Control method and device of tunnel drying oven, terminal equipment and program product

Abstract

The invention provides a tunnel oven control method and device, terminal equipment and a program product. The method comprises the steps that historical parameters and real-time parameters corresponding to multiple control points of a tunnel oven are obtained; the historical parameters comprise historical temperature data and historical atmosphere data, and the real-time parameters comprise real-time temperature data and real-time atmosphere data; predicting prediction parameters respectively corresponding to the plurality of control points at the future moment according to the historical parameters and the real-time parameters by utilizing a prediction model; the prediction parameters comprise predicted temperature data and predicted atmosphere data; determining target set values respectively corresponding to the prediction parameters by using a model prediction control model; the target set value comprises a target temperature set value and a target atmosphere set value; and the temperature and the atmosphere of the tunnel oven are controlled and adjusted according to the target temperature set value and the target atmosphere set value. The control efficiency of the tunnel oven can be improved, and the production efficiency and the product quality are improved.

Description

Technical Field

[0001] The present application relates to the technical field of oven control, and in particular to a control method, device, terminal equipment and program product for a tunnel oven. Background Art

[0002] The tunnel oven is an industrial heat treatment equipment widely used in food processing, chemical, pharmaceutical, building materials and other industries. The working principle of the tunnel oven is based on the heat transfer effect of hot air on objects. Its structure usually includes parts such as the feed port, the discharge port, the heating system, the air duct system and the control system. The heat energy is generated by the heating system, and the hot air is evenly sent into the furnace cavity through the air duct system to achieve the drying, baking or heat treatment of the object. In order to ensure that the temperature and atmosphere in different areas of the tunnel oven can meet the specific process requirements, multiple control points are generally set in the tunnel oven according to the size and shape of the tunnel oven. Each control point is equipped with a temperature sensor, an atmosphere sensor, a temperature regulating device and an atmosphere regulating device. The temperature sensor and the atmosphere sensor are used to collect the temperature and atmosphere in the tunnel oven respectively, and the temperature regulating device and the atmosphere regulating device are used to adjust and control the temperature and atmosphere respectively.

[0003] In the current technical solution, the temperature or atmosphere control is performed for each control point of the tunnel oven. However, the temperature and atmosphere control of the tunnel oven according to the current technical solution is not only cumbersome and inefficient, but also has the following problems: in terms of temperature control, there are problems such as slow response speed of temperature adjustment, lag in heat source control, and large thermal inertia, which lead to insufficient temperature control accuracy of the tunnel oven, large deviation between the actual temperature and the target temperature, and large temperature fluctuations. In terms of atmosphere control, there are problems such as slow response speed of atmosphere adjustment, lag in atmosphere control, and uneven atmosphere. Problems in temperature control and atmosphere control will affect the production efficiency and product quality of the tunnel oven.

[0004] Therefore, how to improve the control efficiency of the tunnel oven and improve production efficiency and product quality is a technical problem that technicians in this field currently need to solve. Summary of the invention

[0005] The purpose of the present application is to provide a control method, device, terminal equipment, computer-readable storage medium and computer program product for a tunnel oven, aiming to improve the control efficiency of the tunnel oven, improve production efficiency and product quality.

[0006] In a first aspect, the present application provides a control method for a tunnel oven. The method comprises:

[0007] Acquire historical parameters and real-time parameters corresponding to a plurality of control points of the tunnel oven; the historical parameters include historical temperature data and historical atmosphere data, and the real-time parameters include real-time temperature data and real-time atmosphere data;

[0008] Using a prediction model to predict prediction parameters corresponding to a plurality of control points at future moments according to the historical parameters and the real-time parameters; the prediction parameters include predicted temperature data and predicted atmosphere data;

[0009] Determining target setting values ​​corresponding to each of the prediction parameters using a model predictive control model; the target setting values ​​include a target temperature setting value and a target atmosphere setting value;

[0010] The temperature and atmosphere of the tunnel oven are controlled and adjusted respectively according to the target temperature setting value and the target atmosphere setting value.

[0011] In one embodiment, the using of the prediction model to predict the prediction parameters corresponding to the plurality of control points at future moments according to the historical parameters and the real-time parameters includes:

[0012] The historical parameters and the real-time parameters are input into the LSTM prediction model, and the LSTM prediction model is used to output the prediction parameters corresponding to the multiple control points at future moments.

[0013] In one embodiment, the method further comprises:

[0014] Obtaining constraints for controlling the tunnel oven;

[0015] The method of using the model predictive control model to determine the target setting values ​​corresponding to the prediction parameters includes:

[0016] A model predictive control model is used to determine target setting values ​​corresponding to each of the prediction parameters based on the prediction parameters and the constraint conditions.

[0017] In one embodiment, the controlling and adjusting the temperature and atmosphere of the tunnel oven according to the target temperature setting value and the target atmosphere setting value respectively includes:

[0018] The target temperature setting value is sent to a temperature PID controller, and a temperature control signal is determined by the temperature PID controller based on the target temperature setting value and the real-time temperature data, so that a temperature regulating device performs temperature control adjustment on the tunnel oven according to the temperature control signal;

[0019] The target atmosphere setting value is sent to an atmosphere PID controller, and the atmosphere PID controller is used to determine an atmosphere control signal based on the target atmosphere setting value and the real-time atmosphere data, so that an atmosphere regulating device performs atmosphere control adjustment on the tunnel oven according to the atmosphere control signal.

[0020] In one embodiment, the method further comprises:

[0021] A data preprocessing operation is performed on the real-time parameters and the historical parameters.

[0022] In one embodiment, the method further comprises:

[0023] The real-time parameters, predicted parameters and target setting values ​​of the tunnel oven are displayed.

[0024] In a second aspect, the present application also provides a control device for a tunnel oven. The device comprises:

[0025] An acquisition module, used to acquire historical parameters and real-time parameters corresponding to a plurality of control points of the tunnel oven; the historical parameters include historical temperature data and historical atmosphere data, and the real-time parameters include real-time temperature data and real-time atmosphere data;

[0026] A prediction module, used for predicting the prediction parameters corresponding to the plurality of control points at future moments respectively according to the historical parameters and the real-time parameters by using a prediction model; the prediction parameters include predicted temperature data and predicted atmosphere data;

[0027] A determination module, used to determine target setting values ​​corresponding to each of the prediction parameters using a model prediction control model; the target setting values ​​include a target temperature setting value and a target atmosphere setting value;

[0028] A control module is used to control and adjust the temperature and atmosphere of the tunnel oven according to the target temperature setting value and the target atmosphere setting value.

[0029] In a third aspect, the present application further provides a terminal device, wherein the terminal device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0030] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0031] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0032] The present application provides a control method for a tunnel oven. After predicting the predicted parameters corresponding to multiple control points at future moments according to the historical parameters and real-time parameters corresponding to multiple control points of the tunnel oven, a model prediction control model is used to determine the target setting value based on the predicted parameters, and then the temperature and atmosphere of the tunnel oven are controlled and adjusted according to the target temperature setting value and the target atmosphere setting value. On the one hand, the historical parameters and the real-time parameters both include temperature data and atmosphere data. The predicted parameters corresponding to multiple control points at future moments are predicted according to the historical parameters and the real-time parameters. That is to say, the method considers multiple control targets at the same time, generates the globally optimized target temperature setting value and the target atmosphere setting value, and the operation process is convenient, improves the temperature and atmosphere adjustment efficiency, and can also reduce the influence of each control point on other control points when it is adjusted independently, and can improve the control accuracy of the temperature and atmosphere in the tunnel oven; on the other hand, the method uses the predicted parameters to adjust and control the temperature and atmosphere of the tunnel oven in advance, rather than just adjusting according to the generated temperature data and atmosphere data, so the method can reduce the time required for the actual state of the tunnel oven to reach the target state and reach a new equilibrium state. Therefore, the method can improve the control efficiency of the tunnel oven, improve production efficiency and product quality.

[0033] It can be understood that the control device, terminal equipment, computer-readable storage medium and computer program product of a tunnel oven provided in the embodiments of the present application have the same beneficial effects as the control method of the tunnel oven described above, and will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 A flow chart of a control method for a tunnel oven provided in an embodiment of the present application;

[0036] Figure 2 A flow chart of a control method for a tunnel oven provided in another embodiment of the present application;

[0037] Figure 3 A schematic diagram of the structure of a control device for a tunnel oven provided in an embodiment of the present application;

[0038] Figure 4 A schematic diagram of the structure of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0040] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0041] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0042] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0043] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0044] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways. "Multiple" means "two or more".

[0045] The tunnel oven is an industrial heat treatment equipment widely used in food processing, chemical, pharmaceutical, building materials and other industries. The working principle of the tunnel oven is based on the heat transfer effect of hot air on objects. Its structure usually includes parts such as the feed port, the discharge port, the heating system, the air duct system and the control system. The heat energy is generated by the heating system, and the hot air is evenly sent into the furnace cavity through the air duct system to achieve the drying, baking or heat treatment of the object. In order to ensure that the temperature and atmosphere in different areas of the tunnel oven can meet the specific process requirements, multiple control points are generally set in the tunnel oven according to the size and shape of the tunnel oven. Each control point is equipped with a temperature sensor, an atmosphere sensor, a temperature regulating device and an atmosphere regulating device. The temperature sensor and the atmosphere sensor are used to collect the temperature and atmosphere in the tunnel oven respectively, and the temperature regulating device and the atmosphere regulating device are used to adjust and control the temperature and atmosphere respectively.

[0046] In the current technical solution, the temperature or atmosphere is controlled at each control point of the tunnel oven. However, the temperature and atmosphere control of the tunnel oven according to the current technical solution is not only cumbersome and inefficient, but also has the following problems:

[0047] In terms of temperature control, there are the following two problems: 1. Since most tunnel oven control systems have problems such as slow response speed, low temperature sensor accuracy, and large thermal inertia, the temperature control accuracy is insufficient, resulting in a large deviation between the target temperature and the actual temperature; secondly, due to the lag in heat source control, low heat transfer efficiency, and non-optimized control system algorithm, the response time of temperature adjustment is slow, resulting in large temperature fluctuations, affecting production efficiency and product quality; 2. Temperature interference between regions: The temperature settings of different regions may affect each other, resulting in temperature fluctuations in adjacent areas, especially in transition zones or areas close to openings where the temperature control effect is poor.

[0048] In terms of atmosphere control, there are two problems: 1. Atmosphere non-uniformity: In tunnel ovens, especially those with longer lengths, atmosphere control often has problems with uneven gas composition, humidity, and oxygen content in different areas; when the gas flow design is unreasonable, it is difficult for the atmosphere composition to remain consistent in each section of the tunnel, affecting the overall process effect. 2. Atmosphere flow control lag: Atmosphere control systems usually rely on gas sensors for feedback adjustment, but due to the large tunnel space, the response time of gas flow is long, resulting in control lag. Failure to adjust parameters such as gas composition and humidity in a timely manner may affect the production process.

[0049] In addition, the system has a slow response speed, and the heating and gas control responses are slow. Since the tunnel oven is usually large in size and has a large thermal inertia, when the heating power or gas flow is adjusted, the system often takes a long time to reach a new equilibrium state. This slow response speed is particularly obvious under rapidly changing process requirements, and it is difficult to adapt to dynamic process requirements.

[0050] In terms of sensor lag, although multiple point sensors are used to monitor temperature and atmosphere, the detection lag of the sensors may cause the control system to be unable to respond in time, especially when the temperature and atmosphere change rapidly, making it difficult to provide precise control.

[0051] Therefore, how to improve the control efficiency of the tunnel oven and improve production efficiency and product quality is a technical problem that technicians in this field currently need to solve.

[0052] The embodiment of the present application provides a control method for a tunnel oven. After predicting the predicted parameters corresponding to multiple control points at future moments according to the historical parameters and real-time parameters corresponding to multiple control points of the tunnel oven, a model prediction control model is used to determine the target setting value based on the predicted parameters, and then the temperature and atmosphere of the tunnel oven are controlled and adjusted according to the target temperature setting value and the target atmosphere setting value. On the one hand, the historical parameters and the real-time parameters both include temperature data and atmosphere data. The predicted parameters corresponding to multiple control points at future moments are predicted according to the historical parameters and the real-time parameters. That is to say, the method considers multiple control targets at the same time, generates the globally optimized target temperature setting value and the target atmosphere setting value, and the operation process is convenient, improves the temperature and atmosphere adjustment efficiency, and can also reduce the influence of each control point on other control points when it is adjusted independently, which can improve the control accuracy of the temperature and atmosphere in the tunnel oven; on the other hand, the method uses the predicted parameters to adjust and control the temperature and atmosphere of the tunnel oven in advance, rather than just adjusting according to the generated temperature data and atmosphere data, so the method can reduce the time required for the actual state of the tunnel oven to reach the target state and reach a new equilibrium state. Therefore, the method can improve the control efficiency of the tunnel oven, improve production efficiency and product quality.

[0053] A control method for a tunnel oven provided in an embodiment of the present application can be executed by a processor of a terminal device when running a corresponding computer program.

[0054] Figure 1 This is a flow chart of a control method for a tunnel oven provided in an embodiment of the present application. For the sake of convenience, only the part related to the present embodiment is shown. The method provided in the present embodiment includes the following steps:

[0055] S100: Acquire historical parameters and real-time parameters corresponding to a plurality of control points of the tunnel oven; the historical parameters include historical temperature data and historical atmosphere data, and the real-time parameters include real-time temperature data and real-time atmosphere data.

[0056] Specifically, temperature sensors are provided at multiple control points of the tunnel oven to collect temperature data corresponding to each control point of the tunnel oven to detect the temperature distribution in the tunnel oven. The temperature sensor may be a thermocouple, which is not limited in this embodiment.

[0057] Specifically, atmosphere sensors are set at multiple control points in the tunnel oven; the atmosphere sensors include atmosphere composition sensors and atmosphere concentration sensors. The atmosphere composition sensors are used to detect the atmosphere composition in the tunnel oven, such as oxygen sensors, nitrogen sensors, etc., and the atmosphere concentration sensors are used to detect the mixing ratio in the tunnel oven.

[0058] It should be noted that the historical parameters in this embodiment refer to the temperature data and atmosphere data corresponding to the historical moments, and the real-time parameters refer to the temperature data and atmosphere data currently collected, that is, in this embodiment, the historical parameters include historical temperature data and historical atmosphere data, and the real-time parameters include real-time temperature data and real-time atmosphere data. It can be understood that the temperature data and atmosphere data are collected in real time using the temperature sensor and the atmosphere sensor, and the historical parameters and the real-time parameters are determined according to the time sequence.

[0059] Specifically, the temperature sensors and atmosphere sensors are connected by a data acquisition system, and the temperature data and atmosphere data respectively collected by the temperature sensors and atmosphere sensors are obtained by the data acquisition system at regular intervals, that is, historical parameters and real-time parameters are obtained. In this embodiment, the data acquisition system can be a programmable logic controller (PLC); the collected temperature data and atmosphere data are stored in the memory of the PLC for subsequent processing.

[0060] S200: using a prediction model to predict prediction parameters corresponding to a plurality of control points at future moments according to historical parameters and real-time parameters; the prediction parameters include predicted temperature data and predicted atmosphere data.

[0061] Specifically, after the processor of the terminal device obtains the historical parameters and the real-time parameters from the data acquisition system, the historical parameters and the real-time parameters are input into the pre-trained prediction model, and the prediction model is used to predict the prediction parameters corresponding to multiple control points at future moments according to the historical parameters and the real-time parameters. Specifically, the prediction model is used to predict the prediction parameters corresponding to the next moment or multiple moments and multiple control points in the future.

[0062] For example, assuming that there are three control points (control point 1, control point 2, and control point 3) in the tunnel oven, each control point can control the temperature and atmosphere of the tunnel oven; this step uses the prediction model to predict the predicted temperature data and predicted atmosphere data corresponding to the three control points at two moments in the future (t1 and t2).

[0063] This embodiment does not limit the specific type of the prediction model. Based on the initial neural network, historical parameters and real-time parameters are used for learning and training to obtain the corresponding prediction model. The initial neural network can be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a long short-term memory neural network (LSTM), and a feed-forward neural network (FNNs).

[0064] S300: Determine the target setting values ​​corresponding to the respective prediction parameters using the model prediction control model; the target setting values ​​include a target temperature setting value and a target atmosphere setting value.

[0065] After the prediction parameters are determined, they are input into the Model Predictive Control (MPC) model, and the target set values ​​corresponding to each prediction parameter are determined by the MPC model; the MPC model is an advanced control model that uses prediction parameters to optimize the current control action. In a specific example, the MPC model can solve the optimization problem by linear programming or quadratic programming to determine the corresponding target set values.

[0066] In a specific example, the temperature target value and the atmosphere target value are determined according to the production requirements of the tunnel oven; the temperature target value refers to the temperature value that the tunnel oven is expected to reach, and the atmosphere target value refers to the atmosphere value that the tunnel oven is expected to reach; the atmosphere value includes the gas composition and the mixing ratio of each gas composition. Then, the temperature target value, the atmosphere target value and the prediction parameters are input into the model predictive control model, and the model predictive control model is used to output the target setting values ​​corresponding to each prediction parameter.

[0067] It should be noted that the target setting value refers to the setting value that needs to be set for the tunnel oven based on the predicted parameters, that is, the target setting value needs to be set for the tunnel oven, and the temperature and atmosphere of the tunnel oven are controlled based on the target setting value so that the tunnel oven can reach the temperature target value and the atmosphere target value. Among them, the target setting value includes the target temperature setting value and the target atmosphere setting value.

[0068] S400: Control and adjust the temperature and atmosphere of the tunnel oven according to the target temperature setting value and the target atmosphere setting value.

[0069] Specifically, after determining the target temperature setting value and the target atmosphere setting value, a temperature control signal is determined based on the target temperature setting value, and the temperature control signal is used to control the temperature adjustment device, and the temperature of the tunnel oven is adjusted by the temperature adjustment device. The temperature adjustment device includes an electric heater, a gas heater or a steam coil, etc., which is used to adjust the temperature of the tunnel oven.

[0070] An atmosphere control signal is determined based on the target atmosphere setting value, and the atmosphere control signal is used to control the atmosphere regulating equipment. The atmosphere of the tunnel oven is adjusted by the atmosphere regulating equipment. The atmosphere regulating equipment includes a gas flow controller for controlling the flow and proportion of various gas components (such as oxygen, nitrogen, hydrogen, etc.), thereby adjusting the atmosphere components in the oven.

[0071] The embodiment of the present application provides a control method for a tunnel oven. After predicting the predicted parameters corresponding to multiple control points at future moments according to the historical parameters and real-time parameters corresponding to multiple control points of the tunnel oven, a model prediction control model is used to determine the target setting value based on the predicted parameters, and then the temperature and atmosphere of the tunnel oven are controlled and adjusted according to the target temperature setting value and the target atmosphere setting value. On the one hand, the historical parameters and the real-time parameters both include temperature data and atmosphere data. The predicted parameters corresponding to multiple control points at future moments are predicted according to the historical parameters and the real-time parameters. That is to say, the method considers multiple control targets at the same time, generates the globally optimized target temperature setting value and the target atmosphere setting value, and the operation process is convenient, improves the temperature and atmosphere adjustment efficiency, and can also reduce the influence of each control point on other control points when it is adjusted independently, which can improve the control accuracy of the temperature and atmosphere in the tunnel oven; on the other hand, the method uses the predicted parameters to adjust and control the temperature and atmosphere of the tunnel oven in advance, rather than just adjusting according to the generated temperature data and atmosphere data, so the method can reduce the time required for the actual state of the tunnel oven to reach the target state and reach a new equilibrium state. Therefore, the method can improve the control efficiency of the tunnel oven, improve production efficiency and product quality.

[0072] Based on the above embodiment, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the prediction model is used to predict the prediction parameters corresponding to multiple control points at future moments according to the historical parameters and the real-time parameters, including:

[0073] The historical parameters and real-time parameters are input into the LSTM prediction model, and the LSTM prediction model is used to output the prediction parameters corresponding to multiple control points at future moments.

[0074] Among them, the LSTM prediction model is a special recurrent neural network (RNN) that is mainly used to process time series data. It solves the problem that long-term dependent information is difficult to capture in traditional RNNs, namely the problem of "gradient disappearance" or "gradient explosion". LSTM controls the flow of information by including three gated units (input gate, forget gate, and output gate), allowing LSTM to remember historical information for a longer period of time and effectively handle long-distance correlations. Among them, the input gate determines whether the new input is added to the cell state; the forget gate determines which old information should be removed from the cell state; the output gate determines how to extract the part used for the current prediction from the cell state. The advantages of the LSTM prediction model include: the ability to learn long-term dependencies: solving the gradient decay problem in RNN; adaptive memory storage: can selectively retain useful information; reduce the risk of overfitting: self-regulate the flow of information through the gate mechanism.

[0075] In practical applications, historical temperature data and historical atmosphere data are obtained and organized into time series data sets to obtain training samples. The model is trained iteratively multiple times according to the training samples until the performance of the model on the training set is stable or reaches a predetermined number of iterations, and the LSTM prediction model is obtained. Using the LSTM prediction model to fit the multivariate dynamic process of the tunnel oven can more effectively predict the changing trends of the temperature and atmosphere in the tunnel oven.

[0076] It should be noted that for temperature prediction, the LSTM prediction model combines historical temperature data, real-time temperature data and the airflow pattern in the tunnel oven to predict the changing trend of temperature distribution. After introducing multiple temperature sensors distributed in different areas of the tunnel oven, the LSTM prediction model is used to learn the changes in the temperature field, thereby performing multi-point prediction and control of the temperature in different areas of the tunnel oven to ensure the uniformity of temperature in each area.

[0077] For atmosphere prediction, the LSTM prediction model can predict changes in the atmosphere, including oxygen content, nitrogen content, hydrogen content, and humidity. Based on the historical atmosphere data collected by the atmosphere sensor, real-time atmosphere data, and process requirements at different production stages, the changing trend of the atmosphere composition, that is, the diffusion rate and mixing ratio of different gases, can be predicted in advance.

[0078] According to the method of this embodiment, the prediction parameters corresponding to the future time can be predicted more efficiently and accurately based on the historical parameters and real-time parameters, thereby improving the accuracy and efficiency of the tunnel oven control.

[0079] On the basis of the above embodiments, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the method further includes:

[0080] Obtain the constraints for tunnel oven control;

[0081] The model predictive control model is used to determine the target setting values ​​corresponding to each prediction parameter, including:

[0082] The model predictive control model is used to determine the target setting values ​​corresponding to each prediction parameter based on the prediction parameters and constraints.

[0083] The constraint condition refers to a restrictive condition or limiting factor that must be followed when determining the target setting value. This embodiment does not limit the specific content of the constraint condition.

[0084] In this embodiment, after obtaining the constraints of the tunnel oven control, the model predictive control model is used to determine the target setting values ​​corresponding to each prediction parameter based on the prediction parameters and the constraints. In a specific example, the constraints include but are not limited to temperature range constraints, atmosphere composition constraints, power limits of heaters and cooling systems, gas flow constraints, pressure constraints, thermal inertia constraints, production efficiency and cost constraints.

[0085] Among them, the temperature range constraint means that the temperature in the tunnel oven must be maintained within a specific temperature range and cannot exceed the upper and lower limits of the temperature range to ensure product quality and safety; the atmosphere composition constraint means that the atmosphere composition (such as oxygen, nitrogen and hydrogen, etc.) in the tunnel oven needs to be maintained within a certain proportion or concentration range; the power limit of the heater and cooling system means that the output power of the heater and cooling system has maximum and minimum limits. When adjusting the temperature and atmosphere control of the tunnel oven, the power limit of the heater and cooling system cannot be exceeded; the gas flow constraint means that the flow of the gas flow controller has maximum and minimum limits to ensure the stability of the atmosphere composition; the pressure constraint means that the pressure inside the tunnel oven needs to be maintained within a safe pressure range; the thermal inertia constraint is to limit the temperature change in the tunnel oven from being too fast, otherwise it may cause structural damage to the tunnel oven or cause product defects; the production efficiency and cost constraints mean that when determining the target setting value, the production efficiency and cost of the tunnel oven need to be considered to maximize economic benefits.

[0086] In this embodiment, the target setting value is determined by further combining the constraint conditions of the tunnel oven control, so that the determined target setting value can enable the tunnel oven to meet the requirements of product quality and production efficiency on a practical basis.

[0087] Figure 2 This is a flow chart of a control method for a tunnel oven provided by another embodiment of the present application. Figure 2 As shown, based on the above embodiment, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the temperature and atmosphere of the tunnel oven are controlled and adjusted according to the target temperature setting value and the target atmosphere setting value, including:

[0088] The target temperature setting value is sent to the temperature PID controller, and the temperature PID controller is used to determine the temperature control signal based on the target temperature setting value and the real-time temperature data, so that the temperature adjustment device performs temperature control adjustment on the tunnel oven according to the temperature control signal;

[0089] The target atmosphere setting value is sent to the atmosphere PID controller, and the atmosphere PID controller is used to determine the atmosphere control signal based on the target atmosphere setting value and real-time atmosphere data, so that the atmosphere adjustment equipment can adjust the atmosphere control of the tunnel oven according to the atmosphere control signal.

[0090] Among them, the PID controller is a feedback controller that determines the control signal according to the difference between the target setting value and the actual value (real-time parameter) to adjust the temperature and atmosphere in the tunnel oven. In this embodiment, after determining the target temperature setting value and the target atmosphere setting value, the temperature adjustment device and the atmosphere adjustment device are controlled by the temperature PID controller and the atmosphere PID controller respectively, thereby realizing the adjustment and control of the temperature and atmosphere of the tunnel oven.

[0091] Specifically, the target temperature setting value is sent to the temperature PID controller, the temperature PID controller calculates the temperature difference between the target temperature setting value and the real-time parameter temperature, performs proportional adjustment, integral adjustment and differential adjustment according to the temperature difference, and determines the corresponding temperature control signal; the temperature control signal is sent to a temperature adjustment device, such as a heater or a cooler, and the temperature in the tunnel oven is adjusted and controlled by the temperature adjustment device.

[0092] Specifically, the target atmosphere setting value is sent to the atmosphere PID controller, and the atmosphere PID controller calculates the atmosphere difference between the target atmosphere setting value and the real-time atmosphere parameters, performs proportional adjustment, integral adjustment and differential adjustment according to the atmosphere difference, and determines the corresponding atmosphere control signal; the atmosphere control signal is sent to the atmosphere adjustment equipment, such as the gas flow controller, and the atmosphere in the tunnel oven is adjusted and controlled by the atmosphere adjustment equipment.

[0093] In actual applications, after the temperature and atmosphere in the tunnel oven are adjusted and controlled by the temperature regulating equipment and the atmosphere regulating equipment, the real-time temperature data and real-time atmosphere data in the tunnel oven will also be affected. Therefore, the step of obtaining the historical parameters and the real-time parameters is returned to continue monitoring the temperature and atmosphere of the tunnel oven.

[0094] According to the method of this embodiment, the local temperature and local atmosphere of the tunnel oven are finely controlled, and the temperature and atmosphere of the tunnel oven can be accurately controlled to ensure that the temperature and atmosphere of the control area corresponding to each control point can reach the corresponding temperature target value and atmosphere target value.

[0095] It should also be noted that the LSTM prediction model in this embodiment can be integrated into the model predictive control model.

[0096] Specifically, this embodiment adopts a double-layer cascade control structure at the control level, with the model predictive control model as the master controller and the PID controller as the slave controller.

[0097] After obtaining the historical parameters and real-time parameters, the historical parameters and real-time parameters are input into the model predictive control model, and the LSTM prediction model in the model predictive control model is used to accurately predict the changes in temperature and atmosphere of multiple control points in the tunnel oven based on the real-time parameters and historical parameters.

[0098] Based on the prediction parameters output by the LSTM prediction model, the model predictive control model considers multiple control objectives (such as multi-point temperatures, multiple atmosphere components) and constraints, effectively coordinates multiple control variables, and generates globally optimized target temperature set values ​​and target atmosphere set values.

[0099] Then, the globally optimized target temperature setting value and target atmosphere setting value generated by the model predictive control model are sent to the temperature PID controller and the atmosphere PID controller respectively; the PID controller compares the target setting value with the real-time parameters, and performs refined control on the local temperature and atmosphere composition, thereby controlling the temperature control equipment and the atmosphere control equipment to achieve high-precision and rapid response, ensuring that the temperature and atmosphere of the control area corresponding to each control point can reach the corresponding temperature target value and atmosphere target value.

[0100] On the basis of the above embodiments, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the method further includes:

[0101] Perform data preprocessing operations on real-time parameters and historical parameters.

[0102] Considering that in actual applications, the collected real-time parameters and historical parameters may include outliers, missing values ​​or noise data, etc., these data may affect the accuracy of determining the prediction parameters, thereby affecting the accuracy of the temperature and atmosphere adjustment of the tunnel oven.

[0103] In this embodiment, the collected real-time parameters and historical parameters are cleaned, and outliers, missing values ​​or noise data are removed to improve the data quality of the real-time parameters and historical parameters, reduce data errors, and improve the accuracy of the predicted parameters, thereby improving the accuracy of the temperature and atmosphere adjustment of the tunnel oven.

[0104] Among them, outliers refer to data that is inconsistent with the overall pattern of historical parameters and real-time parameters collected, which may be caused by measurement or input errors; missing values ​​refer to data that is not recorded or lost in historical parameters and real-time parameters; noise data refers to data that is wrong or deviates from the actual value in historical parameters and real-time parameters, which may be inaccurate data caused by environmental interference or sensor accuracy limitations. Specifically, for outliers, statistical methods (such as Z-score or IQR method) can be used to identify outliers in the data, and the outliers can be deleted or modified using statistical methods; for missing values, records containing missing values ​​can be deleted, filled with mean or median, or a prediction model can be used to estimate missing values; for noisy data, smoothing techniques (such as moving average or median filtering) can be used to reduce noise in the data.

[0105] This embodiment can improve data quality and control accuracy of the tunnel oven by further performing data preprocessing operations on historical parameters and real-time parameters.

[0106] On the basis of the above embodiments, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the method further includes:

[0107] Display the real-time parameters, predicted parameters and target setting values ​​of the tunnel oven.

[0108] In this embodiment, after real-time parameters are collected, predicted parameters are outputted using a prediction model, and target setting values ​​corresponding to each predicted parameter are determined using a model prediction control model, the real-time parameters, predicted parameters, and target setting values ​​of the tunnel oven are displayed. Specifically, the real-time parameters, predicted parameters, and target setting values ​​of the tunnel oven can be displayed through a human-machine interface (HMI), a control panel, or other display devices.

[0109] In practical applications, the display device can be directly connected to the terminal device for communication, and the real-time parameters, predicted parameters and target setting values ​​of the tunnel oven can be directly displayed by the display device. In other embodiments, the real-time parameters, predicted parameters and target setting values ​​of the tunnel oven can be uploaded to the cloud server based on cloud services and Internet of Things technologies, and then the real-time parameters, predicted parameters and target setting values ​​of the tunnel oven can be obtained from the cloud server through the display device and displayed, so as to realize remote access and monitoring of data.

[0110] According to the method of this embodiment, the real-time parameters, predicted parameters and target setting values ​​of the tunnel oven can be displayed in real time, which is convenient for operators to manage and control the tunnel oven.

[0111] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0112] It should be noted that the information collection process (such as face image collection process, fingerprint information collection process, etc.) / feature extraction process involved in this application is performed with the user's knowledge and permission, that is, the information collection process / feature extraction process complies with the requirements of laws and regulations and does not constitute an act that harms the public interest.

[0113] Figure 3 The figure shows a schematic diagram of the structure of a control device for a tunnel oven provided in an embodiment of the present application. Figure 3 As shown, a control device for a tunnel oven in this embodiment includes an acquisition module 310, a prediction module 320, a determination module 330 and a control module 340; wherein,

[0114] An acquisition module 310 is used to acquire historical parameters and real-time parameters corresponding to a plurality of control points of the tunnel oven; the historical parameters include historical temperature data and historical atmosphere data, and the real-time parameters include real-time temperature data and real-time atmosphere data;

[0115] Prediction module 320, used to predict the prediction parameters corresponding to multiple control points at future moments according to historical parameters and real-time parameters using a prediction model; the prediction parameters include predicted temperature data and predicted atmosphere data;

[0116] A determination module 330 is used to determine target setting values ​​corresponding to each prediction parameter using a model predictive control model; the target setting values ​​include a target temperature setting value and a target atmosphere setting value;

[0117] The control module 340 is used to control and adjust the temperature and atmosphere of the tunnel oven according to the target temperature setting value and the target atmosphere setting value.

[0118] A control device for a tunnel oven provided in an embodiment of the present application has the same beneficial effects as the control method for a tunnel oven described above.

[0119] In one embodiment, the prediction module includes:

[0120] The prediction submodule is used to input historical parameters and real-time parameters into the LSTM prediction model, and use the LSTM prediction model to output the prediction parameters corresponding to multiple control points at future moments.

[0121] In one embodiment, a control device for a tunnel oven further includes:

[0122] A constraint condition acquisition module is used to obtain the constraint conditions of tunnel oven control;

[0123] The modules are determined to include:

[0124] The target setting value determination submodule is used to determine the target setting value corresponding to each prediction parameter based on the prediction parameters and constraint conditions using the model predictive control model.

[0125] In one embodiment, the control module includes:

[0126] The temperature control submodule is used to send the target temperature setting value to the temperature PID controller, and use the temperature PID controller to determine the temperature control signal based on the target temperature setting value and the real-time temperature data, so that the temperature adjustment device can adjust the temperature of the tunnel oven according to the temperature control signal;

[0127] The atmosphere control submodule is used to send the target atmosphere setting value to the atmosphere PID controller, and use the atmosphere PID controller to determine the atmosphere control signal based on the target atmosphere setting value and real-time atmosphere data, so that the atmosphere adjustment equipment can adjust the atmosphere control of the tunnel oven according to the atmosphere control signal.

[0128] In one embodiment, a control device for a tunnel oven further includes:

[0129] The preprocessing module is used to perform data preprocessing operations on real-time parameters and historical parameters.

[0130] In one embodiment, a control device for a tunnel oven further includes:

[0131] The display module is used to display the real-time parameters, predicted parameters and target setting values ​​of the tunnel oven.

[0132] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0133] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0134] Figure 4 This is a schematic diagram of the structure of a terminal device provided in an embodiment of the present application. Figure 4 As shown, the terminal device 400 of this embodiment includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and executable on the processor 402; when the processor 402 executes the computer program 403, the steps in the above-mentioned control method embodiments of each tunnel oven are implemented; or when the processor 402 executes the computer program 403, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0135] Exemplarily, the computer program 403 may be divided into one or more modules / units, one or more modules / units are stored in the memory 401, and are executed by the processor 402 to implement the method of the embodiment of the present application. One or more modules / units may be a series of computer program instruction segments that can perform specific functions, and the instruction segments are used to describe the execution process of the computer program 403 in the terminal device 400. For example, the computer program 403 may be divided into an acquisition module, a prediction module, a determination module, and a control module, and the specific functions of each module are as follows:

[0136] An acquisition module is used to acquire historical parameters and real-time parameters corresponding to multiple control points of the tunnel oven; the historical parameters include historical temperature data and historical atmosphere data, and the real-time parameters include real-time temperature data and real-time atmosphere data;

[0137] A prediction module is used to predict the prediction parameters corresponding to multiple control points at future moments according to historical parameters and real-time parameters using a prediction model; the prediction parameters include predicted temperature data and predicted atmosphere data;

[0138] A determination module, used to determine target set values ​​corresponding to each prediction parameter respectively by using a model prediction control model; the target set values ​​include a target temperature set value and a target atmosphere set value;

[0139] The control module is used to control and adjust the temperature and atmosphere of the tunnel oven according to the target temperature setting value and the target atmosphere setting value.

[0140] In application, the terminal device 400 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device 400 may include but is not limited to a memory 401 and a processor 402. Those skilled in the art will appreciate that Figure 4 It is only an example of a terminal device and does not constitute a limitation of the terminal device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.; among which, the input and output devices may include cameras, audio acquisition / playback devices, display screens, etc.; the network access device may include a communication module for wireless communication with external devices.

[0141] In applications, the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0142] In applications, the memory can be an internal storage unit of a terminal device, such as a hard disk or memory of the terminal device; it can also be an external storage device of the terminal device, such as a plug-in hard disk equipped on the terminal device, a smart media card (SMC), a secure digital (SD) card, a flash card, etc.; it can also include both the internal storage unit and the external storage device of the terminal device. The memory is used to store operating systems, applications, boot loaders, data, and other programs, such as program codes of computer programs. The memory can also be used to temporarily store data that has been output or is about to be output.

[0143] The embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0144] The present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying the computer program code to a terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0145] A computer-readable storage medium provided in an embodiment of the present application has the same beneficial effects as the above-mentioned control method of a tunnel oven.

[0146] An embodiment of the present application further provides a computer program product, including a computer program, which can implement the steps in the above-mentioned method embodiments when executed by a processor.

[0147] A computer program product provided in an embodiment of the present application has the same beneficial effects as the above-mentioned control method of a tunnel oven.

[0148] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0149] Those of ordinary skill in the art will appreciate that the devices and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0150] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices, which can be electrical, mechanical or other forms.

[0151] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A control method for a tunnel oven, characterized in that: The method comprises: Acquire historical parameters and real-time parameters corresponding to a plurality of control points of the tunnel oven; the historical parameters include historical temperature data and historical atmosphere data, and the real-time parameters include real-time temperature data and real-time atmosphere data; Using a prediction model to predict prediction parameters corresponding to a plurality of control points at future moments according to the historical parameters and the real-time parameters; the prediction parameters include predicted temperature data and predicted atmosphere data; Determining target setting values ​​corresponding to each of the prediction parameters using a model predictive control model; the target setting values ​​include a target temperature setting value and a target atmosphere setting value; The temperature and atmosphere of the tunnel oven are controlled and adjusted respectively according to the target temperature setting value and the target atmosphere setting value.

2. The method according to claim 1, characterized in that The method of using the prediction model to predict the prediction parameters corresponding to the plurality of control points at future moments according to the historical parameters and the real-time parameters includes: The historical parameters and the real-time parameters are input into the LSTM prediction model, and the LSTM prediction model is used to output the prediction parameters corresponding to the multiple control points at future moments.

3. The method according to claim 1, characterized in that The method further comprises: Obtaining constraints for controlling the tunnel oven; The method of using the model predictive control model to determine the target setting values ​​corresponding to the prediction parameters includes: A model predictive control model is used to determine target setting values ​​corresponding to each of the prediction parameters based on the prediction parameters and the constraint conditions.

4. The method according to claim 1, characterized in that: The controlling and adjusting the temperature and atmosphere of the tunnel oven according to the target temperature setting value and the target atmosphere setting value respectively includes: The target temperature setting value is sent to a temperature PID controller, and a temperature control signal is determined by the temperature PID controller based on the target temperature setting value and the real-time temperature data, so that a temperature regulating device performs temperature control adjustment on the tunnel oven according to the temperature control signal; The target atmosphere setting value is sent to an atmosphere PID controller, and the atmosphere PID controller is used to determine an atmosphere control signal based on the target atmosphere setting value and the real-time atmosphere data, so that an atmosphere adjustment device performs atmosphere control adjustment on the tunnel oven according to the atmosphere control signal.

5. The method according to claim 1, characterized in that The method further comprises: A data preprocessing operation is performed on the real-time parameters and the historical parameters.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: The real-time parameters, predicted parameters and target setting values ​​of the tunnel oven are displayed.

7. A control device for a tunnel oven, characterized in that: The device comprises: An acquisition module, used to acquire historical parameters and real-time parameters corresponding to a plurality of control points of the tunnel oven; the historical parameters include historical temperature data and historical atmosphere data, and the real-time parameters include real-time temperature data and real-time atmosphere data; A prediction module, used to predict the prediction parameters corresponding to the plurality of control points at future moments according to the historical parameters and the real-time parameters using a prediction model; the prediction parameters include predicted temperature data and predicted atmosphere data; A determination module, used to determine target setting values ​​corresponding to each of the prediction parameters using a model prediction control model; the target setting values ​​include a target temperature setting value and a target atmosphere setting value; A control module is used to control and adjust the temperature and atmosphere of the tunnel oven according to the target temperature setting value and the target atmosphere setting value.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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