First-line operator performance assessment method and device, electronic equipment and storage medium

Abstract

The invention provides a front-line operator performance assessment method and device, electronic equipment and a storage medium, and relates to the technical field of performance assessment. The method comprises the following steps: acquiring performance data and assessment index documents of front-line operators in a historical period of time; performing performance calculation on the operation complexity, the operation efficiency and the completion rate to obtain a first performance score; calculating data distribution of operation complexity, operation efficiency and completion rate, and performing tolerance calculation according to the data distribution to obtain a first tolerance interval; according to the performance data and the assessment index document, searching to obtain an associated assessment index; and adjusting the first tolerance interval according to the associated assessment index to obtain a second tolerance interval, and when the operation complexity, the operation efficiency and the completion rate are not in the second tolerance interval, adjusting the first performance score by using the compensation factor according to the exceeding interval to obtain a second performance score, and obtaining a performance assessment result based on the second performance score. The accuracy of performance assessment can be improved.

Description

Technical Field

[0001] This application relates to the field of performance appraisal technology, specifically to a method, device, electronic equipment, and storage medium for performance appraisal of front-line operators. Background Technology

[0002] In an era where corporate management is increasingly moving towards refinement and digitalization, the field of performance appraisal is undergoing continuous development and innovation, with performance appraisal methods constantly iterating and upgrading. The core essence of performance appraisal lies in the accurate evaluation of performance, and the key to performance appraisal focuses on accurately measuring the work results of employees or departments. Performance appraisal plays a crucial role in corporate operations, providing a solid and reliable basis for the formulation of incentive measures, the finalization of promotion decisions, and the arrangement of salary adjustments; it is an indispensable part of corporate management.

[0003] In the actual operation of corporate performance management, performance appraisal systems often focus on evaluating the job performance of front-line operators. However, in reality, in addition to completing their assigned tasks, employees often need to invest time and energy in handling matters outside their job scope. These extra efforts and contributions are often not included in the assessment of their job duties in the current performance appraisal system, resulting in performance appraisal results that cannot comprehensively and accurately reflect employees' actual work achievements, leading to low accuracy in the assessment. Summary of the Invention

[0004] This application provides a method, device, electronic device, and storage medium for performance appraisal of front-line operators, which can realize collaborative performance appraisal at different levels and improve the accuracy of performance appraisal.

[0005] The technical solution of this application embodiment is as follows: In a first aspect, embodiments of this application provide a method for performance evaluation of frontline operators, the method comprising: Obtain performance data and assessment indicator documents of front-line operators over a historical period, including operational complexity, operational efficiency, and completion rate; A first performance score is obtained by performing performance calculations on the operational complexity, operational efficiency, and completion rate. Calculate the data distribution of the operation complexity, the operation efficiency, and the completion rate, and calculate the tolerance based on the data distribution to obtain a first tolerance interval; Keyword extraction is performed on the assessment indicator document to obtain assessment keywords. The assessment keywords are compared with the performance data to obtain comparison results. Based on the comparison results, the related indicators of the comparison results are searched in the assessment indicator document to obtain related assessment indicators. Based on the associated assessment indicators, the first tolerance range is adjusted to obtain a second tolerance range, and it is determined whether the operation complexity, the operation efficiency, and the completion rate are within the second tolerance range. When the first performance score is outside the second tolerance range, the second performance score is adjusted using a compensation factor based on the extent to which the operation complexity, operation efficiency, and completion rate exceed the second tolerance range, and the performance evaluation result is obtained based on the second performance score. The compensation factor is calculated based on the operation complexity, operation efficiency, and completion rate.

[0006] In the above technical solution, the first step is to acquire performance data and assessment indicator documents of frontline operators over a historical period. The performance data includes operational complexity, operational efficiency, and completion rate. Acquiring this data provides a solid data foundation for subsequent performance analysis, ensuring its comprehensiveness and accuracy. Next, performance calculations are performed on operational complexity, operational efficiency, and completion rate to obtain a first performance score. Based on performance calculation rules, performance indicators from different dimensions are transformed into a unified performance score, preparing for subsequent performance adjustments. The data distribution of operational complexity, operational efficiency, and completion rate is calculated, and tolerance is calculated based on this distribution to obtain a first tolerance range. Setting a reasonable tolerance range provides a basis for subsequent performance adjustments. Keyword extraction is performed on the assessment indicator documents to obtain assessment keywords. These keywords are compared with the performance data to obtain comparison results. Based on the comparison results, related indicators are searched in the assessment indicator documents to obtain related assessment indicators. Connectivity analysis can reveal the inherent relationship between performance data and assessment indicators, providing more precise guidance for subsequent performance adjustments. Based on the correlated assessment indicators, the first tolerance range is adjusted to obtain a second tolerance range. Adjusting the tolerance range allows for more flexible responses to employees' non-core work contributions, ensuring the accuracy of performance evaluation and determining whether operational complexity, efficiency, and completion rate fall within the second tolerance range. If these parameters fall outside the second tolerance range, a compensation factor is used to adjust the first performance score based on the extent to which they exceed the second tolerance range, resulting in a second performance score. The performance evaluation result is then derived from this second performance score. The compensation factor is calculated based on operational complexity, efficiency, and completion rate. By considering the above criteria for conforming to the tolerance range and adjusting performance scores using the compensation factor, the actual performance of employees at work can be more accurately reflected, thereby improving the accuracy of performance evaluation.

[0007] In some embodiments of this application, the second tolerance range includes a complexity tolerance range corresponding to operational complexity, an efficiency tolerance range corresponding to operational efficiency, and a completion tolerance range corresponding to completion rate. The step of adjusting the first performance score using a compensation factor based on the operational complexity, operational efficiency, and completion rate exceeding the second tolerance range to obtain a second performance score includes: Calculate the first portion of the operational complexity exceeding the complexity tolerance interval, calculate the second portion of the operational efficiency exceeding the efficiency tolerance interval, and calculate the third portion of the completion rate exceeding the completion tolerance interval; A fuzzy rule base is constructed for the first excess portion, the second excess portion, and the third excess portion. The compensation factor is determined based on the fuzzy rule base. The fuzzy rule base includes fuzzy rules between the operation complexity, the operation efficiency, the completion rate, and the compensation factor. The compensation factor is added to the preset adjustment parameter, and then multiplied by the first performance score to obtain the second performance score.

[0008] In some embodiments of this application, determining the compensation factor based on the fuzzy rule base includes: Bayesian optimization is performed on the fuzzy rules in the fuzzy rule base to obtain a first allocation probability corresponding to the operation complexity, a second allocation probability corresponding to the operation efficiency, and a third allocation probability corresponding to the completion rate. The first allocation probability is multiplied by the complexity compensation coefficient corresponding to the operation complexity to obtain the first compensation weight; the second allocation probability is multiplied by the efficiency compensation coefficient corresponding to the operation efficiency to obtain the second compensation weight; and the third allocation probability is multiplied by the completion compensation coefficient corresponding to the completion rate to obtain the third compensation weight. The compensation factor is obtained by weighting and summing the operational complexity with the first compensation weight, the operational efficiency with the second compensation weight, the completion rate with the third compensation weight.

[0009] In some embodiments of this application, the step of calculating the tolerance based on the data distribution to obtain the first tolerance interval includes: The data distribution is divided into multiple regions using a preset first quantile and second quantile, and a core region is selected from each of the regions. A quantile regression model is constructed using the historical period as the independent variable and the data corresponding to the core region as the dependent variable, and the regression coefficients corresponding to the quantile regression model are estimated. Under the regression coefficients, based on the quantile regression model, the lower tolerance limit corresponding to the first quantile and the upper tolerance limit corresponding to the second quantile at each time point in the historical period are calculated; The first tolerance range is constructed using the lower tolerance limit and the upper tolerance limit.

[0010] In some embodiments of this application, adjusting the first tolerance range according to the associated assessment indicator to obtain a second tolerance range includes: Calculate and dynamically adjust the weights based on the aforementioned related performance indicators; The dynamic adjustment weight is multiplied by the upper tolerance limit and the lower tolerance limit in the first tolerance interval to obtain the adjusted upper tolerance limit and the adjusted lower tolerance limit. The second tolerance range is constructed using the adjusted upper tolerance limit and the adjusted lower tolerance limit.

[0011] In some embodiments of this application, the step of calculating the dynamic adjustment weight based on the associated assessment indicator includes: evaluating the associated assessment indicator to obtain an evaluation value, and searching a preset adjustment weight table based on the evaluation value to determine the adjustment factor, wherein the adjustment weight table includes the correspondence between the evaluation value and the adjustment factor; Based on the adjustment factor, the dynamic adjustment weight is calculated using the dynamic adjustment formula. The dynamic adjustment formula is as follows: ω i The dynamic adjustment weight corresponding to the data at the i-th time point is represented by n, the total number of time points is represented by n, and the adjustment factor is represented by λ.

[0012] In some embodiments of this application, when a first-line operation is combined, the compensation factor is calculated based on the operation complexity, the operation efficiency, and the completion rate, including: The reward value of front-line operations is jointly evaluated to obtain the reward factor; Obtain the fourth compensation weight for the operational complexity, the fifth compensation weight for the operational efficiency, and the sixth compensation weight for the completion rate; The operation compensation factor is obtained by weighting and summing the operation complexity with the fourth compensation weight, the operation efficiency with the fifth compensation weight, the completion rate with the sixth compensation weight. The compensation factor is obtained by adding the reward factor and the operation compensation factor.

[0013] Secondly, embodiments of this application provide a performance appraisal device for frontline operators, the device comprising: The data acquisition module is used to acquire the performance data and assessment indicator documents of front-line operators over a historical period. The performance data includes operational complexity, operational efficiency, and completion rate. The performance evaluation module is used to perform performance calculations on the operational complexity, operational efficiency, and completion rate to obtain a first performance score. The tolerance calculation module is used to calculate the data distribution of the operation complexity, the operation efficiency and the completion rate, and to calculate the tolerance based on the data distribution to obtain a first tolerance interval. The association matching module is used to extract keywords from the assessment indicator document to obtain assessment keywords, compare the assessment keywords with the performance data to obtain comparison results, and search for related indicators of the comparison results in the assessment indicator document to obtain related assessment indicators. The tolerance adjustment module is used to adjust the first tolerance range according to the associated assessment indicators to obtain a second tolerance range, and to determine whether the operation complexity, the operation efficiency and the completion rate are within the second tolerance range. The performance adjustment module is used to adjust the first performance score using a compensation factor when the operation complexity, operation efficiency, and completion rate exceed the second tolerance range, based on the operation complexity, operation efficiency, and completion rate being outside the second tolerance range, to obtain a second performance score, and to obtain a performance appraisal result based on the second performance score, wherein the compensation factor is calculated based on the operation complexity, operation efficiency, and completion rate.

[0014] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, a user interface, a communication bus, and a network interface. The processor, the memory, the user interface, and the network interface are respectively connected to the communication bus. The memory is used to store instructions. The user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method described in any one of the first aspects.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium storing instructions that, when executed, perform the method described in any one of the methods provided in the first aspect above.

[0016] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. The approach first involves acquiring historical performance data and assessment indicator documents for frontline operators over a specific period. This performance data includes operational complexity, efficiency, and completion rate. Acquiring this data provides a solid foundation for subsequent performance analysis, ensuring its comprehensiveness and accuracy. Next, performance calculations are performed on operational complexity, efficiency, and completion rate to obtain an initial performance score. Performance calculation rules are then used to convert different dimensions of performance indicators into a unified score, preparing for subsequent performance adjustments. The data distribution of operational complexity, efficiency, and completion rate is calculated, and tolerance is calculated based on this distribution to obtain an initial tolerance range. Setting a reasonable tolerance range provides a basis for subsequent performance adjustments. Keyword extraction is performed on the assessment indicator documents to obtain assessment keywords. These keywords are then compared with the performance data to obtain comparison results. Based on these results, related indicators are identified in the assessment indicator documents to obtain associated assessment indicators. These related indicators are then used for further analysis. Analysis can reveal the inherent connection between performance data and assessment indicators, providing more precise guidance for subsequent performance adjustments. Based on the associated assessment indicators, the first tolerance range is adjusted to obtain a second tolerance range. Adjusting the tolerance range allows for more flexible responses to employees' non-core work contributions, ensuring the accuracy of performance evaluation and determining whether operational complexity, efficiency, and completion rate fall within the second tolerance range. If these factors fall outside the second tolerance range, a compensation factor is used to adjust the first performance score based on the extent to which operational complexity, efficiency, and completion rate exceed the second tolerance range, resulting in a second performance score. The performance evaluation result is then derived from this second performance score. The compensation factor is calculated based on operational complexity, efficiency, and completion rate. By considering the above-mentioned criteria for conforming to the tolerance range and adjusting performance scores using the compensation factor, the actual performance of employees can be more accurately reflected, thus improving the accuracy of performance evaluation. Therefore, this method effectively solves the problem in related technologies where the exclusion of non-core work tasks from the evaluation scope leads to performance evaluation results that cannot comprehensively and accurately reflect employees' actual work achievements, resulting in low accuracy.

[0017] 2. By constructing a fuzzy rule base for the excess portion, it is possible to link the excess portion with the compensation factor, so that the performance score can be adjusted according to the compensation factor in the future.

[0018] 3. By performing Bayesian optimization, a set of optimal solutions can be found, which allows the compensation factors obtained based on operational complexity, operational efficiency, and completion rate to accurately adjust performance scores, thereby improving the accuracy of performance evaluation.

[0019] 4. Quantile estimation can accurately capture data distribution characteristics, further improving the accuracy of performance evaluation. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a performance evaluation method for frontline operators provided in one embodiment of this application; Figure 2 yes Figure 1 A flowchart illustrating a sub-step of step S300; Figure 3 yes Figure 1 A flowchart illustrating a sub-step of step S600; Figure 4 yes Figure 3 A flowchart illustrating a sub-step of step S620; Figure 5 This is a schematic diagram of the structure of a front-line operator performance appraisal device provided in one embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0022] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0023] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0024] This application provides a method, apparatus, electronic device, and readable storage medium for performance appraisal of frontline operators. The method first acquires performance data and appraisal indicator documents of frontline operators over a historical period. The performance data includes operational complexity, operational efficiency, and completion rate. Acquiring this data provides a solid data foundation for subsequent performance analysis, ensuring its comprehensiveness and accuracy. Next, performance calculations are performed on operational complexity, operational efficiency, and completion rate to obtain a first performance score. Performance indicators from different dimensions are converted into a unified performance score using performance calculation rules, preparing for subsequent performance adjustments. The data distribution of operational complexity, operational efficiency, and completion rate is calculated, and tolerance is calculated based on this distribution to obtain a first tolerance range. Setting a reasonable tolerance range provides a basis for subsequent performance adjustments. Keyword extraction is performed on the appraisal indicator documents to obtain appraisal keywords. These keywords are compared with the performance data to obtain comparison results. Based on the comparison results, a comparison is performed within the appraisal indicator documents. Correlation indicators are derived from the results to obtain correlated performance indicators. Through correlation analysis, the intrinsic link between performance data and performance indicators can be revealed, providing more precise guidance for subsequent performance adjustments. Based on the correlated performance indicators, the first tolerance range is adjusted to obtain the second tolerance range. By adjusting the tolerance range, we can more flexibly address employees' non-core work efforts, ensuring the accuracy of performance evaluation and determining whether operational complexity, operational efficiency, and completion rate are within the second tolerance range. If they are not within the second tolerance range, the first performance score is adjusted using a compensation factor based on the extent to which operational complexity, operational efficiency, and completion rate exceed the second tolerance range, resulting in a second performance score. The performance evaluation result is obtained based on the second performance score, where the compensation factor is calculated based on operational complexity, operational efficiency, and completion rate. By judging whether the performance falls within the tolerance range and adjusting the performance score using the compensation factor, we can more accurately reflect the employee's actual performance at work, thereby improving the accuracy of performance evaluation.

[0025] It should be noted that this performance appraisal method for frontline operators is used to assess frontline operators in the supply chain, including order allocation, supplier management, and order production. It enables collaborative performance appraisal of frontline operators, comprehensively, accurately, and fairly reflecting employees' actual performance at work, and providing a solid foundation for enterprise management.

[0026] The technical solutions provided in the embodiments of this application will be further described below with reference to the accompanying drawings.

[0027] Reference Figure 1 , Figure 1This is a flowchart illustrating the performance appraisal method for frontline operators provided in this application embodiment. The performance appraisal method for frontline operators is applied to a performance appraisal device for frontline operators, and is executed by a processor in an electronic device or a readable storage medium. The performance appraisal method for frontline operators includes steps S100, S200, S300, S400, S500, and S600.

[0028] Step S100: Obtain performance data and assessment indicator documents of front-line operators over a historical period. The performance data includes operational complexity, operational efficiency, and completion rate.

[0029] In one embodiment, the current point in time is used as the time period, and the historical period can be one month, three months, or six months prior to the current point in time. The performance data of frontline operators includes operational complexity, operational efficiency, and completion rate. Operational complexity includes the complexity of workflow steps, required skill levels, and equipment operation difficulty; operational efficiency is the amount of work completed per unit of time and the length of task processing time; the completion rate is the ratio of the actual number of tasks completed to the planned number of tasks to be completed as stipulated by the company. Specifically, operational complexity is assessed through a complexity assessment table, where professionals assign scores to each operational process, operational difficulty, or skill level based on their experience. For example, the complexity is determined by referring to the complexity assessment table based on the operator's actual operation and skill level. Operational efficiency is calculated as the ratio of total completion to time; the completion rate is calculated by the ratio of actual completion to planned completion. The performance evaluation document is a document within the company's performance management system used to clarify performance evaluation standards, content, methods, and related requirements. It records in written form key information such as the performance indicators, weight allocation, scoring standards, data sources, and evaluation cycles set by the company for different positions, departments, or projects. The performance evaluation indicator document can be downloaded from the company's official website. This document includes the evaluation targets (frontline operators), evaluation indicators (operational complexity, operational efficiency, and completion rate), the weighting of each indicator, and the scoring values ​​for each item. These details are not elaborated here. The obtained data is stored in a file. A pre-defined file reading function is used to retrieve historical performance data and the performance evaluation indicator document for frontline operators over a specific period. This multi-dimensional data approach avoids relying on a single data source and provides data support for subsequent analysis. The file reading function can be either `open()` or `read()`.

[0030] Step S200: Calculate the performance of operation complexity, operation efficiency and completion rate to obtain the first performance score.

[0031] In one embodiment, the operational complexity, operational efficiency, and completion rate are first preprocessed to quantify these metrics, avoiding inconsistencies caused by different data units that could hinder subsequent calculations. Specifically, the quantification process involves normalizing operational complexity, operational efficiency, and completion rate to obtain normalized operational complexity, normalized operational efficiency, and normalized completion rate. Then, the first score weight corresponding to operational complexity, the second score weight corresponding to operational efficiency, and the third score weight corresponding to completion rate are obtained. The results of multiplying the normalized operational complexity by the first score weight, the normalized operational efficiency by the second score weight, and the normalized completion rate by the third score weight are summed to obtain the first performance score. This performance calculation rule transforms performance indicators from different dimensions into a unified performance score, preparing for subsequent performance adjustments. It should be noted that the first, second, and third score weights can be the weight allocations specified in the assessment indicator document, or they can be performance calculation weights adjusted by professionals based on experience.

[0032] It should also be noted that data preprocessing includes data cleaning for operational complexity, operational efficiency, and completion rate, removing obvious errors, missing values, and outliers from the performance evaluation indicator data. For example, for operational efficiency indicators, if a data point far exceeds the theoretical maximum efficiency value of normal production equipment, it can be considered an outlier and removed. The cleaned data is then normalized according to the above process to achieve data consistency.

[0033] In another embodiment, operational complexity, operational efficiency, and completion rate can be mapped to scores separately. Specifically: Since operational complexity is obtained as a score from a complexity assessment table, the score is mapped to a value between 0 and 10. For example, if the score found in the complexity assessment table is between 0 and 100, the corresponding value is reduced by a factor of 10. If the score found in the complexity assessment table is between 0 and 10, the operational complexity is retained without any processing. Operational efficiency is mapped to a score between 0 and 10, with professionals assigned different scores. The completion rate is mapped to a percentage, multiplied by 100, the percentage sign removed, and then reduced by a factor of 10, limiting the completion rate to a value between 0 and 10. Then, operational complexity, operational efficiency, and completion rate are added together to obtain the first performance score. The above performance calculation rules transform performance indicators of different dimensions into a unified performance score, preparing for subsequent performance adjustments.

[0034] Step S300: Calculate the data distribution of operation complexity, operation efficiency and completion rate, and calculate the tolerance based on the data distribution to obtain the first tolerance interval.

[0035] In one embodiment, the mean and variance of operational complexity, operational efficiency, and completion rate are calculated separately. Then, the distribution of operational complexity, operational efficiency, and completion rate is calculated with respect to the mean and variance to obtain the data distribution of operational complexity, operational efficiency, and completion rate. Tolerance is calculated based on the data distribution to obtain a first tolerance range. The tolerance is set through the data distribution. By setting a reasonable tolerance range, a basis is provided for subsequent performance adjustments.

[0036] like Figure 2 As shown, tolerance is calculated based on the data distribution to obtain the first tolerance interval, including but not limited to the following steps: Step S310: Divide the data distribution using the preset first quantile and second quantile to obtain multiple partitioned regions, and select the core region from each partitioned region.

[0037] In some possible embodiments of this application, the preset first quantile is 0.25 and the preset second quantile is 0.75. The first and second quantiles can divide the data distribution into four parts, and take the two middle continuous parts as the core area. The middle 50% of the data range can better reflect the central tendency and normal fluctuation range of the data, and can improve the accuracy of subsequent calculations.

[0038] Step S320: Construct a quantile regression model using a historical period as the independent variable and the data corresponding to the core region as the dependent variable, and estimate the regression coefficients corresponding to the quantile regression model.

[0039] In some possible embodiments of this application, various time points within a historical period are used as independent variables. These time points can be divided by days, such as a single day or a five-day interval, and can be adjusted accordingly. Data corresponding to the core region is used as the dependent variable, representing operational complexity, operational efficiency, and completion rate. A first quantile regression model is constructed for the time series and operational complexity, a second quantile regression model for operational efficiency, and a third quantile regression model for completion rate. Linear programming is then used to estimate the regression coefficients of these three models, yielding the regression coefficients for each quantile regression model. Specifically, linear programming involves applying a linear function to the time series and the data corresponding to the core region; selecting two sets of data yields a straight line, and the coefficients corresponding to this line are the regression coefficients. By constructing quantile regression models, data from different dimensions can be fitted, facilitating subsequent calculations of the tolerance interval.

[0040] Step S330: Under the regression coefficients, calculate the lower tolerance limit corresponding to the first quantile and the upper tolerance limit corresponding to the second quantile for each time point in a historical period, based on the quantile regression model.

[0041] In some possible embodiments of this application, the regression coefficients calculated according to step S320 correspond to quantile values ​​for each time point in the quantile regression model. For each time point within a historical period, the 0.25 quantile and 0.75 quantile values ​​are calculated. The value corresponding to the 0.25 quantile is used as the lower tolerance limit, and the value corresponding to the 0.75 quantile is used as the upper tolerance limit. Based on the obtained lower and upper tolerance limits, a first tolerance interval is subsequently obtained.

[0042] Step S340: Construct the first tolerance range using the lower tolerance limit and the upper tolerance limit.

[0043] In some possible embodiments of this application, numerical intervals are constructed based on the lower and upper tolerance limits corresponding to operational complexity to obtain a complexity tolerance interval; numerical intervals are constructed based on the lower and upper tolerance limits corresponding to operational efficiency to obtain an efficiency tolerance interval; and numerical intervals are constructed based on the lower and upper tolerance limits corresponding to completion rate to obtain a completion tolerance interval. Then, the complexity tolerance interval, efficiency tolerance interval, and completion tolerance interval are stored in a list to obtain a first tolerance interval. The storage method involves concatenating the complexity tolerance interval, efficiency tolerance interval, and completion tolerance interval, and distinguishing between different tolerance intervals based on their positional relationships within the list. Constructing tolerance intervals provides a basis for subsequent performance adjustments.

[0044] Step S400: Extract keywords from the assessment indicator document to obtain assessment keywords, compare the assessment keywords with performance data to obtain comparison results, and search for related indicators in the assessment indicator document based on the comparison results to obtain related assessment indicators.

[0045] In one embodiment, natural language processing can be used to process keywords in the assessment indicator document. Specifically, the assessment indicator document is first preprocessed, including converting it into plain text format and removing irrelevant information such as punctuation. Then, the processed text is segmented into words, which can be done using a word segmentation tool. Finally, a keyword extraction algorithm is used to extract keywords from the text to obtain assessment keywords. The keyword extraction algorithm can be either the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm or the BERT model. Both the algorithm and the model are pre-set and will not be described in detail here.

[0046] Then, the operational complexity, operational efficiency, and completion rate of performance data are matched with assessment keywords to establish a mapping relationship. Character matching algorithms can be used to obtain keywords corresponding to operational complexity, operational efficiency, and completion rate. A similarity algorithm is then used to calculate the similarity between these keywords and the assessment keywords. The similarity values ​​are used as comparison results, and each similarity is sorted in descending order. Higher similarity indicates stronger correlation. The top N similarity values ​​are selected to obtain the strongly correlated indicators in the comparison results, and these strongly correlated indicators are used as the associated assessment indicators. Through the above method, associated assessment indicators for operational complexity, operational efficiency, and completion rate are obtained. The scoring weights and scores for each associated assessment indicator can be found in the assessment indicator document. The similarity algorithm is text similarity. For example, operational efficiency and production efficiency have a high similarity and strong correlation. Production efficiency can help operators assist other employees in completing production, and corresponding performance rewards should be given to the operator's performance evaluation; therefore, production efficiency is used as an associated assessment indicator.

[0047] Step S500: Adjust the first tolerance range according to the relevant assessment indicators to obtain the second tolerance range, and determine whether the operation complexity, operation efficiency and completion rate are within the second tolerance range.

[0048] In one embodiment, the first tolerance interval is adjusted according to the associated assessment indicators to obtain a second tolerance interval, including but not limited to: calculating the dynamic adjustment weight according to the associated assessment indicators; multiplying the dynamic adjustment weight by the upper and lower tolerance limits in the first tolerance interval to obtain the adjusted upper and lower tolerance limits; and using the adjusted upper and lower tolerance limits to construct the second tolerance interval.

[0049] Specifically, based on the relevant performance indicators, the dynamic adjustment weights are calculated, including but not limited to: evaluating the relevant performance indicators to obtain evaluation values; looking up a preset adjustment weight table based on the evaluation values ​​to determine the adjustment factors, wherein the adjustment weight table includes the correspondence between evaluation values ​​and adjustment factors; and calculating the dynamic adjustment weights using a dynamic adjustment formula based on the adjustment factors. The dynamic adjustment formula is as follows: ω i λ represents the dynamically adjusted weight corresponding to the data at the i-th time point, n represents the total number of time points, and λ represents the adjustment factor.

[0050] In some possible embodiments of this application, an evaluation value is determined based on the selected relevant assessment indicators and the similarity value corresponding to those indicators. A higher similarity value indicates a stronger correlation, and a higher evaluation value corresponds to a larger adjustment factor. The similarity measure is quantified into a corresponding evaluation value; for example, a similarity of 0.5 results in an evaluation value of 0.5. The adjustment weight table includes the correspondence between evaluation values ​​and adjustment factors. Professionals provide the adjustment factor corresponding to the evaluation value of the relevant assessment indicator. By searching the adjustment weight table, the adjustment factor corresponding to the evaluation value can be found, allowing for subsequent dynamic weight adjustments based on the adjustment factor.

[0051] Then, based on the adjustment factor, the dynamic adjustment weight is calculated using a dynamic adjustment formula. In this formula, by setting an exponential adjustment method, it can adapt to the dynamic changes in the operator's extra workload. Based on the correlation of the extra workload, the decay rate of the weight is controlled by the adjustment factor; for example, the adjustment factor can be 0.1, 0.3, or 0.5. When the correlation between the performance indicators is strong, the adjustment factor can be adjusted from 0.1 to 0.3, thereby achieving dynamic adjustment of the weight.

[0052] In some possible embodiments of this application, the dynamic adjustment weight is multiplied by the upper and lower limits of the tolerance range in the first tolerance range, respectively. Specifically, the dynamic adjustment weight is multiplied by the complexity tolerance range, efficiency tolerance range, and completion tolerance range within the first tolerance range. For example, multiplying the dynamic adjustment weight by the upper and lower limits of the tolerance range in the complexity tolerance range yields the upper and lower limits of the adjustment tolerance range for the complexity tolerance range. Similarly, multiplying the dynamic adjustment weight by the upper and lower limits of the tolerance range in the efficiency tolerance range yields the upper and lower limits of the adjustment tolerance range for the efficiency tolerance range; and multiplying the dynamic adjustment weight by the upper and lower limits of the tolerance range in the completion tolerance range yields the upper and lower limits of the adjustment tolerance range for the completion tolerance range. By adjusting these upper and lower limits, a second tolerance range is subsequently obtained.

[0053] Then, the adjusted complexity tolerance interval, adjusted efficiency tolerance interval, and adjusted completion tolerance interval are listed and stored to obtain the second tolerance interval. The storage method involves concatenating these three intervals and distinguishing them based on their position within the list. Adjusting the first tolerance interval allows for more flexible handling of employees' non-core work contributions, ensuring the accuracy of performance evaluations. It should be noted that, taking operational complexity as an example, when frontline operators engage in non-core work contributions, the operational complexity increases. Adjusting the tolerance interval as described above would lower the tolerance interval data, but the increased complexity value would exceed the tolerance interval. Therefore, a compensation factor is used to compensate for this, thus adjusting the performance score.

[0054] In one embodiment, determining whether operational complexity, operational efficiency, and completion rate are within the second tolerance range specifically involves: determining whether operational complexity is within the adjusted complexity tolerance range, whether operational efficiency is within the adjusted efficiency tolerance range, and whether completion rate is within the adjusted completion tolerance range. This segmented determination provides a basis for accurate subsequent performance score adjustments. All of the above determinations are numerical comparisons. For example, operational complexity is compared with the upper and lower tolerance limits of the adjusted complexity tolerance range. If operational complexity is greater than the lower tolerance limit but less than the upper tolerance limit, it is within the complexity tolerance range; if operational complexity is less than the lower tolerance limit or greater than the upper tolerance limit, it is outside the complexity tolerance range, and the excess portion needs to be calculated. The determination methods for operational efficiency and completion rate are similar to those for operational complexity and will not be elaborated here.

[0055] Step S600: When the performance score is outside the second tolerance range, the first performance score is adjusted using a compensation factor based on the operational complexity, operational efficiency, and completion rate exceeding the second tolerance range to obtain a second performance score. The performance evaluation result is obtained based on the second performance score. The compensation factor is calculated based on the operational complexity, operational efficiency, and completion rate.

[0056] In one embodiment, if an operation is outside the second tolerance range—meaning at least one of operational complexity, operational efficiency, and completion rate is outside its corresponding tolerance range—it indicates that the frontline operator has completed work outside their primary job duties after adjustment, requiring an adjustment to their performance score. Based on the deviations of operational complexity, operational efficiency, and completion rate from the second tolerance range, a compensation factor is used to adjust the first performance score, resulting in a second performance score. This second performance score reflects the frontline operator's work performance under the relevant assessment indicators. Based on the obtained second performance score, a review and confirmation process is implemented to achieve performance evaluation for the frontline operator.

[0057] It should be noted that when all scores are within the second tolerance range, it indicates that the performance score assessment meets the evaluation standards for the operator's job duties. There is no need to adjust the performance score. The first performance score is used as the second performance score. The second performance score is then reviewed and the results are confirmed to achieve the performance evaluation of front-line operators.

[0058] In another embodiment, the second tolerance interval includes a complexity tolerance interval corresponding to operational complexity, an efficiency tolerance interval corresponding to operational efficiency, and a completion tolerance interval corresponding to completion rate; such as Figure 3 As shown, based on the operational complexity, operational efficiency, and completion rate exceeding the second tolerance range, the first performance score is adjusted using a compensation factor to obtain the second performance score, including but not limited to the following steps: Step S610: Calculate the first excess portion of the operation complexity and the complexity tolerance interval, calculate the second excess portion of the operation efficiency and the efficiency tolerance interval, and calculate the third excess portion of the completion rate and the completion tolerance interval.

[0059] In some possible embodiments of this application, calculating the first excess portion of the operational complexity and the complexity tolerance interval specifically involves: comparing the operational complexity with the upper limit of the tolerance; if the operational complexity is greater than the upper limit of the tolerance, subtracting the upper limit of the tolerance from the operational complexity yields the first excess portion. If the operational complexity is less than the upper limit of the tolerance, since it is in the excess portion, it indicates that the operational complexity is also less than the lower limit of the tolerance; subtracting the upper limit of the tolerance from the operational complexity yields the first excess portion. Similarly, the calculation process for the second excess portion of the operational efficiency and the efficiency tolerance interval, and the calculation process for the third excess portion of the completion rate and the completion tolerance interval, are similar to the above calculation methods and will not be elaborated here. The excess portions are calculated to facilitate the subsequent construction of the fuzzy rule base.

[0060] Step S620: Construct a fuzzy rule base for the first, second, and third excess portions, and determine the compensation factor based on the fuzzy rule base. The fuzzy rule base includes fuzzy rules relating to operational complexity, operational efficiency, completion rate, and compensation factor.

[0061] In some possible embodiments of this application, when the operational complexity exceeds the upper tolerance limit, the first excess portion is represented as high; when the operational complexity is less than the lower tolerance limit, the first excess portion is represented as low; and when the operational complexity is less than the upper tolerance limit but greater than the lower tolerance limit, the first excess portion is represented as medium. Similarly, the rules for operational efficiency and completion rate are similar to those for operational complexity, and will not be elaborated here. No fuzzy rules are established for this case. A fuzzy rule base is constructed for the first, second, and third excess portions. The fuzzy rule base includes fuzzy rules relating operational complexity, operational efficiency, completion rate, and compensation factor. The fuzzy rules use if-then statements to establish a correlation between operational complexity, operational efficiency, completion rate, and compensation factor. For example, the fuzzy rule base includes the following fuzzy rules: if operational complexity is high, operational efficiency is low, and completion rate is low, then the compensation factor is a large negative value; if operational complexity is medium, operational efficiency is high, and completion rate is high, then the compensation factor is a large positive value.

[0062] like Figure 4 As shown, the compensation factor is determined based on the fuzzy rule base, including but not limited to the following steps: Step S621: Perform Bayesian optimization on the fuzzy rules in the fuzzy rule base to obtain the first allocation probability corresponding to the operation complexity, the second allocation probability corresponding to the operation efficiency, and the third allocation probability corresponding to the completion rate.

[0063] In some possible embodiments of this application, keywords are extracted from fuzzy rules in the fuzzy rule base to obtain extracted keywords. A trained Bayesian network is then used to optimize the extracted keywords, yielding coefficients corresponding to each rule. Specifically, this results in a first allocation probability corresponding to operational complexity, a second allocation probability corresponding to operational efficiency, and a third allocation probability corresponding to completion rate. These first, second, and third allocation probabilities represent the optimal solutions after Bayesian optimization, allowing for subsequent adjustments to the weights of compensation factors to improve the accuracy of performance evaluation.

[0064] Step S622: Multiply the first allocation probability by the complexity compensation coefficient corresponding to the operation complexity to obtain the first compensation weight; multiply the second allocation probability by the efficiency compensation coefficient corresponding to the operation efficiency to obtain the second compensation weight; and multiply the third allocation probability by the completion compensation coefficient corresponding to the completion rate to obtain the third compensation weight.

[0065] In some possible embodiments of this application, the complexity compensation coefficient, efficiency compensation coefficient, and completion compensation coefficient are all parameters pre-set by professionals, used to obtain the compensation factor by weighted summation with operational complexity, operational efficiency, and completion rate, respectively. The compensation factor can be adjusted by modifying the complexity compensation coefficient, efficiency compensation coefficient, and completion compensation coefficient according to the associated assessment indicators, thereby enabling collaborative performance assessment at different levels and improving the accuracy of the assessment. The first compensation weight is obtained by multiplying the first allocation probability by the complexity compensation coefficient corresponding to operational complexity; the second compensation weight is obtained by multiplying the second allocation probability by the efficiency compensation coefficient corresponding to operational efficiency; and the third compensation weight is obtained by multiplying the third allocation probability by the completion compensation coefficient corresponding to the completion rate. These first, second, and third compensation weights are dynamically adjusted to determine the subsequent compensation factor.

[0066] Step S623: The operation complexity is weighted and summed with the first compensation weight, the operation efficiency, the second compensation weight, the completion rate, and the third compensation weight to obtain the compensation factor.

[0067] In some possible embodiments of this application, the result of multiplying operational complexity by the fourth compensation weight, operational efficiency by the fifth compensation weight, and completion rate by the sixth compensation weight is summed to obtain the operational compensation factor. This operational compensation factor is a compensation parameter used to adjust the performance score of front-line operators when they complete their duties, thereby increasing or decreasing the performance appraisal score. This allows for subsequent adjustments to the performance score using the compensation factor, making the performance appraisal more comprehensive and accurate.

[0068] Step S630: Add the compensation factor to the preset adjustment parameter, and then multiply it by the first performance score to obtain the second performance score.

[0069] In some possible embodiments of this application, the preset adjustment parameter is set to 1 to adjust the compensation factor. That is, the result of adding 1 to the compensation factor is multiplied by the first performance score to obtain the second performance score. This is expressed by the following formula: S2 = S1 * (1 + F), where F is the compensation factor, S1 is the first performance score, and S2 is the second performance score. When the compensation factor is positive, multiplying the first performance score by a value greater than 1 increases the overall performance score, thus improving performance. When the compensation factor is negative, multiplying the first performance score by a value less than 1 decreases the overall performance score, thus reducing performance. By adjusting the performance scores, the second performance score better reflects the work results of frontline operators under the relevant assessment indicators, i.e., the actual performance corresponding to their work, achieving coordinated performance assessment at different levels and improving accuracy.

[0070] In one embodiment, when there is a joint operation on the front line, the compensation factor is calculated based on operational complexity, operational efficiency, and completion rate, including but not limited to: evaluating the reward value of the joint operation on the front line to obtain a reward factor; obtaining a fourth compensation weight for operational complexity, a fifth compensation weight for operational efficiency, and a sixth compensation weight for completion rate; weighting and summing the operational complexity with the fourth compensation weight, the operational efficiency with the fifth compensation weight, and the completion rate with the sixth compensation weight to obtain an operational compensation factor; and adding the reward factor and the operational compensation factor to obtain a compensation factor.

[0071] In some possible embodiments of this application, when there is collaboration on frontline operations—that is, collaboration with other companies or with other departments within the same company—it requires increased skills, operational complexity, or completion rates, placing higher demands on frontline operators. A reward value assessment is conducted for these frontline operation collaborations. Professionals, based on experience, score the required additional skills or workload, and the corresponding score serves as a reward factor. This reward factor is used to improve performance evaluation scores, indicating that the frontline operator possesses outstanding capabilities.

[0072] Then, the fourth compensation weight for operational complexity, the fifth compensation weight for operational efficiency, and the sixth compensation weight for completion rate are obtained. These weights are pre-set by professionals and can be directly read using the `read()` function. The operational compensation factor is obtained by summing the results of multiplying operational complexity by the fourth compensation weight, operational efficiency by the fifth compensation weight, and completion rate by the sixth compensation weight. This operational compensation factor is a compensation parameter used to adjust performance scores when frontline operators complete their duties, adjusting scores accordingly. Finally, the reward factor is added to the operational compensation factor to obtain the compensation factor, which is used to adjust performance scores and improve the accuracy of performance evaluation. It should be noted that the compensation factor should not be too large to avoid deviations from normal performance evaluation standards.

[0073] like Figure 5As shown in the figure, this application embodiment provides a front-line operator performance appraisal device 100. This device 100 acquires performance data and appraisal indicator documents of front-line operators over a historical period through a data acquisition module 110. The performance data includes operational complexity, operational efficiency, and completion rate. Acquiring the performance data and appraisal indicator documents provides a solid data foundation for subsequent performance analysis, ensuring the comprehensiveness and accuracy of the analysis. Then, the performance evaluation module 120 performs performance calculations on operational complexity, operational efficiency, and completion rate to obtain a first performance score. Performance calculation rules convert different dimensions of performance indicators into a unified performance score, preparing for subsequent performance adjustments. Next, the tolerance calculation module 130 calculates the data distribution of operational complexity, operational efficiency, and completion rate, and calculates the tolerance based on the data distribution to obtain a first tolerance range. Setting a reasonable tolerance range provides a basis for subsequent performance adjustments. Finally, the association matching module 140 extracts keywords from the appraisal indicator documents to obtain appraisal keywords. The appraisal keywords are compared with the performance data to obtain comparison results. Based on the comparison results, adjustments are made to the appraisal indicators. By identifying relevant indicators in the benchmark document and comparing the results, related assessment indicators can be obtained. Through correlation analysis, the inherent connection between performance data and assessment indicators can be revealed, providing more precise guidance for subsequent performance adjustments. Then, using the tolerance adjustment module 150, the first tolerance range is adjusted based on the related assessment indicators to obtain a second tolerance range. Adjusting the tolerance range allows for more flexible responses to employees' non-core work contributions, ensuring the accuracy of performance evaluation and determining whether operational complexity, efficiency, and completion rate are within the second tolerance range. When these factors are outside the second tolerance range, the performance adjustment module 160 adjusts the first performance score using a compensation factor based on the extent to which operational complexity, efficiency, and completion rate exceed the second tolerance range, resulting in a second performance score. The performance evaluation result is then derived based on this second performance score. The compensation factor is calculated based on operational complexity, efficiency, and completion rate. By considering the above judgment of whether the performance falls within the tolerance range and introducing a compensation factor to adjust the performance score, the actual performance of employees at work can be more accurately reflected, thereby improving the accuracy of performance evaluation.

[0074] It should be noted that the data acquisition module 110 is connected to the performance evaluation module 120, the performance evaluation module 120 is connected to the tolerance calculation module 130, the tolerance calculation module 130 is connected to the association matching module 140, the association matching module 140 is connected to the tolerance adjustment module 150, and the tolerance adjustment module 150 is connected to the performance adjustment module 160. The aforementioned performance appraisal method for frontline operators is applied to the frontline operator performance appraisal device 100. The device acquires historical performance data and appraisal indicator documents for frontline operators over a specific period. The performance data includes operational complexity, operational efficiency, and completion rate. This acquisition provides a solid data foundation for subsequent performance analysis, ensuring the comprehensiveness and accuracy of the analysis. Performance calculations are then performed on operational complexity, operational efficiency, and completion rate to obtain a first performance score. Performance indicators from different dimensions are converted into a unified performance score using performance calculation rules, preparing for subsequent performance adjustments. The data distribution of operational complexity, operational efficiency, and completion rate is calculated, and tolerance is calculated based on the data distribution to obtain a first tolerance range. Setting a reasonable tolerance range provides a basis for subsequent performance adjustments. Keyword extraction is performed on the appraisal indicator documents to obtain appraisal keywords. These keywords are compared with the performance data to obtain comparison results. Based on the comparison results, the comparison results are searched within the appraisal indicator documents. By identifying related indicators, we can obtain related performance indicators. Through correlation analysis, we can reveal the intrinsic connection between performance data and performance indicators, providing more precise guidance for subsequent performance adjustments. Based on the related performance indicators, we adjust the first tolerance range to obtain the second tolerance range. By adjusting the tolerance range, we can more flexibly address employees' non-core work efforts, ensuring the accuracy of performance evaluation and determining whether operational complexity, efficiency, and completion rate are within the second tolerance range. If they are not within the second tolerance range, we adjust the first performance score using a compensation factor based on the extent to which operational complexity, efficiency, and completion rate exceed the second tolerance range, obtaining the second performance score. The performance evaluation result is then obtained based on the second performance score. The compensation factor is calculated based on operational complexity, efficiency, and completion rate. By judging whether the performance falls within the tolerance range and adjusting the performance score using the compensation factor, we can more accurately reflect the employee's actual performance at work, thereby improving the accuracy of performance evaluation.

[0075] It should also be noted that the apparatus provided in the above embodiments is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0076] This application also discloses an electronic device. (See reference...) Figure 6 , Figure 6 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.

[0077] The communication bus 502 is used to enable communication between these components.

[0078] The user interface 503 may include a display screen and a camera. Optionally, the user interface 503 may also include a standard wired interface and a wireless interface.

[0079] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0080] The processor 501 may include one or more processing cores. The processor 501 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 505, and by calling data stored in memory 505. Optionally, the processor 501 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 501 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 501 and may be implemented as a separate chip.

[0081] The memory 505 may include random access memory (RAM) or read-only memory. Optionally, the memory 505 may include a non-transitory computer-readable storage medium. The memory 505 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 505 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 505 may also be at least one storage device located remotely from the aforementioned processor 501. (Refer to...) Figure 6 The memory 505, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for evaluating the performance of frontline operators.

[0082] exist Figure 6In the illustrated electronic device 500, the user interface 503 is mainly used to provide an input interface for the user and to acquire user input data; while the processor 501 can be used to call an application program stored in the memory 505 for a performance evaluation method for front-line operators. When executed by one or more processors 501, the electronic device 500 performs one or more methods as described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0083] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0084] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0085] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0086] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0087] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0088] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0089] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A performance appraisal method for front-line operators, characterized in that, The method includes: Obtain performance data and assessment indicator documents of front-line operators over a historical period, including operational complexity, operational efficiency, and completion rate; A first performance score is obtained by performing performance calculations on the operational complexity, operational efficiency, and completion rate. Calculate the data distribution of the operation complexity, the operation efficiency, and the completion rate, and calculate the tolerance based on the data distribution to obtain a first tolerance interval; Keyword extraction is performed on the assessment indicator document to obtain assessment keywords. The assessment keywords are compared with the performance data to obtain comparison results. Based on the comparison results, the related indicators of the comparison results are searched in the assessment indicator document to obtain related assessment indicators. Based on the associated assessment indicators, the first tolerance range is adjusted to obtain a second tolerance range, and it is determined whether the operation complexity, the operation efficiency, and the completion rate are within the second tolerance range. When the first performance score is outside the second tolerance range, the second performance score is adjusted using a compensation factor based on the extent to which the operation complexity, operation efficiency, and completion rate exceed the second tolerance range, and the performance evaluation result is obtained based on the second performance score. The compensation factor is calculated based on the operation complexity, operation efficiency, and completion rate.

2. The method according to claim 1, characterized in that, The second tolerance range includes the complexity tolerance range corresponding to the operational complexity, the efficiency tolerance range corresponding to the operational efficiency, and the completion tolerance range corresponding to the completion rate; The step of adjusting the first performance score using a compensation factor based on the operational complexity, operational efficiency, and completion rate exceeding the second tolerance range to obtain a second performance score includes: Calculate the first portion of the operational complexity exceeding the complexity tolerance interval, calculate the second portion of the operational efficiency exceeding the efficiency tolerance interval, and calculate the third portion of the completion rate exceeding the completion tolerance interval; A fuzzy rule base is constructed for the first excess portion, the second excess portion, and the third excess portion. The compensation factor is determined based on the fuzzy rule base. The fuzzy rule base includes fuzzy rules between the operation complexity, the operation efficiency, the completion rate, and the compensation factor. The compensation factor is added to the preset adjustment parameter, and then multiplied by the first performance score to obtain the second performance score.

3. The method according to claim 2, characterized in that, Determining the compensation factor based on the fuzzy rule base includes: Bayesian optimization is performed on the fuzzy rules in the fuzzy rule base to obtain a first allocation probability corresponding to the operation complexity, a second allocation probability corresponding to the operation efficiency, and a third allocation probability corresponding to the completion rate. The first allocation probability is multiplied by the complexity compensation coefficient corresponding to the operation complexity to obtain the first compensation weight; the second allocation probability is multiplied by the efficiency compensation coefficient corresponding to the operation efficiency to obtain the second compensation weight; and the third allocation probability is multiplied by the completion compensation coefficient corresponding to the completion rate to obtain the third compensation weight. The compensation factor is obtained by weighting and summing the operational complexity with the first compensation weight, the operational efficiency with the second compensation weight, the completion rate with the third compensation weight.

4. The method according to claim 1, characterized in that, The step of calculating the tolerance based on the data distribution to obtain the first tolerance interval includes: The data distribution is divided into multiple regions using a preset first quantile and second quantile, and a core region is selected from each of the regions. A quantile regression model is constructed using the historical period as the independent variable and the data corresponding to the core region as the dependent variable, and the regression coefficients corresponding to the quantile regression model are estimated. Under the regression coefficients, based on the quantile regression model, the lower tolerance limit corresponding to the first quantile and the upper tolerance limit corresponding to the second quantile at each time point in the historical period are calculated; The first tolerance range is constructed using the lower tolerance limit and the upper tolerance limit.

5. The method according to claim 4, characterized in that, The step of adjusting the first tolerance range according to the associated assessment indicators to obtain the second tolerance range includes: Calculate and dynamically adjust the weights based on the aforementioned related performance indicators; The dynamic adjustment weight is multiplied by the upper tolerance limit and the lower tolerance limit in the first tolerance interval to obtain the adjusted upper tolerance limit and the adjusted lower tolerance limit. The second tolerance range is constructed using the adjusted upper tolerance limit and the adjusted lower tolerance limit.

6. The method according to claim 5, characterized in that, The step of calculating and dynamically adjusting weights based on the associated performance indicators includes: The relevant assessment indicators are evaluated to obtain evaluation values. Based on the evaluation values, a preset adjustment weight table is searched to determine the adjustment factors. The adjustment weight table includes the correspondence between evaluation values ​​and adjustment factors. Based on the adjustment factor, the dynamic adjustment weight is calculated using the dynamic adjustment formula. The dynamic adjustment formula is as follows: ω i The dynamic adjustment weight corresponding to the data at the i-th time point is represented by n, the total number of time points is represented by n, and the adjustment factor is represented by λ.

7. The method according to claim 1, characterized in that, In the presence of joint operations, the compensation factor is calculated based on the operational complexity, operational efficiency, and completion rate, including: The reward value of front-line operations is jointly evaluated to obtain the reward factor; Obtain the fourth compensation weight for the operational complexity, the fifth compensation weight for the operational efficiency, and the sixth compensation weight for the completion rate; The operation compensation factor is obtained by weighting and summing the operation complexity with the fourth compensation weight, the operation efficiency with the fifth compensation weight, the completion rate with the sixth compensation weight. The compensation factor is obtained by adding the reward factor and the operation compensation factor.

8. A performance evaluation device for front-line operators, characterized in that, The device includes: The data acquisition module (110) is used to acquire the performance data and assessment indicator documents of front-line operators over a historical period of time. The performance data includes operational complexity, operational efficiency, and completion rate. The performance evaluation module (120) is used to perform performance calculations on the operation complexity, operation efficiency and completion rate to obtain a first performance score; The tolerance calculation module (130) is used to calculate the data distribution of the operation complexity, the operation efficiency and the completion rate, and to calculate the tolerance based on the data distribution to obtain a first tolerance interval; The association matching module (140) is used to extract keywords from the assessment indicator document to obtain assessment keywords, compare the assessment keywords with the performance data to obtain comparison results, and search for the associated indicators of the comparison results in the assessment indicator document to obtain associated assessment indicators. The tolerance adjustment module (150) is used to adjust the first tolerance range according to the associated assessment indicators to obtain a second tolerance range, and to determine whether the operation complexity, the operation efficiency and the completion rate are within the second tolerance range. The performance adjustment module (160) is used to adjust the first performance score using a compensation factor when the operation complexity, operation efficiency and completion rate are outside the second tolerance range, based on the range of the operation complexity, operation efficiency and completion rate being outside the second tolerance range, to obtain a second performance score, and to obtain a performance appraisal result based on the second performance score, wherein the compensation factor is calculated based on the operation complexity, operation efficiency and completion rate.

9. An electronic device, characterized in that, The device includes a processor (501), a memory (505), a user interface (503), a communication bus (502), and a network interface (504). The processor (501), the memory (505), the user interface (503), and the network interface (504) are respectively connected to the communication bus (502). The memory (505) is used to store instructions. The user interface (503) and the network interface (504) are used to communicate with other devices. The processor (501) is used to execute the instructions stored in the memory (505) so that the electronic device (500) performs the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1-7.

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