Manufacturing industry operation performance indicator management system

Abstract

The embodiment of the invention provides a manufacturing industry operation performance indicator management system, and relates to the technical field of performance management. The system comprises a data source management module which is used for managing data sources required by indexes and fusing collected data and KPI indexes; the KPI definition management module is used for performing addition, deletion, modification and checking on the KPI, configuring attribute information and establishing a logic association relationship with the business operation model; the manufacturing industry operation model management module is used for constructing business operation models and associating the operation resource information with the business operation models; and the KPI management module is used for calculating the credibility of the KPI and monitoring and processing the data missing condition and the data conflict condition. According to the embodiment of the invention, the corresponding business operation model is constructed in combination with the enterprise characteristics, the applicability of the performance indicators is ensured, and meanwhile, the efficiency and quality of data acquisition are ensured through data source management, so that the efficiency and accuracy of operation performance management are effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of performance management, and in particular, to a manufacturing operations performance indicator management system. Background Art

[0002] Driven by globalization and technological progress, factories are facing increasingly fierce market competition and diversified customer needs. In order to maintain their market leadership, factories must continuously improve production efficiency, ensure product quality, optimize resource allocation, and respond to market changes in a timely manner. As an effective tool to achieve these goals, operational performance management can help companies clarify production goals, monitor business key performance indicators (KPIs), improve operational efficiency, and promote continuous improvement. Therefore, conducting operational performance management is an inevitable choice for factories to adapt to market changes, improve internal management levels, and achieve sustainable development.

[0003] However, the current operational performance management solutions of enterprises generally have the following deficiencies: the use of common KPI templates leads to limited data analysis and prediction capabilities; data collection efficiency is low, and data quality and accuracy are difficult to ensure, etc. In summary, under the combined effect of the above problems, the efficiency and accuracy of manufacturing operational performance management are low. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a manufacturing operation performance indicator management system to improve the efficiency and accuracy of manufacturing operation performance management.

[0005] In a first aspect, an embodiment of the present application provides a manufacturing operation performance indicator management system, including:

[0006] The data source management module is used to mark the source information of the data for indicator calculation; it is also used to integrate the collected data with the corresponding KPI indicators through the preset business operation model;

[0007] The KPI indicator definition management module is used to manage the addition, deletion, modification and query of KPI indicators; it is also used to configure the attribute information of KPI indicators; it is also used to logically associate KPI indicators with preset business operation models;

[0008] A manufacturing operation model management module, used to build a business operation model based on the information of the manufacturing enterprise; and also used to associate the operation resource information of the manufacturing enterprise with each business operation model;

[0009] The KPI indicator management module is used to configure and calculate the credibility rules of the data involved in calculating the KPI indicators; it is also used to monitor and handle data missing in the KPI indicator calculation process; it is also used to monitor and handle data conflicts in the KPI indicator calculation process.

[0010] In the embodiment of the present application, a corresponding business operation model is constructed in combination with the characteristics of the enterprise to ensure the applicability of the performance indicators. At the same time, the efficiency and quality of data collection are guaranteed through data source management, thereby effectively improving the efficiency and accuracy of operational performance management.

[0011] In some possible embodiments, a KPI indicator monitoring module is also included, which is used to monitor the calculation results of the KPI indicator based on preset monitoring standards; it is also used to display detailed information of the KPI indicator; it is also used to monitor abnormal situations in the calculation of the KPI indicator and display abnormal alarm information.

[0012] In an embodiment of the present application, the manufacturing operation performance indicator management system also includes KPI indicator monitoring, specifically including result monitoring, detailed information display and abnormality monitoring, etc., to further improve the accuracy of operation performance management.

[0013] In some possible embodiments, the data source management module includes an IoT communication management unit, a business data access unit, and a manual entry unit;

[0014] The IoT communication management unit is used to collect the equipment and sensor data on the production line in real time through IoT technology, and mark the source of the data as IoT collection;

[0015] The business data access unit is used to connect to and collect data from the business system, and mark the source of the data as IoT collection, vouchered computing data, or manual experience evaluation data according to the type of the data;

[0016] The manual input unit is used to provide a data input interface to collect data input by the user based on the data input interface, and mark the source of the data as vouchered calculation data or manual experience evaluation data.

[0017] In the embodiment of the present application, the data source management module specifically includes three source management units, which further improve the efficiency and accuracy of collection and acquisition by separately managing data from different sources.

[0018] In some possible embodiments, the KPI indicator definition management module includes a KPI indicator definition unit, an indicator operation model dimension marking unit, and an indicator data source management unit;

[0019] The KPI indicator definition unit is used to provide the functions of adding, deleting, modifying and checking KPI indicators, and configure the attribute information of each KPI indicator; wherein the attribute information includes at least one of an indicator ID, an indicator name, a business description, an indicator type, a data source and a calculation method;

[0020] The indicator operation model dimension marking unit is used to logically associate the KPI indicator with the preset business operation model;

[0021] The indicator data source management unit is used to manage the atomic indicators corresponding to each KPI indicator and its data source based on the calculation relationship between the KPI indicators and the corresponding associated operation structure model.

[0022] In an embodiment of the present application, the KPI indicator definition management module specifically includes indicator definition, model dimension labeling and indicator data source management. By refining the indicator definition management process, the accuracy of indicator calculation is further improved.

[0023] In some possible embodiments, the manufacturing operation model management module includes a hierarchical model unit, a business process model unit, and an operation resource inventory unit;

[0024] The hierarchical structure model unit is used to construct multiple hierarchical structure models based on the structural characteristics of the enterprise;

[0025] The business process model unit is used to construct multiple process structure models based on the process characteristics of the enterprise;

[0026] The operation resource list unit is used to maintain the operation resources of the enterprise during the operation process; and is also used to associate the maintained operation resource information with each business operation model.

[0027] In an embodiment of the present application, the applicability and reliability of the business operation model are further improved by further subdividing the manufacturing operation model management into a hierarchical model, a business process model unit, and an operation resource list management.

[0028] In some possible embodiments, the KPI indicator management module includes a data trust rule configuration unit, a KPI indicator missing data management unit, and a KPI indicator conflict data management unit;

[0029] The data credibility rule configuration unit is used to configure credibility rules for data involved in calculating KPI indicators; and is also used to calculate the credibility of KPI indicator results;

[0030] The KPI indicator missing data management unit is used to monitor the data missing situation in the KPI indicator calculation process and process it based on the missing data processing strategy that matches the current data missing situation; wherein the information items of the data missing situation include the data source type and the degree of missing abnormality; the missing processing strategy includes ignoring, alarming and transferring to manual processing and automatic filling of system data;

[0031] The KPI indicator conflict data management unit is used to monitor the data conflict situation in the KPI indicator calculation process and handle it based on the conflict handling strategy that matches the current data conflict situation; wherein the conflict handling strategy includes ignoring, transferring alarm to manual processing and adopting the result with higher data credibility.

[0032] In the embodiment of the present application, the accuracy and reliability of the indicator calculation are further improved by further refining the KPI indicator management into data trust rule configuration, data missing management and data conflict management.

[0033] In some possible embodiments, the KPI indicator management module also includes a KPI system management unit, which is used to construct a KPI indicator system corresponding to the enterprise's operational management needs based on the compiled KPI indicators; it is also used to mark the KPI indicators of interest selected by the user; it is also used to configure the data statistical dimensions of the KPI indicators and the indicator display results; it is also used to configure the assessment standards of the KPI indicators, and monitor the results of the corresponding KPI indicators based on the assessment standards.

[0034] In an embodiment of the present application, the KPI indicator management module also includes KPI system management, which further improves the flexibility of KPI indicator management by providing functions for personalized configuration of KPI indicators, focus settings and expansion of detailed information.

[0035] In some possible embodiments, the KPI indicator management module further includes an alarm warning rule management unit, which is used to configure abnormality determination rules and alarm strategies for KPI indicator results.

[0036] In the embodiment of the present application, the KPI indicator management module also includes alarm warning rule management, which further improves the reliability of performance indicator monitoring by personalizing the abnormal judgment rules and alarm strategies of the indicators.

[0037] In some possible embodiments, the KPI indicator management module also includes an exception handling knowledge base unit, which is used to collect abnormal information on KPI indicator results that have occurred in the system and the corresponding exception handling records, and extract keywords based on these abnormal information and exception handling records to mark them and form abnormal knowledge and store them in the abnormal knowledge base; it is also used to perform knowledge matching on real-time KPI indicator abnormal events based on the abnormal knowledge base and push corresponding exception handling strategies.

[0038] In an embodiment of the present application, the KPI indicator management module also includes an exception handling knowledge base, which collects historical indicator anomalies and extracts key information and stores it in the knowledge base for subsequent knowledge matching and provision of corresponding reference strategies, thereby further improving the efficiency and accuracy of indicator exception handling.

[0039] In some possible embodiments, the KPI indicator monitoring module includes a KPI indicator real-time monitoring and prediction unit and a KPI abnormal alarm and processing unit;

[0040] The KPI indicator real-time monitoring and prediction unit is used to monitor the calculation results of the KPI indicator based on the preset monitoring standard; it is also used to display the credibility of the KPI indicator results; it is also used to expand the detailed data of each node of the KPI indicator and the credibility of each data;

[0041] The KPI abnormal alarm and processing unit is used to monitor the abnormal situation of KPI indicator calculation and display abnormal alarm information; it is also used to analyze abnormal information based on the monitored abnormal situation, and match corresponding abnormal processing strategies based on the abnormal information obtained by analysis.

[0042] In the embodiment of the present application, the KPI indicator monitoring module includes real-time monitoring and prediction of indicators and abnormal alarm and processing, and refines the detailed data management matters and abnormal handling strategies of indicator monitoring, thereby further improving the system's ability to monitor indicators and handle abnormalities. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A schematic diagram of the structure of a manufacturing operation performance indicator management system provided in an embodiment of the present application;

[0045] Figure 2A schematic diagram of the integration and association of data sources and business operation models provided in the embodiment of the present application;

[0046] Figure 3 A schematic diagram of calculating the KPI indicators in a hierarchical model provided in an embodiment of the present application;

[0047] Figure 4 A schematic diagram of data source management of KPI indicators provided in an embodiment of the present application;

[0048] Figure 5 A schematic diagram showing an example of a common hierarchical structure model provided in an embodiment of the present application;

[0049] Figure 6 A schematic diagram of the configuration of the shift time dimension provided in an embodiment of the present application;

[0050] Figure 7 An example schematic diagram of a common business process model provided in an embodiment of the present application;

[0051] Figure 8 A schematic diagram of a process for calculating the credibility of KPI indicator data provided in an embodiment of the present application;

[0052] Fig. 9 A schematic diagram of the process of constructing the KPI indicator system provided in the embodiment of the present application;

[0053] Fig.10 A flowchart for confirming the root cause of an abnormal KPI indicator provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0055] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0056] It should be noted that the existing manufacturing operation performance management solutions generally have deficiencies, which are mainly reflected in: the use of universal KPI templates leads to limited data analysis and prediction capabilities; data collection involves repetitive work and is inefficient, and the lack of an automatic correction mechanism for data leads to resource waste and data accuracy issues; data quality and accuracy are difficult to guarantee, especially when objective data and subjective evaluation data are mixed together; in addition, the system lacks real-time monitoring and prediction capabilities and cannot quickly respond to immediate changes in production; in addition, the root cause analysis and problem solving when KPI indicators are abnormal rely too much on personal experience, lack effective knowledge accumulation and solution query, resulting in difficulty in inheriting experiential knowledge. The combined effect of these problems leads to low efficiency and accuracy of the operation performance management solution, and the above problems are specifically expanded as follows:

[0057] 1. The indicator system lacks business logic: Existing performance management systems often use a common KPI template, and the KPI indicators of each level / department / individual are isolated, failing to fully consider the complex logical relationship between data in the manufacturing industry. Due to the lack of correlation logic between indicators, the same KPI needs to be repeatedly entered and calculated by different personnel during the data collection process at different times or in different business scopes, which not only wastes a lot of manpower and material resources, is inefficient, but also increases the risk of errors.

[0058] 2. Lack of purpose in data collection: Existing manufacturing enterprises are often in a blind state when collecting data. Since data indicators involve various levels such as equipment level, production line level, department level, etc., a large number of forms to be filled out in various links such as work orders, orders, sales, and procurement are generated in the process of collecting data, but there are always data missing when the data is actually used. This leads to the situation of "too much when collecting, too little when using, and not knowing what data is missing".

[0059] 3. Lack of data correction and filling mechanism: The strong logical relationship between manufacturing business data is ignored. The system can usually only provide basic data statistics for KPI indicators, lacks correlation analysis between data, and cannot confirm its credibility when the data is abnormal (for example, different results are obtained from two different statistics of the same KPI indicator). In the case of missing data, there is a lack of effective supplementation and filling mechanism.

[0060] 4. The accuracy and reliability of KPI results are uncontrollable: The objective source data and subjective evaluation data are mixed in the results of KPI indicators, making it difficult to ensure the quality and accuracy of the data. This mixture further increases the complexity of the upper-level indicator calculation, affects the reliability of the final KPI results, and leads to the lack of comprehensive data support for the final overall decision.

[0061] 5. Lack of real-time and predictive capabilities: The performance appraisal method adopted by the existing system is to manually fill in the results after they are generated. It lacks real-time monitoring capabilities and cannot quickly capture and respond to immediate changes in the production process, resulting in a lack of necessary real-time data support and predictive capabilities for decision-making.

[0062] 6. Low experience support and inheritance: When KPI results are abnormal, root cause analysis and problem solutions often rely on the experience of the person handling the problem, lacking a systematic knowledge management and solution query mechanism. With the turnover of business personnel, valuable experience and knowledge are difficult to be effectively accumulated and inherited.

[0063] In view of the problems existing in the above-mentioned prior art, an embodiment of the present application provides a manufacturing operation performance indicator management system, which improves the efficiency of performance management and the quality of decision-making by introducing a more refined and intelligent performance indicator management mechanism.

[0064] Specifically, the embodiment of the present application introduces a variety of business operation models for factory production operations, logically associates the collected data with the business operation model, deeply explores the business logic relationship between the data, effectively helps enterprises build a KPI performance indicator system in combination with existing operational needs, eliminates information islands between business modules, and confirms effective data resource needs. In addition, the use of Internet of Things technology and real-time data collection and processing capabilities ensures real-time monitoring and rapid response of KPI indicators, analyzes the root causes and abnormal points at the first time of abnormality, and recommends processing solutions based on the knowledge base. In addition, in response to data integrity and accuracy issues, by using intelligent algorithms to predict and fill in missing data based on the business logic relationship of the KPI itself, the credibility of the KPI indicator results is analyzed based on the indicator lineage and sensitivity, avoiding the overall failure of the relevant business chain due to the lack of a certain KPI data. Based on this, the embodiment of the present application not only realizes a more intelligent manufacturing operation performance management model and provides comprehensive data analysis support, but also improves the efficiency and effectiveness of factory performance management.

[0065] like Figure 1 As shown, the embodiment of the present application provides a manufacturing operation performance indicator management system, which may include a data source management module 11, a KPI indicator definition management module 12, a manufacturing operation model management module 13, a KPI indicator management module 14 and a KPI indicator monitoring module 15, wherein:

[0066] The data source management module 11 is used to mark the source information of the data for indicator calculation; it is also used to integrate the collected data with the corresponding KPI indicators through a preset business operation model;

[0067] The KPI indicator definition management module 12 is used to manage the addition, deletion, modification and query of KPI indicators; it is also used to configure the attribute information of KPI indicators; it is also used to logically associate KPI indicators with preset business operation models;

[0068] The manufacturing operation model management module 13 is used to build a business operation model based on the information of the manufacturing enterprise; and is also used to associate the operation resource information of the manufacturing enterprise with each business operation model;

[0069] The KPI indicator management module 14 is used to configure and calculate the credibility rules of the data involved in calculating the KPI indicator; it is also used to monitor and process the data missing situation in the KPI indicator calculation process; it is also used to monitor and process the data conflict situation in the KPI indicator calculation process.

[0070] The KPI indicator monitoring module 15 is used to monitor the calculation results of the KPI indicator based on the preset monitoring standards; it is also used to display the detailed information of the KPI indicator; it is also used to monitor the abnormal situation of the KPI indicator calculation and display the abnormal alarm information.

[0071] Further, the data source management module 11 includes an IoT communication management unit 111, a business data access unit 112, and a manual input unit 113;

[0072] The IoT communication management unit 111 is used to collect the equipment and sensor data on the production line in real time through IoT technology, and mark the source of the data as IoT collection;

[0073] The business data access unit 112 is used to connect to and collect data from the business system, and mark the source of the data as IoT collection, vouchered computing data, or manual experience evaluation data according to the type of the data;

[0074] The manual input unit 113 is used to provide a data input interface to collect data input by the user based on the data input interface, and mark the source of the data as vouchered calculation data or manual experience evaluation data.

[0075] Specifically, the data source management module 11 is used to build an efficient, multi-channel, and source-verifiable indicator data source management channel to provide a solid data foundation for the KPI indicators of enterprise manufacturing performance management.

[0076] Among them, the IoT communication management unit 111 uses IoT technology to monitor various devices and sensors on the production line in real time to collect key performance data such as the status, output, and energy consumption of each device. The source type of these data can be marked as "IoT collection";

[0077] The business data access unit 112 is used to integrate data from business systems such as ERP and MES to achieve automatic data extraction. The source types of these data may be marked as "Internet of Things Collection", "Computational Data with Vouchers" and "Manual Experience Evaluation";

[0078] The manual input unit 113 is used to provide an interface for operators to manually input data. It is suitable for data points that cannot be collected automatically. This unit can be used as a way to enter qualitative KPI indicators in the system and KPI indicators that the enterprise does not have the ability to collect digitally. The source type of its data may be marked as "calculated data with credentials" and "manual experience evaluation".

[0079] It should be noted that the data source management module 11 will integrate the data from the above different sources with each KPI indicator by associating the business operation model, or directly associate the data with the KPI indicator. Figure 2 As shown, all source data, in addition to having a unique ID, will also be marked with data name, data entry method (IoT collection (IoT communication management unit 111), system docking (business data access unit 112), manual entry (manual entry unit 113)), data source type (IoT collection, voucher-based calculation, manual experience evaluation), associated hierarchical model (multiple models can be associated, each model can only be associated with 1 node), associated business process model (multiple models can be associated, each model can only be associated with 1 node), associated operational resources (only 1 node of 1 resource type can be associated), update frequency and associated KPI atomic indicators.

[0080] It should be noted that KPI indicators may be integrated with data sources in the following three ways: first, the indicator type is an atomic indicator, which is directly associated with the data source (the data source is one of the indicators in the calculation range); second, the indicator is a derived indicator and a composite indicator, which is associated with the data source through the atomic indicators involved in the calculation; third, the KPI indicator and the data source are associated with the same hierarchical model and business process model, and the calculation relationship between the KPI indicator and the data source is constructed through the association relationship between the models.

[0081] For example, the enterprise pre-configures the production hierarchy of "factory-workshop-production line"; imports the operation resource equipment-equipment list-equipment; and configures the corresponding equipment resources under the "production line" level of the production hierarchy; clarifies which equipment each production line contains; the enterprise creates a KPI indicator named "equipment output" and associates it with the "production hierarchy", and the hierarchical KPI data inheritance and propagation method is "sum"; the enterprise accesses the "output" data of a single device through the IoT communication management unit 111, and this data is reported once every minute through the model. Then, this data source will be marked as: data name [equipment output]; data entry method [IoT collection]; data source [IoT collection]; associated hierarchy [ignore]; associated process model [ignore]; associated operation resource [equipment list-equipment ID]; associated indicator [equipment output]; update frequency [1 minute / time]. After that, whether the enterprise counts the output of this equipment, the production line where it is located, and the equipment output of the workshop where it is located according to dimensions such as day / month / year, the data is related to this data source.

[0082] In another case, the enterprise has already connected to MES. The team leader will copy the production data of each device every day and enter the total into the system. MES pushes the data "daily production of workshop equipment" to the system every day. The data source will be marked as: data name [daily production of workshop equipment]; data entry method [system connection]; data source [calculation with voucher]; associated hierarchy [production hierarchy-workshop-workshop ID]; associated process model [ignore]; associated operation resources [ignore]; associated indicators [equipment production]; update frequency [1 day / time]. If the enterprise needs to calculate the monthly / yearly equipment production, the data can be settled directly. If it needs to calculate the equipment production per hour at a lower granularity, it needs to fill in the data according to the data missing filling rules.

[0083] In some possible embodiments, the KPI indicator definition management module 12 includes a KPI indicator definition unit 121, an indicator operation model dimension marking unit 122, and an indicator data source management unit 123;

[0084] The KPI indicator definition unit 121 is used to provide the functions of adding, deleting, modifying and checking the KPI indicators, and configure the attribute information of each KPI indicator; wherein the attribute information includes at least one of the indicator ID, indicator name, business description, indicator type, data source and calculation method;

[0085] The indicator operation model dimension marking unit 122 is used to logically associate the KPI indicator with the preset business operation model;

[0086] The indicator data source management unit 123 is used to manage the atomic indicators corresponding to each KPI indicator and its data source based on the calculation relationship between the KPI indicators and the corresponding associated operation structure model.

[0087] Specifically, the KPI indicator definition management module 12 is the core part of the performance management system, which is responsible for defining individual indicators, defining the calculation logic of indicators, and integrating indicators and the business operation model of the enterprise at the KPI indicator layer.

[0088] Among them, the KPI indicator definition unit 121 is used to provide management functions such as adding, deleting, modifying and checking KPI indicators. When adding a new KPI indicator, its ID (unique and non-duplicate), KPI Chinese name, business description, indicator type (atomic indicator, derived indicator, composite indicator) need to be defined. The atomic indicator needs to specify its data source (associated source data ID), and the derived indicator and composite indicator need to edit their calculation formula;

[0089] It can be understood that atomic indicators are those that can be directly obtained from data sources to determine basic measurements in the business, such as production, inventory, etc.;

[0090] Derived indicators are derived from a single atomic indicator, combined with the time period, and obtained through certain mathematical operations or logical processing. These operations may include but are not limited to addition, subtraction, multiplication, division, average, maximum, minimum, standard deviation, growth rate, etc. Such as hourly average output, inventory growth, etc.

[0091] A composite indicator refers to a complex indicator constructed by two or more atomic indicators through specific mathematical operations (such as addition, subtraction, multiplication, division, proportion calculation, etc.); for example, product yield = number of qualified products / product output × 100%.

[0092] The calculation methods involved, such as addition, subtraction, multiplication, division, maximum, minimum, average, standard deviation, and growth rate, are all built into the configuration process of the calculation formula. Users need to clarify the indicators involved in the calculation process and the calculation methods between indicators, which is related to the credibility of subsequent indicator data and the blood relationship analysis between indicators.

[0093] It should be noted that when defining KPI indicators, only data logic processing is performed without distinguishing their business levels. For example, "factory output" and "workshop output" only need to define one KPI indicator "output", while the factory and workshop are just the statistical results of the same KPI indicator at different business levels.

[0094] The indicator operation model dimension marking unit 122 is used to mark the hierarchical model associated with the indicator (multiple models can be enabled, but they do not need to be associated with nodes) and the inheritance propagation method of each model, or mark the associated business process model (multiple models can be enabled, but they do not need to be associated with nodes), operation resources (multiple resources can be enabled, and each resource can be associated with multiple resources) and the statistical method between resources.

[0095] For a single KPI indicator, after associating and enabling a business operation model (hierarchical model / business process model), it means that any hierarchical node in this model can calculate the KPI indicator results within its business scope, which is why it is sufficient to just enable the model without associating it to a specific node. Enabling a model means that a family of associated KPI indicators can be derived through the inheritance propagation method of the model.

[0096] When multiple business operation models are enabled for an indicator at the same time, the data superposition relationship between multiple models can be configured: and (and the superposition calculation between the two models), or (or, the two models are calculated independently), not (not, the data of the two models are mutually exclusive). For example: when calculating the KPI indicator "output", the two hierarchical structure models of "production hierarchy: factory-factory area-workshop-production line-workstation" and "product hierarchy: factory-product type-product model" are enabled at the same time, and the relationship is configured as "and", then the KPI indicator of output can derive "output statistics based on factory-factory area-workshop-production line-workstation", "output statistics based on factory-product type-product model" and the superimposed "output of a certain factory area-a certain type of product" at the same time.

[0097] It should be noted that the "inheritance propagation method" here refers to the calculation method of indicator data between nodes in the tree hierarchy model and the business process model. For the data structure, from bottom to top is propagation, and from top to bottom is inheritance. For the process model, from front to back is propagation, and from back to front is inheritance.

[0098] like Figure 3 As shown, still taking the KPI indicator "output" as an example: the enterprise has previously configured the production hierarchy of "factory-workshop-production line"; the data source is the "output" of the MES system, which is associated with the "workshop level"; the user has enabled the "production hierarchy" for the KPI indicator "output" in the indicator operation model dimension tag unit 122, and clearly stated that the propagation method is "sum" and the inheritance method is "average". At this point, the system can calculate the output of the entire factory by summing and the output of each production line by averaging (data filling).

[0099] Understandably, Figure 3What is shown is the most ideal data source state, that is, there is a fixed data source in a certain layer of the model, but in the actual operation process, due to the uneven degree of informatization construction of workshop 1 and workshop 2, the production data of workshop 1 is collected by connecting to MES, the production data of production line 3 in workshop 2 is collected through IoT equipment, and the data of production line 4 is manually reported once a day by the team leader. At this time, the calculation method of the embodiment of the present application will greatly exceed the traditional method in terms of convenience and intelligence.

[0100] The indicator data source management unit 123 can manage the atomic indicators involved in the indicators and their data sources based on the calculation relationship between the KPI indicators and the associated business operation models.

[0101] It should be noted that for KPI indicators that have been defined and associated with business operation models, users can expand the indicators associated with their calculation process layer by layer until they reach the atomic indicator layer. Then, at the atomic indicator layer, expand the business operation model (hierarchical model / business process model) or resource list associated with the KPI indicators to confirm that the data source in the corresponding model or list is complete.

[0102] Taking the KPI indicator "Qualified Rate" as an example, qualified rate = (output - number of defective products) / output × 100%, and the qualified rate indicator has been associated with the production hierarchy of "factory-workshop-production line", such as Figure 4 As shown, the system expands the related indicators of the pass rate indicator "Number of defective products" and "Output" from the "Pass rate", and continues to expand the related production hierarchy to view the source type of the indicator data and the missing status of the source data in each node.

[0103] In some possible embodiments, the manufacturing operation model management module 13 includes a hierarchical structure model unit 131, a business process model unit 132, and an operation resource inventory unit 133;

[0104] A hierarchical structure model unit 131, used to construct multiple hierarchical structure models based on the structural characteristics of the enterprise;

[0105] A business process model unit 132, used to construct multiple process structure models based on the process characteristics of the enterprise;

[0106] The operation resource list unit 133 is used to maintain the operation resources of the enterprise during the operation process; and is also used to associate the maintained operation resource information with each business operation model.

[0107] Specifically, the manufacturing operation model management module 13 is used to construct a common business operation model in a manufacturing enterprise, construct the organizational model, operation model, and business process in the enterprise according to the actual situation of the enterprise, and at the same time associate the enterprise's operation resources into the corresponding model to build a digitized enterprise operation resource model.

[0108] Among them, the hierarchical model unit 131 is used to configure the model with tree structure characteristics in the enterprise. The common hierarchical model in manufacturing enterprises can be built into the unit, such as the production structure dimension based on factory-factory-workshop-production line-workstation; the regional dimension based on factory-region-building-floor; the organizational structure dimension based on factory-department-group-personnel; the product structure dimension based on factory-product type-product model; the inventory dimension based on factory-warehouse type-warehouse-shelf; the equipment structure dimension based on system-equipment type-equipment model, etc. Figure 5 As shown, enterprises can define and build multiple hierarchical models based on the business that needs to be analyzed.

[0109] In the hierarchical structure, the default "time structure dimension" cannot be deleted. In addition to the common time dimensions such as "year-quarter-month-week-day-hour-minute", the concept of "shift" needs to be constructed according to the operational characteristics of the manufacturing industry. The shift can be defined within the scope of one day / multi-day by defining the start and end time and a set of time to determine the different shifts of the factory within one day. In this way, business statistics are performed on each operating shift in the manufacturing industry. The "shift" time dimension, combined with the company's "shift scheduling plan", can refine the production operation KPI assessment results to each production team. If the team is associated with the "personnel" resource, the KPI assessment results can be drilled down to the employee.

[0110] like Figure 6 As shown in the figure, the "shift" of the time structure dimension in the system is configured with the day shift (start at 9 am - get off at 5 pm), the mid-shift (start at 5 pm - get off at 1 am the next day), and the night shift (start at 1 am - get off at 9 am). The system will be able to use "shift" as a unit to count the KPI performance results of each shift, and then match the shift schedule (shift-organization / personnel) to refine the results of the KPI indicators to the production personnel of the corresponding shift. From a different perspective, for production personnel, they are only responsible for the production performance (KPI performance) of the shift they are responsible for, which is not only more refined, but also more reasonable.

[0111] The business process model unit 132 is used to configure the model with business process characteristics in the enterprise. Common business process models in manufacturing enterprises can be built into this unit, such as sales order management process, production work order management process, defective product management process, product process flow, equipment maintenance management process, employee training process, etc. Figure 7 As shown, enterprises can define and build multiple process structure models based on the business that needs to be analyzed.

[0112] The obvious difference between the process-based structure and the hierarchical structure is that the process-based structure is often used to describe the business management status. The entire process consists of steps one by one, and the time for each step is recorded. During the calculation process, indicators often calculate the quantity in a single step, the ratio between two steps, the time between two steps, etc.

[0113] The operation resource list unit 133 is used to manage the lists of people, machines, materials, methods and environment involved in the operation process of the enterprise, such as employee lists, equipment lists, production material lists, product lists, sales order lists, production work order lists, etc.

[0114] After the inventory resources are entered, they will be associated and tagged to the business operation model (hierarchical model / business process model) according to their actual business attributes. Each inventory resource can be tagged to multiple models. The hierarchical model needs to be associated to a specific node, but the business process model only needs to be associated to the structural model.

[0115] For example, after the equipment list is entered, it can be associated with the workstation node and production line node of the production hierarchy model according to the "production hierarchy structure" to mark that the equipment is used for the production of a certain workstation or production line, but at the same time, the equipment is also associated with a certain workshop node in the "factory area structure" to mark the location of the equipment. At the same time, the equipment can be associated with the "equipment maintenance management process" to record the status and time statistics according to the different states of the equipment "operation → maintenance → failure → shutdown".

[0116] In some possible embodiments, the KPI indicator management module 14 includes a data trust rule configuration unit 141, a KPI indicator missing data management unit 142, and a KPI indicator conflict data management unit 143;

[0117] The data trust rule configuration unit 141 is used to configure the trust rules of the data involved in calculating the KPI indicator; and is also used to calculate the trustworthiness of the KPI indicator result;

[0118] The KPI indicator missing data management unit 142 is used to monitor the data missing situation in the KPI indicator calculation process and process it based on the missing data processing strategy that matches the current data missing situation; the information items of the data missing situation include the data source type and the degree of missing abnormality; the missing processing strategy includes ignoring, transferring alarm to manual processing and automatic filling of system data;

[0119] The KPI indicator conflict data management unit 143 is used to monitor the data conflict situation in the KPI indicator calculation process and handle it based on the conflict handling strategy that matches the current data conflict situation; wherein the conflict handling strategy includes ignoring, transferring alarm to manual processing and adopting the result with higher data credibility.

[0120] Specifically, the KPI indicator management module 14 is used to manage the manufacturing operation KPI indicator system that the enterprise is concerned about, including defining the entire system of KPI indicators that the enterprise is concerned about, the operational statistical dimensions of each indicator in the system, the alarm thresholds of the indicator results, the method of handling missing data, trusted evaluation rules, conflict handling rules, and the business knowledge summary of the historical KPI indicator exception handling accumulation.

[0121] Among them, the data credibility rule configuration unit 141 is used to define the credibility calculation rules, factor weight ratios, visualization performance colors, etc. of the KPI indicator data results. When the final KPI results are presented, the credibility of the current KPI indicator calculation results is simultaneously displayed to the user.

[0122] like Figure 8 As shown, the KPI indicator credibility evaluation calculation process may include the following steps:

[0123] Step 81, based on the indicator data source management unit 123, we can drill down layer by layer to the atomic indicators associated with the KPI indicator and the data source of each atomic indicator. For example, after penetrating the indicator Y layer by layer to the atomic indicator Xi, Y can be regarded as the dependent variable and Xi is the independent variable; if there are multiple independent variables, the model form is as follows:

[0124] Y=β0+β1·X1+β2·X2+...+βn·Xn+ε

[0125] Here, βi represents the impact of the i-th independent variable on Y. When other conditions remain unchanged, for every increase of one unit Xi, Y is expected to change by βi units. ε is the sensitivity parameter of the corresponding atomic indicator Xi to the KPI indicator Y.

[0126] Step 82, because Xi is an atomic indicator, we can determine the data source of Xi. In the existing historical data, all data are arranged from small to large, and the mode of the first 50% of the data is taken as the lower limit of the change min, and the mode of the last 50% of the data is taken as the upper limit of the change max. In the range of min to max, 100 equal divisions can be made to obtain 100 change values ​​of Xi. In the same way, the change values ​​of all atomic indicators from X1 to Xn can be obtained.

[0127] Step 83, in the KPI indicator definition unit 121, we have defined the calculation rules of the indicator. By putting the change values ​​of X1~Xn into the calculation rules, we can obtain a set of observation results of Y. Use the software to substitute the independent variables and observation results into the linear regression model, set the confidence interval to 95%, and then solve the value of the unknown coefficients {β0,β1,β2,β3,…βn}.

[0128] Step 84: Normalize the values ​​of {β0, β1, β2, β3, ... βn}. The method adopted here is "accumulation and normalization" to ensure that the final sum of β0, β1, β2, β3, ... βn approaches 1. Single data after normalization

[0129] Step 85. Determine the data source credibility Xicr of the atomic indicator Xi. For example, the data source is "Internet of Things Collection" with a credibility of cr = 1 (completely credible), "credible calculation data" cr = 0.8 (credible), "manual experience evaluation" cr = 0.6 (general), and "other data" (such as data with unlabeled data sources, the system fills in the reference value) cr = 0.2 (for reference only). Users can adjust the credibility cr weights of various source data in the range of 0 to 1 according to actual usage needs to meet the actual needs of the enterprise. At the same time, users can configure data of different credibility levels and color markings on the final visualization results. For example, cr between 0.8 and 1 is marked as green, and below 0.5 is marked as red, etc.

[0130] Step 86. The calculation result of the credibility of KPI indicator Y is Y cr = Xicr × βnorm

[0131] Where βnorm is the weight after βi normalization, Xicr is the credibility of Xi. The processed Y cr data range will also be between 0 and 1, that is, 0 to 100%.

[0132] The KPI indicator missing data management unit 142 is used to handle the compensation method for the sudden missing of KPI indicator data during the calculation process. According to the management method of the indicator data source management unit 123, in theory, each KPI indicator has sufficient data source support and there will be no data missing. However, in the actual operation process, due to network disconnection, API interface abnormality, untimely manual filling, insufficient granularity of source data, etc., data missing may still occur in the statistical process.

[0133] It should be noted that the system can monitor the data from each source according to the update frequency (cycle) of the data source, and give the common reasons for missing data and processing strategies. Users can enable the response strategy and configure the execution priority of the strategy according to the actual situation of the data source. The processing strategies include but are not limited to: [Ignore], [Alarm to manual processing], [System data filling: mean interpolation / median interpolation / mode interpolation / regression interpolation]. It should be noted that the structure of the processing strategy can be configured as: [Data source type] + [Degree of missing abnormality] + [System processing strategy], for example: [Manually reported data] + [≥1 cycle] + [Alarm to manual processing]; another example: [Internet of Things data collection] + [≤3 data cycles] + [System data filling: mean interpolation].

[0134] It is understandable that the data automatically filled in by the system will be marked as lower credibility by default, such as 0.2, during the data credibility assessment.

[0135] The KPI indicator conflict data management unit 143 is used to manage data conflicts that occur during the monitoring process of the platform KPI indicators: Since the KPI operation indicator system involves multiple data sources and complex data processing processes, inconsistencies will inevitably occur in different sources, at different time points or at different processing stages. The system will display the data conflict results currently detected by the system in a list format, and provide multiple strategies to help users automatically resolve current and subsequent data conflict problems.

[0136] This system checks the data conflicts of KPI indicators from four dimensions: first, the inheritance propagation method based on the hierarchical model at the same time point; second, the data relationship based on the time hierarchy model for the same business; third, the data timestamp based on the business process model; fourth, the data business form changes based on the business process model.

[0137] Taking the "inheritance and propagation method of the hierarchical model" as an example, for the output indicator, in the calculation method of the "production hierarchy", the output of the "production line" at the low node cannot be higher than the output of the "factory" at the high node; switching to the time dimension, the "hourly" output of the same node "production line 1" cannot be greater than the "daily" output; for another example, taking the "data timestamp of the business process model" as an example, the completion and warehousing time of a work order cannot be earlier than the material preparation time; switching to the data business form, the number of defective products in a work order will not be more than the amount of product data planned for the work order.

[0138] Users can configure and enable data conflict response strategies and the execution priority of response strategies according to the actual situation of data services. Response strategies include but are not limited to: [Ignore], [Transfer alarm to manual processing], [Use the result with higher data credibility].

[0139] In some possible embodiments, the KPI indicator management module 14 also includes a KPI system management unit 144, which is used to construct a KPI indicator system corresponding to the enterprise operation management needs based on the compiled KPI indicators; it is also used to mark the KPI indicators of interest selected by the user; it is also used to configure the data statistical dimensions of the KPI indicators and the indicator display results; it is also used to configure the assessment standards of the KPI indicators, and monitor the results of the corresponding KPI indicators based on the assessment standards.

[0140] Specifically, based on the KPI system management unit 144, the enterprise can build its own KPI operation management system according to the operation situation. The construction here mainly refers to extracting the required indicators and statistical dimensions from the existing KPI indicators that have been compiled, and configuring them as the responsibility KPI indicators of certain departments, positions, and personnel.

[0141] like Fig. 9 As shown, illustratively, the construction process of the KPI indicator system may include the following steps:

[0142] Step 91. The enterprise builds its own assessment system structure according to the operational management needs. For example, the enterprise can directly adopt the organizational structure as the system structure, and associate indicators between factories, departments and people at different levels in the structure;

[0143] Step 92: Select the node for which KPI indicators need to be formulated in the architecture, and add the KPI indicators of interest (one node may correspond to multiple KPI indicators);

[0144] Step 93: Configure the data statistical dimensions that the current node focuses on for each KPI indicator. If not configured, the root node will be used by default. The dimension configuration here refers to configuring the default indicator display results. Subsequent users can also manually switch between multiple levels and process structures when viewing the real-time monitoring status of the indicator.

[0145] For example, in the organizational structure, the director of Workshop 1, although also concerned about the "output" indicator of his workshop, is more concerned with the statistical results of the "output" indicator under the dimension of Workshop 1. He can also manually switch to the higher-dimensional "factory" to view the factory output, or manually drill down to each "production line" under Workshop 1 to view the production line output.

[0146] Step 94: If there is a need to assess the KPI indicator of the current node, the assessment standard of the indicator can be set according to the node. The assessment standard is the normal range of the KPI indicator. This range may be unilateral (only the upper limit or only the lower limit), with upper and lower limits, or a fixed value (such as a status value of 0 or 1). When the indicator monitoring value exceeds the standard, the system automatically issues an alarm and notifies the user immediately.

[0147] In some possible embodiments, the KPI indicator management module 14 further includes an alarm warning rule management unit 145, which is used to configure abnormality determination rules and alarm strategies for KPI indicator results.

[0148] Specifically, the alarm warning rule management unit 145 is used to define the strategy for issuing alarm warnings when the system KPI indicators are abnormal (data missing, data conflict, exceeding the monitoring value), so that the system can notify different business managers based on the abnormal source indicators, abnormality degree, and abnormality location (node).

[0149] Exemplarily, the structure of an abnormal event can be configured as: [abnormal indicator] + [abnormal triggering rule] + [abnormal degree] + [abnormal occurrence location (node)] + [handling strategy].

[0150] Among them, [abnormal indicator] is the name of the indicator of the monitored abnormality; [abnormal trigger rule] can be single trigger, continuous trigger (3 consecutive cycles), majority trigger (at least 2 abnormalities in 3 cycles), average trigger, maximum / minimum trigger, etc.; [abnormal degree] refers to the abnormal degree that exceeds the monitoring standard. For example, when it exceeds the standard by 10%, the processing strategy is to ignore; when it exceeds 30%, the processing strategy is to notify relevant personnel; [processing strategy] can include ignoring, monitoring interface pop-up window, (triggering the device through the system's IoT module) buzzer alarm, (notifying corresponding personnel through system messages, emails, text messages, smart phones, etc.), etc.

[0151] In some possible embodiments, the KPI indicator management module 14 also includes an exception handling knowledge base unit 146, which is used to collect abnormal information on KPI indicator results that have occurred in the system and corresponding exception handling records, and extract keywords based on these abnormal information and exception handling records to mark them and form abnormal knowledge and store them in the abnormal knowledge base; it is also used to perform knowledge matching on real-time KPI indicator abnormal events based on the abnormal knowledge base and push corresponding exception handling strategies.

[0152] Specifically, in the exception handling knowledge base unit 146, the system's historical indicator abnormality data, root cause analysis and exception handling records can be collected. The system will use "abnormal indicator + abnormal occurrence model-node" as the granularity, perform keyword extraction on the exception handling records, refine / manually label the high-frequency keywords, and the next time the same abnormal situation occurs, query the knowledge base and push the matching reference processing strategy as soon as possible.

[0153] Exemplarily, the processing information of an indicator abnormality may include the following contents: abnormality occurrence time, abnormality indicator, abnormal operation location, root cause model node, root cause indicator, root cause data source, manual root cause analysis confirmation, abnormality processing record, and abnormality resolution time.

[0154] Among them, the operation location where the abnormality occurs refers to the node associated with the abnormal indicator alarm in the KPI system management unit 144, and is also the node for configuring the monitoring standard for the indicator; for example, if the qualification rate of a certain type of product at the factory level is abnormal, then the abnormal indicator is the "product qualification rate", and the operation location where the abnormality occurs is the "factory level" node.

[0155] The root cause model node refers to the node with real data source and abnormal data in the hierarchical model / business process model associated with the abnormal KPI indicator. When multiple business operation models are associated or superimposed, their abnormal nodes need to be pointed out separately. In the above example, the qualified rate of a certain model of product at the factory level is abnormal. The production hierarchy of "product qualified rate occurs" is most likely not at the factory level, but at a certain workshop under the factory level (model data source) and a certain model under the "product hierarchy" (model data source). Therefore, the abnormal indicator model node should be "a certain workshop" and "a certain product model".

[0156] The root cause indicator refers to the atomic indicator associated with the abnormal KPI indicator after being expanded layer by layer according to the calculation logic, that is, the atomic indicator that produces abnormal data; still in the above example, the qualification rate of a certain model of products at the factory level is abnormal, and the abnormal KPI indicator is "product qualification rate", product qualification rate = (output - number of defective products) / output × 100%, and the root cause atomic indicators are "number of defective products" and "output".

[0157] like Fig.10 As shown, manual root cause data confirmation means that after the alarm information occurs, the system pushes multiple tags such as the operation location where the abnormality occurred, the root cause model node, the root cause indicator, and the alarm information to the front desk, and then manually confirms the root cause factor + abnormal classification (man / machine / material / method / environment). Still in the above example, the qualified rate of a certain model of product at the factory level is abnormal, and the system pushes multiple tags such as "factory", "certain workshop", "certain product model", "number of defective products", and "output" (all effective by default). After manual confirmation, it is found that the product is defective due to the abnormality of a certain equipment in the workshop, so the "factory" and "product model" tags are manually cancelled, and the abnormality type is marked as "machine".

[0158] The root cause label + exception classification (also used as a label) will be used as the classification standard for the system exception handling information. The exception handling information with the same label will be classified into one category, and the processing strategy analysis of the exception handling records will be performed. When the same exception alarm occurs in the future, the reference processing strategy will be recommended to the user.

[0159] In addition to confirming the root cause, the user will describe the exception handling process and measures in the [Exception Handling Record]. Since this part is a subjective description text of the user, the system can use the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm to automatically extract several "keywords" in the text, and display the previous processing records in reverse order according to the degree of match between each processing record and the "keyword". Users can also manually mark the keywords of the exception handling record in the knowledge base.

[0160] The TF-IDF algorithm is a commonly used weighted technology for common information retrieval and text mining. It includes two parts: TF (Term Frequency) and IDF (Inverse Document Frequency). TF(t,d) = (number of times t appears in document d) / (total number of terms in document d), which indicates the frequency of occurrence of term (keyword) t in a document; IDF(t,D) = log e(total number of documents / number of documents containing term t), which indicates the general importance of a term.

[0161] At the same time, for exception handling information with the same label, the average handling time (exception resolution time - exception occurrence time) will be calculated as subsequent reference information.

[0162] In some possible embodiments, the KPI indicator monitoring module 15 includes a KPI indicator real-time monitoring and prediction unit 151 and a KPI abnormality alarm and processing unit 152;

[0163] The KPI indicator real-time monitoring and prediction unit 151 is used to monitor the calculation results of the KPI indicator based on the preset monitoring standard; it is also used to display the credibility of the KPI indicator results; it is also used to expand the detailed data of each node of the KPI indicator and the credibility of each data;

[0164] The KPI abnormal alarm and processing unit 152 is used to monitor the abnormal situation of KPI indicator calculation and display abnormal alarm information; it is also used to analyze abnormal information based on the monitored abnormal situation, and match corresponding abnormal processing strategies based on the abnormal information obtained by analysis.

[0165] Specifically, the KPI indicator monitoring module 15 is used to monitor and view the operation KPI indicators at the user end, and to handle the abnormal alarm as soon as possible. It is the front-end presentation layer and operation layer of the entire system.

[0166] Among them, when the user enters the "KPI indicator real-time monitoring and prediction unit 151" for the first time, he can select the node he is concerned about in the manufacturing operation KPI system defined by the enterprise. The system will display the corresponding operation KPI indicator according to the indicator associated with the node in the KPI system management unit 144, and calculate the indicator calculation result according to the model dimension defined by the indicator; in addition, the calculation result of the indicator is monitored according to the monitoring standard set in the system to show whether it is within the normal range; in addition, according to the result of the data credibility rule configuration unit 141, the credibility of the current indicator is displayed; in addition, the system will display the operation structure model entrance associated with the current operation KPI indicator. After the user clicks on the corresponding structure, the current indicator can be expanded to display the detailed data and credibility of each node in the structure.

[0167] It should be noted that when an abnormal event is detected, the system will pop up an alarm on the interface, or the user will manually enter the interface corresponding to the KPI abnormal alarm and processing unit 152, and the system will display the unprocessed abnormal alarm information to the user. This information may include: abnormal occurrence time, abnormal indicators, abnormal operation location, root cause model node, root cause indicator, root cause data source, at this time, the user can manually confirm the root cause analysis and abnormal processing records, and the system will automatically record the abnormality resolution time when submitting.

[0168] Among them, when the user views the alarm information, the system will retrieve the most similar historical alarm information in the exception handling knowledge base unit 146 according to the initial default "root cause label", give the keywords of the previous processing strategy, display the most similar processing strategy in reverse order (the most similar is at the front), and give the possible processing reference time. After that, the user can manually adjust and confirm the "root cause label" and "abnormal classification", and the system will give the processing strategy and reference processing time again according to the latest root cause label result.

[0169] Compared with the existing performance indicator management solution, the embodiment of the present application has the following beneficial effects:

[0170] 1. By constructing two operational structure models (hierarchical model and business process model) and configuring various operational resources of the enterprise, a business production and operation model corresponding to the manufacturing industry is built, and the complex logical relationship between the operational data of the manufacturing industry is straightened out. Based on this, the originally loose and scattered KPI indicator system is linked through business logic to form an indicator system that fits the enterprise. After that, it only needs to complete the data collection of the atomic indicators in the system to realize the data calculation of the entire indicator system efficiently and accurately.

[0171] 2. By combining the two logics of KPI indicator lineage and operation structure model, it helps enterprises quickly sort out the data sources of the indicator system and visualizes which underlying original data are missing in the calculation process, greatly improving the collection efficiency and utilization rate of enterprise data resources.

[0172] 3. The KPI indicator system formed by strong logical relationships can not only complete effective data supplement and filling when some data is missing, but also discover the missing source of indicator data through topology, and detect data anomalies and conflicts when there are multiple data sources.

[0173] 4. By integrating multiple data source management methods such as real-time collection of the Internet of Things (Internet of Things communication management), API interface synchronization (business data access), manual filling (manual entry), etc., the timeliness, reliability and comprehensiveness of data collection are ensured. While KPI indicators are monitored in real time, they can also actively warn when data is not updated in time (default) to remind users to complete data updates;

[0174] 5. By conducting data credibility assessment on each KPI indicator, users can not only see the statistical results of the KPI indicators, but also evaluate the authenticity of the results, thus avoiding misjudgments based on experience. At the same time, it can also indirectly encourage users to further improve data quality and credibility.

[0175] 6. By providing a knowledge base construction method based on the root cause analysis and processing strategy after the abnormality of the operation KPI indicator system, the alarm information can be effectively classified by root cause. At the same time, the subjective information expression interference caused by personal language and description habits is reduced through the TF-IDF algorithm. It can save a large amount of previous user work exception handling experience and help the current user determine the possible exception handling strategy and the approximate duration required as soon as the exception occurs.

[0176] Some embodiments of the present application further provide a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the system functions described in any one of the embodiments are implemented.

[0177] Some embodiments of the present application further provide a computer program product, which, when executed on a computer, enables the computer to implement the system functions described in any one of the embodiments.

[0178] In several embodiments provided by the present application, it should be understood that the disclosed system and method can also be implemented in other ways. The system embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0179] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0180] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0181] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0182] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0183] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A manufacturing industry operation performance indicator management system, characterized in that: include: The data source management module is used to mark the source information of the data calculated by the indicator; It is also used to integrate the collected data with the corresponding KPI indicators through the preset business operation model; The KPI indicator definition management module is used to manage the addition, deletion, modification and query of KPI indicators; it is also used to configure the attribute information of KPI indicators; it is also used to logically associate KPI indicators with preset business operation models; A manufacturing operation model management module, used to build a business operation model based on the information of the manufacturing enterprise; and also used to associate the operation resource information of the manufacturing enterprise with each business operation model; The KPI indicator management module is used to configure and calculate the credibility rules of the data involved in calculating the KPI indicators; it is also used to monitor and handle data missing in the KPI indicator calculation process; it is also used to monitor and handle data conflicts in the KPI indicator calculation process.

2. The manufacturing operation performance index management system according to claim 1 is characterized in that: It also includes a KPI indicator monitoring module, which is used to monitor the calculation results of KPI indicators based on preset monitoring standards; it is also used to display detailed information of KPI indicators; it is also used to monitor abnormal situations in KPI indicator calculations and display abnormal alarm information.

3. The manufacturing operation performance index management system according to claim 1, characterized in that: The data source management module includes an IoT communication management unit, a business data access unit and a manual input unit; The IoT communication management unit is used to collect the equipment and sensor data on the production line in real time through IoT technology, and mark the source of the data as IoT collection; The business data access unit is used to connect to and collect data from the business system, and mark the source of the data as IoT collection, vouchered computing data, or manual experience evaluation data according to the type of the data; The manual input unit is used to provide a data input interface to collect data input by the user based on the data input interface, and mark the source of the data as vouchered calculation data or manual experience evaluation data.

4. The manufacturing operation performance index management system according to claim 1, characterized in that: The KPI indicator definition management module includes a KPI indicator definition unit, an indicator operation model dimension marking unit and an indicator data source management unit; The KPI indicator definition unit is used to provide the functions of adding, deleting, modifying and checking KPI indicators, and configure the attribute information of each KPI indicator; wherein the attribute information includes at least one of an indicator ID, an indicator name, a business description, an indicator type, a data source and a calculation method; The indicator operation model dimension marking unit is used to logically associate the KPI indicator with the preset business operation model; The indicator data source management unit is used to manage the atomic indicators corresponding to each KPI indicator and its data source based on the calculation relationship between the KPI indicators and the corresponding associated operation structure model.

5. The manufacturing operation performance index management system according to claim 1, characterized in that: The manufacturing operation model management module includes a hierarchical model unit, a business process model unit and an operation resource list unit; The hierarchical structure model unit is used to construct multiple hierarchical structure models based on the structural characteristics of the enterprise; The business process model unit is used to construct multiple process structure models based on the process characteristics of the enterprise; The operation resource list unit is used to maintain the operation resources of the enterprise during the operation process; It is also used to associate the maintenance-based operation resource information with each business operation model.

6. The manufacturing operation performance index management system according to claim 1, characterized in that: The KPI indicator management module includes a data trust rule configuration unit, a KPI indicator missing data management unit and a KPI indicator conflict data management unit; The data trust rule configuration unit is used to configure the trust rules of the data involved in calculating the KPI indicator; It is also used to calculate the credibility of KPI indicator results; The KPI indicator missing data management unit is used to monitor the data missing situation in the KPI indicator calculation process and process it based on the missing data processing strategy that matches the current data missing situation; wherein the information items of the data missing situation include the data source type and the degree of missing abnormality; the missing processing strategy includes ignoring, alarming and transferring to manual processing and automatic filling of system data; The KPI indicator conflict data management unit is used to monitor the data conflict situation in the KPI indicator calculation process and handle it based on the conflict handling strategy that matches the current data conflict situation; wherein the conflict handling strategy includes ignoring, transferring alarm to manual processing and adopting the result with higher data credibility.

7. The manufacturing operation performance index management system according to claim 6, characterized in that: The KPI indicator management module also includes a KPI system management unit, which is used to build a KPI indicator system corresponding to the enterprise's operational management needs based on the compiled KPI indicators; it is also used to mark the KPI indicators of interest selected by the user; it is also used to configure the data statistical dimensions of the KPI indicators and the indicator display results; it is also used to configure the assessment standards of the KPI indicators, and monitor the results of the corresponding KPI indicators based on the assessment standards.

8. The manufacturing operation performance index management system according to claim 6, characterized in that: The KPI indicator management module also includes an alarm warning rule management unit, which is used to configure abnormality determination rules and alarm strategies for KPI indicator results.

9. The manufacturing operation performance index management system according to claim 6, characterized in that: The KPI indicator management module also includes an exception handling knowledge base unit, which is used to collect abnormal information on KPI indicator results that have occurred in the system and the corresponding exception handling records, and extract keywords based on these abnormal information and exception handling records to mark them and form abnormal knowledge and store them in the abnormal knowledge base; it is also used to perform knowledge matching on real-time KPI indicator abnormal events based on the abnormal knowledge base and push corresponding exception handling strategies.

10. The manufacturing operation performance index management system according to claim 2, characterized in that: The KPI indicator monitoring module includes a KPI indicator real-time monitoring and prediction unit and a KPI abnormal alarm and processing unit; The KPI indicator real-time monitoring and prediction unit is used to monitor the calculation results of the KPI indicator based on the preset monitoring standard; and is also used to display the credibility of the KPI indicator results; It is also used to expand the detailed data of each node of the KPI indicator and the credibility of each data; The KPI abnormal alarm and processing unit is used to monitor the abnormal situation of KPI indicator calculation and display abnormal alarm information; it is also used to analyze abnormal information based on the monitored abnormal situation, and match corresponding abnormal processing strategies based on the abnormal information obtained by analysis.

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