CN109885647B

CN109885647B - User history verification method, device, electronic equipment and storage medium

Info

Publication number: CN109885647B
Application number: CN201811628088.6A
Authority: CN
Inventors: 邹铁山; 孙积慧
Original assignee: Kaola Credit Service Co ltd
Current assignee: Kaola Credit Service Co ltd
Priority date: 2018-12-28
Filing date: 2018-12-28
Publication date: 2021-07-09
Anticipated expiration: 2038-12-28
Also published as: CN109885647A

Abstract

The embodiment of the disclosure discloses a user history verification method, a user history verification device, electronic equipment and a storage medium. Wherein, the method comprises the following steps: acquiring related information of a target user; wherein the related information at least comprises identity information and resume information; the biographical information comprises at least one first associated combination of the target user, the first associated combination comprising an associated first time interval, first institution and first skill information; acquiring scientific research public data of the target user according to the related information; and verifying the history of the target user according to the scientific research public data and the related information.

Description

User history verification method, device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of computers, and in particular, to a user history verification method, apparatus, electronic device, and storage medium.

Background

Background investigation is an important link in the field of human resources, and the candidate is generally investigated manually for professional background conditions, such as professional qualifications, academic records and/or academic backgrounds, skills and experience, and the like. However, the conventional method is time-consuming and labor-consuming, wastes a lot of resources, and there may be a case where the information acquisition is not accurate, which results in inaccurate investigation results. With the rise of the internet and big data, especially the breakthrough of recent artificial intelligence technology, the professional background investigation based on the internet becomes possible. Through big data and artificial intelligence technology, the professional background investigation platform can acquire public information about individuals from different channels, and can automatically and quickly provide professional background information of a candidate through integrated processing of the public information and under the condition of authorization of a target user. In some cases, the most important feature of a candidate is its expertise, such as university researchers, enterprise research and development engineers, and the like. At this time, in addition to general information, professional skill investigation on a candidate becomes important.

However, the inventor finds that at least the following problems exist in the related art in the process of implementing the related technical scheme of the embodiment of the present disclosure: the expert skill involves specific domain knowledge, making it extremely difficult for non-skilled persons to conduct surveys.

Disclosure of Invention

In view of the above technical problems in the prior art, the embodiments of the present disclosure provide a user history verification method, apparatus, electronic device and computer-readable storage medium, so as to solve the problem that a person who is not in the field is difficult to investigate due to professional skills involving specific field knowledge.

A first aspect of the embodiments of the present disclosure provides a user resume verification method, including:

acquiring related information of a target user; wherein the related information at least comprises identity information and resume information; the biographical information comprises at least one first associated combination of the target user, the first associated combination comprising an associated first time interval, first institution and first skill information;

acquiring scientific research public data of the target user according to the related information;

and verifying the history of the target user according to the scientific research public data and the related information.

In some embodiments, verifying the resume of the target user based on the scientific research disclosure data and the related information comprises:

analyzing the scientific research public data and determining at least one second association combination related to the scientific research public data; the second associated combination comprises an associated second institution, a second time interval, and second skill information;

and verifying the history of the target user according to the first association combination and the second association combination.

In some embodiments, verifying the target user's biographies based on the first and second association combinations includes at least:

determining whether the first and second mechanisms are consistent and the second time interval matches a first association set and a second association set within the first time interval;

and verifying the history of the target user according to the similarity of the first skill information and the second skill information aiming at the matched first association combination and second association combination.

In some embodiments, verifying the target user's biographies based on the similarity of the first and second skill information comprises:

determining the similarity between the first skill information and the second skill information according to a pre-trained artificial intelligence model;

and when the similarity is larger than or equal to a preset threshold value, determining that the history of the target user is real.

In some embodiments, the second skill information comprises at least: technical field, technical problem and/or technical means.

In some embodiments, the scientific published data comprises patent literature data; and/or the presence of a gas in the gas,

the resume information also comprises the patent document identification applied by the target user.

In some embodiments, parsing the scientific research disclosure data comprises:

at least one of the title, abstract, claims and specification in the patent document data is analyzed.

A second aspect of an embodiment of the present disclosure provides a user history verification apparatus, including:

the first acquisition module is used for acquiring relevant information of a target user; wherein the related information at least comprises identity information and resume information; the biographical information comprises at least one first associated combination of the target user, the first associated combination comprising an associated first time interval, first institution and first skill information;

the second acquisition module is used for acquiring scientific research public data of the target user according to the related information;

and the verification module is used for verifying the history of the target user according to the scientific research public data and the related information.

In some embodiments, the verification module comprises:

the first determining submodule is used for analyzing the scientific research public data and determining at least one second association combination related to the scientific research public data; the second associated combination comprises an associated second institution, a second time interval, and second skill information;

and the first verification sub-module is used for verifying the history of the target user according to the first association combination and the second association combination.

In some embodiments, the first verification sub-module comprises:

the matching submodule is used for matching a first association combination and a second association combination within the first time interval according to whether the first mechanism and the second mechanism are consistent or not and whether the second time interval is matched with the first association combination and the second association combination within the first time interval or not;

and the second verification sub-module is used for verifying the history of the target user according to the similarity of the first skill information and the second skill information aiming at the matched first association combination and second association combination.

In some embodiments, the second verification sub-module comprises:

the second determining submodule is used for determining the similarity between the first skill information and the second skill information according to a pre-trained artificial intelligence model;

and the third determining submodule is used for determining that the history of the target user is real when the similarity is greater than or equal to a preset threshold value.

In some embodiments, the first determining sub-module includes:

and the analysis submodule is used for analyzing at least one of the title, the abstract, the claims and the specification in the patent literature data.

A third aspect of the embodiments of the present disclosure provides an electronic device, including:

a memory and one or more processors;

wherein the memory is communicatively coupled to the one or more processors, and the memory stores instructions executable by the one or more processors, and when the instructions are executed by the one or more processors, the electronic device is configured to implement the method according to the foregoing embodiments.

A fourth aspect of the embodiments of the present disclosure provides a computer-readable storage medium having stored thereon computer-executable instructions, which, when executed by a computing device, may be used to implement the method according to the foregoing embodiments.

A fifth aspect of embodiments of the present disclosure provides a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are operable to implement a method as in the preceding embodiments.

According to the embodiment of the disclosure, the related information of the target user is acquired, wherein the related information at least comprises identity information and resume information; the biographical information comprises at least one first associated combination of the target user, the first associated combination comprising an associated first time interval, first institution and first skill information; and acquiring scientific research public data of the target user through the related information, and finally verifying the history of the target user according to the scientific research public data and the related information. Through the technical scheme of the embodiment of the disclosure, scientific research public data of the target user can be automatically acquired, whether the target user really has the claimed skill information can be verified, and the authenticity of the historical information of the target object can be quickly and accurately verified without related professional knowledge of a person who performs verification.

Drawings

The features and advantages of the present disclosure will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the disclosure in any way, and in which:

FIG. 1 is a schematic flow diagram of a user resume verification method according to some embodiments of the present disclosure;

FIG. 2 is a schematic flow chart of step S102 according to the embodiment shown in FIG. 1;

FIG. 3 is a schematic flow chart of step S202 according to the embodiment shown in FIG. 2;

FIG. 4 is a schematic diagram illustrating a skill-time segment generated by machine learning, according to an embodiment of the present disclosure;

FIG. 5 illustrates a schematic diagram of verification results over different time slices, according to an embodiment of the present disclosure;

FIG. 6 is a schematic flow chart of step S302 according to the embodiment shown in FIG. 3;

FIG. 7 is a schematic diagram of a user resume authentication apparatus according to some embodiments of the present disclosure.

FIG. 8 is a schematic diagram of an electronic device suitable for implementing a user biographical verification method according to an embodiment of the present disclosure.

Detailed Description

In the following detailed description, numerous specific details of the disclosure are set forth by way of examples in order to provide a thorough understanding of the relevant disclosure. However, it will be apparent to one of ordinary skill in the art that the present disclosure may be practiced without these specific details. It should be understood that the use of the terms "system," "apparatus," "unit" and/or "module" in this disclosure is a method for distinguishing between different components, elements, portions or assemblies at different levels of sequence. However, these terms may be replaced by other expressions if they can achieve the same purpose.

It will be understood that when a device, unit or module is referred to as being "on" … … "," connected to "or" coupled to "another device, unit or module, it can be directly on, connected or coupled to or in communication with the other device, unit or module, or intervening devices, units or modules may be present, unless the context clearly dictates otherwise. For example, as used in this disclosure, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present disclosure. As used in the specification and claims of this disclosure, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" are intended to cover only the explicitly identified features, integers, steps, operations, elements, and/or components, but not to constitute an exclusive list of such features, integers, steps, operations, elements, and/or components.

These and other features and characteristics of the present disclosure, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will be better understood by reference to the following description and drawings, which form a part of this specification. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the disclosure. It will be understood that the figures are not drawn to scale.

Various block diagrams are used in this disclosure to illustrate various variations of embodiments according to the disclosure. It should be understood that the foregoing and following structures are not intended to limit the present disclosure. The protection scope of the present disclosure is subject to the claims.

FIG. 1 is a schematic diagram of a user resume authentication method according to some embodiments of the present disclosure, as shown in FIG. 1, the user resume authentication method including the steps of:

s101, acquiring related information of a target user; wherein the related information at least comprises identity information and resume information; the biographical information includes at least one first associated combination of the target user, the first associated combination including an associated first time interval, first institution and first skill information.

Specifically, the user resume verification method provided by the embodiment of the present disclosure may be implemented on a background investigation platform, which may be a software system running on a server for providing investigation and verification of resume backgrounds. For example, a researcher sends a survey request to a background survey platform through a terminal device, such as a PC or a smart phone, via a network, and the background survey platform sends related background survey information to the researcher after obtaining authorization information of the researcher, that is, a target user.

The human resource investigator sends an investigation request to a background investigation platform through a network through a terminal device, such as a PC, a smart phone and the like, and the background investigation platform sends related information of a target user to the investigator after obtaining authorization information of the target user. The related information of the target user can be provided by the target user or obtained by other modes in the network. The target user may be any user, such as a job seeker. The relevant information of the target user may include, but is not limited to, a resume of the target user. The related information at least comprises identity information and resume information, wherein the identity information can comprise but is not limited to contact ways such as name, age, identity card, mobile phone number and the like of a target user; the biographical information includes, but is not limited to, learning experiences, professional experiences, etc. of the target user in past time intervals. The first time interval may be in the dimension of a year, a month, etc., and the first mechanism may be a school, an incumbent company, an entity, a department, etc., involved in learning skills by the target user. The first skill information includes, but is not limited to, major and minor expertise, minor and self-study expertise learned by the target user in the course of learning industry, industry engaged in the course of practicing industry, nature of work, professional knowledge involved, and the like.

When the related information of the target user is the resume of the target user, the resume data may be a structured information or a general text information, which is described below by way of example, and the first association combination may be obtained from the work experience and the education background.

For example, a piece of resume information obtained by the background survey platform is as follows:

name: zhang three

Experience of work

2011-2013: baidu, software engineer

During the work period, the online map database development and maintenance work is mainly responsible.

2013-2015: alibara, artificial intelligence architect

During the working period, the distributed storage system participates in the design and development of an artificial intelligence system of the Alibaca.

The education background is as follows:

2008-2011: beijing university, computer Master

In the research period, the method participates in the algorithm development of intelligent semantic analysis, and uses methods such as a deep neural network and a Huffman tree.

By analyzing the work experience and the education background in the above example, the background investigation platform can obtain three first association combinations, which are: (1)2008 + 2011, Beijing university: semantic analysis, a deep neural network, and a Huffman tree; (2) 2011-: developing an online map and a database; (3)2013-2015, Alibama: distributed storage, artificial intelligence.

And S102, acquiring scientific research public data of the target user according to the related information.

Specifically, the target user may generate scientific research disclosure data in past work, and the scientific research disclosure data includes, but is not limited to, published articles, papers, patent documents, scientific news, internet (forums, blogs, SNS, question and answer system, etc.), and the like. According to related information of the target user, such as name and/or identification number, scientific research public data of the target user are obtained, and the scientific research public data comprise professional knowledge and time node information. For example, the scientific research public data is a journal article published by a target user, the expertise is expertise related to the journal article, and the time node information includes publication time of the journal article. For another example, the scientific research public data is a patent document, the professional knowledge is technical knowledge related to the invention point of the patent document, and the time node information includes application time of the patent document and the like.

In some embodiments, the background investigation platform includes at least one patent information database, which may be a locally stored database or a database capable of being remotely accessed, and scientific research disclosure data, i.e., patent literature data, of the target user may be obtained by accessing the patent information database on the background investigation platform. Patent literature data can also be acquired by accessing existing patent databases at home and abroad, such as patent databases provided by a retrieval platform of the national intellectual property office of China, a PCT international patent retrieval website and the like.

In other embodiments, the background survey platform may obtain the thesis data of the target user by accessing a database of all parties, a national knowledge network, and the like, and obtain the data disclosed by the scientific and technical news or the internet (forum, blog, SNS, question and answer system, and the like) of the target user by accessing a search engine such as Baidu, Google, and the like.

S103, verifying the history of the target user according to the scientific research public data and the related information.

Specifically, verification of biographical information includes, but is not limited to, verifying the authenticity of work that the target user is engaged in over an interval of time and at an incumbent unit. For example, whether the target user's functional units in the same time interval are consistent with the institutions related in the scientific research public data is verified, and if so, the fact that the resume information of the target user is real can be determined; or whether the working skill of the target user in a time interval is similar to the professional knowledge related in the scientific research public data or not can be verified, and if the working skill and the professional knowledge belong to different technical fields or the similarity is low, the fact that the resume information of the target user is not true can be determined; or whether the target user's job unit and work skill in a time interval are consistent with the institution and professional knowledge related to the scientific research public data or not can be verified, and if so, the resume information of the target user is real.

In some optional embodiments, as shown in fig. 2, the step S103 of verifying the history of the target user according to the scientific research public data and the related information includes:

s201, analyzing the scientific research public data, and determining at least one second association combination related to the scientific research public data; the second associated combination comprises an associated second institution, a second time interval, and second skill information;

s202, verifying the history of the target user according to the first association combination and the second association combination.

In some embodiments, when the scientific research public data is structured data such as journal articles, papers, and patent documents published by the target user, the second association combination may be analyzed from the structured data by means of regular matching and the like. For example, the structured information of patent documents includes applicant, application date, abstract, claims and specification, etc., and the structured information of journal articles and papers includes title, author, abstract, keyword, communication unit and reference document, etc.; wherein the second association combination includes the associated second institution, the second time interval, and the second skill information. Each item in the second associative combination corresponds to each item in the first associative combination. The second institution may be a scientific institution involved in scientific public data, such as a school, a job department, etc. where the target user is located when papers and/or articles are published, an applicant institution of patent documents. The second time interval includes, but is not limited to, a time interval referred to by publication time of articles, papers, etc., a time interval referred to by application date of patent document.

In other embodiments, when the scientific research public data is an unstructured data type such as scientific news or internet (forum, blog, SNS, question and answer system), the scientific research public data may be obtained first, structured processing may be performed on the key information, and at least one second association combination related to the scientific news or internet (forum, blog, SNS, question and answer system, etc.) may be analyzed and determined.

One implementation of analyzing and determining at least one second association combination of patent documents will be specifically described below by taking patent document data as an example, and assuming that patent document data of yesterthree is obtained by accessing a patent database by name yesterthree:

CN2011xxx, university of beijing: speech recognition method

CN2012xxx, hundred degrees: automatic map data updating method

CN2013xxx, Baidu: method for quickly obtaining geographic information data

CN2015xxx, alisbaba: neural network training method

Although the patent literature data generally has structured data for describing the technical directions, such as IPC and CPC classification numbers, the classification numbers are generally wide and accurate technical information cannot be obtained. Therefore, a method of classifying fields using structured techniques will not be able to perform accurate matches with the history. In some embodiments, the title, abstract, claims, specification, and the like of the patent document may be parsed using a pre-trained artificial intelligence model, such as a neural network model, for extracting technical feature words of the patent. The neural network module can complete training through the labeled training data so as to extract more accurate technical feature words. For example, after the analysis, the skill information of the patent document at the time point of the application date can be obtained, that is, the second related combination of the patent document data can be expressed as:

2011, university of Beijing: speech recognition, word2vector, NLP, waveform detection, database

2012, hundred degrees: map data, GIS, data acquisition, data update, distributed

2013, Baidu: human-machine interaction, GIS, geographic information system, error calibration

2015, Alibama: neural network, CNN, RNN, training, convergence, parallel computing

Further, in some embodiments, the second association combination extracted from the patent document data and the first association combination analyzed in the history are cross-verified to obtain the historical verification information.

In some alternative embodiments, as shown in fig. 3, the step S202 of verifying the resume of the target user according to the first association combination and the second association combination includes:

s301, according to whether the first mechanism and the second mechanism are consistent or not and whether the second time interval is matched with a first association combination and a second association combination in the first time interval or not;

s302, verifying the history of the target user according to the similarity of the first skill information and the second skill information aiming at the matched first association combination and the second association combination.

In this embodiment, the first and second associative combinations are matched based on whether the first and second mechanisms are consistent and whether the second time interval matches within the first time interval. Assuming that the first mechanism and the second mechanism are consistent, the following will specifically describe by taking the data type of the patent document as an example, the first association combination and the second association combination are matched in the first time interval, and since the second time interval in the second association combination, that is, the patent application time is a specific date, a filtering is required to be performed on the time point to obtain a time interval, the filtering can take the continuity of the research and development into consideration, and a general research and development time period can be convolved with the time point to obtain time periods of a plurality of technical characteristics. For example, generating a patented technology means that at least one year of development cycle is involved, so convolving data points with one year's cycle results in different skill cycles:

a computer: 2011-2015

Semantic analysis: 2011-2012

And (3) voice recognition: 2011-2012

GIS：2012-2013

A neural network: 2015-2016

Since the filtering method is equivalent to directly expanding a time point into a time period, the consistency of the development experience of the target user has an inherent technical relevance. For example, the natural evolution of a target user's research topic from 3G mobile communication to 4G mobile communication within ten years is a normal process, whereas the switching from a biological research to an artificial intelligence research implies a major occupational change. Therefore, the skill-time segment in the second related information may also be generated based on patent document data using a method of machine learning. For example, a large number of applicant identifications are obtained first, skill characteristic words at specific time points are obtained through the above method, time slices are further labeled in a manual mode, and optionally, skill labeling can be performed on the time slices, for example, the technical field of the skill is labeled manually. After a large amount of training data is obtained, a machine learning model is trained to obtain a skill-time segment segmentation and classification model. Furthermore, after obtaining patent data of a target user, the model is input, and the model automatically gives division and marking of skill-time segments.

Fig. 4 is a schematic diagram of a skill-time segment generated by machine learning according to an embodiment of the present disclosure, and as shown in fig. 4, in the skill-time segment 1, the skills are evolved from the database technology to the distributed storage technology, and both are evolved from the database technology, so the model synthesizes the two into one segment. Further, skills evolve from databases into the field of artificial intelligence. In skill-time slice 2, the skill evolves naturally from 3G related technologies, such as CDMA, UMTS, to 4G related technologies, such as OFDM, since the model has absorbed the features in the training data, two pieces of skill will be judged to be wireless communication skills. Furthermore, the skills are greatly transformed into the fields of gene sequencing and gene editing, so that the skill segments are obviously transformed into the biotechnology.

In this embodiment, the system first verifies the history of the target user according to the similarity between the first skill information and the second skill information, if the first time interval associated with the first association combination and the second time interval associated with the first mechanism are respectively identical to the second time interval associated with the second association combination and the second mechanism, that is, the first association combination and the second association combination are matched, according to whether the first mechanism and the second mechanism are identical and whether the second time interval is matched within the first time interval. A simpler verification result is a time slice according to the history, and the verification results in different time slices are given. Fig. 5 is a schematic diagram illustrating verification results in different time slices according to an embodiment of the disclosure, as shown in fig. 5, wherein the time slices are history information segmented according to history information.

In some embodiments, the first association set of the resume analysis comprises a first association set of the scientific published data analysis, where the resume may be determined to be authentic, for example:

history data:

2008-2011: semantic analysis

Scientific research published data analysis:

2010-2011: semantic analysis

In other embodiments, the first skill information in the first associated set of historical resolutions is not identical to the second skill information in the first associated set of scientific published data resolutions, for example:

history:

2011-2013: online map

And (4) scientific research discloses data:

2012-2013：GIS

at this time, the online map and the GIS are skill characteristics which cannot be completely matched, word distance information between two words is calculated to obtain the similarity of the first skill information and the second skill information, and then the history of the target user is verified according to the similarity of the first skill information and the second skill information.

In some optional embodiments, as shown in fig. 6, the step S302 of verifying the history of the target user according to the similarity between the first skill information and the second skill information includes:

s601, determining the similarity between the first skill information and the second skill information according to a pre-trained artificial intelligence model;

s602, when the similarity is larger than or equal to a preset threshold value, determining that the history of the target user is real.

In the present embodiment, one implementation of determining the similarity between the first skill information and the second skill information according to the artificial intelligence model trained in advance will be specifically described below by taking the patent document data type as an example. Since the technical feature words in the patent documents are all non-daily words, and the daily words cannot reflect the division of the professional field, the word vectors of the technical feature words are calculated by patents in a large number of fields. Specifically, patent documents in the related field, for example, a large number of patent documents in the computer field, are input to a pre-trained artificial intelligence model, for example, a neural network model, and the term of the patent documents is extracted and the term vector representation of the term is calculated. At this time, by calculating a word vector between the two skill words "online map" and "GIS", the similarity between the first skill information and the second skill information, that is, a matching value, can be obtained. At this time, although the two technical words are not completely related, through a large amount of technical description texts existing in patent documents, the technical words belonging to the same field will have a higher word distance, which also means that historical information under a higher probability is verified by the patent documents, for example, the similarity between the "GIS" and the "online map" will be higher, and the similarity between the "GIS" and the "pesticide" is lower. And when the similarity is larger than or equal to a preset threshold value, determining that the current first associated combination information in the history of the target user is real.

After obtaining all similarities between the first skill information and the second skill information, the system may perform comprehensive verification on the entire history. When the patent analysis data can be verified by the plurality of first association combinations analyzed by the history, the verification score of the entire history information can be obtained.

In one case, the second skill information analyzed from the patent data is empty, and since the development work does not necessarily have to generate a patent application, it is considered that the section of history information cannot be verified at this time.

In some optional embodiments, when the history of the target user is verified according to the first association combination and the second association combination, when the first association combination and the second association combination have a low matching degree, or the similarity between the first skill information and the second skill information is low, the alarm information may be generated. For example:

history:

2011-2013: on-line map, Baidu

And (4) scientific research discloses data:

2011-2012: unmanned aerial vehicle, Shenzhen Dajiang technology

The first mechanism in the first association combination is 'Baidu', the second mechanism in the second association combination is 'Shenzhen Dajiang science and technology', the two mechanisms are not consistent, and the matching degree of the first association combination and the second association combination can be considered to be low; meanwhile, the first skill information in the first association combination is "online map", and the second skill information in the second association combination is "unmanned plane", that is, the similarity between the first skill information and the second skill information is low, so that an alarm message will be generated.

For another example:

history:

2011-2013: on-line map, Baidu

And (4) scientific research discloses data:

2011-2012: instant messaging, hundredth degree

At this time, although the scientific public data can verify the historical information in terms of time segments and organization information, the system will generate an alarm message when the correlation between the on-line map and the instant messaging skill is low.

In some optional embodiments, the second skill information comprises at least: technical field, technical problem and/or technical means.

In the present embodiment, when analyzing the patent document data, the second skill information of the target user can be determined at least by analyzing the technical field, the technical problem, and/or the technical means involved in the patent document data.

In some alternative embodiments, the scientific published data comprises patent literature data; and/or the resume information further comprises a patent document identifier applied by the target user.

In this alternative implementation, the resume information of the target user may further include a patent document identifier that the target user has applied for, for example, a patent application number, a publication number, and/or a publication date. When the history information of the target user is verified, the corresponding patent document is extracted from the patent database by the patent document identifier, and the application information of the patent document, i.e., the applicant, the inventor, the application date, and the like, is acquired and verified in matching with the name, etc., of the target user in the history information.

In some optional embodiments, parsing the scientific research disclosure data comprises: at least one of the title, abstract, claims and specification in the patent document data is analyzed.

In this alternative implementation, the expert knowledge related to the patent document data may be acquired by analyzing the title, abstract, claims, specification, and other contents in the patent document data, and the second skill information of the target user may be determined to be used for verifying the history information of the target user.

The above is a specific embodiment of the user history verification method provided by the present disclosure.

FIG. 7 is a schematic diagram of a user resume authentication apparatus according to some embodiments of the present disclosure. As shown in fig. 7, the user resume authentication apparatus 700 includes a first obtaining module 701, a second obtaining module 702, and an authentication module 703, wherein:

a first obtaining module 701, configured to obtain relevant information of a target user; wherein the related information at least comprises identity information and resume information; the biographical information comprises at least one first associated combination of the target user, the first associated combination comprising an associated first time interval, first institution and first skill information;

a second obtaining module 702, configured to obtain scientific research public data of the target user according to the relevant information;

the verification module 703 is configured to verify the history of the target user according to the public scientific research data and the related information.

In some optional embodiments, the verification module 703 comprises:

In some optional embodiments, the first verification sub-module comprises:

In some optional embodiments, the second verification sub-module comprises:

In some alternative embodiments, the scientific published data comprises patent literature data; and/or the presence of a gas in the gas,

In some optional embodiments, the first determining sub-module includes:

The user history verification apparatus provided in the above embodiment corresponds to the user history verification method, and specific details can be referred to the above description of the user history verification method, which is not described herein again.

As shown in fig. 8, the electronic apparatus 800 includes a Central Processing Unit (CPU)801 that can execute various processes in the embodiment shown in fig. 1 described above according to a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. In the RAM808, various programs and data necessary for the operation of the electronic apparatus 800 are also stored. The CPU801, ROM802, and RAM808 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.

The following components are connected to the I/O interface 805: an input portion 806 including a keyboard, a mouse, and the like; an output section 807 including a signal such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 808 including a hard disk and the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. A drive 810 is also connected to the I/O interface 805 as necessary. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as necessary, so that a computer program read out therefrom is mounted on the storage section 808 as necessary.

In particular, according to embodiments of the present disclosure, the method described above with reference to fig. 1 may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a medium readable thereby, the computer program comprising program code for performing the method of fig. 1. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section 809 and/or installed from the removable medium 811.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, a program segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units or modules described in the embodiments of the present disclosure may be implemented by software or hardware. The units or modules described may also be provided in a processor, and the names of the units or modules do not in some cases constitute a limitation of the units or modules themselves.

As another aspect, the present disclosure also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the apparatus in the above-described embodiment; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for use by one or more processors in performing the methods described in the present disclosure.

In summary, the present disclosure provides a user history verification method, an apparatus, an electronic device and a computer-readable storage medium thereof. According to the technical scheme, the scientific research public data of the target user can be automatically acquired, whether the target user really has the claimed technical information can be verified, and the authenticity of the historical information of the target object can be quickly and accurately verified without related professional knowledge of a person who performs verification.

It is to be understood that the above-described specific embodiments of the present disclosure are merely illustrative of or illustrative of the principles of the present disclosure and are not to be construed as limiting the present disclosure. Accordingly, any modification, equivalent replacement, improvement or the like made without departing from the spirit and scope of the present disclosure should be included in the protection scope of the present disclosure. Further, it is intended that the following claims cover all such variations and modifications that fall within the scope and bounds of the appended claims, or equivalents of such scope and bounds.

Claims

1. A user resume verification method, comprising:

analyzing the scientific research public data and determining at least one second association combination related to the scientific research public data; the second time interval is obtained by filtering the time points, wherein the filtering comprises a research and development continuity factor, and the research and development continuity has relevance of an internal technology; generating skill-time segments in the second correlation information based on the patent literature data using a method of machine learning;

verifying the history of the target user according to the first association combination and the second association combination; wherein, according to the time segment of the resume, the verification results in different time segments are given.

2. The user biographical verification method of claim 1 wherein the second associative combination comprises an associated second institution, a second time interval and second skill information.

3. The user resume verification method of claim 2, wherein verifying the resume of the target user based on the first and second association combinations comprises at least:

4. The user resume verification method of claim 3, wherein verifying the resume of the target user based on the similarity of the first skill information and the second skill information comprises:

5. The user biographical verification method of claim 3 or 4, wherein the second skill information comprises at least: technical field, technical problem and/or technical means.

6. The user resume verification method of claim 3 or 4, wherein the scientific research disclosure data comprises patent literature data; and/or the presence of a gas in the gas,

7. The method of claim 6, wherein parsing the published scientific data comprises:

8. An apparatus for user biographical verification, comprising:

the first determining submodule is used for analyzing the scientific research public data and determining at least one second association combination related to the scientific research public data; the second time interval is obtained by filtering the time points, wherein the filtering comprises a research and development continuity factor, and the research and development continuity has relevance of an internal technology; generating skill-time segments in the second correlation information based on the patent literature data using a method of machine learning;

the first verification sub-module is used for verifying the history of the target user according to the first association combination and the second association combination; wherein, according to the time segment of the resume, the verification results in different time segments are given.

9. The user biographical verification apparatus of claim 8 wherein the second association combination comprises an associated second institution, a second time interval and second skill information.

10. The user biographical verification apparatus of claim 9, said first verification sub-module comprising:

11. The user biographical verification device of claim 10, said second verification sub-module comprising:

12. The user biographical verification apparatus according to claim 10 or 11, wherein the second skill information includes at least: technical field, technical problem and/or technical means.

13. The user resume verification apparatus of claim 10 or 11, wherein the scientific research disclosure data comprises patent literature data; and/or the presence of a gas in the gas,

14. The user biographical verification apparatus of claim 13, wherein the first determination sub-module comprises:

15. An electronic device, comprising:

a memory and one or more processors;

wherein the memory is communicatively coupled to the one or more processors and has stored therein instructions executable by the one or more processors, the electronic device being configured to implement the method of any of claims 1-7 when the instructions are executed by the one or more processors.

16. A computer-readable storage medium having stored thereon computer-executable instructions operable, when executed by a computing device, to implement the method of any of claims 1-7.