CN117522223A - Performance evaluation method and device, electronic equipment and readable storage medium - Google Patents
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Abstract
The invention relates to a performance evaluation method, a performance evaluation device, electronic equipment and a readable storage medium, which can be applied to the field of big data or the field of finance, and each secondary index in a preset index evaluation system can be subjectively weighted by adopting a hierarchical analysis method. And objectively giving weight to each level of index in a preset index evaluation system by a preset weight giving method. And comprehensively considering the subjective weighting result and the objective weighting result, calculating the comprehensive weight of the secondary index after subjective and objective comprehensive weighting by using a linear weighting method, and performing performance scoring by using the comprehensive weight. The limitation of weighting by a single method is overcome, subjective deviation and objective one-sided are eliminated, the determined weight simultaneously reflects subjective information and objective information, and the obtained evaluation result is more accurate and reliable.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a performance evaluation method, a performance evaluation device, an electronic device, and a readable storage medium.
Background
At present, most of higher institutions, scientific research institutions and the like adopt a teacher scientific research performance evaluation system combining annual end evaluation and evaluation to perform performance evaluation on teachers, scientific research personnel and the like. The teacher scientific research performance assessment method is formulated by schools, and each department organizes a corresponding assessment team to assess the teacher on the basis of the guidance and the requirements of the assessment method. The specification of various weights is formulated completely by means of manual experience, so that performance evaluation has high subjectivity, and reliability and accuracy of the performance evaluation are seriously affected.
Disclosure of Invention
The invention provides a performance evaluation method, a performance evaluation device, electronic equipment and a readable storage medium, and performance evaluation is more accurate and reliable.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the performance evaluation method provided by the embodiment of the invention comprises the following steps:
acquiring a preset index evaluation system of a target evaluation object, wherein the preset index evaluation system comprises two evaluation levels, a first evaluation level comprises a plurality of first-level indexes, and a second evaluation level comprises a plurality of second-level indexes under each first-level index;
determining the weight of each secondary index based on an analytic hierarchy process;
determining the weight of each level index based on a preset weighting method;
combining the weight of each secondary index with the weight of the corresponding primary index based on a linear weighting method to obtain the comprehensive weight of each secondary index;
and performing performance scoring on the target evaluation object based on the comprehensive weight of each secondary index and the score of each secondary index.
Further, the performance evaluation method further includes:
clustering the performance scores of the target evaluation objects based on a hierarchical clustering method;
and grading the scores of the target evaluation objects based on the clustering result.
Further, the determining the weight of each secondary index based on the analytic hierarchy process includes:
processing the secondary index under each primary index based on a preset scale method to obtain a judgment matrix of each primary index;
and calculating the feature vector of each judgment matrix based on a sum-product method to obtain the weight of each secondary index under each primary index.
Further, after processing the secondary index under each primary index based on a preset scale method to obtain the judgment matrix of each primary index, the method further comprises:
and carrying out consistency check on the obtained judgment matrix, and correcting the judgment matrix when the consistency index of the judgment matrix does not meet the corresponding consistency standard so as to enable the corrected judgment matrix to meet the consistency standard.
Further, the determining the weight of each level index based on the preset weighting method includes:
taking the absolute value of the standard deviation coefficient between the first-level indexes as a correlation coefficient for calculating the conflict degree in the weighting method to obtain the preset weighting method;
and calculating the weight of each primary index based on the preset weighting method.
Further, the combining the weight of each secondary index and the weight of the corresponding primary index based on the linear weighting method to obtain the comprehensive weight of each secondary index includes:
constructing a calculation formula of the comprehensive weight of each secondary index based on the linear weighting method;
and solving the proportion of the corresponding secondary index in the calculation formula of the comprehensive weight of each secondary index based on a difference coefficient method, and then bringing the solved value into the corresponding calculation formula to obtain the comprehensive weight of each secondary index.
Further, the clustering of the performance scores of the target evaluation objects based on the hierarchical clustering method includes:
clustering sample data of performance scores of the target objects based on Euclidean distances to obtain a preset number of classification levels;
and obtaining the grade of the target object based on the score interval of each classification grade and the performance score of the target object.
According to an embodiment of the present invention, there is provided a performance evaluation device including:
the system comprises an evaluation system acquisition module, a target evaluation object acquisition module and a target evaluation object generation module, wherein the target evaluation object acquisition module is used for acquiring a preset index evaluation system of the target evaluation object, the preset index evaluation system comprises two evaluation levels, a first evaluation level comprises a plurality of first-level indexes, and a second evaluation level comprises a plurality of second-level indexes under each first-level index;
the first weight determining module is used for determining the weight of each secondary index based on an analytic hierarchy process;
the second weight determining module is used for determining the weight of each level of index based on a preset weighting method;
the weight combination module is used for combining the weight of each secondary index and the weight of the corresponding primary index based on a linear weighting method to obtain the comprehensive weight of each secondary index; and
and the performance scoring module is used for scoring the performance of the target evaluation object based on the comprehensive weight of each secondary index and the score of each secondary index.
According to an embodiment of the present invention, there is provided an electronic device including: a memory and a processor;
the memory is used for storing programs;
the processor is configured to execute the program to implement the steps of the performance evaluation method as described above.
A readable storage medium is provided according to an embodiment of the present invention, on which a computer program is stored, which, when being executed by a processor, implements the steps of the performance evaluation method as described above.
According to the technical scheme, the performance evaluation method is disclosed, and subjective weighting can be carried out on each secondary index in a preset index evaluation system by adopting a hierarchical analysis method. And objectively giving weight to each level of index in a preset index evaluation system by a preset weight giving method. And comprehensively considering the subjective weighting result and the objective weighting result, calculating the comprehensive weight of the secondary index after subjective and objective comprehensive weighting by using a linear weighting method, and performing performance scoring by using the comprehensive weight. The limitation of weighting by a single method is overcome, subjective deviation and objective one-sided are eliminated, the determined weight simultaneously reflects subjective information and objective information, and the obtained evaluation result is more accurate and reliable.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a performance evaluation method provided by an embodiment of the present invention;
fig. 2 is a block diagram of a performance evaluation apparatus according to an embodiment of the present invention;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
At present, when performance evaluation is carried out, the prior art builds a model from the perspective of traditional comprehensive evaluation, and has high subjectivity. Meanwhile, the whole evaluation process has long loop length and low working efficiency, and the data asset cannot be fully utilized. In view of these practical problems, referring to fig. 1, an embodiment of the present invention provides a performance evaluation method, which may include the following steps:
101. the method comprises the steps of obtaining a preset index evaluation system of a target evaluation object, wherein the preset index evaluation system comprises two evaluation levels, a first evaluation level comprises a plurality of first-level indexes, and a second evaluation level comprises a plurality of second-level indexes under each first-level index.
Specifically, taking teacher scientific research performance evaluation as an example, the corresponding preset index evaluation system can be composed of two evaluation index system tables with the following structures:
index specification table
Scoring table
The first-level index in each table can comprise basic information of teachers, scientific research projects, scientific research papers, scientific research expenses and copyright information. The secondary index is the subdivision content under the corresponding primary index, for example, the secondary index under the scientific research item of the teacher can comprise: country level, province level, hall level, and others, the corresponding index may be a quantized unit "individual". The corresponding scores can be set according to the different grades of the secondary indexes, such as the national grade is 100 grades, the provincial grade is 85 grades, the office grade is 70 grades, and the other grades are 55 grades. The contents of the other primary index and the secondary index may be specifically shown in the above index specification table and the scoring table, and will not be described herein. Wherein the target rating object can be a college teacher, a full-time scientific research person or a person with both.
It can be understood that, a person skilled in the art can flexibly set the evaluation system according to the needs of the actual application scenario, which is not described herein.
102. Weights for each secondary index are determined based on an analytic hierarchy process.
Specifically, given that m factors have relative importance to a factor of the upper layer, according to a specific scale, the ith factor (i=1, 2, …, m) andthe j-th factor (j=1, 2, …, m) is compared and judged to be the relative importance degree a ij . The m-order matrix thus constructed can be used to solve for the priority of each factor with respect to certain criteria, referred to as the decision matrix, denoted as a= (a) ij ) m×m 。
The judgment matrix can judge the scale rule of the relative importance degree of the two factors based on specific pair comparison, so that the relative importance degree of any two factors has a certain quantity standard. The "1-9 scale method" shown in the following table may be used.
The judgment matrix respectively constructed for the 5 primary indexes is as follows:
basic information:
scientific research projects:
scientific research paper:
scientific research cost:
authoring information:
then, the relative importance is calculated to obtain the maximum characteristic root lambda of each judgment matrix max And the corresponding specialThe sign vector W, i.e. A i W＝λ max W. Calculate the component (W 1 ,W 2 ,...,W m ) That is, the relative importance, i.e., the weight coefficient, corresponding to the m elements.
The calculation method of the weight coefficient comprises a sum-product method and a root method, and the specific calculation process of the weight of the secondary index by taking the sum-product method as an example is as follows:
normalizing each column of the judgment matrix:
and summing the judgment matrix normalized by columns according to rows:
normalized vector
Then w= [ W ] 1 W 2 … W m ] T The feature vector is obtained, and the hierarchical single-order matrix can be obtained according to the feature vector, so that the corresponding weight value is obtained.
Wherein the calculation formula of the maximum characteristic root:wherein AW i Representing the ith component of vector AW.
103. And determining the weight of each level index based on a preset weighting method.
Specifically, the CRITIC weighting method can be improved to obtain a preset weighting method, and the weight of the first-level index of the teacher scientific research performance evaluation is calculated. CRITIC weighting comprehensively considers two important factors of the discrimination of indexes and the conflict among indexes. Firstly, the discrimination of the index refers to the difference of the same index for different sample values, and the larger the difference of the evaluation index in different sample values is, the more the index can distinguish different samples, and the stronger the discrimination of the index is; the smaller the difference in the values of the evaluation index on different samples, the weaker the discrimination of the index is. Next, the collision of the indices means the correlation between the indices, and if the correlation between the two indices is stronger, it is explained that the collision of the two indices is smaller.
The index of the content C of information j The calculation formula is as follows:
wherein S is j For the fluctuation degree, r ij Representing the correlation coefficient of the i-th index and the j-th index.
Since the correlation coefficient between the indices may have a negative value, the correlation between the indices reflected by the positive correlation and the negative correlation, which have the same absolute value, is the same. Thus, the conventional CRITIC weighting method can be improved:
the discrimination of the index is measured by using the standard deviation coefficient and calculatedThe metrics measure the conflict between the metrics.
Improved index information quantity C j ' is:
let X kj Representing the value of the kth sample on the jth index, the mean value of each indexAnd standard deviation S j The method comprises the following steps:
let the correlation coefficient between the ith index and the jth index be r ij Then:
let the j index weight be W j The calculation formula is as follows:
the weight of each first-level index can be obtained through the above formula.
104. And combining the weight of each secondary index and the weight of the corresponding primary index based on a linear weighting method to obtain the comprehensive weight of each secondary index.
Specifically, a combined calculation formula of the primary index and the secondary index obtained based on the linear weighting method is as follows:
W j ＝θα j +(1-θ)β j
wherein alpha is i The weight of the secondary index calculated by the analytic hierarchy process, beta j For the weight of the primary index calculated by the improved CRITIC weighting method, the coefficient θ represents the proportion of the weight of the secondary index to the combined weight.
The coefficient theta can be solved by adopting a difference coefficient method, and the calculation formula is as follows:
wherein P is j (j=1, 2, …, n) is a vector in which the weights of the secondary indexes are sequentially ordered in ascending order, and n is the number of secondary indexes.
105. And performing performance scoring on the target evaluation object based on the comprehensive weight of each secondary index and the score of each secondary index.
And carrying out weighted summarization on the comprehensive weight of the obtained secondary index and each score of the teacher scientific research data to finally obtain the comprehensive score of the teacher scientific research performance. According to the performance evaluation method, subjective weighting is carried out on each secondary index by using an analytic hierarchy process, objective weighting is carried out on the primary index by using an improved CRITIC (CRITIC information technology) method, the subjective and objective comprehensive weighting values are calculated by comprehensively considering the subjective weighting method and the objective weighting method by using a linear weighting method, the limitation of single-method weighting is overcome, subjective deviation and objective one-sided are eliminated, the determined weights reflect subjective information and objective information at the same time, and the obtained evaluation result is more objective, fair and reliable.
In another embodiment of the present invention, in order to make the weight of the obtained secondary index more accurate and reliable, after obtaining the judgment matrix of each primary index, the performance evaluation method further includes:
and carrying out consistency check on the obtained judgment matrix, and correcting the judgment matrix when the consistency index of the judgment matrix does not meet the corresponding consistency standard so as to enable the corrected judgment matrix to meet the consistency standard.
Specifically, the consistency index CI of the judgment matrix may be:
wherein m is the order of the judgment matrix, lambda max To determine the maximum eigenvalue of the matrix.
The larger the CI, the greater the bias consistency; conversely, the smaller the deviation consistency. In general, if CI is less than or equal to 0.01, the judgment matrix is considered to have consistency. In addition, the larger the order m of the judgment matrix is, the larger the deviation caused by the subjective factor of judgment is, and the larger the deviation consistency is; conversely, the smaller the deviation consistency. But when m is less than or equal to 2, ci=0, the judgment matrix has complete consistency. Therefore, the random uniformity index RI can be introduced and changed along with the order change of the judgment matrix. The specific values are shown in the following table, and the RI index values of the 1-15 order judgment matrix are listed:
the RI index values are obtained by constructing a judgment matrix by a random method, repeatedly calculating for more than 500 times, obtaining a consistency index, and averaging.
The ratio CR of the consistency index CI to the same-order random consistency index RI is called the consistency ratio, namely: cr=ci/RI, the smaller the CR, the better the consistency of the decision matrix. Generally, when CR is less than or equal to 0.1, the judgment matrix accords with the consistency standard, and the result of hierarchical single sequencing is acceptable; otherwise, the judgment matrix needs to be corrected until the test passes.
In some embodiments of the present invention, in order to facilitate the user to intuitively display and feel the evaluation score, the performance scores of the target evaluation objects may be clustered based on a hierarchical clustering method, and then a level corresponding to the corresponding score may be obtained.
Specifically, sample data of performance scores of the target objects can be clustered based on Euclidean distances to obtain a preset number of classification levels. And obtaining the grade of the target object based on the score interval of each classification grade and the performance score of the target object.
For example, the distance between different kinds of data of the teacher is calculated and compared by Euclidean distance, and the data with the smallest distance value is combined. And then regarding each sample of the teacher as a cluster, calculating the similarity between each cluster, searching for the two nearest clusters, classifying the two clusters into one type, and repeating the clustering process until all the samples are classified into one type. The maximum clustering number can be set to be 4, and finally 4 classes are obtained, namely, the comprehensive score of the scientific research is divided into 4 grades, and the four grades can be respectively excellent, good, general and qualified. The subjectivity of artificial division is avoided, and the objective fairness of the classification result is ensured.
Based on the same design concept, referring to fig. 2, the embodiment of the invention further provides a performance evaluation device, where the device can implement each step of the performance evaluation method during operation, and the device may include:
the evaluation system acquisition module 201 is configured to acquire a preset index evaluation system of a target evaluation object, where the preset index evaluation system includes two evaluation levels, and a first evaluation level includes a plurality of primary indexes and a second evaluation level includes a plurality of secondary indexes under each primary index.
A first weight determination module 202 is configured to determine a weight of each secondary indicator based on a hierarchical analysis.
And a second weight determining module 203, configured to determine the weight of each level of index based on a preset weighting method.
And the weight combination module 204 is configured to combine the weight of each secondary index with the weight of the corresponding primary index based on a linear weighting method, so as to obtain a comprehensive weight of each secondary index. And
And the performance scoring module 205 is configured to score the performance of the target evaluation object based on the comprehensive weight of each secondary index and the score of each secondary index.
Further, the performance scoring module 205 is further configured to:
and clustering the performance scores of the target evaluation objects based on a hierarchical clustering method.
And grading the scores of the target evaluation objects based on the clustering result.
Further, the first weight determining module 202 is specifically configured to:
and processing the secondary index under each primary index based on a preset scale method to obtain a judgment matrix of each primary index.
And calculating the feature vector of each judgment matrix based on a sum-product method to obtain the weight of each secondary index under each primary index.
Further, the first weight determining module 202 is further specifically configured to:
and carrying out consistency check on the obtained judgment matrix, and correcting the judgment matrix when the consistency index of the judgment matrix does not meet the corresponding consistency standard so as to enable the corrected judgment matrix to meet the consistency standard.
Further, the second weight determining module 203 is specifically configured to:
and taking the absolute value of the standard deviation coefficient between the first-level indexes as a correlation coefficient for calculating the conflict degree in the weighting method to obtain the preset weighting method.
And calculating the weight of each primary index based on the preset weighting method.
Further, the weight combining module 204 is specifically configured to:
and constructing a calculation formula of the comprehensive weight of each secondary index based on the linear weighting method.
And solving the proportion of the corresponding secondary index in the calculation formula of the comprehensive weight of each secondary index based on a difference coefficient method, and then bringing the solved value into the corresponding calculation formula to obtain the comprehensive weight of each secondary index.
Further, the performance scoring module 205 is further specifically configured to: and clustering sample data of the performance scores of the target objects based on Euclidean distances to obtain a preset number of classification levels.
And obtaining the grade of the target object based on the score interval of each classification grade and the performance score of the target object.
The embodiment of the present invention shown with reference to fig. 3 also provides an electronic device, which may include: a memory 301 and a processor 302.
A memory 301 for storing a program;
a processor 302, configured to execute the program, and implement the steps of the performance evaluation method as described in the foregoing embodiment.
The embodiments of the present invention also provide a readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the performance evaluation method described in the above embodiments.
It should be noted that the performance evaluation method, the device, the electronic equipment and the readable storage medium provided by the invention can be used in the big data field or the financial field. The foregoing is merely an example, and does not limit the application fields of the performance evaluation method, the apparatus, the electronic device and the readable storage medium provided by the present invention.
For the foregoing method embodiments, for simplicity of explanation, the methodologies are shown as a series of acts, but one of ordinary skill in the art will appreciate that the present invention is not limited by the order of acts, as some steps may, in accordance with the present invention, occur in other orders or concurrently. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present invention.
It should be noted that, in the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described as different from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other. For the apparatus class embodiments, the description is relatively simple as it is substantially similar to the method embodiments, and reference is made to the description of the method embodiments for relevant points.
The steps in the method of each embodiment of the invention can be sequentially adjusted, combined and deleted according to actual needs, and the technical features described in each embodiment can be replaced or combined.
The modules and the submodules in the device and the terminal of the embodiments of the invention can be combined, divided and deleted according to actual needs.
In the embodiments provided in the present invention, it should be understood that the disclosed terminal, apparatus and method may be implemented in other manners. For example, the above-described terminal embodiments are merely illustrative, and for example, the division of modules or sub-modules is merely a logical function division, and there may be other manners of division in actual implementation, for example, multiple sub-modules or modules may be combined or integrated into another module, 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 modules, which may be in electrical, mechanical, or other forms.
The modules or sub-modules illustrated as separate components may or may not be physically separate, and components that are modules or sub-modules may or may not be physical modules or sub-modules, i.e., may be located in one place, or may be distributed over multiple network modules or sub-modules. Some or all of the modules or sub-modules may be selected according to actual needs to achieve the purpose of the embodiment.
In addition, each functional module or sub-module in the embodiments of the present invention may be integrated in one processing module, or each module or sub-module may exist alone physically, or two or more modules or sub-modules may be integrated in one module. The integrated modules or sub-modules may be implemented in hardware or in software functional modules or sub-modules.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software unit executed by a processor, or in a combination of the two. The software elements may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A performance evaluation method, comprising:
acquiring a preset index evaluation system of a target evaluation object, wherein the preset index evaluation system comprises two evaluation levels, a first evaluation level comprises a plurality of first-level indexes, and a second evaluation level comprises a plurality of second-level indexes under each first-level index;
determining the weight of each secondary index based on an analytic hierarchy process;
determining the weight of each level index based on a preset weighting method;
combining the weight of each secondary index with the weight of the corresponding primary index based on a linear weighting method to obtain the comprehensive weight of each secondary index;
and performing performance scoring on the target evaluation object based on the comprehensive weight of each secondary index and the score of each secondary index.
2. The performance evaluation method according to claim 1, characterized by further comprising:
clustering the performance scores of the target evaluation objects based on a hierarchical clustering method;
and grading the scores of the target evaluation objects based on the clustering result.
3. The performance evaluation method according to claim 1, wherein the determining the weight of each secondary index based on the hierarchical analysis method includes:
processing the secondary index under each primary index based on a preset scale method to obtain a judgment matrix of each primary index;
and calculating the feature vector of each judgment matrix based on a sum-product method to obtain the weight of each secondary index under each primary index.
4. The performance evaluation method according to claim 3, wherein after processing the secondary index under each primary index based on a preset scale to obtain the judgment matrix of each primary index, further comprising:
and carrying out consistency check on the obtained judgment matrix, and correcting the judgment matrix when the consistency index of the judgment matrix does not meet the corresponding consistency standard so as to enable the corrected judgment matrix to meet the consistency standard.
5. The performance evaluation method according to claim 1, wherein the determining the weight of each primary index based on the preset weighting method includes:
taking the absolute value of the standard deviation coefficient between the first-level indexes as a correlation coefficient for calculating the conflict degree in the weighting method to obtain the preset weighting method;
and calculating the weight of each primary index based on the preset weighting method.
6. The performance evaluation method according to claim 1, wherein the combining the weight of each secondary index and the weight of the corresponding primary index based on the linear weighting method to obtain the comprehensive weight of each secondary index includes:
constructing a calculation formula of the comprehensive weight of each secondary index based on the linear weighting method;
and solving the proportion of the corresponding secondary index in the calculation formula of the comprehensive weight of each secondary index based on a difference coefficient method, and then bringing the solved value into the corresponding calculation formula to obtain the comprehensive weight of each secondary index.
7. The performance evaluation method according to claim 2, wherein the clustering of the performance scores of the target evaluation objects based on the hierarchical clustering method includes:
clustering sample data of performance scores of the target objects based on Euclidean distances to obtain a preset number of classification levels;
and obtaining the grade of the target object based on the score interval of each classification grade and the performance score of the target object.
8. A performance evaluation device, comprising:
the system comprises an evaluation system acquisition module, a target evaluation object acquisition module and a target evaluation object generation module, wherein the target evaluation object acquisition module is used for acquiring a preset index evaluation system of the target evaluation object, the preset index evaluation system comprises two evaluation levels, a first evaluation level comprises a plurality of first-level indexes, and a second evaluation level comprises a plurality of second-level indexes under each first-level index;
the first weight determining module is used for determining the weight of each secondary index based on an analytic hierarchy process;
the second weight determining module is used for determining the weight of each level of index based on a preset weighting method;
the weight combination module is used for combining the weight of each secondary index and the weight of the corresponding primary index based on a linear weighting method to obtain the comprehensive weight of each secondary index; and
and the performance scoring module is used for scoring the performance of the target evaluation object based on the comprehensive weight of each secondary index and the score of each secondary index.
9. An electronic device, comprising: a memory and a processor;
the memory is used for storing programs;
the processor for executing the program to implement the respective steps of the performance evaluation method as claimed in any one of claims 1 to 7.
10. A readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the steps of the performance evaluation method according to any one of claims 1 to 7.
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