Process optimization method and system for multilayer flexible circuit board production line

Abstract

The invention provides a process optimization method and system for a multilayer flexible circuit board production line, and relates to the technical field of artificial intelligence. According to the method, firstly, a lamination process vector corresponding to target lamination process data of lamination process parameters to be optimized is mined, and at least two data modes included in the target lamination process data are determined; secondly, performing process optimization operation based on a lamination process vector corresponding to the first lamination process data and a lamination process vector corresponding to the second lamination process data, and generating a first optimized lamination process parameter and a second optimized lamination process parameter; and then, based on the first optimized lamination process parameter and the second optimized lamination process parameter, determining a target optimized lamination process parameter so as to complete the optimization operation of the to-be-optimized lamination process parameter. Based on the method, the problem that in the prior art, the reliability of process optimization of a multi-layer flexible circuit board production line is relatively low can be solved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a process optimization method and system for a multi-layer flexible circuit board production line. Background Art

[0002] The lamination process is one of the key processes in the production of multi-layer flexible printed circuits (FPCs). It plays a vital role in stacking and pressing the layers of circuit boards to form the final product structure. This process ensures that the layers of the circuit board can be firmly bonded and meet the requirements of electrical and mechanical performance. Therefore, the optimization of the lamination process is the quality assurance of multi-layer flexible circuit boards. However, in the prior art, the relevant staff generally optimizes the lamination process according to experience (such as the optimization of parameters such as temperature, pressure, and time). However, since the relevant staff are limited by knowledge, experience and other factors, it is difficult to optimize the lamination process effectively and reliably. Therefore, there is a problem that the reliability of the lamination process optimization is relatively low. Summary of the invention

[0003] In view of this, an object of the present invention is to provide a process optimization method and system for a multi-layer flexible circuit board production line, so as to improve the problem of relatively low reliability of process optimization of a multi-layer flexible circuit board production line in the prior art.

[0004] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions: A process optimization method for a multi-layer flexible circuit board production line, comprising: Using a semantic mining model in a process optimization network, a lamination process vector corresponding to target lamination process data of a lamination process parameter to be optimized is mined, and at least two data modes included in the target lamination process data are determined, wherein the process optimization network belongs to a neural network and also includes a process optimization model, the lamination process parameters to be optimized are applied to a multi-layer flexible circuit board production line, the target lamination process data include first lamination process data and second lamination process data obtained by lamination operations based on the lamination process parameters to be optimized, respectively, the at least two data modes include a lamination process parameter text and a lamination process monitoring image, the lamination process parameter text is at least used to describe temperature, pressure and time, and the lamination process monitoring image is at least used to describe the lamination deformation of the multi-layer flexible circuit board; Using the process optimization model, performing process optimization operations based on a lamination process vector corresponding to the first lamination process data and a lamination process vector corresponding to the second lamination process data, respectively, to generate a first optimized lamination process parameter and a second optimized lamination process parameter; Based on the first optimized lamination process parameter and the second optimized lamination process parameter, a target optimized lamination process parameter is determined to complete the optimization operation of the lamination process parameter to be optimized, wherein the target optimized lamination process parameter is applied to a multi-layer flexible circuit board production line.

[0005] In some preferred embodiments, in the above-mentioned process optimization method for a multi-layer flexible circuit board production line, the step of using the process optimization model to perform process optimization operations based on the lamination process vector corresponding to the first lamination process data and the lamination process vector corresponding to the second lamination process data to generate the first optimized lamination process parameter and the second optimized lamination process parameter includes: Using the internal focusing unit included in the process optimization model, a focusing mining operation is performed inside the data modality, the internal focusing parameters corresponding to the at least two data modalities are output, and based on the internal focusing parameters and the lamination process vector, a lamination process internal focusing vector is determined; Using the external focusing unit included in the process optimization model, a focus mining operation is performed between data modes, an external focusing parameter corresponding to the internal focusing vector of the lamination process is output, and based on the external focusing parameter and the internal focusing vector of the lamination process, an external focusing vector of the lamination process is determined; Using a first decoding output unit included in the process optimization model, based on a lamination process external focus vector corresponding to the first lamination process data, a first optimized lamination process parameter is generated; A second decoding output unit included in the process optimization model is used to generate second optimized lamination process parameters based on the lamination process external focus vector corresponding to the second lamination process data.

[0006] In some preferred embodiments, in the above-mentioned process optimization method for a multi-layer flexible circuit board production line, the at least two data modalities include a target data modality, wherein the step of using the internal focusing unit included in the process optimization model to perform a focusing mining operation inside the data modality, outputting the internal focusing parameters corresponding to the at least two data modalities, and determining the internal focusing vector of the lamination process based on the internal focusing parameters and the lamination process vector includes: extracting a lamination process local vector of the target lamination process data in the target data mode from the lamination process vector; Performing a focus mining operation inside the data modality to perform a semantic focus operation on the lamination process local vector in the target data modality, and outputting an internal focus sub-parameter corresponding to the target data modality; After obtaining the internal focus sub-parameter corresponding to each data modality, combining and forming the internal focus parameters corresponding to the at least two data modalities based on the internal focus sub-parameter corresponding to each of the data modalities; Based on the internal focus parameter and the lamination process vector, a lamination process internal focus vector corresponding to the target lamination process data is determined.

[0007] In some preferred embodiments, in the above-mentioned process optimization method for a multi-layer flexible circuit board production line, the internal focusing sub-parameter includes a first internal focusing sub-parameter and a second internal focusing sub-parameter; The step of performing a focus mining operation within the data modality to perform a semantic focus operation on the lamination process local vector in the target data modality and outputting the internal focus sub-parameters corresponding to the target data modality includes: Performing a focus mining operation inside the data modality to perform a vector compression operation on a lamination process local vector of the target data modality, and outputting a first internal focus sub-parameter corresponding to the target data modality, wherein the first internal focus sub-parameter includes a parameter value; Determine the mean of each vector parameter in the local vector of the lamination process of the target data modality, calculate the square value of the difference between each vector parameter and the mean, and perform mean calculation on each square value to obtain a second internal focusing sub-parameter corresponding to the target data modality, wherein the second internal focusing sub-parameter includes a parameter value.

[0008] In some preferred embodiments, in the above-mentioned process optimization method for a multi-layer flexible circuit board production line, after obtaining the internal focusing sub-parameter corresponding to each data modality, the step of combining and forming the internal focusing parameters corresponding to the at least two data modalities based on the internal focusing sub-parameter corresponding to each data modality includes: splicing the first internal focus sub-parameter corresponding to each of the data modalities to form a corresponding first internal focus parameter, and splicing the second internal focus sub-parameter corresponding to each of the data modalities to form a corresponding second internal focus parameter; Multiplying the first internal focusing parameter by a predetermined first weight parameter to output a corresponding first internal weighted focusing parameter, and multiplying the second internal focusing parameter by a predetermined second weight parameter to output a corresponding second internal weighted focusing parameter; An addition operation is performed on the first internal weight focusing parameter and the second internal weight focusing parameter, and a linear mapping operation is performed on the result of the addition operation to form internal focusing parameters corresponding to the at least two data modalities.

[0009] In some preferred embodiments, in the above-mentioned process optimization method for a multi-layer flexible circuit board production line, the internal focus parameter includes an internal focus sub-parameter corresponding to each of the data modalities, wherein the step of determining the lamination process internal focus vector corresponding to the target lamination process data based on the internal focus parameter and the lamination process vector includes: Performing a multiplication operation on the internal focusing sub-parameter corresponding to the target data modality and the lamination process local vector in the target data modality, and outputting the lamination process local focusing vector of the target data modality; After outputting the lamination process local focusing vector corresponding to each of the data modes, the lamination process local focusing vectors corresponding to each of the data modes are spliced ​​to form the lamination process internal focusing vector of the target lamination process data.

[0010] In some preferred embodiments, in the above-mentioned process optimization method for a multi-layer flexible circuit board production line, the at least two data modalities include a first data modality and a second data modality, wherein the step of using the external focusing unit included in the process optimization model to perform a focusing mining operation between the data modalities, outputting the external focusing parameters corresponding to the internal focusing vector of the lamination process, and determining the external focusing vector of the lamination process based on the external focusing parameters and the internal focusing vector of the lamination process includes: extracting respectively from the lamination process vectors a first lamination process local vector of the target lamination process data in the first data modality and a second lamination process local vector of the target lamination process data in the second data modality; Using a first external focusing subunit in an external focusing unit included in the process optimization model, a focusing mining operation is performed based on the first lamination process local vector and the second lamination process local vector, a first external focusing parameter corresponding to the lamination process vector is output, and based on the first external focusing parameter and the lamination process vector, a first external focusing vector corresponding to the target lamination process data is determined; extracting, from the lamination process internal focus vector, a lamination process local focus vector of the target lamination process data in the first data modality and a lamination process local focus vector of the target lamination process data in the second data modality respectively; Using a second external focusing subunit in the external focusing unit, a focusing mining operation is performed based on a lamination process local focusing vector in the first data modality and a lamination process local focusing vector in the second data modality, a second external focusing parameter corresponding to the lamination process internal focusing vector is output, and based on the second external focusing parameter and the lamination process internal focusing vector, a second external focusing vector corresponding to the target lamination process data is determined; The first external focusing vector and the second external focusing vector are spliced ​​to obtain a lamination process external focusing vector corresponding to the target lamination process data.

[0011] In some preferred embodiments, in the above-mentioned process optimization method for a multi-layer flexible circuit board production line, the step of using the first external focusing subunit in the external focusing unit included in the process optimization model to perform a focusing mining operation based on the first lamination process local vector and the second lamination process local vector, outputting a first external focusing parameter corresponding to the lamination process vector, and determining the first external focusing vector corresponding to the target lamination process data based on the first external focusing parameter and the lamination process vector includes: Based on the first lamination process local vector, performing a focused mining operation at multiple levels on the second lamination process local vector, and outputting corresponding first focused mining results at multiple levels; Based on the second lamination process local vector, performing a plurality of levels of focused mining operations on the first lamination process local vector, and outputting a plurality of levels of corresponding second focused mining results; Aggregating the first focused mining results of the multiple levels and the second focused mining results of the multiple levels to obtain a first external focused vector corresponding to the target lamination process data; Among them, on the first focused mining path, in the focused mining operation of the first level, the dot product between the first lamination process local vector and the transposed vector of the second lamination process local vector is used as the corresponding first external focused parameter, and the result of the weighted summation between the first external focused parameter and the second lamination process local vector is used as the first focused mining result of the first level, in the focused mining operation of the second level and each subsequent level, the first lamination process local vector and the first focused mining result of the first level are respectively subjected to hole convolution to form the corresponding first lamination process convolution vector and the second lamination process convolution vector, and the dot product between the first lamination process convolution vector and the transposed vector of the second lamination process convolution vector is used as the corresponding first external focused parameter, and the result of the weighted summation between the first external focused parameter and the second lamination process convolution vector is used as the first focused mining result of the corresponding level, and the expansion factor of the hole convolution has a positive correlation with the corresponding level.

[0012] In some preferred embodiments, in the above-mentioned process optimization method for a multi-layer flexible circuit board production line, the step of using the second external focusing subunit in the external focusing unit to perform a focusing mining operation based on the lamination process local focusing vector in the first data modality and the lamination process local focusing vector in the second data modality, outputting a second external focusing parameter corresponding to the lamination process internal focusing vector, and determining the second external focusing vector corresponding to the target lamination process data based on the second external focusing parameter and the lamination process internal focusing vector, includes: Determine a dot product between a lamination process local focus vector in the first data modality and a transposed vector of the lamination process local focus vector in the second data modality, and use the dot product as a corresponding second external focus parameter, and, based on the second external focus parameter, perform a weighted sum calculation on the lamination process local focus vector in the second data modality to obtain a corresponding first second focus mining result; Determine a dot product between a lamination process local focus vector in the second data modality and a transposed vector of the lamination process local focus vector in the first data modality, and use the dot product as a corresponding second external focus parameter, and, based on the second external focus parameter, perform a weighted sum calculation on the lamination process local focus vector in the first data modality to obtain a corresponding second second focus mining result; The first second focused mining result and the second second focused mining result are aggregated to obtain a second external focused vector corresponding to the target lamination process data.

[0013] An embodiment of the present invention also provides a process optimization system for a multi-layer flexible circuit board production line, including a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to execute the computer program to implement the above-mentioned process optimization method for a multi-layer flexible circuit board production line.

[0014] The process optimization method and system for a multi-layer flexible circuit board production line provided by an embodiment of the present invention, first, dig out the lamination process vector corresponding to the target lamination process data of the lamination process parameter to be optimized, and determine at least two data modes included in the target lamination process data; secondly, perform process optimization operations based on the lamination process vector corresponding to the first lamination process data and the lamination process vector corresponding to the second lamination process data, respectively, to generate the first optimized lamination process parameter and the second optimized lamination process parameter; then, based on the first optimized lamination process parameter and the second optimized lamination process parameter, determine the target optimized lamination process parameter to complete the optimization operation of the lamination process parameter to be optimized. Based on the above method, on the one hand, the powerful learning ability of the neural network can be utilized, so that the reliability of the optimization operation can be higher. On the other hand, in the process of performing the optimization operation, the first lamination process data and the second lamination process data corresponding to the lamination process parameter to be optimized will be optimized respectively, so that the limitations and one-sidedness existing when optimizing based on a single lamination process data can be improved, so that the reliability of the optimization can be further improved. In addition, since the lamination process data includes at least two data modes, it is possible to aggregate semantic information of multiple data modes, thereby further improving the reliability of the optimization basis, and then improving the reliability of the optimization. The problem of relatively low reliability of process optimization of multi-layer flexible circuit board production lines in the prior art can be fully improved to ensure the quality of multi-layer flexible circuit board production.

[0015] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A structural block diagram of a process optimization system for a multi-layer flexible circuit board production line provided by an embodiment of the present invention.

[0017] Figure 2 A schematic flow chart of the steps of a process optimization method for a multi-layer flexible circuit board production line provided in an embodiment of the present invention.

[0018] Figure 3 A schematic diagram of a focused mining operation at multiple levels on a first focused mining path provided for an embodiment of the present invention.

[0019] Figure 4 A schematic diagram of a focused mining operation at multiple levels on a second focused mining path provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] like Figure 1 As shown, an embodiment of the present invention provides a process optimization system for a multi-layer flexible circuit board production line, wherein the process optimization system for a multi-layer flexible circuit board production line may include a memory and a processor.

[0023] In detail, the memory and the processor are electrically connected directly or indirectly to achieve data transmission or interaction. For example, they can be electrically connected to each other through one or more communication buses or signal lines. The memory can store at least one software function module (computer program) that can exist in the form of software or firmware. The processor can be used to execute the executable computer program stored in the memory, thereby realizing the process optimization method for a multi-layer flexible circuit board production line provided by an embodiment of the present invention (as described later).

[0024] Optionally, the memory may be, but not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc. The processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a system on chip (SoC), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0025] and, Figure 1 The structure shown is for illustration only. The process optimization system for a multi-layer flexible circuit board production line may also include Figure 1 More or fewer components as shown, or with Figure 1 The different configurations shown, for example, may include a communication unit for information exchange with other devices. In an alternative example, the process optimization system for a multi-layer flexible circuit board production line may be a server with data processing capabilities.

[0026] Combination Figure 2 The embodiment of the present invention further provides a process optimization method for a multi-layer flexible circuit board production line, which can be applied to the process optimization system for a multi-layer flexible circuit board production line. The method steps defined in the process related to the process optimization method for a multi-layer flexible circuit board production line can be implemented by the process optimization system for a multi-layer flexible circuit board production line (hereinafter referred to as the process optimization system).

[0027] The following will Figure 2 The specific process shown is explained in detail.

[0028] Step S110, using the semantic mining model in the process optimization network, mining out the lamination process vector corresponding to the target lamination process data of the lamination process parameters to be optimized, and determining at least two data modes included in the target lamination process data.

[0029] In an embodiment of the present invention, the process optimization system can utilize the semantic mining model in the process optimization network to mine the lamination process vector corresponding to the target lamination process data of the lamination process parameters to be optimized, and determine at least two data modes included in the target lamination process data. Wherein, the process optimization network belongs to a neural network and also includes a process optimization model, the lamination process parameters to be optimized are applied to a multi-layer flexible circuit board production line, the target lamination process data include first lamination process data and second lamination process data obtained by lamination operations based on the lamination process parameters to be optimized (it can be two tests based on the lamination process parameters to be optimized, or it can be actual production based on the lamination process parameters to be optimized. It can be understood that in other embodiments, based on reliability requirements, third lamination process data, fourth lamination process data, fifth lamination process data, etc. can also be included), the at least two data modes include lamination process parameter text and lamination process monitoring image, the lamination process parameter text is at least used to describe temperature, pressure and time (or, it can also include other parameters, such as adhesive film materials, such as polyimide film or epoxy resin film, which will bond the layers under high temperature and high pressure), and the lamination process monitoring image is at least used to describe the lamination deformation of the multi-layer flexible circuit board (such as after the lamination process is completed, the multi-layer flexible circuit board is imaged). In addition, as an example, the lamination process parameter text may include the following content: Lamination temperature: set temperature: 180°C; insulation time: 20 minutes; temperature control: uniform heating system; Lamination pressure: Pressure: 2.1 MPa; Pressure time: Maintain pressure for 20 minutes; Lamination time: Total lamination time: 50 minutes, including heating time, heat preservation time, and cooling time; heating time: 10 minutes, heating from room temperature (20°C) to 180°C; heat preservation time: 20 minutes, maintaining a constant temperature at the set temperature; Cooling method: Natural cooling until the temperature drops to room temperature (20°C); Vacuum degree: Use a vacuum bag to remove air, the vacuum degree is -0.9 bar (relative pressure).

[0030] It should be noted that, since the target lamination process data includes at least two data modalities, and different data modalities generally have corresponding semantic mining methods, based on this, the semantic mining model may include a first semantic mining unit and a second semantic mining unit. Among them, the first semantic mining unit may be a word embedding model, which may be used to perform word embedding processing on the lamination process parameter text to obtain the corresponding word embedding vector, and then concatenate or mean-calculate each word embedding vector to obtain the corresponding lamination process local vector. The second semantic mining unit may be a convolutional neural network, which may be used to perform convolution processing on the lamination process monitoring image to obtain the corresponding lamination process local vector. Taking "using a vacuum bag to remove air, the vacuum degree is -0.9 bar" as an example, the word embedding processing is illustrated, and the corresponding word embedding vectors can be obtained: "use": [0.2, -0.1, 0.3, 0.5, -0.2, 0.1, -0.1, 0.6, 0.4, -0.3, 0.3, ..., 0.2]; "vacuum bag": [0.1, -0.3, 0.4, 0.6, -0.1, 0.5, 0.2, 0.4, 0.1, -0.2, 0.4, ..., 0.5]; "come": [0.05, 0.1, -0.2, 0.4, 0.3, 0.2, 0.6, -0.1, 0.5, -0.3, 0.2, ..., 0.1]; "exclude": [0.3, -0.1, 0.5, 0.2, -0.3, 0.1, 0.3, 0.4, 0.2, 0.6, -0.1, ..., 0.3]; "air": [0.6, -0.1, 0.2, 0.4, 0.1, -0.3, 0.4, 0.2, 0.3, 0.1, -0.2, ..., 0.5]; "vacuum degree": [0.4, -0.2, 0.3, 0.6, 0.1, 0.5, 0.2, 0.7, -0.1, 0.4, 0.3, ..., -0.2]; "for": [0.2, 0.1, 0.3, 0.4, -0.1, 0.2, 0.5, -0.3, 0.4, 0.6, -0.2, ..., 0.1]; "-0.9": [0.1, 0.3, -0.2, 0.5, 0.3, -0.1, 0.2, 0.4, -0.3, 0.1, 0.2, ..., 0.0]; "bar": [0.3, 0.1, 0.5, -0.1, 0.4, 0.2, 0.4, 0.5, -0.3, 0.3, 0.2, ..., -0.1].

[0031] Step S120, using the process optimization model, performing process optimization operations based on the lamination process vector corresponding to the first lamination process data and the lamination process vector corresponding to the second lamination process data, respectively, to generate first optimized lamination process parameters and second optimized lamination process parameters.

[0032] In an embodiment of the present invention, after obtaining the lamination process vector, the process optimization system can use the process optimization model to perform process optimization operations based on the lamination process vector corresponding to the first lamination process data and the lamination process vector corresponding to the second lamination process data, respectively, to generate first optimized lamination process parameters and second optimized lamination process parameters. In other words, a process optimization operation can be performed based on the lamination process vector corresponding to the first lamination process data to generate first optimized lamination process parameters, and a process optimization operation can be performed based on the lamination process vector corresponding to the second lamination process data to generate second optimized lamination process parameters.

[0033] Step S130, based on the first optimized lamination process parameter and the second optimized lamination process parameter, determine the target optimized lamination process parameter to complete the optimization operation of the lamination process parameter to be optimized.

[0034] In an embodiment of the present invention, after obtaining the first optimized lamination process parameter and the second optimized lamination process parameter, the process optimization system can determine the target optimized lamination process parameter based on the first optimized lamination process parameter and the second optimized lamination process parameter to complete the optimization operation of the lamination process parameter to be optimized; illustratively, for quantifiable parameters, such as time, temperature, pressure, etc., the first optimized lamination process parameter and the second optimized lamination process parameter can be averaged to obtain the target optimized lamination process parameter, and for non-quantifiable parameters, such as adhesive film materials, any one of the first optimized lamination process parameter and the second optimized lamination process parameter can be used as the target optimized lamination process parameter. Wherein, the target optimized lamination process parameter is applied to a multi-layer flexible circuit board production line.

[0035] Based on the above method, on the one hand, the powerful learning ability of the neural network can be utilized to make the reliability of the optimization operation higher. On the other hand, in the process of the optimization operation, the first lamination process data and the second lamination process data corresponding to the lamination process parameters to be optimized will be optimized respectively, so that the limitations and one-sidedness existing in the optimization based on a single lamination process data can be improved, and thus, the reliability of the optimization can be further improved. In addition, since the lamination process data includes at least two data modes, the aggregation of semantic information of multiple data modes can be achieved, thereby further improving the reliability of the optimization basis, and then improving the reliability of the optimization, so that the problem of relatively low reliability of process optimization of multi-layer flexible circuit board production lines existing in the prior art can be fully improved to ensure the quality of multi-layer flexible circuit board production.

[0036] On the basis of the above-mentioned implementation, it should be noted that for step S120, the specific manner of performing the process optimization operation based on the lamination process vector corresponding to the first lamination process data and the lamination process vector corresponding to the second lamination process data is not limited.

[0037] For example, in a specific application scenario, in order to improve the reliability of the process optimization operation, the above step S120 may further include step S121, step S122, step S123 and step S124. The implementation content of each step is as follows.

[0038] Step S121, using the internal focusing unit included in the process optimization model, performing a focusing mining operation inside the data modality, outputting the internal focusing parameters corresponding to the at least two data modalities, and determining the internal focusing vector of the lamination process based on the internal focusing parameters and the lamination process vector.

[0039] In the embodiment of the present invention, the internal focusing unit included in the process optimization model can be used to perform a focus mining operation inside the data modality, output the internal focus parameters corresponding to the at least two data modalities, and determine the lamination process internal focus vector based on the internal focus parameters and the lamination process vector. In other words, for each data modality, a focus mining operation can be performed inside the data modality, so that key information inside the data modality is mined.

[0040] Step S122, using the external focusing unit included in the process optimization model, perform focusing mining operations between data modes, output external focusing parameters corresponding to the internal focusing vector of the lamination process, and determine the external focusing vector of the lamination process based on the external focusing parameters and the internal focusing vector of the lamination process.

[0041] In the embodiment of the present invention, the external focusing unit included in the process optimization model can also be used to perform a focus mining operation between data modes, output the external focus parameters corresponding to the internal focus vector of the lamination process, and determine the external focus vector of the lamination process based on the external focus parameters and the internal focus vector of the lamination process. In other words, a focus mining operation can be performed between data modes, so that the information associated between the data modes is mined.

[0042] Step S123: using the first decoding output unit included in the process optimization model, based on the lamination process external focus vector corresponding to the first lamination process data, generate a first optimized lamination process parameter.

[0043] In an embodiment of the present invention, after obtaining the lamination process external focusing vector corresponding to the first lamination process data, the first decoding output unit included in the process optimization model can be used to generate a first optimized lamination process parameter based on the lamination process external focusing vector corresponding to the first lamination process data.

[0044] Step S124: using the second decoding output unit included in the process optimization model, based on the lamination process external focus vector corresponding to the second lamination process data, generate second optimized lamination process parameters.

[0045] In an embodiment of the present invention, after obtaining the lamination process external focus vector corresponding to the second lamination process data, the second decoding output unit included in the process optimization model can be used to generate a second optimized lamination process parameter based on the lamination process external focus vector corresponding to the second lamination process data. It should be noted that the first decoding output unit and the second decoding output unit can be the same decoding output unit or different decoding output units. For example, they can have the same network architecture, but the network parameters can be different. The specific network parameters can be formed during the training process. Among them, the decoding output unit is a decoder, and its working principle can be that at the first time step, the input vector of the decoder (such as the lamination process external focus vector) will be mapped to the size of the vocabulary (i.e., the number of words in the vocabulary) through a linear transformation (usually a fully connected layer), and then, the result of the full connection is converted into a probability distribution through the Softmax function, that is, the probability of each word in the vocabulary, and then, the word with the largest probability is used as the first output word, and then, at the second time step, the first output word can be embedded to obtain the corresponding word embedding vector, and the word embedding vector and the input vector of the decoder at the first time step are cross-attention processed or spliced ​​+ convolution processed to obtain the input vector of the decoder at the second time step, and then, based on the input vector, the second output word is determined, and in this way, the corresponding optimized lamination process parameters can be generated by cyclic processing in sequence. Among them, the specific working principle of the decoder can refer to the relevant prior art, and no specific limitation and description are made here. The focus of the present invention is how to dig out a reliable lamination process external focus vector.

[0046] It can be selected that, in the above step S121, the specific manner of performing the focus mining operation within the data modality is not limited. For example, in a specific application scenario, the at least two data modalities include a target data modality (the target data modality can be any data modality). In order to be able to perform a focus mining operation within the data modality to mine the internal focus vector of the lamination process with higher semantic representation accuracy, the above step S121 can further include step S121a, step S121b, step S121c and step S121d. The implementation content of each step is as follows.

[0047] Step S121a: extracting the lamination process local vector of the target lamination process data in the target data mode from the lamination process vector.

[0048] In an embodiment of the present invention, a lamination process local vector of the target lamination process data in the target data modality can be extracted from the lamination process vector, such as the lamination process local vector corresponding to the lamination process parameter text (i.e., the semantic mining result of the semantic mining model), or the lamination process local vector corresponding to the lamination process monitoring image.

[0049] Step S121b, performing a focus mining operation inside the data modality to perform a semantic focus operation on the local vector of the lamination process in the target data modality, and outputting the internal focus sub-parameters corresponding to the target data modality.

[0050] In an embodiment of the present invention, a focus mining operation can be performed within the data modality to perform a semantic focus operation on the local vector of the lamination process in the target data modality, and output internal focus sub-parameters corresponding to the target data modality, that is, to mine out the key semantic information in the local vector of the lamination process in the target data modality and represent it through internal focus sub-parameters.

[0051] Step S121c, after obtaining the internal focus sub-parameters corresponding to each data modality, based on the internal focus sub-parameters corresponding to each of the data modalities, combine to form the internal focus parameters corresponding to the at least two data modalities.

[0052] In an embodiment of the present invention, after obtaining the internal focus sub-parameters corresponding to each data modality (i.e., each data modality is respectively used as the target data modality), the internal focus parameters corresponding to the at least two data modalities can be combined based on the internal focus sub-parameters corresponding to each of the data modalities. For example, when two data modalities are included, the internal focus parameters may include two internal focus sub-parameters; when three data modalities are included, the internal focus parameters may include three internal focus sub-parameters; when four data modalities are included, the internal focus parameters may include four internal focus sub-parameters.

[0053] Step S121d: determining a lamination process internal focusing vector corresponding to the target lamination process data based on the internal focusing parameter and the lamination process vector.

[0054] In an embodiment of the present invention, the lamination process internal focus vector corresponding to the target lamination process data can be determined based on the internal focus parameter and the lamination process vector. In this way, the important information represented by the internal focus parameter can be integrated into the lamination process vector, so that the semantic representation capability of the obtained lamination process internal focus vector can be better.

[0055] Optionally, in the above step S121b, the specific manner of performing the semantic focusing operation on the lamination process local vector in the target data modality is not limited. For example, in a specific application scenario, in order to mine more important information, the internal focusing sub-parameter includes a first internal focusing sub-parameter and a second internal focusing sub-parameter. Based on this, the above step S121b may further include the following implementable contents: First, a focus mining operation may be performed within the data modality to perform a vector compression operation on the lamination process local vector of the target data modality, and output a first internal focus sub-parameter corresponding to the target data modality, wherein the first internal focus sub-parameter includes a parameter value; illustratively, the mean value of the lamination process local vector may be calculated to obtain the corresponding first internal focus sub-parameter, so that, in the case of achieving data compression, the lamination process local vector may also be effectively characterized by the first internal focus sub-parameter; Secondly, the mean of each vector parameter in the local vector of the lamination process of the target data modality can be determined, and the square value of the difference between each vector parameter and the mean can be calculated, and the mean of each square value can be calculated to obtain the second internal focus sub-parameter corresponding to the target data modality, wherein the second internal focus sub-parameter includes a parameter value. In this way, while realizing data compression, the volatility between the parameters in the local vector of the lamination process can also be reflected through the second internal focus sub-parameter, thereby realizing the characterization of important semantic information.

[0056] Optionally, in the above step S121c, the specific manner of combining and forming the internal focus parameters corresponding to the at least two data modalities is not limited. For example, in a specific application scenario, in order to further improve the characterization capability of the internal focus parameters, the above step S121c may further include the following executable contents: First, the first internal focusing sub-parameters corresponding to each of the data modalities may be concatenated to form corresponding first internal focusing parameters (such as {first internal focusing sub-parameters corresponding to data modality 1, first internal focusing sub-parameters corresponding to data modality 2}), and the second internal focusing sub-parameters corresponding to each of the data modalities may be concatenated to form corresponding second internal focusing parameters (such as {second internal focusing sub-parameters corresponding to data modality 1, second internal focusing sub-parameters corresponding to data modality 2}); Secondly, the first internal focusing parameter and a predetermined first weight parameter may be multiplied to output a corresponding first internal weight focusing parameter, and the second internal focusing parameter and a predetermined second weight parameter may be multiplied to output a corresponding second internal weight focusing parameter; illustratively, the first weight parameter and the second weight parameter may be used as network parameters of the internal focusing unit and formed in a corresponding training process, so that due to different focusing methods, the importance of the focused information is different, and therefore, it can be characterized by the corresponding weight parameters; Then, the first internal weight focusing parameter and the second internal weight focusing parameter can be added (for example, the two sub-parameters corresponding to data modality 1 can be added, and the two sub-parameters corresponding to data modality 2 can be added), and the result of the addition operation can be linearly mapped (such as through a corresponding linear activation function, and the specific linear activation function can be selected according to actual needs) to form the internal focusing parameters corresponding to the at least two data modalities.

[0057] Optionally, in the above step S121d, the specific manner of determining the lamination process internal focus vector corresponding to the target lamination process data is not limited. For example, in a specific application scenario, the internal focus parameter includes an internal focus sub-parameter corresponding to each of the data modalities. Thus, the above step S121d may include the following implementable contents: First, the internal focus sub-parameter corresponding to the target data mode and the lamination process local vector in the target data mode can be multiplied to output the lamination process local focus vector of the target data mode, that is, the internal focus sub-parameter is used as the weight of the corresponding data mode to weight the corresponding lamination process local vector. In this way, since different data modes have different importances, the representation accuracy of the corresponding vector can be made higher by weighting; Secondly, after outputting the local focusing vector of the lamination process corresponding to each of the data modes, the local focusing vectors of the lamination process corresponding to each of the data modes can be spliced ​​to form the internal focusing vector of the lamination process of the target lamination process data, such as {local focusing vector of the lamination process corresponding to data mode 1, local focusing vector of the lamination process corresponding to data mode 2}. For example, when the size of the local focusing vector of the lamination process is A*B, if the data mode is 2, the size of the internal focusing vector of the lamination process is 2*A*B; if the data mode is 3, the size of the internal focusing vector of the lamination process is 3*A*B; if the data mode is 4, the size of the internal focusing vector of the lamination process is 4*A*B.

[0058] Optionally, in the above step S122, the specific manner of performing the focused mining operation between the data modalities is not limited. For example, in a specific application scenario, the at least two data modalities include a first data modality and a second data modality. Based on this, in order to achieve full integration of semantic information between different data modalities, the above step S122 may further include step S122a, step S122b, step S122c, step S122d and step S122e. The implementation content of each step is as follows.

[0059] Step S122a: extracting respectively from the lamination process vectors a first lamination process local vector of the target lamination process data in the first data mode and a second lamination process local vector of the target lamination process data in the second data mode.

[0060] In an embodiment of the present invention, a first lamination process local vector of the target lamination process data in the first data modality and a second lamination process local vector of the target lamination process data in the second data modality are extracted from the lamination process vector, respectively. Exemplarily, the first lamination process local vector may be the lamination process local vector corresponding to the lamination process parameter text, and the second lamination process local vector may be the lamination process local vector corresponding to the lamination process monitoring image.

[0061] Step S122b, utilizing the first external focusing sub-unit in the external focusing unit included in the process optimization model, performing focusing mining operations based on the first lamination process local vector and the second lamination process local vector, outputting the first external focusing parameters corresponding to the lamination process vector, and determining the first external focusing vector corresponding to the target lamination process data based on the first external focusing parameters and the lamination process vector.

[0062] In an embodiment of the present invention, after obtaining the first lamination process local vector and the second lamination process local vector, the first external focusing subunit in the external focusing unit included in the process optimization model can be used to perform focusing mining operations based on the first lamination process local vector and the second lamination process local vector, output the first external focusing parameter corresponding to the lamination process vector, and determine the first external focusing vector corresponding to the target lamination process data based on the first external focusing parameter and the lamination process vector. In other words, the first lamination process local vector and the second lamination process local vector can be associated and fused so that the semantic information of different data modalities can be fused. In addition, directly processing the original first lamination process local vector and the second lamination process local vector can capture the associated information in the original semantic information and avoid the problem of semantic loss during deep processing.

[0063] Step S122c, extracting respectively from the lamination process internal focus vector the lamination process local focus vector of the target lamination process data in the first data modality and the lamination process local focus vector of the target lamination process data in the second data modality.

[0064] In an embodiment of the present invention, a lamination process local focus vector of the target lamination process data in the first data modality and a lamination process local focus vector of the target lamination process data in the second data modality can also be extracted from the lamination process internal focus vector. Exemplarily, the first data modality may refer to the modality corresponding to the lamination process parameter text, and the second data modality may refer to the modality corresponding to the lamination process monitoring image.

[0065] Step S122d, utilizing the second external focusing sub-unit in the external focusing unit, performing focusing mining operations based on the lamination process local focusing vector in the first data modality and the lamination process local focusing vector in the second data modality, outputting a second external focusing parameter corresponding to the lamination process internal focusing vector, and determining a second external focusing vector corresponding to the target lamination process data based on the second external focusing parameter and the lamination process internal focusing vector.

[0066] In an embodiment of the present invention, after obtaining the local focusing vectors of the lamination process in the two data modalities, the second external focusing subunit in the external focusing unit can be used to perform a focusing mining operation based on the local focusing vector of the lamination process in the first data modality and the local focusing vector of the lamination process in the second data modality, output a second external focusing parameter corresponding to the internal focusing vector of the lamination process, and determine the second external focusing vector corresponding to the target lamination process data based on the second external focusing parameter and the internal focusing vector of the lamination process. In other words, the local focusing vectors of the lamination process in the two data modalities can be associated and fused, so that the semantic information of different data modalities can be fused.

[0067] Step S122e, splicing the first external focusing vector and the second external focusing vector to obtain a lamination process external focusing vector corresponding to the target lamination process data.

[0068] In an embodiment of the present invention, after obtaining the first external focusing vector and the second external focusing vector, the first external focusing vector and the second external focusing vector can be spliced ​​to obtain the lamination process external focusing vector corresponding to the target lamination process data. Exemplarily, in some applications, the vectors formed by splicing can also be processed by convolution, pooling, etc. to obtain the corresponding lamination process external focusing vector. Based on this, since the first external focusing vector is obtained by focusing on the original semantic information, more detailed information can be obtained. In addition, since the second external focusing vector is obtained by focusing on the semantic information that has been internally focused and has a certain depth, more abstract information can be obtained. In this way, after splicing the two external focusing vectors, the formed lamination process external focusing vector can take into account both detailed information and abstract information, so as to have better semantic representation capabilities. In addition, due to the existence of the first external focusing vector, it is also possible to improve problems such as overfitting caused by using the second external focusing vector alone, and avoid the problem of distortion of the extracted semantic information.

[0069] Optionally, in the above step S122b, the specific manner of performing the focus mining operation based on the first lamination process local vector and the second lamination process local vector is not limited. For example, in a specific application scenario, in order to achieve full integration of the first lamination process local vector and the second lamination process local vector, the above step S122b may further include the following implementable contents: First, based on the first lamination process local vector, a focused mining operation of multiple levels can be performed on the second lamination process local vector, and corresponding first focused mining results of multiple levels can be output, wherein the semantic information represented by the first focused mining results of each level is different; Secondly, based on the second lamination process local vector, a focused mining operation of multiple levels can be performed on the first lamination process local vector, and corresponding second focused mining results of multiple levels can be output, wherein the semantic information represented by the second focused mining results of each level is different; Then, the first focused mining results of the multiple levels and the second focused mining results of the multiple levels are aggregated to obtain a first external focused vector corresponding to the target lamination process data; illustratively, the sizes of the first focused mining results of the multiple levels and the second focused mining results of the multiple levels can be adjusted to be the same, and then the adjusted first focused mining results of the multiple levels and the second focused mining results of the multiple levels are averaged to obtain the corresponding first external focused vector. For example, the large-sized focused mining results can be pooled so that the pooled results can have the same size as the small-sized focused mining results.

[0070] Among them, combined Figure 3 , on the first focused mining path, in the focused mining operation of the first level, the dot product between the first lamination process local vector and the transposed vector of the second lamination process local vector is used as the corresponding first external focused parameter, and the result of the weighted summation between the first external focused parameter and the second lamination process local vector is used as the first focused mining result of the first level, in the focused mining operation of the second level and each subsequent level, the first lamination process local vector and the first focused mining result of the first level are respectively subjected to hole convolution to form the corresponding first lamination process convolution vector and the second lamination process convolution vector, and the dot product between the first lamination process convolution vector and the transposed vector of the second lamination process convolution vector is used as the corresponding An external focusing parameter is obtained, and the result of the weighted summation between the first external focusing parameter and the second lamination process convolution vector is used as the first focusing mining result of the corresponding level, and the dilation factor of the hole convolution has a positive correlation with the corresponding level, that is, as the level increases, the dilation factor of the hole convolution can gradually increase. For example, in the focusing mining operation of the second level, the dilation factor of the hole convolution can be equal to 1, which belongs to the standard convolution, and the convolution kernel has no holes. In the focusing mining operation of the third level, the dilation factor of the hole convolution can be equal to 2, and a hole is inserted between every two adjacent elements of the convolution kernel. In this way, by adding holes, the receptive field of the convolution operation (i.e., the range of the input data that can be perceived) can be increased, so that the network can capture more contextual information. In this way, for focusing mining operations at different levels, different semantic information can be fused, so that more semantic information can be represented by the first focusing mining results of multiple levels.

[0071] In some other application scenarios, on the first focused mining path, in the focused mining operation of the first level, the dot product between the first lamination process local vector and the transposed vector of the second lamination process local vector is used as the corresponding first external focusing parameter, and the result of the weighted summation between the first external focusing parameter and the second lamination process local vector is used as the first focused mining result of the first level; in the focused mining operation of the second level, the first lamination process local vector and the first focused mining result of the first level are respectively subjected to hole convolution to obtain the first lamination process convolution vector and the second lamination process convolution vector of the second level, and the dot product between the first lamination process convolution vector and the transposed vector of the second lamination process convolution vector is taken as the first external focusing parameter, and the result of the weighted summation between the first external focusing parameter and the second lamination process local vector is taken .... The product is used as the corresponding first external focusing parameter, and the result of the weighted summation between the first external focusing parameter and the second lamination process convolution vector is used as the first focused mining result of the second level; in the focused mining operation of the third level and each level thereafter, the first lamination process convolution vector of the previous level and the first focused mining result of the previous level are respectively subjected to hole convolution to form the first lamination process convolution vector and the second lamination process convolution vector of the current level, and the dot product between the first lamination process convolution vector and the transposed vector of the second lamination process convolution vector is used as the corresponding first external focusing parameter, and the result of the weighted summation between the first external focusing parameter and the second lamination process convolution vector is used as the first focused mining result of the current level.

[0072] Likewise, combining Figure 4 , on the second focused mining path, in the focused mining operation of the first level, the dot product between the second lamination process local vector and the transposed vector of the first lamination process local vector is used as the corresponding first external focused parameter, and the result of the weighted summation between the first external focused parameter and the first lamination process local vector is used as the second focused mining result of the first level, in the focused mining operation of the second level and each subsequent level, the second lamination process local vector and the second focused mining result of the first level are respectively subjected to hole convolution to form the corresponding second lamination process convolution vector and the first lamination process convolution vector, and the dot product between the second lamination process convolution vector and the transposed vector of the first lamination process convolution vector is used as the corresponding first external focused parameter, and the result of the weighted summation between the first external focused parameter and the first lamination process convolution vector is used as the second focused mining result of the corresponding level, and the expansion factor of the hole convolution has a positive correlation with the corresponding level, and the specific content is as described above.

[0073] Optionally, in the above step S122d, the specific manner of performing the focus mining operation based on the lamination process local focus vector in the first data modality and the lamination process local focus vector in the second data modality is not limited. For example, in a specific application scenario, in order to avoid the problems of overfitting and semantic distortion while achieving full integration of semantic information of different data modalities, the above step S122d may include the following implementable contents: First, a dot product between a lamination process local focus vector in the first data modality and a transposed vector of the lamination process local focus vector in the second data modality may be determined, and the dot product may be used as a corresponding second external focus parameter, and based on the second external focus parameter, a weighted sum calculation may be performed on the lamination process local focus vector in the second data modality to obtain a corresponding first second focus mining result; Secondly, the dot product between the local focus vector of the lamination process in the second data mode and the transposed vector of the local focus vector of the lamination process in the first data mode can be determined, and the dot product can be used as the corresponding second external focusing parameter, and, based on the second external focusing parameter, the local focus vector of the lamination process in the first data mode is weighted and summed to obtain the corresponding second second focusing mining result; it should be noted that in some other application scenarios, before performing the dot product and weighted summation calculations, a vector mapping operation can also be performed first. Taking the second external focusing sub-unit as an example, the second external focusing sub-unit can carry the first mapping vector, the second mapping vector and the third A mapping vector (which may be formed during the network training process), a local focusing vector of the lamination process in the second data mode may be matrix multiplied with the first mapping vector to obtain a first focusing mapping vector, and a local focusing vector of the lamination process in the first data mode may be matrix multiplied with the second mapping vector and the third mapping vector respectively to obtain a corresponding second focusing mapping vector and a third focusing mapping vector, and then, a dot product between the transposed vectors of the first focusing mapping vector and the second focusing mapping vector may be used as a second external focusing parameter, and then, based on the second external focusing parameter, a weighted sum calculation is performed on the third focusing mapping vector to obtain a corresponding second second focusing mining result; Finally, the first second focused mining result and the second second focused mining result can be aggregated to obtain the second external focused vector corresponding to the target lamination process data; exemplarily, the first second focused mining result and the second second focused mining result can be averaged, or splicing, convolution, pooling and other processing can be performed to obtain the corresponding second external focused vector.

[0074] Finally, it should be noted that in order to ensure the data processing reliability of the process optimization network, before executing the above step S110, the process optimization network can also be trained. Specifically, the first lamination process sample and the second lamination process sample corresponding to the lamination process sample to be optimized can be obtained, and then semantic mining is performed based on the semantic mining model to obtain the corresponding first sample lamination process vector and the second sample lamination process vector. Then, the process optimization model can be used to process the first sample lamination process vector and the second sample lamination process vector respectively to obtain the corresponding first optimized lamination process sample and the second optimized lamination process sample. After that, the target optimized lamination process sample is determined based on the first optimized lamination process sample and the second optimized lamination process sample. Finally, the network parameters of the process optimization network can be updated along the direction of reducing the error between the target optimized lamination process sample and the optimized lamination process label corresponding to the lamination process sample to be optimized (which can be determined based on other reliable neural networks or determined based on corresponding experts) until the error is reduced to a preset value, etc., thereby completing the training of the network.

[0075] In summary, the process optimization method and system for a multi-layer flexible circuit board production line provided by the present invention, first, dig out the lamination process vector corresponding to the target lamination process data of the lamination process parameter to be optimized, and determine at least two data modes included in the target lamination process data; secondly, perform process optimization operations based on the lamination process vector corresponding to the first lamination process data and the lamination process vector corresponding to the second lamination process data, respectively, and generate the first optimized lamination process parameter and the second optimized lamination process parameter; then, based on the first optimized lamination process parameter and the second optimized lamination process parameter, determine the target optimized lamination process parameter to complete the optimization operation of the lamination process parameter to be optimized. Based on the above method, on the one hand, the powerful learning ability of the neural network can be utilized, so that the reliability of the optimization operation can be higher. On the other hand, in the process of performing the optimization operation, the first lamination process data and the second lamination process data corresponding to the lamination process parameter to be optimized will be optimized respectively, so that the limitations and one-sidedness existing when optimizing based on a single lamination process data can be improved, so that the reliability of the optimization can be further improved. In addition, since the lamination process data includes at least two data modes, it is possible to aggregate semantic information of multiple data modes, thereby further improving the reliability of the optimization basis, and then improving the reliability of the optimization. The problem of relatively low reliability of process optimization of multi-layer flexible circuit board production lines in the prior art can be fully improved to ensure the quality of multi-layer flexible circuit board production.

[0076] In several embodiments provided in the embodiments of the present invention, it should be understood that the disclosed apparatus and method can also be implemented in other ways. The apparatus and method embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the apparatus, method and computer program product according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0077] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0078] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, an electronic device, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code. It should be noted that in this article, the term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such process, method, article or device. Without more constraints, an element defined by the phrase "comprising a..." does not exclude the existence of other identical elements in the process, method, article or apparatus comprising the element.

[0079] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A process optimization method for a multi-layer flexible circuit board production line, characterized in that: The process optimization method for a multi-layer flexible circuit board production line includes: Using a semantic mining model in a process optimization network, a lamination process vector corresponding to target lamination process data of a lamination process parameter to be optimized is mined, and at least two data modes included in the target lamination process data are determined, wherein the process optimization network belongs to a neural network and also includes a process optimization model, the lamination process parameters to be optimized are applied to a multi-layer flexible circuit board production line, the target lamination process data include first lamination process data and second lamination process data obtained by lamination operations based on the lamination process parameters to be optimized, respectively, the at least two data modes include a lamination process parameter text and a lamination process monitoring image, the lamination process parameter text is at least used to describe temperature, pressure and time, and the lamination process monitoring image is at least used to describe the lamination deformation of the multi-layer flexible circuit board; Using the process optimization model, performing process optimization operations based on a lamination process vector corresponding to the first lamination process data and a lamination process vector corresponding to the second lamination process data, respectively, to generate a first optimized lamination process parameter and a second optimized lamination process parameter; Based on the first optimized lamination process parameter and the second optimized lamination process parameter, a target optimized lamination process parameter is determined to complete the optimization operation of the lamination process parameter to be optimized, wherein the target optimized lamination process parameter is applied to a multi-layer flexible circuit board production line.

2. The process optimization method for a multi-layer flexible circuit board production line according to claim 1, characterized in that: The step of using the process optimization model to perform process optimization operations based on the lamination process vector corresponding to the first lamination process data and the lamination process vector corresponding to the second lamination process data to generate first optimized lamination process parameters and second optimized lamination process parameters comprises: Using the internal focusing unit included in the process optimization model, a focusing mining operation is performed inside the data modality, the internal focusing parameters corresponding to the at least two data modalities are output, and based on the internal focusing parameters and the lamination process vector, a lamination process internal focusing vector is determined; Using the external focusing unit included in the process optimization model, a focus mining operation is performed between data modes, an external focusing parameter corresponding to the internal focusing vector of the lamination process is output, and based on the external focusing parameter and the internal focusing vector of the lamination process, an external focusing vector of the lamination process is determined; Using a first decoding output unit included in the process optimization model, based on a lamination process external focus vector corresponding to the first lamination process data, a first optimized lamination process parameter is generated; A second decoding output unit included in the process optimization model is used to generate second optimized lamination process parameters based on the lamination process external focus vector corresponding to the second lamination process data.

3. The process optimization method for a multi-layer flexible circuit board production line according to claim 2, characterized in that: The at least two data modalities include a target data modality, wherein the step of using the internal focusing unit included in the process optimization model to perform a focusing mining operation inside the data modality, outputting internal focusing parameters corresponding to the at least two data modalities, and determining a lamination process internal focusing vector based on the internal focusing parameters and the lamination process vector comprises: extracting a lamination process local vector of the target lamination process data in the target data mode from the lamination process vector; Performing a focus mining operation inside the data modality to perform a semantic focus operation on the lamination process local vector in the target data modality, and outputting an internal focus sub-parameter corresponding to the target data modality; After obtaining the internal focus sub-parameter corresponding to each data modality, combining and forming the internal focus parameters corresponding to the at least two data modalities based on the internal focus sub-parameter corresponding to each of the data modalities; Based on the internal focus parameter and the lamination process vector, a lamination process internal focus vector corresponding to the target lamination process data is determined.

4. The process optimization method for a multi-layer flexible circuit board production line according to claim 3, characterized in that: The internal focus sub-parameters include a first internal focus sub-parameter and a second internal focus sub-parameter; The step of performing a focus mining operation within the data modality to perform a semantic focus operation on the lamination process local vector in the target data modality and outputting the internal focus sub-parameters corresponding to the target data modality includes: Performing a focus mining operation inside the data modality to perform a vector compression operation on a lamination process local vector of the target data modality, and outputting a first internal focus sub-parameter corresponding to the target data modality, wherein the first internal focus sub-parameter includes a parameter value; Determine the mean of each vector parameter in the local vector of the lamination process of the target data modality, calculate the square value of the difference between each vector parameter and the mean, and perform mean calculation on each square value to obtain a second internal focusing sub-parameter corresponding to the target data modality, wherein the second internal focusing sub-parameter includes a parameter value.

5. The process optimization method for a multi-layer flexible circuit board production line according to claim 4, characterized in that: After obtaining the internal focus sub-parameter corresponding to each data modality, the step of combining and forming the internal focus parameters corresponding to the at least two data modalities based on the internal focus sub-parameter corresponding to each data modality comprises: splicing the first internal focus sub-parameter corresponding to each of the data modalities to form a corresponding first internal focus parameter, and splicing the second internal focus sub-parameter corresponding to each of the data modalities to form a corresponding second internal focus parameter; Multiplying the first internal focusing parameter by a predetermined first weight parameter to output a corresponding first internal weighted focusing parameter, and multiplying the second internal focusing parameter by a predetermined second weight parameter to output a corresponding second internal weighted focusing parameter; An addition operation is performed on the first internal weight focusing parameter and the second internal weight focusing parameter, and a linear mapping operation is performed on the result of the addition operation to form internal focusing parameters corresponding to the at least two data modalities.

6. The process optimization method for a multi-layer flexible circuit board production line according to claim 3, characterized in that: The internal focus parameter includes an internal focus sub-parameter corresponding to each of the data modalities, wherein the step of determining the lamination process internal focus vector corresponding to the target lamination process data based on the internal focus parameter and the lamination process vector comprises: Performing a multiplication operation on the internal focusing sub-parameter corresponding to the target data modality and the lamination process local vector in the target data modality, and outputting the lamination process local focusing vector of the target data modality; After outputting the lamination process local focusing vector corresponding to each of the data modes, the lamination process local focusing vectors corresponding to each of the data modes are spliced ​​to form the lamination process internal focusing vector of the target lamination process data.

7. The process optimization method for a multi-layer flexible circuit board production line according to any one of claims 2 to 6, characterized in that: The at least two data modalities include a first data modality and a second data modality, wherein the step of using the external focusing unit included in the process optimization model to perform a focus mining operation between the data modalities, outputting an external focusing parameter corresponding to the internal focusing vector of the lamination process, and determining the external focusing vector of the lamination process based on the external focusing parameter and the internal focusing vector of the lamination process includes: extracting respectively from the lamination process vectors a first lamination process local vector of the target lamination process data in the first data modality and a second lamination process local vector of the target lamination process data in the second data modality; Using a first external focusing subunit in an external focusing unit included in the process optimization model, a focusing mining operation is performed based on the first lamination process local vector and the second lamination process local vector, a first external focusing parameter corresponding to the lamination process vector is output, and based on the first external focusing parameter and the lamination process vector, a first external focusing vector corresponding to the target lamination process data is determined; extracting, from the lamination process internal focus vector, a lamination process local focus vector of the target lamination process data in the first data modality and a lamination process local focus vector of the target lamination process data in the second data modality respectively; Using a second external focusing subunit in the external focusing unit, a focusing mining operation is performed based on a lamination process local focusing vector in the first data modality and a lamination process local focusing vector in the second data modality, a second external focusing parameter corresponding to the lamination process internal focusing vector is output, and based on the second external focusing parameter and the lamination process internal focusing vector, a second external focusing vector corresponding to the target lamination process data is determined; The first external focusing vector and the second external focusing vector are spliced ​​to obtain a lamination process external focusing vector corresponding to the target lamination process data.

8. The process optimization method for a multi-layer flexible circuit board production line according to claim 7, characterized in that: The step of using the first external focusing subunit in the external focusing unit included in the process optimization model to perform a focusing mining operation based on the first lamination process local vector and the second lamination process local vector, outputting a first external focusing parameter corresponding to the lamination process vector, and determining a first external focusing vector corresponding to the target lamination process data based on the first external focusing parameter and the lamination process vector, comprises: Based on the first lamination process local vector, performing a focused mining operation at multiple levels on the second lamination process local vector, and outputting corresponding first focused mining results at multiple levels; Based on the second lamination process local vector, performing a plurality of levels of focused mining operations on the first lamination process local vector, and outputting a plurality of levels of corresponding second focused mining results; Aggregating the first focused mining results of the multiple levels and the second focused mining results of the multiple levels to obtain a first external focused vector corresponding to the target lamination process data; Among them, on the first focused mining path, in the focused mining operation of the first level, the dot product between the first lamination process local vector and the transposed vector of the second lamination process local vector is used as the corresponding first external focused parameter, and the result of the weighted summation between the first external focused parameter and the second lamination process local vector is used as the first focused mining result of the first level, in the focused mining operation of the second level and each subsequent level, the first lamination process local vector and the first focused mining result of the first level are respectively subjected to hole convolution to form the corresponding first lamination process convolution vector and the second lamination process convolution vector, and the dot product between the first lamination process convolution vector and the transposed vector of the second lamination process convolution vector is used as the corresponding first external focused parameter, and the result of the weighted summation between the first external focused parameter and the second lamination process convolution vector is used as the first focused mining result of the corresponding level, and the expansion factor of the hole convolution has a positive correlation with the corresponding level.

9. The process optimization method for a multi-layer flexible circuit board production line according to claim 7, characterized in that: The step of using the second external focusing subunit in the external focusing unit to perform a focusing mining operation based on the lamination process local focusing vector in the first data modality and the lamination process local focusing vector in the second data modality, outputting a second external focusing parameter corresponding to the lamination process internal focusing vector, and determining a second external focusing vector corresponding to the target lamination process data based on the second external focusing parameter and the lamination process internal focusing vector, comprises: Determine a dot product between a lamination process local focus vector in the first data modality and a transposed vector of the lamination process local focus vector in the second data modality, and use the dot product as a corresponding second external focus parameter, and, based on the second external focus parameter, perform a weighted sum calculation on the lamination process local focus vector in the second data modality to obtain a corresponding first second focus mining result; Determine a dot product between a lamination process local focus vector in the second data modality and a transposed vector of the lamination process local focus vector in the first data modality, and use the dot product as a corresponding second external focus parameter, and, based on the second external focus parameter, perform a weighted sum calculation on the lamination process local focus vector in the first data modality to obtain a corresponding second second focus mining result; The first second focused mining result and the second second focused mining result are aggregated to obtain a second external focused vector corresponding to the target lamination process data.

10. A process optimization system for a multi-layer flexible circuit board production line, characterized in that: It comprises a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to execute the computer program to implement the process optimization method for a multi-layer flexible circuit board production line as described in any one of claims 1 to 9.

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