CN112883018A - Banking talent information prediction method and device based on big data analysis - Google Patents
Banking talent information prediction method and device based on big data analysis Download PDFInfo
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
Abstract
The invention provides a banking talent information prediction method and device based on big data analysis, which comprises the following steps: preprocessing acquired talent information key data; performing data description and establishing a model according to four elements of time, place, people and content by using key data of talent information; carrying out label mining on key data of talent information to obtain a talent image; and (5) carrying out portrait clustering by using the established model and the talent portrait to obtain a talent information prediction result. The talent evaluation system is based on a series of flow structures such as data acquisition, user modeling, label mining and image clustering, images are performed on talents and an image set is established, a comprehensive quality digital image system of the talents is established, the intelligence and comprehensiveness of a talent evaluation system in the banking industry are improved, in addition, tools such as a visual radar map are used for establishing a post and comprehensive quality matching degree, a basis is provided for self-cognition of the talents, professional development planning is optimized, and reference is provided for a manager to perfect a talent culture quality scheme.
Description
Technical Field
The application belongs to the technical field of big data analysis, and particularly relates to a banking talent information prediction method and device based on big data analysis.
Background
At present, talents are generally evaluated and classified by resume evaluation, on-site question-answering method and psychological test in talent screening, and except for the modes of basic information, learning experience, working experience, behavior description and the like, the key is that the evaluation is taken as the main standard in the quality evaluation mechanism. The traditional evaluation system can not timely, comprehensively and scientifically evaluate various performances of talents, neglects the culture of talent capacity and inhibits the culture of talent autonomous development consciousness. And the evaluation mode is single, the comprehensive quality of the talents cannot be really reflected, and reasonable occupational planning of the talents cannot be guided.
Disclosure of Invention
The application provides a banking talent information prediction method and device based on big data analysis, and aims to at least solve the problem that a traditional talent evaluation system cannot timely, comprehensively and scientifically evaluate various performances of talents.
According to one aspect of the application, a banking talent information prediction method based on big data analysis is provided, and comprises the following steps:
preprocessing acquired talent information key data;
performing data description and establishing a model according to four elements of time, place, people and content by using the preprocessed talent information key data;
carrying out label mining on key data of talent information to obtain a talent image;
and (4) carrying out portrait clustering by using the established model and the talent portrait to obtain the bank talent information prediction result.
In one embodiment, the preprocessing of the acquired key data of talent information includes:
data cleaning is carried out on the key data of talent information from different sources to obtain a uniform data format and a uniform content specification; the data cleaning mode comprises the following steps: constant substitution, mean filling and regression prediction
In one embodiment, the method for performing data description and modeling by using the preprocessed key data of the talent information according to four elements of time, place, people and content comprises the following steps:
performing data description on the preprocessed talent information key data according to four elements of time, place, people and content;
matching the behavior information in the talent information key data with talent identification information;
and modeling all the behaviors of the talents by defining time attenuation factors and weights of various types of behavior information.
In one embodiment, the tag mining of the key data of the talent information to obtain the talent picture comprises the following steps:
dividing the tag metadata and then calibrating the key data of the talent information by using the divided tag metadata;
capturing talent information key data according to the type of the tag metadata and classifying the talent information key data according to the tag metadata;
and (4) performing talent portrayal according to the classified talent information key data and preset weight.
According to another aspect of the present application, a banking talent information prediction apparatus based on big data analysis comprises:
the preprocessing unit is used for preprocessing the acquired key data of the talent information;
the user modeling unit is used for performing data description and establishing a model according to four elements of time, place, people and content by utilizing the preprocessed talent information key data;
the label mining unit is used for performing label mining on the key data of the talent information to obtain a talent image;
and the portrait clustering unit is used for clustering portraits by using the established model and the talent portraits to obtain a bank talent information prediction result.
In one embodiment, the pre-processing unit comprises:
the data cleaning module is used for cleaning the key data of talent information from different sources to obtain a uniform data format and a uniform content specification; the data cleaning mode comprises the following steps: constant substitution, mean filling and regression prediction
In an embodiment, the user modeling unit comprises:
the data description module is used for carrying out data description on the preprocessed talent information key data according to four elements of time, place, people and content;
the matching module is used for matching the behavior information in the talent information key data with the talent identification information;
and the behavior modeling module is used for modeling all behaviors of the talents by defining time attenuation factors and weights of various types of behavior information.
In one embodiment, the tag mining unit includes:
the calibration module is used for dividing the tag metadata and then calibrating the key data of the talent information by using the divided tag metadata;
the classification module is used for capturing the talent information key data according to the type of the label metadata and classifying the talent information key data according to the label metadata;
and the talent portrait module is used for portrait of talents according to the classified key data of talent information and preset weight.
The talent evaluation system is based on a series of flow structures such as data acquisition, user modeling, label mining and image clustering, images are performed on talents and an image set is established for the talents, a comprehensive quality digital image system of the talents is established, the intelligence and comprehensiveness of the talent evaluation system are improved, in addition, the post and comprehensive quality matching degree is established by using tools such as a visual radar map and the like, the basis is provided for the self-cognition of the talents, the occupational development planning is optimized, and the reference is provided for the teaching management of managers and the quality scheme for perfecting talent culture.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a banking talent information prediction method based on big data analysis according to the present application.
Fig. 2 is a flowchart of a method for performing data description and modeling on data in the embodiment of the present application.
Fig. 3 is a flowchart of a method for obtaining a talent image by performing label mining on key data of talent information in the embodiment of the present application.
Fig. 4 is a block diagram of a banking talent information prediction device based on big data analysis according to the present application.
Fig. 5 is a block diagram of a structure of a user modeling unit in the embodiment of the present application.
Fig. 6 is a block diagram of a structure of a tag mining unit in the embodiment of the present application.
Fig. 7 is a specific implementation of an electronic device in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The traditional talent evaluation mode cannot truly reflect the comprehensive quality of talents and cannot guide talents to carry out reasonable occupational planning. By utilizing a big data technology, a brand-new visual and broad idea is provided for comprehensive quality evaluation. The acquisition range is expanded to various environments and working performances through a big data technology, and the comprehensive quality of talents can be objectively mapped. As the requirements and standards for talent evaluation of various industries are different, a talent evaluation method more suitable for the characteristics of the banking industry is needed for talent selection evaluation of the banking industry, and talent evaluation basis is provided for the same industry.
As shown in fig. 1, the present application provides a banking talent information prediction method based on big data analysis, including:
s101: and preprocessing the acquired key data of the talent information.
S102: and performing data description and establishing a model according to four elements of time, place, people and content by utilizing the preprocessed key data of the talent information.
S103: and (4) carrying out label mining on the key data of the talent information to obtain a talent image.
S104: and (4) carrying out portrait clustering by using the established model and the talent portrait to obtain the bank talent information prediction result.
In one embodiment, the preprocessing of the acquired key data of talent information includes:
data cleaning is carried out on the key data of talent information from different sources to obtain a uniform data format and a uniform content specification; the data cleaning mode comprises the following steps: constant substitution, mean filling and regression prediction
In an embodiment, the preprocessed key data of talent information is used to perform data description and model according to four elements of time, place, people and content, as shown in fig. 2, including:
s201: and performing data description on the preprocessed talent information key data according to four elements of time, place, people and content.
S202: and matching the behavior information in the talent information key data with the talent identification information.
S203: and modeling all the behaviors of the talents by defining time attenuation factors and weights of various types of behavior information.
In an embodiment, the tag mining is performed on the key data of talent information to obtain a talent image, as shown in fig. 3, including:
s301: and dividing the tag metadata and then calibrating the key data of the talent information by using the divided tag metadata.
S302: and capturing the talent information key data according to the type of the tag metadata and classifying the talent information key data according to the tag metadata.
S303: and (4) performing talent portrayal according to the classified talent information key data and preset weight.
In a specific embodiment, the performance of talents in different environments, such as data of learning activities, life performance and the like, is collected, information key data covering more than 80% of talents is collected, computing power is provided for the application by using a private cloud platform based on super fusion, 5 computing nodes are provided, and distributed storage is about 30T.
In the aspect of data source acquisition, the data center of a human unit collects educational administration data, book data, internet data and the like of talents during a school period to the data center by relying on an ETL technology. A data stream collection tool flume, a crawler tool python, etc. Data modeling is carried out according to the data sources, and the specification of data formats and contents is carried out in a data cleaning mode aiming at the heterogeneous conditions of the data sources, so that the data has good universality and readability. In the process of cleaning data, Sklearn and Pandas tools in Python can be called to realize constant replacement, mean filling and regression prediction of the data, so that the noise of the data is greatly reduced, the data has good normalization after being coded and converted, the data is consistent in representation, and the regularization of the data can be realized.
In the data modeling process, four elements of time, address, character and content need to be described in a data mode, behavior data of talents are effectively processed in time, and the following modes can be adopted for processing: talent identification + time + behavior type + application system + content. By defining the time decay factor and system and content weight of various types of behaviors, the whole behavior of talents can be modeled.
The tag metadata is data for describing tag classification, and is divided into a basic tag, a score tag, a knowledge tag, a training tag, a working ability tag, a character tag, a psychological tag, a learning tag, an idea tag, and the like. The label division is a post capability label which is made according to the capability required by various posts of the banking industry deeply discussed and researched from multiple aspects of occupation, psychology, communication, learning and the like of relevant business departments. Meanwhile, according to a tree hierarchy discipline knowledge system set by the platform, each node on the tree is associated with the designated capability tag to form a multi-dimensional tag system, and a complete employee capability model is established by mining the tags and setting the weights of various tags. In practice, thought dynamic labels are mined by grabbing and accessing URL logs, and score labels and the like are set by analyzing a teaching platform and a educational administration system. The multidimensional label system effectively integrates learner behavior data of a educational administration system, a learning platform, an information platform and the like. Meanwhile, a multidimensional portrait label display system is established, and data support is provided for more accurate learning support service design. By mining the labels of the data and setting the weight for the labels, a talent can be completely described by the model, namely, the talent is portrait.
And finally, constructing a comprehensive prime digital portrait system of talents, and establishing post and comprehensive prime matching degree by using tools such as a visual radar map and the like. The data of the evaluation system has both result data and process data, and the actual situation of talents can be reflected comprehensively and accurately.
Based on the same inventive concept, the embodiment of the present application further provides a banking talent information prediction apparatus based on big data analysis, which can be used to implement the method described in the above embodiments, as described in the following embodiments. The principle of solving the problems of the banking talent information prediction device based on the big data analysis is similar to that of the banking talent information prediction method based on the big data analysis, so the implementation of the banking talent information prediction device based on the big data analysis can be referred to the implementation of the banking talent information prediction method based on the big data analysis, and repeated parts are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
As shown in fig. 4, the present application further provides a banking talent information prediction apparatus based on big data analysis, including:
the preprocessing unit 401 is configured to preprocess the acquired key data of the talent information;
the user modeling unit 402 is used for performing data description and establishing a model according to four elements of time, place, people and content by utilizing the preprocessed talent information key data;
a label mining unit 403, configured to perform label mining on the key data of talent information to obtain a talent image;
and the portrait clustering unit 404 is used for performing portrait clustering by using the established model and the talent portrait to obtain a banking talent information prediction result.
In one embodiment, the pre-processing unit comprises:
the data cleaning module is used for cleaning the key data of talent information from different sources to obtain a uniform data format and a uniform content specification; the data cleaning mode comprises the following steps: constant substitution, mean filling and regression prediction
In one embodiment, as shown in FIG. 5, the user modeling unit 402 includes:
the data description module 501 is used for performing data description on the preprocessed talent information key data according to four elements of time, place, people and content;
a matching module 502, configured to match behavior information in the talent information key data with talent identification information;
and the behavior modeling module 503 is configured to model all behaviors of the talents by defining time attenuation factors and weights of various types of behavior information.
In one embodiment, as shown in fig. 6, the label mining unit 403 includes:
the calibration module 601 is used for dividing the tag metadata and then calibrating the key data of the talent information by using the divided tag metadata;
the classification module 602 is configured to capture talent information key data according to the type of the tag metadata and classify the talent information key data according to the tag metadata;
the talent portrayal module 603 is used for portrayal talents according to the classified key data of talent information and preset weight.
The talent evaluation system is based on a series of flow structures such as data acquisition, user modeling, label mining and image clustering, images are performed on talents and an image set is established for the talents, a comprehensive quality digital image system of the talents is established, the intelligence and comprehensiveness of the talent evaluation system are improved, in addition, the post and comprehensive quality matching degree is established by using tools such as a visual radar map and the like, the basis is provided for the self-cognition of the talents, the occupational development planning is optimized, and the reference is provided for the teaching management of managers and the quality scheme for perfecting talent culture.
An embodiment of the present application further provides a specific implementation manner of an electronic device capable of implementing all steps in the method in the foregoing embodiment, and referring to fig. 7, the electronic device specifically includes the following contents:
a processor (processor)701, a memory 702, a communication Interface 703, a bus 704, and a nonvolatile memory 705;
the processor 701, the memory 702 and the communication interface 703 complete mutual communication through the bus 704;
the processor 701 is configured to call the computer programs in the memory 702 and the nonvolatile memory 705, and when the processor executes the computer programs, the processor implements all the steps in the method in the foregoing embodiments, for example, when the processor executes the computer programs, the processor implements the following steps:
s101: and preprocessing the acquired key data of the talent information.
S102: and performing data description and establishing a model according to four elements of time, place, people and content by utilizing the preprocessed key data of the talent information.
S103: and (4) carrying out label mining on the key data of the talent information to obtain a talent image.
S104: and (4) carrying out portrait clustering by using the established model and the talent portrait to obtain the bank talent information prediction result.
Embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the method in the above embodiments, where the computer-readable storage medium stores thereon a computer program, and the computer program when executed by a processor implements all the steps of the method in the above embodiments, for example, the processor implements the following steps when executing the computer program:
s101: and preprocessing the acquired key data of the talent information.
S102: and performing data description and establishing a model according to four elements of time, place, people and content by utilizing the preprocessed key data of the talent information.
S103: and (4) carrying out label mining on the key data of the talent information to obtain a talent image.
S104: and (4) carrying out portrait clustering by using the established model and the talent portrait to obtain the bank talent information prediction result.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment. Although embodiments of the present description provide method steps as described in embodiments or flowcharts, more or fewer steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or end product executes, it may execute sequentially or in parallel (e.g., parallel processors or multi-threaded environments, or even distributed data processing environments) according to the method shown in the embodiment or the figures. 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, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded. For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, in implementing the embodiments of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, and the like. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. 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. As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description 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 so forth) having computer-usable program code embodied therein. The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of an embodiment of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction. The above description is only an example of the embodiments of the present disclosure, and is not intended to limit the embodiments of the present disclosure. Various modifications and variations to the embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present specification should be included in the scope of the claims of the embodiments of the present specification.
Claims (10)
1. A banking talent information prediction method based on big data analysis is characterized by comprising the following steps:
preprocessing acquired talent information key data;
performing data description and establishing a model according to four elements of time, place, people and content by utilizing the preprocessed key data of the talent information;
performing label mining on the key data of the talent information to obtain a talent picture;
and carrying out portrait clustering by using the established model and the talent portrait to obtain a bank talent information prediction result.
2. The banking talent information prediction method based on big data analysis according to claim 1, wherein the preprocessing of the key data of the acquired talent information comprises:
data cleaning is carried out on the key data of talent information from different sources to obtain a uniform data format and a uniform content specification; the data cleaning mode comprises the following steps: constant substitution, mean filling, and regression prediction.
3. The banking talent information prediction method based on big data analysis according to claim 1, wherein the data description and modeling by using the preprocessed key data of talent information according to four elements of time, place, people and content comprises:
performing data description on the preprocessed talent information key data according to four elements of time, place, people and content;
matching the behavior information in the talent information key data with talent identification information;
and modeling all the behaviors of the talents by defining time attenuation factors and weights of various types of behavior information.
4. The banking talent information prediction method based on big data analysis according to claim 1, wherein the tag mining of the critical data of talent information to obtain talent portrayal comprises:
dividing the label metadata and then calibrating the key data of the talent information by using the divided label metadata;
capturing the talent information key data according to the type of the tag metadata and classifying the talent information key data according to the tag metadata;
and performing talent portrayal according to the classified key data of the talent information and preset weight.
5. A banking talent information prediction device based on big data analysis, comprising:
the preprocessing unit is used for preprocessing the acquired key data of the talent information;
the user modeling unit is used for performing data description and establishing a model according to four elements of time, place, people and content by utilizing the preprocessed talent information key data;
the label mining unit is used for performing label mining on the key data of the talent information to obtain a talent picture;
and the portrait clustering unit is used for carrying out portrait clustering by utilizing the established model and the talent portrait to obtain the bank talent information prediction result.
6. The banking talent information prediction device based on big data analysis according to claim 5, wherein said preprocessing unit comprises:
the data cleaning module is used for cleaning the key data of talent information from different sources to obtain a uniform data format and a uniform content specification; the data cleaning mode comprises the following steps: constant substitution, mean filling, and regression prediction.
7. The banking talent information prediction device based on big data analysis according to claim 5, wherein said user modeling unit comprises:
the data description module is used for carrying out data description on the preprocessed talent information key data according to four elements of time, place, people and content;
the matching module is used for matching the behavior information in the talent information key data with the talent identification information;
and the behavior modeling module is used for modeling all behaviors of the talents by defining time attenuation factors and weights of various types of behavior information.
8. The banking talent information prediction device based on big data analysis according to claim 5, wherein said tag mining unit comprises:
the calibration module is used for dividing the label metadata and then calibrating the key data of the talent information by using the divided label metadata;
the classification module is used for capturing the talent information key data according to the type of the label metadata and classifying the talent information key data according to the label metadata;
and the talent portrait module is used for portrait of talents according to the classified key data of talent information and preset weight.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the big data analysis-based banking talent information prediction method according to any one of claims 1 to 5 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the banking talent information prediction method based on big data analysis according to any one of claims 1 to 5.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113313470A (en) * | 2021-06-10 | 2021-08-27 | 郑州科技学院 | Employment type evaluation method and system based on big data |
| CN113360733A (en) * | 2021-06-17 | 2021-09-07 | 林宏佳 | Talent data tag classification method and system based on artificial intelligence and cloud platform |
-
2021
- 2021-01-28 CN CN202110119521.9A patent/CN112883018A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113313470A (en) * | 2021-06-10 | 2021-08-27 | 郑州科技学院 | Employment type evaluation method and system based on big data |
| CN113313470B (en) * | 2021-06-10 | 2023-06-09 | 郑州科技学院 | Employment type assessment method and system based on big data |
| CN113360733A (en) * | 2021-06-17 | 2021-09-07 | 林宏佳 | Talent data tag classification method and system based on artificial intelligence and cloud platform |
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