Production data management system, method, program product and electronic device

Abstract

The invention provides a production data management system and method, a program product and electronic equipment, and relates to the technical field of data processing. The system comprises a data acquisition module which is configured to be in butt joint with different data sources through preset interfaces and acquire production data from the data sources; the data processing module is configured to preprocess the production data to obtain standardized data and convert the standardized data into one or more business data sets; the analysis and decision module is configured to process the service data set by adopting an analysis model to obtain service decision information; the visualization module is configured to carry out business visualization display of multiple levels; and the event management module is configured to perform service event monitoring and generate an event processing instruction in response to the monitored specific service event. According to the method and the device, centralized acquisition and integration of production data, comprehensive and accurate production situation awareness and accurate decision making from a global level are realized, and the data management efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of data processing, and in particular, to a production data management system, method, program product and electronic device. BACKGROUND

[0002] With the rapid development of digital technology and its widespread application in the field of industrial production, enterprises will generate a large amount of production data in the daily production process. The traditional production management mode lacks effective management and use of data, and it is difficult to meet the needs of enterprises for production efficiency, quality control, cost control and risk prediction under the background of digitization. In related data management schemes, due to the dispersion of data in different business systems, there is a lack of unified data integration and centralized management mechanism, which makes it difficult to form a comprehensive and accurate production situation awareness, and it is impossible to make accurate decisions from a global perspective. The overall data management efficiency is low, especially the need to develop and execute data management processes for different business systems increases the production and operation cost of enterprises. SUMMARY

[0003] The present disclosure provides a production data management system, method, program product and device to at least partially solve the problem of lack of unified data integration and centralized management mechanism in the related art.

[0004] According to a first aspect of the present disclosure, a production data management system is provided, comprising: a data acquisition module configured to interface with different data sources through a preset interface and acquire production data from the data sources; a data processing module configured to preprocess the production data to obtain standardized data, and convert the standardized data into one or more business data sets according to a business correlation relationship; an analysis and decision module configured to process the business data sets using one or more analysis models related to business needs to obtain business decision information; a visualization module configured to perform multi-level business visualization display according to the standardized data or the business data sets; and an event management module configured to monitor business events based on the standardized data or the business data sets, and generate event processing instructions in response to monitoring a specific business event.

[0005] Optionally, the data processing module comprises: a data preprocessing submodule configured to preprocess the business data to obtain standardized data; preprocessing includes data cleaning, data deduplication, and data format standardization conversion; a data modeling submodule configured to determine one or more business field sets according to a business correlation relationship, and convert the standardized data into one or more business data sets according to the business field sets; the business data set comprises a data table; and a data storage submodule configured to store the standardized data or the business data sets in a hierarchical manner based on a preset data hierarchical standard system.

[0006] Optionally, the analysis model comprises a front-end control model, a process control model, and a quantitative evaluation model; the analysis decision module comprises: a front-end control sub-module configured to process the business data set by using the front-end control model to obtain front-end control decision information; a process control sub-module configured to process the business data set by using the process control model to obtain process control decision information; and a quantitative evaluation sub-module configured to process the business data set by using the quantitative evaluation model to obtain business improvement decision information.

[0007] Optionally, the front-end control model comprises a material supply model, an equipment failure prediction model, and a personnel qualification evaluation model; the process control model comprises a single-station tact achievement rate analysis model, a first inspection pass rate model, a station tact achievement rate analysis model, a production plan completion rate analysis model, and an inter-process inventory analysis and control model; and the quantitative evaluation model comprises a personnel efficiency evaluation model, a standard station evaluation model, a personnel capability matrix model, and a production line evaluation model.

[0008] Optionally, the visualization module comprises: a multi-device adaptation sub-module configured to determine display configuration information corresponding to different device categories; a hierarchical display sub-module configured to visually process the standardized data or the business data set according to the display configuration information to obtain a plurality of hierarchical business views; and an interaction sub-module configured to execute an interaction instruction corresponding to an interaction operation on the business view in response to the interaction operation on the business view.

[0009] Optionally, the event management module comprises: an event classification sub-module configured to determine event characteristic information of different categories of business events; an event monitoring sub-module configured to monitor the standardized data or the business data set, analyze monitoring information of the standardized data or the business data set according to the event characteristic information, to determine whether a specific business event occurs; an event analysis sub-module configured to analyze a specific business event according to an event correlation network in response to monitoring the specific business event, to determine an event cause; and an event handling sub-module configured to generate an event handling instruction according to the specific business event and the event cause, execute the event handling instruction or send the event handling instruction to a designated object.

[0010] Optionally, the data management system further comprises a resource management module configured to perform resource demand prediction according to the standardized data or the business data set, and perform resource allocation optimization according to a resource demand prediction result and resource configuration information.

[0011] According to a second aspect of the present disclosure, a production data management method is provided, comprising: connecting with different data sources through a preset interface, collecting production data from the data sources; preprocessing the production data to obtain standardized data, converting the standardized data into one or more business data sets according to a business correlation relationship; processing the business data sets using one or more analysis models related to business requirements to obtain business decision information; performing multi-level business visualization display according to the standardized data or the business data sets; performing business event monitoring based on the standardized data or the business data sets, and generating event processing instructions in response to monitoring a specific business event.

[0012] According to a third aspect of the present disclosure, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the method of the second aspect and possible implementation manners thereof.

[0013] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising a processor and a memory; wherein the memory is configured to store executable instructions of the processor; and the processor is configured to execute the executable instructions to perform the method of the second aspect and possible implementation manners thereof.

[0014] The technical solution of the present disclosure has the following beneficial effects: On the one hand, the data collection module connects multiple scattered data sources through a preset interface, and the data processing module unifies the production data of different data sources into standardized data through preprocessing, realizing centralized acquisition and integration of production data, ensuring the accuracy, integrity and consistency of data, solving the problem of data dispersion and inability to integrate in related technologies, providing a solid and reliable data foundation for global data management and production command decision-making, without the need to develop and execute data management processes for different business systems, improving the overall data management efficiency and reducing enterprise production and operation costs. On the other hand, the data processing module converts the standardized data into one or more business data sets according to a business correlation relationship, the analysis and decision module analyzes the business data sets using an analysis model to obtain business decision information, and the visualization module performs multi-level business visualization display, and the event management module performs business event monitoring and generates event processing instructions for specific business events monitored, thereby providing multi-dimensional and in-depth data analysis and processing capabilities, enabling real-time and accurate mining of production process rules and potential risks behind the data, providing timely and effective decision support, and realizing comprehensive and accurate production situation awareness and accurate decision-making from a global perspective. The production command is effectively improved in terms of refinement and intelligence, and the production continuity and production efficiency are ensured. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1a schematic diagram of a data processing module in an embodiment of the present disclosure is shown; Figure 2 a schematic diagram of a data processing module in an embodiment of the present disclosure is shown Figure 3 a schematic diagram of an analysis decision module in an embodiment of the present disclosure is shown; Figure 4 a schematic diagram of a visualization module in an embodiment of the present disclosure is shown; Figure 5 a schematic diagram of an event management module in an embodiment of the present disclosure is shown; Figure 6 a flow chart of a production data management method in an embodiment of the present disclosure is shown; Figure 7 a schematic diagram of the structure of an electronic device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0016] Exemplary embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings.

[0017] The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this application, illustrate embodiments of the present disclosure and together with the description serve to explain the principles of the present disclosure. In the drawings:

[0018] In related data management solutions, data is scattered in different business systems, and there is a lack of unified data integration and centralized management mechanism, which makes it difficult to form a comprehensive and accurate production situation awareness, and it is impossible to make accurate decisions from a global perspective. In addition, the overall data management efficiency is low, especially the need to develop and execute data management processes for different business systems increases the cost of enterprise production and operation. In addition, the data analysis and processing capability in related solutions is limited, and it is difficult to provide timely and effective decision support by real-time and accurate mining of the production process rules and potential risks behind the data.

[0019] In view of one or more of the above problems, embodiments of the present disclosure provide a production data management system. Reference is made to Figure 1 As shown in FIG. 1, the production data management system 100 includes a data collection module 110, a data processing module 120, an analysis decision module 130, a visualization module 140, and an event management module 150. Each module is described in detail below.

[0020] The data collection module 110 is configured to interface with different data sources through preset interfaces to collect production data from the data sources. The data sources can refer to business systems or data carriers in an enterprise that generate or store production data, such as ERP (Enterprise Resource Planning, enterprise resource planning, which can generate planning type production data), QMS (Quality Management System, quality management system, which can generate quality type production data), MES (Manufacturing Execution System, manufacturing execution system, which can generate execution type production data), WMS (Warehouse Management System, warehouse management system, which can generate inventory type production data), and the like. The preset interfaces provide standardized data transmission channels between the data collection module 110 and the data sources, which can be interfaces based on any protocol, such as API (Application Programming Interface, application programming interface), ETL (Extract Transform Load, extract, transform, load), ODS (Operational Data Store, operational data store), and the like, which can achieve seamless connection between the data collection module 110 and the data sources.

[0021] Production data can include various types of key data automatically collected or recorded during production, such as production plans, material requirements, equipment operating status, quality test results, inventory information, and the like. In an embodiment, the production data can also include production related index data. For example, the data management interface provides data entry functions to support users to manually enter new indexes, so users can enter indexes that have not yet been established with system support (such as indexes that are not originally supported by the system for collection or calculation), and connect them to the data management system. In this way, the data collection module 110 can automatically collect data for the indexes.

[0022] In an embodiment, the data collection module 110 comprises a data interface submodule and a data entry module. The data interface submodule is configured to interface with different data sources through a preset interface, and collect production data from the data sources, so as to ensure the real-time and accuracy of the collected production data. The data entry module is configured to enter a new index (such as the number of safe production days) in the data management system according to the data entry instruction of a user, so that the data interface submodule collects production-related index data corresponding to the index.

[0023] In an embodiment, the data collection module 110 can perform preliminary integration processing on the production data from different data sources, such as data format conversion, field matching and data fusion operation according to preset data mapping rules and integration logic, so as to ensure the consistency, integrity and accuracy of the data.

[0024] The data processing module 120 is configured to preprocess the production data to obtain standardized data, and convert the standardized data into one or more business data sets according to the business association relationship. The preprocessing is a series of operations such as correction and integration on the original production data, which can eliminate data errors, noises, outliers, etc., and unify the data format and coding rules to form standardized data. For example, the production data collected from different data sources may have different fields but the same meaning, and the time format in the production data may be different. The preprocessing unifies the field name and the time format, and realizes the integration of the production data.

[0025] The business association relationship is a data association rule based on production business logic, and reflects the internal relationship between standardized data of different types and different fields. The business data set is a data set formed by grouping and integrating the standardized data according to the business association relationship, or the standardized data can be further calculated, such as calculating the index required by business analysis and calculating the change trend of a certain production data. Each business data set can correspond to a specific business scenario or business type (such as device management, material supply, production execution), which facilitates the subsequent modules to be called on demand and avoids data confusion.

[0026] The data processing module 120 preprocesses the production data to obtain standardized data, and the standardized data includes standardized business fields. Then, the data processing module 120 arranges the business fields having association according to the business association relationship to form a business field set, each business field set is a collection of related business fields under a certain business type or a specific business category, and the standardized data corresponding to the business field set is integrated to form one or more business data sets.

[0027] The analysis decision module 130 is configured to process the business data set by using one or more analysis models related to business needs to obtain business decision information. The analysis model is a data processing algorithm or rule set based on statistics, machine learning or business rules, and each analysis model corresponds to one or a type of business needs (such as prediction, efficiency analysis, quality evaluation), ensuring that the analysis result is in line with the actual production needs. For example, the analysis model can include a device failure prediction model, a production efficiency analysis model, a material demand prediction model, a quality pass rate analysis model, etc. The analysis decision module 130 uses the analysis model to carry out targeted analysis on the business data set, and outputs business decision information that can directly guide production in a business needs-oriented manner.

[0028] The visualization module 140 is configured to perform multi-level business visualization display according to the standardized data or the business data set. The data source of the visualization module 140 can be standardized data or integrated business data set, and can be flexibly selected according to display needs, such as using standardized data for real-time monitoring of conventional indicators and using business data set for complex scene analysis. The business visualization display method includes but is not limited to icons, dashboards, etc. The visualization module 140 visualizes the core information of the standardized data or the business data set and supports differentiated display according to management levels. The multiple levels can be display dimensions divided according to enterprise management architecture, business attribution, etc., and the visualization display contents of different levels are different to focus on the core concerns of the level and avoid information overload or insufficient information.

[0029] The event management module 150 is configured to monitor business events based on standardized data or business data sets and generate event processing instructions in response to monitoring specific business events. The event management module 150 is the core of the abnormal response of the entire data management system 100, which monitors abnormal or key events in production data in real time and automatically triggers event processing instructions to ensure production continuity.

[0030] The event management module 150 can monitor abnormal information in the standardized data or the business data set through preset rules, define the abnormal information as a business event, and generate executable event processing instructions if a specific business event is identified. The specific business event can be a production abnormal event that requires management personnel intervention or attention, which can include warning events and emergency events, and is a key node affecting production continuity, quality or efficiency. The event management module 150 can directly execute the event processing instruction, or push the event processing instruction to the corresponding person in charge to realize timely disposal of production abnormalities.

[0031] Based on the above production data management system, on the one hand, the data acquisition module is connected to multiple scattered data sources through a preset interface, the data processing module unifies the production data of different data sources into standardized data through preprocessing, realizes centralized acquisition and integration of production data, ensures the accuracy, integrity and consistency of data, solves the problem of data dispersion and inability to integrate in related technologies, provides a solid and reliable data foundation for global data management and production command decision-making, eliminates the need to develop and execute data management processes for different business systems, improves the overall data management efficiency, and reduces the enterprise production operation cost. On the other hand, the data processing module converts the standardized data into one or more business data sets according to the business correlation relationship, the analysis and decision module analyzes the business data sets using an analysis model to obtain business decision information, the visualization module performs multi-level business visualization display, and the event management module monitors business events and generates event processing instructions for specific business events monitored. Thus, a multi-dimensional and in-depth data analysis and processing capability is provided, which can real-time and accurately mine the production process rules and potential risks behind the data, provide timely and effective decision support, realize comprehensive and accurate production situation awareness, and accurate decision-making from a global perspective. The fine and intelligent level of production command is effectively improved, and the production continuity and production efficiency are ensured.

[0032] In an embodiment, the production data management system can be implemented as a production command data management tool (such as a cockpit tool). Based on the functions of the above modules, the production data classification and hierarchical level, production information visualization display, intelligent analysis, resource optimization configuration, event monitoring and disposal, user interaction response, etc. are provided in the management tool, and these functions can be integrated in the production command management interface (such as the cockpit interface) for intuitive viewing and one-key operation by the user.

[0033] In an embodiment, as shown in FIG. 1, the production data management system can include a data acquisition module 110, a data processing module 120, an analysis and decision module 130, a visualization module 140, an event management module 150, and a user interaction module 160. Figure 2

[0034] ​The data preprocessing submodule 1201 is configured to preprocess the business data to obtain standardized data; the preprocessing includes data cleaning, data deduplication, and data format standardization conversion. The data cleaning is used to eliminate invalid and erroneous data in the original production data, for example, the production data with missing product batch number and other key fields is directly eliminated. The data deduplication is used to delete redundant records in the original production data, for example, by comparing the collection time, equipment number, and index name, the production data repeatedly collected due to network delay is deleted. The data format standardization conversion is used to unify the heterogeneous data formats of different data sources, for example, the date formats of the production data of the ERP system and the MES system are different, and they are converted into the date format of “YYYY-MM-DD”, thereby ensuring the consistency of the standardized data. The data preprocessing submodule 1201 can solve the problems of missing, erroneous, redundant, and format heterogeneous data in the original production data collected by the data collection module 110, and convert the original production data into unified and clean standardized data through a series of fixed and flow pre-processing operations such as cleaning, deduplication, and format standardization conversion.

[0035] The data modeling submodule 1202 is configured to determine one or more business field sets according to the business association relationship, and convert the standardized data into one or more business data sets according to the business field sets; the business data set includes a data table. The data modeling submodule 1202 is a core component for realizing data business classification. The business field set is a collection of related fields under a certain business type or category, for example, the field set of the production execution business scenario includes order number, production line number, planned production, actual production, production time, etc. The data modeling submodule 1202 filters and integrates the records in the standardized data that meet the business field set to form a structured business data set, and the core expression form is a data table. The data table can be a row-column structure, where the row represents a single record and the column corresponds to the business field. The data modeling submodule 1202 can construct a data wide table covering various links of the production process, including material supply, equipment failure prediction, personnel qualification evaluation, single station beat achievement rate, first inspection qualified rate, station beat achievement rate, production plan completion rate, and inter-process inventory, and other multi-dimensional analysis related fields and data, to provide comprehensive, accurate, and real-time data basis for production command.

[0036] The data storage submodule 1203 is configured to store the standardized data or business data set in different levels based on a preset data level standard system. The data storage submodule 1203 is responsible for the persistent management of data. The preset data level standard system can match the level system of the visual business display, or can be independent of the level system. For example, different data levels can be divided according to business levels, data viewing permissions, data processing depth, use scenarios, and the like, and the definition, classification, and management requirements of different data levels are specified, and the data collection, storage, processing, and use processes are standardized, thereby establishing a data level standard system. The data storage submodule 1203 stores the standardized data or business data set in different levels based on the data level standard system, to ensure the security, reliability, and traceability of the data. In addition, the hierarchical storage method not only ensures the real-time data calling efficiency, but also realizes the ordered management of data.

[0037] Based on the functions of the above three submodules and combinations thereof, the data processing module 120 covers the standardized processing, structured modeling, and hierarchical storage of production data, realizes the full-process standardized processing, improves the data quality and management efficiency, and provides reliable structured data support for subsequent analysis and decision-making.

[0038] In an embodiment, the analysis model includes a front-end control model, a process control model, and a quantitative evaluation model. Referring to FIG. 1, the analysis and decision-making module 130 includes a front-end control submodule 1301, a process control submodule 1302, and a quantitative evaluation submodule 1303. Figure 3

[0039] The front-end control submodule 1301 is configured to process the business data set using the front-end control model to obtain front-end control decision information. For example, the front-end control model includes a material supply model, a device fault prediction model, a personnel qualification evaluation model, and the like. The front-end control submodule 1301 processes the business data set (which can be a data wide table covering the whole production process, or a business data set related to the front end) using these front-end control models, and outputs corresponding information such as material demand prediction, potential device fault identification, personnel skill qualification evaluation, and subsequent measures, which are collectively referred to as front-end control decision information, for implementing front-end control decisions, which can optimize production resource allocation from the source and prevent production interruption and quality problems.

[0040] ​The process control sub-module 1302 is configured to process the business data set by using a process control model to obtain process control decision information. Exemplarily, the process control model includes a single station tact time achievement rate (a ratio of actual production time of a single product to a preset planned tact time, which is used to quantify whether the production efficiency of a single product meets the standard, and is a core efficiency index in process control) analysis model, a first inspection pass rate (a pass rate when a product is submitted for inspection for the first time, which directly reflects the initial quality level of the production process) model, a station tact time achievement rate (a ratio of actual completion time of a single production station to a preset planned tact time of the station, which is used to locate the efficiency bottleneck of the production process) analysis model, a production plan completion rate analysis model, a process inventory analysis and control model, etc. The process control sub-module 1302 processes the business data set (which can be a data wide table covering the whole production process, or a process-related business data set) by using these process control models, and outputs information such as reasons for low index, improvement measures, etc., which are collectively referred to as process control decision information, for executing process control decisions, and can accurately locate bottlenecks and problems in the production process, and realize optimization and efficiency improvement of the production process.

[0041] The quantitative evaluation sub-module 1303 is configured to process the business data set by using a quantitative evaluation model to obtain business improvement decision information. Exemplarily, the quantitative evaluation model includes a personnel efficiency evaluation model, a standard station evaluation model, a personnel capability matrix model, a production line evaluation model, etc. The quantitative evaluation sub-module 1303 processes the business data set (which can be a data wide table covering the whole production process, or a quantitative evaluation-related business data set) by using these quantitative evaluation models, and comprehensively and objectively quantitatively evaluates the comprehensive performance of personnel, stations, and even the whole production line, and outputs corresponding evaluation information and subsequent measure information, which are collectively referred to as business improvement decision information, to provide clear direction and data support for continuous improvement of production management.

[0042] The above three sub-modules respectively perform data analysis from the front-end, process, and quantitative evaluation dimensions and output corresponding front-end control decision information, process control decision information, and business improvement decision information, realize a closed loop of business decisions, and comprehensively improve the scientificity and timeliness of production decisions.

[0043] In an embodiment, referring to FIG. 14, Figure 4 The visualization module 140 includes a multi-device adaptation sub-module 1401, a hierarchical display sub-module 1402, and an interaction sub-module 1403.

[0044] The multi-device adaptation sub-module 1401 is configured to determine the display configuration information corresponding to different device categories. The multi-device adaptation sub-module 1401 sets differentiated display configuration information according to the screen size, resolution, operation mode and other characteristics of different terminal devices (such as LED display screens, personal computers, mobile terminals, etc.), and the display configuration information can include layout, font, chart type, data granularity, etc. The adaptation of one set of data (i.e. standardized data or business data set) to different terminals is implemented, ensuring that the visualized content on various devices is clear and usable, and meeting the display requirements in different scenarios.

[0045] The hierarchical display sub-module 1402 is configured to perform visual processing on the standardized data or business data set according to the display configuration information, to obtain a plurality of hierarchical business views. For example, the hierarchical display sub-module 1402 can divide the business visualization display into a company-level display mode and a plant-level display mode according to the management requirements of different levels. The company-level display mode focuses on the overall display of the company's overall production status from a macro perspective, providing data support for strategic decision-making. The plant-level display mode focuses on details and practical operations, allowing each plant to make individualized configuration and partition adjustment according to its own production characteristics, facilitating the plant management to quickly respond to changes in the production site, and achieving precise scheduling and efficient management. Based on the determination of the visualization level, the hierarchical display sub-module 1402 performs visual processing on the standardized data or business data set according to the display configuration information and the visualization level, to obtain a plurality of hierarchical business views, meeting the visualization and management requirements of different levels.

[0046] The interaction sub-module 1403 is configured to respond to the interactive operation on the business view, and execute the interaction instruction corresponding to the interactive operation on the business view. For example, the instruction logic corresponding to common interactive operations such as data drilling, filtering, view switching, real-time refreshing, etc. can be preset in the production command and management interface, and the user can trigger the corresponding interaction instruction by clicking, dragging, inputting, etc. Operation to dynamically adjust the view, so as to realize convenient production management interaction.

[0047] Through the cooperation of the above three sub-modules, the visualization module 140 guarantees the use efficiency of different devices and different levels of users, enhances the convenience of data interaction, enables the management personnel at each level to quickly obtain accurate information, and further strengthens the production situation awareness capability.

[0048] In an embodiment, as shown in FIG. 15, Figure 5 The event management module 150 includes an event classification sub-module 1501, an event monitoring sub-module 1502, an event analysis sub-module 1503, and an event handling sub-module 1504.

[0049] The event classification submodule 1501 is configured to determine event characteristic information of different categories of business events. Some of the event characteristic information can include definitions, attributes, and impact of various events in the production process on production operations, providing a basis for monitoring, analysis, and processing of events. For example, the event classification submodule 1501 divides events into major categories (such as equipment, quality, material, and production) according to production business scenarios, and further subdivides specific event types (such as temperature abnormalities, breakdowns, and other types of equipment). The event classification submodule 1501 determines the definition, classification standard, attribute, and determination condition (such as numerical threshold or numerical range corresponding to an index) of each specific event type, and establishes an event characteristic information library for calling by other modules.

[0050] The event monitoring submodule 1502 is configured to monitor standardized data or business data sets and analyze monitoring information of the standardized data or business data sets according to event characteristic information to determine whether a specific business event has occurred. For example, the event monitoring submodule 1502 reads standardized data or business data sets at a preset frequency and extracts monitoring indicators, compares them with event characteristic information, and determines that a corresponding specific business event has occurred if the determination condition is met, and records event information (such as occurrence time, object, abnormal value, etc.). The event monitoring submodule 1502 can also send the monitored specific business event information to the visualization module 140 for display.

[0051] The event analysis submodule 1503 is configured to analyze a specific business event according to an event correlation network to determine the cause of the event in response to monitoring the specific business event. The event correlation network is a pre-constructed relationship network of event-related factors. For example, business events, business impact factors (including personnel, equipment, materials, methods, and environment), and the like can be used as nodes, and edges can be generated between nodes with a correlation relationship, thereby constructing an event correlation network. In the case where a specific business event is determined to have occurred, the event analysis submodule 1503 can search for nodes associated with the specific business event in the event correlation network, analyze and determine the cause of the event according to the relationship between the specific business event and the associated nodes.

[0052] The event handling submodule 1504 is configured to generate event handling instructions according to a specific business event and its event cause, execute the event handling instructions, or send the event handling instructions to a designated object. The event handling instructions can include information such as handling measures, responsible person, time limit requirements, etc., and the designated object can be the person in charge of the event. For example, the event handling submodule 1504 finds the handling measures corresponding to the "event + cause" in the pre-constructed knowledge base according to a specific business event and its event cause, and generates event handling instructions in combination with the person in charge of the specific business event, business requirements, etc. The event handling submodule 1504 can directly execute the event handling instructions, such as sending control instructions to the device causing the anomaly, or the event handling submodule 1504 can not directly execute the event handling instructions, but send them to the person in charge for handling.

[0053] Through the closed-loop operation of the above four submodules, the event management module 150 realizes the whole-process management and control of timely discovery, accurate analysis, and rapid handling of production anomalies, effectively reduces the impact of abnormal situations on production, and ensures production continuity.

[0054] In an embodiment, the data management system further includes a resource management module configured to perform resource demand prediction based on standardized data or business data sets, and perform resource allocation optimization based on resource demand prediction results and resource configuration information. The resources related to the production process include human resources, equipment resources, material resources, etc. The resource management module predicts the resource demand, combines the current resource configuration state, and outputs the resource optimization allocation scheme to ensure that the resource allocation result can meet the production demand of each link to the greatest extent, avoid production interruption or production delay caused by unreasonable resource allocation, and reduce resource waste.

[0055] In an embodiment, the resource management module includes a resource coordination submodule, a resource demand prediction submodule, and a resource optimization configuration submodule. The resource coordination submodule is configured to coordinate the management of various production resources such as manpower, equipment, and materials, integrate production plan data, historical resource usage records, and other information, and realize real-time monitoring and mastering of the resource state. The resource demand prediction submodule is configured to use big data analysis and other technologies to accurately predict resource demand, and according to multi-dimensional information such as production plans, order demands, and historical data, to estimate the quantity, type, and time node of the required resources in advance, providing a forward-looking basis for resource allocation. The resource optimization configuration submodule is configured to dynamically allocate resources based on the actual production situation under the consideration of multiple constraints such as cost, efficiency, and risk, to realize the reasonable allocation and efficient use of resources, and to ensure smooth production.

[0056] In an embodiment, the data management system further comprises a matching management module for implementing other related matching functions. Exemplarily, the matching management module comprises one or more of the following sub-modules: A user management sub-module configured to manage and maintain user basic information, including operations such as adding, editing, querying, viewing details, deleting, and one-key password resetting, supports user classification management, assigns corresponding permissions to users of different roles, and ensures the security and confidentiality of system data.

[0057] A permission management sub-module configured to control function permissions based on roles, configures all menus and buttons in the system as a resource tree, assigns corresponding permissions to different roles, and implements multi-dimensional permission configuration at the module level, menu level, window level, and button level to meet the differentiated functional needs of management personnel at different levels and of different types.

[0058] A basic table management sub-module supporting management functions for basic tables such as safety days management, threshold management, and data dictionary management, and users with permissions can perform operations such as querying, viewing, modifying, and deleting. Basic table data supports manual input, import, and export, and serves as necessary index data for data collection and analysis.

[0059] The present disclosure also provides a production data management method. Referring to FIG. 6, Figure 6 The production data management method comprises the following steps S610 to S650: Step S610: connecting with different data sources through a preset interface and collecting production data from the data sources; Step S620: preprocessing the production data to obtain standardized data, and converting the standardized data into one or more business data sets according to business association relationships; Step S630: processing the business data sets using one or more analysis models related to business needs to obtain business decision information; Step S640: performing multi-level business visualization display based on the standardized data or the business data sets; Step S650: performing business event monitoring based on the standardized data or the business data sets, and generating event processing instructions in response to monitoring a specific business event.

[0060] The graph-based approach, on the one hand, connects to multiple dispersed data sources through pre-defined interfaces. Preprocessing unifies production data from different sources into standardized data, enabling centralized acquisition and integration of production data. This ensures data accuracy, completeness, and consistency, resolving the issues of data fragmentation and incompatibility in related technologies. It provides a solid and reliable data foundation for comprehensive global data management and production command decision-making, eliminating the need to develop and execute separate data management processes for different business systems, thus improving overall data management efficiency and reducing enterprise production and operating costs. On the other hand, based on business relationships, the standardized data is converted into one or more business datasets. Analytical models are used to analyze these datasets to obtain business decision-making information, enabling multi-level business visualization and monitoring. Furthermore, it allows for business event monitoring and the generation of event handling instructions for specific monitored events. This provides multi-dimensional and in-depth data analysis and processing capabilities, enabling real-time and accurate discovery of production process patterns and potential risks behind the data, providing timely and effective decision support, achieving comprehensive and accurate production situation awareness, and precise decision-making at the global level. This effectively improves the refinement and intelligence of production command, ensuring production continuity and efficiency.

[0061] In one implementation, preprocessing includes data cleaning, data deduplication, and data format standardization conversion.

[0062] In one implementation, converting the standardized data into one or more business datasets based on business relationships includes: determining one or more business field sets based on business relationships, and converting the standardized data into one or more business datasets according to the business field sets; the business datasets include data tables.

[0063] In one embodiment, the method further includes: storing the standardized data or the business dataset in a hierarchical manner based on a preset data hierarchy standard system.

[0064] In one implementation, the analysis model includes a front-end control model, a process control model, and a quantitative evaluation model; the step of processing the business dataset using one or more analysis models related to business needs to obtain business decision information includes: processing the business dataset using the front-end control model to obtain front-end control decision information; processing the business dataset using the process control model to obtain process control decision information; and processing the business dataset using the quantitative evaluation model to obtain business improvement decision information.

[0065] In an implementation, the front-end management model comprises a material supply model, a device failure prediction model, and a personnel qualification evaluation model; the process management model comprises a single-station tact achievement rate analysis model, a first inspection qualified rate model, a station tact achievement rate analysis model, a production plan completion rate analysis model, and an inter-process inventory analysis and management model; and the quantitative evaluation model comprises a personnel efficiency evaluation model, a standard station evaluation model, a personnel capability matrix model, and a production line evaluation model.

[0066] In an implementation, the multi-level business visualization display based on the standardized data or the business data set comprises: determining display configuration information corresponding to different device categories; performing visualization processing on the standardized data or the business data set according to the display configuration information to obtain multi-level business views; and in response to an interactive operation on the business views, executing an interactive instruction corresponding to the interactive operation for the business views.

[0067] In an implementation, the business event monitoring based on the standardized data or the business data set comprises, in response to monitoring a specific business event, generating an event processing instruction, which comprises: determining event characteristic information of different categories of business events; monitoring the standardized data or the business data set, and analyzing monitoring information of the standardized data or the business data set according to the event characteristic information to determine whether a specific business event occurs; in response to monitoring a specific business event, analyzing the specific business event according to an event correlation network to determine an event cause; generating an event processing instruction according to the specific business event and the event cause, and executing the event processing instruction or sending the event processing instruction to a specified object.

[0068] In an implementation, the method further comprises: performing resource demand prediction according to the standardized data or the business data set, and performing resource allocation optimization according to a resource demand prediction result and resource configuration information.

[0069] The specific technical details of the above production data management method can refer to the implementation of the production data management system part, which will not be repeated here.

[0070] The present disclosure also provides a computer program product. The computer program product comprises a computer program, which, when executed by a processor, implements the above method.

[0071] In one embodiment, the computer program product can be a tangible product, such as a computer-readable storage medium having the computer program stored thereon. The computer-readable storage medium can be based on electrical, magnetic, optical, electromagnetic, infrared, or any other type of signals. Examples of the computer-readable storage medium include, but are not limited to, RAM, ROM, tape, floppy disks, flash memory (Flash), hard disks (HDD), solid state disks (SSD), and the like. For example, the computer program product can be a non-volatile storage medium, such as a read-only memory (ROM), a Nand Flash, or the like, which stores the computer program.

[0072] In one embodiment, the computer program product can be an intangible product. For example, the computer program product can be a virtual digital product, such as an executable file or an installation package containing the computer program.

[0073] The code of the computer program can be written in one or more programming languages. Examples of the programming language include C, Java, C++, and the like. The program code can be executed entirely on the user computing device, or partially on the user computing device, or as a standalone software package, or partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case involving a remote computing device, the remote computing device can be connected to the user computing device through any kind of network, such as a local area network (LAN), a wide area network (WAN), or the like, or can be connected to an external computing device (for example, through an Internet connection provided by an operator).

[0074] The computer program can be carried or transmitted by electrical, magnetic, optical, electromagnetic, infrared, or any other type of signals. The electronic device can convert the signals carrying the computer program into digital signals, and then run the computer program. When the computer program is run on the electronic device, its code is used to make the electronic device perform (more specifically, can make the processor of the electronic device perform) the method steps of various embodiments of the present disclosure, for example Figure 6 the method shown.

[0075] The above method steps are implemented by a computer program, and have the following technical effects: on the one hand, a plurality of scattered data sources are connected through a preset interface, and production data of different data sources are unified into standardized data through preprocessing, so that centralized acquisition and integration of production data are realized, the accuracy, integrity and consistency of data are ensured, the problem of scattered data and inability to integrate in related technologies is solved, a solid and reliable data foundation is provided for global data management and production command decision, data management processes do not need to be developed and executed for different business systems, the overall data management efficiency is improved, and the enterprise production operation cost is reduced. On the other hand, the standardized data are converted into one or more business data sets according to the business correlation relationship, the business data sets are analyzed by using an analysis model to obtain business decision information, multi-level business visualization display is performed, business event monitoring is performed, and event processing instructions are generated for specific business events monitored, thereby providing multi-dimensional and in-depth data analysis and processing capabilities, being able to real-time and accurately mine the production process rules and potential risks behind the data, providing timely and effective decision support, realizing comprehensive and accurate production situation awareness and accurate decision from a global level. The fine and intelligent level of production command is effectively improved, and the production continuity and production efficiency are ensured.

[0076] The exemplary embodiments of the present disclosure also provide an electronic device. The electronic device can be used to deploy a production data management system, and can be any device in the production data management system. The electronic device includes a processor and a memory. The memory stores executable instructions of the processor, which can be a computer program. The processor executes the method steps of various exemplary embodiments of the present disclosure by executing the executable instructions.

[0077] The following refers to Figure 7 The electronic device is exemplarily illustrated in the form of a general computing device. It should be understood that Figure 7 The electronic device 700 shown is merely an example, and should not limit the functions and use range of the embodiments of the present disclosure.

[0078] As Figure 7 shown, the electronic device 700 can include a processor 710, a memory 720, a bus 730, an I / O (input / output) interface 740, and a network adapter 750.

[0079] The memory 720 can include volatile memory, such as RAM 721, cache unit 722, and can also include non-volatile memory, such as ROM 723. The memory 720 can also include one or more program modules 724, which include, but are not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or a combination can include implementation of a network environment. For example, the program modules 724 can include the various modules in the apparatus described above.

[0080] The processor 710 can include one or more processing units, such as: the processor 710 can include an AP (Application Processor, application processor), a modem processor, a GPU (Graphics Processing Unit, graphics processor), an ISP (Image Signal Processor, image signal processor), a controller, an encoder, a decoder, a DSP (Digital Signal Processor, digital signal processor), a baseband processor, and / or an NPU (Neural-Network Processing Unit, neural network processor), etc.

[0081] The processor 710 can be used to execute executable instructions stored in the memory 720 to perform the method steps of various embodiments of the present disclosure, such as the method of Figure 6 .

[0082] By executing the above method steps through the processor 710, the following technical effects are achieved: on the one hand, by connecting multiple scattered data sources through a preset interface, and by preprocessing to unify the production data of different data sources into standardized data, the centralized acquisition and integration of production data are realized, the accuracy, integrity and consistency of the data are ensured, the problem of data scattering and inability to integrate in related technologies is solved, a solid and reliable data foundation is provided for global data management and production command decision-making, without the need to develop and execute data management processes for different business systems, the overall data management efficiency is improved, and the enterprise production and operation cost is reduced. On the other hand, the standardized data is converted into one or more business data sets according to the business association relationship, the business data sets are analyzed using an analysis model to obtain business decision information, multi-level business visualization is displayed, business event monitoring is performed, and event processing instructions are generated for specific business events monitored, thereby providing multi-dimensional and in-depth data analysis and processing capabilities, which can real-time and accurately mine the production process rules and potential risks behind the data, provide timely and effective decision support, and realize comprehensive and accurate production situation awareness and accurate decision-making from a global perspective. The fine and intelligent level of production command is effectively improved, and the production continuity and production efficiency are ensured.

[0083] Bus 730 is used to enable the connection between the various components of electronic device 700, and can include a data bus, an address bus, and a control bus.

[0084] Electronic device 700 can communicate with one or more external devices 800 (e.g., a keyboard, a mouse, a printer, etc.) via I / O interface 740.

[0085] Electronic device 700 can communicate with one or more networks via network adapter 750, which can provide a mobile communication solution such as 3G / 4G / 5G, or a wireless communication solution such as a wireless local area network, Bluetooth, near-field communication, etc. Network adapter 750 can communicate with other modules of electronic device 700 via bus 730.

[0086] Although Figure 7 Other hardware and / or software modules can also be included in electronic device 700, such as a display, microcode, device drivers, redundant processors, external disk drive arrays, RAID (Redundant Arrays of Independent Disks) systems, tape drives, and data backup storage systems, etc., which are not shown in FIG. 7.

[0087] As can be seen, the technical solutions of the present disclosure can be implemented as a method, an apparatus, a system, a computer program product, a storage medium, an electronic device, and the like. Those skilled in the art can understand that various aspects of the present disclosure can be implemented in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, such as a "circuit", a "module", or a "system".

[0088] It should be understood that the present disclosure is not limited to the specific method steps or structures described above and shown in the drawings, and various modifications and changes can be made without departing from the scope of the present disclosure. Those skilled in the art, based on the specific embodiments provided by the present disclosure, will easily think of other embodiments. Therefore, the specific embodiments provided by the present disclosure are only exemplary, and the scope and spirit of the present disclosure are indicated by the claims, and should cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure, and include common knowledge or conventional technical means in the technical field of the present disclosure that are not disclosed by the present disclosure.

Claims

1. A production data management system, characterized in that, include: The data acquisition module is configured to interface with different data sources through a preset interface and acquire production data from the data sources. The data processing module is configured to preprocess the production data to obtain standardized data, and convert the standardized data into one or more business datasets according to business relationships. The analysis and decision-making module is configured to process the business dataset using one or more analysis models related to business needs to obtain business decision information; The visualization module is configured to perform multi-level business visualization based on the standardized data or the business dataset. The event management module is configured to monitor business events based on the standardized data or the business dataset, and generate event handling instructions in response to the detection of a specific business event.

2. The data management system according to claim 1, characterized in that, The data processing module includes: The data preprocessing submodule is configured to preprocess the business data to obtain standardized data; the preprocessing includes data cleaning, data deduplication, and data format standardization conversion. The data modeling submodule is configured to determine one or more business field sets based on business relationships, and to convert the standardized data into one or more business datasets according to the business field sets; the business datasets include data tables. The data storage submodule is configured to store the standardized data or the business dataset in a hierarchical manner based on a preset data hierarchy standard system.

3. The data management system according to claim 1, characterized in that, The analytical model includes a front-end control model, a process control model, and a quantitative evaluation model. The analysis and decision-making module includes: The front-end management and control submodule is configured to process the business dataset using the front-end management and control model to obtain front-end management and control decision information; The process control submodule is configured to process the business dataset using the process control model to obtain process control decision information; The quantitative evaluation submodule is configured to process the business dataset using the quantitative evaluation model to obtain business improvement decision information.

4. The data management system according to claim 3, characterized in that, The front-end control model includes a material supply model, an equipment failure prediction model, and a personnel qualification assessment model. The process control model includes a single unit cycle time achievement rate analysis model, a first-pass inspection pass rate model, a workstation cycle time achievement rate analysis model, a production plan completion rate analysis model, and an inter-process inventory analysis and control model. The quantitative evaluation model includes a personnel efficiency evaluation model, a standard workstation evaluation model, a personnel capability matrix model, and a production line evaluation model.

5. The data management system according to claim 1, characterized in that, The visualization module includes: The multi-device adaptation submodule is configured to determine the display configuration information corresponding to different device categories; The hierarchical display submodule is configured to perform visualization processing on the standardized data or the business dataset according to the display configuration information to obtain multiple levels of business views. The interaction submodule is configured to respond to an interaction operation on the business view and execute the interaction instruction corresponding to the interaction operation on the business view.

6. The data management system according to claim 1, characterized in that, The event management module includes: The event classification submodule is configured to determine the event characteristic information of different categories of business events; The event monitoring submodule is configured to monitor the standardized data or the business dataset, and parse the monitoring information of the standardized data or the business dataset according to the event feature information to determine whether a specific business event has occurred. The event analysis submodule is configured to analyze a specific business event based on an event correlation network in response to the monitoring of that specific business event in order to determine the cause of the event. The event handling submodule is configured to generate event handling instructions based on the specific business event and its cause, execute the event handling instructions, or send the event handling instructions to a specified object.

7. The data management system according to any one of claims 1 to 6, characterized in that, Also includes: The resource management module is configured to predict resource demand based on the standardized data or the business dataset, and optimize resource allocation based on the resource demand prediction results and resource configuration information.

8. A production data management method, characterized in that, include: The system connects to different data sources via a pre-defined interface to collect production data from those data sources. The production data is preprocessed to obtain standardized data, and the standardized data is converted into one or more business datasets according to business relationships. The business dataset is processed using one or more analytical models related to business needs to obtain business decision information; Based on the standardized data or the business dataset, perform multi-level business visualization; Based on the standardized data or the business dataset, business events are monitored, and in response to the detection of a specific business event, an event handling instruction is generated.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of claim 8.

10. An electronic device, characterized in that, Including processor and memory; The memory is used to store executable instructions of the processor; the processor is configured to execute the method of claim 8 by executing the executable instructions.

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