CN117333068A - Multi-dimension quantitative assessment method and system - Google Patents
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Abstract
The invention relates to the technical field of information processing, and discloses a multi-dimension quantitative assessment method and a system, wherein the method comprises the following steps: acquiring department and post responsibilities corresponding to the object to be checked; determining an assessment flow according to departments and post responsibilities corresponding to the objects to be assessed, and decomposing the assessment flow into a plurality of assessment nodes; assembling each check node according to the enterprise management requirement of the image to be checked, and determining the execution sequence of each check node; calculating each assessment node in turn according to the execution sequence, and determining an assessment value of each assessment node; according to the assessment items and the man-hour distribution participated in the assessment period of the images to be assessed, weighting calculation is carried out on the assessment values of all assessment nodes, and the assessment score of the objects to be assessed is determined; meanwhile, the universality of quantitative assessment of multiple dimensions is also expanded.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to a multi-dimension quantitative assessment method and system.
Background
With the development of enterprises, information automation has become one of the indispensable technical means in enterprise management and operation. Aiming at numerous staff and regulations in large and medium-sized enterprises, accurate and effective management cannot be obviously performed only by manpower, so that information automation performance management is required for the enterprises.
In the related art, performance management generally sets a set of fixed assessment flows by distinguishing different posts, and starts the assessment flow after one assessment period is over. However, since different positions in enterprises have large difference in working contents and large difference in position properties, performance assessment cannot be performed by adopting the same set of judgment standard system, if a set of assessment flow is implemented for each position, the number of positions is numerous and new positions can appear at any time, so that performance management cost is huge, and therefore, a scheme capable of quantitatively assessing for any enterprise is needed.
Disclosure of Invention
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview, and is intended to neither identify key/critical elements nor delineate the scope of such embodiments, but is intended as a prelude to the more detailed description that follows.
In view of the shortcomings of the prior art, the invention discloses a multi-dimensional quantitative assessment method and a system, which are used for solving the technical problems of high implementation cost and lack of universality of the conventional performance assessment.
In a first aspect, the present invention provides a multi-dimensional quantitative assessment method, including: acquiring department and post attributes corresponding to an object to be checked, wherein the post attributes comprise post responsibilities; determining an assessment flow according to departments and post responsibilities corresponding to the objects to be assessed, and decomposing the assessment flow into a plurality of assessment nodes; assembling each assessment node according to the enterprise management requirement of the image to be assessed, and determining an execution sequence of each assessment node; calculating each assessment node in turn according to the execution sequence, and determining an assessment value of each assessment node; and carrying out weighted calculation on the assessment value of each assessment node according to the assessment items and the working hour distribution participated in the assessment period of the image to be assessed, and determining the assessment score of the object to be assessed.
In a possible embodiment of the present invention, the determining an assessment process according to the department and post responsibilities corresponding to the object to be assessed, and decomposing the assessment process into a plurality of assessment nodes includes: determining an assessment flow of the image performance to be assessed according to the belonged departments and the post responsibilities, extracting the assessment flow, and decomposing the assessment flow into a plurality of assessment nodes, wherein each assessment node comprises a subjective assessment node and an objective assessment node, each assessment node comprises a responsibilities person, a starting time, an ending time, a score and a weight, the subjective assessment node is characterized as a node needing to participate in assessment artificially, and the objective assessment node is characterized as an automatic assessment node.
In a possible embodiment of the present invention, the assembling each of the check nodes according to the enterprise management requirement to which the to-be-checked image belongs, and determining the execution sequence of each of the check nodes includes: determining the type and logic time sequence of the assessment node corresponding to the object to be assessed according to the enterprise management requirement; assembling the assessment nodes of each type according to the logic time sequence, and determining an execution sequence of each assessment node, wherein each assessment node corresponds to a unique sequence label, and the sequence label is used for representing the execution sequence of each assessment node.
In a possible embodiment of the present invention, the step of performing weighted calculation on the assessment value of each assessment node according to the assessment item and the working hour distribution participated in the assessment period of the image to be assessed, and determining the assessment score of the object to be assessed includes: judging whether the checking period is ended, if so, generating a checking record, wherein the checking period comprises a month period, a quarter period or a year period; determining whether the examination project participated in the examination period of the image to be examined is ended or not according to the examination record; and if the assessment project is finished, weighting and calculating the assessment value of each assessment node according to the man-hour distribution of the image to be assessed in the assessment project, and determining the assessment score of the object to be assessed.
In a possible embodiment of the present invention, the determining whether the checking period is over or not, if the checking period is over, generating a checking record, further includes:
determining unique identification information corresponding to the object to be checked;
matching according to the identification information related to the image to be checked, and obtaining a checking record of the image to be checked, wherein the checking record comprises a personnel contract, an attendance record and a checking target completion degree, the personnel contract comprises a preset checking period, the attendance record comprises the image to be checked, a card punching record of the image to be checked on work and off work, the completion state of a participating project and the project activity attendance data, and the checking target completion degree is the target checking completed in the checking period;
judging whether the assessment period is a result or not based on the preset assessment period of the image to be assessed, and if the current assessment period of the image to be assessed is not less than the preset assessment period, determining that the assessment period is ended.
In a possible embodiment of the present invention, the weighting calculation is performed on the assessment value of each assessment node according to the man-hour distribution of the image to be assessed in the assessment project, to determine the assessment score of the object to be assessed, and the method further includes:
If the assessment node is a subjective assessment node, matching a preset position responsible person according to a position, a position title, a position number, a professional skill and a past history assessment project corresponding to the image to be assessed, and distributing the subjective assessment node to the preset position responsible person so that the preset position responsible person refers to the attendance record and the assessment target completion degree to score, and generating and displaying an assessment result;
if the assessment node is an objective assessment node, acquiring project data participated by the image to be assessed according to the assessment record, preprocessing the project data, and determining man-hour distribution, targets and target achievement values of the image to be assessed in an assessment period, wherein the preprocessing comprises at least one of data cleaning, data transformation and data integration;
and carrying out weighted calculation on the assessment value of each assessment node according to the working hour distribution, the target and the target achievement value of the assessment period of the image to be assessed, determining the assessment score of the object to be assessed, and taking the assessment score as an assessment result.
In a possible embodiment of the present invention, the method further includes:
determining a preset assessment template based on the post attribute corresponding to the object to be assessed, wherein the preset assessment template comprises post responsibilities, a corresponding relation between the assessment node type and at least one assessment item content, and a corresponding relation between the assessment node type of the object to be assessed and the assessment node;
And determining the assessment value of each assessment node corresponding to the image to be assessed by adopting the preset assessment template, carrying out weighted calculation on the assessment value of each assessment node according to the assessment project and the working hour distribution of the image to be assessed participated in the assessment period, determining the assessment score of the object to be assessed, and outputting the assessment score.
In a possible embodiment of the present invention, the computing, according to the execution sequence, each of the check nodes in turn determines a check value of each of the check nodes, and further includes: determining at least one check text associated with the check image to be checked at each check node, wherein the check text is used for representing the execution condition of the check image to be checked in the check project; coding at least one check text by utilizing a semantic classification model, extracting semantic features of the coded text to obtain semantic features corresponding to the check text, and determining the evaluation grade of each check text by processing the semantic features corresponding to each check text; and determining the assessment value of each assessment node according to the assessment grade of the assessment text associated with each assessment node.
In a possible embodiment of the present invention, the semantic classification model is a large model, and the method for constructing the semantic classification model includes: acquiring a data set for constructing the semantic classification model, wherein the data set comprises a training set and a verification set, and the training set and each examination text marked by the verification set carry semantic corresponding evaluation grades; coding training samples in the training set through a coding layer of the semantic classification model; processing the encoded training sample through a semantic feature extraction layer of the semantic classification model to obtain semantic features of the training sample; processing semantic features of the training sample through a semantic classification layer of the semantic classification model to obtain a predicted evaluation grade of the training sample; obtaining a loss value based on the predicted evaluation level and the evaluation level indicated by the label, and training the semantic classification model based on the loss value until the training ending condition is met to obtain a trained semantic classification model; and verifying by utilizing the verification sample in the verification set, if the accuracy of the semantic classification model reaches the preset accuracy, determining that the verification is passed, and determining the semantic classification model.
In a second aspect, the present invention also provides a multi-dimensional quantitative assessment system, including: the acquisition module is used for acquiring department and post attributes corresponding to the object to be checked, wherein the post attributes comprise post responsibilities; the node determining module is used for determining an assessment flow according to departments and post responsibilities corresponding to the objects to be assessed, and decomposing the assessment flow into a plurality of assessment nodes; the sequence determining module is used for assembling each examination node according to the enterprise management requirement of the object to be examined and determining the execution sequence of each examination node; the first assessment determining module is used for sequentially calculating each assessment node according to the execution sequence and determining an assessment value of each assessment node; and the second assessment determining module is used for carrying out weighted calculation on the assessment value of each assessment node according to the assessment items and the working hour distribution participated in the assessment period of the to-be-assessed object to determine the assessment score of the to-be-assessed object.
The invention has the beneficial effects that:
acquiring department and post responsibilities corresponding to the object to be checked; determining an assessment flow according to departments and post responsibilities corresponding to the objects to be assessed, and decomposing the assessment flow into a plurality of assessment nodes; assembling each assessment node according to the enterprise management requirement of the image to be assessed, and determining an execution sequence of each assessment node; calculating each assessment node in turn according to the execution sequence, and determining an assessment value of each assessment node; according to the assessment items and the man-hour distribution participated in the assessment period of the images to be assessed, weighting calculation is carried out on assessment values of all assessment nodes, assessment scores of the objects to be assessed are determined, through the mode, each assessment flow is abstracted to form the assessment nodes, any assembly of different assessment links is supported, assessment flows aiming at different assessed objects can be formed by configuring any several assessment links, the effect of realizing a set of assessment flow customized for each type of assessed object is achieved, follow-up reuse and dynamic assembly are facilitated, and therefore implementation cost of performance assessment can be effectively reduced; meanwhile, the universality of quantitative assessment of multiple dimensions is expanded, and the assessment requirements of different posts of different enterprises can be met.
Drawings
FIG. 1 is a flow chart of a multi-dimensional quantitative assessment method according to an exemplary embodiment of the present invention;
FIG. 2 is a flow chart of an embodiment of a multi-dimensional quantitative assessment method according to an exemplary embodiment of the present invention;
FIG. 3 is a flow chart illustrating another implementation of a multi-dimensional quantitative assessment method according to an exemplary embodiment of the present invention;
fig. 4 is a schematic diagram of a multi-dimensional quantitative assessment system according to an exemplary embodiment of the present invention.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that, without conflict, the following embodiments and sub-samples in the embodiments may be combined with each other.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
In the following description, numerous details are set forth in order to provide a more thorough explanation of embodiments of the present invention, it will be apparent, however, to one skilled in the art that embodiments of the present invention may be practiced without these specific details, in other embodiments, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the embodiments of the present invention.
The terms first, second and the like in the description and in the claims of the embodiments of the disclosure and in the above-described figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe embodiments of the present disclosure. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. The term "plurality" means two or more, unless otherwise indicated. In the embodiment of the present disclosure, the character "/" indicates that the front and rear objects are an or relationship. For example, A/B represents: a or B. The term "and/or" is an associative relationship that describes an object, meaning that there may be three relationships. For example, a and/or B, represent: a or B, or, A and B.
Referring to fig. 1, a flowchart of a multi-dimension quantitative assessment method according to an exemplary embodiment of the invention is shown. Referring to fig. 1, in an exemplary embodiment, the multi-dimension quantitative assessment method at least includes steps S101 to S105, which are described in detail as follows:
step S101, acquiring department and post attributes corresponding to an object to be checked, wherein the post attributes comprise post responsibilities;
specifically, the object to be checked is an employee of a certain position of a certain enterprise or a certain department of a company, for example, taking a salesman of a certain 4S store of a certain sales department under a Jili automobile as an example, by determining the department and the position of the salesman, the check flow corresponding to the employee is further determined.
Step S102, determining an assessment flow according to departments and post responsibilities corresponding to the objects to be assessed, and decomposing the assessment flow into a plurality of assessment nodes;
specifically, an assessment flow to be assessed for the image performance is determined according to the responsibility of departments and posts, the assessment flow is extracted and decomposed into a plurality of assessment nodes, each assessment node comprises a subjective assessment node and an objective assessment node, each assessment node comprises a responsible person, a starting time, an ending time, a score and a weight, the subjective assessment node is characterized as a node needing to participate in assessment artificially, and the objective assessment node is characterized as an automatic assessment node.
For example, subjective evaluation nodes are characterized as nodes requiring human participation in evaluation, i.e., scoring for human participation in evaluation, and objective evaluation nodes are characterized as nodes for automatic evaluation, i.e., automatic evaluation nodes without human participation.
It should be noted that, the checking flow corresponding to the departments and the job responsibilities, that is, the text checking flow, is determined by identifying the entity of the text checking flow, so that each execution step in the flow is determined as an checking node.
Step S103, assembling each check node according to the enterprise management requirement of the object to be checked, and determining the execution sequence of each check node;
specifically, determining the type and logic time sequence of an assessment node corresponding to an object to be assessed according to enterprise management requirements; assembling the examination nodes of each type according to the logic time sequence, and determining the execution sequence of each examination node, wherein each examination node corresponds to a unique sequence label, and the execution sequence of each examination node is represented by the sequence label.
By the method, the assessment nodes corresponding to the objects to be assessed of each enterprise are determined to be assembled, and the execution sequence of each assessment node is determined.
Step S104, sequentially calculating each assessment node according to the execution sequence, and determining the assessment value of each assessment node;
specifically, each of the evaluation nodes is sequentially calculated with reference to the order of the execution sequence, and thus, the evaluation value of each of the evaluation nodes is determined.
Step S105, weighting calculation is carried out on the assessment values of all assessment nodes according to the assessment items and the working hour distribution participated in the assessment period of the image to be assessed, and the assessment score of the object to be assessed is determined.
Specifically, judging whether an assessment period is over, if so, generating an assessment record, wherein the assessment period comprises a month period, a quarter period or a year period;
determining whether the examination project participated in the examination period of the image to be examined is ended or not according to the examination record;
if the assessment project is finished, weighting calculation is carried out on assessment values of all assessment nodes according to the man-hour distribution of the to-be-assessed object in the assessment project, and assessment scores of the to-be-assessed object are determined.
In the embodiment, department and post responsibilities corresponding to the object to be checked are obtained; determining an assessment flow according to departments and post responsibilities corresponding to the objects to be assessed, and decomposing the assessment flow into a plurality of assessment nodes; assembling each check node according to the enterprise management requirement of the image to be checked, and determining the execution sequence of each check node; calculating each assessment node in turn according to the execution sequence, and determining an assessment value of each assessment node; according to the method, the assessment values of all assessment nodes are weighted and calculated according to assessment projects and man-hour distribution participated in an assessment period of an image to be assessed, and the assessment score of the object to be assessed is determined; meanwhile, the universality of quantitative assessment of multiple dimensions is expanded, and the assessment requirements of different posts of different enterprises can be met.
In some embodiments, determining whether the assessment period is over, if so, generating an assessment record, and further comprising:
determining unique identification information corresponding to an object to be checked;
according to the identification information associated with the images to be checked, matching is carried out, and an examination record of the objects to be checked including personnel contracts, attendance records and the completion degree of the examination targets is obtained, wherein the personnel contracts include preset examination periods, the attendance records include the images to be checked on duty and duty card records (data such as open workers, false matters, sick and false matters), the completion state of the participation projects and the activity attendance data (activity time, activity track and the like) of the participation projects, and the completion degree of the examination targets is the target examination completed in the examination periods;
judging whether the checking period is a result or not based on a preset checking period of the object to be checked, and if the current checking period of the object to be checked is not smaller than the preset checking period, determining that the checking period is ended.
Specifically, unique identification information is set by each image to be checked, and relevant checking information is obtained outside the checking system by using the identification information, for example, an external database corresponding to a working hour system, a OKR (Objectives and Key Results, i.e. a target and key achievement method) system, a message center and the like is matched, and then the image to be checked is grabbed to include personnel contracts, attendance records and checking records of the completion degree of the checking target.
Through the mode, the assessment record corresponding to the object to be assessed can be obtained from the multi-dimensional angle, the performance of the object to be assessed in work can be reflected more accurately, and the phenomenon of public compliance caused by one-sided performance assessment is avoided.
In some embodiments, the weighting calculation is performed on the assessment value of each assessment node according to the man-hour distribution of the object to be assessed in the assessment project, so as to determine the assessment score of the object to be assessed, and the method further includes:
if the assessment node is a subjective assessment node, matching a preset post responsible person according to a post, a post title, a post number, a professional skill and a past history assessment item corresponding to the image to be assessed, and distributing the subjective assessment node to the preset post responsible person so as to score the preset post responsible person attendance record and the assessment target completion degree, and generating and displaying an assessment result;
for example, in the subjective evaluation node, as the scoring person is required to score, how to determine who is to score, the preset post responsible person is matched through the post, post title, post number, professional skill and past history evaluation items corresponding to the object to be evaluated, the scoring person (preset post responsible person) is determined, and the scoring person needs to score with reference to the attendance record and the completion degree of the evaluation target, so as to generate and display the evaluation result.
Through the method, the preset post responsible person of each subjective evaluation node is determined, performance management is facilitated, and the automatic quantification result of the performance index is more accurate through the staged result collection and manual calibration.
If the assessment node is an objective assessment node, acquiring project data participated in by an image to be assessed according to the assessment record, preprocessing the project data, and determining man-hour distribution, targets and target achievement values of an assessment period of the object to be assessed, wherein the preprocessing comprises at least one of data cleaning, data transformation and data integration;
and carrying out weighted calculation on the assessment values of all the assessment nodes according to the working hour distribution, the targets and the target achievement values of the assessment period of the to-be-assessed object, determining the assessment score of the to-be-assessed object, and taking the assessment score as an assessment result.
The preprocessing includes data cleaning, data integration, data transformation and data reduction. Data cleaning refers to "cleaning" data by filling in missing values, smoothing noise data, identifying or deleting outliers and resolving inconsistencies. The project data about the objects to be checked and participated in are captured in the processing mode, so that the project data are more accurate and more beneficial to scoring processing.
Through the mode, in the link of objective rating, the automatic quantification result of the performance index is more accurate through the mode of staged result collection and manual calibration; the task evaluation and the index evaluation are combined, so that the realization process and the result are unified, and the evaluation is more real; through automatic performance evaluation, human intervention is stopped, so that the evaluation is fairer, more fair and more convenient, the working capacity of staff is continuously improved, the performance level of the company and the staff is continuously improved through performance analysis and application, and the enterprise management and business process is continuously optimized.
In some embodiments, the multi-dimensional quantitative assessment method further comprises: determining a preset assessment template based on the post attribute corresponding to the object to be assessed, wherein the preset assessment template comprises post responsibilities, a corresponding relation between the assessment node type and at least one assessment item content, and a corresponding relation between the assessment node type of the object to be assessed and the assessment node;
and determining the assessment value of each assessment node corresponding to the object to be assessed by adopting a preset assessment template, carrying out weighted calculation on the assessment value of each assessment node according to the assessment items and the man-hour distribution of the object to be assessed participated in the assessment period, determining the assessment score of the object to be assessed, and outputting the assessment score.
Specifically, a plurality of preset assessment templates are stored in a client or a memory of the server, and each preset assessment template is provided with a template identifier so as to obtain the preset assessment template corresponding to the template identifier when the post attribute of the object to be assessed is matched with the template identifier, thereby realizing the purpose of quickly calling the corresponding preset assessment template on the premise of obtaining the post attribute of the object to be assessed. The preset assessment template comprises a corresponding relation between the type of the assessment node and at least one assessment item content, when the number of the nodes to be assessed is one, the type of the assessment node and the assessment item content are in one-to-one correspondence relation, and when the number of the nodes to be assessed is a plurality of the nodes, the type of the assessment node and the assessment item content are in one-to-many correspondence relation; the preset assessment template also comprises a corresponding relation between the type of the assessment node of the object to be assessed and the assessment node, the image to be assessed is determined corresponding to the type of the assessment node, for example, when the type of the assessment node is a subjective assessment node, a scoring person is allocated to score according to a scoring rule, when the type of the assessment node is an objective assessment node, automatic scoring is not needed by people, performance scoring efficiency can be improved, meanwhile, scoring is conducted on subjective and objective objects, and performance assessment from multiple dimensions is facilitated.
Through the mode, compared with the performance assessment process using the unified assessment table template in the existing scheme, the performance assessment process using the preset assessment template corresponding to the post attribute of the image to be assessed in the embodiment has more flexibility, and the performance assessment efficiency is improved.
In some embodiments, each assessment node is sequentially calculated according to an execution sequence to determine an assessment value of each assessment node, and the method further includes:
determining at least one examination text associated with the object to be examined at each examination node, wherein the examination text is used for representing the execution condition of the object to be examined in the examination project;
coding at least one check text by utilizing a semantic classification model, extracting semantic features of the coded text to obtain semantic features corresponding to the check text, and determining the evaluation level of each check text by processing the semantic features corresponding to each check text;
and determining the assessment value of each assessment node according to the assessment grade of the assessment text associated with each assessment node.
In some embodiments, the semantic classification model is a large model, and the method of constructing the semantic classification model comprises:
acquiring a data set for constructing a semantic classification model, wherein the data set comprises a training set and a verification set, and each examination text marked by the training set and the verification set carries a semantic corresponding evaluation grade;
Coding training samples in the training set through a coding layer of the semantic classification model; processing the encoded training sample through a semantic feature extraction layer of the semantic classification model to obtain semantic features of the training sample; processing semantic features of the training sample through a semantic classification layer of the semantic classification model to obtain a predicted evaluation grade of the training sample;
obtaining a loss value based on the predicted evaluation level and the evaluation level indicated by the label, and training the semantic classification model based on the loss value until the training ending condition is met, so as to obtain a trained semantic classification model; and verifying by utilizing a verification sample in the verification set, and if the accuracy of the semantic classification model reaches the preset accuracy, determining that the verification passes, and determining the semantic classification model.
In some embodiments, collecting a plurality of audit texts of the division associated with each (to-be-checked) object, wherein the audit texts are used for describing the execution condition of the division associated with the object; encoding a plurality of audit texts through an encoding layer of the semantic classification model; extracting semantic features of the plurality of encoded audit texts through a semantic feature extraction layer of the semantic classification model to obtain semantic features of each audit text; processing semantic features of each audit text through a semantic classification layer of the semantic classification model to obtain evaluation grades corresponding to the audit texts; based on the respective evaluation levels and the number of audit texts included in the respective evaluation levels, the scores of the respective subjects for executing the division associated with the subject are counted.
According to the method, the score of each division of the object execution can be obtained based on the audit text, the influence of subjective factors caused by manual scoring can be avoided, furthermore, when the audit text is processed, the semantic features of each audit text are extracted, the actual performance of the object in each work can be highlighted, and therefore the objectivity, the authenticity and the fairness of performance assessment can be guaranteed.
In some embodiments, based on the identity of the object, the audit text corresponding to the object is obtained from a database (man-hour system, message center, and OKR, i.e., objectives and Key Results, review system). In other embodiments, the target organization is associated with an evaluation webpage, the service object of the target organization may log in the evaluation webpage, submit the execution condition evaluation of the division associated with any object in the target organization through the evaluation webpage, and obtain the audit text corresponding to the object from the evaluation webpage based on the identification of the object. The embodiments of the present application do not limit the source of the audit text.
In some embodiments, the collected audit text needs to be normalized before encoding the audit text by the encoding layer of the semantic classification model, the process comprising: carrying out security risk detection on the collected audit texts, and filtering audit texts with security risks from the collected audit texts; evaluator information removed from the filtered audit text; formatting the audit text after removing the evaluator information to remove redundant blank spaces in the audit text, and unifying the text length of the audit text.
Through the mode, the safety risk detection, the evaluator information removal and the normalization are carried out on the audit text, so that the data safety can be ensured, the privacy of the evaluator can be protected, the uniform format of the audit text input into the semantic classification model can be ensured, and the accuracy of the output result of the semantic classification model can be further ensured.
It should be noted that, the above manner of collecting the audit text of each division associated with each object to count the score of each division associated with each object executed by each object is merely exemplary.
In some embodiments, training the semantic classification model to obtain a trained semantic classification model includes: inputting a training sample into a semantic classification model, wherein the training sample is a text with labels, and the labels indicate evaluation grades corresponding to the semantics of the text; coding the training samples through a coding layer of the semantic classification model; processing the encoded training sample through a semantic feature extraction layer of the semantic classification model to obtain semantic features of the training sample; processing semantic features of the training sample through a semantic classification layer of the semantic classification model to obtain a predicted evaluation grade of the training sample; and obtaining a loss value based on the predicted evaluation level and the evaluation level indicated by the label, and training the semantic classification model based on the loss value until the training ending condition is met, so as to obtain the trained semantic classification model. The method comprises the steps of collecting a plurality of original audit texts, normalizing the plurality of original audit texts, and labeling the normalized audit texts to generate a plurality of training samples. The specific process of normalizing the original audit text is the same as the normalization process of the audit text, and is not repeated.
Through the method, the semantic classification model is trained, so that the semantic classification model has the capability of dividing different audit texts into different evaluation grades, the score of each object to execute each work can be counted based on the classification result of the semantic classification model on the audit texts, and further the manpower resources required in the performance assessment process can be greatly reduced, and the efficiency and the accuracy of the performance assessment are improved.
In some embodiments, the multi-dimensional quantitative assessment method further comprises:
if the assessment score exceeds the preset score corresponding to the object to be assessed, determining that the assessment passes, determining the assessment grade of the object to be assessed based on the target interval where the assessment score is located, and displaying a congratulatory message passing the association;
if the assessment score does not exceed the preset score corresponding to the object to be assessed, determining that the assessment is not passed, analyzing according to the attendance record, the work place track and the behavior record of the object to be assessed in the assessment period, generating a supplementary explanation, and determining additional scores of the object to be assessed according to the number of assessment items corresponding to the object to be assessed and the difficulty level of the assessment items.
Specifically, whether the object to be checked passes or not can be determined by comparing the checking score with a preset score, if so, the checking grade of the object to be checked is determined according to a target interval where the checking score is located, for example, the checking score is 90-100, the checking grade A is 80-90, the checking grade B is 70-80, the checking grade C is 60-70, the checking grade D is 60-70, if not, and different blessing words are displayed according to different checking grades.
In this embodiment, according to the analysis of the attendance record, the work place track and the behavior record of the object to be checked in the checking period, a supplementary description is generated, for example, the supplementary description may describe the attendance record, the work place track and the behavior record of the object to be checked in the checking period, so as to supplement the reason of the failure of checking.
Referring to fig. 2, a flowchart of an implementation of a multi-dimensional quantitative assessment method according to an exemplary embodiment of the present invention is shown; the details are as follows:
firstly, abstracting an assessment flow associated with an object to be assessed into an assessment node, and assembling the assessment flow according to the position of the object to be assessed;
judging whether an assessment period in which an assessment flow is located is over, if not, not generating an assessment record, and if so, generating the assessment record; assigning evaluation tasks (i.e. subjective evaluation nodes) to a preset responsible person for scoring, judging whether the object to be checked completes all the evaluation tasks, if not, receiving all the evaluation tasks, and displaying the check result; and if the task is completed, displaying the checking result.
Referring to fig. 3, a flowchart of another implementation of the multi-dimension quantitative assessment method according to an exemplary embodiment of the present invention is shown; the details are as follows:
firstly, abstracting an assessment flow associated with an object to be assessed into an assessment node, and assembling the assessment flow according to the position of the object to be assessed;
judging whether an assessment period in which an assessment flow is located is over, if not, not generating an assessment record, and if so, generating the assessment record; and respectively collecting project data, targets and achievement values participated by the object to be checked, automatically calculating scores, and displaying check results.
In this embodiment, each step in performance assessment is abstracted into individual assessment nodes, and these nodes may exist independently, so that arbitrary serialization between the nodes is ensured. Assessment nodes are classified into two types, one is a node requiring human participation (i.e., subjective evaluation node), such as "employee self-evaluation", "upper-level evaluation", "critique evaluation", and the other is a node where the system automatically completes evaluation (i.e., objective evaluation node), such as "objective evaluation". The subjective evaluation nodes are distributed to a preset post responsible person for scoring until the end of the assessment project participated in the assessment period where the object to be assessed is determined to be located, and assessment results are displayed;
Abstracting the assessment step as an assessment node, and finding the common attribute of the assessment step, wherein the assessment node is designed to comprise the following attributes: link responsibility people, starting time, ending time, score and weight, wherein objective evaluation nodes automatically complete evaluation by the system, and the responsibility people are empty.
The independent assessment nodes are assembled into an assessment flow according to enterprise management requirements, and because each node in the flow has a certain sequence, the number of each assessment node needs to be increased, and finally, the assessment nodes are connected in series to form a complete assessment flow, and the assessment flow can be dynamically and flexibly adjusted according to enterprise management modes and staff posts.
After an examination process is assembled, the scope of action of the process, namely the scope of the examined object, is set, and the scope is specified by departments and posts, so that the examination process needs to have department and post attributes.
After the checking period is finished, the checking flow takes effect, firstly, staff are inquired according to departments and posts of the checking flow, then checking records are generated for the staff, if node responsibility in the flow is not empty, evaluation tasks are also required to be created for corresponding responsibility people, and task data are stored in an independent task table.
In addition, the performance assessment system collects cross-system data, and the collection is divided into two parts, and the details are as follows:
and the first part is used for collecting working hour data of the evaluated object in the last checking period from the working hour system and obtaining the project in which the evaluated object participates in the last checking period and the working hour distribution proportion of the project. The specific implementation logic is as follows: the working hour system acquires working hour data of staff every day in a period, then eliminates data such as legal holidays, leave-in-place adjustment and the like, if the working hour data of the staff does not exist in a working day of a certain working period, the working hour system automatically fills non-project working hours, gathers according to the dimension of the checking period which is divided into a month period and a quarter period according to whether the working hour system is converted into the month period and the quarter period, and finally obtains projects participated in by the staff in the period and the energy proportion of the projects participated by the staff. The data can be used as project evaluation nodes to determine the basis of evaluation assessment personnel and project score weights. For example, in the past evaluation period, the staff member takes part in A, B, C three items, and the proportion of input man-hours is 50%, 30% and 20%, so that the staff member of the staff member is the responsible person of A, B, C three items, and the weight of each responsible person is 50%, 30% and 20%.
And the second part is used for collecting business targets and target achievement values set in the last assessment period of the assessed object from the OKR system, obtaining data such as projects, performance targets, achievement values and the like, and then taking the data as a score basis of an objective assessment link, and further, automatically calculating scores without manual scoring.
In the embodiment, by abstracting each assessment link, the repeated utilization and dynamic assembly of the assessment links are realized, so that the system realization cost is controllable, and the assessment links cannot be increased along with the increase of the types of the evaluated objects; in addition, in the objective rating link, the automatic quantification result of the performance index is more accurate by means of staged result collection and manual calibration; furthermore, on the premise of controllable cost, the invention realizes a set of performance management system with accurate performance results and is applicable to all checked objects.
Referring to fig. 4, a schematic structural diagram of a multi-dimensional quantitative assessment system according to an exemplary embodiment of the present invention is shown. As shown in fig. 4, the exemplary multi-dimensional quantitative assessment system includes an acquisition module 401, a node determination module 402, a sequence determination module 403, a first assessment determination module 404, and a second assessment determination module 405, which are described in detail below:
The acquiring module 401 is configured to acquire a department and a post attribute corresponding to an object to be checked, where the post attribute includes post responsibility;
the node determining module 402 is configured to determine an assessment flow according to a department and a post responsibility corresponding to an object to be assessed, and decompose the assessment flow into a plurality of assessment nodes;
a sequence determining module 403, configured to assemble each assessment node according to the enterprise management requirement to which the image to be assessed belongs, and determine an execution sequence of each assessment node;
the first assessment determining module 404 is configured to sequentially calculate each assessment node according to the execution sequence, and determine an assessment value of each assessment node;
the second assessment determining module 405 is configured to perform weighted calculation on the assessment values of the assessment nodes according to the assessment items and the working hours distribution of the assessment objects to be assessed in the assessment period of the assessment objects to be assessed, so as to determine an assessment score of the assessment objects to be assessed.
It should be noted that, the multi-dimension quantitative assessment system provided by the above embodiment and the multi-dimension quantitative assessment method provided by the above embodiment belong to the same concept, and the specific manner of performing the operation of each step has been described in detail in the system embodiment, which is not repeated here.
By adopting the multidimensional quantitative assessment system provided by the embodiment of the disclosure, departments and post responsibilities corresponding to the objects to be assessed are obtained; determining an assessment flow according to departments and post responsibilities corresponding to the objects to be assessed, and decomposing the assessment flow into a plurality of assessment nodes; assembling each check node according to the enterprise management requirement of the image to be checked, and determining the execution sequence of each check node; calculating each assessment node in turn according to the execution sequence, and determining an assessment value of each assessment node; according to the method, the assessment values of all assessment nodes are weighted and calculated according to assessment projects and man-hour distribution participated in an assessment period of an image to be assessed, and the assessment score of the object to be assessed is determined; meanwhile, the universality of quantitative assessment of multiple dimensions is expanded, and the assessment requirements of different posts of different enterprises can be met.
The flowcharts and block diagrams in the figures described above illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
Claims (10)
1. The multi-dimensional quantitative assessment method is characterized by comprising the following steps of:
acquiring department and post attributes corresponding to an object to be checked, wherein the post attributes comprise post responsibilities;
determining an assessment flow according to departments and post responsibilities corresponding to the objects to be assessed, and decomposing the assessment flow into a plurality of assessment nodes;
assembling each assessment node according to the enterprise management requirement of the image to be assessed, and determining an execution sequence of each assessment node;
calculating each assessment node in turn according to the execution sequence, and determining an assessment value of each assessment node;
and carrying out weighted calculation on the assessment value of each assessment node according to the assessment items and the working hour distribution participated in the assessment period of the image to be assessed, and determining the assessment score of the object to be assessed.
2. The method for quantitatively evaluating multiple dimensions according to claim 1, wherein determining an evaluation flow according to the department and post responsibilities corresponding to the object to be evaluated, and decomposing the evaluation flow into a plurality of evaluation nodes comprises:
determining an assessment flow of the image performance to be assessed according to the belonged departments and the post responsibilities, extracting the assessment flow, and decomposing the assessment flow into a plurality of assessment nodes, wherein each assessment node comprises a subjective assessment node and an objective assessment node, each assessment node comprises a responsibilities person, a starting time, an ending time, a score and a weight, the subjective assessment node is characterized as a node needing to participate in assessment artificially, and the objective assessment node is characterized as an automatic assessment node.
3. The method of claim 2, wherein the assembling each of the assessment nodes according to the enterprise management requirement to which the image to be assessed belongs, and determining the execution sequence of each of the assessment nodes, comprises:
determining the type and logic time sequence of the assessment node corresponding to the object to be assessed according to the enterprise management requirement; assembling the assessment nodes of each type according to the logic time sequence, and determining an execution sequence of each assessment node, wherein each assessment node corresponds to a unique sequence label, and the sequence label is used for representing the execution sequence of each assessment node.
4. The method of claim 2, wherein the weighting calculation is performed on the assessment value of each assessment node according to the assessment item and the man-hour distribution participated in the assessment period of the image to be assessed, and determining the assessment score of the object to be assessed comprises:
judging whether the checking period is ended, if so, generating a checking record, wherein the checking period comprises a month period, a quarter period or a year period;
Determining whether the examination project participated in the examination period of the image to be examined is ended or not according to the examination record;
and if the assessment project is finished, weighting and calculating the assessment value of each assessment node according to the man-hour distribution of the image to be assessed in the assessment project, and determining the assessment score of the object to be assessed.
5. The method of claim 4, wherein the determining whether the assessment period is over, if so, generating an assessment record, further comprises:
determining unique identification information corresponding to the object to be checked;
matching according to the identification information related to the image to be checked, and obtaining a checking record of the image to be checked, wherein the checking record comprises a personnel contract, an attendance record and a checking target completion degree, the personnel contract comprises a preset checking period, the attendance record comprises the image to be checked, a card punching record of the image to be checked on work and off work, the completion state of a participating project and the project activity attendance data, and the checking target completion degree is the target checking completed in the checking period;
judging whether the assessment period is a result or not based on the preset assessment period of the image to be assessed, and if the current assessment period of the image to be assessed is not less than the preset assessment period, determining that the assessment period is ended.
6. The method according to claim 5, wherein the weighting calculation is performed on the assessment values of the assessment nodes according to the man-hour distribution of the images to be assessed in the assessment project, and determining the assessment score of the objects to be assessed further comprises:
if the assessment node is a subjective assessment node, matching a preset position responsible person according to a position, a position title, a position number, a professional skill and a past history assessment project corresponding to the image to be assessed, and distributing the subjective assessment node to the preset position responsible person so that the preset position responsible person refers to the attendance record and the assessment target completion degree to score, and generating and displaying an assessment result;
if the assessment node is an objective assessment node, acquiring project data participated by the image to be assessed according to the assessment record, preprocessing the project data, and determining man-hour distribution, targets and target achievement values of the image to be assessed in an assessment period, wherein the preprocessing comprises at least one of data cleaning, data transformation and data integration;
and carrying out weighted calculation on the assessment value of each assessment node according to the working hour distribution, the target and the target achievement value of the assessment period of the image to be assessed, determining the assessment score of the object to be assessed, and taking the assessment score as an assessment result.
7. The multi-dimensional quantization assessment method according to claim 2, further comprising:
determining a preset assessment template based on the post attribute corresponding to the object to be assessed, wherein the preset assessment template comprises post responsibilities, a corresponding relation between the assessment node type and at least one assessment item content, and a corresponding relation between the assessment node type of the object to be assessed and the assessment node;
and determining the assessment value of each assessment node corresponding to the image to be assessed by adopting the preset assessment template, carrying out weighted calculation on the assessment value of each assessment node according to the assessment project and the working hour distribution of the image to be assessed participated in the assessment period, determining the assessment score of the object to be assessed, and outputting the assessment score.
8. The method of claim 1, wherein the computing each of the assessment nodes in turn according to the execution sequence, determining an assessment value of each of the assessment nodes, further comprises:
determining at least one check text associated with the check image to be checked at each check node, wherein the check text is used for representing the execution condition of the check image to be checked in the check project;
Coding at least one check text by utilizing a semantic classification model, extracting semantic features of the coded text to obtain semantic features corresponding to the check text, and determining the evaluation grade of each check text by processing the semantic features corresponding to each check text;
and determining the assessment value of each assessment node according to the assessment grade of the assessment text associated with each assessment node.
9. The multi-dimensional quantitative assessment method according to claim 8, wherein the semantic classification model is a large model, and the method for constructing the semantic classification model comprises the following steps:
acquiring a data set for constructing the semantic classification model, wherein the data set comprises a training set and a verification set, and the training set and each examination text marked by the verification set carry semantic corresponding evaluation grades;
coding training samples in the training set through a coding layer of the semantic classification model; processing the encoded training sample through a semantic feature extraction layer of the semantic classification model to obtain semantic features of the training sample; processing semantic features of the training sample through a semantic classification layer of the semantic classification model to obtain a predicted evaluation grade of the training sample;
Obtaining a loss value based on the predicted evaluation level and the evaluation level indicated by the label, and training the semantic classification model based on the loss value until the training ending condition is met to obtain a trained semantic classification model; and verifying by utilizing the verification sample in the verification set, if the accuracy of the semantic classification model reaches the preset accuracy, determining that the verification is passed, and determining the semantic classification model.
10. A multi-dimensional quantitative assessment system, comprising:
the acquisition module is used for acquiring department and post attributes corresponding to the object to be checked, wherein the post attributes comprise post responsibilities;
the node determining module is used for determining an assessment flow according to departments and post responsibilities corresponding to the objects to be assessed, and decomposing the assessment flow into a plurality of assessment nodes;
the sequence determining module is used for assembling each examination node according to the enterprise management requirement of the object to be examined and determining the execution sequence of each examination node;
the first assessment determining module is used for sequentially calculating each assessment node according to the execution sequence and determining an assessment value of each assessment node;
And the second assessment determining module is used for carrying out weighted calculation on the assessment value of each assessment node according to the assessment items and the working hour distribution participated in the assessment period of the to-be-assessed object to determine the assessment score of the to-be-assessed object.
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