A temperature control method, system and storage medium for base material heating

Abstract

The present invention relates to the field of temperature control systems, and discloses a temperature control method, system and storage medium for heating a base material. The method includes obtaining measurement data of n points in a container, where the measurement data at least includes temperature; setting m independently controlled heating units on the container; recording the measurement data at each time node at fixed time intervals; recording the heating power matrix corresponding to the heating units W ; calculating a coefficient matrix from the measurement data and the heating power matrix K , and independently adjusting the heating power of each heating unit by the coefficient matrix K . By calculating the coefficient matrix K , and the coefficient matrix K can reflect the coefficient of each heating unit corresponding to the point, thereby adjusting the heating power of several heating units to avoid the occurrence of local high temperature, and solving the problem that the local high temperature situation cannot be adaptively adjusted.

Description

Technical Field

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

[0002] In some base material processing techniques, various raw materials are placed in a container for heating, and the heating process is optimized through iterative learning to improve the flavoring effect. Chinese Patent C N 110488888A discloses a temperature control method for a resistance heating furnace based on adaptive iterative learning. This method is applied in the field of automatic control. The temperature control method for the resistance heating furnace includes the following steps: placing a thermocouple in the resistance furnace to be studied, and regarding the resistance furnace, electronic execution device, and thermocouple as a generalized object; introducing an adaptive iterative learning control scheme to make the temperature rising process and the constant temperature maintaining process in the resistance furnace as stable and efficient as possible to achieve the purpose of temperature control; recording the real-time temperature in the resistance furnace, and adaptively iteratively adjusting the input voltage of the resistance furnace through steps during the temperature rising process and the constant temperature maintaining process. Draw a scatter plot of the obtained temperature data, and after the drawing is completed, judge whether the adaptive iterative learning temperature control meets the design requirements to obtain the stability and efficiency of the adaptive iterative learning temperature control in the temperature rising process and the constant temperature maintaining process of the resistance furnace temperature.

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

[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 to solve the problem that it is impossible to perform adaptive adjustment for the situation of local high temperature. Summary of the Invention

[0005] The purpose of the present invention is: aiming at the existing temperature control methods, it is difficult to regulate the local temperature in the furnace, and it is impossible to perform adaptive adjustment for the situation of local high temperature, and solve the problem that it is impossible to perform adaptive adjustment for the situation of local high temperature.

[0006] In the first aspect, the present invention provides a temperature control method for heating the base material, including:

[0007] Obtain the measurement data of n points in the container, and the measurement data includes at least temperature;

[0008] Set m independently controlled heating units on the container;

[0009] Record the measurement data at each time node at fixed time intervals;

[0010] Record the heating power matrix corresponding to the heating unit ;

[0011] Based on the measurement data and the heating power matrix W Calculate the coefficient matrix K , and independently adjust the heating power of each heating unit according to the coefficient matrix K ;

[0012] Wherein n 、 m are both positive integers, W is a row matrix.

[0013] For the existing temperature control method, it is difficult to regulate the local temperature in the furnace and cannot perform adaptive adjustment for the case of local high temperature. In this solution, by calculating the coefficient matrix K , the temperature values at n points are related to the heating powers of m heating units. When the measured data temperature value at any point is relatively high, the coefficient matrix K can reflect the coefficient of each heating unit corresponding to this point, and then adjust the heating powers of several heating units to avoid the occurrence of local high temperature, solving the problem that adaptive adjustment cannot be performed for the case of local high temperature.

[0014] Furthermore, this solution does not uniquely define the specific calculation method of the coefficient matrix K . One feasible solution is as follows: calculating the coefficient matrix W from the measurement data and the heating power matrix K includes:

[0015] Generate a temperature matrix from the measurement data, and calculate the coefficient matrix K using the following formula:

[0016] ,

[0017] Wherein W 0 is the power matrix at the previous time node of W , W 0 is a row matrix, T and T 0 are both column matrices, T 0 is the temperature matrix at the previous time node of T , is the transpose matrix of T , is T0 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 .

[0018] Furthermore, 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 Afterwards, the coefficient matrix K By performing iterative calculations, this scheme can repeatedly correct the initial value, thereby improving accuracy.

[0019] 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:

[0020] like or ,but The value of ,

[0021] 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.

[0022] Furthermore, in order to improve the correction accuracy, one feasible solution is: the coefficient matrix K Perform iterative operations, including:

[0023] like and , calculate the rate of change ;

[0024] If 5%< r <20%, ​​then The value of ;

[0025] like r <5%, then The value of ;

[0026] If three consecutive iterations are performed, r If both are greater than 20%, then The value remains unchanged,

[0027] where w is the weight value, 0 ≤ w ≤ 1.

[0028] When adopting this scheme, by performing weighted correction on the elements at the same position in the two coefficient matrices K , the accuracy can be improved. w The larger the value of K , the greater the influence of the previous coefficient matrix

[0029] Furthermore, in order to facilitate predicting the temperature data at each point, one feasible scheme is: the measurement data further includes viscosity and the height difference between this point and the bottom of the container, and predicting the temperature matrix , is a column matrix. When adopting this scheme, by combining viscosity, height difference and heating power data, the accuracy of temperature data prediction can be improved.

[0030] Furthermore, this scheme does not uniquely limit the power adjustment method of each heating unit. One feasible scheme is: independently adjusting the heating power of each heating unit by the coefficient matrix, including:

[0031] Setting the temperature rise amount corresponding to each time interval according to the heating process ;

[0032] Calculating the power adjustment matrix by the following formula:

[0033] ;

[0034] ,

[0035] where A is a n ×1 all - 1 matrix, is the temperature offset matrix, is the transpose matrix of A, is transpose matrix.

[0036] When adopting this scheme, since the temperature gradually increases during the heating process and the heat dissipation also increases accordingly, the influence of the temperature rise amount on the power adjustment amount gradually decreases, and the influence of the temperature offset amount gradually increases, thereby balancing the temperature at each point to avoid local high temperature.

[0037] In a second aspect, the present invention provides a temperature control system for bottom - material heating, used to execute the above - mentioned temperature control method for bottom - material heating, including:

[0038] A measurement module for obtaining measurement data at n several points within the container;

[0039] A heating module, including m several heating units arranged at intervals on the container;

[0040] A recording module for recording the measurement data and the corresponding power matrix at each time node at fixed time intervals;

[0041] A calculation module for calculating the coefficient matrix K and independently controlling each heating unit.

[0042] When adopting this solution, 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 independently controls the heating power of each heating unit by the coefficient matrix K to balance the temperatures at each point and avoid local high temperatures.

[0043] Furthermore, in order to further prevent local high temperatures, one feasible solution is that the temperature control system further includes a prediction module, and the prediction module is used to predict the temperature matrix at the next time node according to the measurement data and assist in controlling the heating module. When adopting this solution, by predicting the temperatures at each point at the next time node and assisting in controlling the heating module through the predicted values, local high temperatures can be further prevented.

[0044] 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 base material heating.

[0045] Compared with the existing technology, the beneficial effects of the present invention are:

[0046] 1. By calculating the coefficient matrix K , the temperature values at n several points are related to the heating powers of m several heating units. When the measured temperature value at any point is relatively high, the coefficient matrix K can reflect the coefficients of each heating unit corresponding to this point, and then adjust the heating powers of several heating units to avoid the occurrence of local high temperatures, solving the problem that the local high temperature situation cannot be adaptively adjusted;

[0047] 2. After the second calculation of the coefficient matrix K , iterative operations are performed on the coefficient matrix K to repeatedly correct the initial value and thus improve the accuracy;

[0048] 3. Independently adjusting the heating power of each heating unit by the coefficient matrix, including:

[0049] Setting the temperature rise amount corresponding to each time interval according to the heating process ;

[0050] Calculating the power adjustment matrix by the following formula :

[0051] ;

[0052] ,

[0053] where A is a n ×1 all - 1 matrix, is the temperature offset matrix, is the transpose matrix of A, is the transpose matrix of.

[0054] When adopting this scheme, since the temperature gradually rises during the heating process, the heat dissipation also increases accordingly, and the influence of the temperature rise amount on the power adjustment amount gradually decreases, while the influence of the temperature offset amount gradually increases, thereby balancing the temperatures at each point to avoid local high temperatures. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a schematic flow chart of a temperature control method for bottom material heating provided by Embodiment 1 of the present invention;

[0056] Figure 2 is a schematic diagram of a temperature control system for bottom material heating provided by Embodiment 2 of the present invention.

[0057] Reference Signs:

[0058] 100, measurement module; 200, heating module; 300, recording module; 400, calculation module; 500, prediction module. DETAILED DESCRIPTION OF THE INVENTION

[0059] It should be noted that relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variation thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0060] The features and performance of the present invention will be further described in detail below in conjunction with embodiments.

[0061] Embodiment 1:

[0062] Please refer to Figure 1 , a temperature control method for heating a base material, comprising:

[0063] S100. Obtain measurement data at n points within the container, where the measurement data includes at least temperature;

[0064] S200. Set m independently controlled heating units on the container;

[0065] S300. Record the measurement data at each time node at fixed time intervals;

[0066] S400. Record the heating power matrix corresponding to the heating units ;

[0067] S500. Calculate the coefficient matrix W from the measurement data and the heating power matrix K , and independently adjust the heating power of each heating unit from the coefficient matrix K ;

[0068] where n , m are both positive integers, and W is a row matrix.

[0069] In the existing temperature control method, it is difficult to regulate the local temperature in the furnace, and it is impossible to perform adaptive adjustment for the case of local high temperature. In this solution, by calculating the coefficient matrix K , the temperature values at n points are compared with mThe 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.

[0070] This scheme does not uniquely limit the coefficient matrix K A specific calculation method of the heating power matrix is ​​as follows: W Calculate the coefficient matrix K ,include:

[0071] S501, generating a temperature matrix from measurement data , the coefficient matrix is ​​calculated by the following formula K :

[0072] ,

[0073] 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 .

[0074] 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 K By performing iterative calculations, this scheme can repeatedly correct the initial value, thereby improving accuracy.

[0075] 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:

[0076] S601, if or , then The value of ,

[0077] where is the element in the coefficient matrix obtained from the previous calculation , is the element in the coefficient matrix obtained at the current time node K , i = 1, 2... m , j = 1, 2... n . When adopting this scheme, the generation of zero values can be reduced.

[0078] To improve the calibration accuracy, one feasible scheme is: perform iterative operations on the coefficient matrix K , and it also includes:

[0079] S602. If and , calculate the change rate ;

[0080] S603. If 5% < r < 20%, then The value of is

[0081] S604. If r < 5%, then The value of is

[0082] If in three consecutive iterative operations, r are all greater than 20%, then The value remains unchanged,

[0083] where w is the weight value, 0 ≤ w ≤ 1.

[0084] When adopting this scheme, by performing weighted calibration on the elements at the same position in the two coefficient matrices K , the accuracy can be improved, w The larger the value of K , the greater the influence of the previous coefficient matrix

[0085] To facilitate predicting the temperature data at each point, one feasible scheme is: the measurement data also includes viscosity and the height difference between this point and the bottom of the container. After obtaining the measurement data, execute S700. Predict the temperature matrix at the next time node, is a column matrix. When adopting this scheme, by combining the viscosity, height difference, and heating power data, the accuracy of temperature data prediction can be improved.

[0086] This scheme does not uniquely limit the specific calculation method of the temperature matrix One feasible scheme is as follows: predict the temperature matrix of the next time node based on the measurement data , including:

[0087] S701. Obtain the measurement data of the current time node , and obtain the measurement data of the historical time node ;

[0088] S702. Calculate the prediction coefficient ;

[0089] S703. Predict the temperature matrix of the next time node by the following formula :

[0090] ;

[0091] where η , h , w are respectively the base material viscosity, the natural cooling heat dissipation coefficient corresponding to the current temperature of the base material, and the average heating power at the current time node, = 1, 2, 3, is the measurement data of the th historical time node, , , are respectively the base material viscosity, the natural cooling heat dissipation coefficient corresponding to the current temperature of the base material, and the average heating power at the th time node, , e is the natural constant, is D 's transpose matrix, is 's inverse matrix, , , are respectively the n×1 column vectors of the temperature matrix corresponding to the historical time nodes, is , , combined to form an n×3 matrix, x is the prediction parameter, x The smaller it is, the more the historical measurement data close to the data of the current time node affects the predicted value, but x being too small will cause the noise in the historical data to have a greater interference on the predicted value.

[0092] Optionally, in this embodiment, x has a value range of 0.04 ≤ x ≤ 0.06, which can make the predicted temperature matrix close to the measured value at the next time node.

[0093] Optionally, the predicted temperature matrix obtained from the previous time node and the temperature matrix T measured at the current time node are used to calculate the variance. If the variance is greater than a preset value, it means that the historical data interferes too much with the predicted temperature, and then the value of w is decreased, and vice versa.

[0094] This solution does not uniquely limit the power adjustment method of each heating unit. One feasible solution is: independently adjust the heating power of each heating unit by the coefficient matrix, including:

[0095] S502. Set the temperature increase amount corresponding to each time interval according to the heating process ;

[0096] S503. Calculate the power adjustment matrix by the following formula :

[0097] ;

[0098] ,

[0099] where A is a n ×1 all-ones matrix, is the temperature offset matrix, is the transpose matrix of A, is transpose matrix.

[0100] When adopting this solution, since the temperature gradually increases during the heating process, the heat dissipation also increases accordingly. The influence of the temperature increase amount on the power adjustment amount gradually decreases, and the influence of the temperature offset amount gradually increases, thereby balancing the temperatures at each point to avoid local high temperatures.

[0101] Embodiment 2:

[0102] Referring to Figure 2 , a temperature control system for bottom material heating, which is used to execute the above-mentioned temperature control method for bottom material heating, includes:

[0103] A measurement module 100, which is used to obtain the measurement data of n points in the container;

[0104] The heating module 200 includes m heating units spaced apart on the container;

[0105] The recording module 300 is used to record the measurement data and the corresponding power matrix at each time node at fixed time intervals;

[0106] The calculation module 400 is used to calculate the coefficient matrix K and independently control each heating unit.

[0107] When adopting this solution, the data is sent to the calculation module 400 through the measurement module 100 and the recording module 300. The calculation module 400 calculates the coefficient matrix K and the heating power of each heating unit is independently controlled by the coefficient matrix K to balance the temperatures at each point and avoid local high temperatures.

[0108] To further prevent local high temperatures, one feasible solution is that the temperature control system further includes a prediction module 500. The prediction module 500 is used to predict the temperature matrix at the next time node according to the measurement data and assist in controlling the heating module 200. When adopting this solution, by predicting the temperatures at each point at the next time node and using the predicted values to assist in controlling the heating module 200, local high temperatures can be further prevented.

[0109] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-RO M optical storage, etc.) containing computer-readable program code.

[0110] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in the process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0111] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device that implements the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 in the block or multiple blocks.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 in the block or multiple blocks.

[0113] Embodiment 3:

[0114] A storage medium stores a computer program thereon, and the program is executed by a processor to implement the above-mentioned temperature control method for base material heating.

[0115] To solve the problem that adaptive adjustment cannot be performed for local high-temperature situations, in this solution, by calculating the coefficient matrix K , n the temperature values at m points are related to the heating powers of K heating units. When the measured data temperature value at any point is high, through the coefficient matrix

[0116] which can reflect the coefficients of each heating unit corresponding to this point, the heating powers of several heating units are then adjusted to avoid the occurrence of local high temperature, thus solving the problem that adaptive adjustment cannot be performed for local high-temperature situations. K To gradually improve the accuracy of the coefficient matrix K , in this solution, after the second calculation of the coefficient matrix K , iterative operations are performed on the coefficient matrix

[0117] to repeatedly correct the initial value, thereby improving the accuracy.

[0118] To balance the temperatures at each point, in this solution, the independent adjustment of the heating power of each heating unit by the coefficient matrix includes: ;

[0119] Calculate the power adjustment matrix by the following formula

[0120] ;

[0121] ,

[0122] where A is n a 1×1 all-ones matrix, is the temperature offset matrix, is the transpose matrix of A, is the transpose matrix of.

[0123] When this solution is adopted, since the temperature gradually increases during the heating process, the heat dissipation also increases accordingly, and the influence of the temperature rise on the power adjustment amount gradually decreases, while the influence of the temperature offset gradually increases, thereby balancing the temperatures at each point to avoid local high temperatures.

[0124] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can 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 broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A temperature control method for heating a base material, characterized in that, Including: Obtain the measurement data at n several points inside the container, and the measurement data includes at least temperature; Set on the container m independently controlled heating units; Recording the measurement data at each time node at fixed time intervals; Record the heating power matrix corresponding to the heating unit ; From the measurement data and the heating power matrix W Calculate the coefficient matrix K , and independently adjust the heating power of each heating unit from the coefficient matrix K ; Generate a temperature matrix from measurement data , calculate the coefficient matrix using the following formula K :[[]] ； Set the temperature increase amount corresponding to each time interval according to the heating process ; The power adjustment matrix is calculated by the following formula :[[]] ； ； wherein n and m are both positive integers, W 0 is the power matrix at the previous time node of W , T is the temperature matrix at the current time node, T 0 is the temperature matrix at the previous time node of T , is the transpose matrix of T , is the transpose matrix of T 0 , A is a n ×1 all-ones matrix, is the temperature offset matrix, is the temperature offset at the i th point, is the transpose matrix of A, is the transpose matrix of .

2. The temperature control method for bottom material heating according to claim 1, characterized in that, From the second calculation of the coefficient matrix K After that, perform iterative operations on the coefficient matrix K ​ 3. A temperature control method for heating a base material according to claim 2, wherein the coefficient matrix K is subjected to iterative operation, characterized in that Including: If or , then The value of , wherein is the coefficient matrix obtained from the previous calculation element in, is the coefficient matrix obtained at the current time node K element in, i = 1, 2... m , j = 1, 2... n .

4. A temperature control method for heating the base material according to claim 3, wherein the coefficient matrix K is subjected to iterative operation, characterized in that Further including: If and , calculate the rate of change ; If 5% < r < 20%, then The value of ; If r < 5%, then The value of ; If in three consecutive iterative operations, r are all greater than 20%, then the value remains unchanged. wherein w is a weight value, 0 ≤ w ≤ 1.

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

6. A temperature control system for heating the base material, characterized in that: For performing a temperature control method for bottom material heating as described in any one of claims 1-5, including: Measurement module, used to obtain n measurement data at several points within the container; Heating module, comprising m heating units spacedly arranged on the container; A recording module for recording the measurement data and the corresponding power matrix at each time node at fixed time intervals; Calculation module for calculating the coefficient matrix K And independently control each heating unit.

7. The temperature control system for heating the base material according to claim 6, characterized in that: The temperature control system further includes a prediction module for predicting the temperature matrix at the next time node based on the measurement data and assisting in controlling the heating module.

8. A storage medium having a computer program stored thereon, characterized in that, This program is executed by a processor to implement a temperature control method for bottom material heating as described in any one of claims 1-5.

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