Process route generation system and process route generation method

Abstract

A process route generation system and a process route generation method. The process route generation system comprises a processor and a storage device. The processor is configured to execute a plurality of modules. A regression analysis module is used for executing a regression algorithm on the basis of demand data to obtain process parameters and material information. An association analysis module is used for executing association analysis on the basis of the process parameters and the material information to respectively obtain first process information and second process information. The association analysis module comprises a process and process association module. The process and process association module is used for obtaining an associated process route on the basis of one of the first process information and the second process information. A deep learning module is used for generating a candidate process route on the basis of the demand data. The processor compares the associated process route and the candidate process route to generate a recommended process route.

Description

Process route generation system and process route generation method

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on January 18, 2024, with application number 202410074861.8 and invention name “Process Route Generation System and Process Route Generation Method”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to a process route recommendation and management technology, in particular to a process route generation system and a process route generation method. Background Art

[0003] In the manufacturing industry, the selection and design of accurate and efficient process routes is crucial for product quality, production efficiency, and cost control. Current methods for generating production process routes and product manufacturing information often rely on extensive practical experience and multiple trials to generate a process route that meets requirements. This results in experienced personnel having to adjust or redesign the process route for each different finished product, leading to inefficiencies and high costs in process route generation. Furthermore, with increasing market demand and the diversification of product cycles, manufacturers are demanding faster and more accurate methods for generating process routes.

[0004] Summary of the Invention

[0005] This application is directed to a process route generation system and a process route generation method, which can provide a suitable process route according to user demand data, and provide a recommended process route based on historical data and mining algorithms.

[0006] According to an embodiment of the present application, the process route generation system of the present application includes a processor and a storage device. The processor is coupled to the storage device and is used to execute multiple modules in the storage device. The multiple modules include a regression analysis module, an association analysis module and a deep learning module. The regression analysis module is used to execute a regression algorithm based on the demand data to obtain process parameters and material information. The association analysis module is used to perform association analysis based on the process parameters to obtain first process information. In addition, the association analysis module obtains second process information based on the material information. The association analysis module includes a process and process association module. The process and process association module is used to obtain an associated process route based on one of the first process information and the second process information. The deep learning module is used to generate candidate process routes based on the demand data. The processor compares the associated process route and the candidate process route to generate a recommended process route.

[0007] According to an embodiment of the present application, the process route generation method of the present application is applicable to a process route generation system. The process route generation method includes the following steps: executing a regression algorithm based on the demand data through a regression analysis module to obtain process parameters and material information; executing an association analysis based on the process parameters through an association analysis module to obtain first process information, and obtaining second process information based on the material information through the association analysis module; obtaining an associated process route based on one of the first process information and the second process information through a process-process association module; generating a candidate process route based on the demand data through a deep learning module; and comparing the associated process route with the candidate process route through a processor to generate a recommended process route.

[0008] Based on the above, the process route generation system and process route generation method of the present application can effectively and automatically generate a process route corresponding to demand data and historical data as well as process parameters and a bill of materials corresponding to the process route.

[0009] In order to make the above features and advantages of the present application more obvious and easy to understand, embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] FIG1 is a schematic diagram of a process route generation system according to an embodiment of the present application;

[0011] FIG2 is a flow chart of a process route generation method according to an embodiment of the present application;

[0012] 3A and 3B are data analysis flow charts of a process route generation method according to an embodiment of the present application;

[0013] 4A and 4B are schematic diagrams of the architecture of a process route generation system according to an embodiment of the present application.

[0014] DESCRIPTION OF NUMERALS 100: Process route generation system; 110: Processor; 120: Storage device; 121: Regression analysis module; 122: Association analysis module; 123: Deep learning module; 124: Procurement calculation module; 301: Process history data; 302: Material inventory and cost data; 303: Association between materials and processes; 304: Association between processes; 305: Optimal process route; 3051: Deep learning model database; 306: Relationship between process parameters and material types, amounts, and performance; 307: Regression model database; 308: Process-to-process association database; 309: Material-to-process association database; 401: Input data; 411: Application data table; 402: Customer requirements; 403: Regression process parameters, material data, and performance result data; 404: Material-to-process parameter association rules; 405: Process-to-process association rules; 406: Final process route recommendation; 4061: Bill of materials; 4062: Process parameters; 407: Inventory information maintenance; 408: Supplier recommendation; S210~S250, S310~S350, S410~S470: Steps; S321~S324, S331~S333, S341~S344, S351~S353: Steps. DETAILED DESCRIPTION

[0015] Reference will now be made in detail to exemplary embodiments of the present application, examples of which are illustrated in the accompanying drawings. Whenever possible, the same reference numerals are used in the drawings and the description to refer to the same or like parts.

[0016] FIG1 is a schematic diagram of a process route generation system according to an embodiment of the present application. Referring to FIG1 , the process route generation system 100 includes a processor 110 and a storage device 120. The processor 110 is coupled to the storage device 120. The storage device 120 stores a regression analysis module 121, an association analysis module 122, a deep learning module 123, and a procurement calculation module 124. In this embodiment, the process route generation system 100 may, for example, be set up on a cloud server, allowing users to connect and execute related business service functions of different application programming interfaces (APIs) also set up on the cloud server. The cloud server may, for example, be a Software as a Service (SaaS) server, and the API corresponds to a SaaS application, but the present application is not limited thereto. In one embodiment, the assembly sequence-based material calculation system 100 is set up on a cloud server, and allows users to log in to individual system accounts online to receive data and instructions such as input data, demand data, selection instructions, and input operation instructions.

[0017] In this embodiment, the processor 110 may include, for example, a central processing unit (CPU), or other programmable general-purpose or special-purpose microprocessor, digital signal processor (DSP), application-specific integrated circuit (ASIC), programmable logic device (PLD), other similar processing circuits, or a combination of these devices. The storage device 120 may include memory and / or a database, wherein the memory may be, for example, non-volatile memory (NVM). The storage device 120 may store relevant programs, modules, systems, or algorithms for implementing various embodiments of the present application, for access and execution by the processor 110 to implement the relevant functions and operations described in various embodiments of the present application. In this embodiment, the regression analysis module 121, the association analysis module 122, the deep learning module 123 and the procurement calculation module 124 can be implemented in a programming language such as JSON (JavaScript Object Notation), Extensible Markup Language (XML) or YAML, but the present application is not limited thereto.

[0018] In this embodiment, a user can, for example, execute the process route generation system 100 through a personal computer device and input initial data, process data, process history data, demand data, and instructions into the process route generation system to execute the corresponding data content. The assembly sequence-based material calculation system 100 can automatically execute the regression analysis module 121, the notification module 112, and the abnormality analysis module 113 based on the request data to automatically generate corresponding delivery plan data and analysis results (such as production gap analysis data). In this embodiment, a user can connect to the material calculation system 100 located on a cloud server and log in to the corresponding system account to input and set the detection path of the material calculation system. Therefore, the assembly sequence-based material calculation system 100 can automatically detect the production plan data 101 stored in the database or the user's electronic device.

[0019] FIG2 is a flowchart of a process route generation method according to one embodiment of the present application. Referring to FIG1 and FIG2 , in this embodiment, the process route generation system 100 may execute steps S210 to S250 to generate a process route and / or process parameters and a bill of materials corresponding to the required data. In step S210, the processor 110, via the regression analysis module 121, executes a regression algorithm based on the required data to obtain process parameters and material information. The required data includes at least one of expected finished product performance, finished product specifications, and a manufacturing budget. For example, a user operates a terminal device to communicate with the process route generation system 100 and then sends a process route generation instruction to the processor 110. Based on the process route generation instruction, the processor 110 retrieves the required data corresponding to the process route instruction from a database, or retrieves the required data within the process route generation instruction. The processor 110 then executes the regression analysis module 121, causing the regression analysis module 121 to execute a regression algorithm based on the required data to obtain corresponding process parameters and material information. In one embodiment, the terminal device is a user's personal desktop computer, smartphone, tablet, or other device.

[0020] In one embodiment, regression analysis module 121 matches corresponding process parameters and material information from process history data stored in storage device 120 or an external database based on performance requirements in the demand data. The performance requirements are multiple product performance information, such as durability, speed, scalability, handling, and stability. For example, performance information may be insulation of 2 meters or withstand voltage of 0.3 MPa, but the present invention is not limited to this. The process history information includes data and parameters related to the finished product, materials, and process operations, such as material inventory, material and process costs, supplier information, process information, and historical performance information.

[0021] In step S220, the processor 110 performs correlation analysis based on the process parameters through the correlation analysis module 122 to obtain first process information, and the correlation analysis module 122 obtains second process information based on the material information (i.e., material information). The process information includes process parameters and material information. The process parameters include the category, sequence, and operation parameters of each process operation in a plurality of process operations. For example, the process parameters include splitting and cutting (i.e., slitting) operations, core and core group testing operations, winding operations, gold spraying operations, welding and assembly operations, dipping operations and assembly operations, welding temperature values, test voltage volt values ​​and current ampere values, dipping environment temperature values, and the like. The present invention should not be limited to this. The material information includes the type of material and the amount of each material used. The material types are, for example, wires, gold, copper foil, insulating materials, impregnating agents, and the like. The present invention should not be limited to this.

[0022] In one embodiment, the association analysis module 122 uses association rule learning based on the process history information to identify multiple association rules and stores the association rules in the storage device 120 or a database. The association rules include association rules between processes and association rules between materials and processes. Thus, the association analysis module 122 performs association analysis based on the process parameters to obtain first process information. The association analysis module 122 then uses the material information generated in step S210 and the association rules between materials and public welfare to generate second process information.

[0023] In one embodiment, the association analysis module 122 includes a process-process association module. In step S230, the processor 110 executes the process-process association module to obtain an associated process route based on one of the first process information and the second process information. For example, the process-process association module verifies the first process information and the second process information according to the verification rules stored in the storage device 120, and then obtains the associated process route. The verification rule includes the matching of process information. When the matching results of the first process information and the second process information are the same, the process-process association module converts the first process information into a process route based on the process history data in the database or the storage device 120, and uses it as the associated process route. When the matching results of the first process information and the second process information are different, the process-process association module outputs the differences to the user, and then converts the corresponding process information into a process route according to the user's selection instruction, and uses it as the associated process route.

[0024] In step S240, the processor 110 generates candidate process routes based on the demand data through the deep learning module 123. Specifically, the deep learning module 123 establishes a deep learning model based on the process history data, and then generates candidate process routes based on the expected performance information in the demand data. The candidate process routes include multiple process parameters, process operation sequences, and material information. In step S250, the processor 110 compares the associated process routes and the candidate process routes to generate a recommended process route. The recommended process route includes the process route (i.e., process operation sequence), process parameters, bill of materials, and supplier information.

[0025] In one embodiment, the deep learning module 123 establishes a deep learning model based on the process history data using a long short-term memory algorithm. In this way, the deep learning module 123 can use the long short-term memory algorithm to process the process data with time series and predict the optimal continuous process steps (i.e., process route), and then generate candidate process routes based on the demand data.

[0026] It is worth noting that the procurement calculation module 124 generates recommended supplier information based on the material inventory, cost data and recommended process routes, and generates recommended procurement information based on the material inventory and recommended supplier information. Specifically, the procurement calculation module 124 calculates the material information that should be purchased based on the material information in the recommended process route and the material inventory in the database or storage device 120, and further calculates the most cost-effective recommended supplier information based on the material type and amount in the supplier information. In other words, the procurement calculation module 124 can compare the material inventory quantity based on the material type and quantity to be used in the recommended process route to obtain the type and data of missing materials. Then, the procurement calculation module 124 calculates the type and quantity of missing materials from the supplier information, and uses the supplier information with the lowest required cost as the recommended supplier information.

[0027] For example, the recommended supplier information includes the type of material in short supply (e.g., copper foil, gold, wire, etc.), the quantity of each material in short supply, the recommended supplier information, and a suggested purchase list and purchase amount (i.e., suggested purchase information). Thus, the processor 110 of the process route generation system 100 outputs a recommended process route, process parameters corresponding to the recommended process route, suggested purchase information, and a list of materials required for the recommended process route.

[0028] Figures 3A and 3B are data analysis flow charts of a process route generation method according to one embodiment of the present application. Referring to Figures 1, 3A, and 3B, the process route generation system 100 may execute steps S310-S350, S321-S324, S331-S333, S341-S344, and S351-S353, thereby establishing and training a deep learning model and a regression model based on the input data. In this embodiment, the storage device 120 also stores a data acquisition module and a data preprocessing module. The data acquisition module is coupled to at least one of a server, a database, and an electronic device to receive input data and demand data. The input data includes process history data 301, customer demand data, and material inventory cost data. The process history data 301 includes historical process routes, process parameters, required material information, and product performance test results. In this embodiment, the data acquisition module receives the process history data 301 and material inventory and cost data 302.

[0029] In step S310, the data preprocessing module performs data preprocessing on the input data received by the data acquisition module (i.e., process history data 301 and material inventory and cost data 302). Data preprocessing includes at least one of data cleaning, missing value processing, and data standardization, thereby ensuring data quality and consistency, making the input data suitable for subsequent data analysis and model building steps.

[0030] In step S320, the association analysis module 122 performs association rule analysis on the normalized data, where the association rule analysis can include the following steps S321 to S324. In steps S321 and S322, a frequent pattern growth algorithm (FP-GROWTH) is used to establish a frequent pattern tree structure to identify frequent item sets. Specifically, the association analysis module 122 uses the frequent pattern growth algorithm to establish a frequent pattern tree structure based on the preprocessed input data to identify frequent item sets.

[0031] In step S323, the association analysis module 122 selects the association rules based on the confidence level greater than the threshold as appropriate association rules. The association analysis module 122 then obtains association rules between multiple process parameters in the process route based on the frequent item sets. In this embodiment, the threshold is 50%. In other words, the association analysis module 122 selects the association rules based on the confidence level greater than 50%, namely the material-process association 303 and the process-process association 304. In this way, the association analysis module 122 can perform association analysis based on the established frequent pattern tree structure. In step S324, the association analysis module 122 stores the material-process association 303 and the process-process association 304 in the storage device 120 or a database. Specifically, the association analysis module 122 stores the material-process association 303 in the process-process association database 308 of the storage device 120, and stores the process-process association 304 in the material-process association database 309 of the storage device 120.

[0032] In step S330, the deep learning module 123 trains a deep learning model based on the preprocessed input data. Step S330 includes steps S331 through S333. In step S331, the deep learning module 123 divides the input data into training data and test data. In one embodiment, the deep learning module 123 divides the input data into training data, test data, and validation data in a ratio of 8:1:1.

[0033] In step S332, the deep learning module 123 trains a long short-term memory (LSTM) model using the training set data, the test set data, and the validation set data based on a neural network. In other words, the LSTM model serves as the deep learning framework. In another embodiment, the deep learning module 123 divides the input data into training set data and test set data, and trains the LSTM model on the training set data and the test set data using a deep neural network.

[0034] In step S333, the deep learning module 123 calculates the accuracy of the trained LSTM model using the mean squared error (MSE), thereby determining the effectiveness of the LSTM model training. In this manner, the deep learning module 123 processes the time-series process data using the LSTM model and predicts the optimal process route 305 corresponding to the demand data (i.e., the candidate process route corresponding to the demand data). The deep learning module 123 stores the optimal process route 305 in the deep learning model database 3051.

[0035] In one embodiment, the storage device 120 further stores a correlation analysis module. In step S340, before the processor 110 trains the regression model using input data, the processor 110 executes the correlation analysis module to perform correlation analysis. Step S340 includes the following steps S341 to S344.

[0036] In step S341, the correlation analysis module performs correlation analysis on the variable parameters and the performance categories in the input data. In step S342, the correlation analysis module performs correlation analysis on the variable parameters and the performance categories in the input data.

[0037] In step S343 and step S344, the correlation analysis module selects variables with confidence correlation coefficients>0.5, and the correlation analysis module organizes and classifies the influencing factors of each performance. In other words, the correlation analysis module uses variables with confidence greater than a threshold (e.g., 0.5) as influencing factors to obtain the influencing factors (i.e., influencing factors) of each performance category. In one embodiment, the correlation analysis module performs correlation analysis on different performance data (i.e., performance classification) to obtain correlation analysis results, and the correlation analysis module can use pandas and numpy modules to perform specific correlation analysis. The correlation analysis results in this embodiment can be measured by the correlation coefficient. The correlation coefficient is, for example, the Pearson coefficient, the Spearman coefficient, or the Kendall coefficient. In one embodiment, since Pearson is applicable to continuous data, the correlation analysis module selects the Pearson correlation coefficient as the correlation coefficient, and selects the correlation coefficient standard as 0.5.

[0038] In step S350, processor 110 executes regression analysis module 121 to build and train a regression model. In other words, processor 110 inputs the relationship between process parameters, material data, and product performance into regression analysis module 121 for modeling. Specifically, regression analysis module 121 is a regression model. Processor 110 trains a regression model based on process parameters, material data, and product performance using a deep neural network.

[0039] In one embodiment, step S350 includes steps S351 to S353. In step S351, the processor 110 divides the performance categories and the influencing factors (ie, influencing factors) generated in step S344 into test set data and training set data in a ratio of 3:7.

[0040] In step S352, the processor 110 executes the regression analysis module 121 to train a regression model based on the test set data and the training set data using a deep neural network. In this embodiment, the deep neural network is a neural network based on the Support Vector Regression (SVR) algorithm. In other words, the processor 110 trains the regression model based on the SVR algorithm. In another embodiment, the processor 110 uses a genetic algorithm to continuously optimize the parameter selection of the SVR.

[0041] In step S353, the processor 110 determines the training effectiveness of the regression model using the R-squared value of the regression model. Specifically, the coefficient of determination (R-squared) of the regression model is used. The regression analysis module 121 determines the model training effectiveness based on the R-squared value and selects the model with the highest R-squared value as the baseline model. In this way, the trained regression model performs regression analysis on the input data using the baseline model to generate a relationship 306 between process parameters, material type and amount, and performance. The processor 110 then stores the relationship 306 between process parameters, material type and amount, and performance in the regression model database 307 of the storage device 120.

[0042] Figures 4A and 4B are schematic diagrams of the architecture of a process route generation system according to an embodiment of the present application. Referring to Figures 1, 4A and 4B, the process route generation system 100 can execute the following steps S410 to S470: data input, model building, and generating a recommended process route based on customer demand 402. In step S410, the data preprocessing module performs data preprocessing on the input data 401. The input data 401 includes process history data 301, material inventory, cost data, supplier information, and other data related to the process and material cost. In this embodiment, the data preprocessing module organizes and organizes the preprocessed input data 401 into an application data table 411. The application data table 411 is a data set organized by collecting and organizing the input data 401 to obtain multiple parameter factors that affect product performance in the process.

[0043] Next, as shown in FIG3 and the process described above regarding establishing multiple models based on input data 401, in this embodiment, processor 110 stores the regression process parameters, material data, and performance result data 403 generated by the trained regression model in regression model database 307. Furthermore, processor 110 stores material-process parameter association rules 404 and process-process association rules 405 generated by performing association analysis on the trained frequent pattern tree structure in material-process association database 309 and process-process association database 308, respectively. In step S420, processor 110 processes the time-series process data based on the trained long short-term memory model and stores the results in deep learning model database 3051.

[0044] In step S430, the regression analysis module 121 inputs the customer requirements 402 into the regression model database 307. Using the trained regression model, the module generates process parameters 4062 and material information based on the expected performance in the customer requirements 402. Customer requirements 402 include data such as the expected performance of the finished product, the specifications for the finished product, and the finished product budget. Furthermore, the regression analysis module 121 stores the process parameters 4062 and material information in the material-process association database 309.

[0045] In step S440, the association analysis module 122 generates corresponding process information (i.e., first process information) based on the process parameters 4062 generated in step S430 and the association relationships between multiple processes in the material-process parameter association rules 404. The association analysis module 122 also generates corresponding process information (i.e., second process information) based on the material information generated in step S430 and the material-process parameter association rules 404. In step S450, the process-process association module of the association analysis module 122 performs a verification process on the first process information and the second process information to determine whether the first process information matches the second process information. If the first process information matches the second process information, the process-process association module obtains a corresponding associated process route from the process-process association rules 405 based on the first process information. In another embodiment, if the first process information does not match the second process information, the process-process association module outputs a prompt message to a corresponding terminal device (i.e., a user's terminal device) and, based on the received selection instruction, selects the process information corresponding to the instruction from the first process information and the second process information as the associated process route.

[0046] In step S460, the processor 110 compares the verified associated process routes with the optimal process route 305 generated by the deep learning model. When the process parameters 4062 between the associated process route and the optimal process route 305 are consistent, the processor 110 selects the associated process route or the optimal process route 305 as a recommended process route (i.e., final process route recommendation 406). The processor 110 also presents a bill of materials 4061 and process parameters 4062 with timing characteristics for the recommended process route. In another embodiment, when the process parameters 4062 between the associated process route and the optimal process route 305 are inconsistent, the processor 110 prioritizes matching based on the process-process association rules 405 and generates a recommended process route that complies with the process-process association rules 405. In other words, the processor 110 matches the associated process routes or the optimal process route 305 with identical process parameters 4062 based on the process-process association rules 405 to generate a recommended process route.

[0047] In step S470, the procurement calculation module 124 performs procurement calculations on the bill of materials 4061 corresponding to the recommended process route based on the inventory data in inventory information maintenance 407. The module then determines whether the inventory is sufficient to determine the bill of materials 4061 and the quantity in the recommended process route, thereby determining whether procurement is necessary. If the material quantity in the inventory data is less than the material list and data in the recommended process route, the procurement calculation module 124 generates a supplier recommendation 408 with the lowest procurement cost based on supplier information (including material prices and material types from multiple suppliers).

[0048] In summary, the process route generation system and method of the present application can generate a corresponding process route based on user-entered demand data by establishing long-short-term memory models, regression models, and association rules between process data based on historical process data. Notably, the process route generation system generates a process route based on the expected performance in the demand data and compares the process route generated based on the association rules with the process route generated by the deep learning model to generate a process route that meets customer needs while maintaining high accuracy.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A process route generation system, characterized in that, Comprising: A storage device for storing a plurality of modules; And A processor coupled to the storage device and configured to execute the plurality of modules, the plurality of modules including: A regression analysis module that performs a regression algorithm on demand data to obtain process parameters and material information; An association analysis module that performs association analysis on the process parameters to obtain first process information, and the association analysis module obtains second process information based on the material information, wherein the association analysis module includes a process-to-process association module, and the process-to-process association module obtains an associated process route based on one of the first process information and the second process information; A deep learning module that generates a candidate process route based on the demand data; Wherein the processor compares the associated process route and the candidate process route to generate a recommended process route.

2. The process route generation system according to claim 1, characterized in that, The plurality of modules further include: A procurement calculation module that generates recommended supplier information based on material inventory, cost data, and the recommended process route, and generates a recommended procurement message based on the material inventory and the recommended supplier information, Wherein the processor outputs the recommended process route, the process parameters corresponding to the recommended process route, the recommended procurement message, and a list of required materials for the recommended process route.

3. The process route generation system according to claim 1, wherein The demand data includes at least one of expected finished product performance, finished product specifications, and manufacturing budget, and the recommended process route includes a process route, process parameters, a material list, and supplier information.

4. The process route generation system according to claim 1, characterized in that The process-to-process association module performs an inspection process on the first process information and the second process information to determine whether the first process information matches the second process information, When the first process information matches the second process information, the process-to-process association module obtains the associated process route based on the first process information, When the first process information does not match the second process information, the process-to-process associa tion module outputs a prompt message to a corresponding terminal device, and the process-to-process association module receives a selection instruction, and then uses the corresponding process information from the first process information and the second process information as the associated process route.

5. The process route generation system according to claim 1, characterized in that, It further includes a data acquisition module and a data preprocessing module, Wherein the data acquisition module is coupled to at least one of a server, a database, and an electronic device to receive input data and the demand data, wherein the input data includes process history data, customer demand data, and material inventory cost data, and the process history data includes historical process routes, process parameters, required material information, and product performance test results; Wherein the data preprocessing module performs data preprocessing on the input data received by the data acquisition module, and the data preprocessing includes at least one of data cleaning, missing value processing, and data standardization.

6. The process route generation system according to claim 1, wherein The association analysis module uses the frequent pattern growth algorithm to establish a frequent pattern tree structure based on the input data, and then obtains the association rules between the multiple process parameters in the process route according to the frequent item set. The association analysis module performs the association analysis based on the frequent pattern tree structure.

7. The process route generation system according to claim 1, wherein The deep learning module divides the input data into training set data, test set data, and validation set data, and the deep learning module trains a long short-term memory model based on a deep neural network for the training set data, the test set data, and the validation set data. The deep learning module generates the candidate process route according to the requirement data by executing the long short-term memory model.

8. The process route generation system according to claim 1, characterized in that, It further includes a correlation analysis module. The correlation analysis module performs a correlation analysis according to the performance categories in the input data and the variable parameters in the input data, and then uses the variables with a confidence greater than a threshold as influencing factors to obtain the influencing factors for each of the performance categories.

9. The process route generation system according to claim 8, characterized in that, The regression analysis module is a regression model. The processor trains the regression model based on a deep neural network for process parameters, material data, and product performance.

10. The process route generation system according to claim 9, wherein The deep neural network is a support vector regression algorithm. The regression analysis module divides the performance categories and the influencing factors into test set data and training set data. The regression analysis module trains the regression model based on a deep neural network for the test set data and the training set data, and then optimizes the parameter selection of the support vector regression through a genetic algorithm. The coefficient of determination of the regression model is R-squared. The regression analysis module judges the training effect of the model according to the coefficient of determination, and then uses the model with the highest value of R-squared as the benchmark model.

11. A process route generation method, characterized in that, It includes: The regression analysis module executes a regression algorithm according to the requirement data to obtain process parameters and material information. The association analysis module performs an association analysis according to the process parameters to obtain first process information, and the association analysis module obtains second process information according to the material information. The process-process association module obtains an associated process route according to one of the first process information and the second process information. The deep learning module generates a candidate process route according to the requirement data. And The processor compares the associated process route and the candidate process route to generate a recommended process route.

12. The process route generation method according to claim 11, wherein The method further includes: The procurement calculation module generates recommended supplier information according to material inventory, cost data, and the recommended process route, and the procurement calculation module generates recommended procurement messages according to material inventory and the recommended supplier information; and The processor outputs the recommended process route, the process parameters corresponding to the recommended process route, the recommended procurement messages, and the bill of materials required for the recommended process route.

13. The process route generation method according to claim 11, characterized in that, The requirement data includes at least one of expected finished product performance, finished product specifications, and manufacturing budget. The recommended process route includes a process route, process parameters, a bill of materials, and supplier information.

14. The process route generation method according to claim 11, characterized in that, The step in which the process-process association module obtains the associated process route according to one of the first process information and the second process information includes: When the first process information matches the second process information, obtain the associated process route according to the first process information through the process and process association module; and When the first process information does not match the second process information, output a prompt message to the corresponding terminal device through the process and process association module, and receive a selection instruction through the process and process association module, and then use the corresponding process information among the first process information and the second process information as the associated process route.

15. The process route generation method according to claim 11, characterized in that, The method further includes: Receiving input data and the requirement data through a data acquisition module, where the input data includes process history data, customer requirement data, and material inventory cost data, and the process history data includes historical process routes, process parameters, required material information, and product performance test results; Performing data preprocessing on the input data received by the data acquisition module through a data preprocessing module, where the data preprocessing includes at least one of data cleaning, missing value processing, and data standardization.

16. The process route generation method according to claim 11, wherein, The steps of performing the association analysis according to the process parameters through the association analysis module to obtain the first process information, and obtaining the second process information according to the material information through the association analysis module include: Using the frequent pattern growth algorithm by the association analysis module to establish a frequent pattern tree structure based on the input data, and then obtaining the association rules between multiple process parameters in the process route according to the frequent item sets by the association analysis module, where the association analysis module performs the association analysis based on the frequent pattern tree structure.

17. The process route generation method according to claim 11, wherein It further includes: Dividing the input data into training set data, test set data, and validation set data through the deep learning module, and training a long short-term memory model for the training set data, the test set data, and the validation set data based on a deep neural network through the deep learning module, where the step of generating the candidate process route according to the requirement data through the deep learning module includes: Generating the candidate process route according to the requirement data by executing the long short-term memory model through the deep learning module.

18. The process route generation method according to claim 11, wherein The process route generation method further includes: Performing correlation analysis according to the performance categories in the input data and the variable parameters in the input data through a correlation analysis module, and then using the variables with a confidence greater than a threshold as influencing factors to obtain the influencing factors for each performance category.

19. The process route generation method according to claim 18, wherein The regression analysis module is a regression model, where the process route generation method further includes: training the regression model for the process parameters, material data, and product performance based on a deep neural network through the processor.

20. The process route generation method according to claim 19, wherein The deep neural network is a support vector regression algorithm, where the process route generation method further includes: Dividing the performance categories and the influencing factors into test set data and training set data through the regression analysis module, The regression analysis module trains the regression model based on a deep neural network for the test set data and the training set data, and then optimizes the parameter selection of the support vector regression through a genetic algorithm. The regression analysis module determines the model training effect according to the coefficient of determination, where the coefficient of determination of the regression model is R-squared; and The regression analysis module then uses the model with the highest R-squared value as the benchmark model.

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