Data cockpit system and production management method based on data cockpit system

Abstract

The invention provides a data cockpit system, a production management method and device based on the data cockpit system, a computer storage medium and electronic equipment, and relates to the technical field of data processing. The system comprises a data acquisition module used for acquiring key production data from a plurality of production management systems; the data processing module is used for processing the key production data according to the business association relationship corresponding to the plurality of production management systems, and constructing a target data wide table in a production link; the analysis decision module is used for inputting the target data wide table into a pre-trained neural network model to obtain production state information; and the data visualization module is used for providing a man-machine interaction function and displaying the production state information. The system expansibility and the data processing efficiency are enhanced, the reliability of the system is greatly guaranteed, and then stable operation and efficient data support of the production command data cockpit in a complex production environment are guaranteed.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of data processing, and in particular, to a data cockpit system, a production management method and device based on the data cockpit system, a computer storage medium and an electronic device. BACKGROUND

[0002] With the continuous expansion of the production scale and the increasing complexity of the production process of industrial manufacturing enterprises, higher production command requirements are put forward for the efficiency and accuracy of the data cockpit system, and enterprises urgently need to rely on digital technology to realize real-time monitoring, intelligent decision-making and resource optimization configuration in the production process, so as to help enterprises transform to digital and intelligent, thereby improving the core competitiveness of enterprises.

[0003] Most of the existing data cockpit systems adopt centralized architecture, which has the problems of single architecture, poor scalability, low data processing efficiency and insufficient system reliability, so it is difficult to guarantee the stable operation and efficient data support of the production command data cockpit in complex production environment. SUMMARY

[0004] The present disclosure provides a data cockpit system, a production management method and device based on the data cockpit system, a computer storage medium and an electronic device, thereby facilitating the unified management and expansion of the data cockpit system, enhancing the system scalability and data processing efficiency, and increasing the reliability of the system, thereby guaranteeing the stable operation and efficient data support of the production command data cockpit in complex production environment.

[0005] In a first aspect, an embodiment of the present disclosure provides a data cockpit system, which is constructed based on a micro-service architecture, and the micro-service architecture includes a plurality of independently deployed service components, including a data collection module, a data processing module, an analysis and decision module, and a data visualization module. The data collection module is connected to a plurality of production management systems outside the data cockpit system, and is configured to collect key production data from the plurality of production management systems. The data processing module is configured to obtain the key production data from the data collection module, and process the key production data according to the business association relationship corresponding to the plurality of production management systems to construct a target data wide table in the production link. The analysis and decision module is configured to input the target data wide table into a pre-trained neural network model to obtain production state information, and the neural network model includes at least one of a first neural network model for a front-end control stage, a second neural network model for a production process control stage, and a third neural network model for a quantitative evaluation stage. The data visualization module is configured to provide human-computer interaction function and display the production state information.

[0006] In an optional embodiment of the present disclosure, the data acquisition module acquires key production data from a plurality of production management systems through one or more data acquisition methods of extraction, transformation and loading (ETL), operational data store (ODS), structured query language (SQL) and application programming interface (API), wherein the production management systems include at least two of enterprise resource planning (ERP), manufacturing execution system (MES), warehouse management system (WMS) and quality management system (QMS).

[0007] In an optional embodiment of the present disclosure, the microservice architecture is a combination framework of Spring Boot and Spring Cloud.

[0008] In an optional embodiment of the present disclosure, the first neural network model in the analysis and decision module includes at least one of a material supply model, a device fault prediction model and a personnel qualification evaluation model; the second neural network model includes at least one of a single station cycle achievement rate analysis model, a production plan completion rate analysis model and a first inspection qualified rate model; and the third neural network model includes at least one of a personnel efficiency evaluation model and a standard station evaluation model.

[0009] In an optional embodiment of the present disclosure, the data visualization module provides a plurality of information display modes, and displays a corresponding target information display mode based on determined user identity information; wherein the plurality of information display modes include at least one of a main level information display mode and a plurality of sub-level information display modes, and first production state information displayed in the main level information display mode includes a plurality of second production state information displayed in the plurality of sub-level information display modes.

[0010] In an optional embodiment of the present disclosure, the data cockpit system includes a fault-tolerant service component, which is used to trigger a switching operation when a service component fails, so as to replace the service component with the fault-tolerant service component to provide corresponding services.

[0011] In a second aspect, an embodiment of the present disclosure provides a production management method based on a data cockpit system, which includes: acquiring key production data from a plurality of connected production management systems through a data acquisition module, and processing the key production data based on business association relationships corresponding to the plurality of production management systems through a data processing module to construct a target data wide table in a production link; inputting the target data wide table into a pre-trained neural network model based on an analysis and decision module to obtain production state information, wherein the neural network model includes at least one of a first neural network model for a front-end control stage, a second neural network model for a production process control stage and a third neural network model for a quantitative evaluation stage; and visualizing the production state information through a data visualization module.

[0012] In an optional embodiment of the present disclosure, the method further comprises: storing system data of the data cockpit system to a plurality of storage media at preset time intervals; wherein the system data stored in the plurality of storage media is consistent; and in response to loss or damage of the system data in a first storage medium currently used, recovering the system data by a second storage medium in the plurality of storage media.

[0013] In an optional embodiment of the present disclosure, after the analysis and decision module inputs the target data wide table into the pre-trained neural network model to obtain the production state information, the method further comprises: in response to a production state value in the production state information not meeting a health state pre-threshold interval, generating an early warning information.

[0014] In a third aspect, an embodiment of the present disclosure provides a production management device based on a data cockpit system, the device comprising: a data acquisition and processing unit configured to acquire key production data from a plurality of connected production management systems through a data acquisition module, and process the key production data based on business association relationships corresponding to the plurality of production management systems through a data processing module to construct a target data wide table in a production link; a state information generation unit configured to input the target data wide table into a pre-trained neural network model based on an analysis and decision module to obtain production state information, the neural network model comprising at least one of a first neural network model of a front-end control stage, a second neural network model of a production process control stage, and a third neural network model of a quantitative evaluation stage; and a display unit configured to visually display the production state information through a data visualization module.

[0015] In an optional embodiment of the present disclosure, the device can further comprise a storage unit and a data recovery unit, the storage unit being configured to store system data of the data cockpit system to a plurality of storage media at preset time intervals; wherein the system data stored in the plurality of storage media is consistent; and the data recovery unit being configured to, in response to loss or damage of the system data in a first storage medium currently used, recover the system data by a second storage medium in the plurality of storage media.

[0016] In an optional embodiment of the present disclosure, the device can further comprise an information generation unit, the information generation module being configured to, in response to a production state value in the production state information not meeting a health state pre-threshold interval, generate an early warning information.

[0017] In a fourth aspect, an embodiment of the present disclosure provides a computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the production management method based on a data cockpit system as described above.

[0018] In a fifth aspect, an embodiment of the present disclosure provides an electronic device, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the data cockpit system-based production management method as above via execution of the executable instructions.

[0019] In a sixth aspect, an embodiment of the present disclosure provides a computer program product, comprising a computer program which, when executed by a processor, implements the data cockpit system-based production management method as above.

[0020] The technical solution of the present disclosure has the following beneficial effects: The data cockpit system described above adopts a micro-service architecture for the basic construction of the overall system architecture, and implements business encapsulation of specific function modules in the micro-service architecture, forming a plurality of independently deployed service components, thereby ensuring that each service runs independently and has an independent service process and database, and supporting inter-service calling and interaction. In the micro-service architecture, the service components are independent and loosely coupled. In this regard, when an enterprise needs to add a function module (such as an energy management module), it only needs to develop a new energy management service based on the micro-service architecture and register the service in the micro-service configuration center, without the need to modify the existing service code. Compared with the centralized architecture used in related technical solutions, the system can easily implement system function expansion, thereby adapting to the digital production needs of the continuously developing enterprise. In addition, the system processes the collected data through the real-time and batch processing capabilities of the data processing layer, and constructs a data wide table according to the business association relationship, so as to provide comprehensive, accurate and real-time data support for production command, and improve the scientificity and timeliness of production command. Furthermore, the system also configures a series of neural network models through the analysis and decision module to predict fault risks and allocate resource configurations, so as to timely discover production delay risks and adjust production plans, and optimize production resource allocation, thereby improving production efficiency and product quality. Finally, the system also interacts and views data through the data visualization module, improves the visualization and intelligent processing process, further accelerates the digital and intelligent transformation of the enterprise, and thereby improves the core competitiveness of the enterprise.

[0021] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0022] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present disclosure and, together with the specification, serve to explain the principles of the present disclosure. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0023] Figure 1 Fig. 1 shows a schematic diagram of an application architecture of a data cockpit system in the present exemplary embodiment; Figure 2 Fig. 2 shows a flow chart of a production management method based on the data cockpit system in the present exemplary embodiment; Figure 3 Fig. 3 shows a flow chart of a data backup method in the present exemplary embodiment; Figure 4 Fig. 4 shows a schematic diagram of a production management device based on the data cockpit system in the present exemplary embodiment; Figure 5 Fig. 5 shows a schematic diagram of an electronic device in the present exemplary embodiment. DETAILED DESCRIPTION

[0024] Example embodiments now will be described more fully hereinafter with reference to the accompanying drawings. Example embodiments, however, can be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of example embodiments to those skilled in the art. Like reference numerals refer to like elements throughout. The terminology used in the description presented herein is not intended to be interpreted in any specific and / or particular manner. Embellished features, structures or characteristics can be incorporated in one or more embodiments in any suitable manner. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present disclosure. One skilled in the relevant art will recognize, however, that the technology disclosed can be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures have not been described in detail to avoid obscuring aspects of the present disclosure.

[0025] Furthermore, the drawings are not necessarily drawn to scale. Like reference numerals refer to like parts throughout the several views. Some block components are shown in block diagram form to more conceptually illustrate implementation of the present disclosure. These block components can be implemented in software, hardware, or a combination of both, and can be implemented in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0026] The flow charts shown in the drawings are merely exemplary illustrations, and do not necessarily include all steps. For example, some steps can be further divided, and some steps can be combined or partially combined, so that the actual execution order can be changed according to actual conditions.

[0027] To help those skilled in the art better understand the technical solutions of the present disclosure, the related content involved in the technical solutions of the present disclosure will be introduced below.

[0028] 1) Production Command Data Dashboard: Also known simply as a data dashboard, it is a visualization tool that integrates data from all aspects of production. Through real-time data acquisition and intelligent analysis, it provides the production command center with an intuitive panoramic view, supporting enterprises in achieving lean management and rapid response.

[0029] 2) Enterprise Resource Planning (ERP): Used to integrate core business processes such as enterprise finance, supply chain, and production, and optimize resource allocation and decision-making efficiency; Manufacturing Execution System (MES): Used to monitor the production site in real time, manage equipment status, process execution and quality traceability, and ensure the implementation of production plans.

[0030] 3) Warehouse Management System (WMS): A real-time computer software system that manages information, resources, activities, inventory, and distribution operations more effectively and efficiently according to operational business rules and algorithms. Its main purpose is to accurately record goods within the warehouse, track inventory, and manage inventory scheduling, thereby achieving comprehensive monitoring and management of warehouse operations.

[0031] 4) Quality Management System (QMS): A software system used to manage and optimize enterprise quality management. It can assist enterprises in formulating and implementing quality management strategies, managing and controlling quality processes, collecting and analyzing quality data, and improving quality management efficiency and quality level.

[0032] After introducing the relevant terms involved in the technical solution of this disclosure, the following will provide an exemplary description of the relevant technical background involved in the technical solution of this disclosure.

[0033] As industrial manufacturing enterprises continue to expand their production scale and their production processes become increasingly complex, higher demands are being placed on the efficiency and accuracy of production command data dashboards. Enterprises are also in urgent need of leveraging digital technologies to achieve real-time monitoring of the production process, intelligent decision-making, and optimized resource allocation. This will enable them to create efficient, intelligent, reliable, and highly available production command data dashboard systems, helping them achieve data-driven transformation and enhance their core competitiveness.

[0034] However, most existing data cockpit systems adopt a centralized architecture, which suffers from problems such as a single architecture, poor scalability, low data processing efficiency, and insufficient availability. As a result, it is difficult to ensure the stable operation and efficient data support of the production command data cockpit in complex production environments, thus failing to meet the actual needs of enterprises.

[0035] Specifically, existing data cockpit systems mainly suffer from the following technical problems: 1) The problem of insufficient architectural flexibility and scalability.

[0036] Because existing data dashboard systems employ a centralized architecture (a system design pattern where all functional modules are concentrated in a single application or server; its core characteristic is single-centralized management, with all data and processing tasks handled uniformly by a single host or main server), they are ill-suited to the needs of expanding enterprise production scale, changing business processes, and integrating new production systems. System functional expansion is difficult; when enterprises introduce new production functional modules or management requirements, they often need to undertake large-scale modifications to the existing data dashboard system, which is not only costly and time-consuming but also carries significant risks, impacting the normal operation of the enterprise.

[0037] 2) Problems with low data processing efficiency and poor data quality.

[0038] Existing data dashboard systems suffer from problems such as untimely data collection, poor data accuracy, and difficulties in data integration when processing large amounts of production data. For example, the inconsistent formats of data collected from ERP, MES, and other systems lead to a significant amount of time being spent on data cleaning and conversion during the data processing stage, making it impossible to provide real-time and accurate data support for production decisions.

[0039] 3) The problem of lack of intelligent analysis and decision support.

[0040] Most production management systems only provide basic data display and query functions, lacking in-depth analysis of production data and intelligent decision support. For example, they cannot predict equipment failures in advance, optimize production plans and resource allocation, leading to frequent problems such as sudden equipment failures causing production stoppages and delays in production schedules.

[0041] 4) Problems with poor user experience and compatibility.

[0042] Existing production management systems often have user interfaces that are only compatible with specific devices, such as PCs, failing to meet the needs of managers in mobile work scenarios. Furthermore, the system interface layout is not flexible enough to be personalized based on the priorities of different levels of management, resulting in low transparency in production management and slow decision-making response.

[0043] 5) Insufficient high availability.

[0044] The existing production management system lacks a sound fault tolerance mechanism and high availability design. In the event of server hardware failure, network fluctuations, or data corruption, the system is prone to interruption, resulting in the loss or inability to obtain production command data, which seriously affects the continuity of enterprise production operations.

[0045] This disclosure addresses the aforementioned issues by proposing a data cockpit system, a production management method based on the data cockpit system, and an apparatus. In this data cockpit system, a microservice architecture is employed to form the foundation of the overall system architecture. Within this microservice architecture, specific functional modules are encapsulated into multiple independently deployed service components, ensuring that each service operates independently, possessing its own service process and database, and supporting inter-service calls and interactions. In the microservice architecture, the service components are independent and loosely coupled. Therefore, when an enterprise needs to add a new functional module (e.g., an energy management module), it only needs to develop a new energy management service based on the microservice architecture and register it with the microservice configuration center, without modifying existing service code. Compared to the centralized architecture used in related technical solutions, this allows for easy expansion of system functionality, adapting to the ever-evolving digital production needs of enterprises. Furthermore, the system can leverage the real-time and batch processing capabilities of the data processing layer to perform refined processing of collected data and construct wide data tables based on business relationships, providing comprehensive, accurate, and real-time data support for production command and control, thereby improving the scientific rigor and timeliness of production command. Furthermore, the system incorporates a series of neural network models in its analysis and decision-making module to predict failure risks and allocate resources. This allows for the timely detection of production delay risks, adjustment of production plans, and optimization of production resource allocation, thereby improving production efficiency and product quality. Finally, the system also features a data visualization module for interaction and data viewing, enhancing visualization and intelligent processing, further accelerating the enterprise's digital and intelligent transformation, and ultimately improving its core competitiveness.

[0046] The following will combine Figure 1 An example of a data cockpit system provided in the embodiments of this disclosure is described.

[0047] Reference Figure 1 The diagram shown is an application architecture diagram of one of the data cockpit systems provided in this embodiment of the present disclosure. The data cockpit system 100 is built based on a microservice architecture, which includes multiple independently deployed service components, namely: a data acquisition module, a data processing module, an analysis and decision-making module, and a data visualization module.

[0048] Microservice architecture is a software architecture style that breaks down a monolithic application into multiple independent, autonomous small service units. Each microservice or service component is built around a specific business capability and runs in an independent process, communicating through lightweight mechanisms (such as HTTP APIs), and can be independently deployed, scaled, and maintained. Compared to the centralized architecture adopted by existing related technical solutions, it allows enterprises to quickly and easily expand system functionality when adding new functional modules or service components without modifying existing service code, thereby improving the system's scalability and flexibility.

[0049] Next, we will provide an exemplary description of the microservice architecture adopted by the above-mentioned data cockpit system with reference to specific embodiments.

[0050] In one optional embodiment of this disclosure, the microservice architecture for building the above-mentioned data cockpit system is a combination of Spring Boot and Spring Cloud frameworks.

[0051] The combination of Spring Boot and Spring Cloud is the mainstream technology for implementing microservice architecture. Spring Boot, as the foundational framework for microservice development, provides independent and runnable basic capabilities for microservices (rapid development, embedded containers, and automatic configuration), thereby simplifying the rapid development and deployment of services. Spring Cloud encapsulates the core governance capabilities of microservices based on Spring Boot, thus providing a microservice governance solution. The combination of the two can quickly build highly available, scalable, distributed microservice systems.

[0052] For example, based on a microservice architecture that combines Spring Boot and Spring Cloud, the Spring Boot framework encapsulates the core governance capabilities of microservices, including components such as: a configuration center (Nacos, used for service registration and discovery), an API gateway (Spring Cloud Gateway), a declarative HTTP client (Feign), and distributed transactions (Seata). The following will provide an illustrative explanation of these components.

[0053] In one optional embodiment, the configuration center can be Nacos middleware, which acts as both a registry and configuration center, enabling dynamic service discovery and centralized configuration management. When a service starts, its service name, IP address, and port are registered with the Nacos configuration center, and the corresponding configuration files are managed uniformly through Nacos. For example, when service component A (e.g., the data processing module in a data dashboard system) needs to call the service provided by service component B (e.g., the data acquisition module in a data dashboard system), there is no need to hardcode B's address; it only needs to retrieve the list of instances corresponding to service component B from the Nacos configuration center, achieving "dynamic addressing" (avoiding call failures due to service address changes).

[0054] For example, inter-service communication calls use Feign as a declarative, modular HTTP client, simplifying remote calls between services. Interfaces are defined using annotations, and load balancing and retry logic are handled automatically. Meanwhile, Spring Cloud Gateway can be used as an API gateway to implement request routing, load balancing, rate limiting, circuit breaking, and other functions, thereby ensuring high availability and security between services.

[0055] Furthermore, based on the combined framework of Spring Boot and Spring Cloud, Seata is used to solve the distributed transaction problem, thereby ensuring that data operations across multiple microservices remain consistent, thus guaranteeing the consistency of distributed transactions. At the same time, the Snowflake algorithm is used to generate globally unique IDs to build a highly available distributed system.

[0056] Having learned about the construction of a data cockpit system using a microservice architecture combining Spring Boot and Spring Cloud provided in this disclosure, the following will provide an exemplary description of the service components deployed in the data cockpit system, in conjunction with specific embodiments.

[0057] Reference Figure 1 As shown, the data acquisition module provides a unified data interface to connect with multiple production management systems outside the data cockpit system (corresponding to...). Figure 1 The production management systems 1, 2, ..., N are connected to collect key production data from each stage of the entire production process from multiple production management systems.

[0058] In one optional embodiment of this disclosure, the production management system includes at least two of the following: Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), Warehouse Management System (WMS), and Quality Management System (QMS).

[0059] ERP systems integrate various departments and business processes within an enterprise, including production, supply chain, sales, finance, and human resources, to simplify complex processes and improve business execution efficiency and quality. MES systems, on the other hand, are production information management systems geared towards the shop floor execution layer of manufacturing enterprises. They sit between the upper-level planning management system and the lower-level industrial control system, providing shop floor-level management information. MES can optimize and manage the entire production process from order placement to product completion through information transmission.

[0060] A Warehouse Management System (WMS) is a real-time computer software system that uses information technology to manage and control warehousing and logistics activities throughout the entire process. A Quality Management System (QMS) is a software system used to manage and control an enterprise's quality management processes, helping companies establish standardized processes, ensure the complete recording of production data, and enable rapid product traceability and recall when needed.

[0061] For example, Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), Warehouse Management System (WMS), and Quality Management System (QMS) are core management systems in enterprise management and operations. Specifically, ERP is responsible for resource planning, MES focuses on production execution, WMS manages warehousing and logistics, and QMS ensures quality control. These systems work together to enable enterprises to achieve resource optimization, efficient production, accurate inventory, and controllable quality.

[0062] It should be noted that the aforementioned production management systems typically cover all aspects of the production process. Therefore, the data acquisition module connects to multiple production management systems and collects key production data from each system, covering all aspects of the production process. This provides comprehensive and accurate data for the data cockpit's production command data, thereby ensuring the reliability of the data cockpit system.

[0063] In one optional embodiment of this disclosure, the data acquisition module collects key production data from multiple production management systems through one or more data acquisition methods, including ETL extraction, transformation and loading, ODS operation data storage, structured query SQL, and application programming interface (API).

[0064] ETL is a core component of data warehouse technology, referring to the process of extracting data from multiple production management systems, cleaning, transforming, and integrating it before loading it into a target data storage system. The core of the extraction process is obtaining raw data from production management systems such as ERP, MES, WMS, and QMS; transformation involves cleaning, standardizing, calculating, and associating the data; and loading involves importing the processed data into the target database or data warehouse for storage.

[0065] Operational Data Store (ODS) is a subject-oriented, integrated, current or near-current collection of data used to support daily decision-making and analytical activities within an enterprise, thereby ensuring real-time data updates. Structured Query Language (SQL) is the standard programming language used to manage relational database systems; in data acquisition, it is primarily used for querying and manipulating data within the database. Application Programming Interface (API) is a set of predefined rules and protocols that allow data exchange and function calls between different software applications.

[0066] For example, the data acquisition module can integrate with the ERP system through ETL (Extract, Transform, Load) to periodically extract financial and supply chain data from the ERP system for building an enterprise-level data warehouse. The data acquisition module can also work with the MES (Manufacturing Execution System) through an Application Programming Interface (API) to obtain real-time production equipment status and process parameters from the MES system, supporting production scheduling decisions. Furthermore, the data acquisition module can link with the WMS (Warehouse Management System) through the ODS (Original Equipment Storage System) to store real-time inventory data from the WMS system, providing data support for warehouse optimization. Finally, the data acquisition module can collaborate with the QMS (Quality Management System) through Structured Query SQL (SQL) to query quality inspection records from the QMS system for subsequent product quality traceability and analysis.

[0067] It should be explained that the above embodiments are merely illustrative of one data acquisition method and are not intended to be limiting. The embodiments disclosed herein do not impose any special limitations on this method.

[0068] In this embodiment, the data acquisition module in the data cockpit system can seamlessly integrate with the enterprise's existing production management system through various data acquisition methods, thereby ensuring real-time data acquisition.

[0069] Further, continue to refer to Figure 1 The data processing module shown can call the services provided by the data acquisition module to obtain key production data from various production management systems. Because the data acquisition module collects data from multiple production management systems in inconsistent formats, preprocessing of the collected data is necessary.

[0070] In related technical solutions, a significant amount of time is often required for data cleaning and format conversion, which affects the real-time data processing capability. The embodiments of this disclosure take into account the above problems and utilize a data processing module for data processing. In one optional embodiment of this disclosure, the data processing module is used to acquire key production data from the data acquisition module and process the key production data according to the business relationships corresponding to multiple production management systems to construct a target data wide table for the production process.

[0071] For example, the data processing module in the data cockpit can leverage the real-time and batch processing capabilities of the enterprise data management platform, combined with data governance standards, to perform refined processing on the collected key production data in order to construct a target data wide table. For instance, it can perform correlation analysis and calculations on production order data, equipment operation data, etc., to provide comprehensive, accurate, and real-time data support for production command, thereby improving the scientific nature and timeliness of production command.

[0072] In this embodiment, the wide table is a core data structure in modern data warehouses and data analysis. By merging multiple related data tables into a single wide table through primary key association, the efficiency of data querying and analysis is greatly improved.

[0073] In one optional embodiment, a hierarchical standard system can be constructed based on enterprise data-related technical specifications and data governance standards to standardize data collection, storage, processing, and usage processes, providing support for the construction of the enterprise's production command data dashboard. This involves managing data at different levels separately to improve data management efficiency.

[0074] Continue to refer to Figure 1 In an optional embodiment of the data cockpit system shown in this disclosure, the analysis and decision module is used to input the target data wide table into a pre-trained neural network model to obtain production status information. The neural network model includes at least one of a first neural network model for the front-end control stage, a second neural network model for the production process control stage, and a third neural network model for the quantitative evaluation stage.

[0075] The analysis and decision-making module in this embodiment adopts a hierarchical control strategy, that is, the neural network model is classified and statistically analyzed according to three core dimensions: front-end control, process control and quantitative evaluation, so as to provide comprehensive, accurate and real-time data basis for production command.

[0076] In the front-end control phase, the first neural network model in the analysis and decision-making module includes at least one of the material supply model, equipment failure prediction model, and personnel qualification assessment model; in the process control phase, the second neural network model in the analysis and decision-making module includes at least one of the single unit cycle time achievement rate analysis model, production plan completion rate analysis model, and first-time inspection pass rate model; in the quantitative evaluation phase, the third neural network model in the analysis and decision-making module includes at least one of the personnel efficiency evaluation model and standard workstation evaluation model.

[0077] For example, in the first neural network model of the front-end control phase: the material supply model determines the material procurement strategy based on material characteristics (e.g., importance, supply risk) parameters to ensure timely material supply. The equipment failure prediction model can use machine learning algorithms to predict and identify potential equipment failures based on equipment operating data and historical failure cases, so that maintenance personnel can perform preventative maintenance in advance. The personnel qualification assessment model is a model that objectively evaluates the abilities, qualifications, and potential of employees.

[0078] For example, in the second neural network model of the process control stage: the single-unit cycle time achievement rate analysis model is used to monitor and analyze the production cycle time achievement of a single piece of equipment or production line within a specific time period, so as to reflect the equipment operating efficiency in real time and facilitate maintenance personnel to quickly locate production bottleneck equipment; the production plan completion rate analysis model is used to track production progress, promptly identify production delay risks and adjust production plans, optimize production resource allocation, and improve production efficiency and product quality. The first-pass inspection pass rate model is used to measure the proportion of products that meet quality standards on the first inspection in the production process, reflecting the stability of the production process and the quality control capability, and thus reflecting the true quality level of the production process, which facilitates the analysis of relevant factors affecting quality problems based on the pass rate.

[0079] For example, in the third neural network model of the quantitative evaluation stage: the personnel efficiency evaluation model focuses on quantifying the work output and resource consumption of individuals or teams, aiming to identify optimization potential. Typical evaluation dimensions of this model include: time efficiency (measuring the ratio of actual working time to standard time, reflecting task completion speed), quality efficiency (assessing output pass rate and rework rate), resource utilization (covering the ratio of equipment, material, and other resource consumption to output, avoiding overuse or idleness), and flexibility efficiency (examining personnel's ability to adapt to multitasking or unexpected demands). The standard workstation evaluation model focuses on optimizing workstation layout and processes to improve overall production efficiency.

[0080] The analysis and decision-making module shown in the above embodiments can perform in-depth analysis based on the collected production data to provide data support for future equipment and resource allocation, thereby improving the reliability of system operation, timely identifying production delay risks and adjusting production plans, optimizing production resource allocation, and improving production efficiency and product quality.

[0081] Continue to refer to Figure 1 The data cockpit system provided in the embodiments of this disclosure also includes a data visualization module for providing human-machine interaction functions and displaying production status information.

[0082] In existing technical solutions, the user interface of current systems is often only compatible with specific devices, such as PCs, failing to meet the needs of administrators in mobile office scenarios. The data visualization module disclosed herein adopts a multi-terminal responsive design, enabling it to adapt to LED displays, PC desktops, mobile devices, and tablets.

[0083] Furthermore, the data visualization module allows for personalized configuration based on the characteristics of workshop production.

[0084] In one optional embodiment of this disclosure, the data visualization module provides multiple information display modes and displays the corresponding target information display mode based on the determined user identity information.

[0085] The multiple information display modes include at least one of a main-level information display mode and multiple sub-level information display modes. The first production status information displayed in the main-level information display mode includes multiple second production status information displayed in the multiple sub-level information display modes.

[0086] For example, in order to provide personalized configurations for different users, a data visualization module can provide multiple information display modes so that the system can display the corresponding target information display mode according to the user's identity information.

[0087] For example, senior management needs to view the overall production status of the company. Therefore, they can be provided with a primary-level information display mode to show the first production status information. The primary production status information is the overall situation of the company headquarters and each branch. For personnel in each branch, only a sub-level information display mode is shown to them, and the second production status information corresponding to that branch is provided through the sub-level information display mode. Other branches do not have permission to view this information.

[0088] For multi-level information display modes, branch plant managers can also customize the settings according to the production characteristics of the workshop. For example, production site managers can view equipment fault warnings and production progress at any time via mobile devices, facilitating rapid response to changes in the production site and achieving precise scheduling and efficient management.

[0089] Based on any of the above embodiments, to avoid single points of failure and thus affect system reliability, the data cockpit system provided in this disclosure also provides a fault tolerance mechanism. Specifically, in an optional embodiment of this disclosure, the data cockpit system includes a fault-tolerant service component. This fault-tolerant service component is used to trigger a switching operation when a service component fails, so that the fault-tolerant service component can replace the service component and provide the corresponding service.

[0090] For example, fault-tolerant service components can be appropriately configured in the system architecture to avoid the impact of a single point of failure on the entire system. When configuring fault-tolerant service components, they can be set up for key service nodes in the data cockpit system, such as the data acquisition module, data processing module, analysis and decision-making module, and data visualization module shown in the above embodiments. Then, when a service component fails and cannot provide the corresponding service, it will automatically switch to a backup fault-tolerant service component to ensure the continuous and stable operation of the system.

[0091] The aforementioned data dashboard system employs a microservices architecture as its foundational framework. Within this architecture, specific functional modules are encapsulated into multiple independently deployed service components. This ensures that each service operates independently, possessing its own service process and database, and supporting inter-service calls and interactions. In this microservices architecture, the service components are independent and loosely coupled. Therefore, when an enterprise needs to add a new functional module (such as an energy management module), it only needs to develop a new energy management service based on the microservices architecture and register it with the microservices configuration center, without modifying existing service code. Compared to the centralized architecture used in related technical solutions, this allows for easy system functional expansion, adapting to the ever-evolving digital production needs of enterprises. Furthermore, the system utilizes the real-time and batch processing capabilities of its data processing layer to perform refined processing of collected data and construct wide data tables based on business relationships. This provides comprehensive, accurate, and real-time data support for production command, enhancing the scientific rigor and timeliness of production command. Furthermore, the system incorporates a series of neural network models in its analysis and decision-making module to predict failure risks and allocate resources. This allows for the timely detection of production delay risks, adjustment of production plans, and optimization of production resource allocation, thereby improving production efficiency and product quality. Finally, the system also features a data visualization module for interaction and data viewing, enhancing visualization and intelligent processing, further accelerating the enterprise's digital and intelligent transformation, and ultimately improving its core competitiveness.

[0092] Furthermore, in an exemplary embodiment of this disclosure, a production management method based on a data cockpit system is also provided.

[0093] Next, we will combine Figure 2 The above-mentioned production management method based on the data cockpit system is illustrated by example. Figure 2 A flowchart illustrating a production management method based on a data cockpit system in this exemplary embodiment is shown below. Figure 2 The production management method based on a data cockpit system provided in this disclosure includes the following steps S201-S203: Step S201: Collect key production data from multiple connected production management systems through the data acquisition module, and process the key production data based on the business relationships corresponding to the multiple production management systems through the data processing module to construct a target data wide table in the production process.

[0094] Step S202: Based on the analysis and decision-making module, the target data wide table is input into the pre-trained neural network model to obtain production status information. The neural network model includes at least one of the following: a first neural network model for the front-end control stage, a second neural network model for the production process control stage, and a third neural network model for the quantitative evaluation stage.

[0095] Step S203: Visualize the production status information using the data visualization module.

[0096] In the above Figure 2 The method described above utilizes the real-time and batch processing capabilities of the data processing layer to refine the collected data and construct a wide data table based on business relationships. This provides comprehensive, accurate, and real-time data support for production command, enhancing its scientific rigor and timeliness. Furthermore, the method employs a series of neural network models configured in the analysis and decision-making module to predict failure risks and allocate resources. This allows for timely detection of production delay risks, adjustment of production plans, and optimization of production resource allocation, thereby improving production efficiency and product quality. Finally, the method also enables interaction and data viewing through a data visualization module, enhancing the visualization and intelligent processing process and further accelerating the enterprise's digital and intelligent transformation, ultimately improving its core competitiveness.

[0097] In an optional embodiment, the method further includes: setting fault-tolerant service components for each service component in the data cockpit system, and switching the target service component to the corresponding fault-tolerant service component in response to a failure of any target service component, so that the fault-tolerant service component can replace the target service component to provide the corresponding service.

[0098] For example, assuming the target service component is a data processing module, and if the currently used data processing module fails, the system switches to the fault-tolerant service component configured for the data processing module. The fault-tolerant service component replaces the data processing module and performs the steps of collecting key production data from multiple connected production management systems, processing the key production data based on the business relationships corresponding to the multiple production management systems, and constructing the target data wide table in the production process.

[0099] Figure 3 This schematically illustrates a flowchart of a data backup method in this exemplary embodiment. Please refer to [link / reference]. Figure 3In an optional embodiment of this disclosure, the method may further include the following steps S301-S302: Step S301: Data of the data cockpit system is stored in multiple storage media at preset time intervals; wherein the system data stored in the multiple storage media is consistent.

[0100] Step S302: In response to the loss or corruption of system data in the currently used first storage medium, the system data is recovered by the second storage medium among the multiple storage media.

[0101] System data refers to the data used or generated by the various service components of the data cockpit system, such as data collected by the data acquisition module, data processed by the data processing module, and production status information obtained by the analysis and decision-making module.

[0102] For example, system data can be backed up periodically at preset time intervals, i.e., a multi-copy storage strategy can be adopted to store system data backups on different storage media and in different geographical locations so that data can be quickly recovered in case of data loss or damage.

[0103] In one optional embodiment, during the step of storing system data of the data cockpit system to multiple storage media at preset time intervals, a consistency check can be performed on the system data stored in the multiple storage media to ensure the accuracy, integrity and consistency of the system data.

[0104] In an optional embodiment, to ensure system stability, the load on each service node can be detected and balanced.

[0105] For example, load balancing devices or software load balancing technologies can be deployed at multiple layers such as the network layer, application layer, and database layer to monitor the load of each server in real time. Based on preset algorithms and strategies, requests can be distributed to different servers to avoid overloading a single server and improve the overall performance and availability of the system.

[0106] In an optional embodiment, after performing the above-described step of inputting the target data wide table into a pre-trained neural network model based on the analysis and decision module to obtain production status information, the method may further include: generating early warning information in response to the production status value in the production status information not conforming to the health status threshold range.

[0107] For example, the system can compare the production status value in the production status information with the value in the health status threshold range, and then issue an early warning for service nodes that do not meet the health status threshold range.

[0108] In addition, it can collect system operation status information in real time, including server performance indicators, network bandwidth, database connection count, etc. By setting thresholds and early warning rules, potential fault risks can be detected in a timely manner and early warning signals can be issued in advance so that operation and maintenance personnel can take measures to deal with them quickly and reduce the probability of fault occurrence and the scope of impact.

[0109] In addition to the above embodiments, an automated operation and maintenance platform can be built to automate and intelligently perform operation and maintenance operations such as system deployment, upgrades, configuration management, and troubleshooting, thereby improving operation and maintenance efficiency and accuracy, reducing human error, and automatically repairing common faults through scripts and tools, thus shortening system fault recovery time.

[0110] In order to implement the above-mentioned production management method based on a data cockpit system, one embodiment of this disclosure provides a production management device based on a data cockpit system. Figure 4 The schematic diagram illustrates the architecture of a production management device based on a data cockpit system.

[0111] The production management device 400 based on the data cockpit system includes a data acquisition and processing unit 401, a status information generation unit 402, and a display unit 403.

[0112] The data acquisition and processing unit 401 is used to collect key production data from multiple connected production management systems through the data acquisition module, and to process the key production data based on the business relationships corresponding to the multiple production management systems through the data processing module to construct a target data wide table in the production process; the status information generation unit 402 is used to input the target data wide table into a pre-trained neural network model based on the analysis and decision module to obtain production status information. The neural network model includes at least one of the first neural network model of the front-end control stage, the second neural network model of the production process control stage, and the third neural network model of the quantitative evaluation stage; the display unit 403 is used to visualize the production status information through the data visualization module.

[0113] In an optional embodiment of this disclosure, the device may further include a storage unit and a data recovery unit. The storage unit is used to store system data of the data cockpit system to multiple storage media at preset time intervals, wherein the system data stored in the multiple storage media is consistent. The data recovery unit is used to recover system data from a second storage media among the multiple storage media in response to the loss or damage of system data in the currently used first storage media.

[0114] In an optional embodiment of this disclosure, the apparatus may further include an information generation unit, which generates a warning message when the production status value in the production status information does not meet the pre-threshold range of the health status.

[0115] The production management device 400 based on the data cockpit system provided in this embodiment can execute the technical solution of the production management method based on the data cockpit system in any of the above embodiments. Its implementation principle and beneficial effects are similar to those of the production management method based on the data cockpit system. Please refer to the implementation principle and beneficial effects of the production management method based on the data cockpit system. It will not be repeated here.

[0116] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the present invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the present invention described in the "Exemplary Methods" section above.

[0117] According to embodiments of the present invention, a program product for implementing the above-described method may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0118] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0119] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0120] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency (RF), or any suitable combination thereof.

[0121] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0122] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0123] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: entirely in hardware, entirely in software (including firmware, microcode, etc.), or in a combination of hardware and software, collectively referred to herein as “circuit,” “module,” or “system.”

[0124] The following reference Figure 5 To describe an electronic device 500 according to this embodiment of the present invention. Figure 5 The electronic device 500 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0125] likeFigure 5 As shown, the electronic device 500 is presented in the form of a general-purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including storage unit 520 and processing unit 510), and a display unit 540.

[0126] The storage unit stores program code, which can be executed by the processing unit 510 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 510 can perform actions such as... Figure 2 Steps S201 to S203 are shown in the figure.

[0127] Storage unit 520 may include readable media in the form of volatile storage units, such as random access memory (RAM) 5201 and / or cache memory 5202, and may further include read-only memory (ROM) 5203.

[0128] Storage unit 520 may also include a program / utility 5204 having a set (at least one) program module 5205, such program module 5205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0129] Bus 530 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0130] Electronic device 500 can also communicate with one or more external devices 1000 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 500, and / or with any device that enables electronic device 500 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 550. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 560. As shown, network adapter 560 communicates with other modules of electronic device 500 via bus 530. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, Redundant Arrays of Independent Disks (RAID) systems, tape drives, and data backup storage systems.

[0131] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0132] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0133] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0134] Other embodiments of this disclosure 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 disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0135] It should be understood that this disclosure is not limited to the precise structures 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 disclosure is defined only by the appended claims.

Claims

1. A data cockpit system, characterized by The data cockpit system is constructed based on a micro-service architecture, and the micro-service architecture includes a plurality of independently deployed service components, including a data collection module, a data processing module, an analysis and decision module, and a data visualization module; The data collection module is connected to a plurality of production management systems outside the data cockpit system, and is configured to collect key production data from the plurality of production management systems; The data processing module is configured to obtain the key production data from the data collection module, and process the key production data according to a business association relationship corresponding to the plurality of production management systems, to construct a target data wide table in a production link; The analysis and decision module is configured to input the target data wide table into a pre-trained neural network model to obtain production state information, and the neural network model includes at least one of a first neural network model for a front-end control stage, a second neural network model for a production process control stage, and a third neural network model for a quantitative evaluation stage; The data visualization module is configured to provide human-computer interaction function and display the production state information.

2. The system of claim 1, wherein, The data collection module collects the key production data from the plurality of production management systems by using one or more data collection methods selected from the group consisting of extraction, transformation and loading (ETL), operational data storage (ODS), structured query language (SQL), and application programming interface (API); The production management systems include at least two of enterprise resource planning (ERP), manufacturing execution system (MES), warehouse management system (WMS), and quality management system (QMS).

3. The system of claim 1 or 2, wherein, The micro-service architecture is a combined framework of Spring Boot and Spring Cloud.

4. The system of claim 1, wherein, The first neural network model in the analysis and decision module includes at least one of a material supply model, a device fault prediction model, and a personnel qualification evaluation model; the second neural network model includes at least one of a single beat achievement rate analysis model, a production plan completion rate analysis model, and a first inspection qualified rate model; and the third neural network model includes at least one of a personnel efficiency evaluation model and a standard work station evaluation model.

5. The system of claim 1, wherein, The data visualization module provides a plurality of information display modes, and displays corresponding target information display modes based on determined user identity information. The plurality of information display modes include at least one of a main level information display mode and a plurality of sub-level information display modes, and first production state information displayed in the main level information display mode includes a plurality of second production state information displayed in the plurality of sub-level information display modes.

6. The system of claim 1, wherein, The data cockpit system includes a fault-tolerant service component, which is configured to trigger a switching operation when a fault of the service component is detected, so as to replace the service component with the fault-tolerant service component to provide corresponding services.

7. A production management method based on a data cockpit system, characterized by, The method is applied to the data cockpit system of any one of claims 1 to 5, and the method includes: The data acquisition module acquires key production data from a plurality of connected production management systems, and the data processing module processes the key production data based on business association relationships corresponding to the plurality of production management systems, to construct a target data wide table in a production link; The analysis and decision module inputs the target data wide table into a pre-trained neural network model to obtain production state information, the neural network model including at least one of a first neural network model for a front-end control stage, a second neural network model for a production process control stage, and a third neural network model for a quantitative evaluation stage; The data visualization module visualizes the production state information.

8. The method of claim 7, wherein, The method further includes: storing system data of the data cockpit system to a plurality of storage media at a preset time interval; wherein the system data stored in the plurality of storage media is consistent; in response to loss or damage of system data in a first storage medium currently in use, restoring the system data from a second storage medium in the plurality of storage media.

9. The method of claim 7, wherein, After the analysis and decision module inputs the target data wide table into a pre-trained neural network model to obtain production state information, the method further includes: in response to a production state value in the production state information not meeting a healthy state pre-threshold interval, generating a warning information.

10. A production management apparatus based on a data cockpit system, characterized by, The device applied to the data cockpit system of any one of claims 1 to 5, the device comprising: a data acquisition and processing unit configured to acquire key production data from a plurality of connected production management systems through a data acquisition module, and process the key production data based on business association relationships corresponding to the plurality of production management systems through a data processing module, to construct a target data wide table in a production link; a state information generation unit configured to input the target data wide table into a pre-trained neural network model based on an analysis and decision module, to obtain production state information, the neural network model including at least one of a first neural network model for a front-end control stage, a second neural network model for a production process control stage, and a third neural network model for a quantitative evaluation stage; a display unit configured to visualize the production state information through a data visualization module.

11. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, implements the production management method based on the data cockpit system of any one of claims 7 to 9.

12. An electronic device, comprising: comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the production management method based on the data cockpit system of any one of claims 7 to 9 by executing the executable instructions.

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