A gradient search method and system for reflow oven parameter setting

Abstract

The present application discloses a gradient search method and system for parameter setting of a reflow soldering furnace, the method comprising: establishing a temperature simulation model of the reflow soldering furnace, fitting heat transfer parameters according to constant temperature experimental data; then grouping the temperature zones in the simulation model according to the temperature zone grouping status of the reflow soldering furnace, setting the action step length, and establishing a gradient search action table according to the step length, establishing an evaluation index according to the control requirement of reducing the error with the reference process curve, calculating the action gradient using the evaluation index, and setting the initial value of the temperature parameter according to the reference process curve; finally, running the gradient search algorithm for iterative calculation to determine the optimal temperature control parameters. The present application aims at the problem that the temperature parameters of the reflow soldering furnace are mutually coupled and the setting is complex, and the temperature parameters of the reflow soldering furnace are set, and the error between the temperature zone curve output by the system and the reference process curve is reduced, which is conducive to quickly finding the optimal temperature parameters, improving the accuracy of temperature control, improving product quality, and reducing the defective rate.

Description

Technical Field

[0001] The invention relates to the field of mechatronics and automatic control technology, and in particular to a gradient search method and system for reflow oven parameter setting. Background Art

[0002] With the continuous development of modern processing theory and mechatronics technology, embedded systems based on printed circuit boards have developed rapidly and are widely used in various industries such as aerospace, electronics, automobiles, and metal processing. In this process, reflow ovens have been widely used due to their high efficiency and low cost. Especially in the context of circuit integration and component miniaturization, traditional welding methods have been difficult to meet the needs. Reflow technology, which is developed in the direction of high efficiency, multi-function and intelligence, has gradually become the core technology in printed circuit board processing.

[0003] Reflow soldering oven has become the core equipment for printed circuit board processing. However, due to the strong coupling and nonlinear characteristics of reflow soldering oven, it is difficult to determine the actual control parameters of the system under a specific process curve. At present, the control parameters of reflow soldering oven are mainly obtained through trial and error based on manual experience, which greatly increases the time cost of industrial production and places very high demands on the professionalism of operators. Considering the errors that may be introduced by manual experience, in actual work, it leads to reduced production efficiency and reduced product quality. This problem can be effectively improved by accurately modeling the existing equipment and optimizing the parameters using algorithms such as gradient optimization. However, in the field of reflow soldering, the development of control parameter optimization methods lags behind, which restricts the application effect of reflow soldering oven systems. Therefore, realizing the automated optimization of reflow soldering oven parameters has become an urgent problem to be solved.

[0004] Gradient descent method is an optimization algorithm that solves the minimum value along the direction of gradient descent. It is a common algorithm used for recursive approximation in machine learning and artificial intelligence. Its purpose is to search for the optimal solution that minimizes the loss function in the problem. In the gradient descent method, a point is first randomly selected in the problem space, and the gradient at this point is calculated. According to a certain step size, the next point is obtained along the gradient direction, and the next iteration is performed according to the gradient until the algorithm converges. The gradient descent method has shown strong performance in solving optimization problems and has been widely used in various fields such as machine learning and parameter optimization, and has achieved good results. However, in the field of reflow oven system parameter setting, parameter optimization methods represented by gradient descent have not yet been applied. Summary of the invention

[0005] In order to overcome the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a gradient search method and system for reflow soldering furnace parameter setting. According to the industrial reference curve of the reflow soldering furnace, the difference between the current parameter curve and the reference curve is calculated through simulation operation, and the optimal compensation parameters are iteratively obtained using the gradient descent method, thereby improving the accuracy of the control parameters and minimizing the error between the actual temperature and the reference curve.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] The first aspect of the present application discloses a gradient search method for reflow oven parameter setting, comprising:

[0008] S1, based on Fourier's law of heat conduction, the temperature simulation model of the reflow oven is carried out, and the formula is:

[0009]

[0010] Among them, α is the heat transfer parameter, T(t) is the temperature of the workpiece in the reflow oven at time t, is the temperature inside the reflow oven at time t, and Δt is the preset time interval;

[0011] S2, fitting the heat transfer parameter α according to the constant temperature experimental data;

[0012] S3, according to the temperature zone grouping state of the reflow oven, the temperature zones in the simulation model are grouped into preheating zone, heating zone and high temperature zone, the action step length is set, and the gradient search action table S for each temperature zone is generated according to the action step length i , i=1,2,3, where S i is a matrix, each column of which represents the possible action changes of the temperature in the temperature zone;

[0013] S4, input a preset reference process curve R, the sampling interval of each point in the reference process curve R is the preset time interval Δt, and a gradient descent loss function l is constructed as an evaluation index, and the loss function l is:

[0014]

[0015] Where A(j) is the simulation curve, R(j) is the reference process curve, and N is the data length of A(j) and R(j);

[0016] S5, setting an initial temperature parameter for each temperature zone, wherein the initial temperature parameter for each temperature zone is equal to the temperature value in the corresponding temperature zone in the reference process curve;

[0017] S6, in the order of preheating zone, heating zone, and high temperature zone, in each temperature zone, iThe elements in each column in the corresponding temperature group are added to the initial temperature of each temperature zone, and the result is input into the simulation model as the temperature parameter for simulation. After the simulation curve is obtained, the corresponding loss function value is calculated until S i After all the simulations of the columns are completed, the temperature parameter corresponding to the minimum loss function value is used as the initial parameter for the next iteration until the preset number of iterations is completed;

[0018] S7, completing parameter calculations for all temperature zones in sequence according to step S6 to obtain optimal temperature control parameters.

[0019] Preferably, the preset time interval Δt is 0.25 s.

[0020] Preferably, step S2 specifically includes the following steps:

[0021] S201, determine the values ​​of multiple heat transfer parameters based on the constant temperature experiment:

[0022] Place the workpiece in the reflow oven, set the temperature of each temperature zone to 150°C, the chain speed v = 1035mm / min, and the fan frequency to a constant Run the reflow oven to conduct experiments, record the workpiece temperature, and calculate the α value based on the three sets of temperature data. i The value is calculated as follows:

[0023]

[0024] S202, obtain a fitting curve and / or fitting equation of the heat transfer parameters changing with the fan frequency by fitting:

[0025] According to the relationship between heat transfer parameters and fan speed Using the known conditions α1, α2, α3, k and b are calculated by the least squares method, and then the fitting curve and / or fitting equation of the heat transfer parameter α changing with the fan frequency is obtained; if the workpiece type is changed, or the temperature field inside the reflow oven is changed, return to step S201 to re-perform the constant temperature experiment and calculate the heat transfer parameters α, k and b.

[0026] Preferably, step S3 specifically includes the following steps:

[0027] According to the temperature zone grouping of the reflow oven, the temperature zones of the reflow oven simulation model are grouped into preheating zone, heating zone and high temperature zone. The number of temperature zones in each group is n. i , set the search action step size to 2℃, and generate the action change matrix A according to the step size s = [-2, 0, 2], generating the action space S for each temperature zone i , S i For one Each column of the matrix represents the possible action change of the temperature in the temperature zone, S i The basic form is:

[0028]

[0029] Preferably, in step S4, the reference process curve R adopts a saddle-shaped reflow curve RSS (Ramp-Sock-Spike).

[0030] Preferably, step S6 specifically includes the following steps:

[0031] In each temperature group, S i The elements in each column in the corresponding temperature group are added to the initial temperature of each temperature zone, and the result is input into the simulation model as the temperature parameter. At the same chain speed as the reference curve, the model established in step S1 is used to simulate the heat transfer parameters calculated in step S2. After the simulation curve is obtained, the corresponding loss function value l is calculated. i , until S i All simulations of each column in are completed; compare all loss function values ​​obtained by simulation, take the temperature parameter corresponding to the minimum loss function value as the initial parameter, and perform the next iteration until the maximum number of iterations.

[0032] More preferably, the maximum number of iterations is set to 30 times.

[0033] The second aspect of the present application discloses a gradient search system for reflow oven parameter setting, comprising:

[0034] The simulation model building module is used to simulate the temperature of the reflow oven according to Fourier's law of heat conduction. The formula is:

[0035]

[0036] Among them, α is the heat transfer parameter, T(t) is the temperature of the workpiece in the reflow oven at time t, is the temperature inside the reflow oven at time t, and Δt is the preset time interval;

[0037] A heat transfer parameter determination module is used to fit the heat transfer parameter α according to the constant temperature experimental data;

[0038] The simulation model temperature zone grouping module is used to group the temperature zones in the simulation model into a preheating zone, a heating zone and a high temperature zone according to the temperature zone grouping state of the reflow oven;

[0039] The action space generation module is used to set the action step length and generate the gradient search action table S for each temperature zone according to the action step length. i , i=1,2,3, where Si is a matrix, each column of which represents the possible action changes of the temperature in the temperature zone;

[0040] The loss function construction module is used to construct a gradient descent loss function l according to the input preset reference process curve R, the sampling interval of each point in the reference process curve R is the preset time interval Δt, and the loss function l is:

[0041]

[0042] Where A(j) is the simulation curve, R(j) is the reference process curve, and N is the data length of A(j) and R(j);

[0043] An initial temperature parameter setting module, used to set the initial temperature parameter of each temperature zone, the initial temperature parameter of each temperature zone being equal to the temperature value in the corresponding temperature zone in the reference process curve;

[0044] The iterative operation module is used to convert S i The elements in each column in the corresponding temperature group are added to the initial temperature of each temperature zone, and the result is input into the simulation model as the temperature parameter for simulation. After the simulation curve is obtained, the corresponding loss function value is calculated until S i All simulations of each column in are completed, and the temperature parameter corresponding to the minimum loss function value is used as the initial parameter for the next iteration until the preset number of iterations is completed to generate the optimal temperature control parameter.

[0045] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0046] 1) The present invention adjusts the temperature parameters of the reflow soldering furnace, reduces the error between the system output temperature zone curve and the reference process curve, and is beneficial to improving the temperature control accuracy, improving product quality, and reducing the defective rate.

[0047] 2) The present invention searches for temperature parameters by a gradient descent method, which is conducive to finding the optimal parameters, achieving the temperature control target, shortening the time for temperature parameter setting, and improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The drawings that constitute part of this application are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0049] Figure 1 It is a schematic flow diagram of the method of the present invention;

[0050] Figure 2(a) to (c) are constant temperature experimental data curves of Example 1 of the present invention;

[0051] Figure 3 is the saddle-shaped reference curve of Example 1 of the present invention;

[0052] Figure 4 This is an example of the actual operation curve of the final optimization parameters of Example 1 of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0054] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0055] Example:

[0056] Figure 1 A flowchart of a gradient search method for reflow oven parameter adjustment is shown in FIG.

[0057] like Figure 1 As shown, a gradient search method for reflow oven parameter setting specifically includes the following steps:

[0058] Step 1: Thermal simulation modeling of a reflow oven.

[0059] The simulation model is modeled according to Fourier's law of heat conduction, and its formula is:

[0060]

[0061] Among them, α is the heat transfer parameter, and its value needs to be determined by the constant temperature experiment in step 2. T(t) is the temperature of the workpiece in the reflow oven at time t. is the temperature inside the reflow oven at time t, and Δt is generally taken as 0.25s.

[0062] Step 2: Isothermal experiments were performed to determine the heat transfer parameters.

[0063] This embodiment uses a reflow soldering furnace device in a certain temperature zone to conduct experiments. The experimental steps are as follows:

[0064] Place the workpiece in the reflow oven, set the temperature of each zone to 150°C, and the chain speed v = 1035mm / min, with a constant fan frequency Run the reflow oven to conduct experiments and record the workpiece temperature. Calculate the α values ​​of the three sets of workpiece temperature data. i The value is calculated as follows:

[0065]

[0066] according to Figure 2 The constant temperature experimental data of (a) to (c) in the figure are used to obtain the α i The values ​​are α1=0.0269, α2=0.0287, and α3=0.0307 respectively.

[0067] Step 3: The fitting curve and / or fitting equation of the heat transfer parameters changing with the fan frequency are obtained by fitting.

[0068] According to the relationship between heat transfer parameters and fan speed Using the known conditions α1, α2, α3, Calculate k and b by the least squares method. i The values ​​are solved to obtain k=0.000685, b=0.02337, and then the fitting curve and / or fitting equation of the heat transfer parameter α varying with the fan frequency are obtained.

[0069] If the workpiece type is changed, or the temperature field inside the reflow oven is changed, return to step 2 to conduct the constant temperature experiment again and calculate the heat transfer parameters α, k and b.

[0070] Step 4: The temperature zones in the simulation model are grouped and action spaces are established for each temperature group.

[0071] According to the temperature zone grouping of the reflow oven, the temperature zones of the reflow oven simulation model are grouped into preheating zone, heating zone and high temperature zone, and the number of medium temperature zones in each group is set to n. i Among them, the number of temperatures in the preheating zone is n1=2, the number of temperatures in the heating zone is n2=7, and the number of temperatures in the high temperature zone is n3=3.

[0072] Set the search action step size to 2°C, and generate the action change matrix A according to the step size. s = [-2, 0, 2], generating the action space S for each temperature zone i , S i Should be one Each column of the matrix represents the possible action change of the temperature in the temperature zone, S i The basic form is:

[0073]

[0074] Then the action space S1 of the preheating zone is:

[0075]

[0076] The action space S2 in the temperature rising zone is:

[0077]

[0078] The action space S3 in the high temperature zone is:

[0079]

[0080] Step 5: Construct the loss function for gradient descent.

[0081] Input reference process curve R. In this embodiment, a saddle-shaped reflow curve RSS (Ramp-Sock-Spike) is used, such as Figure 3 As shown. The interval time between each point in the process curve is still 0.25s, and the loss function l of the gradient descent is established as:

[0082]

[0083] Among them, A(j) is the simulation curve, R(j) is the reference process curve, and N is the data length of A(j) and R(j).

[0084] Step 6: The reflow oven temperature parameters are calculated in the order of preheating zone, heating zone and high temperature zone.

[0085] First, enter a set of initial temperature parameters, specifically:

[0086] T int =[60, 100, 140, 160, 170, 170, 170, 172, 190, 240, 253, 215]

[0087] The initial temperature parameter of each temperature zone should be equal to the temperature value in the temperature zone in the reference process curve. i The elements in each column are added to the initial temperatures of each temperature zone in the corresponding temperature group, and the results are input back into the simulation model as temperature parameters. At the same chain speed as the reference curve, the model established in step 1 is used to simulate the heat transfer parameters calculated in steps 2 and 3. After the simulation curve is obtained, the loss function value l is calculated.i , until S i All simulations in each column are completed.

[0088] All loss function values ​​obtained by simulation are compared, and the temperature parameter corresponding to the minimum loss function value is used as the initial parameter to perform the next iteration until the maximum number of iterations N=30.

[0089] Then the calculation of the next temperature group is carried out until the calculation of all temperature zone parameters is completed.

[0090] Get the final temperature parameter T final for:

[0091] T final =[52,132,158,174,176,176,176,174,178,194,266,255,203];

[0092] Experiments were conducted according to the temperature parameters, and the results were as follows: Figure 4 The temperature curve shown. Figure 4 From the temperature curve, we can see that in the comparison of main indicators, the highest temperature of the standard curve is 250℃, and the highest temperature of the actual operation curve is 253℃. The workpiece in the standard curve is in the high temperature zone for 92 seconds, and in the actual operation it is 98 seconds. The error is within the acceptable range of processing.

[0093] On the other hand, the present application also discloses a gradient search system for reflow oven parameter setting, comprising:

[0094] The simulation model building module is used to simulate the temperature of the reflow oven according to Fourier's law of heat conduction. The formula is:

[0095]

[0096] Among them, α is the heat transfer parameter, T(t) is the temperature of the workpiece in the reflow oven at time t, is the temperature inside the reflow oven at time t, and Δt is the preset time interval;

[0097] A heat transfer parameter determination module is used to fit the heat transfer parameter α according to the constant temperature experimental data;

[0098] The simulation model temperature zone grouping module is used to group the temperature zones in the simulation model into a preheating zone, a heating zone and a high temperature zone according to the temperature zone grouping state of the reflow oven;

[0099] The action space generation module is used to set the action step length and generate the gradient search action table S for each temperature zone according to the action step length. i , i=1,2,3, where S iis a matrix, each column of which represents the possible action changes of the temperature in the temperature zone;

[0100] The loss function construction module is used to construct a gradient descent loss function l according to the input preset reference process curve R, the sampling interval of each point in the reference process curve R is the preset time interval Δt, and the loss function l is:

[0101]

[0102] Where A(j) is the simulation curve, R(j) is the reference process curve, and N is the data length of A(j) and R(j);

[0103] An initial temperature parameter setting module, used to set the initial temperature parameter of each temperature zone, the initial temperature parameter of each temperature zone being equal to the temperature value in the corresponding temperature zone in the reference process curve;

[0104] The iterative operation module is used to convert S i The elements in each column in the corresponding temperature group are added to the initial temperature of each temperature zone, and the result is input into the simulation model as the temperature parameter for simulation. After the simulation curve is obtained, the corresponding loss function value is calculated until S i All simulations of each column in are completed, and the temperature parameter corresponding to the minimum loss function value is used as the initial parameter for the next iteration until the preset number of iterations is completed to generate the optimal temperature control parameter.

[0105] In summary, the present application aims to solve the problem that the temperature parameters of a reflow soldering furnace are mutually coupled and the setting is complex, and provides a gradient search method and system for setting the parameters of a reflow soldering furnace. According to the industrial reference curve of the reflow soldering furnace, the difference between the current parameter curve and the reference curve is calculated through simulation operation, and the optimal compensation parameters are iteratively obtained using the gradient descent method, thereby reducing the error between the system output temperature zone curve and the reference process curve, which is conducive to quickly finding the optimal temperature parameters, improving the accuracy of temperature control, improving product quality, and reducing the defective rate.

[0106] The specific embodiments of the present invention are described in detail above, but they are only examples, and the present invention is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications and substitutions made to the present invention are also within the scope of the present invention. Therefore, the equalization changes and modifications made without departing from the spirit and scope of the present invention should be included in the scope of the present invention.

Claims

1. A gradient search method for reflow oven parameter setting, characterized in that: include: S1, based on Fourier's law of heat conduction, the temperature simulation model of the reflow oven is carried out, and the formula is: Among them, α is the heat transfer parameter, T(t) is the temperature of the workpiece in the reflow oven at time t, is the temperature inside the reflow oven at time t, and Δt is the preset time interval; S2, fitting the heat transfer parameter α according to the constant temperature experimental data; S3, according to the temperature zone grouping state of the reflow oven, the temperature zones in the simulation model are grouped into preheating zone, heating zone and high temperature zone, the action step length is set, and the gradient search action table S for each temperature zone is generated according to the action step length i , i=1,2,3, where S i is a matrix, each column of which represents the possible action changes of the temperature in the temperature zone; S4, input a preset reference process curve R, the sampling interval of each point in the reference process curve R is the preset time interval Δt, and a gradient descent loss function l is constructed as an evaluation index, and the loss function l is: Where A(j) is the simulation curve, R(j) is the reference process curve, and N is the data length of A(j) and R(j); S5, setting an initial temperature parameter for each temperature zone, wherein the initial temperature parameter for each temperature zone is equal to the temperature value in the corresponding temperature zone in the reference process curve; S6, in the order of preheating zone, heating zone, and high temperature zone, in each temperature zone, i The elements in each column in the corresponding temperature group are added to the initial temperature of each temperature zone, and the result is input into the simulation model as the temperature parameter for simulation. After the simulation curve is obtained, the corresponding loss function value is calculated until S i After all the simulations of the columns are completed, the temperature parameter corresponding to the minimum loss function value is used as the initial parameter for the next iteration until the preset number of iterations is completed; S7, completing parameter calculations for all temperature zones in sequence according to step S6 to obtain optimal temperature control parameters; Wherein, step S2 specifically includes the following steps: S201, determine the values ​​of multiple heat transfer parameters based on the constant temperature experiment: Place the workpiece in the reflow oven, set the temperature of each temperature zone to 150°C, the chain speed v = 1035mm / min, and the fan frequency to a constant Run the reflow oven to conduct experiments, record the workpiece temperature, and calculate the α value based on the three sets of temperature data. i The value is calculated as follows: S202, obtain a fitting curve and / or fitting equation of the heat transfer parameters changing with the fan frequency by fitting: According to the relationship between heat transfer parameters and fan speed Using the known conditions α1, α2, α3, k and b are calculated by the least square method, and then the fitting curve and / or fitting equation of the heat transfer parameter α changing with the fan frequency is obtained; if the workpiece type is changed, or the temperature field inside the reflow oven is changed, return to step S201 to re-perform the constant temperature experiment and calculate the heat transfer parameters α, k and b; Wherein, step S3 specifically includes the following steps: According to the temperature zone grouping of the reflow oven, the temperature zones of the reflow oven simulation model are grouped into preheating zone, heating zone and high temperature zone. The number of temperature zones in each group is n. i , set the search action step size to 2℃, and generate the action change matrix A according to the step size s = [-2, 0, 2], generating the action space S for each temperature zone i , S i For one Each column of the matrix represents the possible action change of the temperature in the temperature zone, S i The basic form is:

2. A gradient search method for reflow oven parameter setting according to claim 1, characterized in that: The preset time interval Δt is 0.25s.

3. A gradient search method for reflow oven parameter setting according to claim 1, characterized in that: In step S4, the reference process curve R adopts a saddle-shaped reflow curve RSS.

4. A gradient search method for reflow oven parameter setting according to claim 1, characterized in that: Step S6 specifically includes the following steps: In each temperature group, S i The elements in each column in the corresponding temperature group are added to the initial temperature of each temperature zone, and the result is input into the simulation model as the temperature parameter. At the same chain speed as the reference curve, the model established in step S1 is used to simulate the heat transfer parameters calculated in step S2. After the simulation curve is obtained, the corresponding loss function value l is calculated. i , until S i All simulations of each column in are completed; compare all loss function values ​​obtained by simulation, take the temperature parameter corresponding to the minimum loss function value as the initial parameter, and perform the next iteration until the maximum number of iterations.

5. A gradient search method for reflow oven parameter setting according to claim 4, characterized in that: The maximum number of iterations is set to 30 times.

6. A gradient search system for reflow oven parameter setting, characterized in that: include: The simulation model building module is used to simulate the temperature of the reflow oven according to Fourier's law of heat conduction. The formula is: Among them, α is the heat transfer parameter, T(t) is the temperature of the workpiece in the reflow oven at time t, is the temperature inside the reflow oven at time t, and Δt is the preset time interval; The heat transfer parameter determination module is used to fit the heat transfer parameter α according to the constant temperature experimental data, specifically including: Determine the values ​​of multiple heat transfer parameters according to the constant temperature experiment: Put the workpiece into the reflow oven, set the temperature of each temperature zone to 150℃, the chain speed v=1035mm / min, and keep the fan frequency constant Run the reflow oven to conduct experiments, record the workpiece temperature, and calculate the α value based on the three sets of temperature data. i The value is calculated as follows: And, fitting the fitting curve and / or fitting equation of the heat transfer parameters changing with the fan frequency: According to the relationship formula between the heat transfer parameters and the fan speed Using the known conditions α1, α2, α3, Calculate k and b by the least square method, and then obtain the fitting curve and / or fitting equation of the heat transfer parameter α changing with the fan frequency; if the workpiece type is changed, or the temperature field inside the reflow oven is changed, re-perform the constant temperature experiment and calculate the heat transfer parameters α, k and b; The simulation model temperature zone grouping module is used to group the temperature zones in the simulation model into preheating zones, heating zones and high temperature zones according to the temperature zone grouping status of the reflow oven. The number of temperature zones in each group is n. i ; The action space generation module is used to set the search action step size to 2°C and generate the action change matrix A according to the step size. s = [-2, 0, 2], generating the action space S for each temperature zone i , S i For one Each column of the matrix represents the possible action change of the temperature in the temperature zone, S i The basic form is: The loss function construction module is used to construct a gradient descent loss function l according to the input preset reference process curve R, the sampling interval of each point in the reference process curve R is the preset time interval Δt, and the loss function l is: Where A(j) is the simulation curve, R(j) is the reference process curve, and N is the data length of A(j) and R(j); An initial temperature parameter setting module, used to set the initial temperature parameter of each temperature zone, the initial temperature parameter of each temperature zone being equal to the temperature value in the corresponding temperature zone in the reference process curve; The iterative operation module is used to convert S i The elements in each column in the corresponding temperature group are added to the initial temperature of each temperature zone, and the result is input into the simulation model as the temperature parameter for simulation. After the simulation curve is obtained, the corresponding loss function value is calculated until S i All simulations of each column in are completed, and the temperature parameter corresponding to the minimum loss function value is used as the initial parameter for the next iteration until the preset number of iterations is completed to generate the optimal temperature control parameter.

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