Furnace temperature control method, device and equipment for reflow soldering and storage medium

Abstract

The invention discloses a furnace temperature control method, device and equipment for reflow soldering and a storage medium, and relates to the technical field of temperature control. Determining a coefficient of a PID algorithm by using a neural network according to the target parameter; performing PID calculation on the error between the furnace temperature set value and the furnace temperature actual value according to the coefficient of the PID algorithm to obtain a PID calculation result; and controlling the temperature of the reflow soldering furnace according to the PID calculation result. According to the method, the neural network is adopted to carry out adaptive adjustment on the coefficient of the PID algorithm, then the furnace temperature is controlled based on the coefficient subjected to adaptive adjustment, the furnace temperature can be accurately controlled, and high real-time performance, robustness and accuracy are achieved.

Description

Technical Field

[0001] The present application relates to the field of temperature control technology, and in particular to a reflow soldering furnace temperature control method, device, equipment and computer-readable storage medium. Background Art

[0002] Reflow soldering is a surface mount technology widely used in the electronics production industry for efficient and automated connection of components and circuit boards. The working principle of reflow soldering is based on the thermal expansion and contraction characteristics of materials. The metal powder in the solder paste is melted by heating, and then quickly cooled and solidified, thereby achieving a firm connection between the assembly and the circuit board. The control of the temperature in the reflow soldering furnace directly affects the yield rate of the products passing through the furnace, so timely and accurate control of the furnace temperature is extremely important. In the past, when traditional control methods were used to control the furnace temperature of reflow soldering, there were defects such as large lag and poor temperature control accuracy. Therefore, how to accurately control the furnace temperature to achieve strong real-time, robustness and accuracy has become a technical problem that needs to be solved urgently by technicians in this field. Summary of the invention

[0003] The purpose of this application is to provide a reflow oven temperature control method, device, equipment and computer-readable storage medium, which can accurately control the oven temperature and have strong real-time, robustness and accuracy.

[0004] In order to solve the above technical problems, the present application provides a reflow oven temperature control method, comprising:

[0005] Get target parameters;

[0006] Determining coefficients of a PID algorithm using a neural network according to the target parameters;

[0007] Performing PID calculation on the error between the furnace temperature setting value and the actual furnace temperature value according to the coefficient of the PID algorithm to obtain a PID calculation result;

[0008] The temperature of the reflow oven is controlled according to the PID calculation result.

[0009] In some embodiments, determining coefficients of a PID algorithm using a neural network according to the target parameters comprises:

[0010] According to the target parameters, the coefficients of the PID algorithm are determined using a BP neural network until the reflow oven temperature controlled by the determined coefficients of the PID algorithm reaches the requirement or the number of self-learning times of the BP neural network reaches a preset number.

[0011] In some embodiments, obtaining the target parameter includes:

[0012] Obtain the error between the furnace temperature setting value and the actual furnace temperature value, the furnace temperature setting value, and the actual furnace temperature value.

[0013] In some embodiments, the BP neural network includes four input neurons, five hidden neurons, and three output neurons; the input neurons correspond to the error between the furnace temperature set value and the actual furnace temperature value, the actual furnace temperature value, the actual furnace temperature value and unit 1 respectively; the output neurons correspond to the proportional coefficient, integral coefficient and differential coefficient of the PID algorithm respectively.

[0014] In some embodiments, before performing PID calculation on the error between the furnace temperature setting value and the actual furnace temperature value according to the coefficient of the PID algorithm, the step further includes:

[0015] Determine whether the reflow oven temperature is abnormal;

[0016] If abnormal, a PID calculation is performed on the error between the furnace temperature setting value and the actual furnace temperature value according to the coefficient of the PID algorithm;

[0017] If normal, return to the step of determining whether the reflow oven temperature is abnormal.

[0018] In some embodiments, it also includes:

[0019] After controlling the abnormal reflow oven temperature, determine whether the reflow oven temperature returns to normal;

[0020] If it does not return to normal, an alarm will be issued for manual intervention.

[0021] In some embodiments, it also includes:

[0022] Data is exchanged with the host computer by communicating with the host computer.

[0023] In order to solve the above technical problems, the present application also provides a reflow oven temperature control device, comprising:

[0024] An acquisition unit, used for acquiring target parameters;

[0025] A determination unit, configured to determine coefficients of a PID algorithm using a neural network according to the target parameters;

[0026] A calculation unit, used for performing PID calculation on the error between the furnace temperature setting value and the actual furnace temperature value according to the coefficient of the PID algorithm to obtain a PID calculation result;

[0027] A control unit is used to control the furnace temperature of the reflow soldering according to the PID calculation result.

[0028] In order to solve the above technical problems, the present application also provides a reflow oven temperature control device, comprising:

[0029] Memory for storing computer programs;

[0030] The processor is used to implement the steps of the reflow oven temperature control method as described above when executing the computer program.

[0031] In order to solve the above technical problems, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the reflow soldering furnace temperature control method as described above are implemented.

[0032] The furnace temperature control method for reflow soldering provided in the present application includes: obtaining target parameters; determining the coefficients of a PID algorithm using a neural network according to the target parameters; performing PID calculation on the error between the furnace temperature set value and the actual furnace temperature value according to the coefficients of the PID algorithm to obtain a PID calculation result; and controlling the furnace temperature of the reflow soldering according to the PID calculation result.

[0033] It can be seen that the reflow oven temperature control method provided by the present application uses a neural network to adaptively adjust the coefficients of the PID algorithm, determine a more reasonable coefficient, and make up for the shortcomings of the traditional PID algorithm. Then, the PID algorithm is used to control the oven temperature based on the adaptively adjusted coefficients, and the oven temperature can be accurately controlled, with strong real-time, robustness and accuracy.

[0034] The reflow oven temperature control device, equipment and computer-readable storage medium provided in this application all have the above-mentioned technical effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the prior art and the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 A schematic flow chart of a reflow oven temperature control method provided in an embodiment of the present application;

[0037] Figure 2 A schematic diagram of a furnace temperature control system provided in an embodiment of the present application;

[0038] Figure 3 A BP neural network structure diagram provided in an embodiment of the present application;

[0039] Figure 4 A schematic diagram of a flow chart of PID control combined with BP neural network provided in an embodiment of the present application;

[0040] Figure 5 A schematic diagram of a PID control structure combined with a BP neural network provided in an embodiment of the present application;

[0041] Figure 6 A schematic diagram of furnace temperature control provided in an embodiment of the present application;

[0042] Figure 7 A schematic diagram of a reflow oven temperature control device provided in an embodiment of the present application;

[0043] Figure 8 A schematic diagram of a reflow soldering furnace temperature control device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] The core of this application is to provide a reflow oven temperature control method, device, equipment and computer-readable storage medium, which can accurately control the oven temperature and have strong real-time, robustness and accuracy.

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] Please refer to Figure 1 , Figure 1 A flow chart of a reflow oven temperature control method provided in an embodiment of the present application is provided. Figure 1 As shown, the method includes:

[0047] S101: Obtain target parameters.

[0048] The execution subject of this embodiment is the furnace temperature control system. Figure 2 As shown in the figure, the hardware part of the furnace temperature control system can use the STM32F407 microcontroller as the core device. The furnace temperature control system can exchange data with the host computer through the communication module. The thermocouple and cold end compensation / amplification module in the peripheral circuit of the furnace temperature control system are mainly responsible for collecting the furnace temperature information of the reflow soldering, and inputting the ADC channel of the STM32F407 microcontroller to convert it into a digital signal. The control signal is output from the PWM channel of the STM32F407 microcontroller to the solid-state relay module, which is used to control the heating components in the reflow soldering furnace to control the temperature in the furnace.

[0049] In some embodiments, obtaining the target parameter includes:

[0050] Obtain the error between the furnace temperature setting value and the actual furnace temperature value, the furnace temperature setting value, and the actual furnace temperature value.

[0051] That is, the target parameters include the error between the furnace temperature setting value and the actual furnace temperature value, the furnace temperature setting value, and the actual furnace temperature value.

[0052] S102: Determine the coefficients of the PID algorithm using a neural network according to the target parameters.

[0053] In the case where the target parameters include the error between the furnace temperature setting value and the actual furnace temperature value, the furnace temperature setting value, and the actual furnace temperature value, the coefficients of the PID algorithm are determined using a neural network according to the target parameters including the error between the furnace temperature setting value and the actual furnace temperature value, the furnace temperature setting value, and the actual furnace temperature value. The coefficients of the PID algorithm include a proportional coefficient, an integral coefficient, and a differential coefficient.

[0054] Among them, targeted training of the neural network can be completed through historical furnace temperature control data, the training results can be accurately recorded, the training data can be compared with the predicted value by comparative analysis, and the error between the training data and the predicted value can be directly mapped to the individual fitness value.

[0055] In some embodiments, determining coefficients of a PID algorithm using a neural network according to the target parameters comprises:

[0056] According to the target parameters, the coefficients of the PID algorithm are determined using a BP neural network until the reflow oven temperature controlled by the determined coefficients of the PID algorithm reaches the requirement or the number of self-learning times of the BP neural network reaches a preset number.

[0057] This embodiment uses BP neural network to determine the coefficients of PID algorithm. Combining BP neural network with PID algorithm to control furnace temperature has the strong robustness of PID control and the self-learning and self-optimization capabilities of BP neural network.

[0058] In some embodiments, the BP neural network includes four input neurons, five hidden neurons, and three output neurons; the input neurons correspond to the error between the furnace temperature set value and the actual furnace temperature value, the actual furnace temperature value, the actual furnace temperature value and unit 1 respectively; the output neurons correspond to the proportional coefficient, integral coefficient and differential coefficient of the PID algorithm respectively.

[0059] refer to Figure 3 The BP neural network structure diagram, in this embodiment, the BP neural network structure adopts a 4-5-3 mode, that is, the BP neural network has 4 input neurons, namely , , and unit l. Among them, For input, is the error, is the output; there are 5 hidden neurons. There are 3 output neurons, which are the proportional coefficients , integral coefficient , differential coefficient .

[0060] Number of nodes in the input layer of the BP neural network , the number of hidden layer nodes , the initial values ​​of the weighted coefficients of the input layer and the hidden layer are and , the learning rate of BP neural network is , the inertia coefficient is ,make . According to the furnace temperature setting value And the actual value of furnace temperature , calculate the real-time temperature error . Calculate the input and output of each neuron of the BP neural network, and finally the parameters of the network output layer are the three adjustable coefficients of the PID controller , , .

[0061] Among them, the input function of the input layer is:

[0062] .

[0063] In the above formula, is the input neuron.

[0064] The intermediate function of the hidden layer is:

[0065] .

[0066] The input function and output function of the hidden layer are:

[0067] .

[0068] .

[0069] In the above formula, is the weight coefficient of the hidden layer.

[0070] The input function and output function of the output layer are:

[0071] .

[0072] .

[0073] in, , , .

[0074] Therefore, the transfer function of the output layer neuron is:

[0075] .

[0076] The learning algorithm of weighted coefficients is expressed as follows:

[0077] The performance indicator function is:

[0078] .

[0079] The coefficients are improved according to the echelon descent method, and the correction formula is added:

[0080] .

[0081] In the above formula: For learning efficiency, ; is the inertia coefficient, .

[0082] In summary, the weight learning method of the output layer is:

[0083] .

[0084] in, , is the derivative function.

[0085] The weight learning algorithm of the hidden layer is:

[0086] .

[0087] in, , is the derivative function.

[0088] The coefficients of the determined PID algorithm are transmitted to the PID controller in real time, and the PID controller calculates and outputs By adjusting the coefficients, the self-tuning of PID control is completed. The PID controller transmits the calculation results to the solid-state relay, and controls the temperature of the heating component by controlling the solid-state relay, thereby achieving the purpose of temperature control.

[0089] make , return to the step of calculating the real-time temperature error according to the furnace temperature setting value and the actual furnace temperature value, and start a new round of adaptive adjustment of the PID algorithm coefficient. Until the reflow oven temperature controlled by the determined PID algorithm coefficient reaches the requirement or the number of self-learning of the BP neural network reaches the preset number of times.

[0090] refer to Figure 4 and Figure 5 As shown, the process of PID control combined with BP neural network includes:

[0091] The program is initialized and the furnace temperature is sampled. According to the furnace temperature setting value and the actual furnace temperature value, the real-time temperature error is calculated. The BP neural network calculates the input and output of each neuron, and the parameters of the final network output layer are the three adjustable coefficients of the PID controller. , , The PID controller performs PID control based on the coefficients determined by the BP neural network. The BP neural network performs self-learning to correct the weights of the hidden layer and the output layer. Update parameter k (take k=k+1). Until the reflow oven temperature controlled by the coefficients of the determined PID algorithm reaches the requirement or the number of self-learning times of the BP neural network reaches the preset number.

[0092] S103: Perform PID calculation on the error between the furnace temperature setting value and the actual furnace temperature value according to the coefficient of the PID algorithm to obtain a PID calculation result.

[0093] S104: Controlling the furnace temperature of the reflow soldering according to the PID calculation result.

[0094] The PID controller performs PID calculation on the error between the set value of the furnace temperature and the actual value of the furnace temperature based on the coefficient of the determined PID algorithm, obtains the PID calculation result, and then outputs the PID calculation result, controls the temperature of the heating component by controlling the solid-state relay, and thus controls the furnace temperature of the reflow soldering.

[0095] In some embodiments, before performing PID calculation on the error between the furnace temperature setting value and the actual furnace temperature value according to the coefficient of the PID algorithm, the step further includes:

[0096] Determine whether the reflow oven temperature is abnormal;

[0097] If abnormal, a PID calculation is performed on the error between the furnace temperature setting value and the actual furnace temperature value according to the coefficient of the PID algorithm;

[0098] If normal, return to the step of determining whether the reflow oven temperature is abnormal.

[0099] refer to Figure 6As shown in the figure, the process of controlling the oven temperature of reflow soldering using the PID algorithm includes:

[0100] Determine the application area of ​​the furnace temperature control system and complete the initialization of various parameters inside the furnace temperature control system. Determine whether the furnace temperature of the reflow soldering is abnormal. If the furnace temperature of the reflow soldering is abnormal, use the improved PID algorithm to control the furnace temperature in real time. If the furnace temperature of the reflow soldering is not abnormal, re-determine whether the furnace temperature of the reflow soldering is abnormal. The so-called improved PID algorithm refers to a PID algorithm with adaptive adjustment of coefficients.

[0101] In some embodiments, it also includes:

[0102] Determine whether the reflow oven temperature has returned to normal;

[0103] If it does not return to normal, an alarm will be issued for manual intervention.

[0104] After waiting for the reflow oven temperature control to be completed, determine whether the reflow oven temperature has returned to normal. If the reflow oven temperature has not returned to normal, immediately start the alarm module to alarm so that maintenance can be performed by manual intervention. If the reflow oven temperature returns to normal, start the recording function of the PID algorithm to record the current control temperature of the oven temperature control system, and reset the relevant parameters of the oven temperature control system, including adjusting the coefficients of the PID algorithm within a fixed period.

[0105] In some embodiments, it also includes:

[0106] Data is exchanged with the host computer by communicating with the host computer.

[0107] The furnace temperature control system can receive the furnace temperature set value sent by the host computer, and can upload the actual furnace temperature value, furnace temperature abnormality, furnace temperature recovery status, etc. to the host computer.

[0108] In summary, the reflow oven temperature control method provided by the present application uses a neural network to adaptively adjust the coefficients of the PID algorithm, determine a more reasonable coefficient, and make up for the shortcomings of the traditional PID algorithm. Then, the PID algorithm is used to control the oven temperature based on the adaptively adjusted coefficients, and the oven temperature can be accurately controlled, with strong real-time, robustness, and accuracy.

[0109] The present application also provides a reflow oven temperature control device, and the device described below can be referred to in correspondence with the method described above. Figure 7 , Figure 7 A schematic diagram of a reflow oven temperature control device provided in an embodiment of the present application, combined with Figure 7 As shown, the device comprises:

[0110] An acquisition unit 10, used to acquire target parameters;

[0111] A determination unit 20, configured to determine coefficients of a PID algorithm using a neural network according to the target parameters;

[0112] A calculation unit 30, configured to perform PID calculation on an error between the furnace temperature setting value and the furnace temperature actual value according to the coefficient of the PID algorithm to obtain a PID calculation result;

[0113] The control unit 40 is used to control the furnace temperature of the reflow soldering according to the PID calculation result.

[0114] Based on the above embodiment, as a specific implementation manner, the determining unit 20 is specifically configured to:

[0115] According to the target parameters, the coefficients of the PID algorithm are determined using a BP neural network until the reflow oven temperature controlled by the determined coefficients of the PID algorithm reaches the requirement or the number of self-learning times of the BP neural network reaches a preset number.

[0116] Based on the above embodiment, as a specific implementation mode, the acquisition unit 10 is specifically used for:

[0117] Obtain the error between the furnace temperature setting value and the actual furnace temperature value, the furnace temperature setting value, and the actual furnace temperature value.

[0118] On the basis of the above embodiments, as a specific implementation method, the BP neural network includes four input neurons, five hidden neurons, and three output neurons; the input neurons correspond to the error between the furnace temperature set value and the actual furnace temperature value, the actual furnace temperature value, the actual furnace temperature value and unit 1 respectively; the output neurons correspond to the proportional coefficient, integral coefficient and differential coefficient of the PID algorithm respectively.

[0119] Based on the above embodiment, as a specific implementation method, it also includes:

[0120] The first judgment unit is used to judge whether the furnace temperature of the reflow soldering is abnormal before performing PID calculation on the error between the furnace temperature setting value and the actual furnace temperature value according to the coefficient of the PID algorithm; if abnormal, the calculation unit 30 performs PID calculation on the error between the furnace temperature setting value and the actual furnace temperature value according to the coefficient of the PID algorithm; if normal, return to the step of judging whether the furnace temperature of the reflow soldering is abnormal.

[0121] Based on the above embodiment, as a specific implementation method, it also includes:

[0122] A second judgment unit is used to judge whether the temperature of the reflow soldering furnace returns to normal after the abnormal temperature of the reflow soldering furnace is controlled;

[0123] The alarm unit is used to sound an alarm if the system fails to return to normal so as to facilitate manual intervention.

[0124] Based on the above embodiment, as a specific implementation method, it also includes:

[0125] The communication unit is used to communicate with the host computer and exchange data with the host computer.

[0126] The reflow oven temperature control device provided in the present application uses a neural network to adaptively adjust the coefficients of the PID algorithm to determine a more reasonable coefficient, thereby making up for the shortcomings of the traditional PID algorithm. Then, the PID algorithm is used to control the oven temperature based on the adaptively adjusted coefficients, which can accurately control the oven temperature and has strong real-time, robustness and accuracy.

[0127] The present application also provides a reflow oven temperature control device, referring to Figure 8 As shown, the device includes a memory 1 and a processor 2 .

[0128] Memory 1, used for storing computer programs;

[0129] Processor 2 is used to execute the computer program to implement the following steps:

[0130] Obtain target parameters; determine coefficients of a PID algorithm using a neural network according to the target parameters; perform PID calculation on an error between a set value of the furnace temperature and an actual value of the furnace temperature according to the coefficients of the PID algorithm to obtain a PID calculation result; and control the furnace temperature of the reflow soldering according to the PID calculation result.

[0131] For an introduction to the equipment provided in this application, please refer to the above method embodiments, and this application will not go into details here.

[0132] The reflow oven temperature control device provided in the present application uses a neural network to adaptively adjust the coefficients of the PID algorithm to determine a more reasonable coefficient, thereby making up for the shortcomings of the traditional PID algorithm. Then, the PID algorithm is used to control the oven temperature based on the adaptively adjusted coefficients, which can accurately control the oven temperature and has strong real-time, robustness and accuracy.

[0133] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps can be implemented:

[0134] Obtain target parameters; determine coefficients of a PID algorithm using a neural network according to the target parameters; perform PID calculation on an error between a set value of the furnace temperature and an actual value of the furnace temperature according to the coefficients of the PID algorithm to obtain a PID calculation result; and control the furnace temperature of the reflow soldering according to the PID calculation result.

[0135] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0136] For an introduction to the computer-readable storage medium provided in this application, please refer to the above method embodiment, and this application will not go into details here.

[0137] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the devices, equipment, and computer-readable storage media disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0138] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may 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.

[0139] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0140] The above is a detailed introduction to the reflow oven temperature control method, device, equipment and storage medium provided by the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core ideas of the present application. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A method for controlling the temperature of a reflow soldering furnace, characterized in that: include: Get target parameters; Determining coefficients of a PID algorithm using a neural network according to the target parameters; Performing PID calculation on the error between the furnace temperature setting value and the furnace temperature actual value according to the coefficient of the PID algorithm to obtain a PID calculation result; The furnace temperature of the reflow soldering is controlled according to the PID calculation result.

2. The furnace temperature control method according to claim 1, characterized in that: The method of determining the coefficients of the PID algorithm using a neural network according to the target parameters includes: According to the target parameters, the coefficients of the PID algorithm are determined using a BP neural network until the reflow oven temperature controlled by the determined coefficients of the PID algorithm reaches the requirement or the number of self-learning times of the BP neural network reaches a preset number.

3. The furnace temperature control method according to claim 1, characterized in that: The acquisition target parameters include: Obtain the error between the furnace temperature setting value and the actual furnace temperature value, the furnace temperature setting value, and the actual furnace temperature value.

4. The furnace temperature control method according to claim 2, characterized in that: The BP neural network includes four input neurons, five hidden neurons, and three output neurons; the input neurons correspond to the error between the furnace temperature set value and the actual furnace temperature value, the actual furnace temperature value, the actual furnace temperature value and unit 1 respectively; the output neurons correspond to the proportional coefficient, integral coefficient and differential coefficient of the PID algorithm respectively.

5. The furnace temperature control method according to claim 1, characterized in that: Before performing PID calculation on the error between the furnace temperature setting value and the actual furnace temperature value according to the coefficient of the PID algorithm, the method further includes: Determine whether the reflow oven temperature is abnormal; If abnormal, a PID calculation is performed on the error between the furnace temperature setting value and the actual furnace temperature value according to the coefficient of the PID algorithm; If normal, return to the step of determining whether the reflow oven temperature is abnormal.

6. The furnace temperature control method according to claim 5, characterized in that: Also includes: After controlling the abnormal reflow oven temperature, determine whether the reflow oven temperature returns to normal; If it does not return to normal, an alarm will be issued for manual intervention.

7. The furnace temperature control method according to claim 1, characterized in that: Also includes: Data is exchanged with the host computer by communicating with the host computer.

8. A reflow oven temperature control device, characterized in that: include: An acquisition unit, used for acquiring target parameters; A determination unit, configured to determine coefficients of a PID algorithm using a neural network according to the target parameters; A calculation unit, used for performing PID calculation on the error between the furnace temperature setting value and the actual furnace temperature value according to the coefficient of the PID algorithm to obtain a PID calculation result; A control unit is used to control the furnace temperature of the reflow soldering according to the PID calculation result.

9. A reflow oven temperature control device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the reflow soldering furnace temperature control method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the reflow soldering furnace temperature control method according to any one of claims 1 to 7 are implemented.

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