Supply chain intelligent management system and method, electronic equipment and storage medium

Abstract

The invention relates to a supply chain intelligent management system and method, an electronic device and a storage mechanism, and the method comprises the steps: obtaining and storing the production link data and order monitoring data of a factory through a data management module; a three-dimensional digital twinborn model of a factory is constructed through a construction module based on production link data and order monitoring data; a front-end display module is used for displaying a graphical interface based on a three-dimensional digital twinborn model, and the graphical interface displays board indexes used for reflecting the real-time production condition of a factory. The problems of high labor cost and low efficiency of supply chain management in related technologies are solved. By means of digital and intelligent means, real-time monitoring and visual display of the factory production process are achieved, a more convenient and efficient management mode is provided for enterprises, and the process of transformation of the manufacturing industry to intelligence and digitization is promoted.

Description

Technical Field

[0001] The present application relates to the field of supply chain management, and in particular to a supply chain intelligent management system, method, electronic device and storage medium. Background Art

[0002] With the increasing complexity of the global supply chain and the intensification of market competition, manufacturing companies are facing unprecedented challenges. Especially in the process of building smart factories in the supply chain, data timeliness and problem follow-up efficiency have become key factors that restrict production efficiency and affect the overall pass rate.

[0003] Traditional supply chain management methods often rely on manual collection, organization and analysis of data. This method is not only time-consuming and labor-intensive, but also difficult to ensure the accuracy and timeliness of the data. As a result, companies are unable to grasp the production dynamics of foundries in a timely and accurate manner, which in turn affects the management and control of foundries and the efficiency of business decision-making.

[0004] Currently, no effective solution has been proposed to address the problem of low efficiency of supply chain management systems in related technologies. Summary of the invention

[0005] The embodiments of the present application provide a supply chain intelligent management system, method, computer device and computer-readable storage medium to at least solve the problem of low efficiency of the supply chain management system in the related art.

[0006] In a first aspect, an embodiment of the present application provides a supply chain intelligent management system, the system comprising: a construction module, a data management module and a front-end display module, wherein:

[0007] The data management module is used to obtain and store the factory's production process data and order monitoring data;

[0008] The construction module is used to obtain the production link data and the order monitoring data from the data management module, and to construct a three-dimensional digital twin model of the factory based on the production link data and the order monitoring data;

[0009] The front-end display module is used to display a graphical interface based on the three-dimensional digital twin model, wherein the graphical interface displays kanban indicators that reflect the real-time production status of the factory.

[0010] In some embodiments, the data management module includes: a storage module and an update module, wherein:

[0011] The storage module is constructed based on the MySQL database and is used to receive and manage the source data of the system, wherein the source data includes: production link data and order monitoring data;

[0012] The update module is constructed based on the Hologres database and is used to implement real-time update of the source data through the Binlog synchronization mechanism.

[0013] In some embodiments, the update module includes: an acquisition and parsing module, a conversion and adaptation module, and a writing module, wherein:

[0014] The acquisition and parsing module is used to establish a connection channel with the storage module, detect data change events from the Binlog of the MySQL database through the connection channel, and parse the data change results and the data operation types therein according to the format of Binlog when a data change event occurs;

[0015] The conversion adaptation module is used to convert the data change results parsed from Binlog into data adapted for Hologres storage and analysis according to the data operation type;

[0016] The writing module writes the data adapted for Hologres storage and analysis into a corresponding storage structure in Hologres.

[0017] In some embodiments, the system also includes a service module, wherein the service module is used to generate an asynchronous request in response to a user interaction instruction, and call a background service through the asynchronous request to perform data query and logical operations based on the production link data and the order monitoring.

[0018] In some of the embodiments, the service module is further used to: identify and filter data records belonging to the same product or the same production batch based on the three-dimensional digital twin model and the serial number of the production link data;

[0019] The dashboard index is calculated using the data record as reference information.

[0020] In some embodiments, the construction module includes: a data parsing module and a model construction module, wherein:

[0021] The data parsing module is used to parse the production link data and the order monitoring data to extract key information, and convert the format of the key information to obtain key information suitable for building a three-dimensional digital twin model;

[0022] The model building module is used to use WebGL and Three.js technology to build the basic architecture of the three-dimensional digital twin model according to the actual layout plan of the factory, and map the key information to the corresponding elements of the basic structure to obtain the three-dimensional digital twin model.

[0023] In some embodiments, the kanban indicators include: section yield, defective conditions, order status, comprehensive first pass rate and link production status, where:

[0024] The section yield is obtained by real-time statistical calculation based on the job data of each section of the factory, and is displayed in the graphical interface through charts and data formats;

[0025] The bad conditions are obtained through bad data of each section, including: operation errors, incoming material problems, production anomalies and assembly defects;

[0026] The comprehensive first pass rate is calculated based on the data of each process section and is used to demonstrate the overall production process efficiency and stability of the factory;

[0027] The order status is displayed in charts and data formats to reflect order plans and production progress;

[0028] The waiting-for-delivery status of the described links is used to display the waiting-for-delivery data of each production position.

[0029] In a second aspect, the present application also provides a supply chain intelligent management method, the method comprising:

[0030] Obtain and store the factory's production data and order monitoring data;

[0031] Acquire the production link data and the order monitoring data from the data management module, and build a three-dimensional digital twin model of the factory based on the production link data and the order monitoring data;

[0032] A graphical interface is displayed based on the three-dimensional digital twin model, wherein the graphical interface displays kanban indicators for reflecting the real-time production status of the factory.

[0033] In a third aspect, an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect above when executing the computer program.

[0034] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect above.

[0035] Compared with the related art, the supply chain intelligent management system provided by the embodiment of the present application obtains and stores the factory's production link data and order monitoring data through the data management module; constructs a three-dimensional digital twin model of the factory based on the production link data and order monitoring data through the construction module; and uses the front-end display module to display a graphical interface based on the three-dimensional digital twin model, wherein the graphical interface displays the kanban indicators used to reflect the real-time production situation of the factory. The problem of high labor cost and low efficiency of supply chain management in the related art is solved; through digital and intelligent means, the real-time monitoring and visual display of the factory production process is realized, providing enterprises with a more convenient and efficient management method, and promoting the process of manufacturing industry's transformation to intelligence and digitalization. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0037] Figure 1 is a structural block diagram of a supply chain intelligent management system according to an embodiment of the present application;

[0038] Figure 2 is a schematic diagram of a dashboard indicator according to an embodiment of the present application;

[0039] Figure 3 is a flow chart of a supply chain intelligent management method according to an embodiment of the present application;

[0040] Figure 4 It is a data flow diagram of a supply chain intelligent management method according to an embodiment of the present application;

[0041] Figure 5 is a schematic diagram of a graphical interface according to an embodiment of the present application;

[0042] Figure 6 is a schematic diagram of another graphical interface according to an embodiment of the present application;

[0043] Figure 7 It is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0045] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0046] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0047] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0048] With the increasing complexity of the global supply chain and the intensification of market competition, manufacturing companies are facing unprecedented challenges. Especially in the process of building smart factories in the supply chain, data timeliness and problem follow-up processing efficiency have become key factors that restrict production efficiency and affect the overall straight-through rate. Traditional supply chain management methods often rely on manual collection, organization and analysis of data. This method is not only time-consuming and labor-intensive, but also difficult to ensure the accuracy and timeliness of data, resulting in companies being unable to grasp the production dynamics of foundries in a timely and accurate manner, which in turn affects the control of foundries and the efficiency of business decision-making.

[0049] In order to meet these challenges and improve the management efficiency and overall straight-through rate of supply chain smart factories, this application provides a supply chain intelligent management system. The system aims to achieve real-time monitoring and visual display of the factory production process through digital and intelligent means, thereby solving the problems of data timeliness and problem follow-up processing efficiency.

[0050] An intelligent supply chain management system provided in an embodiment of the present application makes full use of advanced technologies such as big data, the Internet of Things, and cloud computing to build a digital twin model of a factory, and realizes real-time monitoring and visual display of the factory production process through digital and intelligent means, thereby solving the problems of data timeliness and efficiency in problem follow-up processing.

[0051] Figure 1 is a structural block diagram of a supply chain intelligent management system according to an embodiment of the present application, such as Figure 1 As shown, the system includes a data management module 10, a construction module 11, a front-end display module 12 and a service module 13, wherein:

[0052] The data management module 10 is used to obtain the production process data and order monitoring data of the target factory and store and analyze the data;

[0053] The data management module 10 includes a storage module 101 and an update module 102;

[0054] Specifically, the storage module 101 establishes a connection interface with various data sources in the factory (such as production equipment sensors, production management system, order management system, material management system, etc.), and receives production data (such as production progress of each work section, equipment operation status, quality inspection results, etc.), order monitoring data (order number, product specifications, quantity, order time, delivery time, etc.) and material data (material inventory quantity, material in and out records, material flow information on the production line, etc.) from these data sources through the interface;

[0055] Furthermore, the storage module 101 designs a reasonable data storage structure in the MySQL database according to the data type, characteristics and business requirements. For example, a table structure containing fields such as timestamp, work section identification, production quantity, quality index, etc. is created for production data; a table containing fields such as order basic information, product details, order status, etc. is created for order monitoring data; a material inventory table, a material flow table, etc. are created for material data, and the association relationship between each data table is defined. Optionally, the order monitoring data is associated with the production data through the order number, and the material data is associated with the material usage in the production data through the material code.

[0056] Finally, the data received by the storage module 101 is accurately stored in the MySQL database according to the designed storage structure. The data is classified and managed to ensure the integrity and consistency of the data. For example, the production data is classified and stored according to different production batches, product types or time periods to facilitate subsequent data query and analysis.

[0057] In addition, the update module 102 includes an acquisition and analysis module 1021, a conversion and adaptation module 1022, and a writing module 1023; wherein the acquisition and analysis module 1021 is used to establish a connection with the Binlog of the MySQL database, and through the connection channel, monitor the data change events in the Binlog in real time. When new data insertion, update, or deletion operations are recorded in the Binlog, the update module 102 can obtain the detailed information of these changed data in a timely manner, including the operation type, the data tables and fields involved in the operation, and the data values ​​before and after the change.

[0058] In addition, due to the differences in data formats and storage requirements between MySQL and Hologres, the changed data obtained from Binlog is converted and adapted through the conversion and adaptation module 1022; and the converted and adapted data is written to the Hologres database in real time through the writing module 1023 to achieve synchronous update with the MySQL source data.

[0059] During the writing process, it is necessary to ensure the consistency and integrity of the data and handle possible concurrent writing conflicts. For example, a suitable transaction processing mechanism is used to ensure the correctness and stability of the data when multiple threads or processes update data simultaneously.

[0060] At the same time, the update module 102 can also monitor the status of data synchronization and record synchronization logs so that it can be traced and checked when problems occur, ensuring that the data in the Hologres database always remains consistent with the MySQL source data in real time.

[0061] The construction module 11 is used to construct a three-dimensional digital twin model of the factory based on the production link data and order monitoring data of the factory;

[0062] In this embodiment, the digital twin model is constructed by making full use of advanced technologies such as big data, the Internet of Things, and cloud computing. Specifically, the module includes a data analysis module 111 and a model construction module 112, wherein:

[0063] The data analysis module 111 obtains production process data (such as the output of each work section, quality inspection results, equipment operating parameters, etc.) and order data (order number, product type, quantity, delivery time, etc.) from the factory's production management system, equipment sensors and other data sources.

[0064] Parse this raw data, extract key information, and convert it into a format suitable for building a 3D digital twin model.

[0065] The model building module 112 is used to use WebGL and Three.js technology to build the basic architecture of the three-dimensional digital twin model according to the actual factory layout plan, including creating three-dimensional geometric objects representing factory buildings, workshops, production lines, equipment, etc., and accurately setting their position, size, shape and other attributes to ensure that the model is consistent with the actual factory layout.

[0066] Furthermore, the converted data is mapped to the corresponding elements in the three-dimensional digital twin model. Optionally, based on the equipment's operating status data, the dynamic performance of the equipment model in three-dimensional space is determined, such as whether the equipment is in operation, shutdown or fault state, and displayed through animation effects (such as rotating mechanical parts, flashing indicator lights, etc.); based on the order data, the location and flow path of the order-related products on the production line, as well as the order task progress of each production link, are marked in the model.

[0067] The front-end display module 12 is used to display a graphical interface based on the three-dimensional digital twin model, wherein the graphical interface displays dashboard indicators that reflect the real-time production status of the factory.

[0068] In this embodiment, the front-end display module 12 builds a user interface based on the React framework and uses Echarts technology for data visualization.

[0069] The overall interface layout of the digital twin cockpit is designed according to user needs and business processes. The location, size and arrangement of each data display area are determined to ensure the rationality and intuitiveness of information presentation. For example, key production indicators such as section yield, defective conditions, and comprehensive pass rate are displayed in the core area to facilitate managers to quickly obtain important information; special display panels are set up for data such as order status and link production, and different types of data are easy to distinguish and find through reasonable partitions and labels.

[0070] Furthermore, the front-end data visualization library is used to display the production link data and order monitoring data obtained from the back-end in the form of intuitive graphics and charts on the interface. According to the characteristics of the data and the display requirements, the appropriate visualization method is selected, such as a bar chart to compare the yield of different sections, a line chart to show the trend of order production progress over time, and a pie chart to show the classification and proportion of bad conditions, so that users can understand the production situation reflected by the data at a glance.

[0071] In some embodiments, the service module 13 identifies and filters data records belonging to the same product or the same production batch from the serial number of the production link data based on the three-dimensional digital twin model, and calculates the kanban index based on the data records. The front-end display interface displays the index in a graphical interface. Specifically, Figure 2 is a schematic diagram of a dashboard indicator according to an embodiment of the present application, such as Figure 2 As shown, the dashboard indicators include but are not limited to:

[0072] 1) Section yield rate: Based on the job data of the four major sections, the section yield rate of each section is counted and displayed in real time. Through intuitive charts and data, the quality status of each section operation is immediately reflected, helping managers to quickly identify quality fluctuations and take corresponding measures;

[0073] 2) Bad conditions: Real-time summary and display of bad condition data of each section, covering key links in the manufacturing process such as operating errors, incoming material problems, production anomalies and assembly defects. Through detailed data analysis, the team can accurately locate the source of the problem and improve overall production efficiency and product quality.

[0074] 3) Comprehensive straight pass rate: The comprehensive straight pass rate is calculated by combining the data of the four major sections and displayed in real time. This indicator is an important yardstick for measuring the efficiency and stability of the entire production process, which helps the management to grasp the overall situation, optimize the production process, and improve the overall straight pass rate;

[0075] 4) Order status: Using PO (purchase order) as the dimension, the digital twin status of order plan and production progress is monitored in real time. Through dynamically updated data charts, the order execution status is intuitively displayed to ensure that the production progress is highly consistent with sales demand, and to improve the on-time rate and satisfaction of order delivery.

[0076] 5) Waiting for production: Real-time monitoring and display of waiting data for each production position, realizing the digital twin of the production position. Through accurate data analysis, it helps production dispatchers to arrange production plans reasonably, reduce waiting time, and improve the overall operating efficiency and flexibility of the production line.

[0077] In addition, it should be noted that the service module 13 is also used to respond to user interaction instructions and send asynchronous requests to call background services through Ajax and other methods to perform data query and logical operations. Among them, specific data queries can include: yield data query of each section, defect data query, comprehensive pass rate query and order status query, etc.; further, the corresponding logical operations can be yield calculation, defect analysis calculation, comprehensive rate pass rate calculation, etc.

[0078] It should also be noted that the results obtained during the data query and logical operation process, such as the yield rate, defective conditions, production progress and other data of each section, will be fed back to the digital twin model in real time, driving the corresponding elements in the model to update their status display. For example, when the yield rate of a section decreases, the equipment or area of ​​the section in the digital twin model may be intuitively presented through color changes (such as turning to red warning) or animation effects (such as flashing), so that managers can see the changes in production quality at a glance in the model.

[0079] In addition, the digital twin model displays production data and order status in an intuitive three-dimensional visual form. Compared with simple data tables or charts, it helps managers understand complex production relationships and order execution processes. For example, by observing the accumulation of materials in each production link in the model (corresponding to the production data of the link), managers can more quickly determine the bottlenecks in the production process.

[0080] At the same time, based on the data and logical computing capabilities provided by the data management module 10, the digital twin model can perform simulation analysis and prediction. For example, based on the current production schedule and order situation, it simulates the impact of different production plan adjustment plans on the comprehensive pass rate, order delivery time, etc., to help management evaluate the decision-making effect in advance and select the optimal production strategy. This makes the decision-making process more scientific and accurate, avoids the risks that may be caused by decision-making based solely on experience, and further improves production efficiency and order delivery capabilities.

[0081] Through this system, the data management module obtains and stores the factory's production data and order monitoring data; through the construction module 11, a three-dimensional digital twin model of the factory is constructed based on the production data and order monitoring data; through the front-end display module, a graphical interface is displayed based on the three-dimensional digital twin model, wherein the graphical interface displays the Kanban indicators used to reflect the real-time production situation of the factory. The problem of high labor cost and low efficiency of supply chain management in related technologies is solved; through digital and intelligent means, the real-time monitoring and visual display of the factory production process is realized, providing enterprises with a more convenient and efficient management method, and promoting the process of manufacturing industry's transformation to intelligence and digitalization.

[0082] On the other hand, the present application also provides a method for intelligent management of a supply chain. Figure 3 is a flow chart of a supply chain intelligent management method according to an embodiment of the present application, such as Figure 3 As shown, the process includes the following steps:

[0083] S301, obtaining and storing the factory's production process data and order monitoring data;

[0084] S302, obtaining production link data and order monitoring data from the data management module, and building a three-dimensional digital twin model of the factory based on the production link data and order monitoring data;

[0085] S303, displaying a graphical interface based on the three-dimensional digital twin model, wherein the graphical interface displays dashboard indicators for reflecting the real-time production status of the factory.

[0086] also, Figure 4 is a data flow diagram of a supply chain intelligent management method according to an embodiment of the present application, such as Figure 4 As shown in the figure, in this technical solution, the front end initiates asynchronous requests, including viewing the dashboard, querying the yield of each section, querying the production of the link, and querying the bad condition. After the background receives these asynchronous requests, the request for querying the yield of each section will be further decomposed into the query of the assembly section yield, the aging section yield, the factory test section yield, and the packaging section yield, and real-time calculation will be performed; for querying the production of the link and querying the bad condition, the query operation is directly performed. The background will send a query request to the Hologres database, and the Hologres database will query the production of the link and the bad condition respectively, and return the result after the query is successful. At the same time, the MySQL database and the Hologres database are synchronized in real time to ensure data consistency. The whole process realizes the smooth transmission and processing of data from the front-end request to the background processing and then to the database operation. The system realizes real-time monitoring and visual display of the factory production process, provides enterprises with a more convenient and efficient management method, and promotes the process of manufacturing industry transformation to intelligence and digitalization.

[0087] in addition, Figure 5 is a schematic diagram of a graphical interface according to an embodiment of the present application, Figure 6 It is a schematic diagram of another graphical interface according to an embodiment of the present application.

[0088] Through the above steps S301 to S303, compared with the supply chain management method that relies on manual work in the related art, the technical solution of this application obtains and stores the factory's production link data and order monitoring data; builds a three-dimensional digital twin model of the factory based on the production link data and order monitoring data; and uses the front-end display module to display a graphical interface based on the three-dimensional digital twin model. Solve the problems of data timeliness and problem follow-up collaborative efficiency faced by current manufacturing companies in the process of supply chain management. By building a cockpit management system based on digital twin technology, the overall pass rate can be improved, the management and control of the factory can be strengthened, and the business team can be enabled to make more accurate and efficient decisions.

[0089] This embodiment further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0090] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0091] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0092] S1, obtain and store the factory’s production data and order monitoring data;

[0093] S2, obtains production data and order monitoring data from the data management module to build a three-dimensional digital twin model of the factory;

[0094] S3, a graphical interface is displayed based on the three-dimensional digital twin model, in which the kanban indicators used to reflect the real-time production status of the factory are displayed in the graphical interface.

[0095] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0096] In one embodiment, Figure 7 is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application, such as Figure 7 As shown, an electronic device is provided, which may be a server, and its internal structure diagram may be as shown in Figure 7As shown. The electronic device includes a processor, a network interface, an internal memory and a non-volatile memory connected through an internal bus, wherein the non-volatile memory stores an operating system, a computer program and a database. The processor is used to provide computing and control capabilities, the network interface is used to communicate with an external terminal through a network connection, the internal memory is used to provide an environment for the operation of the operating system, the computer program is executed by the processor to implement a supply chain intelligent management method, and the database is used to store data.

[0097] Those skilled in the art will understand that Figure 4 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. Specifically, the electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0098] In addition, in combination with a supply chain intelligent management method in the above embodiment, the present application embodiment can provide a storage medium for implementation. The storage medium stores a computer program; when the computer program is executed by a processor, any supply chain intelligent management method in the above embodiment is implemented.

[0099] In one embodiment, a computer program product is provided, including a computer program, characterized in that when the computer program is executed by a processor, any one of the supply chain intelligent management methods in the above embodiments is implemented.

[0100] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0101] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A supply chain intelligent management system, characterized in that: The system includes: a construction module, a data management module and a front-end display module, wherein: The data management module is used to obtain and store the factory's production process data and order monitoring data; The construction module is used to obtain the production link data and the order monitoring data from the data management module, and to construct a three-dimensional digital twin model of the factory based on the production link data and the order monitoring data; The front-end display module is used to display a graphical interface based on the three-dimensional digital twin model, wherein the graphical interface displays kanban indicators that reflect the real-time production status of the factory.

2. The system according to claim 1, characterized in that The data management module includes: a storage module and an update module, wherein: The storage module is constructed based on the MySQL database and is used to receive and manage the source data of the system, wherein the source data includes: production link data and order monitoring data; The update module is constructed based on the Hologres database and is used to implement real-time update of the source data through the Binlog synchronization mechanism.

3. The system according to claim 2, characterized in that The update module includes: an acquisition and analysis module, a conversion and adaptation module, and a writing module, wherein: The acquisition and parsing module is used to establish a connection channel with the storage module, detect data change events from the Binlog of the MySQL database through the connection channel, and parse the data change results and the data operation types therein according to the format of Binlog when a data change event occurs; The conversion adaptation module is used to convert the data change results parsed from Binlog into data adapted for Hologres storage and analysis according to the data operation type; The writing module writes the data adapted for Hologres storage and analysis into a corresponding storage structure in Hologres.

4. The system according to claim 1, characterized in that The system also includes a service module, wherein the service module is used to generate an asynchronous request in response to a user interaction instruction, and to call a background service through the asynchronous request to perform data query and logical operations based on the production link data and the order monitoring.

5. The system according to claim 4, characterized in that The service module is also used to: identify and filter data records belonging to the same product or the same production batch based on the three-dimensional digital twin model and the serial number of the production link data; The dashboard index is calculated using the data record as reference information.

6. The system according to claim 1, characterized in that The construction module includes: a data analysis module and a model construction module, wherein: The data parsing module is used to parse the production link data and the order monitoring data to extract key information, and convert the format of the key information to obtain key information suitable for building a three-dimensional digital twin model; The model building module is used to use WebGL and Three.js technology to build the basic architecture of the three-dimensional digital twin model according to the actual layout plan of the factory, and map the key information to the corresponding elements of the basic structure to obtain the three-dimensional digital twin model.

7. The system according to claim 1, characterized in that The Kanban indicators include: section yield, defective conditions, order status, comprehensive first-pass rate and link production status, among which: The section yield is obtained based on the job data of each section of the factory through real-time statistical calculation and is displayed in the graphical interface through charts and data formats; The bad conditions are obtained through bad data of each section, including: operation errors, incoming material problems, production anomalies and assembly defects; The comprehensive first pass rate is calculated based on the data of each process section and is used to demonstrate the overall production process efficiency and stability of the factory; The order status is displayed in charts and data formats to reflect order plans and production progress; The waiting-for-delivery status of the described links is used to display the waiting-for-delivery data of each production position.

8. A supply chain intelligent management method, characterized in that: The method comprises: Obtain and store the factory's production data and order monitoring data; Acquire the production link data and the order monitoring data from the data management module, and build a three-dimensional digital twin model of the factory based on the production link data and the order monitoring data; A graphical interface is displayed based on the three-dimensional digital twin model, wherein the graphical interface displays kanban indicators for reflecting the real-time production status of the factory.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to claim 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to claim 8 is implemented.

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