CN112330156B

CN112330156B - KPI management method, apparatus, device and storage medium

Info

Publication number: CN112330156B
Application number: CN202011229841.1A
Authority: CN
Inventors: 杨磊; 樊伟; 张蕴青; 陈欣; 张奇彪
Original assignee: China Unicom Zhejiang Industrial Internet Co Ltd
Current assignee: China Unicom Zhejiang Industrial Internet Co Ltd
Priority date: 2020-11-06
Filing date: 2020-11-06
Publication date: 2024-04-09
Anticipated expiration: 2040-11-06
Also published as: CN112330156A

Abstract

The application provides a KPI management method, a KPI management device, KPI management equipment and a storage medium. The method comprises the following steps: the server acquires first data at a first moment, wherein the first data comprises KPI indexes of all layers at the first moment. When the first index exists in the first data and the value of the first index is not in the preset index range of the first index, the server determines that the first index is abnormal. And the server determines a target optimization scheme and preset completion time according to the index name and the key word of the first index. And the server determines the influence of the target optimization scheme on the upper-layer index and the top-layer index and/or the loss caused by deferred completion according to the actual completion time, the preset completion time and the index related data of the first index of the target optimization scheme. The method provides a practical and effective reference for optimization, improves the intelligent management level of the KPI, and provides a powerful management and tracking tool for continuous improvement of the KPI of enterprises.

Description

KPI management method, apparatus, device and storage medium

Technical Field

The present disclosure relates to the field of KPI management, and in particular, to a KPI management method, apparatus, device, and storage medium.

Background

The key performance indicators (Key Performance Indicator, KPIs) are a target type of quantitative management indicator for measuring process performance.

Currently, in KPI management, KPI indicators are typically used to evaluate the work performance of staff at a workstation. Alternatively, in KPI management, the KPI indicators are also used for overall performance assessment of production lines, workshops, factories.

However, in this KPI management, there is a lack of macroscopic control of KPI indicators by which a practically efficient reference for the optimization of the production line cannot be provided.

Disclosure of Invention

The application provides a KPI management method, a KPI management device, KPI management equipment and a storage medium, which are used for solving the problem that the KPI management lacks macroscopic control of KPI indexes and cannot provide practical and effective references for the optimization of a production line through the KPI indexes.

In a first aspect, the present application provides a KPI management method, including:

acquiring first data of a first moment, wherein the first data comprises KPI indexes of all layers at the first moment;

when a first index exists in the first data and the value of the first index is not in the preset index range of the first index, recommending an optimization database of the first index according to the index name and the keywords of the first index, wherein the optimization database comprises expert cases and historical cases corresponding to the first index;

Recommending a target optimization scheme, preset completion time of the target optimization scheme and a responsible person according to the optimization database and the index related data of the first index;

and determining the influence of the target optimization scheme on the upper-layer indexes and/or the loss caused by deferred completion according to the actual completion time of the target optimization scheme, the preset completion time and a KPI tree structure, wherein the KPI tree structure comprises KPI indexes of each layer, and the correlation and the weight among the KPI indexes.

Optionally, before recommending the target optimization scheme, the preset completion time of the target optimization scheme and the responsible person according to the optimization database and the index related data of the first index, the method further includes:

determining an influence factor set causing index abnormality according to the first index and a preset recommendation algorithm, wherein the influence factor set comprises at least one influence factor;

and determining a significant influence factor of the first index according to an influence factor selection instruction, wherein the influence factor selection instruction is used for indicating the significant influence factor on the first index.

Optionally, the recommending the target optimization scheme, the preset completion time of the target optimization scheme and the responsible person according to the optimization database and the index related data of the first index includes:

Matching similar cases in the optimization database according to the index related data of the first index and the significant influence factors, and generating a similar case set, wherein the similar case set comprises at least one similar case;

determining a target similar case according to the optimization effect of each similar case in the similar case set and/or a case selection instruction, wherein the case selection instruction is used for indicating and selecting one similar case in the similar case set as the target similar case;

and determining a target optimization scheme and preset completion time according to the target similar case.

Optionally, the acquiring the first data at the first moment includes:

acquiring a KPI tree structure, wherein the KPI tree structure comprises KPI indexes of each layer, and correlation and weight among the KPI indexes;

and determining first data according to the KPI tree structure and the original data, wherein the first data is KPI indexes of each layer at a first moment, and the KPI indexes comprise KPI indexes obtained through direct acquisition and KPI indexes obtained through calculation.

Optionally, when acquiring the first moment, KPI indexes of each layer include:

obtaining original data, wherein the original data are parameters required for calculating KPI indexes of each layer;

Preprocessing the original data according to a first preset rule to obtain processed data;

and determining KPI indexes of all layers according to the processed data and a preset evaluation algorithm.

Optionally, the preprocessing the raw data according to a first preset rule to obtain processed data includes:

judging whether abnormal data exists in the original data according to the first preset rule, wherein the abnormal data is abnormal data, and the abnormal data comprises large data abnormality, small data abnormality or data missing;

when the abnormal data does not exist in the original data, carrying out normalization processing on the original data;

when the abnormal data exists in the original data, the original data is processed according to the attribute of the original data.

Optionally, when the abnormal data exists in the original data, processing the original data according to the attribute of the original data includes:

acquiring the attribute of the original data;

when the attribute of the original data is available in calculation, supplementing abnormal data in the original data according to a preset algorithm of the original data and related data of the original data;

Otherwise, the original data or the alarm is abandoned and re-acquired.

Optionally, when the target optimization scheme is completed on time, determining the influence of the target optimization scheme on the upper-layer index and/or the loss caused by deferred completion according to the actual completion time of the target optimization scheme, the preset completion time and the KPI tree structure includes:

determining the advanced completion time of the target optimization scheme according to the actual completion time of the target optimization scheme and the preset completion time;

and determining the influence of the target optimization scheme on the upper-layer index according to the advanced completion time length and the KPI tree structure.

Optionally, when the target optimization scheme is completed in a time-out manner, determining the influence of the target optimization scheme on the upper-layer index and/or the loss caused by deferred completion according to the actual completion time of the target optimization scheme, the preset completion time and the KPI tree structure includes:

determining the overtime completion time of the target optimization scheme according to the actual completion time of the target optimization scheme and the preset completion time;

and determining the loss of the target optimization caused by deferred completion according to the timeout completion duration and the index related data of the first index.

Optionally, the method further comprises:

archiving the target optimization scheme, wherein the obtained archiving data of the target optimization scheme comprises the first data, the first index, the target optimization scheme and the optimization effect or loss of the target optimization scheme;

and storing the archived target optimization scheme into a historical case.

Optionally, when the first index deviation in the first data exceeds a preset index range, the method further includes:

and sending first abnormal information to target equipment, wherein the first abnormal information comprises at least one of the first index, the index name, a preset index range of the first index, a keyword and a time interval, and the target equipment is equipment used by a responsible person of the first index.

Optionally, when the target optimization scheme is completed after timeout, the method further comprises:

and sending second abnormal information to target equipment, wherein the second abnormal information comprises the preset completion time and the target optimization scheme, and the target equipment is equipment used by a responsible person of the first index.

Optionally, the first data includes at least one of KPI indicators including a producer KPI, a production equipment KPI, a production quality KPI, a production material KPI, a production stream KPI, a production cost KPI, a production safety KPI, etc.

Optionally, the KPI indicators of each layer include a station layer KPI indicator, a line layer KPI indicator, a workshop layer KPI indicator, and a factory layer KPI indicator.

In a second aspect, the present application provides a KPI management apparatus, comprising:

the first acquisition module is used for acquiring first data at a first moment, wherein the first data comprises KPI indexes of all layers at the first moment;

the first determining module is used for recommending an optimized database of the first index according to the index name and the keyword of the first index when the first index exists in the first data and the value of the first index is not in the preset index range of the first index, wherein the optimized database comprises expert cases and historical cases corresponding to the first index;

the second determining module is used for recommending a target optimization scheme, preset completion time of the target optimization scheme and a responsible person according to the optimization database and the index related data of the first index;

and the third determining module is used for determining the influence of the target optimization scheme on the upper-layer indexes and/or the loss caused by deferred completion according to the actual completion time of the target optimization scheme, the preset completion time and the KPI tree structure, wherein the KPI tree structure comprises KPI indexes of each layer, and the correlation and the weight among the KPI indexes.

Optionally, before the second determining module, the apparatus further includes:

a fourth determining module, configured to determine, according to the first index and a preset recommendation algorithm, a set of influencing factors that cause an abnormality in the index, where the set of influencing factors includes at least one influencing factor;

and a fifth determining module, configured to determine a significant influence factor of the first indicator according to an influence factor selection instruction, where the influence factor selection instruction is used to indicate a significant influence factor on the first indicator.

Optionally, the second determining module includes:

the generation sub-module is used for matching similar cases in the optimization database according to the index related data of the first index and the significant influence factors, and generating a similar case set, wherein the similar case set comprises at least one similar case;

the first determining submodule is used for determining a target similar case according to the optimizing effect of each similar case in the similar case set and/or a case selection instruction, and the case selection instruction is used for indicating to select one similar case in the similar case set as the target similar case;

and the second determining submodule is used for determining a target optimization scheme and preset completion time according to the target similar case.

Optionally, the first acquisition module includes:

the second acquisition sub-module is used for acquiring a KPI tree structure, wherein the KPI tree structure comprises KPI indexes of each layer, and correlation and weight among the KPI indexes;

and the third acquisition sub-module is used for determining first data according to the KPI tree structure and the original data, wherein the first data is KPI indexes of all layers at a first moment, and the KPI indexes comprise KPI indexes obtained through direct acquisition and KPI indexes obtained through calculation.

Optionally, the third obtaining sub-module is specifically configured to obtain raw data, where the raw data is a parameter required for calculating KPI indexes of each layer; preprocessing the original data according to a first preset rule to obtain processed data; and determining KPI indexes of all layers according to the processed data and a preset evaluation algorithm.

Optionally, preprocessing the original data according to a first preset rule, which specifically includes judging whether abnormal data exists in the original data according to the first preset rule, wherein the abnormal data is abnormal data, and the abnormal data includes data with big abnormality, data with small abnormality or data with missing; when the abnormal data does not exist in the original data, carrying out normalization processing on the original data; when the abnormal data exists in the original data, the original data is processed according to the attribute of the original data.

Optionally, when the abnormal data exists in the original data, the evaluation submodule is specifically configured to obtain an attribute of the original data; when the attribute of the original data is available in calculation, supplementing abnormal data in the original data according to a preset algorithm of the original data and related data of the original data; otherwise, the original data or the alarm is abandoned and re-acquired.

Optionally, when the target optimization scheme is completed on time, the third determining module includes:

the optimization effect calculation sub-module is used for determining the advanced completion time of the target optimization scheme according to the actual completion time of the target optimization scheme and the preset completion time; and determining the influence of the target optimization scheme on the upper-layer index according to the advanced completion time length and the KPI tree structure.

Optionally, when the target optimization scheme is completed in a timeout, the third determining module includes:

the loss calculation sub-module is used for determining the overtime completion time of the target optimization scheme according to the actual completion time of the target optimization scheme and the preset completion time; and determining the loss of the target optimization caused by deferred completion according to the timeout completion duration and the index related data of the first index.

Optionally, the apparatus further comprises:

the archiving module is used for archiving the target optimization scheme, and the obtained archiving data of the target optimization scheme comprise the first data, the first index, the target optimization scheme and the optimization effect or loss of the target optimization scheme; and storing the archived target optimization scheme into a historical case.

Optionally, when the first index deviation in the first data exceeds a preset index range, the device further includes:

the sending module is used for sending first abnormal information to target equipment, wherein the first abnormal information comprises at least one of the first index, the index name, a preset index range of the first index, a keyword and a time interval, and the target equipment is equipment used by a responsible person of the first index.

Optionally, when the target optimization scheme times out to completion,

the sending module is further configured to send second anomaly information to a target device, where the second anomaly information includes the preset completion time and the target optimization scheme, and the target device is a device used by a responsible person of the first index.

In a third aspect, the present application provides a server comprising: a memory and a processor;

the memory is used for storing program instructions;

the processor is configured to invoke program instructions in the memory to perform the KPI management method of the first aspect and any of the possible designs of the first aspect.

In a fourth aspect, the present application provides a readable storage medium, in which executable instructions are stored, when executed by at least one processor of a server, the server performs the KPI management method according to the first aspect and any one of the possible designs of the first aspect.

The KPI management method, device, equipment and storage medium acquire original data from edge equipment according to a preset time interval; according to the performance indexes of all KPIs and the evaluation algorithm thereof, calculating all KPIs by using the original data; when a first index exists in the first data and the value of the first index is not in the preset index range of the first index, determining that the KPI index is abnormal; according to the index name of the first index, searching related cases from an expert database and a historical database, and recommending the related cases to an optimization database; according to the index related data of the first index, further screening similar schemes from the optimization database, and determining a target optimization scheme; according to the target optimization scheme, determining the time required by the target optimization scheme to complete optimization; determining preset completion time and responsible person according to the time required for completing optimization; acquiring the actual completion time of the target optimization scheme; according to the actual completion time, the preset completion time and the KPI tree structure of the target optimization scheme, the optimization effect of the target optimization scheme or the loss caused by deferred completion is determined, the KPI is analyzed and processed, the effect of practically and effectively referencing is provided for the optimization of the production line through the KPI, the intelligent management level of the KPI is improved, and meanwhile, a powerful management and tracking tool is provided for the continuous improvement of the KPI of enterprises.

Drawings

For a clearer description of the technical solutions of the present application or of the prior art, the drawings that are used in the description of the embodiments or of the prior art will be briefly described, it being obvious that the drawings in the description below are some embodiments of the present application, and that other drawings can be obtained from these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic diagram of a KPI management scenario according to an embodiment of the present application;

FIG. 2 is a flowchart of a KPI management method according to an embodiment of the present application;

fig. 3 is a schematic diagram of a KPI tree structure according to an embodiment of the present application;

FIG. 4 is a flowchart of another KPI management method according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of a KPI management device according to an embodiment of the present application;

FIG. 6 is a schematic structural diagram of another KPI management apparatus according to an embodiment of the present application;

FIG. 7 is a schematic structural diagram of a KPI management apparatus according to an embodiment of the present application;

fig. 8 is a schematic hardware structure of a KPI management device according to an embodiment of the present application.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the present application will be clearly and completely described below with reference to the drawings in the present application, and it is apparent that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.

The key performance indicators (Key Performance Indicator, KPIs) are a target type of quantitative management indicator for measuring process performance. Currently, in KPI management, KPI indicators are typically used to evaluate the work performance of staff at a workstation. Alternatively, in KPI management, the KPI indicators are also used for overall performance assessment of production lines, workshops, factories. Currently, in enterprises, key KPI indexes of production lines, workshops and factories are tracked and managed by using an MES system or an ERP system.

In practical use, KPI indicators of a production line, plant, or factory are often affected when problems occur in the production line, plant, or factory. Or, when the manager optimizes the production line, workshop and factory according to the problems, the KPI of the production line, workshop and factory can obviously show the optimizing effect.

However, in the prior art, there are mainly the following problems:

1. in the existing KPI management system, the correlation among KPI indexes of each layer is not clear, and the problems of fuzzy hierarchical relation, unclear influence of lower KPI indexes on upper KPI indexes and the like exist.

2. In the existing KPI management, the on-site problem feedback mechanism and the problem solving mechanism have problems, an optimization scheme cannot be provided for feedback problems in time, the realization effect of the optimization scheme cannot be tracked in time, and problem solving exceeding is easily caused, so that resource waste is caused.

3. In the existing KPI management, a systematic tool is lacking to correlate KPI indexes with an optimization scheme, and intelligent scheme recommendation according to the KPI indexes cannot be realized.

4. In the existing KPI management, systematic tools are lacking to carry out association and correlation analysis on multi-level KPI indexes of enterprises, so that global analysis and improvement on the whole KPI system cannot be realized.

5. In the existing KPI management, the history optimization schemes and improvement experiences of the same production line, different production lines and different workshops are difficult to accumulate, and the correlation with KPI indexes is lacking.

In order to overcome the defects, the application provides a KPI management method, a KPI management device, KPI management equipment and a KPI storage medium. Firstly, the system has the conventional KPI tracking and managing function, and the KPI management device can establish a four-level KPI system comprising stations, production lines, workshops and factories according to the business process and the application scene of enterprises. And the server running the KPI management method monitors the KPI index change condition of each layer in a set time interval. When the KPI index has a first index which is not in the preset index range, the server can determine an optimized database of the first index according to the index name of the first index. The optimization database comprises expert cases and historical cases corresponding to the first index. The server can determine a target optimization scheme and preset completion time according to the optimization database, the index name and the index related data of the first index through an artificial intelligence algorithm. The target optimization scheme can provide a possible optimization scheme reference for an administrator aiming at the index anomaly. The preset completion time can be aimed at the optimization scheme, so that the implementation effect of the optimization scheme can be better monitored, and the optimization effect of the optimization scheme and/or the loss caused by the completion of the delay can be determined.

The technical scheme of the present application is described in detail below with specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.

Fig. 1 shows a schematic view of a KPI management scenario according to an embodiment of the application. As shown in fig. 1, the KPI management system of the present application may include a data processing data management subsystem, a hierarchical intelligent KPI performance assessment subsystem, and a hierarchical KPI visualization, i.e., optimization support subsystem.

Wherein the data processing data management subsystem is used for acquiring data from a data source and preprocessing the data. In particular, the data may include producer data, production equipment data, production process data, production quality data, and production material data.

The hierarchical intelligent KPI performance evaluation subsystem processes the data acquired in the data processing data management subsystem, and calculates to obtain KPI indexes. Specifically, the process includes: calculating the producer data according to the producer KPI performance index and the evaluation algorithm, and determining the producer KPI; calculating production equipment data according to equipment KPI performance indexes and an evaluation algorithm, and determining production equipment KPIs; calculating production process data according to the process KPI performance index and an evaluation algorithm, and determining a production process KPI; calculating production quality data according to the quality KPI performance index and an evaluation algorithm, and determining a product quality KPI; and calculating production material data according to the logistics KPI performance index and the evaluation algorithm, and determining the production material KPI. Furthermore, according to the KPI indexes, the hierarchical intelligent KPI performance evaluation subsystem can realize index anomaly analysis and target optimization scheme analysis.

The hierarchical KPI visualization, namely optimization support subsystem is used for displaying information calculated in the hierarchical intelligent KPI performance evaluation subsystem. According to the displayed information, the hierarchical KPI visualization, namely, optimization support subsystem can be divided into three modules, namely, a KPI index display module, an index analysis module and a target optimization scheme analysis module. The KPI display module is used for displaying KPI indexes of each layer. The index analysis module is used for displaying an analysis result of each abnormal index, wherein an analysis tool used when displaying the analysis result can comprise PDCA, A3 report, fishbone graph, 5W2H and the like. The target optimization scheme analysis module is used for displaying the analysis process and the result of the target optimization scheme, and the analysis method used in the analysis process can comprise a historical index comparison method, a keyword automatic recommendation matching method based on a historical record, an intelligent algorithm and the like.

In the application, the server is taken as an execution main body, and the KPI management method of the following embodiment is executed. In particular, the execution body may be a hardware device of a server, or a software application implementing the embodiments described below in the server, or a computer-readable storage medium on which a software application implementing the embodiments described below is installed.

Fig. 2 shows a flowchart of a KPI management method according to an embodiment of the application. On the basis of the embodiment shown in fig. 1, as shown in fig. 2, with the server as the execution body, the method of this embodiment may include the following steps:

s101, acquiring first data of a first moment, wherein the first data comprises KPI indexes of all layers at the first moment.

In this embodiment, the first moment may be the moment when the administrator starts to view the KPI indicator by starting the KPI management system. Alternatively, the first time may also calculate the end time of the period for each KPI indicator.

The server acquires the KPI of each layer at the first moment, and determines the KPI of each layer as a first index. The KPI indexes of each layer are obtained by the server through calculation according to a preset evaluation algorithm after acquiring original data from edge equipment.

Raw data used for calculating the KPI indexes can be divided into a plurality of categories such as production personnel data, production equipment data, production process data, production quality data, production substance data and the like. The producer data may include, among other things, completion rate, working time, etc. The production quality data may be a completion rate, a qualification rate, a superior rate, etc. The production process data may include, among other things, throughput, overall efficiency of the plant, etc. The production material data may include logistic time, material quality, etc.

The server calculates KPI indexes according to the acquired original data until the first moment. The first data includes at least one KPI indicator.

In one example, the first data may further include at least one of a producer KPI, a production facility KPI, a production process KPI, a production quality KPI, a production material KPI, a production stream KPI, a production cost KPI, a production safety KPI, or the like.

In one example, the individual layer KPI indicators may include a workstation layer KPI indicator, a line layer KPI indicator, a plant layer KPI indicator.

S102, recommending an optimization database of the first index according to the index name and the keyword of the first index when the first index exists in the first data and the value of the first index is not in the preset index range of the first index, wherein the optimization database comprises expert cases and historical cases corresponding to the first index.

In this embodiment, after the server obtains the first data according to S101, the server compares each KPI indicator in the first data with a preset indicator range. Wherein, each KPI index corresponds to a preset index range.

When the value of the first index is not in the preset range of the first index, the server determines that the KPI is abnormal. The server determines the KPI index with the abnormality as a first index. When the number of the abnormal KPI indexes is larger than 1, the server processes the abnormal KPI indexes one by one, and determines the KPI indexes in the process as first indexes.

The server determines an index name of the first index. The index name is used to uniquely identify a KPI index.

The server retrieves the relevant cases from the expert database and the history database according to the index names and keywords, and recommends the relevant cases to the optimization database. The expert database and the history database may be databases storing expert cases and history cases of the production line, the workshop and the factory. The keywords may include line name, material name, batch, fault name occurring in a time period, and the like.

It should be noted that in different KPI management systems, the same KPI indicator may have different names. Since the KPI management system used in the present application requires the use of expert cases and historical cases, specifications need to be made for different names of the same KPI index.

The patent cases and the history cases may exist because of different archiving time and archiving place, and the same KPI index names are different in different cases.

In one implementation, the standard mode may be that when the server changes a new system or imports a new case, the server replaces the index names in the case of the old system or the newly imported case in batches according to the index name mapping relation of the KPI index.

In another implementation manner, the specification manner may be that the server adds all names corresponding to the index names into the search range according to the index name mapping relationship of the KPI index, and further searches the optimization database of the first index according to the expanded search range.

In one example, the server may send the first anomaly information to the target device when there is a first indicator deviation in the first data that exceeds a preset indicator range.

The first abnormal information is used for reminding an administrator that the index in the first data is abnormal. The first abnormal information comprises at least one of a first index, an index name, a preset index range of the first index, a keyword and a time interval.

The target device may be a terminal used by the first index responsible person, for example, a working terminal, a terminal specified by an administrator, or an account number used by the first index responsible person, for example, a mailbox, a mobile phone number, a working number, or the like.

S103, recommending a target optimization scheme, preset completion time of the target optimization scheme and a responsible person according to the optimization database and index related data of the first index.

In this embodiment, after the server retrieves the optimization database according to S102, the server further needs to screen similar schemes from the optimization database according to the index related data of the first index, and determine a target optimization scheme.

The index related data may be production personnel data, production equipment data, production process data, production quality data, production material data, etc.

The server can also determine the time required for the target optimization scheme to complete optimization according to the target optimization scheme. Further, the server determines a preset completion time according to the time required for completing the optimization.

The server can also determine the responsible person according to the first index related data and the target optimization scheme.

S104, determining the influence of the target optimization scheme on the upper-layer index and the top-layer index and/or the loss caused by deferred completion according to the actual completion time, the preset completion time and the KPI tree structure of the target optimization scheme, wherein the KPI tree structure comprises KPI indexes of each layer, and the correlation and the weight among the KPI indexes.

In this embodiment, the server obtains the actual completion time of the target optimization scheme. The actual completion time of the target optimization scheme may be before the preset completion time, or after the preset completion time, or at the preset completion time. The records of the actual completion time and the preset completion time may be a month, a day, a time, etc.

When the actual completion time of the target optimization scheme is before the preset completion time, since the target optimization scheme is completed in advance, the station, production line, plant, factory should be optimized, for example, the cost thereof should be optimized, in the period from the actual completion time to the preset completion time.

When the actual completion time of the target optimization scheme is after the preset completion time, the completion is deferred due to the target optimization scheme. Therefore, in the period from the actual completion time to the preset completion time, the KPI of the station, the production line, the workshop and the factory still has the problem that the preset effect cannot be achieved. During this time period, the station, production line, plant, factory should be at a loss, for example, its cost.

The server can also determine the influence on the KPI of the upper layer after the KPI of the lower layer is optimized according to the KPI tree structure.

The KPI tree structure may be as shown in fig. 3. Including station level, line level, shop level and factory level four-level KPI indicators.

The station-level KPI indexes comprise a primary qualification rate, a rejection rate, a person average efficiency, equipment OEE, a fixture, material consumption and a production plan completion rate.

The production line KPI comprises a primary qualification rate, a rejection rate, production line efficiency, a fixture, material cost and a production plan completion rate. Wherein, the primary qualification rate of the production line level is related to the primary qualification rate of the station level. The reject rate at the production line level is related to the reject rate at the station level. The line efficiency of the line stage is related to the personnel efficiency of the station stage and the equipment OEE. The production line-level tool clamp is related to the station-level tool clamp. The material cost of the production line stage is related to the material cost of the station stage. The production plan completion rate at the production line level is correlated with the production plan completion rate at the station level.

The workshop-level KPI indexes comprise product qualification rate, rejection rate, manufacturing cost, material cost and production plan completion rate. Wherein, the product qualification rate of workshop level is related to the primary qualification rate of production line level. The rejection rate at the plant level is related to the rejection rate at the production line level. The manufacturing cost of the workshop level is related to the rejection rate of the production line level, the production line efficiency and the tooling fixture. The material costs at the plant level are related to the material costs at the production line level. The production plan completion rate at the plant level is correlated with the production plan completion rate at the line level.

Among other things, plant-level KPI indicators include quality, cost, and delivery. Wherein the quality of the plant level is related to the product yield and rejection rate of the plant level. The plant-level costs are related to the plant-level manufacturing costs and the material costs. Factory level delivery is related to shop level production schedule completion rate.

Weights for the metrics may also be included in the KPI tree structure. For example, in the calculation of the production line efficiency, the duty ratio of the average human efficiency to the equipment OEE is 1:3.

according to the KPI management method, the server acquires the original data from the edge equipment according to the preset time interval. And the server calculates all the KPI indexes by using the original data according to all the KPI performance indexes and the evaluation algorithm thereof. The server compares each KPI in the first data with a preset index range. When the first index exists in the first data and the value of the first index is not in the preset index range of the first index, the server determines that the KPI index is abnormal. The server determines the KPI index with the abnormality as a first index. The server retrieves the relevant case from the expert database and the history database according to the index name and the keyword of the first index, and recommends the relevant case to the optimization database. The server further needs to screen similar schemes from the optimization database according to the index related data of the first index, and determine a target optimization scheme. The server can also determine the time and responsible person required by the target optimization scheme to complete optimization according to the target optimization scheme. Further, the server determines a preset completion time according to the time required for completing the optimization. The server obtains the actual completion time of the target optimization scheme. And the server determines the optimization effect of the target optimization scheme or the loss caused by deferred completion according to the actual completion time, the preset completion time and the KPI tree structure of the target optimization scheme. According to the method and the device, the KPI is analyzed and processed by acquiring the first data, the effect of providing practical and effective reference for the optimization of the production line through the KPI is achieved, the intelligence of KPI management is improved, convenience is provided for analysis and optimization work of an administrator, and the working efficiency of the KPI is improved.

Figure 4 illustrates a flow chart of another KPI management method provided by an embodiment of the application. On the basis of the embodiments shown in fig. 1 to 3, as shown in fig. 4, with the server as the execution body, the method of this embodiment may include the following steps:

s201, acquiring a KPI tree structure, wherein the KPI tree structure comprises KPI indexes of each layer and all KPI indexes.

In this embodiment, the server obtains a KPI tree structure, where the KPI tree structure includes correlations and weights between KPI indicators. The server can calculate the upper KPI index through the lower KPI index according to the KPI tree structure.

The KPI tree structure may be stored in a storage device of a server, or in another device communicatively coupled to the server, or in a cloud device communicatively coupled to the server.

S202, determining first data according to a KPI tree structure and original data, wherein the first data is KPI indexes of each layer at a first moment, and the KPI indexes comprise KPI indexes obtained through direct acquisition and KPI indexes obtained through calculation.

In this embodiment, the server may obtain the original data corresponding to the KPI indicators of each layer at the time when it is determined that the KPI indicators need to be obtained. And the server calculates KPI indexes of each layer according to the original data. The process of calculating the KPI index of each layer by the server can comprise the following steps:

Step 1, obtaining original data, wherein the original data are parameters required by calculating KPI indexes of each layer.

In this step, the raw data may include device parameters directly acquired by the edge device, index parameters input by the administrator, and the like.

And 2, preprocessing the original data according to a first preset rule to obtain processed data.

In this step, the first preset rule is used to determine whether there is a data loss or a data abnormality in the original data.

If the original data has data missing or data abnormality, judging whether the missing data or abnormal data can be supplemented or optimized. If the missing data or the abnormal data can be supplemented or optimized, the missing data or the abnormal data is supplemented or optimized. Otherwise, the original data is deleted, and the data is reacquired as the original data.

Wherein, the step of judging and processing the original data by the server may include:

and 2.1, judging whether abnormal data exist in the original data according to a first preset rule, wherein the abnormal data are abnormal data, and the abnormal data comprise large data abnormality, small data abnormality or data missing.

In this step, in the first preset rule of the server, a normal data range of each original data is stored, and when the original data is not in the normal data range or is missing, the server determines that the original data is abnormal data.

Specifically, when the abnormal data is larger than the normal data range of the original data, the server determines that the abnormal data is abnormally large. When the abnormal data is smaller than the normal data range of the original data, the server determines that the abnormal data is abnormally small.

And 2.2, when the original data does not contain abnormal data, carrying out normalization processing on the original data.

And 2.3, when abnormal data exist in the original data, processing the original data according to the attribute of the original data.

In this step, different raw data have different attributes. The attributes may include non-missing, non-unusual, computationally available, etc.

Wherein, the indelible is used for indicating that the original data cannot be subjected to the abnormal deletion, and once the original data is subjected to the deletion, the original data cannot be used.

Wherein, the non-exception is used for indicating that the original data cannot be used, and once the original data is abnormal, the original data cannot be used.

The calculation may be used to indicate that when the original data is abnormal, the original data may be complemented by a preset algorithm calculation.

For the above different properties, step 2.3 may specifically include:

And 2.3.1, acquiring the attribute of the original data.

And 2.3.2, supplementing abnormal data in the original data according to a preset algorithm of the original data and related data of the original data when the attribute of the original data is calculated.

Step 2.3.3, otherwise, discard and reacquire the raw data.

And step 3, determining KPI indexes of each layer according to the processed data and a preset evaluation algorithm.

In this step, the server performs preprocessing on the original data according to step 2, and then obtains preprocessed data. And the server calculates the processed data according to a preset evaluation algorithm to obtain KPI indexes of each layer. Wherein, partial KPI indexes can be directly obtained according to original data. Wherein, partial KPI indexes are obtained through calculation by using a plurality of original data.

S203, acquiring first data of a first moment, wherein the first data comprises KPI indexes of all layers at the first moment.

Step S203 is similar to the implementation of step S101 in the embodiment of fig. 2, and is not described herein.

S204, recommending an optimization database of the first index according to the index name and the keyword of the first index when the first index exists in the first data and the value of the first index is not in the preset index range of the first index, wherein the optimization database comprises expert cases and historical cases corresponding to the first index.

Step S204 is similar to the implementation of step S102 in the embodiment of fig. 2, and is not described herein.

S205, determining an influence factor set which causes index abnormality according to a first index and a preset recommendation algorithm, wherein the influence factor set comprises at least one influence factor.

In this embodiment, the server determines, according to the index name of the first index, a set of influencing factors that may cause an abnormality in the index from a preset mapping table. Wherein each KPI indicator may include one or more influencing factors. And the server screens effective influence factors in the influence factor set according to a preset recommendation algorithm.

The preset mapping table may be stored in a preset database, or the preset mapping table may be stored in a storage device of the server.

The server can analyze the influence factors through analysis tools such as PDCA, A3 report, fishbone diagram and 5W 2H. The server may also present the analysis results obtained by analysis by the analysis tool to an administrator.

In one implementation, after the administrator views the first index and its influencing factors, the administrator may also modify the influencing factors of the first index through the interactive interface of the analysis tool. The modification may include adjusting the order of individual influencing factors, specific gravity, deleting unnecessary influencing factors therein, adding new influencing factors, etc.

S206, determining a significant influence factor of the first index according to an influence factor selection instruction, wherein the influence factor selection instruction is used for indicating the significant influence factor on the first index.

In this embodiment, the server may further obtain a selection instruction sent by the administrator. After the first index and the influence factors thereof are checked by the administrator, the administrator selects one influence factor from the influence factor set according to actual conditions as a significant influence factor. The significant influencing factor is the factor that has the greatest influence on the abnormality of the first index.

S207, matching similar cases in the optimization database according to the index related data of the first index and the significant influencing factors, and generating a similar case set, wherein the similar case set comprises at least one similar case.

In this embodiment, the server further screens the cases in the optimization database according to the index related data of the first index and the significant influencing factors to obtain the similar case set. The set of similar cases includes at least one similar case.

The significant influencing factor of each similar case is the same as or similar to the significant influencing factor of the first index. The index-related data in the similar case is the same as or similar to the index-related data of the first index.

S208, determining a target similar case according to the optimization effect of each similar case in the similar case set and/or a case selection instruction, wherein the case selection instruction is used for indicating to select one similar case in the similar case set as the target similar case.

In this embodiment, the optimization effect of each similar case of the server selects the case with the best optimization effect as the target similar case

Alternatively, the server may rank similar cases according to the optimization effect. And the server displays the ordered similar cases to an administrator through a display interface. And the administrator selects one similar case as a target similar case according to each displayed similar case. The server obtains the selection instruction of the administrator and generates the case selection instruction.

S209, determining a target optimization scheme and preset completion time according to the target similar cases.

In this embodiment, the server determines the target similar case according to S210. And the server determines a target optimization scheme according to the optimization scheme in the target similar case. And the server determines preset completion time according to the completion time of the optimization scheme in the target similar case.

When the target optimization scheme is completed in advance, the method of the embodiment may include:

S210, determining the advanced completion time of the target optimization scheme according to the actual completion time and the preset completion time of the target optimization scheme.

In this embodiment, the server obtains the actual completion time of the target optimization scheme. When the actual completion time is earlier than the preset completion time, the server may determine the advanced completion time according to a time difference between the actual completion time and the preset completion time.

S211, determining the influence of the target optimization scheme on the upper-layer index according to the advanced completion time length and the KPI tree structure.

In this embodiment, after the target optimization scheme is completed, the server continues to acquire the relevant data of the first index. And the server determines whether the first index is optimized after finishing the target optimization scheme according to the related data of the first index. If the value of the first index returns to the normal range, the first index is optimized. Otherwise, the first index still has index anomalies.

The server can also acquire the original data needed to be used by each KPI in the advanced completion time. And the server calculates all KPI indexes in the early completion time period according to the original data and a preset index evaluation algorithm.

In one implementation, the server may calculate the second data according to the original data that needs to be used by each KPI indicator in the early completion period. And the server compares the first data with the second data and determines the optimization effect of the target optimization scheme.

In another implementation manner, the server may compare indexes such as quality, cost, delivery, etc. of the production line, the workshop, and the factory before the execution of the optimization scheme and after the completion of the optimization scheme, so as to determine the optimization effect of each layer.

When the target optimization scheme is completed in a time-out manner, the method of the embodiment may include:

s212, determining the overtime completion time of the target optimization scheme according to the actual completion time and the preset completion time of the target optimization scheme.

In this embodiment, the server obtains the actual completion time of the target optimization scheme. When the actual completion time is later than the preset completion time, the server can determine the timeout completion duration according to the time difference between the actual completion time and the preset completion time.

S213, determining loss of target optimization due to delay completion according to the timeout completion time and index related data of the first index.

The server can also acquire the original data needed by each KPI in the timeout completion time length and the original data needed by each KPI after the target optimization scheme is completed. And the server calculates all KPI indexes in the timeout completion time according to the original data and a preset index evaluation algorithm, and all KPI indexes after the target optimization scheme is completed.

In one implementation, the server may calculate, according to the original data that needs to be used by each KPI indicator in the timeout completion period, to obtain the third data. After the server finishes the target optimization scheme, the server calculates fourth data according to the original data required to be used by each KPI. The server compares the third data with the fourth data and determines the loss condition of the target optimization scheme.

In another implementation, the server may also count the number of indicators of quality, cost, delivery, etc. of the production line, plant, factory, etc. during the timeout completion period. The server compares the value with the value of the index of quality, cost, delivery and the like of the production line, the workshop and the factory after the optimization scheme is finished. Further, the server may determine a loss condition within the timeout completion period.

And S214, when the target optimization scheme is completed in a time-out manner, sending second abnormal information to target equipment, wherein the second abnormal information comprises preset completion time and the target optimization scheme, and the target equipment is equipment used by a responsible person of the first index.

In this embodiment, when the server reaches the preset completion time, it is determined whether the target optimization scheme is completed. If the target optimization scheme is not completed, the server determines that the target optimization scheme is completed overtime. At this time, the server transmits the second abnormality information to the target device.

The second abnormal information is used for reminding an administrator that the target optimization scheme is not completed on time. The second anomaly information may include a preset completion time, a target optimization scheme, and the like.

The execution of step S214 and the execution of steps S212 and S213 in the present embodiment are not limited by the described order of actions, and step S214 may be performed simultaneously with steps S212 and S213, performed before steps S212 and S213, or performed after steps S212 and S213.

S215, archiving the target optimization scheme, wherein the archived data obtained by the target optimization scheme comprises the first data, the first index, the target optimization scheme and the optimization effect or loss of the target optimization scheme.

In this embodiment, after completing execution of the target optimization scheme according to the above steps, the server files the target optimization scheme and related information thereof. The archiving information mainly comprises the analysis process, influence factors, the duty ratio of each influence factor, KPI indexes, the change condition of the KPI indexes and the like.

S216, storing the archived target optimization scheme into a historical case.

In this embodiment, the server stores the archived target optimization schemes in the history cases. When the server again obtains the target optimization solution, the target optimization solution will be one of the cases in the database that are matched.

According to the KPI management method, a server acquires a KPI tree structure, and the KPI tree structure comprises correlations and weights among KPI indexes. The server acquires the original data corresponding to the KPI of the station layer, and calculates to obtain the KPI of the station layer. And the server determines first data at a first moment according to the KPI index and the KPI tree structure of the station layer. When at least one KPI in the first data is out of the range of the preset index, the server determines that the KPI is abnormal. The server determines the KPI index with the abnormality as a first index. The server retrieves the relevant case from the expert database and the history database according to the index name of the first index, and stores the relevant case in the optimization database. The server further needs to screen similar schemes from the optimization database according to the index related data of the first index, and determine a target optimization scheme. And the server determines preset completion time according to the target optimization scheme. When the target optimization scheme is formed in advance, the server determines the optimization effect of the target optimization scheme according to the actual completion time of the target optimization scheme, the preset completion time and the index related data of the first index. When the target optimization scheme is completed in a time-out mode, the server determines the loss of the target optimization scheme according to the actual completion time of the target optimization scheme, the preset completion time and index related data of the first index. After completing the execution of the target optimization scheme according to the steps, the server files the target optimization scheme and related information thereof and stores the target optimization scheme and the related information into a historical case. According to the method and the device, whether the target optimization scheme is completed on time is judged, the effect after completion of the target optimization scheme is achieved, and the loss caused by completion of overtime is counted, so that the optimization effect of the target optimization scheme on the production line is evaluated, the intelligence of KPI management is improved, convenience is brought to analysis and optimization work of an administrator, and the working efficiency of the system is improved.

Fig. 5 is a schematic structural diagram of a KPI management apparatus according to an embodiment of the present application, and as shown in fig. 5, the KPI management apparatus 10 of the present embodiment is configured to implement operations corresponding to a server in any of the above method embodiments, where the KPI management apparatus 10 of the present embodiment includes:

the first obtaining module 11 is configured to obtain first data at a first time, where the first data includes KPI indicators of each layer at the first time.

The first determining module 12 is configured to recommend an optimization database of the first index according to an index name and a keyword of the first index when the first index exists in the first data and a value of the first index is not within a preset index range of the first index, where the optimization database includes expert cases and historical cases corresponding to the first index.

The second determining module 13 is configured to recommend a target optimization scheme, a preset completion time of the target optimization scheme, and a responsible person according to the optimization database and the index related data of the first index.

The third determining module 14 is configured to determine, according to the actual completion time of the target optimization scheme, the preset completion time, and the KPI tree structure, an effect of the target optimization scheme on the upper-layer indicators and/or a loss caused by deferred completion, where the KPI tree structure includes KPI indicators of each layer, and correlations and weights between the KPI indicators.

In one example, the first data includes at least one of a producer KPI, a production facility KPI, a production process KPI, a production quality KPI, a production material KPI, a production stream KPI, a production cost KPI, a production safety KPI, or the like.

In one example, the KPI indicators for each layer include a workstation layer KPI indicator, a line layer KPI indicator, a workshop layer KPI indicator, and a factory layer KPI indicator.

The KPI management device 10 provided in the embodiment of the present application may execute the above method embodiment, and the specific implementation principle and technical effects of the KPI management device may be referred to the above method embodiment, which is not described herein again.

Fig. 6 is a schematic structural diagram of another KPI management apparatus according to an embodiment of the present application, and on the basis of the embodiment shown in fig. 5, as shown in fig. 6, the KPI management apparatus 10 of this embodiment is configured to implement an operation corresponding to a server in any one of the above method embodiments, where the KPI management apparatus 10 of this embodiment further includes:

prior to the second determination module 13, the KPI management apparatus 10 further includes:

the fourth determining module 15 is configured to determine, according to the first index and a preset recommendation algorithm, a set of influencing factors that cause an abnormality in the index, where the set of influencing factors includes at least one influencing factor.

A fifth determining module 16 is configured to determine the significant influencing factor of the first indicator according to a influencing factor selection instruction, where the influencing factor selection instruction is used to indicate the significant influencing factor on the first indicator.

The KPI management apparatus 10 further includes:

an archiving module 17, configured to archive the target optimization scheme. And storing the archived target optimization schemes into historical cases.

The sending module 18 is configured to send first anomaly information to a target device when the first data has a first indicator deviation exceeding a preset indicator range, where the first anomaly information includes at least one of a first indicator, an indicator name, the preset indicator range of the first indicator, a keyword, and a time interval, and the target device is a device used by a responsible person of the first indicator.

The sending module 18 is further configured to send, when the target optimization scheme is completed in a timeout, second exception information to the target device, where the second exception information includes at least one of a preset completion time, the target optimization scheme, and an actual completion time of the target optimization scheme, and the target device is a device used by a responsible person of the first index.

Fig. 7 is a schematic structural diagram of another KPI management apparatus according to an embodiment of the present application, and on the basis of the embodiments shown in fig. 5 and fig. 6, as shown in fig. 7, the KPI management apparatus 10 of this embodiment is configured to implement an operation corresponding to a server in any of the foregoing method embodiments, where the KPI management apparatus 10 of this embodiment includes:

the second determining module 13 specifically includes:

the generating sub-module 131 is configured to match similar cases in the optimization database according to the index related data of the first index and the significant influencing factors, and generate a similar case set, where the similar case set includes at least one similar case.

The first determining sub-module 132 is configured to determine the target similar case according to the optimization effect of each similar case in the similar case set and/or a case selection instruction, where the case selection instruction is configured to instruct to select a similar case in the similar case set as the target similar case.

A second determining sub-module 133, configured to determine a target optimization scheme and a preset completion time according to the target similar case.

The first acquisition module 11 specifically includes:

a second obtaining sub-module 111, configured to obtain a KPI tree structure, where the KPI tree structure includes KPI indexes of each layer, and correlations and weights between the KPI indexes;

The third obtaining sub-module 112 is configured to determine first data according to the KPI tree structure and the original data, where the first data is KPI indexes of each layer at a first moment, and the KPI indexes include KPI indexes obtained by direct collection and KPI indexes obtained by calculation.

In one example, the third obtaining sub-module 112 is specifically configured to obtain raw data, where the raw data is parameters required for calculating KPI indexes of each layer; preprocessing the original data according to a first preset rule to obtain processed data; and determining KPI indexes of each layer according to the processed data and a preset evaluation algorithm.

In an example, the original data is preprocessed according to a first preset rule to obtain processed data, and the third obtaining sub-module 112 is specifically configured to determine, according to the first preset rule, whether abnormal data exists in the original data, where the abnormal data is abnormal data, and the abnormality includes that the data is abnormally large, that the data is abnormally small, or that the data is missing; when no abnormal data exists in the original data, carrying out normalization processing on the original data; when abnormal data exists in the original data, the original data is processed according to the attribute of the original data.

In an example, when the original data has abnormal data, the third obtaining sub-module 112 is specifically configured to obtain the attribute of the original data when the original data is processed according to the attribute of the original data; when the attribute of the original data is available for calculation, supplementing abnormal data in the original data according to a preset algorithm of the original data and related data of the original data; otherwise, the original data is discarded and retrieved.

The third determining module 14 specifically includes:

the judging sub-module 141 is configured to judge whether the target optimization scheme is completed on time.

The optimization effect calculation sub-module 142 is configured to determine an advanced completion duration of the target optimization scheme according to the actual completion time and the preset completion time of the target optimization scheme; and determining the optimization effect of the target optimization scheme according to the advanced completion time length and the index related data of the first index, wherein the optimization effect comprises the optimization effect of the first index and the optimization effect of the upper index.

The loss calculation sub-module 143 is configured to determine a timeout completion duration of the target optimization scheme according to an actual completion time and a preset completion time of the target optimization scheme; and determining the loss of the target optimization scheme according to the time-out completion time length and the index related data of the first index, wherein the loss comprises the influence on the first index and the influence on the upper index.

Fig. 8 shows a schematic hardware structure of a KPI management device according to an embodiment of the application. As shown in fig. 8, the KPI management apparatus 20, configured to implement operations corresponding to a server in any of the foregoing method embodiments, the KPI management apparatus 20 of this embodiment may include: a memory 21, a processor 22 and a communication interface 24.

A memory 21 for storing a computer program.

The Memory may include a high-speed random access Memory (Random Access Memory, RAM), and may further include a Non-Volatile Memory (NVM), such as at least one magnetic disk Memory, and may also be a U-disk, a removable hard disk, a read-only Memory, a magnetic disk, or an optical disk.

A processor 22 for executing a computer program stored in a memory to implement the KPI management method of the above embodiment. Reference may be made in particular to the relevant description of the embodiments of the method described above.

Alternatively, the memory 21 may be separate or integrated with the processor 22.

When the memory 21 is a device separate from the processor 22, the KPI management apparatus 20 may further include:

a bus 23 for connecting the memory 21 and the processor 22.

The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, the buses in the drawings of the present application are not limited to only one bus or one type of bus.

A communication interface 24, the communication interface 24 being connectable with the processor 21 via a bus 23. The Gauge communication interface 24 may be configured to obtain first data from the KPI assessment system, or send first anomaly information or first anomaly information.

The server provided in this embodiment may be used to execute the KPI management method described above, and its implementation manner and technical effects are similar, which is not described herein.

The present application also provides a computer-readable storage medium having a computer program stored therein, which when executed by a processor is adapted to carry out the methods provided by the various embodiments described above.

The computer readable storage medium may be a computer storage medium or a communication medium. Communication media includes any medium that facilitates transfer of a computer program from one place to another. Computer storage media can be any available media that can be accessed by a general purpose or special purpose computer. For example, a computer-readable storage medium is coupled to the processor such that the processor can read information from, and write information to, the computer-readable storage medium. In the alternative, the computer-readable storage medium may be integral to the processor. The processor and the computer readable storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuits, ASIC). In addition, the ASIC may reside in a user device. The processor and the computer-readable storage medium may also reside as discrete components in a communication device.

The computer readable storage medium may be implemented by any type or combination of volatile or non-volatile Memory devices, such as Static Random-Access Memory (SRAM), electrically erasable programmable Read-Only Memory (EEPROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.

The present application also provides a program product comprising execution instructions stored in a computer-readable storage medium. The at least one processor of the device may read the execution instructions from the computer-readable storage medium, the execution instructions being executed by the at least one processor to cause the device to implement the methods provided by the various embodiments described above.

The embodiments also provide a chip including a memory for storing a computer program and a processor for calling and running the computer program from the memory, so that a device on which the chip is mounted performs the method in the above possible embodiments.

It is understood that the processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, e.g., the division of modules is merely a logical function division, and there may be additional divisions of actual implementation, e.g., multiple modules may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.

The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated in one processing unit, or each module may exist alone physically, or two or more modules may be integrated in one unit. The units formed by the modules can be realized in a form of hardware or a form of hardware and software functional units.

The integrated modules, which are implemented in the form of software functional modules, may be stored in a computer readable storage medium. The software functional modules described above are stored in a storage medium and include instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or processor to perform some steps of the methods of the various embodiments of the present application.

Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above. And the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A KPI management method, the method comprising:

when a first index exists in the first data and the value of the first index is not in the preset index range of the first index, recommending an optimization database of the first index according to the index name and the keyword of the first index, wherein the optimization database comprises expert cases and historical cases corresponding to the first index;

determining the influence of the target optimization scheme on upper-layer indexes and top-layer indexes and/or loss caused by deferred completion according to the actual completion time of the target optimization scheme, the preset completion time and a KPI tree structure, wherein the KPI tree structure comprises KPI indexes of each layer, and correlation and weight among the KPI indexes;

according to the optimization database and the index related data of the first index, before recommending a target optimization scheme, the preset completion time of the target optimization scheme and a responsible person, the method further comprises:

determining a significant influence factor of the first index according to an influence factor selection instruction, wherein the influence factor selection instruction is used for indicating the significant influence factor on the first index;

the recommending the target optimization scheme, the preset completion time of the target optimization scheme and the responsible person according to the optimization database and the index related data of the first index comprises the following steps:

determining a target optimization scheme and preset completion time according to the target similar cases;

The acquiring the first data at the first moment includes:

determining first data according to the KPI tree structure and the original data, wherein the first data is KPI indexes of each layer at a first moment, and the KPI indexes comprise KPI indexes obtained by direct acquisition and KPI indexes obtained by calculation;

when the target optimization scheme is completed on time, determining the influence of the target optimization scheme on an upper layer index and/or the loss caused by deferred completion according to the actual completion time of the target optimization scheme, the preset completion time and a KPI tree structure, wherein the method comprises the following steps:

determining the influence of the target optimization scheme on an upper-layer index according to the advanced completion time length and the KPI tree structure;

when the target optimization scheme is completed in a time-out manner, determining the influence of the target optimization scheme on an upper-layer index and/or the loss caused by deferred completion according to the actual completion time of the target optimization scheme, the preset completion time and a KPI tree structure comprises the following steps:

2. The method of claim 1, wherein the determining the first data from the KPI tree structure and the raw data comprises:

3. The method of claim 2, wherein preprocessing the raw data according to a first preset rule to obtain processed data comprises:

4. A method according to claim 3, wherein when the abnormal data exists in the original data, processing the original data according to the attribute of the original data comprises:

acquiring the attribute of the original data;

otherwise, the original data or the alarm is abandoned and re-acquired.

5. The method according to any one of claims 1-4, further comprising:

archiving the target optimization scheme;

and storing the archived target optimization scheme into a historical case.

6. The method according to any one of claims 1-4, wherein when there is a first index deviation in the first data that exceeds a preset index range, the method further comprises:

7. The method of claim 6, wherein when the target optimization scheme times out to completion, the method further comprises:

8. The method of any of claims 1-4, wherein the first data comprises at least one of a producer KPI, a production facility KPI, a production quality KPI, a production material KPI, a production stream KPI, a production cost KPI, a production safety KPI.

9. The method of any of claims 1-4, wherein the KPI indicators for each layer include a station layer KPI indicator, a line layer KPI indicator, a plant layer KPI indicator.

10. A KPI management apparatus, the apparatus comprising:

the acquisition module is used for acquiring first data at a first moment, wherein the first data comprises KPI indexes of each layer at the first moment;

the first determining module is used for determining an optimized database of the first index according to the index name of the first index when the first index deviation exceeds a preset index range in the first data, wherein the optimized database comprises expert cases and historical cases of the index;

the second determining module is used for determining a target optimization scheme, preset completion time and a responsible person according to the optimization database, the index name and the index related data of the first index;

the third determining module is used for determining the optimizing effect of the target optimizing scheme or loss caused by deferred completion according to the actual completion time of the target optimizing scheme, the preset completion time and the index related data of the first index;

wherein, the second determining module is further configured to:

wherein the second determining module is configured to:

wherein, the acquisition module includes:

When the target optimization scheme is completed on time, the third determining module is configured to:

when the target optimization scheme is completed in a time-out mode, the third determining module is used for:

11. A KPI management apparatus, the apparatus comprising: a memory, a processor and a communication interface;

a memory; executable instructions for storing the processor, and data in an optimization database storing the respective metrics;

a processor for implementing the KPI management method according to any one of claims 1-9, according to executable instructions stored by said memory;

And the communication interface is used for acquiring the first data and the second data and sending information.

12. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are for implementing the KPI management method according to any of the claims 1-9.