Method and device for performing performance assessment on task, computer readable medium and computer program product

Abstract

A method, an apparatus, a computer readable medium, and a computer program product for performing performance assessment on a task are provided. The method comprises the steps of performing basic data configuration on a task; receiving task data related to the task; matching a plurality of algorithm models based on the basic data configuration, and processing the task data using the plurality of algorithm models to generate a plurality of weights; and calculating a contribution score of the task for the task data based on the plurality of generated weights so as to perform performance assessment on the task.

Description

Technical Field

[0001] The present disclosure relates to a method, an apparatus, a computer-readable medium and a computer program product for performing performance appraisal on a task. Specifically, the present disclosure relates to performing performance appraisal on a worker, especially an IT (information technology) worker, in a data-driven manner. Background Art

[0002] In modern enterprises, many departments, especially IT departments, play a vital role in supporting and promoting the digital transformation and technological innovation of enterprises. However, with the continuous development of technology, the increasing complexity and diversity of these tasks make it difficult for traditional performance appraisal methods to comprehensively, accurately and efficiently evaluate the contributions of staff.

[0003] In the traditional office model, the performance appraisal system mainly relies on manual operations. The specific process and the defects of each process involved in the process are as follows:

[0004] 1. Collect performance information offline: Performance management personnel need to collect performance reporting information from each department offline. This process is usually time-consuming and prone to errors.

[0005] 2. Reporting and Summarization: After completing the performance information reporting, all participants in the assessment will summarize the materials to the performance management department. Due to the lack of a unified reporting standard, the accuracy and completeness of the information is difficult to guarantee.

[0006] 3. Manual statistics and management: The performance management department manually counts and manages the collected materials. This process is not only cumbersome, but also prone to human errors, which affects the fairness and accuracy of performance appraisal.

[0007] 4. Scoring by superiors: Superiors assess and score employees based on their daily performance. However, due to the lack of standardized assessment indicators and algorithms, the assessment results are often subjective and difficult to fully and fairly reflect the actual performance of employees.

[0008] The entire performance appraisal process under the traditional office model lacks online management methods, information transmission efficiency is low, and data is difficult to update and share in real time, which affects the overall efficiency and transparency of performance management. In order to overcome the shortcomings of the performance appraisal system under the traditional office model, online management tools and standardized appraisal indicator systems can be introduced. Through graphical user interfaces and automated systems, the efficiency, accuracy and transparency of performance appraisals can be improved, and more fair and scientific performance management can be achieved.

[0009] Therefore, there is a need to provide scientific, fair and efficient performance appraisal solutions for employees. Such solutions can enhance the work enthusiasm and innovation ability of employees and help enterprises achieve greater success in digital transformation and technological innovation.

[0010] To this end, the present invention is committed to developing a performance appraisal program for staff, which improves the scientificity, fairness and efficiency of performance appraisal through a data-driven approach. Summary of the invention

[0011] According to one aspect of the present disclosure, a method for performance appraisal of tasks is provided, comprising: configuring basic data for the task; receiving task data related to the task; matching multiple algorithm models based on the basic data configuration, and processing the task data using the multiple algorithm models to generate multiple weights; and calculating the contribution score of the task for the task data based on the generated multiple weights for performance appraisal of the task. Through the method of the present disclosure, task data related to staff members can be processed using the matched multiple algorithm models, thereby conducting comprehensive, accurate and efficient appraisal and evaluation of complex and diverse work tasks.

[0012] In one aspect, configuring the basic data of the task includes one or more of the following operations: configuring the roles of the personnel involved in the task to obtain role information configuration; configuring the category of the task to obtain task category configuration; and configuring the implementation conditions of the task to obtain implementation condition configuration. Through this method, standardized configuration can be performed on various complex and diverse work tasks, so that these work tasks can be accurately and uniformly assessed and evaluated.

[0013] In one aspect, the method further includes initiating an evaluation of the basic data configuration after completing the basic data configuration to determine whether the basic data configuration matches a predetermined standard. Through this method, it is possible to evaluate whether the work task is correctly standardized, thereby further improving the accuracy and uniformity of the assessment of the work task.

[0014] In one aspect, matching multiple algorithm models based on the basic data configuration includes: matching corresponding algorithm models in the multiple algorithm models based on one or more of the obtained role information configuration, task category configuration, and implementation condition configuration. In addition, using the multiple algorithm models to process the task data to generate multiple weights includes: using the corresponding algorithm models matched in the multiple algorithm models to process the task data to generate corresponding weights. Through this method, for different aspects of complex and diverse work tasks, corresponding algorithm models are matched and used for processing to obtain corresponding weights, so that different aspects of the work tasks can be comprehensively and accurately assessed and evaluated.

[0015] According to one aspect of the present disclosure, there is provided an apparatus for performing performance appraisal on a task, comprising: a configuration module for performing basic data configuration on the task; a receiving module for receiving task data related to the task; a matching module for matching a plurality of algorithm models based on the basic data configuration, and processing the task data using the plurality of algorithm models to generate a plurality of weights; and an appraisal module for calculating a contribution score of the task for the task data based on the generated plurality of weights, for performing performance appraisal on the task.

[0016] The configuration module is also used to perform one or more of the following operations: configuring the roles of the personnel involved in the task to obtain role information configuration; configuring the category of the task to obtain task category configuration; and configuring the implementation conditions of the task to obtain implementation condition configuration.

[0017] In one aspect, the apparatus further comprises an evaluation module for initiating an evaluation of the basic data configuration after completing the basic data configuration to determine whether the basic data configuration matches a predetermined criterion.

[0018] In one aspect, the matching module is also used to match a corresponding algorithm model among multiple algorithm models based on one or more of the obtained role information configuration, task category configuration and implementation condition configuration, and wherein the matching module is also used to process the task data using the corresponding algorithm model matched among the multiple algorithm models to generate corresponding weights.

[0019] According to one aspect of the present disclosure, a computer readable medium having instructions stored thereon is provided, wherein when the instructions are executed, the computing device is caused to perform the method described in accordance with the present disclosure.

[0020] According to one aspect of the present disclosure, a computer program product is provided comprising instructions, which, when executed, cause a computing device to perform the method described according to the present disclosure.

[0021] Through the present disclosure, multiple algorithm models are matched and used to perform corresponding assessments and evaluations on various aspects of the staff's tasks, so that the objectivity and fairness of the performance evaluation of the work tasks are improved, and subjective bias is avoided, so that the work performance of the staff can be objectively and impartially evaluated. By implementing standardized configurations for various complex and diverse work tasks, the transparency of performance appraisal is improved, thereby providing a transparent performance appraisal process and results, so that the staff can clearly understand their performance scores and improvement directions. Through such standardized and comprehensive assessments, on the one hand, the incentives and work efficiency obtained by the staff are improved: through standard and comprehensive performance appraisals, the staff are encouraged to improve their work efficiency and innovation capabilities. On the other hand, the scientific nature of the company's management decisions is improved: using data analysis and algorithms, decision support is provided to the management, and team management and resource allocation are optimized. Therefore, the present disclosure can provide scientific, fair and efficient performance appraisal solutions for staff, improve their work enthusiasm and innovation capabilities, and help companies achieve greater success in digital transformation and technological innovation. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Specific exemplary embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, the present disclosure may be implemented in many different forms and should not be interpreted as being limited to the embodiments set forth herein; rather, these embodiments are provided to make the disclosure comprehensive and complete and will fully convey the scope of the present disclosure to those skilled in the art. The terms used in the detailed description of specific exemplary embodiments shown in the accompanying drawings are not intended to be limiting of the present disclosure. In the accompanying drawings, like numbers refer to like parts.

[0023] Figure 1 is a flow chart showing a method for performing performance appraisal on a task according to an embodiment of the present disclosure;

[0024] Figure 2 is a schematic diagram showing an interface of basic data configuration according to another embodiment of the present disclosure;

[0025] Figure 3 is a schematic diagram showing a review interface according to another embodiment of the present disclosure;

[0026] Figure 4 is a schematic diagram showing a task information input interface according to another embodiment of the present disclosure;

[0027] Figure 5 is a schematic diagram of an interface showing details of personnel assessment results and result rankings according to another embodiment of the present disclosure;

[0028] Figure 6 is a flow chart showing a method for performing performance appraisal on a task according to another embodiment of the present disclosure;

[0029] Figure 7 is a block diagram showing an apparatus for performing performance evaluation on a task according to another embodiment of the present disclosure;

[0030] Figure 8 is a block diagram showing a system architecture for performing performance appraisal on tasks according to another embodiment of the present disclosure; and

[0031] Fig. 9 is a block diagram illustrating a computing device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of the present disclosure more clearly understood, the technical solutions of the present disclosure are further described below with reference to the accompanying drawings and embodiments.

[0033] As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless expressly stated otherwise. It should be further understood that, as used in this specification, the term "comprising" means the presence of stated features, integers, steps, operations, parts and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, parts, components and / or groups thereof.

[0034] Figure 1 FIG. 1 is a flow chart showing a method 100 for performing performance appraisal on a task according to an embodiment of the present disclosure. Figure 1 As shown in, at step 102, basic data configuration is performed on the task. As described above, with the continuous development of technology, the complexity and diversity of various jobs, especially IT jobs, are increasing day by day. Correspondingly, the tasks of these jobs also involve many aspects. Therefore, at step 102, basic data configuration is performed on the tasks of these jobs to obtain configurations related to various aspects of the tasks. As an example, the work tasks may include but are not limited to one or more of the following aspects: the roles of the personnel involved in the tasks; the categories to which the tasks belong; and the implementation conditions for performing the tasks, etc. More specifically, in the example where the work tasks involve IT work, according to the role classification characteristics of the staff performing the tasks, the roles of the staff may include but are not limited to: project manager, sub-project manager, technical manager, product designer, project member, etc. According to the task characteristics of the staff performing the tasks, the categories to which the tasks belong may include but are not limited to: operation categories, key operation categories, regulatory operation categories, key regulatory operation categories, internal incubation categories, external procurement and maintenance categories, external procurement and construction categories, etc. According to the implementation characteristics of the tasks, the implementation conditions for performing the tasks may include but are not limited to: the expenses required to implement the tasks, such as a specific amount; and the cycle required to implement the tasks, etc.

[0035] In the example where the task includes the above aspects, accordingly, the basic data configuration of the task may also include one or more of the following operations: configuring the roles of the personnel involved in the task to obtain the role information configuration; configuring the category of the task to obtain the task category configuration; and configuring the implementation conditions of the task to obtain the implementation condition configuration. In one example, by performing basic data configuration on the task, templates corresponding to various aspects of the task and the attribute information of the templates may also be obtained. For example, in the above example, one or more of the following templates may be obtained: a role template for role information configuration; a task category template for task category configuration; and an implementation condition template for implementation condition configuration. In one example, the obtained templates may be used by various data processing software or applications, including but not limited to: Excel, WPS Office, MySQL, Oracle, SPSS, Python, etc., and the present disclosure is not limited thereto. In one example, the attribute information of the obtained template may be implemented by specifying a data field to indicate the basic data configuration corresponding to the template, that is, the corresponding aspect of the task. In Figure 2 , an example of an interface for basic data configuration according to an embodiment of the present disclosure is shown. Figure 2 As shown in , for example, for a newly added project of a task, the basic data of the task can be configured to obtain the role information configuration (as shown in the project role coefficient and project role declaration table), the task category configuration (as shown in the project attribute coefficient table) and the implementation condition configuration (as shown in the newly added project declaration table). Figure 2 In the example shown, the attribute information of the template may include, for example, one or more of the following: a “role” field, a “work type” field, and an “amount” or “period” field, and the like.

[0036] Through the basic data configuration of work tasks described above, standardized configuration can be performed on various complex and diverse work tasks, so that accurate and unified assessment and evaluation can be performed on all aspects of these work tasks.

[0037] Additionally or alternatively, the method may also include, after completing the basic data configuration, initiating an evaluation of the basic data configuration to determine whether the basic data configuration matches a predetermined standard. In one example, the obtained basic data configuration (e.g., template) may be compared with, for example, an industry standard to determine whether it matches a predetermined standard. For example, for the role information configuration of an IT task, it may be compared with the roles included in the personnel role group involved in the IT work to determine whether the role information configuration is included in the role group. As described in the example above, the role group may include, but is not limited to: project manager, sub-project manager, technical manager, product designer, project member, etc. For the task category configuration of an IT task, it may be compared with the task category included in the task category group involved in the IT work to determine whether the task category configuration is included in the task category group. As described in the example above, the task category group may include, but is not limited to: operation category, key operation category, regulatory operation category, key regulatory operation category, internal incubation category, external procurement and operation category, external procurement and construction category, etc. For the implementation condition configuration of the IT task, it can be compared with the implementation conditions included in the implementation condition group involved in the IT work to determine whether the implementation condition configuration is included in the implementation condition group. As described in the above example, the implementation condition group may include but is not limited to: the expenses required for implementing the task, such as a specific amount; and the period required for implementing the task, etc. In addition, the basic data configuration (e.g., template) can be compared with the technical specifications to determine whether it matches the predetermined standard. For example, the format of the above basic data configuration (e.g., template) can be compared with the format required by the software or application used to determine whether it matches the target format. Further, the basic data configuration (e.g., template) can also be compared with an existing (e.g., previously created and evaluated) basic data configuration to determine whether it matches the predetermined standard. In this regard, the existing basic data configuration can be trained as training data using artificial intelligence technologies such as machine learning to obtain an evaluation model, and then the basic data configuration is evaluated using the trained evaluation model to determine whether it matches the predetermined standard. Additionally or alternatively, after completing the basic data configuration, a full staff (such as an expert group) review can also be initiated to evaluate the basic data configuration. For example, in Figure 3 , an example of a review interface according to an embodiment of the present disclosure is shown.

[0038] At step 104, task data related to the task is received. In one example, relevant task data can be received from a staff member who performs the task. For example, the staff member can input data related to the plan of the task and data related to the actual completion of the task. More specifically, on the one hand, the staff member can input work plan data for a corresponding time period based on the work plan for a time period such as a day, a month, a quarter, a year, etc., such as but not limited to: task type, planned start time, planned end time, planned workload and other information. On the other hand, the staff member can input retrospective data of the planned work tasks for a corresponding time period based on the actual work completion status for a time period such as a day, a month, a quarter, a year, etc., such as but not limited to: the actual start time of the task, the actual end time, the actual workload and other information. In one example, the staff member can use the template obtained as described above to input the above information. In Figure 4 , an example of a task information input interface according to an embodiment of the present disclosure is shown. Figure 4 As shown in, the staff can use their electronic devices (e.g., computers, mobile phones, tablet computers, etc.) to download the various templates obtained as described above, and then use the templates to input the corresponding data, such as information related to the task plan and information related to the task completion status, etc. Then, the staff can select the time period to which the input information belongs, such as one day, one month, one year, etc. Then, the staff can select the software or application to be imported, such as Excel, WPS Office, MySQL, Oracle, SPSS, Python, etc. described above. Finally, the staff can choose to save the data and initiate a calculation report, and the software or application automatically calculates and generates task data in a predetermined format accordingly.

[0039] At step 106, a plurality of algorithm models are matched based on the basic data configuration, and the task data is processed using the plurality of algorithm models to generate a plurality of weights. In one example, the plurality of algorithm models may include one or more of the following: a plan achievement algorithm model, for calculating and generating a weight corresponding to the degree of completion of the task based on the data related to the task plan input by the staff member as described above and the data related to the actual completion of the task; a work type algorithm model, for calculating and generating a weight corresponding to the task category based on the work type involved in the task; a ranking algorithm model, for calculating and generating a weight corresponding to the implementation conditions based on the implementation conditions involved in the task; and a role algorithm model, for calculating and generating a weight corresponding to the role based on the role of the staff member involved in the task, and so on.

[0040] In one example, matching multiple algorithm models based on the basic data configuration includes: matching corresponding algorithm models in multiple algorithm models based on one or more of the obtained role information configuration, task category configuration, and implementation condition configuration. According to the present disclosure, various methods can be used to match algorithm models. In one example, the attribute information of the template obtained as described above can be used to match the algorithm model. For example, the "role" field, the "work type" field, and the "amount" or "cycle" field can be used to match the role algorithm model, the work type algorithm model, and the ranking algorithm model, etc. respectively. In addition, in one example, the algorithm model can also be matched based on the attribute information of the task data related to the task received at step 104. For example, in the above example, the attribute information of the task data may include fields such as "plan", "task status", and "workload". Accordingly, one or more of these fields can be used to match the plan achievement algorithm model.

[0041] In one example, using the multiple algorithm models to process the task data to generate multiple weights includes: using the corresponding algorithm model matched in the multiple algorithm models to process the task data to generate corresponding weights. According to the present disclosure, various methods can be used to generate weights for various aspects of the task through the algorithm model. For example, after matching the planned achievement algorithm model, one or more of the following rules can be used to generate weights corresponding to the completion of the task: 1) Early completion reward: When the planned work is completed ahead of schedule, a predetermined early completion reward (e.g., 2%) is given; 2) Evaluation deviation rate: If the planned workload evaluation deviation rate (actual workload / planned workload) is greater than or equal to a predetermined threshold (e.g., 98%), no relevant reward is given. For example, the received task data indicates that the planned workload of task A this month is 50 days and the actual workload is 49 days. The task data can be processed by the planned achievement algorithm model to calculate the deviation rate as 49 / 50=0.98, and the completion weight of the task is 50 / 49=1.02041. As another example, the received task data indicates that the planned workload for Task A this month is 50 days and the actual workload is 48 days. The task data can be processed by the plan achievement algorithm model to calculate the deviation rate as 48 / 50=0.96 (less than the predetermined threshold of 98%), and the completion weight of the task is 50 / 48+reward (2%)=1.06166.

[0042] In one example, after matching the work type algorithm model, one or more of the following rules can be used to generate weights corresponding to the task categories of the tasks: 1) Daily work order processing, permission activation, data collection, report field adjustment, report development and other tasks are given weights corresponding to the operational task categories, such as 1.12; 2) Batch or relatively concentrated tasks (for example: institutional research, system transformation for Meituan business) are given weights corresponding to the key operational task categories, such as 1.13; 3) Daily supervision-related work orders, report development and other tasks are given weights corresponding to the regulatory operation task categories, such as 1.13 ; 4) Batch or related centralized supervision tasks (for example: Tongxin reporting, EAST reporting) are given weights corresponding to the key supervision operation task categories, such as 1.14; 5) Internal competition but no relevant project establishment process, internal project establishment standards (workload assessment exceeds, for example, 3 man-months) are given weights corresponding to the internal incubation task categories, such as 1.15; 6) The centralized implementation construction period of the project has been completed and the subsequent operation is carried out through procurement. It is given a weight corresponding to the external procurement operation task category, such as 1.15; 7) New projects implemented through external procurement are given weights corresponding to external procurement construction projects, such as 1.16. Therefore, the various types of work involved in various work tasks can be divided into several task categories and corresponding weights are given to each task category. The received task data is processed by the work type algorithm to determine the task category to which the task belongs and generate the corresponding weight.

[0043] In one example, after matching the ranking algorithm model, one or more of the following rules can be used to generate weights corresponding to the implementation conditions of the task. In one example, the task categories of the project can include 7 categories, which can be divided into two categories: operation category and external procurement category. 1) The external procurement category is for projects with external procurement budget amounts. The ranking is based on the average monthly input (external procurement budget amount / construction cycle (month)). The weights are divided into 3 levels. For example, each level is allocated according to the ratio of 30%, 40%, and 30%, and the ranking weight is finally obtained. For example: if there are 10 outsourcing projects, the number of first-level projects is 10*30%=3, and the ranking weight is 1.18, the number of second-level projects is 10*40%=4, and the ranking weight is 1.15, and the number of third-level projects is 10*30%=3, and the ranking weight is 1.12. 2) There is no budget amount in the operation category. According to the actual workload and projects entered by the staff, each task category is ranked separately, and the weight is divided into 3 levels. For example, the number of each level is allocated according to the ratio of 30%, 40%, and 30%, and the ranking weight is finally obtained. For example: the operation category contains four task categories, and each work category can include 10 staff with workload. The staff and actual workload are ranked separately, and task category A is obtained: 3 first-level staff, the weight is 1.18, 4 second-level staff, the weight is 1.15, 3 third-level staff, the weight is 1.12, and so on to obtain the ranking weights of the personnel in the remaining three task categories in this implementation cycle (for example, by quarter). Therefore, the received task data can be processed through a ranking algorithm model to determine the implementation conditions, and corresponding weights can be generated according to the implementation conditions (such as implementation budget amount / implementation construction period).

[0044] In one example, after matching the role algorithm model, one or more of the following rules can be used to generate weights corresponding to the roles of the staff involved in the task: 1) Project manager: responsible for the implementation management of the overall project schedule, the role weight is, for example, 1.15; 2) Technical manager: responsible for system-related technical processing, the role weight is, for example, 1.14, 1.13 or 1.12; 3) Product designer: responsible for project design implementation, the role weight is, for example, 1.14, 1.13 or 1.12; 4) Sub-project manager: This role, for example, can be limited to the weight assigned to group projects, and the role needs to be confirmed in advance by the project manager, the team leaders and other staff, and will take effect after the public announcement without problems, and the role weight is, for example, 1.14, 1.13, 1.12, 1.15. In one example, the role weight can be floated by the project manager according to the needs of the project, and the role weight is selected; and multiple project managers are not allowed in the future, and the actual project manager is determined as the role of the project manager. Therefore, the received task data can be processed by the role algorithm model to determine the role of the staff involved in the task, and the corresponding weight is generated according to their role.

[0045] It should be understood that the specific values ​​of the various weights given above are for illustrative purposes only, and the various weights may have other values, and the present disclosure is not limited thereto.

[0046] At step 108, based on the generated multiple weights, the contribution score of the task is calculated for the task data for performance appraisal of the task. According to the present disclosure, various methods can be used to calculate the contribution score of a task. In one example, the contribution score can be calculated as follows: contribution score = actual workload * planned achievement weight * work type weight * role weight * ranking weight. In an example, the planned achievement weight can be, for example, the weight corresponding to the degree of completion of the task described above, the work type weight can be, for example, the weight corresponding to the task category of the task described above, the role weight can be, for example, the weight corresponding to the role of the staff involved in the task described above, and the ranking weight can be, for example, the weight corresponding to the implementation conditions of the task as described above.

[0047] Furthermore, various visualization methods can be used to display the contribution scores calculated for various tasks, as well as the results of statistical analysis of the assessment of each staff member in the corresponding assessment period based on the contribution scores. Figure 5 In FIG. 1 , an example of an interface for detailing personnel assessment results and ranking results according to an embodiment of the present disclosure is shown. Additionally or alternatively, in addition to Figure 5In addition to the visual display through tables as shown, various statistical analysis methods can also be used to display the details of the assessment results and the ranking of the results, including but not limited to: bar charts, pie charts, scatter plots, etc., but the present disclosure is not limited thereto.

[0048] Figure 6 FIG. 6 is a flow chart showing a method 600 for performing performance evaluation on a task according to another embodiment of the present disclosure. Figure 6 As shown, method 600 can be divided into four parts: task data input, basic data configuration, matching algorithm model and assessment result display. However, it should be understood that such division is only for illustrative purposes, and the present disclosure is not limited thereto.

[0049] Method 600 starts at step 602, where role information configuration is performed. As described above, the role information configuration is obtained by configuring the roles of the persons involved in the task. In one example, through the role information configuration, a role template for the role information configuration can be obtained. Then, method 600 proceeds to step 604, where task category configuration is performed. As described above, the task category configuration is obtained by configuring the category of the task. In one example, through the task category configuration, a task category template for the task category configuration can be obtained. Then, the method proceeds to step 606, where task implementation condition configuration is performed. As described above, the implementation condition configuration is obtained by configuring the implementation condition of the task. In one example, through the task implementation condition configuration, an implementation condition template for the implementation condition configuration can be obtained. Figure 1 The details of various examples used to illustrate the above configuration steps are described and will not be repeated here.

[0050] Next, the method 600 proceeds to the review step 608, where the obtained role information configuration, task category configuration, and implementation condition configuration are evaluated. Figure 1 As described, after completing the basic data configuration, an assessment of the basic data configuration can be initiated to determine whether the basic data configuration matches a predetermined standard. If the review is passed, method 600 proceeds to step 610 in the task data input part, wherein, for example, a task plan is input by a staff member. Then, method 600 proceeds to step 612, wherein, for example, task actual completion information is input by a staff member. In one example, the task plan and task actual completion information can correspond to various task cycles, such as one day, one month, one quarter, one year, etc. On the contrary, if the review is not passed, then return to step 602 to restart the configuration.

[0051] After completing the input of the task data, the task data can be sent to multiple algorithm models together with the various configurations obtained and reviewed (e.g., role information configuration, task category configuration, and implementation condition configuration). Then, method 600 can proceed to the matching algorithm model part, where the corresponding algorithm models in the multiple algorithm models (e.g., plan achievement algorithm model 614, work type algorithm model 616, ranking algorithm model 618, and role algorithm model 620) can be matched based on these configurations and / or task data (e.g., their attribute information) to process the task data to generate corresponding weights, as described above with reference to Figure 1 Then, the method 600 proceeds to step 622, where a contribution score of the task can be calculated for the task data based on the generated multiple weights, as described above with reference to Figure 1 as described.

[0052] Next, method 600 proceeds to the evaluation result display section, where a detailed list of evaluation results for each staff member can be generated based on the calculated contribution score at step 624, and a ranking of evaluation results for each staff member can be generated based on the calculated contribution score at step 626 for viewing by the staff member and the evaluator, as described above. Figure 1 as described.

[0053] Figure 7 FIG. 7 is a block diagram showing an apparatus 700 for performing performance evaluation on a task according to another embodiment of the present disclosure. Figure 7As shown, the device 700 may include a configuration module 702, a receiving module 704, a matching module 706 and an assessment module 708. The configuration module 702 may be used to configure the basic data for the task. For example, the basic data configuration may be performed by the various methods described above, such as configuring the roles of the personnel involved in the task to obtain the role information configuration, configuring the categories of the task to obtain the task category configuration, and configuring the implementation conditions of the task to obtain the implementation condition configuration. The receiving module 704 may be used to receive the task data related to the task. The matching module 706 may be used to match a plurality of algorithm models based on the basic data configuration, and use the plurality of algorithm models to process the task data to generate a plurality of weights. For example, the corresponding algorithm models in the plurality of algorithm models may be matched based on one or more of the obtained role information configuration, task category configuration and implementation condition configuration by the various methods described above. The assessment module 708 may be used to calculate the contribution score of the task for the task data based on the generated plurality of weights, so as to perform performance assessment on the task. For example, the task data may be processed using the corresponding algorithm model matched in the multiple algorithm models through the various methods described above to generate corresponding weights. In various embodiments, the corresponding modules in the apparatus 700 may be used to perform the steps of any method described herein.

[0054] Figure 8 FIG. 1 is a block diagram showing a system architecture for performing performance evaluation on tasks according to another embodiment of the present disclosure. Figure 8 As shown, the entire system is divided into three layers from the architecture: source data layer 802, algorithm model layer 804 and data application layer 806. In one example, the source data layer 802 can be the source of data, and the data source of the entire performance appraisal is divided into two parts: basic data configuration and task data input. The basic data configuration requires the following relevant basic information configuration before the appraisal: role information configuration, task category configuration and implementation condition configuration. Task data input can include staff entering task plans and entering actual task completion information. The algorithm model layer 804 matches the relevant algorithm model through the basic configuration data and the task-related information entered by the staff, and calculates the corresponding contribution score of the task. The data application layer 806 can provide a detailed list of each work task to the staff and the appraiser for review based on the calculated contribution score; it can also summarize the contribution scores according to the appraisal cycle and then rank them for display.

[0055] Additionally or alternatively, the above methods, universal docking modules, service platforms or third-party platforms of the present disclosure may be implemented on one or more computers or servers or similar devices using computer processors, memory units, storage devices, computer software and other components. A high-level block diagram of such a computer or server is shown in Fig. 9 . Herein, computers, servers, or other devices including processors are collectively referred to as computing devices. Computing device 902 includes processor 904, which controls the operation of computer 902 by executing computer program instructions that define the overall operation. The computer program instructions can be stored in a storage device 912 (e.g., a disk) and loaded into memory 910 when the computer program instructions need to be executed. Therefore, reference is made to Figure 1 and Figure 6 The steps of the method may be defined by computer program instructions stored in the memory 910 and / or storage device 912 and controlled by the processor 904 executing the computer program instructions. The computing device 902 also includes one or more network interfaces 906 for communicating with other devices via a network. The computing device 902 also includes other input / output devices 908 (e.g., a display, keyboard, mouse, speakers, buttons, etc.) that enable a user to interact with the computer 902. Those skilled in the art will recognize that the implementation of the actual computer may also include other components, and Fig. 9 is a high-level representation of some of the components of such a computer for illustration purposes.

[0056] The storage device 912 and the memory 910 each include a tangible, non-transitory computer-readable storage medium. The storage device 912 and the memory 910 may each include a high-speed random access memory, such as a dynamic random access memory (DRAM), a static random access memory (SRAM), a double data rate synchronous dynamic random access memory (DDR RAM), or other random access solid-state memory devices, and may include non-volatile memory, such as one or more magnetic disk storage devices (such as internal hard disks and removable disks), magneto-optical disk storage devices, optical disk storage devices, flash memory devices, semiconductor memory devices (such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)), compact disk read-only memory (CD-ROM), digital versatile disk read-only memory (DVD-ROM) disks, or other non-volatile solid-state storage devices.

[0057] In another embodiment, the above method, universal docking module, service platform or third-party platform can be implemented in a network-based cloud computing system. In such a network-based cloud computing system, the server communicates with one or more client computers via a network. The client computer can communicate with the server, for example, via a web browser application resident on and running on the client computer. The client computer can store data on the server and access the data via the network. The client computer can transmit data requests or online service requests to the server via the network. The server can implement the requested service and provide the data to (one or more) client computers. The server can also transmit data adapted to enable the client computer to implement a specified function (e.g., implement calculations, display specified data on the screen, etc.). Some steps of the above method can be implemented by a server or by other computers / processors in a network-based cloud computing system. Some steps of the above method can be implemented locally by a client computer in a network-based cloud computing system. The steps of the above method can be implemented in any combination by one or more devices in a network-based cloud computing system or by a local client computer.

[0058] It should be appreciated that certain features of the disclosure described in the context of separate embodiments for clarity may also be provided in combination in a single embodiment. Conversely, various features of the disclosure described in the context of a single embodiment for simplicity may also be provided individually or in any suitable sub-combination or as appropriate in any other described embodiment of the disclosure. Certain features described in the context of various embodiments should not be considered essential features of those embodiments unless the embodiment is ineffective without those elements.

[0059] Although the present disclosure has been described in conjunction with the specific embodiments of the present disclosure, it is obvious that many substitutions, modifications and changes will be obvious to those skilled in the art. Therefore, it is intended to cover all such substitutions, modifications and changes that fall within the spirit and broad scope of the appended claims.

[0060] All publications, patents, and patent applications mentioned in this specification are incorporated herein by reference in their entirety to the same extent as if each individual publication, patent, or patent application was specifically and particularly indicated to be incorporated herein by reference. In addition, citation or identification of any reference in this disclosure should not be construed as an admission that such reference is available as prior art to the present disclosure. Where section headings are used, they should not be construed as necessarily limiting.

Claims

1. A method for performance appraisal of a task, comprising: Performing basic data configuration for the task; receiving task data associated with the task; Matching a plurality of algorithm models based on the basic data configuration, and processing the task data using the plurality of algorithm models to generate a plurality of weights; as well as Based on the generated multiple weights, a contribution score of the task is calculated for the task data to be used for performance appraisal of the task.

2. The method according to claim 1, wherein configuring the basic data of the task includes one or more of the following operations: configuring the roles of the personnel involved in the task to obtain role information configuration; configuring the category of the task to obtain task category configuration; and configuring the implementation conditions of the task to obtain implementation condition configuration.

3. The method according to claim 2, further comprising: After the basic data configuration is completed, an evaluation of the basic data configuration is initiated to determine whether the basic data configuration matches a predetermined criterion.

4. The method according to claim 3, wherein matching a plurality of algorithm models based on the basic data configuration comprises: Matching a corresponding algorithm model in a plurality of algorithm models based on one or more of the obtained role information configuration, task category configuration, and implementation condition configuration, and The step of using the multiple algorithm models to process the task data to generate multiple weights includes: using a corresponding algorithm model matched among the multiple algorithm models to process the task data to generate corresponding weights.

5. A device for performing performance appraisal on a task, comprising: A configuration module, used for performing basic data configuration on the task; A receiving module, used for receiving task data related to the task; A matching module, configured to match a plurality of algorithm models based on the basic data configuration, and process the task data using the plurality of algorithm models to generate a plurality of weights; as well as An assessment module is used to calculate the contribution score of the task for the task data based on the generated multiple weights, so as to perform performance assessment on the task.

6. The device according to claim 5, wherein the configuration module is also used to perform one or more of the following operations: configuring the roles of the personnel involved in the task to obtain the role information configuration; configuring the category of the task to obtain the task category configuration; and configuring the implementation conditions of the task to obtain the implementation condition configuration. 7 . The apparatus according to claim 6 , further comprising an evaluation module, configured to initiate an evaluation of the basic data configuration after completing the basic data configuration to determine whether the basic data configuration matches a predetermined standard.

8. The apparatus according to claim 7, wherein the matching module is further used to match a corresponding algorithm model in the plurality of algorithm models based on one or more of the obtained role information configuration, task category configuration and implementation condition configuration, and The matching module is further used to process the task data using a corresponding algorithm model matched among the multiple algorithm models to generate corresponding weights.

9. A computer readable medium having stored thereon instructions which, when executed, cause a computing device to perform the method of any one of claims 1-4.

10. A computer program product comprising instructions which, when executed, cause a computing device to perform the method according to any one of claims 1 to 4.

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