmentation process first sets sensitivity thresholds, including high and low sensitivity thresholds, as the basis for stage division. The stage identification algorithm traverses the entire time series, comparing the temperature and pressure sensitivity values ​​at each time point with the preset thresholds. When both temperature and pressure sensitivity exceed the high sensitivity threshold, the time period is classified as a full-dimensional critical stage. When only one of the temperature or pressure sensitivity exceeds the high sensitivity threshold, the time period is classified as a single-factor control stage. When both temperature and pressure sensitivity are below the low sensitivity threshold, the time period is classified as a transition stage. The segmentation algorithm also includes stage boundary optimization, identifying abrupt changes and inflection points in sensitivity by analyzing the derivatives and second derivatives of the sensitivity curves, and using these feature points as candidate locations for stage boundaries. The boundary determination process considers the minimum stage duration constraint to avoid excessively short process stages. A stage merging mechanism is used to handle adjacent stages of the same type; when two stages of the same type are separated only by a very short stage of another type, they are merged into a single continuous stage. The segmentation results are output as a stage sequence containing information such as stage type, start time, end time, and main characteristic parameters, providing a basic framework for subsequent differentiated control strategies.

[0046] Specifically, regarding the division of the hot pressing process, step S204 further includes S2041-S2045.

[0047] S2041: Based on the preset temperature sensitivity threshold, the temperature sensitivity curve is divided into a high-precision temperature segment and a low-precision temperature segment. Among them, the preset temperature sensitivity threshold refers to the critical value used to judge the sensitivity of a material to temperature changes. This threshold is determined in advance based on the material characteristics and process requirements, and serves as a boundary standard to distinguish between high-sensitivity and low-sensitivity regions. The high-precision temperature range represents the time period in which the temperature sensitivity value exceeds the preset threshold. In these ranges, the material exhibits significant response characteristics to temperature changes, requiring precise temperature control. The low-precision temperature range represents the time period in which the temperature sensitivity value is lower than the preset threshold. In these ranges, the material's response to temperature changes is relatively insensitive, and the temperature control precision requirement is relatively low.

[0048] Specifically, the segmentation process first retrieves a preset temperature sensitivity threshold from the process database. This threshold is determined comprehensively based on factors such as material type, product quality requirements, and equipment precision. The algorithm iterates through each data point on the temperature sensitivity curve, comparing the sensitivity value with the preset threshold. When the sensitivity value is greater than or equal to the preset threshold, the time point is marked as a high-precision temperature point; when the sensitivity value is less than the preset threshold, the time point is marked as a low-precision temperature point. The segment identification process merges adjacent time points of the same type into continuous segments through continuity detection. The algorithm also includes segment boundary smoothing processing, which determines the precise segment boundary position by analyzing the changing trend of the sensitivity curve near the threshold, avoiding frequent state switching. To prevent the generation of excessively short segments, a minimum segment duration constraint is set. When a segment with an excessively short duration is detected, it is merged into an adjacent main segment. The segmentation results are recorded in time series form, including information such as the start time, end time, and segment type of each segment, providing basic data for subsequent process stage combinations.

[0049] S2042: Based on the preset pressure sensitivity threshold, the pressure sensitivity curve is divided into a high-precision pressure segment and a low-precision pressure segment. Among them, the preset pressure sensitivity threshold refers to the critical value used to judge the sensitivity of a material to pressure changes. This threshold is preset according to the mechanical properties of the material and the process pressure control requirements. The high-precision pressure range represents the time period in which the pressure sensitivity value exceeds the preset threshold. In these ranges, the material shows a strong response to pressure changes and requires strict pressure control. The low-precision pressure range represents the time period in which the pressure sensitivity value is lower than the preset threshold. In these ranges, the material is less sensitive to pressure changes and the pressure control precision requirements are relatively relaxed.

[0050] Specifically, the processing reads a preset pressure sensitivity threshold from the system configuration, which is determined through historical process data analysis and expert experience. The algorithm iterates through all data points of the pressure sensitivity curve in chronological order, performing threshold comparison operations. When the pressure sensitivity value reaches or exceeds the preset threshold, the time point is classified as a high-precision pressure point; when the pressure sensitivity value is below the preset threshold, the time point is classified as a low-precision pressure point. The segment formation process combines consecutive time points with the same classification label into complete segments through state continuity analysis. The algorithm includes a segment boundary optimization function, which accurately locates the time point of segment transition by analyzing the gradient change of sensitivity near the threshold. The processing also includes a noise filtering mechanism to eliminate transient state switching caused by data fluctuations, and ensures the stability and usability of segments by setting a minimum segment width parameter. The final output contains detailed information on all high-precision and low-precision pressure segments, including segment identification, time range, sensitivity characteristics, and other attributes, preparing for the combined analysis in the process stage.

[0051] S2043: The process flow stage in which both high-precision temperature and high-precision pressure sections occur simultaneously is divided into a full-dimensional critical stage. Specifically, firstly, a segment mapping table is established on the time axis, arranging and marking high-precision temperature and pressure segments in chronological order. The intersection calculation process traverses all high-precision temperature segments, calculating the time overlap between each temperature segment and all high-precision pressure segments. Time overlap is determined by comparing the start and end times of the segments; if the start time of a temperature segment is less than the end time of a pressure segment, and the end time of a temperature segment is greater than the start time of a pressure segment, time overlap is confirmed. The overlap time period is calculated by taking the larger of the start times of the two segments as the intersection start time and the smaller of the end times as the intersection end time. The algorithm also includes minimum duration verification, filtering out intersection segments with excessively short durations to ensure sufficient time length for effective control of the full-dimensional key stage. The merging of adjacent intersection segments is achieved through interval analysis; when the time interval between two adjacent intersection segments is less than a preset threshold, they are merged into a single continuous full-dimensional key stage.

[0052] S2044: Process flow segments in which high-precision temperature and low-precision pressure ranges occur simultaneously, or process flow segments in which low-precision temperature and high-precision pressure ranges occur simultaneously, are classified as single-factor master control stages. This step identifies single-factor control stages through two independent intersection operations. The first intersection operation handles the time overlap between high-precision temperature segments and low-precision pressure segments, while the second intersection operation handles the time overlap between low-precision temperature segments and high-precision pressure segments. The algorithm establishes two parallel computational flows to handle temperature-controlled and pressure-controlled single-factor stages respectively. The identification process for temperature-controlled stages traverses all high-precision temperature segments, calculating the time intersection of each temperature segment with all low-precision pressure segments to obtain temperature-sensitive but pressure-insensitive process periods. The identification process for pressure-controlled stages traverses all high-precision pressure segments, calculating the time intersection of each pressure segment with all low-precision temperature segments to obtain pressure-sensitive but temperature-insensitive process periods. The two types of intersection results are merged by time sorting to form a complete sequence of single-factor control stages. The algorithm includes a control attribute labeling function, marking the main control parameter type for each single-factor control stage; temperature-controlled stages are labeled as temperature-priority control, and pressure-controlled stages are labeled as pressure-priority control. The overlapping conflict handling mechanism is used to resolve situations where two single-factor conditions are met simultaneously within the same time period. By comparing the numerical values ​​of temperature sensitivity and pressure sensitivity, the parameter with higher sensitivity is selected as the controlling factor.

[0053] S2045: The process flow stage in which both low-precision temperature and low-precision pressure sections occur simultaneously is classified as a transition stage.

[0054] Specifically, a similar intersection operation method as described above is used, but the processing objects are two types of low-precision segments. The calculation process traverses all low-precision temperature segments, searching for low-precision pressure segments that overlap with each temperature segment in time. The criteria for judging time overlap are consistent with the previous steps, determining the existence of intersection by comparing the time boundaries of the segments. The intersection time range is determined by taking the intersection of the time ranges of the two segments. The algorithm includes an integrity verification function to ensure that all process times are allocated to a specific stage, avoiding unclassified time periods. The merging optimization of transition stages is achieved through adjacent segment analysis. When adjacent transition stages are separated only by a very short other type of stage, it is evaluated whether the short intermediate stage should also be merged into a transition stage.

[0055] Based on the above embodiments, as an optional implementation method, the method of mapping the influence value of the secondary observation attribute on the primary observation attribute to the second process parameter curve in S104 to obtain the inner tolerance zone can be specifically implemented through the following steps S301-S303.

[0056] S301: Determine the level of influence of secondary observed attributes on primary observed attributes based on temperature and pressure feedback data; The impact level refers to the severity of the interference caused by secondary observed attributes to primary observed attributes, typically categorized into high, medium, and low impact levels. It quantifies the intensity of the impact of secondary parameter changes on the control accuracy of primary parameters. The determination of the impact level is based on historical data analysis and real-time data assessment, reflecting the actual degree of impact of secondary observed attribute changes on the stability of primary observed attributes under specific process conditions.

[0057] Specifically, the analysis process first extracts time-series data of secondary and primary observed attributes from the temperature and pressure feedback data, establishing a correspondence between the two parameters. The impact level is determined using correlation analysis to calculate the statistical correlation between changes in secondary observed attributes and deviations in primary observed attributes. The calculation process includes data preprocessing, filtering and normalizing the raw feedback data to eliminate noise interference and dimensional differences. Correlation calculation is performed using a sliding time window method, calculating the correlation coefficient between changes in secondary attributes and deviations in primary attributes within each time window. The impact level is determined based on the numerical range of the correlation coefficient, with different threshold intervals corresponding to different impact levels. A high impact level corresponds to a correlation coefficient absolute value greater than the high threshold, indicating a significant impact of secondary attribute changes on primary attributes. A medium impact level corresponds to a correlation coefficient absolute value within a medium range, indicating a moderate impact of secondary attribute changes on primary attributes. A low impact level corresponds to a correlation coefficient absolute value less than the low threshold, indicating a relatively small impact of secondary attribute changes on primary attributes.

[0058] S302: Calculate the impact value of the secondary observation attribute on the primary observation attribute based on the preset impact weight coefficient of the impact level; The influence weighting coefficient is a pre-set numerical coefficient based on the influence level, used to quantify the influence intensity corresponding to different influence levels. Higher influence levels correspond to larger weighting coefficients, while lower influence levels correspond to smaller weighting coefficients. The influence value represents the numerical result of the degree of influence of the secondary observed attribute on the primary observed attribute, obtained through mathematical calculation. This value directly reflects the actual amount of interference caused by changes in the secondary attribute on the control of the primary attribute.

[0059] The calculation process first retrieves preset weighting coefficients corresponding to different impact levels from the configuration database. These coefficients are determined based on material properties, process requirements, and historical experience. The weighting coefficients are set according to an increasing principle, with higher impact levels having significantly larger weighting coefficients than medium and low impact levels. The impact value is calculated using a weighted product method, multiplying the actual change of the secondary observed attribute by the corresponding weighting coefficient for each impact level. The calculation process includes change extraction, calculating the deviation of the secondary observed attribute from the standard value using temperature and pressure feedback data. This deviation can be positive or negative, reflecting the actual direction and magnitude of the secondary attribute's change. The application of weighting coefficients considers the time factor, adjusting the impact weights at different times using a time-weighted method, assigning higher weights to recent impacts and lower weights to long-term impacts. The impact value calculation also includes cumulative effect analysis; when a secondary observed attribute changes continuously over a period of time, its cumulative impact effect is calculated, and the overall impact value is obtained through integration or summation.

[0060] S303: Map the influence values ​​to the second process parameter curve to obtain the inner layer tolerance zone.

[0061] Specifically, the mapping process first establishes a functional relationship between the influence value and the tolerance zone adjustment amount. This relationship is determined based on process control theory and practical experience. The design of the mapping function considers the positive and negative aspects and magnitude of the influence value. A positive influence value causes the tolerance zone to shift in one direction, while a negative influence value causes it to shift in the opposite direction. The absolute value of the influence value determines the magnitude of the shift. The generation process of the inner tolerance zone is based on the second process parameter curve. First, the standard value at each time point on the process parameter curve is extracted. Then, the upper and lower boundaries of the tolerance zone are calculated based on the influence value at the corresponding time. The upper boundary is calculated by adding a positive adjustment amount to the standard value, and the lower boundary is calculated by subtracting a negative adjustment amount from the standard value. The adjustment amount is calculated using a proportional mapping method, multiplying the influence value by a preset mapping coefficient to obtain the specific tolerance adjustment amount.

[0062] Based on the above embodiments, as an optional implementation method, the method of generating the first compensation control command according to the predicted temperature and pressure change curve and the preset process parameter curve in step S103 can be specifically implemented through the following steps S401-S403.

[0063] S401: Within a preset time step, calculate the prediction deviation between the parameter values ​​on the predicted temperature and pressure change curve and the target values ​​on the process parameter curve. Among them, the preset time step refers to the fixed time interval for the control system to perform predictive calculations and control adjustments. It is usually determined according to the process characteristics and control accuracy requirements. The selection of the time step needs to balance the control response speed and the computational load. The predicted temperature and pressure change curve represents the expected change trajectory of temperature and pressure parameters in the future period of time based on the current process status and trend analysis. The prediction deviation is the difference between the predicted parameter value and the target parameter value. This difference reflects the degree and direction of the process parameters deviating from the ideal state at future moments.

[0064] Specifically, the calculation process first extracts the target temperature and target pressure values ​​corresponding to the current time step from the process parameter curves. These target values ​​represent the ideal process control state. Predicted parameter values ​​are obtained by accessing the predicted temperature and pressure change curves, which contain predicted future parameter values ​​based on current process trends and historical data analysis. Deviation calculations employ an interpolation method to calculate temperature and pressure prediction deviations separately. The temperature prediction deviation is obtained by subtracting the target temperature value from the predicted temperature value; a positive value indicates the predicted temperature is higher than the target temperature, and a negative value indicates the predicted temperature is lower than the target temperature. The pressure prediction deviation is obtained by subtracting the target pressure value from the predicted pressure value; a positive value indicates the predicted pressure is higher than the target pressure, and a negative value indicates the predicted pressure is lower than the target pressure. The calculation process also includes deviation normalization, converting deviations of different dimensions into a unified relative deviation value, facilitating subsequent comprehensive analysis and control decisions.

[0065] S402: When the prediction deviation is greater than the temperature difference adjustment threshold, the feedforward control algorithm is used to calculate the compensation adjustment amount to offset the prediction deviation. Among them, the temperature difference adjustment threshold refers to the critical deviation value that triggers the feedforward control action. When the predicted deviation exceeds this threshold, compensation control is initiated. The setting of this threshold needs to take into account the process tolerance requirements and the response capability of the control system. The feedforward control algorithm refers to the algorithm that calculates the control adjustment amount in advance based on the predicted deviation information. This algorithm can start the compensation action before the deviation actually occurs, realizing active control. The compensation adjustment amount represents the control adjustment amount that needs to be applied to offset the predicted deviation, including temperature compensation amount and pressure compensation amount.

[0066] Threshold judgments are performed for temperature and pressure deviations respectively. When the absolute value of the predicted temperature deviation exceeds the temperature adjustment threshold, temperature feedforward control is triggered; when the absolute value of the predicted pressure deviation exceeds the pressure adjustment threshold, pressure feedforward control is triggered. The feedforward control algorithm is calculated based on the process model and control gain parameters. The temperature compensation adjustment is calculated by multiplying the predicted temperature deviation by the temperature feedforward gain coefficient, which is determined based on the temperature response characteristics and process requirements of the hot-pressing equipment. The pressure compensation adjustment is calculated by multiplying the predicted pressure deviation by the pressure feedforward gain coefficient, which is determined based on the dynamic characteristics and control accuracy requirements of the pressure system.

[0067] For example, the feedforward control algorithm can be implemented based on the inverse model of a thermo-pressing system. A forward model of a thermo-pressing system describes "what kind of temperature and pressure changes will result from a given amount of heating power / pressure regulation", while its inverse model answers the opposite question: "how much heating power / pressure regulation needs to be applied to achieve the target temperature and pressure change (i.e., to offset the prediction deviation)".

[0068] The temperature feedforward controller calculates the compensation heating power Power_ff required to offset ΔT_pred based on the temperature inverse model established by the system's thermal inertia and other characteristics. This relationship can be expressed as Power_ff = f_inv_T(ΔT_pred), where f_inv_T is the inverse model function of the temperature system.

[0069] The pressure feedforward controller calculates the compensation pressure adjustment Adj_ff (e.g., the change in valve opening) required to offset ΔP_pred based on the pressure inverse model established by the system's pressure response characteristics. This relationship can be expressed as Adj_ff = f_inv_P(ΔP_pred), where f_inv_P is the inverse model function of the pressure system.

[0070] The calculated compensation adjustments, Power_ff and Adj_ff, are converted into executable instructions for the equipment. This inverse model-based feedforward control can proactively compensate for deviations before they actually occur, significantly improving the system's tracking accuracy of the set process curve. This is particularly important for critical stages where temperature and pressure parameters are highly sensitive. Finally, this feedforward control instruction can be superimposed on the traditional PID feedback control instruction to form the final control output: final control quantity = feedforward compensation quantity + feedback adjustment quantity, balancing predictability and robustness to unpredictable disturbances.

[0071] S403: Convert the compensation adjustment amount into a temperature regulation command and / or pressure regulation command for the hot pressing equipment to obtain the first compensation control command.

[0072] Among them, the temperature regulation command refers to the specific operation command sent by the control system to the temperature control module of the hot press equipment, including specific control actions such as adjusting the heating power and adjusting the cooling flow rate; the pressure regulation command refers to the specific operation command sent by the control system to the pressure control module of the hot press equipment, including specific control actions such as increasing or decreasing pressure and maintaining pressure; the first compensation control command represents the complete set of control commands generated based on the feedforward control algorithm, which includes all necessary equipment operation commands and parameter settings.

[0073] Specifically, the conversion process first involves dimension conversion, transforming the compensation adjustment quantities from process parameter units to equipment control units. Temperature compensation adjustment quantities are converted through a temperature-power mapping relationship, transforming the temperature adjustment quantity into a heater power adjustment quantity or a cooling system flow adjustment quantity based on the equipment's temperature control characteristics. Pressure compensation adjustment quantities are converted through a pressure-actuator mapping relationship, transforming the pressure adjustment quantity into a hydraulic cylinder displacement quantity or a pneumatic valve opening adjustment quantity based on the characteristics of the pressure control system. The instruction formatting process organizes control instructions according to the equipment communication protocol requirements, including standard format elements such as instruction headers, parameter fields, and checksums. Temperature adjustment instructions include specific parameters such as target temperature setting, heating power allocation, and temperature slope control, and also contain execution priority and time constraint information. Pressure adjustment instructions include specific parameters such as target pressure setting, pressure change rate, and pressure holding time, ensuring the smoothness and accuracy of the pressure adjustment process. The instruction integration process combines temperature and pressure adjustment instructions according to execution order and timing requirements, forming a coordinated and consistent control instruction sequence.

[0074] Based on the above embodiments, as an optional implementation method, the method of generating the second compensation control command in S104 according to the tolerance zone of the main feedback data corresponding to the main observed attributes in the temperature and pressure feedback data can be specifically implemented through the following steps S501-S503.

[0075] S501: When the main feedback data corresponding to the main observed attribute in the temperature and pressure feedback data is within the inner tolerance zone, a second compensation control command is generated to maintain the current operating state of the temperature and pressure equipment. The primary feedback data refers to the measured parameter values ​​in the temperature and pressure feedback data that directly correspond to the main observed attributes. This data reflects the actual state of key parameters in the process. The second compensation control command refers to the equipment operation command generated based on the feedback control logic. It differs from the first compensation control command based on feedforward control and is mainly used for adjustment based on the actual feedback state. When the primary feedback data is within the inner tolerance zone, it indicates that the process parameters are under ideal control and do not require significant adjustment.

[0076] Specifically, the execution process first performs a position determination, extracting the current measured values ​​corresponding to the main observed attributes from the temperature and pressure feedback data, and comparing these measured values ​​with the upper and lower boundaries of the inner tolerance zone. Position determination uses a numerical comparison method; when the measured value is greater than the lower boundary of the inner tolerance zone but less than the upper boundary, the main feedback data is confirmed to be within the inner tolerance zone. The state assessment process analyzes the stability of the current process parameters, evaluating whether the parameters are in a stable state by calculating the variance and variation amplitude of recent data points. The generation of sustaining control commands is based on the current equipment operating state, maintaining existing key operating parameters such as temperature setpoints, pressure setpoints, heating power, and cooling flow rate unchanged. The command generation process includes reading the current state parameters, obtaining all current operating parameter settings from the equipment control system, including the setpoints of the temperature controller and pressure regulator, and the current positions of each actuator. The parameter verification mechanism ensures that the current operating parameters are still suitable for maintaining the ideal state, verifying the validity of the parameters by comparing them with standard process parameters. The fine-tuning and optimization function makes subtle adjustments based on the specific location of the main feedback data within the inner tolerance zone. When the data deviates from the tolerance zone boundary, preventative fine-tuning is performed to ensure that the parameters always remain in the central area of ​​the tolerance zone.

[0077] S502: When the main feedback data corresponding to the main observed attribute in the temperature and pressure feedback data is located between the inner tolerance zone and the outer tolerance zone, a second compensation control command is generated based on the rate of change and trend of the main feedback data to bring the main feedback data back to the inner tolerance zone. Among them, the rate of change refers to how quickly the main feedback data changes over time, obtained by calculating the amount of change in parameter values ​​per unit time, reflecting the urgency of the parameter deviating from the ideal state; the trend of change refers to the direction and development trend of the main feedback data, including upward trend, downward trend, oscillation trend, etc., used to predict the future direction of parameter change. These two indicators together determine the strength and direction of the regression control strategy.

[0078] Specifically, region determination is achieved through dual boundary comparison, verifying that the data exceeds the inner tolerance zone boundary but does not reach the outer tolerance zone boundary. The rate of change is calculated using a difference method, dividing the difference between the current data value and the previous data value by the time interval to obtain the instantaneous rate of change. Multi-point rate of change analysis is performed using a sliding time window method, calculating the rate of change at multiple consecutive time points to obtain the average rate of change and the magnitude of change. Trend analysis employs a trend detection algorithm, analyzing the overall direction of change in the data sequence through linear or polynomial fitting methods. Trend strength assessment is performed by calculating the slope and correlation coefficient of the fitted straight line; the sign of the slope indicates the trend direction, and the absolute value of the slope indicates the trend strength. The regression control strategy is formulated based on a comprehensive analysis of the rate of change and the trend. When the rate of change is large and the trend points towards the outer tolerance zone, strong regression control is used; when the rate of change is small and the trend is relatively flat, mild regression control is used. The control quantity is calculated using a proportional-integral method, calculating the required adjustment amount based on the current deviation, rate of change, and trend strength.

[0079] S503: When the main feedback data corresponding to the main observed attribute in the temperature and pressure feedback data reaches the outer tolerance zone, a second compensation control command with the highest response priority is generated.

[0080] Among them, the highest response priority refers to the most urgent control level in the control system. Control commands at this level have the highest execution priority, can interrupt other ongoing control actions, and immediately obtain system resources for emergency adjustments. When the main feedback data reaches the outer tolerance zone, it indicates that the process parameters have deviated from the normal range and reached a critical state of quality risk, requiring immediate and forceful corrective measures.

[0081] Specifically, the first step is to determine the critical state, detecting whether the main feedback data has reached or exceeded the boundary value of the outer tolerance zone. The critical state determination employs a strict boundary comparison; when the data value is equal to or exceeds the upper boundary of the outer tolerance zone, or equal to or below the lower boundary, an emergency control response is immediately triggered. The emergency response mechanism includes interrupting the current control action, stopping all currently executing routine control commands, and freeing up system resources for emergency control. The generation of the highest priority control command is based on a preset emergency control strategy, determining the maximum permissible adjustment range according to the direction and degree of deviation. Emergency temperature regulation is achieved through maximum power heating or maximum flow cooling, while emergency pressure regulation is achieved through rapid pressurization or rapid depressurization.

[0082] Based on the above embodiments, as an optional implementation method, the method of reducing the sampling frequency of temperature and pressure feedback data according to the rate of change of temperature and pressure parameters in step S105 can be implemented through the following steps: Determine if the rate of change of temperature and pressure parameters is less than the rate of change judgment threshold; if the rate of change of temperature and pressure parameters is less than the rate of change judgment threshold, then reduce the sampling frequency of temperature and pressure feedback data based on the difference between the rate of change judgment threshold and the temperature and pressure feedback data.

[0083] The rate of change threshold refers to a pre-set critical value for the rate of change. When the actual rate of change is lower than this threshold, it indicates that the process is in a relatively stable state, and the monitoring density can be appropriately reduced. The sampling frequency refers to the reciprocal of the time interval between the acquisition of temperature and pressure feedback data by the data acquisition system. A higher sampling frequency means more frequent data acquisition, while a lower sampling frequency means longer acquisition intervals.

[0084] Specifically, the process begins with rate of change calculation. Continuous temperature and pressure values ​​are extracted from the most recent temperature and pressure feedback data, and the rates of change for temperature and pressure are calculated separately. The temperature rate of change is calculated by subtracting the previous temperature value from the current temperature value and then dividing by the time interval. The pressure rate of change is calculated by subtracting the previous pressure value from the current pressure value and then dividing by the time interval. A weighted average method is used for comprehensive rate of change evaluation, combining the temperature and pressure rates of change according to preset weights to obtain a comprehensive rate of change for temperature and pressure parameters. Threshold comparison is achieved through numerical comparison, comparing the calculated rate of change for temperature and pressure parameters with preset rate of change judgment thresholds. When the rate of change is less than the threshold, a sampling frequency reduction mechanism is triggered. The difference calculation process determines the numerical difference between the rate of change judgment threshold and the actual rate of change; this difference reflects the degree of deviation of the current process state from the stable state baseline. The sampling frequency adjustment algorithm determines the magnitude of the frequency reduction based on the magnitude of the difference; a larger difference indicates a more stable process, and the greater the reduction in sampling frequency. Frequency adjustment adopts a graded frequency reduction method, setting different frequency reduction levels according to the range of the difference. The new sampling frequency is calculated by multiplying the current sampling frequency by a down-rate factor, which is selected from a preset coefficient table based on the difference.

[0085] It should be understood that, in this embodiment, the pressure regulating device includes: an air filling pipe, an air inlet valve, a mass flow meter, a pressure transmitter, a vent valve, a primary pump, a secondary pump, and a vacuum valve. Furthermore, the technical solution provided in this application is applicable to PLC control modules and configuration and temperature control modules. Because the actual process flow involves slow heating rates and long holding times, a 50-step curve is designed, where the press pressure curve is combined with the temperature control curve. This allows for the selection of a constant pressurization rate or a rate correlated with the heating time, with the pressurization rate calculated by the program.

[0086] The following are system embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the system embodiments of this application, please refer to the method embodiments of the application.

[0087] Please see Figure 2This illustration shows a schematic diagram of the structure of an automatic hot-pressing control system provided in an exemplary embodiment of this application. The system can be implemented as all or part of a system through software, hardware, or a combination of both. The automatic hot-pressing control system includes: The stage division module is used to acquire the process flow data of the material to be sintered, and divide the process flow of the material to be sintered into multiple hot pressing stages based on the process flow data. The hot pressing stages include all-dimensional key stages, single-factor main control stages, or transition stages. The monitoring module is used to monitor the temperature and pressure feedback data and the rate of change of temperature and pressure parameters of the material to be sintered during hot pressing in real time. The first control module is used to input the preset first process parameter curve, temperature and pressure feedback data, and temperature and pressure parameter change rate of the full-dimensional critical stage into the temperature and pressure parameter prediction model when the hot pressing process is in the full-dimensional critical stage, so as to obtain the predicted temperature and pressure change curve; and generate a first compensation control command based on the predicted temperature and pressure change curve and the preset process parameter curve, so as to constrain the actual change trajectory of the temperature and pressure feedback parameters within the range limited by the temperature and pressure adjustment threshold of the first process parameter curve. The second control module is used to determine the primary and secondary observed attributes under the single-factor master control stage when the hot pressing process is in the single-factor master control stage; generate the outer tolerance zone corresponding to the primary observed attribute based on the preset second process parameter curve of the single-factor master control stage; map the influence value of the secondary observed attribute on the primary observed data to the second process parameter curve to obtain the inner tolerance zone; generate the second compensation control command based on the tolerance zone where the primary feedback data corresponding to the primary observed attribute in the temperature and pressure feedback data is located; the threshold range of the outer tolerance zone is greater than the threshold range of the inner tolerance zone. The third control module is used to reduce the sampling frequency of temperature and pressure feedback data according to the rate of change of temperature and pressure parameters when the hot pressing process is in the transition stage, and to generate a third compensation control command when the rate of change of temperature and pressure parameters is greater than the preset change threshold. The execution module is used to send a first compensation control command, a second compensation control command, or a third compensation control command to the hot pressing equipment when the corresponding hot pressing stage is reached.

[0088] This application also provides a computer storage medium that can store multiple instructions. The instructions are adapted to be loaded by a processor and executed as described in the above embodiments of the hot pressing automatic control method. For the specific execution process, please refer to the detailed description of the embodiments, which will not be repeated here.

[0089] Please see Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3As shown, the electronic device 300 may include: at least one processor 301, at least one network interface 304, user interface 303, memory 305, and at least one communication bus 302.

[0090] The communication bus 302 is used to enable communication between these components.

[0091] The user interface 303 may include a display screen and a camera.

[0092] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0093] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of digital signal processing, field-programmable gate array, or programmable logic array. The processor 301 may integrate one or more of the following: a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0094] The memory 305 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 305 may include a non-transitory computer-readable medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. Figure 3 As shown, the memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for an automatic hot-pressing control method.

[0095] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 301 can be used to call an application program of a hot-press automatic control method stored in the memory 305. When executed by one or more processors, the electronic device executes one or more methods as described in the above embodiments.

[0096] An electronic device readable storage medium stores instructions that, when executed by one or more processors, cause the electronic device to perform one or more methods as described in the above embodiments.

[0097] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0098] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0100] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0101] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0102] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0103] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and practical application disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure.

Claims

1. A method for automatically controlling hot pressing, characterized by, The method comprises: acquiring process flow data of a material to be sintered, and dividing a process flow of the material to be sintered into multiple hot-pressing processing stages according to the process flow data, wherein the hot-pressing stages include a full-dimension key stage, a single-factor main control stage or a transition stage; real-time monitoring of temperature and pressure feedback data of the material to be sintered in the hot-pressing processing and a change rate of temperature and pressure parameters; when the hot-pressing processing stage is in the full-dimension key stage, inputting preset first process parameter curves of the full-dimension key stage, the temperature and pressure feedback data and the change rate of the temperature and pressure parameters into a temperature and pressure parameter prediction model to obtain a predicted temperature and pressure change curve; and generating a first compensation control instruction according to the predicted temperature and pressure change curve and preset process parameter curves, so that an actual change track of the temperature and pressure feedback parameters is constrained within a range defined by a temperature and pressure adjustment threshold of the first process parameter curves; when the hot-pressing processing stage is in the single-factor main control stage, determining a main observation attribute and a secondary observation attribute in the single-factor main control stage; generating an outer tolerance band corresponding to the main observation attribute according to preset second process parameter curves of the single-factor main control stage; and mapping an influence value of the secondary observation attribute on the main observation data to the second process parameter curves to obtain an inner tolerance band; generating a second compensation control instruction according to a tolerance band in which main feedback data corresponding to the main observation attribute in the temperature and pressure feedback data is located; and a threshold range of the outer tolerance band is greater than a threshold range of the inner tolerance band; when the hot-pressing processing stage is in the transition stage, reducing a sampling frequency of the temperature and pressure feedback data according to the change rate of the temperature and pressure parameters, and generating a third compensation control instruction when the change rate of the temperature and pressure parameters is greater than a preset change threshold; when a corresponding hot-pressing processing stage is reached, sending the first compensation control instruction, the second compensation control instruction or the third compensation control instruction to a hot-pressing device.

2. The method of claim 1, wherein, The acquiring process flow data of a material to be sintered, and dividing a process flow of the material to be sintered into multiple hot-pressing processing stages according to the process flow data, comprises: acquiring process flow data of a material to be sintered, wherein the process flow data includes production process parameters and material performance data of the material to be sintered; performing correlation analysis on the production process parameters and the material performance parameters to obtain temperature sensitivity and pressure sensitivity at different time points; fitting the temperature sensitivity and the pressure sensitivity at the different time points into a temperature sensitivity curve and a pressure sensitivity curve, respectively; dividing a process flow of the material to be sintered into multiple hot-pressing processing stages according to the temperature sensitivity curve and the pressure sensitivity curve.

3. The method of claim 2, wherein, The dividing a process flow of the material to be sintered into multiple hot-pressing processing stages according to the temperature sensitivity curve and the pressure sensitivity curve, comprises: dividing the temperature sensitivity curve into a high-precision temperature section and a low-precision temperature section according to a preset temperature sensitivity threshold; dividing the pressure sensitivity curve into a high-precision pressure section and a low-precision pressure section according to a preset pressure sensitivity threshold; The process phase in which the high-precision temperature section and the high-precision pressure section appear simultaneously is divided into a full-dimension key phase; The process section in which the high-precision temperature section and the low-precision pressure section appear simultaneously, or the process section in which the low-precision temperature section and the high-precision pressure section appear simultaneously, is divided into a single-factor main control phase; The process phase in which the low-precision temperature section and the low-precision pressure section appear simultaneously is divided into a transition phase.

4. The method of claim 1, wherein, The mapping of the influence value of the secondary observation attribute on the primary observation attribute into the second process parameter curve to obtain an inner tolerance band comprises: According to the temperature-pressure feedback data, determining an influence level of the secondary observation attribute on the primary observation attribute; According to a preset influence weight coefficient of the influence level, calculating an influence value of the secondary observation attribute on the primary observation attribute; Mapping the influence value into the second process parameter curve to obtain an inner tolerance band.

5. The method of claim 1, wherein, The generation of a first compensation control instruction according to the predicted temperature-pressure change curve and a preset process parameter curve comprises: In a preset time step, calculating a predicted deviation between a parameter value on the predicted temperature-pressure change curve and a target value of the process parameter curve; When the predicted deviation is greater than the temperature difference adjustment threshold, using a feedforward control algorithm to calculate a compensation adjustment amount that offsets the predicted deviation; Converting the compensation adjustment amount into a temperature adjustment instruction and / or a pressure adjustment instruction for the hot-pressing equipment to obtain a first compensation control instruction.

6. The method of claim 1, wherein, The generation of a second compensation control instruction according to a tolerance band in which primary feedback data corresponding to the primary observation attribute in the temperature-pressure feedback data is located comprises: When the primary feedback data corresponding to the primary observation attribute in the temperature-pressure feedback data is located within the inner tolerance band, generating a second compensation control instruction that maintains a current operating state of the temperature-pressure equipment; Or, When the primary feedback data corresponding to the primary observation attribute in the temperature-pressure feedback data is located between the inner tolerance band and the outer tolerance band, generating a second compensation control instruction that makes the primary feedback data return to the inner tolerance band according to a change rate and a change trend of the primary feedback data; Or, When the primary feedback data corresponding to the primary observation attribute in the temperature-pressure feedback data reaches the outer tolerance band, generating a second compensation control instruction with the highest response priority.

7. The method of claim 1, wherein, The reduction of the sampling frequency of the temperature-pressure feedback data according to the temperature-pressure parameter change rate comprises: Judging whether the temperature-pressure parameter change rate is less than the change rate judgment threshold; If the temperature-pressure parameter change rate is less than the change rate judgment threshold, reducing the sampling frequency of the temperature-pressure feedback data according to a difference between the change rate judgment threshold and the temperature-pressure feedback data.

8. A hot pressing automatic control system, characterized by, The system comprises: A phase division module is configured to acquire process flow data of a material to be sintered, and divide a process flow of the material to be sintered into a plurality of hot-pressing processing phases according to the process flow data, wherein the hot-pressing phases include a full-dimension key phase, a single-factor main control phase, or a transition phase; The monitoring module is configured to monitor, in real time, temperature and pressure feedback data of the material to be sintered during the hot-pressing process and a change rate of temperature and pressure parameters. The first control module is configured to, when the hot-pressing process stage is in the full-dimension key stage, input preset first process parameter curves of the full-dimension key stage, the temperature and pressure feedback data, and the change rate of temperature and pressure parameters into a temperature and pressure parameter prediction model to obtain a predicted temperature and pressure change curve; and generate a first compensation control instruction according to the predicted temperature and pressure change curve and preset process parameter curves, so that an actual change track of the temperature and pressure feedback parameters is constrained within a range defined by a temperature and pressure adjustment threshold of the first process parameter curves. The second control module is configured to, when the hot-pressing process stage is in the single-factor master control stage, determine a main observation attribute and a secondary observation attribute in the single-factor master control stage; generate an outer tolerance band corresponding to the main observation attribute according to preset second process parameter curves of the single-factor master control stage; map an influence value of the secondary observation attribute on the main observation data to the second process parameter curves to obtain an inner tolerance band; and generate a second compensation control instruction according to a tolerance band in which main feedback data corresponding to the main observation attribute in the temperature and pressure feedback data is located; a threshold range of the outer tolerance band is greater than a threshold range of the inner tolerance band. The third control module is configured to, when the hot-pressing process stage is in the transition stage, reduce a sampling frequency of the temperature and pressure feedback data according to the change rate of temperature and pressure parameters, and generate a third compensation control instruction when the change rate of temperature and pressure parameters is greater than a preset change threshold. The execution module is configured to, when a corresponding hot-pressing process stage is reached, send the first compensation control instruction, the second compensation control instruction, or the third compensation control instruction to a hot-pressing device.

9. A computer storage medium, characterized in that The computer storage medium stores a plurality of instructions suitable for being loaded and executed by a processor to perform the method of any one of claims 1-7.

10. An electronic device, comprising: The electronic device includes a processor, a memory, and a transceiver. The memory is configured to store instructions. The transceiver is configured to communicate with other devices. The processor is configured to execute the instructions stored in the memory to cause the electronic device to perform the method of any one of claims 1-7.

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