Electronic archive information planning and sorting system for managing human resources through big data

Abstract

The invention relates to the technical field of human resource information management, in particular to an electronic archive information planning and sorting system for managing human resources through big data. The system comprises an evaluator division updating module and an evaluation score calculation module. According to the invention, the evaluator dividing and updating module divides evaluator identities according to evaluation scores of each person, distinguishes the evaluator identities according to credits, obtains evaluation contents of each type of evaluators, improves evaluation diversity, guarantees evaluation effectiveness, and improves evaluation efficiency. Meanwhile, in the evaluation process, the identities of all evaluators are hidden, evaluation results are directly obtained, misleading of the evaluators with high credits to statistical personnel is avoided, and finally evaluation score subtraction processing is conducted on all the evaluators through an evaluation score calculation module in combination with a difference report, so that the evaluation score calculation efficiency is improved. And an evaluator division updating module is matched to carry out evaluation score updating on each evaluator, the identity of each evaluator is adjusted in real time, and the effectiveness of each evaluation is further improved.

Description

Technical Field

[0001] This invention relates to the field of human resource information management technology, and more specifically, to an electronic file information planning and organization system for big data management of human resources. Background Technology

[0002] Human resource management refers to the effective utilization of human resources by an enterprise or organization through a series of management activities to support the organization's mission and goals. The importance of human resource management in an enterprise is self-evident. It not only involves recruitment and training, but also plays multiple roles such as shaping corporate culture, improving employee satisfaction, and promoting organizational development.

[0003] In the process of human resource management, in order to improve the effectiveness of resource allocation, it is necessary to evaluate managers and obtain their performance in different jobs as a basis for subsequent allocation. Especially for assembly line factories, how to allocate human resources has become an urgent problem to be solved.

[0004] To address these issues, it is necessary to evaluate work projects during employees' work hours. However, existing evaluation methods primarily rely on manual evaluations from managers and related personnel (i.e., those working in the same area). Due to the long-term nature of these evaluations, there is a tendency for evaluators and those being evaluated to cover up for each other, leading to distorted evaluation results. It is difficult to accurately obtain the true evaluations of each individual, resulting in the inability to allocate resources reasonably based on the current evaluations later on, thus affecting the effectiveness of resource management.

[0005] To address the aforementioned issues, there is an urgent need for a big data-driven electronic record information planning and organization system for human resources that can perform implicit evaluation. Summary of the Invention

[0006] The purpose of this invention is to provide an electronic record information planning and organization system for big data management of human resources. This system includes an evaluator segmentation and update module (evaluation score), an evaluation report random matching module (evaluation score), a difference report extraction module (evaluation score), and an evaluation score calculation module. The evaluator segmentation and update module categorizes evaluators based on their evaluation scores and randomly labels each evaluator to obtain their unique identifier for hidden evaluation. Simultaneously, the evaluation score calculation module, combined with the difference report, deducts points from each evaluator's score, calculates the revised evaluation score, and feeds this revised score back to the evaluator segmentation and update module for score updates. The updated evaluation score is then used for a secondary update of the evaluator's identity, thus solving the problems mentioned in the background art. The inability to accurately obtain the true evaluations of each individual makes it impossible to make reasonable allocations based on the current evaluations later on.

[0007] Specifically, the evaluator segmentation and update module categorizes evaluators based on their evaluation scores, using these scores to reflect each evaluator's credibility. A comprehensive evaluation is conducted using evaluators with different identities, improving the overall comprehensiveness of the final evaluation. Simultaneously, during evaluation statistics, each evaluator is randomly labeled to obtain their unique identifier, enabling hidden evaluations. Furthermore, the evaluation report random matching module, combined with big data from the production process, divides evaluation relationships within the same scope. Evaluation items for the target audience are randomly assigned to evaluators within this network, preventing evaluators from knowing in advance who is being evaluated. Additionally, statistical personnel can only participate in the evaluation statistics without knowing the identities of the evaluators, further improving the effectiveness of the evaluation and preventing collusion to submit false evaluations.

[0008] Furthermore, to prevent arbitrarily evaluated reviews, a difference report extraction module is used in conjunction with feedback reviews in the evaluation relationship network to obtain the difference reviews and identify the reviewer with the current number. This is then combined with the evaluation score calculation module to update the evaluation based on the difference report. By analyzing the evaluation results of each reviewer and updating the evaluation score, the arbitrators are constrained, ensuring the authenticity of each evaluation and improving the timeliness of evaluations for each target group.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: This big data-driven electronic archive information planning and organization system for human resources management uses an evaluator segmentation and update module to segment evaluators based on their evaluation scores and distinguish them according to their creditworthiness. It also acquires the evaluation content of each type of evaluator, increasing evaluation diversity and ensuring evaluation effectiveness. Furthermore, by hiding the identities of individual evaluators during the evaluation process and directly obtaining the evaluation results, it avoids misleading statistical personnel due to high-credit evaluators. Finally, the evaluation score calculation module, combined with a discrepancy report, deducts points from each evaluator's score. This, along with the evaluator segmentation and update module, updates the evaluation scores of each evaluator in real time, further improving the effectiveness of each evaluation. Attached Figure Description

[0010] Figure 1 This is a block diagram of the overall system structure of the present invention; Figure 2 This is a schematic diagram of the same-range evaluation relationship network simulation of the present invention; Figure 3 This is a diagram showing the division of the same region according to the present invention.

[0011] The meanings of the labels in the diagram are as follows: 10. Evaluator segmentation and update module; 20. Random matching module for evaluation reports; 30. Difference Report Extraction Module; 40. Evaluation score calculation module. Detailed Implementation

[0012] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] Please see Figure 1 As shown, an electronic record information planning and organization system for big data management of human resources is provided, including an evaluator division and update module 10, an evaluation report random matching module 20, a difference report extraction module 30, and an evaluation score calculation module 40. The evaluator segmentation and update module 10 segments the evaluators' identities based on their evaluation scores, randomly labels each evaluator, obtains each evaluator's number, and performs hidden evaluation. The evaluation report random matching module 20 combines big data of production process projects to divide the evaluation relationship network within the same scope, and randomly assigns the evaluation items of the person to be evaluated to the evaluators in the evaluation relationship network. The difference report extraction module 30 combines the feedback evaluations in the evaluation relationship network to obtain the difference evaluations and identify the evaluator of the current label; The evaluation score calculation module 40, in conjunction with the difference report, deducts points from the evaluation scores of each evaluator, calculates the post-evaluation scores, and feeds back the calculated evaluation scores of each evaluator to the evaluator classification update module 10 for score updates. The evaluator's identity is then updated a second time based on the updated evaluation scores.

[0014] The details are as follows: During the evaluation process, it is necessary to ensure the quality of each evaluation. Therefore, the evaluators should be able to know the work content of the person being evaluated. For example, for workers on the same assembly line, the evaluation should be based on the current workload and yield rate. To improve evaluation diversity, different evaluators need to be identified. Therefore, during the specific evaluation, the evaluator identification and update module 10 first identifies the evaluators based on their evaluation scores. The evaluator identities involved in this scheme include top evaluators, associates, and target evaluators, and the identification method is as follows: First, determine the initial evaluation score. Each evaluator's initial evaluation score All are the same. After the evaluation is completed, the evaluation score is updated, and an evaluation score threshold is set. When a reviewer's score, after being updated, falls below the score threshold... At that time, they will no longer be eligible for top ratings and will be downgraded to associates; Among them, the excellent reviewers are those whose evaluation scores are not lower than the evaluation score threshold. The evaluators include managers or supervisors in the same evaluation network. Of course, ordinary employees can also be evaluated as excellent evaluators because of their high evaluation scores (i.e., high credibility). The related party is the one whose evaluation score is lower than the evaluation score threshold. The evaluator; The target is the person to be evaluated that needs to be evaluated, and the top evaluators, related parties and the target belong to the same evaluation relationship network; It is worth noting that although the target is the person being evaluated, their self-evaluation is still used as a reference.

[0015] To prevent follow-up reviews, such as when associates simultaneously submit reviews based on those of top reviewers, and given the potential conflict of interest between the top reviewer and the target party, leading to distorted evaluation results, it's crucial to avoid disclosing reviewers' identities during the evaluation analysis process. This would negatively impact subsequent evaluations. For instance, if a reviewer is a manager, the corresponding statistical personnel could easily be misled by management, hindering impartial statistical analysis. Therefore, the evaluation process requires random labeling of each reviewer, obtaining their unique identifier, and implementing hidden evaluation. The specific steps are as follows: First, the evaluators, associates, and target individuals will be randomly labeled. The target individual's label will then be selected as the central label for the evaluation relationship network within the same scope. Figure 2 As shown, each circular region corresponds to a network of evaluation relationships within the same range for the target, i.e. Figure 2 Numbers 1, 6, 7, and 9 are all located at the center of the circular area. To ensure the diversity of evaluation results, it is necessary to ensure that there is at least one top evaluator in each evaluation network within the same scope. However, the number of top evaluators is relatively small. Therefore, when multiple targets need to be evaluated, in most cases, the same top evaluator needs to evaluate multiple targets within the same evaluation network, such as... Figure 2 Among them, 7, 11, 12, and 17 are all top-performing raters, and each of them evaluated the target individuals in two evaluation networks within the same scope. The remaining 15, 16, 18, 19, 20, 21, 22, 25, 30, 33, 50, and 66 are all associated individuals in their respective evaluation networks within the same scope. It is worth noting that the establishment of each evaluation network within the same scope is only for evaluation classification. When conducting statistics, the statisticians can only obtain the corresponding evaluation results and cannot know the specific evaluation results. Figure 2The classification method further improves the effect of hidden evaluation, allowing statisticians to maintain neutrality in evaluation statistics; Furthermore, to conduct a reasonable evaluation, evaluation items need to be formulated in advance. These evaluation items are determined by the work content of the target employee, such as their specific work steps, finished product quality standards, and completion quantities. Therefore, by combining the evaluation report random matching module 20 with big data on the production process project, an evaluation relationship network within the same scope is divided. The evaluation items of the employee to be evaluated are randomly assigned to the evaluators in the evaluation relationship network. The corresponding division content is as follows: Obtain the management area of ​​each manager, and designate all staff members within that area as target individuals. Correspondingly, the manager will be designated as an associate or evaluator of the target individuals within that area. The specific division will depend on the manager's evaluation score. Obtain the production process steps of staff members within the same area, and based on their actual locations, mark the corresponding intervals within that area. Figure 3 As shown, workers 1-15 constitute the production line. Each worker has different tasks, but they cooperate to produce products in sequence, thus creating a connection between them. The production line is divided into sections based on proximity, such as... Figure 3 As shown, 1-6, 4-9, 7-12, and 10-15 form different region intervals. When the target is labeled 1, the corresponding associates are 2-6. When the target is labeled 4, the corresponding associates are 1, 2, 3, 5, 6, 7, 8, and 9. The subsequent associate matching is carried out through the pre-defined region intervals. It is worth noting that the evaluation item allocation strategy is as follows: the relevant parties matched by the target audience are randomly assigned corresponding evaluation items, and the top evaluators matched by the target audience are all assigned corresponding evaluation items.

[0016] In the process of randomly assigning corresponding evaluation items to matched associates, this scheme uses random sampling for sampling. First, the total sample frame is determined. Figure 3 The range included by the ellipse shown forms the corresponding total sample frame. A random number sequence is generated for each label in the total sample frame. A specified number of samples are drawn according to the random numbers as the associated persons of the target person in the current area interval. The corresponding evaluation items are then assigned to the matched associated persons for evaluation.

[0017] After completing the evaluation item allocation, the differential report extraction module 30, combined with the feedback evaluations in the evaluation relationship network, is used to obtain the differential evaluations and identify the evaluator of the current label. The specific steps are as follows: First, by combining the evaluation items of the target user's associates and top reviewers, the numerical data of each evaluation item is collected. For each individual evaluation item, numerical statistics are performed, and the values ​​are sorted by size. The maximum and minimum values ​​are obtained, and the average value of the remaining values ​​is calculated. Finally, the difference between the maximum and minimum values ​​and the average value is calculated, and a difference threshold is established. If the difference between the maximum and the average value does not exceed the difference threshold, the reviewer corresponding to the maximum value of the current evaluation item is not considered a differentiator for that item; otherwise, the reviewer corresponding to the maximum value is marked. Similarly, if the difference between the minimum and the average value does not exceed the difference threshold, the reviewer corresponding to the minimum value of the current evaluation item is not considered a differentiator for that item; otherwise, the reviewer corresponding to the minimum value is marked. The difference selection for each evaluation item is performed sequentially according to the above steps.

[0018] To improve the effectiveness of each evaluation, it is necessary to update the evaluation scores of each evaluator in real time to prevent arbitrary evaluations by top evaluators. Therefore, the evaluation score calculation module 40, in conjunction with the difference report, needs to deduct points from each evaluator's score, calculate the post-evaluation score, and feed the calculated scores of each evaluator back to the evaluator classification update module 10 for score updates. The evaluator's identity is then updated a second time based on the updated scores. The specific steps are as follows: First, a differential reduction score is established for each individual evaluation item. The number of times the current evaluator is marked as having differential evaluation in the same batch of evaluations is obtained. The same batch refers to the evaluation work carried out at the same time, including evaluations by the evaluator targeting different individuals. Finally, the final reduction score is calculated as the product of the differential reduction score for each individual evaluation item and the number of differential evaluations. This gives the evaluator the score that needs to be reduced. The evaluation score is then updated based on the evaluator's previous evaluation score, serving as the basis for distinguishing between high-performing evaluators and those with vested interests.

[0019] This invention uses an evaluator segmentation and update module 10 to segment evaluators based on their evaluation scores, differentiate evaluator identities according to their creditworthiness, and obtain the evaluation content of each type of evaluator, thereby improving evaluation diversity and ensuring evaluation effectiveness. Simultaneously, by hiding the identities of each evaluator during the evaluation process, the evaluation results are directly obtained, thus avoiding misleading statistical personnel due to evaluators with high creditworthiness. Finally, the evaluation score calculation module 40, combined with a difference report, deducts points from each evaluator's evaluation score, and the evaluator segmentation and update module 10 updates the evaluation scores of each evaluator, adjusting the identities of each evaluator in real time, further improving the effectiveness of each evaluation.

[0020] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A big data-driven electronic record information planning and organization system for human resources management, characterized in that: It includes an evaluator segmentation and update module (10), an evaluation report random matching module (20), a difference report extraction module (30), and an evaluation score calculation module (40); The evaluator segmentation and update module (10) segments the evaluator identities according to the evaluation scores of each person, randomly marks each evaluator, obtains the label of each evaluator, and performs hidden evaluation. The evaluation report random matching module (20) combines the big data of the production process project to divide the evaluation relationship network within the same scope and randomly assigns the evaluation project of the person to be evaluated to the evaluator in the evaluation relationship network. The difference report extraction module (30) combines the feedback evaluation in the evaluation relationship network to obtain the difference evaluation and identify the evaluator of the current label; The evaluation score calculation module (40) combines the difference report to deduct the evaluation score of each evaluator, calculates the evaluation score after evaluation, and feeds back the calculated evaluation score of each evaluator to the evaluator division update module (10) to update the evaluation score. The evaluator's identity is updated a second time through the updated evaluation score.

2. The electronic file information planning and organization system for big data management of human resources according to claim 1, characterized in that: The evaluator identities in the evaluator segmentation and update module (10) include top evaluators, associates, and target users.

3. The electronic file information planning and organization system for big data management of human resources according to claim 1, characterized in that: The method for classifying evaluator identities in the evaluator classification and update module (10) includes the following steps: S101. Formulate initial evaluation scores Each evaluator's initial evaluation score They are all the same; S102. Excellent reviewers are those whose evaluation scores are not lower than the evaluation score threshold. The evaluator; S103, Related parties are those whose evaluation scores are below the evaluation score threshold. The evaluator; S104. The target is the person to be evaluated who needs to be evaluated at present.

4. The electronic file information planning and organization system for big data management of human resources according to claim 1, characterized in that: The method for performing hidden evaluation in the evaluator segmentation and update module (10) includes the following steps: S105. Random numbering will be applied to those who receive excellent ratings, associates, and target individuals. S106. Select the target's label as the center label of the evaluation relationship network within the same scope; S107. Assign outstanding reviewers to ensure that each evaluation network within the same scope has at least one outstanding reviewer.

5. The electronic file information planning and organization system for big data management of human resources according to claim 1, characterized in that: The method for dividing the evaluation relationship network within the same scope in the evaluation report random matching module (20) includes the following steps: S201. Obtain the management area of ​​each manager and treat each employee in their management area as the target. S202. Obtain the production process steps of staff in the same area, and mark the corresponding intervals in the same area based on the actual location, as the same range evaluation relationship network for the current target.

6. The electronic record information planning and organization system for big data management of human resources according to claim 5, characterized in that: The allocation strategy for evaluation items in the evaluation report random matching module (20) is as follows: the associated persons matched by the target are randomly assigned corresponding evaluation items, and the excellent reviewers matched by the target are all assigned corresponding evaluation items.

7. The electronic record information planning and organization system for big data management of human resources according to claim 6, characterized in that: The evaluation items for which the associated parties are randomly assigned are determined by random sampling, and the specific steps are as follows: S203. Form the corresponding total sample frame by grouping the number of people in the same evaluation network; S204. Generate a random number sequence for each label in the total sample frame, and draw a specified number of samples according to the random numbers.

8. The electronic file information planning and organization system for big data management of human resources according to claim 1, characterized in that: The method for obtaining the difference evaluation in the difference report extraction module (30) includes the following steps: S301. Combine the evaluation items of the target's corresponding associates and top reviewers, and collect the numerical data of each evaluation item. S302. Perform numerical statistics on a single evaluation item, sort the values ​​in order of magnitude, obtain the maximum and minimum values, and calculate the average value of the remaining values. S303. Calculate the difference between the maximum and minimum values ​​and the average value, and determine the difference threshold; When the difference between the maximum value and the average value does not exceed the difference threshold, the evaluator corresponding to the maximum value of the current evaluation item is not considered to have made a difference evaluation for that item; otherwise, the evaluator corresponding to the maximum value is marked. When the difference between the minimum value and the average value does not exceed the difference threshold, the evaluator corresponding to the minimum value of the current evaluation item is not considered to have made a difference evaluation for that item; otherwise, the evaluator corresponding to the minimum value is marked. S304. Following the steps above, select the differences for each evaluation item in sequence.

9. The electronic file information planning and organization system for big data management of human resources according to claim 1, characterized in that: The method for calculating the evaluation score in the evaluation score calculation module (40) includes the following steps: S401. Develop a decreasing score for individual evaluation items and obtain the number of times the current evaluator is marked as having a discrepancy in the same batch of evaluations. S402. Calculate the final reduction score = the product of the individual evaluation item difference reduction score and the number of difference evaluations, to obtain the score that the current evaluator needs to reduce.

Images