CN116579671A - Performance assessment method, system, terminal and storage medium for automatically matching indexes - Google Patents
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Abstract
The application discloses a performance assessment method, a system, a terminal and a storage medium for automatically matching indexes, which belong to the technical field of performance assessment and comprise the following steps: acquiring personnel information and performance data of personnel to be checked, and matching index allocation types according to the personnel information; performing data processing on the acquired performance data to obtain performance data to be checked; constructing a performance assessment model according to a plurality of assessment indexes and algorithm types aiming at each assessment index; and processing the performance data to be assessed through the performance assessment model to obtain a performance assessment result, and storing the performance assessment result into a pre-constructed assessment result library. The application has the effects of greatly improving the performance assessment efficiency without matching corresponding assessment indexes one by one according to personnel information of assessment personnel and determining specific algorithms one by one according to the assessment indexes.
Description
Technical Field
The application relates to the technical field of performance assessment, in particular to a performance assessment method, a system, a terminal and a storage medium for automatically matching indexes.
Background
The enterprise performance assessment system is an intelligent system aiming at enterprise employee performance assessment, and an artificial intelligence technology and a data visualization technology are adopted, so that enterprise management personnel can be helped to realize automatic assessment and analysis of employee work performance.
In the existing performance assessment method, when performance assessment is carried out on personnel to be assessed each time, personnel information and performance data of the personnel to be assessed are required to be obtained, each assessment index of the personnel to be assessed is determined according to the personnel information, and the performance data of the personnel to be assessed is scored through an algorithm corresponding to the assessment index, so that assessment results of the personnel to be assessed are obtained.
In carrying out the present application, the inventors have found that the above-described technique has at least the following problems: when performance assessment is carried out each time, corresponding assessment indexes are matched one by one according to personnel information of personnel to be assessed, and then a specific algorithm is confirmed according to the matched assessment indexes, so that performance assessment efficiency is low.
Disclosure of Invention
In order to solve the problem that performance assessment efficiency is low because corresponding assessment indexes are required to be matched one by one according to personnel information of personnel to be assessed and a specific algorithm is confirmed according to the matched assessment indexes, the application provides an automatic index matching performance assessment method, an automatic index matching performance assessment system, a terminal and a storage medium.
In a first aspect, the present application provides a performance assessment method for automatically matching an index, which adopts the following technical scheme:
a performance assessment method for automatically matching indexes comprises the following steps:
acquiring personnel information and performance data of personnel to be checked, and matching index allocation types according to the personnel information, wherein the index allocation types comprise a plurality of check indexes aiming at the personnel to be checked and algorithm types aiming at each check index, and the personnel information comprises names, work numbers and posts;
performing data processing on the acquired performance data to obtain performance data to be checked;
constructing a performance assessment model according to a plurality of assessment indexes and algorithm types aiming at each assessment index;
processing performance data to be assessed through a performance assessment model to obtain performance assessment results, and storing the performance assessment results into a pre-constructed assessment result library;
acquiring a query instruction, and calling out an assessment result consistent with query content in the query instruction from the assessment result library according to the query instruction.
By adopting the technical scheme, the assessment indexes are bound to specific assessment types, the algorithm type corresponding to each assessment index is also corresponding to the assessment indexes, the personnel to be assessed are matched with the specific assessment types, and then the corresponding assessment types can be matched by acquiring personnel information of the personnel to be assessed in performance assessment, and the assessment types comprise a plurality of assessment indexes aiming at the personnel to be assessed and algorithm types aiming at each assessment index, so that the corresponding assessment indexes do not need to be matched one by one according to personnel information of the assessment personnel, the specific algorithm is determined one by one according to the assessment indexes, and the performance assessment efficiency is greatly improved.
In a specific embodiment, before the step of acquiring the personnel information and performance data of the personnel to be examined, the method includes the following steps:
acquiring an assessment index and assessment standards of different posts;
performing word segmentation on the assessment indexes, and performing semantic analysis on the assessment indexes subjected to the word segmentation;
matching the assessment standard of the corresponding post according to the semantic analysis result, and generating a corresponding index allocation type aiming at the post;
acquiring an index type of an assessment index, and selecting a corresponding algorithm type from a preset algorithm library according to the index type;
and matching the assessment index and the algorithm type with the index allocation type.
By adopting the technical scheme, the setting method of the index allocation type is disclosed, analysis processing is carried out on the assessment indexes, one assessment index possibly corresponds to a plurality of posts, each assessment index also corresponds to a corresponding algorithm type, and all assessment indexes of each post are matched with the index allocation type in advance, so that when performance assessment is carried out, all assessment indexes of the post can be directly obtained by directly matching personnel to be assessed with the index allocation type, and the performance assessment efficiency is improved.
In a specific embodiment, before the obtaining the index type of the assessment index and selecting the corresponding algorithm type from the preset algorithm library according to the index type, the method further includes the following steps:
acquiring a plurality of history checking algorithms, and storing the plurality of history checking algorithms into a preset algorithm library;
acquiring a historical assessment index and an index type of the historical assessment index, and constructing an algorithm matching training model;
matching the historical assessment indexes with a plurality of historical assessment algorithms one by one through an algorithm matching training model, and selecting a historical assessment algorithm matched with the historical assessment indexes;
and extracting the characteristics of all the history assessment algorithms matched with the history assessment indexes to generate algorithm types.
By adopting the technical scheme, the historical assessment algorithms are classified, different algorithms can be applicable to the same assessment index, so that the historical assessment algorithms are trained by an algorithm matching training model, a plurality of historical assessment algorithms matched with the same assessment index in the selection process are used as one class, and the same class of historical assessment algorithms can be used as algorithms corresponding to the assessment index in the subsequent performance assessment process.
In a specific embodiment, the algorithm library includes an evaluation score method, a comparison method, a score method, a number difference product accumulation method, a step number score product accumulation addition method, an indefinite selection algorithm, a section percentage fixed standard number algorithm and a section percentage average standard number algorithm.
By adopting the technical scheme, the algorithm library for performance assessment comprises 8 algorithms, and the indexes are matched with different algorithms according to different indexes, so that the efficiency of performance assessment is improved.
In a specific embodiment, the data processing is performed on the obtained performance data to obtain performance data to be checked, and specifically includes the following steps:
according to the assessment index corresponding to the performance data, performing data cleaning, data conversion and data normalization on the performance data to obtain first performance data;
acquiring the number of algorithms included in the algorithm type aiming at the assessment index, and judging whether the number of algorithms is larger than 1;
if the number of the algorithms is not more than 1, taking the first performance data as performance data to be checked, and taking an algorithm contained in the algorithm type as an algorithm for calculating the performance data to be checked;
and if the number of the algorithms is greater than 1, performing data standard analysis on the first performance data, selecting a corresponding algorithm from algorithms contained in the algorithm type according to an analysis result as an algorithm for calculating the performance data to be checked, and taking the first performance data as the performance data to be checked.
By adopting the technical scheme, the quality and the accuracy of the data can be ensured, but because of the diversity of the data, the requirements of different algorithms on the quality and the accuracy of the data are different, and the same assessment index corresponds to a plurality of algorithms, so that the algorithm which is most suitable for the assessment index is selected from the plurality of algorithms by analyzing the quality and the accuracy of the data, thereby improving the accuracy of performance assessment.
In a specific embodiment, the data standard analysis on the first performance data specifically includes the following steps:
acquiring algorithm data calculation standards of each algorithm included in the preset algorithm type;
analyzing the first performance data to obtain actual data calculation standards;
comparing the similarity degree of the actual data calculation standard and the algorithm data calculation standard of each algorithm;
and selecting an algorithm with highest similarity degree between the algorithm data calculation standard and the actual data calculation standard as an algorithm for calculating the performance data to be checked.
By adopting the technical scheme, the quality and the accuracy of the data can be ensured, but because of the diversity of the data, the requirements of different algorithms on the quality and the accuracy of the data are different, and the same assessment index corresponds to a plurality of algorithms, so that the algorithm which is most suitable for the assessment index is selected from the plurality of algorithms by analyzing the quality and the accuracy of the data, thereby improving the accuracy of performance assessment.
In a specific implementation manner, the performance assessment model processes performance data to be assessed to obtain a performance assessment result, and specifically includes the following steps:
calculating performance data to be checked corresponding to an algorithm according to the algorithm corresponding to each check index;
and acquiring a calculation result of each assessment index and a weight of each assessment index, and calculating to obtain a performance assessment result according to the weight of each assessment index.
By adopting the technical scheme, the performance assessment result is obtained through calculation according to the weight of each assessment index, so that the final performance assessment result is more in accordance with the actual demand.
In a second aspect, the present application provides a performance assessment system for automatically matching an index, which adopts the following technical scheme:
a performance assessment system that automatically matches an indicator, comprising:
the data acquisition layer is used for acquiring personnel information and performance data of personnel to be checked;
the data processing layer is used for matching the index allocation type according to the personnel information, carrying out data processing on the acquired performance data to obtain performance data to be checked, constructing a performance check model according to the index allocation type, and processing the performance data to be checked through the performance check model to obtain a performance check result;
the data storage layer is used for storing performance assessment results of each person to be examined;
the application layer is used for providing a graphical user interface, so that a user can conveniently inquire performance assessment results of personnel to be examined.
By adopting the technical scheme, the assessment indexes are bound to specific assessment types, the algorithm type corresponding to each assessment index is also corresponding to the assessment indexes, the personnel to be assessed are matched with the specific assessment types, and then the corresponding assessment types can be matched by acquiring personnel information of the personnel to be assessed in performance assessment, and the assessment types comprise a plurality of assessment indexes aiming at the personnel to be assessed and algorithm types aiming at each assessment index, so that the corresponding assessment indexes do not need to be matched one by one according to personnel information of the assessment personnel, the specific algorithm is determined one by one according to the assessment indexes, and the performance assessment efficiency is greatly improved.
In a third aspect, the present application provides an intelligent terminal, which adopts the following technical scheme:
an intelligent terminal comprising a memory, a processor and a computer program stored in the memory and operable on the processor, wherein the computer program when executed by the processor implements a performance assessment method for automatically matching an indicator as described in any one of the above.
In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:
a computer readable storage medium comprising a readable storage medium and a computer program stored for execution on the readable storage medium, the computer program loaded and executed by a processor to implement a performance assessment method for automatically matching metrics as described in any of the above.
In summary, the present application includes at least one of the following beneficial technical effects:
1. binding the assessment indexes to specific assessment types, wherein the algorithm type corresponding to each assessment index is also corresponding to the assessment indexes, the personnel to be assessed are matched with the specific assessment types, and then the corresponding assessment types can be matched by acquiring personnel information of the personnel to be assessed when performance assessment is carried out, and the assessment types comprise a plurality of assessment indexes aiming at the personnel to be assessed and algorithm types aiming at each assessment index, so that the corresponding assessment indexes are not required to be matched one by one according to the personnel information of the assessment personnel, and then the specific algorithms are determined one by one according to the assessment indexes, thereby greatly improving the efficiency of performance assessment.
2. The quality and the accuracy of the data can be ensured, but because of the diversity of the data, the requirements of different algorithms on the quality and the accuracy of the data are different, and the same assessment index corresponds to a plurality of algorithms, the algorithm which is most suitable for the assessment index is selected from the plurality of algorithms by analyzing the quality and the accuracy of the data, so that the accuracy of performance assessment is improved.
Drawings
Fig. 1 is a schematic overall structure of a performance assessment system for automatically matching indicators according to an embodiment of the present application.
Fig. 2 is an overall flowchart of a performance assessment method for automatically matching indicators in an embodiment of the present application.
Fig. 3 is a flowchart of setting the allocation type of the index according to an embodiment of the present application.
Fig. 4 is a flow chart of algorithm type setting in the embodiment of the application.
FIG. 5 is a flow chart illustrating selection of a specific algorithm in an embodiment of the present application.
FIG. 6 is a schematic overall flow chart of a specific algorithm selection in an embodiment of the application.
Fig. 7 is a flowchart illustrating calculation of a final performance assessment result according to an embodiment of the present application.
Reference numerals illustrate:
1. a data acquisition layer; 2. a data processing layer; 21. an evaluation standard module; 22. an artificial intelligence module; 3. a data storage layer; 4. an application layer.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, embodiments of the present application will be described in further detail with reference to the accompanying drawings.
An embodiment of the application discloses a performance assessment system capable of automatically matching indexes, and referring to fig. 1, the system comprises a data acquisition layer, a data processing layer, a data storage layer and an application layer.
Specifically, the data acquisition layer is used for acquiring personnel information and performance data of personnel to be checked.
In practice, the data collection layer is responsible for collecting personnel performance data of various business systems in an enterprise, including personnel management systems, financial management systems, sales management systems, technical management systems, and the like. The collected personnel performance data is transmitted to the data processing layer in the form of files or data streams.
The data processing layer is used for matching the index allocation type according to the personnel information, carrying out data processing on the acquired performance data to obtain performance data to be checked, constructing a performance check model according to the index allocation type, and processing the performance data to be checked through the performance check model to obtain a performance check result.
In implementation, the data processing layer is a core part of the system, and mainly realizes cleaning, conversion and normalization of performance data. The data processing layer further comprises an evaluation standard module and an artificial intelligent module, the evaluation standard module defines the weight of each assessment index in the corresponding index allocation type, the artificial intelligent module analyzes performance data based on deep learning and natural language processing technology, and a performance assessment model is built according to the index allocation type, so that automatic performance assessment is achieved.
The data storage layer is used for storing performance assessment results of each person to be examined.
It should be noted that, the storage of the performance assessment results by the data storage layer may be implemented by using a relational database, a NoSQL database, or other manners, or may also implement rapid access and processing of data by using technologies such as cloud computing.
In the embodiment of the application, the data storage layer stores performance assessment results by using a MySQL database. The MySQL database is an efficient and reliable and extensible relational database, and can provide efficient data query and operation support so as to ensure the integrity, the correctness and the reliability of system data.
The application layer is used for providing a graphical user interface, so that a user can conveniently inquire performance assessment results of personnel to be checked.
Referring to fig. 2, another embodiment of the present application provides a performance assessment method for automatically matching indicators, including the following steps:
s10, acquiring personnel information and performance data of personnel to be checked, and matching index allocation types according to the personnel information;
specifically, the personnel information includes name, job number and post, and the index allocation type includes a plurality of assessment indexes for the personnel to be examined and an algorithm type for each assessment index. The index allocation type is generated according to a specific post stage, and referring to fig. 3, the method specifically comprises the following steps:
a10, acquiring an assessment index and assessment standards of different posts;
a20, performing word segmentation on the assessment indexes, and performing semantic analysis on the assessment indexes subjected to the word segmentation;
a30, matching the assessment standard of the corresponding post according to the semantic analysis result, and generating a corresponding index allocation type aiming at the post;
it should be noted that one evaluation index may correspond to a plurality of posts, and the evaluation standard of each post is set in advance. And matching the semantic analysis result with the assessment standard of each post, and when the matching degree is greater than a preset similarity threshold value, mapping the assessment index to the post.
A40, acquiring an index type of the assessment index, and selecting a corresponding algorithm type from a preset algorithm library according to the index type;
a50, matching the assessment index and the algorithm type with the index allocation type.
In implementation, before selecting a corresponding algorithm type from a preset algorithm library according to an index type, the history checking algorithm is classified by summarizing the history checking algorithm, and referring to fig. 4, the method specifically includes the following steps:
b10, acquiring a plurality of history checking algorithms, and storing the plurality of history checking algorithms into a preset algorithm library;
b20, acquiring historical assessment indexes and index types of the historical assessment indexes, and constructing an algorithm matching training model;
b30, matching the historical assessment indexes with a plurality of historical assessment algorithms one by one through an algorithm matching training model, and selecting a historical assessment algorithm matched with the historical assessment indexes;
and B40, extracting the characteristics of all the history assessment algorithms matched with the history assessment indexes, and generating algorithm types.
In implementation, the checking algorithm in the algorithm library is not limited to the acquired history checking algorithm, and the algorithm library can be updated by newly adding a new checking algorithm in the algorithm library.
In the embodiment of the application, the algorithm library comprises 8 algorithms, and specifically comprises an evaluation score method, a comparison method, a score method, a quantity difference product accumulation method, a step quantity score multiplication accumulation addition method, an indefinite item selection algorithm, a section percentage fixed standard quantity algorithm and a section percentage average standard quantity algorithm.
S20, performing data processing on the acquired performance data to obtain performance data to be checked;
it should be noted that, each algorithm has a corresponding algorithm type, and the algorithms of the same algorithm type can process the assessment data under the same assessment index, but because there are many algorithms in the algorithm library and there are also a plurality of situations that the algorithms belong to the same algorithm type, when the algorithm corresponding to the assessment index is selected, the algorithm belonging to the same algorithm type needs to be preferentially selected, referring to fig. 5, specifically including the following steps:
c10, performing data cleaning, data conversion and data normalization on the performance data according to the assessment index corresponding to the performance data to obtain first performance data;
c20, acquiring the number of algorithms included in the algorithm type aiming at the assessment index, and judging whether the number of algorithms is larger than 1;
if the number of algorithms is not more than 1, taking the first performance data as performance data to be checked, and taking an algorithm contained in the algorithm type as an algorithm for calculating the performance data to be checked;
and if the number of algorithms is greater than 1, carrying out data standard analysis on the first performance data, selecting a corresponding algorithm from algorithms contained in the algorithm types according to analysis results as an algorithm for calculating the performance data to be checked, and taking the first performance data as the performance data to be checked.
And when the algorithms contained in the same algorithm type are one, the algorithm is used as an algorithm for calculating the performance data to be checked. When the algorithms contained in the same algorithm type are multiple, the accuracy calculated by the algorithms is different under different accuracy due to different accuracy requirements of different algorithms on performance data to be checked.
In implementation, the data standard degree analysis is performed on the first performance data, and an optimal algorithm is selected to calculate the performance data to be assessed, and referring to fig. 6, the method specifically includes the following steps:
d10, acquiring algorithm data calculation standards of each algorithm included in the preset algorithm type;
d20, analyzing the first performance data to obtain actual data calculation standards;
d30, comparing the similarity degree of the actual data calculation standard and the algorithm data calculation standard of each algorithm;
and D40, selecting an algorithm with highest similarity degree between the algorithm data calculation standard and the actual data calculation standard as an algorithm for calculating the performance data to be checked.
In one embodiment of the present application, the same algorithm type includes a first algorithm and a second algorithm, the first algorithm includes a first standard degree threshold, the second algorithm includes a second standard degree threshold, and the performance data is subjected to three steps of data cleaning, data conversion and data normalization in the process of performing data processing, so as to obtain first performance data. The first performance data and the initial performance data are subjected to data processing to have certain difference, data standard degree analysis is performed on the first performance data at the moment, and then analysis results are respectively compared with a first standard degree threshold value and a second standard degree threshold value to obtain the similarity degree of the two comparisons. And when the similarity between the analysis result and the first standard degree threshold is greater than the similarity between the analysis result and the second standard degree threshold, using the first algorithm as an algorithm for calculating the performance data to be checked.
S30, constructing a performance assessment model according to a plurality of assessment indexes and algorithm types aiming at each assessment index;
s40, processing performance data to be assessed through a performance assessment model to obtain a performance assessment result, and storing the performance assessment result into a pre-constructed assessment result library;
in implementation, each post has a plurality of assessment standards, and the algorithm corresponding to each assessment standard is different, so that a summary of the results calculated by each algorithm is needed to obtain a final performance assessment result, and referring to fig. 7, the method specifically includes the following steps:
e10, calculating performance data to be checked corresponding to the algorithm according to the algorithm corresponding to each check index;
and E20, acquiring a calculation result of each assessment index and a weight of each assessment index, and calculating to obtain a performance assessment result according to the weight of each assessment index.
In implementation, according to the weight of each assessment index, a performance assessment result is obtained through calculation, so that the final performance assessment result is more suitable for actual requirements.
S50, acquiring a query instruction, and calling out a check result consistent with query content in the query instruction from a check result library according to the query instruction.
In implementation, the assessment indexes are bound to specific assessment types, the algorithm type corresponding to each assessment index is also corresponding to the assessment indexes, the personnel to be assessed are matched with the specific assessment types, and then the corresponding assessment types can be matched by acquiring personnel information of the personnel to be assessed in performance assessment, and the assessment types comprise a plurality of assessment indexes aiming at the personnel to be assessed and algorithm types aiming at each assessment index, so that the corresponding assessment indexes do not need to be matched one by one according to the personnel information of the assessment personnel, and then the specific algorithms are determined one by one according to the assessment indexes, thereby greatly improving the efficiency of performance assessment.
Based on the same inventive concept, a further embodiment of the present application also discloses a computer readable storage medium, where at least one instruction, at least one section of program, code set or instruction set is stored, where the at least one instruction, at least one section of program, code set or instruction set can be loaded and executed by a processor to implement the steps of a performance assessment method for automatically matching an index provided by the foregoing method embodiment.
The computer-readable storage medium includes, for example: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to perform all or part of the functions described above. The specific working processes of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which are not described herein.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, apparatuses and methods may be implemented in other manners, for example, the apparatus embodiments described above are merely illustrative, for example, the modules or units are divided into only one kind of logic function, and there may be other manners of dividing actually being implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.
The foregoing embodiments are only used to describe the technical solution of the present application in detail, but the descriptions of the foregoing embodiments are only used to help understand the method and the core idea of the present application, and should not be construed as limiting the present application. Variations or alternatives, which are easily conceivable by those skilled in the art, are included in the scope of the present application.
Claims (8)
1. The performance assessment method for automatically matching the indexes is characterized by comprising the following steps of:
acquiring personnel information and performance data of personnel to be checked, and matching index allocation types according to the personnel information, wherein the index allocation types comprise a plurality of check indexes aiming at the personnel to be checked and algorithm types aiming at each check index, and the personnel information comprises names, work numbers and posts;
performing data processing on the acquired performance data to obtain performance data to be checked;
constructing a performance assessment model according to a plurality of assessment indexes and algorithm types aiming at each assessment index;
processing performance data to be assessed through a performance assessment model to obtain performance assessment results, and storing the performance assessment results into a pre-constructed assessment result library;
acquiring a query instruction, and calling out an examination result consistent with query content in the query instruction from the examination result library according to the query instruction;
the data processing is carried out on the acquired performance data to obtain the performance data to be checked, and the method specifically comprises the following steps:
according to the assessment index corresponding to the performance data, performing data cleaning, data conversion and data normalization on the performance data to obtain first performance data;
acquiring the number of algorithms included in the algorithm type aiming at the assessment index, and judging whether the number of algorithms is larger than 1;
if the number of the algorithms is not more than 1, taking the first performance data as performance data to be checked, and taking an algorithm contained in the algorithm type as an algorithm for calculating the performance data to be checked;
if the number of algorithms is greater than 1, performing data standard analysis on the first performance data, selecting a corresponding algorithm from algorithms contained in the algorithm type according to an analysis result as an algorithm for calculating the performance data to be checked, and taking the first performance data as the performance data to be checked;
the data standard analysis is performed on the first performance data, and specifically comprises the following steps:
acquiring algorithm data calculation standards of each algorithm included in the preset algorithm type;
analyzing the first performance data to obtain actual data calculation standards;
comparing the similarity degree of the actual data calculation standard and the algorithm data calculation standard of each algorithm;
and selecting an algorithm with highest similarity degree between the algorithm data calculation standard and the actual data calculation standard as an algorithm for calculating the performance data to be checked.
2. The performance assessment method of claim 1, wherein before the step of obtaining the personnel information and performance data of the personnel to be assessed, the method comprises the following steps:
acquiring an assessment index and assessment standards of different posts;
performing word segmentation on the assessment indexes, and performing semantic analysis on the assessment indexes subjected to the word segmentation;
matching the assessment standard of the corresponding post according to the semantic analysis result, and generating a corresponding index allocation type aiming at the post;
acquiring an index type of an assessment index, and selecting a corresponding algorithm type from a preset algorithm library according to the index type;
and matching the assessment index and the algorithm type with the index allocation type.
3. The performance assessment method according to claim 2, wherein before the obtaining the index type of the assessment index and selecting the corresponding algorithm type from the preset algorithm library according to the index type, the performance assessment method further comprises the steps of:
acquiring a plurality of history checking algorithms, and storing the plurality of history checking algorithms into a preset algorithm library;
acquiring a historical assessment index and an index type of the historical assessment index, and constructing an algorithm matching training model;
matching the historical assessment indexes with a plurality of historical assessment algorithms one by one through an algorithm matching training model, and selecting a historical assessment algorithm matched with the historical assessment indexes;
and extracting the characteristics of all the history assessment algorithms matched with the history assessment indexes to generate algorithm types.
4. The performance assessment method according to claim 3, wherein the algorithm library comprises an evaluation score method, a comparison method, a score method, a quantity difference product accumulation method, a step quantity score product accumulation addition method, an indefinite item selection algorithm, a section percentage fixed standard quantity algorithm and a section percentage average standard quantity algorithm.
5. The performance assessment method of automatically matching indexes according to claim 1, wherein the performance assessment model is used for processing performance data to be assessed to obtain a performance assessment result, and the performance assessment method specifically comprises the following steps:
calculating performance data to be checked corresponding to an algorithm according to the algorithm corresponding to each check index;
and acquiring a calculation result of each assessment index and a weight of each assessment index, and calculating to obtain a performance assessment result according to the weight of each assessment index.
6. A performance assessment system that automatically matches an indicator, comprising:
the data acquisition layer (1) is used for acquiring personnel information and performance data of personnel to be checked;
the data processing layer (2) is used for matching the index allocation type according to the personnel information, carrying out data processing on the acquired performance data to obtain performance data to be checked, constructing a performance check model according to the index allocation type, and processing the performance data to be checked through the performance check model to obtain a performance check result;
the data storage layer (3) is used for storing performance assessment results of each person to be examined;
and the application layer (4) is used for providing a graphical user interface, so that a user can conveniently inquire the performance assessment results of the personnel to be examined.
7. An intelligent terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program when executed by the processor implements a performance assessment method for automatically matching metrics as claimed in any one of claims 1 to 5.
8. A computer readable storage medium comprising a readable storage medium and a computer program stored for execution on the readable storage medium, the computer program loaded and executed by a processor to implement a performance assessment method for automatically matching metrics as claimed in any one of claims 1 to 5.
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