Intelligent production management system and intelligent production management method based on circuit board manufacturing

Abstract

The invention provides an intelligent production management system and an intelligent production management method based on circuit board manufacturing, and relates to the field of intelligent manufacturing. The intelligent production management system comprises a data sharing center which is in communication connection with a plurality of discrete intelligent production scheduling modules and a plurality of discrete intelligent data processing modules and is used for realizing data sharing of each module; the discrete intelligent data processing module can be connected with equipment in the corresponding discrete production area and is used for acquiring original data generated in the circuit board production process and sending state data to the data sharing center, and the original data comprises structured data and unstructured data; the discrete intelligent production scheduling module can be connected with equipment in the corresponding discrete production area, and is used for determining a production plan of the discrete production area according to the state data in the data sharing center, and carrying out the production scheduling of the circuit board according to the production plan. According to the intelligent production management system, the production efficiency of the circuit board is improved.

Description

Technical Field

[0001] This application relates to the field of intelligent manufacturing technology, and in particular to an intelligent production management system and intelligent production management method based on circuit board manufacturing. Background Technology

[0002] With the advancement of industry and intelligent manufacturing, traditional manufacturing industries have begun to introduce advanced automated equipment and intelligent systems to improve production efficiency and product quality, and reduce production costs, such as the circuit board (PCB) manufacturing industry.

[0003] In related technologies, the circuit board manufacturing industry has introduced Advanced Planning and Scheduling (APS) systems to realize the production planning and scheduling of circuit boards. An APS system is a computer-based production planning and scheduling system that can calculate the optimal production plan through optimization algorithms and then allocate the plan to various workshops and machines, optimizing the allocation of production resources and improving production efficiency. However, in practical applications, APS systems are not well-suited to the circuit board manufacturing industry, resulting in low circuit board production efficiency.

[0004] Therefore, there is a need for an intelligent production management solution that can improve the efficiency of circuit board production. Summary of the Invention

[0005] This application provides an intelligent production management system and intelligent production management method based on circuit board manufacturing, which can improve the production efficiency of circuit boards.

[0006] In a first aspect, embodiments of this application provide an intelligent production management system based on circuit board manufacturing, comprising:

[0007] Multiple discrete intelligent production scheduling modules, multiple discrete intelligent data processing modules, and a data sharing center;

[0008] The data sharing center is communicatively connected to the plurality of discrete intelligent production scheduling modules and the plurality of discrete intelligent data processing modules, respectively, to realize data sharing among the modules;

[0009] The discrete intelligent data processing module is connected to the equipment in the corresponding discrete production area, and is used to acquire the raw data generated during the circuit board production process in the discrete production area, process the raw data to obtain status data, and send the status data to the data sharing center. The raw data includes structured data and unstructured data.

[0010] The discrete intelligent production scheduling module is connected to the equipment in the corresponding discrete production area, and is used to determine the production plan of the discrete production area according to the status data in the data sharing center, and to schedule the production of circuit boards according to the production plan.

[0011] In one possible implementation, the discrete intelligent data processing module is connected to production equipment and / or control equipment and / or sensing equipment and / or monitoring equipment and / or artificial intelligence equipment in the corresponding discrete production area.

[0012] The discrete intelligent data processing module is specifically used for:

[0013] Through the interface / communication modules on each device, raw data generated during the circuit board production process in the discrete production area is collected. The raw data includes one or more of the following: equipment status data, material consumption data, warehousing data, environmental data, product quality data, and production progress data.

[0014] In one possible implementation, the discrete intelligent data processing module is specifically used for:

[0015] The original data is processed using big data technology to achieve data isomorphism, resulting in the first data.

[0016] The first data is integrated and processed using preset data integration rules to obtain the second data. The data integration rules include one or more of the following: quality information extraction rules, working condition information extraction rules, production progress information extraction rules, and multi-source heterogeneous manufacturing process information processing and extraction rules.

[0017] The second data is processed using a deep learning algorithm to extract the status data of the circuit board production process in the discrete production area. The status data includes equipment status information, material information, environmental information, product quality information, production progress information, operating condition information, logistics information, and efficiency information.

[0018] In one possible implementation, the discrete intelligent data processing module is further configured to:

[0019] Based on the status data and the preset control strategy, generate interactive / control commands for the target device;

[0020] The interface / communication module sends interactive / control commands to the target device to complete information interaction / control.

[0021] In one possible implementation, the discrete intelligent data processing module is further configured to:

[0022] Computer vision technology is used to identify unstructured data in the raw data in order to determine whether there are product defects in the circuit board manufacturing process;

[0023] If a product defect is found, a defect alarm message will be output.

[0024] In one possible implementation, the discrete intelligent data processing module is a Supervisory Control and Data Acquisition (SCADA) system that learns autonomously using neural network algorithms and raw data and status data from the circuit board manufacturing process.

[0025] In one possible implementation, the discrete intelligent production scheduling module is connected to the production equipment, and / or control equipment, and / or artificial intelligence equipment in the corresponding discrete production area.

[0026] The discrete intelligent production scheduling module is specifically used for:

[0027] Obtain the status data corresponding to the discrete production area in the data sharing center;

[0028] Based on the status data corresponding to the discrete production area and the genetic algorithm, the production plan for the discrete production area is determined.

[0029] Based on the production plan and natural language processing technology, the operation of each piece of equipment is controlled to perform corresponding material management, production management, equipment management, quality management, process management, and personnel management, so as to complete the circuit board production scheduling of the discrete production area.

[0030] In one possible implementation, the discrete intelligent production scheduling module is obtained by the advanced planning and scheduling (APS) system through autonomous learning using neural network algorithms and status data and production plans during the circuit board production process.

[0031] In one possible implementation, the intelligent production management system further includes a data storage module, which is communicatively connected to the plurality of discrete intelligent data processing modules;

[0032] The data storage module is used to store the raw data and status data generated by each discrete intelligent data processing module.

[0033] Secondly, embodiments of this application provide an intelligent production management method, applied to the intelligent production management system based on circuit board manufacturing as described in any one of the first aspects, comprising:

[0034] The system acquires raw data generated during the circuit board production process in each discrete production area, processes the raw data to obtain status data, and sends the status data to the data sharing center. The raw data includes structured data and unstructured data.

[0035] The production plan for each discrete production area is determined based on the status data in the data sharing center, and the production scheduling of circuit boards is carried out according to the production plan.

[0036] This application provides an intelligent production management system and method based on circuit board manufacturing. A data sharing center can communicate with multiple discrete intelligent production scheduling modules and multiple discrete intelligent data processing modules, thereby enabling data sharing and collaborative work among the modules. The discrete intelligent data processing modules can collect and process data generated during the circuit board production process in each discrete production area, and upload the processed status data to the data sharing center, adapting to the discrete production process of circuit boards. The discrete intelligent production scheduling module can obtain the status data corresponding to the discrete production areas from the data sharing center, and perform production scheduling accordingly, adapting to the discrete production process of circuit boards. Through this setup, the discrete intelligent production scheduling module and the discrete intelligent data processing module adapted to circuit board manufacturing can be effectively integrated. The discrete intelligent production scheduling module does not require manual input; it can determine the production plan by using the data shared by the discrete intelligent data processing module through the data sharing center, and schedule circuit board production according to the production plan, greatly improving the production efficiency of circuit boards.

[0037] In addition, the discrete intelligent production scheduling module can also process the collected unstructured data to ensure the comprehensiveness and accuracy of the status data, thereby improving the efficiency and accuracy of the discrete intelligent production scheduling module in scheduling production based on the status data, and further improving the production efficiency of circuit boards. Attached Figure Description

[0038] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0039] Figure 1 This is a schematic diagram of the structure of an intelligent production management system based on circuit board manufacturing according to an embodiment of this application;

[0040] Figure 2 This is a flowchart of an embodiment of the intelligent production management method of this application.

[0041] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0042] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0043] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0044] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0045] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0046] It should also be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0047] The intelligent production management system and intelligent production management method based on circuit board manufacturing of this application can be used in the field of intelligent manufacturing, or in any field other than intelligent manufacturing, such as the field of circuit boards. The application field of the intelligent production management system and intelligent production management method based on circuit board manufacturing of this application is not limited.

[0048] The intelligent production management system and intelligent production management method based on circuit board manufacturing of this application can be applied to scenarios such as circuit board manufacturing, electronic equipment manufacturing, and machinery manufacturing. Any scenario involving circuit board production can apply the intelligent production management system and intelligent production management method based on circuit board manufacturing of this application.

[0049] First, let me explain the terms used in this application:

[0050] APS (Advanced Planning and Scheduling) is a computer-based production planning and scheduling system primarily used to solve production scheduling and production planning problems. It utilizes advanced planning techniques and information technology to fill Gantt charts through system simulation or mathematical programming, aiming for balanced planning between supply and demand. Under limited resources, APS achieves the most efficient planning through techniques such as constraint theory, job study, and genetic algorithms.

[0051] SCADA (Supervisory Control and Data Acquisition) systems are automated systems used to monitor and manage factories, equipment, or processes. They improve efficiency by monitoring and controlling production processes in real time and are widely used in industries such as power, oil and gas, water and wastewater treatment, transportation, and manufacturing. The core functions of SCADA systems include data collection, monitoring, control, and alarms. Through these functions, enterprises can more effectively monitor and control operations, reduce operating costs, and improve production efficiency and safety.

[0052] Structured data, also known as row data, is data logically expressed and implemented using a two-dimensional table structure. It strictly adheres to data format and length specifications and is primarily stored and managed through relational databases. In contrast to structured data is unstructured data, which is unsuitable for representation in a two-dimensional database table. This includes all formats of office documents, XML, HTML, various reports, images, and audio / video information.

[0053] In related technologies, the circuit board manufacturing industry has introduced Advanced Planning and Scheduling (APS) systems to realize the production planning and scheduling of circuit boards. An APS system is a computer-based production planning and scheduling system that can calculate the optimal production plan through optimization algorithms, and then allocate the plan to various workshops and machines, optimizing the allocation of production resources and improving production efficiency.

[0054] However, in practical applications, APS systems are not well-suited to the circuit board manufacturing industry, resulting in low circuit board production efficiency. Specifically,

[0055] (1) The circuit board manufacturing industry is characterized by long process, many and dense processes, and it is impossible to complete the production of circuit boards using a single production line. They are usually produced in multiple factories in a discrete manner. The APS system is usually suitable for production line scheduling, but not for scheduling of discrete production, resulting in low production efficiency.

[0056] (2) The circuit board manufacturing industry usually has a large number of equipment, and the equipment is of different ages and models. Some equipment can be automated, some cannot be automated, and some do not even have data acquisition interfaces. As a result, the APS system cannot obtain the status information of the equipment for production scheduling, and manual input of information is required, which greatly reduces production efficiency.

[0057] (3) The APS system can only process structured data. It cannot effectively process and utilize a large amount of unstructured data, such as images, videos, and audio, which may result in the production plan being less than optimal and affect production efficiency.

[0058] Based on the above-mentioned technical problems, the inventive concept of this application is: how to provide an intelligent production management system that can improve the production efficiency of circuit boards.

[0059] This application provides an intelligent production management system and method based on circuit board manufacturing. It can improve existing APS and SCADA systems to obtain discrete intelligent production scheduling modules and discrete intelligent data processing modules that are more suitable for circuit board manufacturing. The discrete intelligent production scheduling module and discrete intelligent data processing module are effectively integrated through a data sharing center, thereby realizing data sharing and collaborative work among the modules. The discrete intelligent production scheduling module does not require manual input of information. It can perform production scheduling and production scheduling by using the data sharing center to obtain the data shared by the discrete intelligent data processing module, which greatly improves the production efficiency of circuit boards.

[0060] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0061] Figure 1 This is a schematic diagram of the structure of an intelligent production management system based on circuit board manufacturing according to an embodiment of this application, as shown below. Figure 1 As shown, the intelligent production management system may include: multiple discrete intelligent data processing modules 1, multiple discrete production scheduling intelligent modules 2, and a data sharing center 3.

[0062] The data sharing center 3 can communicate with multiple discrete intelligent data processing modules 1 and multiple discrete intelligent production scheduling modules 2 respectively to realize data sharing among the modules;

[0063] The discrete intelligent data processing module 1 can be connected to the equipment in the corresponding discrete production area to obtain the raw data generated during the circuit board production process in the discrete production area, process the raw data to obtain status data, and send the status data to the data sharing center 3. The raw data includes structured data and unstructured data.

[0064] The discrete intelligent production scheduling module 2 can be connected to the equipment in the corresponding discrete production area to determine the production plan of the discrete production area based on the status data in the data sharing center 3, and to schedule the production of circuit boards according to the production plan.

[0065] In this embodiment, multiple discrete intelligent production scheduling modules 2 and multiple discrete intelligent data processing modules 1 can adopt a distributed architecture and communicate with the data sharing center 3 through a local area network to achieve data sharing among the modules.

[0066] In this embodiment, the number of discrete intelligent data processing modules 1 can correspond one-to-one with the number of discrete production areas. That is, one discrete intelligent data processing module 1 collects and processes the data generated during the circuit board production process of one discrete production area. Similarly, the number of discrete intelligent production scheduling modules 2 can also correspond one-to-one with the number of discrete production areas. That is, one discrete intelligent production scheduling module 2 performs production planning and scheduling based on the data of one discrete production area.

[0067] In this embodiment, the discrete intelligent data processing module 1 can be trained based on data from the SCADA system and the circuit board manufacturing process to adapt to the data acquisition and data processing in circuit board manufacturing.

[0068] In this embodiment, the discrete intelligent production scheduling module 2 can be trained based on the APS system and data from the circuit board manufacturing process to adapt to the production plan and scheduling of circuit board manufacturing.

[0069] In this embodiment, the discrete production area can be a discrete production and processing area such as a factory or workshop in the circuit board manufacturing process.

[0070] In this embodiment, the discrete intelligent data processing module 1 can be connected to various intelligent sensing and control devices in the corresponding discrete production area, such as sensors, cameras, RFID readers, and production equipment, to collect a large amount of raw data generated during the circuit board manufacturing process. Subsequently, the discrete intelligent data processing module 1 can also perform unstructured data processing on the collected raw data, as well as integrate and extract data to form reliable real-time status data at the workshop level, and send the status data to the data sharing center 3.

[0071] In this embodiment, the discrete intelligent production scheduling module 2, upon receiving a production scheduling instruction, can obtain the corresponding status data from the data sharing center 3 and generate a production plan accordingly to schedule the production of circuit boards, and is responsible for scheduling the daily processing work orders of the workshop machines. The discrete intelligent production scheduling module 2 can also send the production progress information of the discrete production area to the data sharing center 3.

[0072] In this embodiment, the data sharing center 3 may include a display interface, through which staff can view various data stored in the data sharing center, thereby monitoring the status information and production progress of each discrete production area.

[0073] In this embodiment, the data sharing center can communicate with multiple discrete intelligent production scheduling modules and multiple discrete intelligent data processing modules, thereby enabling data sharing and collaborative work among the modules. The discrete intelligent data processing modules can collect and process data generated during the circuit board production process in each discrete production area, and upload the processed status data to the data sharing center, adapting to the discrete production process of the circuit boards. The discrete intelligent production scheduling module can obtain the status data corresponding to the discrete production area from the data sharing center, and perform production scheduling accordingly, adapting to the discrete production process of the circuit boards. Through this setup, the discrete intelligent production scheduling module and the discrete intelligent data processing module adapted for circuit board manufacturing can be effectively integrated. The discrete intelligent production scheduling module does not require manual input; it can determine the production plan by obtaining data shared by the discrete intelligent data processing module through the data sharing center, and schedule circuit board production according to the production plan, greatly improving the production efficiency of the circuit boards. Furthermore, the discrete intelligent production scheduling module can also process the collected unstructured data to ensure the comprehensiveness and accuracy of the status data, thereby improving the efficiency and accuracy of the discrete intelligent production scheduling module in scheduling production based on the status data, further improving the production efficiency of the circuit boards.

[0074] In one possible implementation, the discrete intelligent data processing module 1 can be connected to production equipment, and / or control equipment, and / or sensing equipment, and / or monitoring equipment, and / or artificial intelligence equipment in the corresponding discrete production area.

[0075] Discrete intelligent data processing module 1 can be used for:

[0076] Through the interface / communication modules on each device, raw data generated during the circuit board production process in discrete production areas is collected. The raw data includes one or more of the following: equipment status data, material consumption data, warehousing data, environmental data, product quality data, and production progress data.

[0077] In this embodiment, all equipment in each discrete production area can be uniformly equipped with an interface or communication module so that the discrete intelligent data processing module 1 can collect data according to the interface or communication module.

[0078] In this embodiment, the artificial intelligence device can use artificial intelligence technology for signal control and data acquisition, thereby improving the efficiency and intelligence of data acquisition.

[0079] In this embodiment, the specific raw data can be flexibly set by those skilled in the art, and is not limited to the above content; no restrictions are imposed here.

[0080] In this embodiment, since the equipment in the circuit board manufacturing industry varies in age and model, and some equipment does not even have an interface for acquiring data, an interface or communication module can be uniformly set on all equipment in each discrete production area. The discrete intelligent data processing module can collect the raw data generated during the circuit board production process in the discrete production area through the interface / communication module on each device, thereby improving the comprehensiveness and efficiency of data collection.

[0081] In one possible implementation, the discrete intelligent data processing module 1 can be used for:

[0082] The original data is processed using big data technology to achieve data isomorphism, resulting in the first data.

[0083] The first data is integrated and processed using preset data integration rules to obtain the second data. The data integration rules include one or more of the following: quality information extraction rules, working condition information extraction rules, production progress information extraction rules, and multi-source heterogeneous manufacturing process information processing and extraction rules.

[0084] The second data is processed using deep learning algorithms to extract the status data of the circuit board production process in the discrete production area. The status data includes equipment status information, material information, environmental information, product quality information, production progress information, working condition information, logistics information, and efficiency information.

[0085] In this embodiment, data isomorphism processing of the original data can be performed to process unstructured data in the original data, such as images, videos, and audio, into structured data that can be used by the discrete intelligent production scheduling module, thereby enabling the discrete intelligent production scheduling module to obtain more comprehensive and valuable data.

[0086] In this embodiment, the specific data integration rules and status data can be flexibly set by those skilled in the art, and are not limited to the above content. No restrictions are imposed here.

[0087] In this embodiment, the discrete intelligent data processing module 1 can use Hadoop clusters and Spark and other big data processing frameworks for big data processing; it can clean, integrate and analyze data using preset data integration rules to obtain valuable information; it can use deep learning algorithms for intelligent extraction; and it can use artificial intelligence technology to automate the data processing process, thereby improving the efficiency and accuracy of data processing.

[0088] In this embodiment, the discrete intelligent data processing module can integrate big data technology and machine learning technology. Big data technology is used to process unstructured data to achieve data homogenization and ensure data comprehensiveness. Data is integrated using data integration rules and extracted using machine learning technology, thereby improving the efficiency and accuracy of data processing.

[0089] In one possible implementation, the discrete intelligent data processing module 1 can also be used for:

[0090] Based on the status data and the preset control strategy, generate interactive / control commands for the target device; send the interactive / control commands to the target device through the interface / communication module to complete information interaction / control.

[0091] In this embodiment, the specific control strategy can be flexibly set by those skilled in the art, and no restrictions are imposed here.

[0092] In this embodiment, the discrete intelligent data processing module can also use status data and preset control strategies to interact with / control the device, thereby improving the intelligence and automation of data acquisition.

[0093] In one possible implementation, the discrete intelligent data processing module 1 can also be used for:

[0094] Computer vision technology is used to identify unstructured data in the raw data to determine whether there are product defects in the circuit board manufacturing process; if product defects are found, a defect alarm message is output.

[0095] In this embodiment, computer vision technology can be used to automatically identify product defects on the production line, issue timely warnings, and prevent the production of defective products.

[0096] In this embodiment, the discrete intelligent data processing module can also integrate computer vision technology to monitor product quality and improve the production quality of circuit boards.

[0097] In one possible implementation, the discrete intelligent data processing module 1 can be a Supervisory Control and Data Acquisition (SCADA) system, which uses neural network algorithms and raw data and status data from the circuit board manufacturing process to perform autonomous learning.

[0098] In this embodiment, the discrete intelligent data processing module can be trained by an existing SCADA system using neural network algorithms and relevant data from circuit board manufacturing. This not only enables the discrete intelligent data processing module to adapt to data acquisition and processing in the circuit board manufacturing industry, but also allows the discrete intelligent data processing module to learn and evolve autonomously, improving the efficiency and accuracy of data acquisition and processing.

[0099] In one possible implementation, the discrete intelligent production scheduling module 2 is connected to the production equipment, and / or control equipment, and / or artificial intelligence equipment in the corresponding discrete production area.

[0100] Discrete intelligent production scheduling module 2 can be used for:

[0101] Obtain the status data corresponding to the discrete production areas in Data Sharing Center 3;

[0102] Based on the state data corresponding to the discrete production area and the genetic algorithm, the production plan for the discrete production area is determined.

[0103] Based on the production plan and natural language processing technology, the operation of each piece of equipment is controlled separately, and corresponding material management, production management, equipment management, quality management, process management and personnel management are carried out to complete the circuit board production scheduling in discrete production areas.

[0104] In this embodiment, after receiving the production scheduling instruction, the discrete intelligent production scheduling module 2 can obtain the corresponding status data from the data sharing center 3 and use genetic algorithms to schedule production and optimize the processing work orders of the workshop machines.

[0105] In this embodiment, after the discrete intelligent production scheduling module 2 determines the production plan, it can control the operation of each device through the interface or communication module of each device to complete the production scheduling. During this process, the discrete intelligent production scheduling module 2 can use natural language processing technology to automatically parse and execute production instructions, ensuring the smooth progress of production and processing.

[0106] In this embodiment, the discrete intelligent production scheduling module can integrate natural language processing technology. After determining the production plan based on the status data in the data sharing center and the genetic algorithm, it can control the operation of each device according to the production plan and natural language processing technology to complete the circuit board production scheduling of the discrete production area, thereby improving the intelligence and efficiency of circuit board production.

[0107] In one possible implementation, the discrete intelligent production scheduling module 2 can be obtained by the advanced planning and scheduling (APS) system through autonomous learning using neural network algorithms and status data and production plans during the circuit board production process.

[0108] In this embodiment, the discrete intelligent production scheduling module can be trained by an existing APS system using neural network algorithms and relevant data from circuit board manufacturing. This not only enables the discrete intelligent production scheduling module to adapt to the production scheduling and scheduling of the circuit board manufacturing industry, but also allows the discrete intelligent production scheduling module to learn and evolve autonomously, thereby improving the efficiency and accuracy of circuit board production scheduling.

[0109] In one possible implementation, the intelligent production management system may further include a data storage module 4, which is communicatively connected to multiple discrete intelligent data processing modules 1.

[0110] The data storage module 4 can be used to store the raw data and status data generated by each discrete intelligent data processing module 1.

[0111] In this embodiment, data storage module 4 can use MySQL for data storage and management, and provide a data access interface to the outside world through a web service. This module can provide supporting data for quality assurance, production process monitoring, and process improvement to upper-level production management service platforms such as ERP.

[0112] In this embodiment, after the discrete intelligent data processing module collects and processes the data, it can store the collected raw data and the processed state data in the data storage module for subsequent data traceability and support in the circuit board production process.

[0113] Figure 2 This is a flowchart of an intelligent production management method according to an embodiment of this application. In this embodiment, the executing entity is... Figure 1 The intelligent production management system based on circuit board manufacturing, as shown, illustrates this intelligent production management method. For example... Figure 2 As shown, this intelligent production management method may include the following steps:

[0114] S201: Obtain the raw data generated during the circuit board production process in each discrete production area.

[0115] In this embodiment, the intelligent production management system may include multiple discrete intelligent production scheduling modules, multiple discrete intelligent data processing modules, and a data sharing center.

[0116] In this embodiment, multiple discrete intelligent production scheduling modules and multiple discrete intelligent data processing modules can adopt a distributed architecture and communicate with the data sharing center through a local area network to achieve data sharing among the modules.

[0117] In this embodiment, the number of discrete intelligent data processing modules can correspond one-to-one with the number of discrete production areas. That is, one discrete intelligent data processing module collects and processes the data generated during the circuit board production process in one discrete production area. Similarly, the number of discrete intelligent production scheduling modules can also correspond one-to-one with the number of discrete production areas. That is, one discrete intelligent production scheduling module performs production planning and scheduling based on the data from one discrete production area.

[0118] In this embodiment, the discrete intelligent data processing module can be trained based on data from the SCADA system and the circuit board manufacturing process to adapt to the data acquisition and processing in circuit board manufacturing.

[0119] In this embodiment, the discrete intelligent production scheduling module can be trained based on the APS system and data from the circuit board manufacturing process to adapt to the production plan and scheduling of circuit board manufacturing.

[0120] In this embodiment, the discrete production area can be a discrete production and processing area such as a factory or workshop in the circuit board manufacturing process.

[0121] In this embodiment, the discrete intelligent data processing module can be connected to various intelligent sensing and control devices in the corresponding discrete production area, such as sensors, cameras, RFID readers, production equipment, etc., to collect a large amount of raw data generated during the circuit board manufacturing process.

[0122] In this embodiment, the specific implementation method for obtaining the raw data generated during the circuit board production process in each discrete production area can be referred to the above-described structural embodiment of the intelligent production management system, and will not be repeated here.

[0123] S202: Process the raw data to obtain status data and send the status data to the data sharing center. The raw data includes structured data and unstructured data.

[0124] In this embodiment, the discrete intelligent data processing module can also perform unstructured data processing on the collected raw data, as well as integrate and extract it to form reliable real-time status data of the workshop bottom layer, and send the status data to the data sharing center.

[0125] In this embodiment, the data sharing center may include a display interface, through which staff can view various data stored in the data sharing center, thereby monitoring the status information and production progress of each discrete production area.

[0126] In this embodiment, the specific implementation method for processing the raw data to obtain the status data can be referred to the above-described structural embodiment of the intelligent production management system, and will not be repeated here.

[0127] S203: Determine the production plan for each discrete production area based on the status data in the data sharing center, and schedule the production of circuit boards according to the production plan.

[0128] In this embodiment, the discrete intelligent production scheduling module can obtain the corresponding status data from the data sharing center after receiving the production scheduling instruction, and generate a production plan accordingly to schedule the production of circuit boards, and is responsible for scheduling the daily processing work orders of the workshop machines.

[0129] In this embodiment, the specific implementation method for determining the production plan of each discrete production area based on the status data in the data sharing center and scheduling the production of circuit boards according to the production plan can be referred to the above-described intelligent production management system structural embodiment, and will not be repeated here.

[0130] In this embodiment, the data sharing center can communicate with multiple discrete intelligent production scheduling modules and multiple discrete intelligent data processing modules, thereby enabling data sharing and collaborative work among the modules. The discrete intelligent data processing modules can collect and process data generated during the circuit board production process in each discrete production area, and upload the processed status data to the data sharing center, adapting to the discrete production process of the circuit boards. The discrete intelligent production scheduling module can obtain the status data corresponding to the discrete production area from the data sharing center, and perform production scheduling accordingly, adapting to the discrete production process of the circuit boards. Through this setup, the discrete intelligent production scheduling module and the discrete intelligent data processing module adapted for circuit board manufacturing can be effectively integrated. The discrete intelligent production scheduling module does not require manual input; it can determine the production plan by obtaining data shared by the discrete intelligent data processing module through the data sharing center, and schedule circuit board production according to the production plan, greatly improving the production efficiency of the circuit boards. Furthermore, the discrete intelligent production scheduling module can also process the collected unstructured data to ensure the comprehensiveness and accuracy of the status data, thereby improving the efficiency and accuracy of the discrete intelligent production scheduling module in scheduling production based on the status data, further improving the production efficiency of the circuit boards.

[0131] The application process of the intelligent production management system based on circuit board manufacturing of this application will be described below with a specific embodiment.

[0132] In one specific embodiment, a circuit board manufacturing enterprise includes multiple discrete production areas, which utilize an intelligent production management system for circuit board production management. This intelligent production management system includes multiple discrete intelligent production scheduling modules, multiple discrete intelligent data processing modules, and a data sharing center. Equipment in each discrete production area is connected to both a discrete intelligent data processing module and a discrete intelligent production scheduling module. These modules are communicatively connected to the data sharing center, enabling data sharing and collaborative operation among the modules. The specific application process of this intelligent production management system is as follows:

[0133] The discrete intelligent data processing module connects to the equipment in the corresponding discrete production area. Through the interface / communication modules on each device, it collects the raw data generated during the circuit board production process in the discrete production area. The raw data includes structured and unstructured data. The module then processes the raw data using big data technology, data integration rules, machine learning technology, and artificial intelligence technology to obtain the status data of the circuit board production process in the discrete production area.

[0134] The discrete intelligent data processing module also stores the collected raw data and the processed state data in the data storage module to achieve data traceability.

[0135] After receiving the production scheduling instruction, the discrete intelligent production scheduling module obtains the status data from the data sharing center, determines the production plan for the discrete production area based on the status data and the genetic algorithm, and schedules the production of circuit boards according to the production plan.

[0136] The discrete intelligent data processing module sends status data to the data sharing center, and the discrete intelligent production scheduling module sends production progress information to the data sharing center. The data sharing center stores and displays the status data and production progress information of each discrete production area.

[0137] It should be understood that the above system embodiments are merely illustrative, and the system of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components can be combined, or integrated into another system, or some features can be ignored or not executed.

[0138] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0139] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0140] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the appended claims.

[0141] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. An intelligent production management system based on circuit board manufacturing, characterized in that, include: Multiple discrete intelligent production scheduling modules, multiple discrete intelligent data processing modules, and a data sharing center; The data sharing center is communicatively connected to the plurality of discrete intelligent production scheduling modules and the plurality of discrete intelligent data processing modules, respectively, to realize data sharing among the modules; The discrete intelligent data processing module is connected to the equipment in the corresponding discrete production area, and is used to acquire the raw data generated during the circuit board production process in the discrete production area, process the raw data to obtain status data, and send the status data to the data sharing center. The raw data includes structured data and unstructured data. The discrete intelligent production scheduling module is connected to the equipment in the corresponding discrete production area, and is used to determine the production plan of the discrete production area according to the status data in the data sharing center, and to schedule the production of circuit boards according to the production plan.

2. The intelligent production management system based on circuit board manufacturing according to claim 1, characterized in that, The discrete intelligent data processing module is connected to the production equipment and / or control equipment and / or sensing equipment and / or monitoring equipment in the corresponding discrete production area, and / or artificial intelligence equipment. The discrete intelligent data processing module is specifically used for: Through the interface / communication modules on each device, raw data generated during the circuit board production process in the discrete production area is collected. The raw data includes one or more of the following: equipment status data, material consumption data, warehousing data, environmental data, product quality data, and production progress data.

3. The intelligent production management system based on circuit board manufacturing according to claim 2, characterized in that, The discrete intelligent data processing module is specifically used for: The original data is processed using big data technology to achieve data isomorphism, resulting in the first data. The first data is integrated and processed using preset data integration rules to obtain the second data. The data integration rules include one or more of the following: quality information extraction rules, working condition information extraction rules, production progress information extraction rules, and multi-source heterogeneous manufacturing process information processing and extraction rules. The second data is processed using a deep learning algorithm to extract the status data of the circuit board production process in the discrete production area. The status data includes equipment status information, material information, environmental information, product quality information, production progress information, operating condition information, logistics information, and efficiency information.

4. The intelligent production management system based on circuit board manufacturing according to claim 3, characterized in that, The discrete intelligent data processing module is also used for: Based on the status data and the preset control strategy, generate interactive / control commands for the target device; The interface / communication module sends interactive / control commands to the target device to complete information interaction / control.

5. The intelligent production management system based on circuit board manufacturing according to claim 1, characterized in that, The discrete intelligent data processing module is also used for: Computer vision technology is used to identify unstructured data in the raw data in order to determine whether there are product defects in the circuit board manufacturing process; If a product defect is found, a defect alarm message will be output.

6. The intelligent production management system based on circuit board manufacturing according to any one of claims 1-5, characterized in that, The discrete intelligent data processing module is a Supervisory Control and Data Acquisition (SCADA) system that uses neural network algorithms and raw data and status data from the circuit board manufacturing process to perform autonomous learning.

7. The intelligent production management system based on circuit board manufacturing according to any one of claims 1-5, characterized in that, The discrete intelligent production scheduling module is connected to the production equipment, and / or control equipment, and / or artificial intelligence equipment in the corresponding discrete production area. The discrete intelligent production scheduling module is specifically used for: Obtain the status data corresponding to the discrete production area in the data sharing center; Based on the status data corresponding to the discrete production area and the genetic algorithm, the production plan for the discrete production area is determined. Based on the production plan and natural language processing technology, the operation of each piece of equipment is controlled to perform corresponding material management, production management, equipment management, quality management, process management, and personnel management, so as to complete the circuit board production scheduling of the discrete production area.

8. The intelligent production management system based on circuit board manufacturing according to claim 7, characterized in that, The discrete intelligent production scheduling module is obtained by the Advanced Planning and Scheduling (APS) system through autonomous learning using neural network algorithms and status data and production plans during the circuit board production process.

9. The intelligent production management system based on circuit board manufacturing according to any one of claims 1-5, characterized in that, The intelligent production management system also includes a data storage module, which is communicatively connected to the plurality of discrete intelligent data processing modules. The data storage module is used to store the raw data and status data generated by each discrete intelligent data processing module.

10. An intelligent production management method, applied to an intelligent production management system based on circuit board manufacturing as described in any one of claims 1-9, characterized in that, include: The system acquires raw data generated during the circuit board production process in each discrete production area, processes the raw data to obtain status data, and sends the status data to the data sharing center. The raw data includes structured data and unstructured data. The production plan for each discrete production area is determined based on the status data in the data sharing center, and the production scheduling of circuit boards is carried out according to the production plan.

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