Temperature control method and system for heating backing material and storage medium

Abstract

The invention relates to the field of temperature control systems, and discloses a temperature control method and system for backing material heating and a storage medium, the method comprises the following steps: obtaining measurement data of n point locations in a container, the measurement data at least comprising temperature; m independently controlled heating units are arranged on the container; recording measurement data of each time node at a fixed time interval; recording a heating power matrix W corresponding to the heating unit; and calculating a coefficient matrix K according to the measured data and the heating power matrix, and independently adjusting the heating power of each heating unit according to the coefficient matrix K. According to the method, the coefficient matrix K is calculated, and the coefficient of each heating unit corresponding to the point position can be reflected through the coefficient matrix K, so that the heating power of the plurality of heating units is adjusted, local high temperature is avoided, and the problem that self-adaptive adjustment cannot be performed under the condition of local high temperature is solved.

Description

Technical Field

[0001] The invention relates to the field of temperature control systems, and in particular to a temperature control method, system and storage medium for heating base materials. Background Art

[0002] In some base material processing techniques, the raw materials are placed in a container for heating, and the heating process is optimized through iterative learning to improve the flavoring effect. N 110488888A discloses a temperature control method for a resistance heating furnace based on adaptive iterative learning, which is applied to the field of automatic control. The temperature control method for a resistance heating furnace includes the following steps: placing a thermocouple in the resistance furnace to be studied, and regarding the resistance furnace, the electronic actuator, and the thermocouple as a generalized object; introducing an adaptive iterative learning control scheme to make the temperature rise process and the constant temperature maintenance process in the resistance furnace as smooth and efficient as possible, so as to achieve the purpose of temperature control; recording the real-time temperature in the resistance furnace, and performing adaptive iterative learning adjustment of the input voltage of the resistance furnace through steps during the temperature rise process and the constant temperature maintenance process. A scatter plot is drawn for the acquired temperature data, and after the drawing is completed, it is determined whether the adaptive iterative learning temperature control meets the design requirements, and the stability and efficiency of the adaptive iterative learning temperature control in the temperature rise process and the constant temperature maintenance process in the resistance furnace are obtained.

[0003] The existing temperature control method has the following problems: it is difficult to regulate the local temperature in the furnace, and it is impossible to perform adaptive adjustment for local high temperature conditions.

[0004] Based on the above situation, there is an urgent need for a temperature control method, system and storage medium for heating the base material, which can solve the problem that adaptive adjustment cannot be performed in the case of local high temperature. Summary of the invention

[0005] The purpose of the present invention is to solve the problem that the existing temperature control method is difficult to regulate the local temperature in the furnace and cannot perform adaptive adjustment for local high temperature conditions.

[0006] In a first aspect, the present invention provides a temperature control method for heating a substrate, comprising: Get the container n The measurement data of each point includes at least temperature; Set on the container m An independently controlled heating unit; Record the measurement data of each time node at fixed time intervals; Record the heating power matrix corresponding to the heating unit ; Based on the measured data and heating power matrix W Calculate the coefficient matrix K , by the coefficient matrix K Independently adjust the heating power of each heating unit; in n , m are all positive integers, W is a row matrix.

[0007] The existing temperature control method is difficult to regulate the local temperature in the furnace, and cannot perform adaptive adjustment for local high temperature conditions. In this scheme, by calculating the coefficient matrix K ,Will n The temperature value of each point m The heating power of each heating unit is connected. When the temperature value of the measured data at any point is high, the coefficient matrix K It can reflect the coefficient of each heating unit corresponding to the point, and then adjust the heating power of several heating units to avoid the occurrence of local high temperature, solving the problem that adaptive adjustment cannot be performed for the situation of local high temperature.

[0008] Furthermore, this scheme does not uniquely limit the coefficient matrix K A specific calculation method, one of which is as follows: W Calculate the coefficient matrix K ,include: Generate temperature matrix from measurement data , the coefficient matrix is ​​calculated by the following formula K : , in W 0 for W The power matrix of the previous time node, W 0 is a row matrix, T and T 0 are column matrices, T 0 for T The temperature matrix of the previous time node, for T The transposed matrix of for T 0 When this solution is adopted, the above formulas corresponding to several time nodes are combined to obtain the coefficient matrix K The values ​​of each element in .

[0009] Furthermore, in order to improve the coefficient matrix KTo improve the accuracy of the coefficient matrix, one feasible solution is to calculate the coefficient matrix from the second K Afterwards, the coefficient matrix K By performing iterative calculations, this scheme can repeatedly correct the initial value, thereby improving accuracy.

[0010] Furthermore, since the temperature data of each point is positively correlated with the heating power of each heating unit, the coefficient matrix K The elements of should all be positive values. One correction scheme is: the coefficient matrix K Perform iterative operations, including: like or ,but The value of , in is the coefficient matrix obtained from the last calculation The elements in The coefficient matrix obtained for the current time node K The elements in i =1, 2... m , j =1, 2... n When this solution is adopted, the generation of zero values ​​can be reduced.

[0011] Furthermore, in order to improve the correction accuracy, one feasible solution is: the coefficient matrix K Perform iterative operations, including: like and , calculate the rate of change ; If 5%< r <20%, ​​then The value of ; like r <5%, then The value of ; If three consecutive iterations are performed, r If both are greater than 20%, then The value of remains unchanged. in w is the weight value, 0≤ w ≤1.

[0012] When this scheme is adopted, the two coefficient matrices K Weighted correction of elements at the same position in the image can improve accuracy. w The larger the value of is, the larger the coefficient matrix is. K The greater the impact.

[0013] Furthermore, in order to facilitate the prediction of the temperature data of each point, one feasible solution is: the measurement data also includes the viscosity and the height difference between the point and the bottom of the container, and the temperature matrix of the next time node is predicted based on the measurement data , It is a column matrix. When this scheme is adopted, the accuracy of temperature data prediction can be improved by combining viscosity, height difference and heating power data.

[0014] Furthermore, this solution does not limit the power adjustment method of each heating unit. One feasible solution is: the heating power of each heating unit is independently adjusted by the coefficient matrix, including: Set the temperature rise corresponding to each time interval according to the heating process ; The power regulation matrix is ​​calculated by the following formula : ; , Where A is n ×1 matrix of all 1s, is the temperature offset matrix, is the transposed matrix of A, for The transposed matrix of .

[0015] When this solution is adopted, as the temperature gradually increases during the heating process, the heat dissipation also increases accordingly. The influence on power regulation gradually decreases, and the temperature offset The impact gradually increases, thereby balancing the temperature at each point to avoid local high temperature.

[0016] In a second aspect, the present invention provides a temperature control system for heating a bottom material, which is used to execute the above-mentioned temperature control method for heating a bottom material, comprising: Measuring module, used to obtain the n Measurement data of each point; Heating module, including m A heating unit is disposed on the container at intervals; A recording module, used to record the measurement data and the corresponding power matrix of each time node at a fixed time interval; Calculation module, used to calculate the coefficient matrix K And control each heating unit independently.

[0017] When this solution is adopted, the data is sent to the calculation module through the measurement module and the recording module, and the calculation module calculates the coefficient matrix K And by the coefficient matrixK The heating power of each heating unit is independently controlled to balance the temperature at each point to avoid local high temperature.

[0018] Furthermore, in order to further prevent local high temperature, one feasible solution is: the temperature control system also includes a prediction module, which is used to predict the temperature matrix of the next time node based on the measurement data, and assist in controlling the heating module. When this solution is adopted, by predicting the temperature of each point at the next time node and assisting in controlling the heating module through the predicted value, local high temperature can be further prevented.

[0019] In a third aspect, the present invention also provides a storage medium on which a computer program is stored, and the program is executed by a processor to implement the above-mentioned temperature control method for heating a base material.

[0020] Compared with the prior art, the present invention has the following beneficial effects: 1. By calculating the coefficient matrix K ,Will n The temperature value of each point m The heating power of each heating unit is connected. When the temperature value of the measured data at any point is high, the coefficient matrix K It can reflect the coefficient of each heating unit corresponding to the point, and then adjust the heating power of several heating units to avoid the occurrence of local high temperature, solving the problem that adaptive adjustment cannot be performed for local high temperature. 2. Calculate the coefficient matrix from the second time K Afterwards, the coefficient matrix K Iterative calculations can repeatedly correct initial values ​​to improve accuracy; 3. The method of independently adjusting the heating power of each heating unit by the coefficient matrix includes: Set the temperature rise corresponding to each time interval according to the heating process ; The power regulation matrix is ​​calculated by the following formula : ; , Where A is n ×1 matrix of all 1s, is the temperature offset matrix, is the transposed matrix of A, for The transposed matrix of .

[0021] When this solution is adopted, as the temperature gradually increases during the heating process, the heat dissipation also increases accordingly. The influence on power regulation gradually decreases, and the temperature offset The impact gradually increases, thereby balancing the temperature at each point to avoid local high temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic flow chart of a temperature control method for heating a base material provided in the first embodiment of the present invention; Figure 2 A schematic diagram of a temperature control system for heating a base material provided in the second embodiment of the present invention.

[0023] Reference numerals: 100, measurement module; 200, heating module; 300, recording module; 400, calculation module; 500, prediction module. DETAILED DESCRIPTION

[0024] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0025] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0026] Embodiment 1: Please refer to Figure 1 , a temperature control method for heating a base material, comprising: S100, get the container n The measurement data of each point includes at least temperature; S200, set on the container m An independently controlled heating unit; S300, recording the measurement data of each time node at a fixed time interval; S400, record the heating power matrix corresponding to the heating unit ; S500, measured data and heating power matrix W Calculate the coefficient matrix K , by the coefficient matrixK Independently adjust the heating power of each heating unit; in n , m are all positive integers, W is a row matrix.

[0027] The existing temperature control method is difficult to regulate the local temperature in the furnace, and cannot perform adaptive adjustment for local high temperature conditions. In this scheme, by calculating the coefficient matrix K ,Will n The temperature value of each point m The heating power of each heating unit is connected. When the temperature value of the measured data at any point is high, the coefficient matrix K It can reflect the coefficient of each heating unit corresponding to the point, and then adjust the heating power of several heating units to avoid the occurrence of local high temperature, solving the problem that adaptive adjustment cannot be performed for the situation of local high temperature.

[0028] This scheme does not uniquely limit the coefficient matrix K A specific calculation method is as follows: W Calculate the coefficient matrix K ,include: S501, generating a temperature matrix from measurement data , the coefficient matrix is ​​calculated by the following formula K : , in W 0 for W The power matrix of the previous time node, W 0 is a row matrix, T and T 0 are all column matrices, T 0 for T The temperature matrix of the previous time node, for T The transposed matrix of for T 0 When this solution is adopted, the above formulas corresponding to several time nodes are combined to obtain the coefficient matrix K The values ​​of each element in .

[0029] In order to improve the coefficient matrix K To improve the accuracy of the coefficient matrix, one feasible solution is to calculate the coefficient matrix from the second K Then, execute S600, the coefficient matrix KBy performing iterative calculations, this scheme can repeatedly correct the initial value, thereby improving accuracy.

[0030] Since the temperature data of each point is positively correlated with the heating power of each heating unit, the coefficient matrix K The elements of should all be positive values. One correction scheme is: K Perform iterative operations, including: S601, if or ,but The value of , in is the coefficient matrix obtained from the last calculation The elements in The coefficient matrix obtained for the current time node K The elements in i =1, 2... m , j =1, 2... n When this solution is adopted, the generation of zero values ​​can be reduced.

[0031] In order to improve the correction accuracy, one feasible solution is to: K Perform iterative operations, including: S602, if and , calculate the rate of change ; S603, if 5%< r <20%, ​​then The value of ; S604, if r <5%, then The value of ; If three consecutive iterations are performed, r If both are greater than 20%, then The value of remains unchanged. in w is the weight value, 0≤ w ≤1.

[0032] When this scheme is adopted, the two coefficient matrices K Weighted correction of elements at the same position in the image can improve accuracy. w The larger the value of is, the larger the coefficient matrix is. K The greater the impact.

[0033] In order to facilitate the prediction of the temperature data of each point, one feasible solution is: the measurement data also includes the viscosity and the height difference between the point and the bottom of the container. After obtaining the measurement data, execute S700 to predict the temperature matrix of the next time node according to the measurement data. , It is a column matrix. When this scheme is adopted, the accuracy of temperature data prediction can be improved by combining viscosity, height difference and heating power data.

[0034] This scheme does not uniquely limit the temperature matrix A specific calculation method is as follows: predict the temperature matrix of the next time node based on the measured data ,include: S701, obtain the measurement data of the current time node , get the measurement data of historical time nodes ; S702, calculate prediction coefficient ; S703: Predict the temperature matrix of the next time node using the following formula : ; in η , h , w They are the base material viscosity at the current time node, the natural cooling heat dissipation coefficient corresponding to the current temperature of the base material, and the average heating power. =1, 2, 3, For the The measurement data of historical time nodes, , , Respectively The viscosity of the base material at each time node, the natural cooling coefficient corresponding to the current temperature of the base material, and the average heating power. , e is a natural constant, for D The transposed matrix of for The inverse matrix of , , are the n×1 column vectors of the temperature matrix corresponding to the historical time nodes, for , , The n×3 matrix formed by the combination, x is the prediction parameter, x The smaller it is, the greater the impact of historical measurement data close to the current time node data on the predicted value, butx If it is too small, the noise in the historical data will cause great interference to the predicted value.

[0035] Optionally, in this embodiment, x The value range is 0.04≤ x ≤0.06, which enables the predicted temperature matrix The measurement value close to the next time node.

[0036] Optionally, the predicted temperature matrix obtained from the previous time node The temperature matrix obtained from the measurement at the current time node T Calculate the variance. If the variance is greater than the preset value, the historical data interferes too much with the predicted temperature. w The value of decreases, and vice versa.

[0037] This solution does not limit the power adjustment method of each heating unit. One feasible solution is to independently adjust the heating power of each heating unit by a coefficient matrix, including: S502: Set the temperature rise amount corresponding to each time interval according to the heating process ; S503, calculate the power regulation matrix by the following formula : ; , Where A is n ×1 matrix of all 1s, is the temperature offset matrix, is the transposed matrix of A, for The transposed matrix of .

[0038] When this solution is adopted, as the temperature gradually increases during the heating process, the heat dissipation also increases accordingly. The influence on power regulation gradually decreases, and the temperature offset The impact gradually increases, thereby balancing the temperature at each point to avoid local high temperature.

[0039] Embodiment 2: Reference Figure 2 , a temperature control system for heating a bottom material, used to execute the above-mentioned temperature control method for heating a bottom material, comprising: The measuring module 100 is used to obtain the n Measurement data of each point; The heating module 200 includes m A heating unit is disposed on the container at intervals; The recording module 300 is used to record the measurement data and the corresponding power matrix of each time node at a fixed time interval; Calculation module 400, used to calculate the coefficient matrix K And control each heating unit independently.

[0040] When this solution is adopted, the data is sent to the calculation module 400 through the measurement module 100 and the recording module 300, and the calculation module 400 calculates the coefficient matrix K And by the coefficient matrix K The heating power of each heating unit is independently controlled to balance the temperature at each point to avoid local high temperature.

[0041] In order to further prevent local high temperature, one feasible solution is: the temperature control system also includes a prediction module 500, which is used to predict the temperature matrix of the next time node based on the measurement data, and assist in controlling the heating module 200. When this solution is adopted, by predicting the temperature of each point at the next time node and assisting in controlling the heating module 200 through the predicted value, local high temperature can be further prevented.

[0042] It should be understood by those skilled in the art that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer-readable storage medium (including but not limited to a disk storage, a CD-ROM, or a computer program product) containing computer-readable program code. M , optical storage, etc.).

[0043] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0044] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0045] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0046] Embodiment three: A storage medium stores a computer program, which is executed by a processor to implement the above-mentioned temperature control method for heating a base material.

[0047] In order to solve the problem that adaptive adjustment cannot be performed for local high temperature, in this scheme, the coefficient matrix is ​​calculated K ,Will n The temperature value of each point m The heating power of each heating unit is connected. When the temperature value of the measured data at any point is high, the coefficient matrix K It can reflect the coefficient of each heating unit corresponding to the point, and then adjust the heating power of several heating units to avoid the occurrence of local high temperature, solving the problem that adaptive adjustment cannot be performed for the situation of local high temperature.

[0048] In order to gradually improve the coefficient matrix K The accuracy of the coefficient matrix is ​​calculated from the second K Afterwards, the coefficient matrix K Iterative calculations can repeatedly correct the initial value and thus improve accuracy.

[0049] In order to balance the temperature of each point, in this solution, the heating power of each heating unit is independently adjusted by the coefficient matrix, including: Set the temperature rise corresponding to each time interval according to the heating process ; The power regulation matrix is ​​calculated by the following formula : ; , Where A is n ×1 matrix of all 1s, is the temperature offset matrix, is the transposed matrix of A, for The transposed matrix of .

[0050] When this solution is adopted, as the temperature gradually increases during the heating process, the heat dissipation also increases accordingly. The influence on power regulation gradually decreases, and the temperature offset The impact gradually increases, thereby balancing the temperature at each point to avoid local high temperature.

[0051] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A temperature control method for heating a base material, characterized in that: include: Get the container n The measurement data of each point includes at least temperature; Set on the container m An independently controlled heating unit; Record the measurement data of each time node at fixed time intervals; Record the heating power matrix corresponding to the heating unit ; Based on the measured data and heating power matrix W Calculate the coefficient matrix K , by the coefficient matrix K Independently adjust the heating power of each heating unit; in n , m All are positive integers.

2. A temperature control method for heating a bottom material according to claim 1, wherein the temperature control method is based on the measurement data and the heating power matrix. W Calculate the coefficient matrix K , characterized in that, Generate temperature matrix from measurement data , the coefficient matrix is ​​calculated by the following formula K : ， in W 0 for W The power matrix of the previous time node, T is the temperature matrix of the current time node, T 0 for T The temperature matrix of the previous time node, for T The transposed matrix of for T 0 The transposed matrix of .

3. A temperature control method for heating a base material according to claim 2, characterized in that: Calculate the coefficient matrix from the second K Afterwards, the coefficient matrix K Perform iterative operations.

4. According to the temperature control method for heating the bottom material according to claim 3, the coefficient matrix K An iterative operation is performed, characterized in that: include: like or ,but The value of , in is the coefficient matrix obtained from the last calculation The elements in The coefficient matrix obtained for the current time node K The elements in i =1, 2... m , j =1, 2... n .

5. A temperature control method for heating a base material according to claim 4, wherein the coefficient matrix K An iterative operation is performed, characterized in that: Also includes: like and , calculate the rate of change ; If 5%< r <20%, ​​then The value of ; like r <5%, then The value of ; If three consecutive iterations are performed, r If both are greater than 20%, then The value of remains unchanged. in w is the weight value, 0≤ w ≤1.

6. A temperature control method for heating a base material according to claim 1, characterized in that: The measurement data also includes viscosity and the height difference between the point and the bottom of the container. The temperature matrix of the next time node is predicted based on the measurement data. .

7. A temperature control method for heating a base material according to claim 1, wherein the coefficient matrix K The heating power of each heating unit is independently adjusted, characterized in that: include: Set the temperature rise corresponding to each time interval according to the heating process ; The power regulation matrix is ​​calculated by the following formula : ； ， Where A is n ×1 matrix of all 1s, is the temperature offset matrix, For the i The temperature offset of each point, is the transposed matrix of A, for The transposed matrix of .

8. A temperature control system for heating a base material, characterized in that: Used to implement a temperature control method for heating a base material as claimed in any one of claims 1 to 7, comprising: Measuring module, used to obtain the n Measurement data of each point; Heating module, including m A heating unit disposed at intervals on the container; A recording module, used to record the measurement data and the corresponding power matrix of each time node at a fixed time interval; Calculation module, used to calculate the coefficient matrix K And control each heating unit independently.

9. A temperature control system for heating a bottom material according to claim 8, characterized in that: The temperature control system further comprises a prediction module, which is used to predict the temperature matrix of the next time node according to the measurement data and assist in controlling the heating module.

10. A storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement a temperature control method for heating a base material as described in any one of claims 1 to 7.

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