CN115168736A - Bidding evaluation expert recommendation method, device, equipment and medium - Google Patents
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Abstract
The invention belongs to the technical field of expert bid evaluation, and particularly discloses a bid evaluation expert recommendation method, device, equipment and medium, which comprise the following steps: exporting the personal information of the bid evaluation expert from the bid evaluation expert library; adopting a method for constructing a digital portrait of the bid evaluation expert to tag the derived personal information of the bid evaluation expert to obtain the digital portrait of the bid evaluation expert; clustering the bid evaluation experts after the digital portrait, and labeling the bid evaluation experts required by the bid inviting project, wherein the tag of the bid evaluation experts required by the bid inviting project is consistent with the tag of the bid evaluation experts after the digital portrait; recommending the bid evaluation experts through a bid evaluation expert recommendation strategy in a mixed recommendation mode to obtain a recommendation scheme, and outputting the recommendation scheme. The invention solves the problem that the existing expert professional ability evaluation form is too simple, makes full use of historical evaluation information, and can express the objective difference existing between expert professional abilities, so that the selection of the bid evaluation expert is more refined and accurate.
Description
Technical Field
The invention belongs to the technical field of expert bid evaluation, and particularly relates to a bid evaluation expert recommendation method, device, equipment and medium.
Background
In the purchasing activity of an enterprise, the purchasing project review work is a purchasing core link, and a bid evaluation expert is used as a referee in the bid inviting purchasing process and is very important for a purchasing result, and factors such as professional background, business level, professional morality and behavior preference of the bid evaluation expert directly influence the review quality of the purchasing project. The selection of the bid evaluation expert currently has the following problems:
(1) The professional ability evaluation of experts is equal to professional classification labels, and the accuracy and the flexibility are insufficient;
(2) The historical performance of the experts in the bid evaluation activity is not reflected in the expert professional ability evaluation;
(3) The capability difference of the same expert in different professional directions in the research field is not reflected;
(4) The capability difference of different experts in the same professional direction is not reflected.
The defects reflect that the existing expert professional ability evaluation form is too simple, the historical evaluation information cannot be fully utilized, objective differences among expert professional abilities cannot be expressed, and the further improvement of the expert extraction function towards refinement and precision is restricted.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a method, a device, equipment and a medium for recommending evaluation experts so as to solve the problems that the current expert professional ability evaluation form is too simple, the historical evaluation information cannot be fully utilized, and the objective difference existing among expert professional abilities cannot be expressed.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a bid evaluation expert recommending method, which comprises the following steps:
s1: exporting personal information of the bid evaluation expert from the bid evaluation expert library; adopting a method for constructing a digital portrait of the bid evaluation expert to tag the derived personal information of the bid evaluation expert to obtain the digital portrait of the bid evaluation expert;
s2: clustering the bid evaluation experts after the digital portrait, and labeling the bid evaluation experts required by the bid inviting project, wherein the tag of the bid evaluation experts required by the bid inviting project is consistent with the tag of the bid evaluation experts after the digital portrait; recommending the bid evaluation experts through a bid evaluation expert recommendation strategy in a mixed recommendation mode to obtain a recommendation scheme, and outputting the recommendation scheme.
Further, the method for constructing the evaluation expert digital portrait comprises four steps: the method comprises the steps of raw data acquisition, data preprocessing, portrait dimension design and portrait label generation.
Further, the raw data is obtained as follows: exporting personal information of the bid evaluation experts in the bid evaluation expert database to obtain original data;
the data preprocessing comprises the following steps: preprocessing original data in a mode of manual extraction, data cleaning, data mining and main body extraction, and converting semi-structured and unstructured data into structured data;
the portrait dimensions are designed as: extracting the attribute of the bid evaluation expert by analyzing and processing the original data; aiming at the dimensionality of the evaluation expert portrait, extracting features according to application requirements or preset data dimensionality; converting potential connections hidden in data into evaluation expert portrait attribute dimensions, and making the potential connections hidden in the data explicit in a data mining mode;
the portrait label is generated as follows: designing a label according to different attribute dimensions of the image of the label evaluation expert; integrating the data of the bid evaluation experts and the data of the evaluation objects into a whole; and (4) extracting results according to the attribute characteristics of the bid evaluation experts, and classifying the results into four types of label systems, namely a basic information label, a professional information label, a social relation label and a bid evaluation capability information label.
Further, the original data comprises the personal basic information of the bid evaluation experts, the personal learning experience of the bid evaluation experts, the personal work information of the bid evaluation experts, the personal bid evaluation experience of the bid evaluation experts, the personal declaration and bid evaluation information of the bid evaluation experts and the scoring of the bid evaluation experts of the project manager; the data mining method comprises text mining, association analysis and machine learning.
Further, the basic information tag includes: ID. Gender, political aspect, health, residential address, academic calendar; the professional information labels comprise professional, work experience, manufacture, working age, and academic ability; the social relationship label includes: partnerships, affiliations, corporate relationships; the bid evaluation capability information tag comprises: and (4) bid evaluation experience, bid evaluation scoring and bid evaluation professional declaration.
Further, clustering is carried out on the bid evaluation experts after the digital portrait, tagging is carried out on the bid evaluation experts who meet the bid inviting project requirement, wherein the step that the tag of the bid evaluation expert who meet the bid inviting project requirement is consistent with the tag of the bid evaluation expert after the digital portrait specifically comprises the following steps:
s21: processing by an evaluation expert;
according to project requirements, similarity calculation or clustering is carried out on the standard evaluation experts after the digital images are imaged by using a Jaccard similarity, cosine similarity or Pearson correlation coefficient similarity calculation method or a k-means clustering algorithm, so as to obtain a clustered labeled standard evaluation expert group;
s22: processing the bidding project requirement;
s221: the listed bidding projects are required by bid evaluation experts;
s222: and labeling the bid evaluation experts required by the bid inviting project, wherein the tag of the bid evaluation experts required by the bid inviting project is consistent with the tag of the bid evaluation experts after the digital portrait.
Further, the method for recommending the bid evaluation experts through the bid evaluation expert recommendation strategy in a mixed recommendation mode to obtain a recommendation scheme and outputting the recommendation scheme specifically comprises the following steps:
s23: recommending bid evaluation experts by using collaborative filtering;
s231: calculating past bidding items approximate to the bidding requirement by using the similarity of Jaccard, the similarity of cosine or the similarity of Pearson correlation coefficient;
s232: extracting the selected bid evaluation experts of the similar past bid inviting items;
s233: extracting the label from the clustered labeled bid evaluation expert group in S21, extracting the bid evaluation experts with the labels similar to the label of the bid evaluation expert selected in the step S232 from the similar past bid inviting item, and selecting topN bid evaluation experts by sequencing; n is a positive integer;
s24: using a content-based recommendation bid evaluation expert;
if the similarity of the bidding item requirements is not satisfied with the past bidding item requirements, calculating topN recommendation lists with the bidding item requirement labels most similar to the labeled bidding expert group labels clustered in S21 by directly using the Jaccard similarity, the cosine similarity or the Pearson correlation coefficient similarity, matching the bidding item bidding expert requirement labels with the clustered labeled bidding expert group labels clustered in S21, and listing topN bidding experts to finally obtain a bidding expert recommendation scheme; and outputting the recommended scheme.
In a second aspect, an evaluation expert recommending apparatus includes:
the bid evaluation expert exporting module and the bid evaluation expert digital portrait constructing module are used for exporting the personal information of the bid evaluation expert from the bid evaluation expert database; adopting a method for constructing a digital portrait of the bid evaluation expert to tag the derived personal information of the bid evaluation expert to obtain the digital portrait of the bid evaluation expert;
the bid evaluation expert recommending module is used for clustering bid evaluation experts after the digital portrait and labeling bid evaluation experts who are required for bidding projects, wherein the tag of the bid evaluation expert required for the bidding projects is consistent with the tag of the bid evaluation expert after the digital portrait; recommending the bid evaluation experts through a bid evaluation expert recommendation strategy in a mixed recommendation mode to obtain a recommendation scheme, and outputting the recommendation scheme.
A third aspect comprises a memory, a processor and a computer program stored in the memory and operable on the processor, wherein the processor implements a bid evaluation expert recommendation method according to any one of the above aspects when executing the computer program.
In a fourth aspect, a computer-readable storage medium stores a computer program, wherein the computer program is configured to implement any one of the above described comment expert recommendation methods when executed by a processor.
The invention has at least the following beneficial effects:
the invention constructs an expert digital portrait and carries out tagging on expert information; the expert evaluation method comprises the steps of extracting experts, using an expert digital image processing method to image evaluation experts, and extracting the experts through an expert recommendation strategy in a mixed recommendation mode.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
In the drawings:
FIG. 1 is a schematic diagram of a main process for constructing an evaluation expert portrait;
FIG. 2 is a diagram of a label system of an evaluation expert portrait;
FIG. 3 is a schematic diagram illustrating a bid evaluation expert recommendation strategy flow in a hybrid recommendation manner;
FIG. 4 is a schematic diagram of the architecture of the bid evaluation expert recommender.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further details of the invention. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
Example 1
As shown in fig. 1, a bid evaluation expert recommendation method includes:
s1: exporting the personal information of the bid evaluation expert from a bid evaluation expert library of the enterprise; and adopting a method for constructing a digital portrait of the bid evaluation expert to tag the derived personal information of the bid evaluation expert to obtain the digital portrait of the bid evaluation expert.
The method for constructing the evaluation expert digital portrait mainly comprises four steps: the method comprises the steps of raw data acquisition, data preprocessing, portrait dimension design and portrait label generation. The construction of the evaluation expert portrait is the result of the synergy of data and analysis.
S11: raw data acquisition
The data is the basis for constructing the image of the bid evaluation expert, comprehensive information of the bid evaluation expert is obtained in the first step of constructing the digital image of the bid evaluation expert, personal information of the bid evaluation expert is exported from the bid evaluation expert library, namely the personal information of the bid evaluation expert is exported through an electronic commerce platform-expert management module and a participated project management evaluation table of each item, and original data is obtained, wherein the original data comprises personal basic information of the bid evaluation expert, personal learning experience of the bid evaluation expert, personal work information of the bid evaluation expert, personal bid evaluation experience of the bid evaluation expert, personal bid evaluation information of the bid evaluation expert and scoring of the project management expert. The data are categorized as shown in the following table:
TABLE 1 data classification Table
S12: data preprocessing: the method comprises the steps of preprocessing original data through modes of manual extraction, data cleaning, data mining, main body extraction and the like, and converting semi-structured and unstructured data into structured data. The method mainly carries out structural processing on the study history, the work history, the review history and the like.
S13: and (3) portrait dimension design: the purpose of extracting the attribute of the bid evaluation expert is achieved by analyzing and processing the original data. Aiming at the portrait dimension of the bid evaluation expert, feature extraction can be carried out according to application requirements or preset common data dimensions, such as age, working age, academic calendar and the like; some potential connections hidden in the data can be converted into image attribute dimensions of bid evaluation experts, so that the potential connections hidden in the data need to be developed in a data mining mode, for example, the association degrees of academic expertise, work experience and declaration review information, the association degrees of the located units and bid companies and other association coefficients are mined, and the data mining method comprises text mining, association analysis, machine learning and other modes.
S14: portrait label generation: the method is a technology for establishing the most core and key of a user portrait and designing a label according to different attribute dimensions of a bid evaluation expert portrait. To find the most suitable expert for bid evaluation, the data of the bid evaluation expert and the fine data of the review object in all aspects need to be integrated into a whole so as to become effective information. According to the result of the attribute feature extraction of the bid evaluation expert, four types of tag systems can be divided into a basic information tag, a professional information tag, a social relation tag and a bid evaluation capability information tag. FIG. 2 is a schematic diagram of a label system of an evaluation expert portrait;
the basic information tag includes: ID. Gender, political aspect, health, residential address, academic calendar; the professional information labels comprise professions, work experiences, manufacture, age of workers, and academic abilities; the social relationship label includes: partnerships, affiliations, company relationships; the bid evaluation capability information tag comprises: and (4) bidding evaluation experience, bidding evaluation scoring and professional bidding evaluation declaration.
S2: as shown in fig. 3, clustering the evaluation experts after the digital portrait; labeling the bidding evaluation experts required by the bidding program, wherein the labeling evaluation expert label required by the bidding program is consistent with the labeling evaluation expert label behind the digital portrait; recommending the bid evaluation experts through a bid evaluation expert recommendation strategy in a mixed recommendation mode to obtain a recommendation scheme; outputting a recommended scheme;
s21: processing by an evaluation expert;
according to project requirements, similarity calculation methods such as Jaccard similarity, cosine similarity and Pearson correlation coefficient or k-means clustering algorithm are used for calculating or clustering similarity of the standard evaluation experts after the digital image is drawn, and a labeled standard evaluation expert group after clustering is obtained;
s22: processing the bidding project requirement;
s221: listing bid items versus bid evaluation expert requirements such as expertise, age, address, and other requirements;
s222: and labeling the bid evaluation experts required by the bid inviting project, wherein the tag of the bid evaluation experts required by the bid inviting project is consistent with the tag of the bid evaluation experts after the digital portrait.
S23: recommending bid evaluation experts based on collaborative filtering;
s231: calculating the past bidding items similar to the bidding requirement by using the similarity of Jaccard similarity, cosine similarity, pearson correlation coefficient and the like;
s232: extracting the selected bid evaluation experts of the similar past bid inviting items;
s233: extracting the label from the clustered labeled bid evaluation expert group in S21, extracting the bid evaluation experts with the labels similar to the label of the bid evaluation expert selected in the step S232 from the similar past bid inviting item, and selecting topN bid evaluation experts by sequencing; n is a positive integer.
S24: a content-based recommendation bid evaluation expert is used.
If the similarity of the bidding item requirement does not meet the requirement of the past bidding item, namely no related item exists, calculating topN recommendation lists with the bidding item requirement label most similar to the labeled bidding expert group label clustered in S21 by directly using the similarity of Jaccard similarity, cosine similarity, pearson correlation coefficient and the like, matching the bidding item bidding expert requirement label with the clustered labeled bidding expert group label clustered in S21, and listing topN bidding experts to finally obtain a bidding expert recommendation scheme; and outputting the recommended scheme.
Example 2
As shown in fig. 4, an evaluation expert recommending apparatus includes:
the bid evaluation expert exporting and bid evaluation expert digital portrait constructing module is used for exporting the personal information of the bid evaluation expert from the bid evaluation expert database; adopting a method for constructing a digital portrait of the bid evaluation expert to tag the derived personal information of the bid evaluation expert to obtain the digital portrait of the bid evaluation expert;
the bid evaluation expert recommending module is used for clustering bid evaluation experts after the digital portrait and labeling the bid evaluation experts required by the bid inviting project, wherein the tag of the bid evaluation expert required by the bid inviting project is consistent with the tag of the bid evaluation experts after the digital portrait; recommending the bid evaluation experts through a bid evaluation expert recommendation strategy in a mixed recommendation mode to obtain a recommendation scheme, and outputting the recommendation scheme.
Example 3
The invention provides computer equipment which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and is characterized in that the processor executes the computer program to realize the bid evaluation expert recommendation method in embodiment 1.
Example 4
The invention provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the method for recommending a bid evaluation expert is implemented in embodiment 1.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A bid evaluation expert recommendation method is characterized by comprising the following steps:
s1: exporting the personal information of the bid evaluation expert from the bid evaluation expert library; adopting a method for constructing a digital portrait of the bid evaluation expert to tag the derived personal information of the bid evaluation expert to obtain the digital portrait of the bid evaluation expert;
s2: clustering the bid evaluation experts after the digital portrait, and labeling the bid evaluation experts required by the bid inviting project, wherein the tag of the bid evaluation experts required by the bid inviting project is consistent with the tag of the bid evaluation experts after the digital portrait; recommending the bid evaluation experts through a bid evaluation expert recommendation strategy in a mixed recommendation mode to obtain a recommendation scheme, and outputting the recommendation scheme.
2. The bid evaluation expert recommendation method according to claim 1, wherein the method for constructing the bid evaluation expert digital representation comprises four steps: the method comprises the steps of raw data acquisition, data preprocessing, image dimension design and image label generation.
3. The bid evaluation expert recommendation method according to claim 2, wherein the raw data is obtained by: exporting personal information of the bid evaluation experts in the bid evaluation expert database to obtain original data;
the data preprocessing comprises the following steps: preprocessing original data in a mode of manual extraction, data cleaning, data mining and main body extraction, and converting semi-structured and unstructured data into structured data;
the portrait dimensions are designed as: extracting the attribute of the bid evaluation expert by analyzing and processing the original data; aiming at the dimensionality of the evaluation expert portrait, extracting features according to application requirements or preset data dimensionality; for the relation hidden in the data is converted into the attribute dimension of the evaluation expert portrait, the potential relation hidden in the data is made explicit through a data mining mode;
the portrait label is generated as follows: designing a label according to different attribute dimensions of the benchmarking expert portrait; integrating the data of the bid evaluation experts and the data of the evaluation objects into a whole; and (4) extracting results according to the attribute characteristics of the bid evaluation experts, and classifying the results into four types of label systems, namely a basic information label, a professional information label, a social relation label and a bid evaluation capability information label.
4. The bid evaluation expert recommendation method according to claim 3, wherein the raw data comprises bid evaluation expert personal basic information, bid evaluation expert personal learning experience, bid evaluation expert personal work information, bid evaluation expert personal bid evaluation experience, bid evaluation expert personal declaration and bid evaluation information, and a project manager bid evaluation expert score; the data mining method comprises text mining, association analysis and machine learning.
5. The bid evaluation expert recommendation method according to claim 3, wherein the basic information tag comprises: ID. Gender, political aspect, health, residential address, academic calendar; the professional information labels comprise professions, work experiences, manufacture, age of workers, and academic abilities; the social relationship label includes: partnerships, affiliations, corporate relationships; the bid evaluation capability information tag comprises: and (4) bidding evaluation experience, bidding evaluation scoring and professional bidding evaluation declaration.
6. The method according to claim 1, wherein the clustering of the bidding experts after the digital portrait and the tagging of the bidding experts who are in demand for the bidding project, wherein the step of matching the tag of the bidding expert who is in demand for the bidding project with the tag of the bidding experts after the digital portrait specifically comprises:
s21: processing by an evaluation expert;
according to project requirements, similarity calculation or clustering is carried out on the standard evaluation experts after the digital images are imaged by using a Jaccard similarity, cosine similarity or Pearson correlation coefficient similarity calculation method or a k-means clustering algorithm, so as to obtain a clustered labeled standard evaluation expert group;
s22: processing the bidding project requirement;
s221: the listed bidding items meet the requirements of bid evaluation experts;
s222: and labeling the bid evaluation experts required by the bid inviting project, wherein the tag of the bid evaluation experts required by the bid inviting project is consistent with the tag of the bid evaluation experts after the digital portrait.
7. The bid evaluation expert recommendation method according to claim 6, wherein the steps of recommending bid evaluation experts through a bid evaluation expert recommendation strategy in a hybrid recommendation mode, obtaining a recommendation scheme, and outputting the recommendation scheme specifically comprise:
s23: recommending bid evaluation experts based on collaborative filtering;
s231: calculating past bidding items approximate to the bidding requirements by using the Jaccard similarity, the cosine similarity or the Pearson correlation coefficient similarity;
s232: extracting the selected bid evaluation experts of the similar past bid inviting items;
s233: extracting the label from the clustered labeled bid evaluation expert group in S21, extracting the bid evaluation experts with the labels similar to the label of the bid evaluation expert selected in the step S232 from the similar past bid inviting item, and selecting topN bid evaluation experts by sequencing; n is a positive integer;
s24: using a content-based recommendation bid evaluation expert;
if the similarity of the bidding project requirements is not satisfied with the past bidding project requirements, calculating topN recommendation lists with the bidding project requirement labels most similar to the clustered labeled expert panel labels in S21 by directly using Jaccard similarity, cosine similarity or Pearson correlation coefficient similarity, matching the bidding project expert panel requirement labels with the clustered labeled expert panel labels in S21, listing topN evaluation experts, and finally obtaining an expert panel recommendation scheme; and outputting the recommended scheme.
8. An evaluation expert recommending device, comprising:
the bid evaluation expert exporting and bid evaluation expert digital portrait constructing module is used for exporting the personal information of the bid evaluation expert from the bid evaluation expert database; adopting a method for constructing a digital portrait of the bid evaluation expert to tag the derived personal information of the bid evaluation expert to obtain the digital portrait of the bid evaluation expert;
the bid evaluation expert recommending module is used for clustering bid evaluation experts after the digital portrait and labeling the bid evaluation experts required by the bid inviting project, wherein the tag of the bid evaluation expert required by the bid inviting project is consistent with the tag of the bid evaluation experts after the digital portrait; recommending the bid evaluation experts through a bid evaluation expert recommendation strategy in a mixed recommendation mode to obtain a recommendation scheme, and outputting the recommendation scheme.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements a method of bid evaluation expert recommendation according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, implements a bid evaluation expert recommendation method according to any one of claims 1 to 7.
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| CN116956097A (en) * | 2023-09-18 | 2023-10-27 | 湖南华菱电子商务有限公司 | Expert portrait analysis method and system based on K-means |
| CN117634979A (en) * | 2023-12-08 | 2024-03-01 | 长春领航科技有限公司 | Power grid bid-recruitment bid-evaluation expert management system |
| CN118333349A (en) * | 2024-06-13 | 2024-07-12 | 辽宁省网联数字科技产业有限公司 | Evaluation method and system for matching degree of label evaluation expert working experience based on artificial intelligence |
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- 2022-07-29 CN CN202210910448.1A patent/CN115168736A/en active Pending
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116257681A (en) * | 2022-12-21 | 2023-06-13 | 中国民航科学技术研究院 | Civil aviation event investigator recommendation method based on portrait technology |
| CN116257681B (en) * | 2022-12-21 | 2023-10-13 | 中国民航科学技术研究院 | Civil aviation event investigator recommendation method based on portrait technology |
| CN116956097A (en) * | 2023-09-18 | 2023-10-27 | 湖南华菱电子商务有限公司 | Expert portrait analysis method and system based on K-means |
| CN116956097B (en) * | 2023-09-18 | 2023-12-12 | 湖南华菱电子商务有限公司 | Expert portrait analysis method and system based on K-means |
| CN117634979A (en) * | 2023-12-08 | 2024-03-01 | 长春领航科技有限公司 | Power grid bid-recruitment bid-evaluation expert management system |
| CN117634979B (en) * | 2023-12-08 | 2024-05-14 | 长春领航科技有限公司 | Power grid bid-recruitment bid-evaluation expert management system |
| CN118333349A (en) * | 2024-06-13 | 2024-07-12 | 辽宁省网联数字科技产业有限公司 | Evaluation method and system for matching degree of label evaluation expert working experience based on artificial intelligence |
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