Performance management method and system, electronic equipment and storage medium

Abstract

The invention provides a performance management method and system, an electronic device and a storage medium, and the method comprises the steps: obtaining the original data of a production process, determining the actual value of a total performance indicator according to the actual values of a plurality of basic performance indicators, under the condition that the actual value of the total performance index does not reach the standard, the actual value of a target basic performance index is determined from the actual values of the multiple basic performance indexes, and a target improvement measure is determined based on the actual value of the target basic performance index so as to adjust the production process; the actual value of the total performance indicator is firstly determined, then the target basic performance indicator is reversely positioned, so that the root cause is determined, the primary and secondary irregularities are avoided, the target improvement measure is determined based on the actual value of the target basic performance indicator, the performance is converted from experience driving to data driving, a management closed loop is realized, and the efficiency is improved. The management efficiency is improved; and the subjectivity caused by artificial experience is effectively avoided.

Description

Technical Field

[0001] This invention relates to the field of performance management technology, and in particular to a performance management method, system, electronic device, and storage medium. Background Technology

[0002] Steel companies are typical process manufacturing enterprises, characterized by numerous processes, long workflows, high energy and material consumption, and dense equipment. Effective performance management transforms lengthy, complex, and costly processes into clear, collaborative, and controlled value streams, thereby ensuring equipment stability and optimizing production efficiency.

[0003] However, traditional performance management methods rely heavily on manual data collection, which is time-consuming, slow to provide feedback, and unable to achieve real-time performance monitoring. Furthermore, when the actual value of the overall performance indicator fails to meet the target, improvement measures also largely depend on individual experience. This approach to performance evaluation is subjective, lacks standardized criteria, is prone to errors, and is inefficient. Summary of the Invention

[0004] This invention provides a performance management method, system, electronic device, and storage medium to solve the technical problems of traditional performance management methods, which often rely on manual labor, have subjective performance evaluations, lack unified standards, are prone to errors, and are inefficient.

[0005] The present invention provides a performance management method, the method comprising: acquiring raw data of a production process, the raw data including actual values ​​of multiple basic performance indicators; determining an actual value of a total performance indicator based on the actual values ​​of the multiple basic performance indicators, and determining an actual value of a target basic performance indicator from the actual values ​​of the multiple basic performance indicators if the actual value of the total performance indicator fails to meet the target; and determining target improvement measures based on the actual value of the target basic performance indicator to adjust the production process.

[0006] In one embodiment of the present invention, determining the actual value of the total performance indicator based on the actual values ​​of the plurality of basic performance indicators includes: pre-constructing a performance indicator tree according to the production process, the performance indicator tree including performance indicators at each level, and parent-child aggregation relationships between upper-level composite performance indicators and lower-level performance indicators of adjacent two levels, wherein the performance indicators at each level include at least one type of basic performance indicators and composite performance indicators; determining the actual values ​​of the composite performance indicators at the middle and top levels by aggregating the actual values ​​of the plurality of basic performance indicators in a layer-by-layer upward manner, wherein the actual value of a composite performance indicator is obtained by aggregating the actual values ​​of the child performance indicators corresponding to the composite performance indicator at the lower level, wherein the child performance indicators include at least one type of basic performance indicators and composite performance indicators; and using the actual value of the top-level composite performance indicator as the actual value of the total performance indicator.

[0007] In one embodiment of the present invention, determining the actual value of a target basic performance indicator from the actual values ​​of the plurality of basic performance indicators includes: comparing the actual value of each performance indicator with the corresponding benchmark value according to the hierarchy of each performance indicator in the performance indicator tree and the parent-child aggregation relationship, in a drill-down manner; if the actual value of a basic performance indicator does not meet the corresponding benchmark value, and the actual values ​​of all upstream composite performance indicators of the basic performance indicator do not meet the corresponding benchmark value, then the basic performance indicator is determined as the target basic performance indicator, and the actual value of the target basic performance indicator is obtained.

[0008] In one embodiment of the present invention, determining target improvement measures based on the actual values ​​of the target basic performance indicators includes: determining the actual value of a target behavioral indicator from the actual values ​​of multiple behavioral indicators according to the target basic performance indicators and the relationship between preset behavioral indicators and performance indicators, wherein the original data also includes the actual values ​​of the multiple behavioral indicators; determining the deviation of the target basic performance indicators according to the actual values ​​of the target basic performance indicators and the corresponding benchmark values, and determining the deviation of the target behavioral indicators according to the actual values ​​of the target behavioral indicators and the corresponding benchmark values; and combining the deviations of the target basic performance indicators and the deviations of the target behavioral indicators to match the target improvement measures from a preset knowledge graph, wherein the preset knowledge graph includes the historical deviations of the basic performance indicators and the historical deviations of the behavioral indicators, respectively, with historical improvement measures.

[0009] In one embodiment of the present invention, the target improvement measures are matched from a preset knowledge graph by comprehensively considering the deviations of the target basic performance indicators and the deviations of the target behavioral indicators. This includes: determining a first target historical improvement measure based on the deviations of the target basic performance indicators and the correlation between historical deviations of the basic performance indicators and historical improvement measures in the preset knowledge graph; determining a second target historical improvement measure based on the deviations of the target behavioral indicators and the correlation between historical deviations of the behavioral indicators and historical improvement measures in the preset knowledge graph; comparing the improvement effects of the first target historical improvement measure and the second target historical improvement measure, and determining the target improvement measure from the first target historical improvement measure and the second target historical improvement measure based on the comparison result, wherein the preset knowledge graph also includes the improvement effects of the historical improvement measures.

[0010] In one embodiment of the present invention, after obtaining the raw data of the production process, the method further includes: calculating the deviation of each basic performance indicator based on the actual value and the corresponding benchmark value of each basic performance indicator, and determining the score of each basic performance indicator based on the deviation of each basic performance indicator and the corresponding deviation interval, wherein each deviation interval has a corresponding score; and determining the score of the composite performance indicator of the middle layer and the top layer by aggregating the scores of the multiple basic performance indicators in a layer-by-layer upward manner according to the hierarchy of each performance indicator in the performance indicator tree and the parent-child aggregation relationship.

[0011] In one embodiment of the present invention, after determining the actual values ​​of the composite performance indicators of the intermediate layer and the top layer, the method further includes: generating early warning information based on the target data of a performance indicator when the target data of a performance indicator exceeds a corresponding preset threshold value, wherein the target data of the performance indicator includes the actual value or deviation of the performance indicator, and the deviation of the performance indicator is determined based on the actual value of the performance indicator and the corresponding benchmark value; generating early warning information based on the target data of a behavioral indicator when the target data of a behavioral indicator exceeds a corresponding preset threshold value, wherein the original data also includes the actual values ​​of multiple behavioral indicators, the target data of the behavioral indicators includes the actual value or deviation of the behavioral indicators, and the deviation of the behavioral indicators is determined based on the actual value of the behavioral indicators and the corresponding benchmark value.

[0012] The present invention also provides a performance management system, the system comprising: a data acquisition module for acquiring raw data of the production process, the raw data including actual values ​​of multiple basic performance indicators; an information processing module for determining the actual value of a total performance indicator based on the actual values ​​of the multiple basic performance indicators, and, if the actual value of the total performance indicator fails to meet the target, determining the actual value of a target basic performance indicator from the actual values ​​of the multiple basic performance indicators, and determining target improvement measures based on the actual value of the target basic performance indicator; and a responsibility module for receiving the target improvement measures and adjusting the production process accordingly.

[0013] The present invention also provides an electronic device, the electronic device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device enables the performance management method described above.

[0014] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer's processor, causes the computer to perform the performance management method as described above.

[0015] The beneficial effects of this invention are as follows: This invention proposes a performance management method, system, electronic device, and storage medium. This method determines the actual value of the overall performance indicator by comprehensively analyzing the actual values ​​of various basic performance indicators, achieving standardized and comprehensive performance evaluation. This overcomes the shortcomings of independently evaluating individual basic performance indicators. When the overall performance fails to meet the target, the method then reversely identifies the key influencing factors (target basic performance indicators) from among the multiple basic performance indicators to determine the root cause and avoid misclassification. Based on the identification of the actual values ​​of the target basic performance indicators, the underlying process, operational, or equipment reasons are analyzed to determine the target improvement measures. This transforms performance management from experience-driven to data-driven, achieving a closed-loop management system, improving management efficiency, and effectively avoiding the subjectivity of relying on human experience. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0017] In the attached diagram: Figure 1 A schematic diagram illustrating the implementation environment of a performance management method according to an embodiment of the present invention; Figure 2A flowchart illustrating a performance management method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a performance indicator tree provided in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the establishment of a behavior-performance correlation model according to an embodiment of the present invention; Figure 5 A flowchart illustrating the construction and recommendation of a knowledge graph according to an embodiment of the present invention; Figure 6 This is a schematic diagram illustrating a performance management interface provided in an embodiment of the present invention; Figure 7 A flowchart of performance management provided in an embodiment of the present invention; Figure 8 This is a block diagram of a performance management system provided in an embodiment of the present invention; Figure 9 This is a structural diagram of a performance management system provided in another embodiment of the present invention; Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0019] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. The drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0020] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0021] It should be noted that traditional performance management methods typically have the following problems: (1) Isolation: The performance indicators of each branch factory, workshop and process are independent of each other, and there is a lack of consistency from the company strategy to the job operation, resulting in local optimization rather than global optimization.

[0022] (2) Lag: Performance data relies heavily on manual collection and report statistics, which has a long cycle and slow feedback, making it impossible to achieve real-time or near-real-time performance monitoring and early warning.

[0023] (3) Subjectivity: The decomposition and assessment of performance targets rely heavily on experience and lack scientific and quantitative model support. Fairness and accuracy need to be improved. In particular, there is a lack of unified and automated scoring rules, making it difficult to standardize the evaluation and make horizontal comparisons of diverse performance results.

[0024] (4) Difficulty in reverse tracing: When the profit or cost of a final product fluctuates, it is difficult to quickly and accurately trace back to which link, which piece of equipment, or which process parameter caused the problem.

[0025] (5) Decision-making is disconnected from knowledge: Even if the root cause of the problem is located, how to quickly match historical best practices or expert experience to form effective improvement measures still depends heavily on personal experience and ability, and lacks a systematic knowledge accumulation and reuse mechanism.

[0026] (6) Disconnect between behavior and performance: The inability to quantitatively link macro performance results with micro, actionable job operation behaviors leads to unclear approaches to performance improvement.

[0027] (7) Rigid standards: Performance gap analysis often uses static and fixed target values, which cannot adapt to the dynamic changes in production conditions, resulting in a lack of scientificity and guidance in the analysis results.

[0028] To address these issues, embodiments of the present invention provide a performance management method, a performance management system, an electronic device, a computer-readable storage medium, and a computer program product, which will be described in detail below.

[0029] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating the implementation environment of a performance management method according to an embodiment of the present invention, such as... Figure 1As shown, the implementation environment may include data acquisition device 110 and computer device 120. Data acquisition device 110 may be an automated control system such as MES (Manufacturing Execution System), ERP (Enterprise Resource Planning), PLC (Programmable Logic Controller), or DCS (Distributed Control System), as well as data interfaces such as operation logs and inspection systems. It may also be a signal acquisition system for collecting signals from the aforementioned systems or data interfaces; no limitation is imposed here. Computer device 120 may be at least one of a computer, computing cluster, microcomputer, embedded computer, neural network computer, etc.; no limitation is imposed here either. Raw data from the production process can be collected by data acquisition device 110 and provided to computer device 120 for processing.

[0030] In a schematic manner, computer device 120 acquires raw data of the production process through data acquisition device 110. This raw data includes the actual values ​​of multiple basic performance indicators. Based on the actual values ​​of these basic performance indicators, the actual value of the overall performance indicator is determined. If the actual value of the overall performance indicator fails to meet the target, the actual value of the target basic performance indicator is determined from the actual values ​​of the multiple basic performance indicators. Based on the actual value of the target basic performance indicator, target improvement measures are determined to adjust the production process. Therefore, the technical solution of this embodiment of the invention determines the actual value of the overall performance indicator by comprehensively considering the actual values ​​of each basic performance indicator, achieving standardized and comprehensive performance evaluation, and overcoming the drawbacks of independently evaluating individual basic performance indicators. When the overall performance fails to meet the target, the key influencing factors (target basic performance indicators) are located from the multiple basic performance indicators in reverse, thereby identifying the root cause and avoiding a lack of focus. Based on the location of the actual value of the target basic performance indicator, the underlying process, operation, or equipment reasons are analyzed to determine target improvement measures, transforming performance evaluation from experience-driven to data-driven, achieving a management closed loop, improving management efficiency, and effectively avoiding the subjectivity of relying on human experience.

[0031] It should be noted that the performance management method provided in this embodiment of the invention is generally executed by computer device 120.

[0032] Please see Figure 2 , Figure 2 This is a flowchart illustrating a performance management method according to an embodiment of the present invention. This performance management method can be applied to... Figure 1The implementation environment shown is specifically executed by computer device 120 within that implementation environment. It should be understood that this performance management method can also be applied to other exemplary implementation environments and executed by devices in other implementation environments; this embodiment does not limit the implementation environment to which this performance management method is applicable. Figure 2 As shown, in an exemplary embodiment, the performance management method includes at least steps S210 to S230, which are described in detail below: Step S210: Obtain the raw data of the production process.

[0033] In one embodiment of the present invention, the raw data includes actual values ​​of multiple basic performance indicators, which at least include hot blast temperature, ore grade, oxygen enrichment rate, silicon content in molten iron, foreman adjustment frequency, coke unit price, pulverized coal unit price, raw material cost per ton of iron, and power cost per ton of iron. The actual values ​​required for each performance indicator can be automatically and in real-time collected through data interfaces such as MES, ERP, automated control systems (e.g., PLC or DCS), operation logs, and inspection systems.

[0034] Step S220: Based on the actual values ​​of multiple basic performance indicators in the original data, determine the actual value of the total performance indicator, and if the actual value of the total performance indicator does not meet the target, determine the actual value of the target basic performance indicator from the actual values ​​of the multiple basic performance indicators.

[0035] In one embodiment of the present invention, multiple quantitative relationships between basic performance indicators and the overall performance indicator can be pre-established. Based on these quantitative relationships and the actual values ​​of the multiple basic performance indicators, the actual value of the overall performance indicator is calculated. A benchmark value or benchmark range for the overall performance indicator is pre-set. If the actual value of the overall performance indicator meets the benchmark value or benchmark range, the actual value of the overall performance indicator is considered to have met the target; otherwise, it is considered not to have met the target. When the actual value of the overall performance indicator does not meet the target, the key basic performance indicator, i.e., the target basic performance indicator, is then searched in reverse to determine the root cause and avoid a haphazard approach. The deviation between the actual value of the target basic performance indicator and the benchmark value corresponding to the target basic performance indicator is greater than the preset deviation corresponding to the target basic performance indicator.

[0036] In one embodiment of the present invention, determining the actual value of the total performance indicator based on the actual values ​​of multiple basic performance indicators includes: pre-constructing a performance indicator tree according to the production process; the performance indicator tree includes performance indicators at each level, and parent-child aggregation relationships between upper-level composite performance indicators and lower-level performance indicators of adjacent layers; the performance indicators at each level include at least one type of basic performance indicators and composite performance indicators; based on the hierarchy and parent-child aggregation relationships of each performance indicator in the performance indicator tree, determining the actual values ​​of the composite performance indicators at the middle and top levels by aggregating the actual values ​​of multiple basic performance indicators layer by layer upwards; wherein the actual value of a composite performance indicator is obtained by aggregating the actual values ​​of the child performance indicators corresponding to the composite performance indicator at the lower level, and the child performance indicators include at least one type of basic performance indicators and composite performance indicators; and using the actual value of the top-level composite performance indicator as the actual value of the total performance indicator.

[0037] In this embodiment, the top layer is the highest level, the bottom layer is the lowest level, and the middle layers are located between the bottom and top layers. There can be one or more middle layers. "Upper layer" and "lower layer" are relative and do not represent a fixed level. Based on the steel production process, a multi-level, structured performance indicator tree can be constructed using a layer-by-layer decomposition method. This performance indicator tree includes at least: company level, branch plant / process level, workshop / unit level, team level, and job level. Upper-level performance indicators guide and set goals for lower-level performance indicators, while lower-level performance indicators support and decompose upper-level performance indicators. Please refer to [link / reference]. Figure 3 , Figure 3 This is a schematic diagram of a performance indicator tree provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the performance indicator tree, from top to bottom, includes the company level, process level, workshop level, and job level. The company level is the top or highest level, the process and workshop levels are intermediate levels, and the job level is the bottom or lowest level. The company level contains one composite performance indicator: blast furnace iron cost. The process level contains two basic performance indicators and one composite performance indicator: the two basic indicators are raw material cost per ton of iron and power cost per ton of iron, and the composite performance indicator is fuel cost per ton of iron. The workshop level contains two basic performance indicators and one composite performance indicator: the two basic indicators are coke price per unit and pulverized coal price per unit, and the composite performance indicator is fuel ratio. The job level contains more than five basic performance indicators, including hot blast temperature, ore grade, oxygen enrichment rate, silicon content in molten iron, and foreman adjustment frequency. The cost of blast furnace iron at the company level has a parent-child aggregation relationship with the cost of raw materials, power, and fuel per ton of iron at the process level. The fuel cost per ton of iron at the process level has a parent-child aggregation relationship with the unit price of coke, unit price of pulverized coal, and fuel ratio at the workshop level. The fuel ratio at the workshop level has a parent-child aggregation relationship with the hot blast temperature, ore grade, oxygen enrichment rate, silicon content of molten iron, and frequency of foreman adjustments at the job level.

[0038] In the performance indicator tree, basic performance indicators are those that cannot be decomposed, while composite performance indicators are those that can be decomposed.

[0039] Based on production process mechanisms, historical data analysis, and machine learning algorithms, mathematical function models can be established for logically related (parent-child aggregation) upper and lower level indicators in a performance indicator tree. These models serve as quantitative correlation models between upper and lower level indicators, and can be specifically represented as follows: Y = f ( X 1, X 2,..., X n Equation (1) in, Y As a performance indicator for higher levels, X 1, X 2,..., X n These are the lower-level performance indicators, i.e., the direct influencing factors. A mathematical model clarifies the quantitative relationship of performance contributions, establishing a unified performance evaluation standard.

[0040] By utilizing a quantitative correlation model between upper and lower level indicators and the actual values ​​of multiple basic performance indicators, the performance results are dynamically calculated starting from the lowest level of job positions. The results are then aggregated upwards level by level, updating the performance results in real time at the team level, workshop / unit level, branch plant / process level, and finally the company level, forming a global real-time performance view.

[0041] In one embodiment of the present invention, determining the actual value of a target basic performance indicator from the actual values ​​of multiple basic performance indicators includes: comparing the actual value of each performance indicator with the corresponding benchmark value according to the hierarchy and parent-child aggregation relationship of each performance indicator in the performance indicator tree, in a drill-down manner; if the actual value of a basic performance indicator does not meet the corresponding benchmark value, and the actual values ​​of all upstream composite performance indicators of the basic performance indicator do not meet the corresponding benchmark value, then the basic performance indicator is determined as the target basic performance indicator, and the actual value of the target basic performance indicator is obtained.

[0042] In this embodiment, the upstream composite performance indicator refers to a composite performance indicator that is at a higher level than the basic performance indicator and is related to the basic performance indicator. This includes parent composite performance indicators, grandparent composite performance indicators, great-grandparent composite performance indicators, and so on, up to the top-level composite performance indicator. For example: Figure 3In the performance indicator tree shown, the upstream composite performance indicators for oxygen enrichment rate include fuel ratio, fuel cost per ton of iron, and blast furnace iron cost; the upstream composite performance indicators for coke unit price include fuel cost per ton of iron and blast furnace iron cost; and the upstream composite performance indicator for raw material cost per ton of iron is blast furnace iron cost. Starting from the next layer below the top layer, the actual value (i.e., actual performance value) of each performance indicator in this layer is compared with the corresponding benchmark value (i.e., performance benchmark value). The absolute gap and relative gap are calculated. Based on the absolute gap, relative gap, and the corresponding preset gap (preset deviation), it is determined whether the performance indicator meets the corresponding benchmark value. If the actual value of a basic performance indicator in this layer does not meet the corresponding benchmark value, this basic performance indicator is taken as the target basic performance indicator. If the actual values ​​of all composite performance indicators in this layer meet the corresponding benchmark values, the drill-down ends. If the actual value of a composite performance indicator in this layer does not meet the corresponding benchmark value, the drill-down continues using the quantitative correlation model between the upper and lower layer indicators. The actual values ​​of each sub-level performance indicator of the composite performance indicator are compared with the corresponding benchmark values. The absolute gap and relative gap are calculated. Based on the absolute gap, relative gap, and the corresponding preset deviation, it is determined whether the performance indicator meets the corresponding benchmark value. The process involves determining whether each sub-level performance indicator meets its corresponding benchmark value. If the actual value of a sub-level basic performance indicator of the composite performance indicator does not meet its benchmark value, that sub-level basic performance indicator is taken as the target basic performance indicator. If the actual values ​​of all sub-level composite performance indicators of the composite performance indicator meet their corresponding benchmark values, the drill-down process ends. If the actual value of a sub-level composite performance indicator of the composite performance indicator does not meet its benchmark value, the drill-down continues using a quantitative correlation model between upper and lower level indicators. The actual values ​​of each sub-level performance indicator (i.e., the grandchild performance indicators of the composite performance indicator) are compared with their corresponding benchmark values ​​to calculate the absolute and relative gaps. Based on the absolute and relative gaps and the corresponding preset gaps, it is determined whether each grandchild performance indicator meets its corresponding benchmark value. This process continues until all target basic performance indicators are obtained.

[0043] In one embodiment of the present invention, before comparing the actual values ​​of performance indicators with the corresponding benchmark values, the method further includes: establishing a dynamic benchmark library for performance indicators, setting a dynamic standard library for each performance indicator in the performance indicator tree as a comparison benchmark for gap analysis, including: theoretical standard value, historical best value, benchmarking standard value, and rolling budget value. Before comparison, one or more of the theoretical standard value, historical best value, benchmarking standard value, and rolling budget value can be selected as the comparison benchmark, i.e., the benchmark value.

[0044] For example, the most suitable dynamic benchmark value can be automatically selected or weighted and calculated based on current production conditions such as product specifications and raw material conditions.

[0045] Step S230: Determine target improvement measures based on the actual values ​​of the target basic performance indicators in order to adjust the production process.

[0046] In one embodiment of the present invention, a knowledge graph can be pre-established based on historical performance issues, corresponding improvement measures, and improvement effects. Based on the actual values ​​of the target basic performance indicators, the knowledge graph is used to find the corresponding target improvement measures, and the production process is adjusted according to the target improvement measures to achieve a closed loop of performance management.

[0047] In one embodiment of the present invention, determining target improvement measures based on the actual values ​​of target basic performance indicators includes: determining the actual value of a target behavioral indicator from the actual values ​​of multiple behavioral indicators according to the target basic performance indicators, the relationship between preset behavioral indicators and performance indicators, wherein the original data also includes the actual values ​​of multiple behavioral indicators; determining the deviation of the target basic performance indicators according to the actual values ​​of the target basic performance indicators and the corresponding benchmark values, and determining the deviation of the target behavioral indicators according to the actual values ​​of the target behavioral indicators and the corresponding benchmark values; and matching target improvement measures from a preset knowledge graph by combining the deviations of the target basic performance indicators and the deviations of the target behavioral indicators, wherein the preset knowledge graph includes the historical deviations of the basic performance indicators and the historical deviations of the behavioral indicators and their respective associations with historical improvement measures.

[0048] In this embodiment, quantifiable and monitorable behavioral indicators can be predefined for key positions. These behavioral indicators are process behaviors that lead to results, including but not limited to: procedure compliance rate, inspection timeliness rate, frequency of operation adjustments, and response time. For example, key behavioral indicators can be defined for the blast furnace foreman, including: furnace temperature adjustment frequency, process procedure compliance rate, and on-time tapping rate.

[0049] Please see Figure 4 , Figure 4 This is a schematic diagram illustrating the establishment of a behavior-performance correlation model according to an embodiment of the present invention, as shown below. Figure 4 As shown, correlation analysis, regression analysis, or machine learning methods can be used to establish a quantitative correlation model between behavioral indicators and related process or cost performance indicators, representing the relationship between behavioral and performance indicators. The quantitative correlation model between behavioral and performance indicators can be expressed as follows: Z = g ( B 1, B 2,..., B m Equation (2) in, Z For performance indicators, B 1, B 2,..., Bm For behavioral indicators.

[0050] Through data interfaces such as MES, ERP, automated control systems (PLC / DCS, etc.), operation logs, and inspection systems, the actual values ​​required for each behavioral indicator are automatically and in real time collected.

[0051] By utilizing a quantitative correlation model between target-based performance indicators, behavioral indicators, and performance indicators, target behavioral indicators are determined. Then, the actual values ​​of the target behavioral indicators are extracted from the actual values ​​of multiple behavioral indicators. Deviations in target-based performance indicators characterize the root causes of process or cost issues, while deviations in target behavioral indicators characterize the behavioral root causes behind process or cost deviations.

[0052] This embodiment drills down along the performance indicator tree, uses a quantitative correlation model between upper and lower level indicators to quickly locate abnormal lower level performance indicators, and calls the quantitative correlation model between behavioral indicators and performance indicators to analyze the behavioral reasons behind the abnormal process or cost parameters, thereby achieving full-link root cause tracing from macro results to micro process parameters and then to job operation behavior.

[0053] A knowledge graph for action management can also be pre-built, containing performance issues, root causes, improvement action entities, and the relationships between them. See also... Figure 5 , Figure 5 A flowchart illustrating the construction and recommendation process of a knowledge graph, as provided in an embodiment of the present invention, is shown below. Figure 5 As shown, constructing a knowledge graph includes: Define core knowledge entities, including: performance issues, root causes, improvement measures, equipment, process parameters, product specifications, responsible persons, and improvement effects; Construct semantic relationships between entities, such as: (a performance problem) - [caused by...] -> (root cause 1), (root cause 2), ..., (root cause n) - [can be taken] -> (improvement measure 1), (improvement measure 2), ..., (improvement measure n) - [applies to] -> (equipment / product), (improvement measure i) - [has improved... indicator] -> (performance indicator i), etc.; Knowledge extraction and storage includes: extracting entities and relationships from unstructured texts such as historical work orders, expert experience bases, technical procedures, and accident reports using NLP (Natural Language Processing) technology, and storing them in a graph database to form a dynamically growing knowledge graph for measure management.

[0054] like Figure 5As shown, after locating a specific root cause (including process root causes and behavioral root causes, etc.), it is automatically used as a query condition to perform matching and reasoning in the knowledge graph, obtain target improvement measures, sort them, and generate a list of recommended measures. The measures can be sorted according to the priority of the target basic performance indicators and target behavioral indicators, or according to the improvement effect of the target improvement measures.

[0055] In one embodiment of the present invention, the method of matching target improvement measures from a preset knowledge graph by considering the deviations of the target basic performance indicators and the deviations of the target behavioral indicators includes: determining a first target historical improvement measure based on the deviations of the target basic performance indicators and the correlation between historical deviations of the basic performance indicators and historical improvement measures in the preset knowledge graph; determining a second target historical improvement measure based on the deviations of the target behavioral indicators and the correlation between historical deviations of the behavioral indicators and historical improvement measures in the preset knowledge graph; comparing the improvement effects of the first target historical improvement measure and the second target historical improvement measure, and determining the target improvement measure from the first target historical improvement measure and the second target historical improvement measure based on the comparison results, wherein the preset knowledge graph also includes the improvement effects of the historical improvement measures.

[0056] In this embodiment, the knowledge graph includes historical performance problems, root causes, historical improvement measures and their effects, and the relationship chains between these parameters. Historical performance problems refer to deviations in historical abnormal performance indicators, and root causes include the deviations of the historical abnormal performance indicators themselves (generally indicating process-related reasons) and the deviations of the corresponding abnormal behavioral indicators (generally indicating behavioral reasons). The knowledge graph retrieves all effective improvement measures (i.e., first-target historical improvement measures and second-target historical improvement measures) historically taken for the root cause (including process root causes and behavioral root causes, etc.), and recommends them in order of historical effectiveness (i.e., the quality of historical improvement effects). For example, the historical improvement measures with the best historical improvement effects can be prioritized from the first-target and second-target historical improvement measures.

[0057] In some embodiments, adjusting the production process includes: generating recommended action work orders based on target improvement measures and pushing them to the mobile terminals or MES systems of relevant responsible persons for execution; and also tracking and recording information such as the executor and execution time of each measure through the system to complete feedback on the implementation of the measures.

[0058] In some embodiments, after adjusting the production process, the method includes: after the target improvement measures are implemented, monitoring the changes in the target basic performance indicators and their upstream composite basic performance indicators, quantitatively evaluating the actual improvement effect of the target improvement measures, and feeding back the new relationship of the successful "root cause-measure-effect" and updating it to the knowledge graph, completing the effect verification and knowledge graph self-learning, and realizing the self-evolution and iterative optimization of the knowledge graph.

[0059] In one embodiment of the present invention, after obtaining the raw data of the production process, the method further includes: calculating the deviation of each basic performance indicator based on the actual value and the corresponding benchmark value of each basic performance indicator, and determining the score of each basic performance indicator based on the deviation of each basic performance indicator and the corresponding deviation interval, wherein each deviation interval has a corresponding score; and determining the score of the composite performance indicator of the middle layer and the top layer by aggregated up layer by layer according to the hierarchy and parent-child aggregation relationship of each performance indicator in the performance indicator tree.

[0060] In this embodiment, scoring rules can be predefined for each performance indicator in the performance indicator tree to map the performance gap, i.e., the deviation between the actual value of the performance indicator and the dynamic benchmark value, into a standardized score. The scoring rules include one or more of linear scoring rules and segmented scoring rules. Different weighting coefficients can also be configured for different performance indicators within the scoring rules.

[0061] In some embodiments, defining scoring rules includes at least one of the following: Define a linear scoring rule, divide the score into one or more intervals, and set the same or different scoring slopes for each interval; Define discrete scoring rules, predefine several clear grade intervals and the score for each interval. If the performance gap falls into a certain interval, it directly obtains the fixed score corresponding to that interval, and no linear interpolation calculation is performed within the interval.

[0062] In some embodiments, adjustable weighting coefficients can be set for performance indicators at different levels and in different dimensions, such as output, cost, quality, efficiency, and safety, to calculate the weighted composite score of the parent performance indicator.

[0063] When calculating scores, the scoring rules are used to calculate a standardized score for each basic performance indicator based on the deviation of each basic performance indicator. Based on the hierarchical structure of the performance indicator tree and the preset weights, the weighted comprehensive performance score of the team, workshop, branch factory and company is automatically calculated from bottom to top.

[0064] In some embodiments, rating rules can be predefined to classify performance levels based on scores. Based on the rating rules and calculated scores, a rating for each performance indicator is determined, generating performance levels such as A, B, C, D, or Excellent, Good, Satisfactory, and Needs Improvement. For example, rating rules may include Excellent (≥90 points), Good (80~89 points), Satisfactory (70~79 points), and Needs Improvement (<70 points), supporting a "veto" option.

[0065] In one embodiment of the present invention, after determining the actual values ​​of the composite performance indicators of the intermediate layer and the top layer, the method further includes: generating early warning information based on the target data of a performance indicator when the target data of the performance indicator exceeds the corresponding preset threshold value, wherein the target data of the performance indicator includes the actual value or deviation of the performance indicator, and the deviation of the performance indicator is determined based on the actual value of the performance indicator and the corresponding benchmark value; generating early warning information based on the target data of a behavioral indicator when the target data of the behavioral indicator exceeds the corresponding preset threshold value, wherein the original data also includes the actual values ​​of multiple behavioral indicators, the target data of the behavioral indicators includes the actual value or deviation of the behavioral indicators, and the deviation of the behavioral indicators is determined based on the actual value of the behavioral indicators and the corresponding benchmark value.

[0066] In this embodiment, a corresponding threshold is preset as a critical point or threshold value. When the actual performance value (actual value of the performance indicator) or performance deviation (deviation of the performance indicator), or the actual value of the behavioral indicator or behavioral deviation (deviation of the behavioral indicator) exceeds the corresponding critical point, an early warning message is automatically triggered and pushed to the relevant responsible person via SMS, email or system message.

[0067] In some embodiments, at least one of the following can be displayed: performance indicator-related information, behavioral indicator-related information, performance indicator scoring information, root cause information, target improvement measures, and early warning information. Please see [link to relevant documentation]. Figure 6 , Figure 6 This is a schematic diagram illustrating a performance management interface provided in an embodiment of the present invention, as shown below. Figure 6 As shown, the screen display panel can visualize performance results (including the actual values ​​of performance indicators at each level), the actual values ​​of behavioral indicators, performance scores and levels, root cause tracing paths, target improvement measures, and early warning information.

[0068] The technical solution of this invention will be described below using "blast furnace molten iron cost" as an example.

[0069] Please see Figure 7 , Figure 7 A flowchart of the overall performance management provided in an embodiment of the present invention is shown below. Figure 7 As shown, the overall process of performance management is as follows: S1. Constructing a performance indicator tree The company-level performance indicator "blast furnace iron cost (yuan / ton)" is broken down to the ironmaking process level or blast furnace branch plant level. The performance indicators include "raw material cost per ton of iron", "fuel cost per ton of iron", and "power cost per ton of iron". "Fuel cost per ton of iron" is further broken down to the blast furnace workshop or team level. The performance indicators include "fuel ratio (kg / t)", "coke load", and "pulverized coal injection rate". "Fuel ratio" can ultimately be linked to job operation indicators, such as "hot blast temperature", "ore grade", "oxygen enrichment rate", and "foreman's furnace temperature adjustment frequency".

[0070] S2. Establish a quantitative correlation model among performance indicators. Based on the blast furnace smelting mechanism and a large amount of historical operation data, a key quantitative correlation model is established using machine learning algorithms. The expression of the quantitative correlation model is as follows: Blast furnace molten iron cost = Raw material cost per ton of iron + Fuel cost per ton of iron + Power cost per ton of iron Fuel cost per ton of iron = Fuel ratio * ((coke percentage * coke price per unit) + (pulverized coal percentage * pulverized coal price per unit)) Fuel ratio = f (Hot blast temperature, ore grade, oxygen enrichment rate, furnace gas volume, silicon content in molten iron [Si], slag-to-iron ratio, foreman adjustment frequency, ...) S3. Constructing a knowledge graph Knowledge is extracted from blast furnace operation manuals, expert experience databases, and historical accident analysis reports. For example, relationships are established: (increased fuel ratio) - [caused by] -> (decreased hot blast temperature), (decreased hot blast temperature) - [can be addressed] -> (check for blockage of hot blast stove grid bricks), (check hot blast stove grid bricks) - [previously caused an increase in... index] -> (hot blast temperature) [increase range: ≥30℃]; (large fluctuations in [Si] content) - [can be addressed] -> (optimize coke load).

[0071] S4. Define behavioral indicators and establish a quantitative correlation model between behavioral indicators and performance indicators. Define key behavioral indicators for blast furnace foremen, such as: Furnace temperature adjustment frequency: the number of times the amount of pulverized coal is manually adjusted per shift; Process compliance rate: the percentage of time that key operating parameters (such as air temperature, air pressure, and top pressure) are within the preset optimal range; Iron tapping on time rate: Whether slag and iron are completely removed within the prescribed time; Utilize correlation analysis, regression analysis, or machine learning methods to establish a quantitative correlation model between behavioral indicators and relevant process / cost performance indicators.

[0072] S5. Establish a dynamic benchmark library A dynamic benchmark library is set for the "fuel ratio". When using high-grade ore (>62%), the benchmark value is the historical best value (e.g., 495 kg / t). When using low-grade ore, the system automatically adjusts the benchmark value to a higher budget value (e.g., 510 kg / t) based on the model and historical data to make the evaluation fairer.

[0073] S6. Define scoring rules Set segmented scoring rules for "fuel ratio", including: Better than the benchmark: 100 points (Grade A); Baseline value ~ Baseline value + 5 kg / t: 90 points (Grade B); Baseline value +5kg / t ~ Baseline value +10kg / t: 80 points (Grade C); Greater than the benchmark value +10kg / t: 60 points (Grade D); A higher weight was given to "fuel cost per ton of iron" to reflect its importance.

[0074] S7, Data Acquisition The system collects hundreds of parameters in real time from blast furnace PLC / DCS, MES, ERP and other systems, including hot blast temperature, air pressure, [Si] content, ore grade, coke / coal powder consumption, and unit price of various raw materials, and automatically calculates the foreman's behavior indicators from the operation log.

[0075] S8. Dynamically calculate the actual values ​​of the composite performance indicators of the intermediate and top layers. Every 15-30 minutes (for one batch of molten iron), the latest fuel ratio and fuel cost per ton of iron are dynamically calculated using the latest data and advanced models, and the blast furnace molten iron cost is updated by summarizing and updating the data level by level.

[0076] S9. Gap Analysis and Tracing If it is found that the "blast furnace iron cost" of blast furnace No. 2 exceeds the budget by 20 yuan / ton, further investigation reveals that the main reason is an increase of 18 yuan / ton in "fuel cost per ton of iron". Continuing to investigate, the core reason is found to be an increase in the "fuel ratio" from 500 kg / t to 518 kg / t. Using a machine learning model for fuel ratio to perform root cause analysis, the model simulation calculation indicates that the most contributing factor is a 25°C decrease in the average hot blast temperature (contribution rate 50%), followed by a 0.8% decrease in the grade of ore fed into the furnace (contribution rate 30%). Meanwhile, the behavior-performance model indicates that the shift foreman adjusted the furnace temperature twice as frequently as the average, and the compliance rate with process procedures decreased by 15%. This behavior pattern is highly similar to historically recorded periods of furnace instability.

[0077] S10. Automatically calculate performance scores and ratings. The system invoked the scoring rules and calculated the "fuel ratio" score for this shift to be 75 points (Grade C). Based on the weights, the "fuel cost per ton of iron" score was calculated, and the overall performance rating of this shift was ultimately Grade C.

[0078] S11. Recommended Improvement Measures and Closed-Loop The system identifies "reduced hot air temperature" and "unstable furnace conditions" as root causes and queries the knowledge graph. The map shows a series of measures, sorted by historical effectiveness: ① Check the sealing of hot blast stove valves and the unobstructed flow of qualified bricks; ② Communicate with the purchasing department to verify the stability of ore grade; ③ Organize foremen to conduct training on furnace condition judgment and stable operation. The equipment department prioritized inspections based on recommendations, discovered a leaking hot air valve, and replaced it; the training department organized training for the foremen. After the measures were implemented, the hot air temperature recovered, the foreman's operation became more stable, and the fuel ratio dropped to 503 kg / t. This successful case was used to form a relationship pair of "hot air valve leakage -> hot air valve replacement -> hot air temperature recovery +30℃ -> fuel ratio reduction of 15 kg / t", and stored as new knowledge in the knowledge graph.

[0079] S12, Early Warning When the hot blast temperature remains low and the frequency of foreman adjustments increases abnormally, a level-two warning is sent to the blast furnace director to achieve advance warning.

[0080] This embodiment presents the overall performance status in real time, enabling staff at all levels to clearly understand the relationship between their contributions and the overall goals. It also achieves a complete digital closed loop from problem identification to measure implementation and effect verification. By tracing back from top-level performance indicators, problems can be quickly located to specific processes and equipment, transforming "experience-driven" into "data-driven." Knowledge graphs make implicit expert experience explicit and structured, changing "people searching for knowledge" to "knowledge finding people," providing immediate data-driven decision support after root cause identification, significantly shortening decision time and improving decision quality. By transforming the performance improvement process into reusable knowledge stored in the graph, the company's ability to address similar problems is continuously enhanced. Furthermore, by establishing a quantitative correlation between job behavior indicators and performance indicators, macro-level performance gaps are mapped to micro-level operational gaps. In terms of behavior, it provides clear, specific, and actionable directions for employee performance improvement, achieving a deepening from "performance management" to "behavioral management." By establishing a multi-dimensional and dynamic performance benchmark library, gap analysis is freed from the constraints of single, rigid target values, enabling the selection of the most reasonable evaluation benchmark based on different internal and external conditions, making the analysis results more scientific and fair. Through a predefined scoring rule library, different types of performance gaps are automatically converted into unified, comparable standardized scores and grades, completely eliminating subjective human factors in the evaluation process, making performance evaluation results more fair, just, and transparent. Through bottom-up weighted comprehensive scoring, it can quickly generate overall performance scores for the company, branch factories, workshops, and even work teams, making the performance level of each level of the organization clear at a glance, greatly facilitating horizontal comparison and vertical management.

[0081] Please see Figure 8 , Figure 8 This is a block diagram of a performance management system provided in an embodiment of the present invention. This system can be applied to... Figure 1 The implementation environment shown can also be applied to other exemplary implementation environments. This embodiment does not limit the implementation environment to which the system is applicable.

[0082] like Figure 8 As shown, the exemplary performance management system includes: a data acquisition module 810, used to acquire raw data of the production process, the raw data including actual values ​​of multiple basic performance indicators; an information processing module 820, used to determine the actual value of the total performance indicator based on the actual values ​​of the multiple basic performance indicators, and, if the actual value of the total performance indicator fails to meet the target, to determine the actual value of the target basic performance indicator from the actual values ​​of the multiple basic performance indicators, and to determine target improvement measures based on the actual value of the target basic performance indicator; and a responsibility module 830, used to receive the target improvement measures and to adjust the production process according to the target improvement measures.

[0083] In this embodiment, the data acquisition module 810 can be a data interface such as MES, ERP, automation control system (such as PLC or DCS), operation log, and inspection system, or a signal acquisition system for collecting signals from the above systems or interfaces. The information processing module 820 can be a computer, computing cluster, microcomputer, embedded computer, neural network computer, processor, processing chip, etc. The responsibility module 830 can be a mobile terminal such as a mobile phone, tablet computer, or laptop computer, or an MES system. There are no restrictions here.

[0084] Please see Figure 9 , Figure 9 This is a structural diagram of a performance management system provided in another embodiment of the present invention, as shown below. Figure 9 As shown, the performance management system includes: The system includes several modules: a data acquisition module for automatically collecting raw production data from enterprise information systems; a performance model management module for building and maintaining performance indicator trees and providing visualization tools for users to define and configure quantitative correlation models between performance indicators; a knowledge graph construction and management module for building and maintaining knowledge graphs; a behavioral indicator management module for defining behavioral indicators and building and managing quantitative correlation models between behavioral and performance indicators; a dynamic benchmark management module for maintaining and managing a multi-dimensional dynamic performance benchmark library; a scoring rule management module for defining and maintaining scoring rules, rating rules, and performance indicator weighting systems; and a performance calculation module. The system comprises four modules: a module for dynamically calculating the actual values ​​of composite performance indicators at each level based on a quantitative correlation model between performance indicators and raw data; a module for calculating performance scores and grades for each level of performance indicators based on scoring rules, rating rules, performance indicator weighting systems, and the actual values ​​of performance indicators at each level; and a module for updating and aggregating actual performance values ​​and scores from the bottom to the top level. The analysis and traceability module is used for performance gap analysis and full-link root cause tracing. The intelligent recommendation and closed-loop management module is used for querying the knowledge graph based on root causes, recommending measures, and tracking implementation effects. The visualization and early warning module is used for result display, knowledge graph visualization exploration, and anomaly warnings.

[0085] The performance calculation module supports distributed computing to handle large-scale real-time data streams. By connecting to various information systems within the enterprise and configuring data extraction rules, it enables automatic and real-time collection and cleaning of multi-source heterogeneous data. The knowledge graph construction and management module integrates NLP tools. By defining knowledge entities, constructing semantic relationships between entities, and extracting knowledge using integrated NLP extraction tools, it forms a knowledge graph. The analysis and traceability module performs performance gap analysis based on dynamic benchmarks and end-to-end root cause tracing combined with process and behavioral models, enabling multi-dimensional performance analysis. The intelligent recommendation and closed-loop management module integrates a query engine. After receiving root cause information, it uses the graph query engine to query the knowledge graph, generate recommended measures, and track the implementation and feedback of the measures' effectiveness. The visualization and early warning module supports multiple access methods, including web and mobile devices. It provides performance dashboards, behavior monitoring panels, scoring and rating dashboards, a knowledge graph visualization exploration interface, and a standard management interface on both web and mobile platforms. It also offers configurable early warning rule settings and personalized panel settings.

[0086] It should be noted that the performance management system provided in the above embodiments and the performance management method provided in the above embodiments belong to the same concept. The specific way each module performs its operation has been described in detail in the method embodiments, and will not be repeated here. In practical applications, the performance management system provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0087] In one embodiment of the present invention, an electronic device is also provided, comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device enables the performance management method provided in the above embodiments.

[0088] Please see Figure 10 , Figure 10 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. It should be noted that... Figure 10 The electronic device 1000 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0089] like Figure 10 As shown, the electronic device 1000 includes a processor 1001, a memory 1002, and a communication bus 1003; the communication bus 1003 is used to connect the processor 1001 and the memory 1002; the processor 1001 is used to execute a computer program stored in the memory 1002 to implement one or more methods in the above embodiments.

[0090] In one embodiment of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored. When executed by a computer's processor, the computer program causes the computer to perform the performance management method as described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not incorporated into the electronic device.

[0091] In one embodiment of the present invention, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the performance management methods provided in the various embodiments described above.

[0092] The electronic device provided in this embodiment of the invention includes a processor, a memory, a transceiver, and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication between them. The memory is used to store computer programs, the communication interface is used to perform communication, and the processor and the transceiver are used to run the computer programs, so that the electronic device performs the various steps of the above method.

[0093] In embodiments of the present invention, the memory may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0094] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0095] As will be understood by those skilled in the art, the computer-readable storage medium in the embodiments of the present invention can implement all or part of the steps of the above method embodiments by hardware related to computer programs. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM (Read-Only Memory), RAM (Random Access Memory), magnetic disks, or optical disks.

[0096] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A performance management method, characterized in that, The method includes: Obtain raw data from the production process, including actual values ​​of multiple basic performance indicators; Based on the actual values ​​of the multiple basic performance indicators, the actual value of the total performance indicator is determined, and if the actual value of the total performance indicator fails to meet the target, the actual value of the target basic performance indicator is determined from the actual values ​​of the multiple basic performance indicators. Based on the actual values ​​of the target basic performance indicators, target improvement measures are determined to adjust the production process.

2. The performance management method according to claim 1, characterized in that, Based on the actual values ​​of the aforementioned basic performance indicators, the actual value of the overall performance indicator is determined, including: Based on the production process, a performance indicator tree is pre-constructed. The performance indicator tree includes performance indicators at each level, as well as parent-child aggregation relationships between the composite performance indicators of the upper level and the performance indicators of the lower level in adjacent two levels. The performance indicators at each level include at least one type of basic performance indicators and composite performance indicators. Based on the hierarchy of each performance indicator in the performance indicator tree and the parent-child aggregation relationship, the actual values ​​of the multiple basic performance indicators are determined by aggregating them upwards layer by layer to determine the actual values ​​of the composite performance indicators in the middle and top layers. The actual value of a composite performance indicator is obtained by aggregating the actual values ​​of the child performance indicators corresponding to the lower layer of the composite performance indicator. The child performance indicators include at least one type of basic performance indicators and composite performance indicators. The actual value of the top-level composite performance indicator is used as the actual value of the total performance indicator.

3. The performance management method according to claim 2, characterized in that, Determining the actual value of the target basic performance indicator from the actual values ​​of the multiple basic performance indicators includes: Based on the hierarchy of each performance indicator in the performance indicator tree and the parent-child aggregation relationship, the actual value of the performance indicator is compared with the corresponding benchmark value by drilling down layer by layer. If the actual value of a basic performance indicator does not meet the corresponding benchmark value, and the actual values ​​of all upstream composite performance indicators of the basic performance indicator do not meet the corresponding benchmark values, then the basic performance indicator is determined as the target basic performance indicator, and the actual value of the target basic performance indicator is obtained.

4. The performance management method according to any one of claims 1-3, characterized in that, Based on the actual values ​​of the aforementioned target performance indicators, target improvement measures are determined, including: Based on the target basic performance indicators, the relationship between preset behavioral indicators and performance indicators, the actual value of the target behavioral indicator is determined from the actual values ​​of multiple behavioral indicators, wherein the original data also includes the actual values ​​of the multiple behavioral indicators; Based on the actual values ​​of the target basic performance indicators and the corresponding benchmark values, the deviation of the target basic performance indicators is determined; based on the actual values ​​of the target behavioral indicators and the corresponding benchmark values, the deviation of the target behavioral indicators is determined. By combining the deviations of the target basic performance indicators and the target behavioral indicators, the target improvement measures are matched from a preset knowledge graph. The preset knowledge graph includes the correlation between the historical deviations of the basic performance indicators, the historical deviations of the behavioral indicators, and the historical improvement measures.

5. The performance management method according to claim 4, characterized in that, Based on the deviations of the target basic performance indicators and the target behavioral indicators, improvement measures are matched from a pre-defined knowledge graph, including: Based on the deviation of the target basic performance indicators and the correlation between the historical deviation of the basic performance indicators and historical improvement measures in the preset knowledge graph, the first target historical improvement measures are determined. Based on the deviation of the target behavior indicator and the correlation between the historical deviation of the behavior indicator and historical improvement measures in the preset knowledge graph, the second target historical improvement measures are determined. The improvement effects of the first target historical improvement measures and the second target historical improvement measures are compared. Based on the comparison results, the target improvement measures are determined from the first target historical improvement measures and the second target historical improvement measures. The preset knowledge graph also includes the improvement effects of the historical improvement measures.

6. The performance management method according to claim 2, characterized in that, After acquiring the raw data from the production process, the method further includes: Based on the actual values ​​and corresponding benchmark values ​​of each basic performance indicator, the deviation of each basic performance indicator is calculated, and the score of each basic performance indicator is determined based on the deviation of each basic performance indicator and the corresponding deviation range. Each deviation range has a corresponding score. Based on the hierarchy of each performance indicator in the performance indicator tree and the parent-child aggregation relationship, the scores of the multiple basic performance indicators are determined by a hierarchical upward aggregation method to determine the scores of the composite performance indicators of the intermediate and top layers.

7. The performance management method according to claim 2, characterized in that, After determining the actual values ​​of the composite performance indicators for the middle and top layers, the method further includes: If the target data of a performance indicator exceeds the corresponding preset threshold, an early warning message is generated based on the target data of the performance indicator. The target data of the performance indicator includes the actual value or deviation of the performance indicator, and the deviation of the performance indicator is determined based on the actual value of the performance indicator and the corresponding benchmark value. If the target data of a behavior indicator exceeds the corresponding preset threshold, an early warning message is generated based on the target data of the behavior indicator. The original data also includes the actual values ​​of multiple behavior indicators. The target data of the behavior indicator includes the actual value or deviation of the behavior indicator. The deviation of the behavior indicator is determined based on the actual value of the behavior indicator and the corresponding benchmark value.

8. A performance management system, characterized in that, The system includes: The data acquisition module is used to acquire raw data from the production process, including the actual values ​​of multiple basic performance indicators. The information processing module is used to determine the actual value of the total performance indicator based on the actual values ​​of the multiple basic performance indicators, and, if the actual value of the total performance indicator fails to meet the target, to determine the actual value of the target basic performance indicator from the actual values ​​of the multiple basic performance indicators, and to determine the target improvement measures based on the actual value of the target basic performance indicator. The responsibility module is used to receive the target improvement measures and adjust the production process accordingly.

9. An electronic device, characterized in that, The electronic device includes: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the performance management method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by the computer's processor, causes the computer to perform the performance management method as described in any one of claims 1-7.

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