CN112381525A - Talent database analysis system and method based on big data - Google Patents

Talent database analysis system and method based on big data Download PDF

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CN112381525A
CN112381525A CN202011349251.2A CN202011349251A CN112381525A CN 112381525 A CN112381525 A CN 112381525A CN 202011349251 A CN202011349251 A CN 202011349251A CN 112381525 A CN112381525 A CN 112381525A
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李欢
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Guizhou Feiyoumo Talent Big Data Co ltd
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Abstract

The invention provides a talent database analysis system and method based on big data, wherein the analysis method comprises the following steps: the data processing module searches the tentatively determined talent identity information to be analyzed in the current station library point; if the current site library is not searched, searching the site library in which the latest work of talents to be analyzed is positioned; if the site where the latest work is located is not searched, sequentially searching the sites where the work or practice experiences of talents to be analyzed are located according to the working year time, if the sites where the work experiences are not searched, sequentially searching the sites from near to far away from the current site until the search or the search of all the sites is completed, and if the sites where the work or practice experiences are not searched, creating a new site; and comparing the data information searched by the talents to be analyzed with the same secondary identity with the history information, determining the data information according to the comparison result to generate a real-time talent curve, and determining the type of the talents to be analyzed according to the difference range of the data information and the standard talent curve.

Description

Talent database analysis system and method based on big data
Technical Field
The invention relates to the technical field of talent analysis of an internet platform, in particular to a talent database analysis system and method based on big data.
Background
The human resource management is increasingly important due to the close relation of the human resource management and the internal factors of people, enterprise management is turning to the management of emphasizing people from the management of emphasizing objects, the result of aggravation of competition is obtained, enterprise-level software is in the revolution of technology upgrading, the management of people in the software is gradually strengthened, the existing human resource management solution facing large and medium-sized enterprise and public institutions and clients provides organization and construction, personnel management and attendance management by using internet technology, and standardized post and competence construction is performed by using big data based on statistical significance, namely, a typical industry and job template or a typical high-efficiency employee model is performed.
The following problems still exist in the prior art: when the talents are analyzed, the real identities and the situations of the talents are not confirmed, and when the prior data are faced, the situations of the talents can be judged only through human subjectives, so that the analysis deviation of the talents is huge.
Disclosure of Invention
Therefore, the invention provides a talent database analysis system and method based on big data, which are used for overcoming the problem of the prior data deviation of the talents to be analyzed when the talents to be analyzed are analyzed in the prior art.
In order to achieve the above objects, the present invention provides a system and a method for talent database analysis based on big data, wherein a talent database analysis method based on big data comprises,
the method comprises the following steps: the data processing module preliminarily determines the identity information of the talents to be analyzed according to the current website, searches in the current website according to the identity information of the talents after preliminary determination, and compares the searched data information of the talents to be analyzed with the history information provided by the talents to be analyzed if the data information of the talents to be analyzed is searched in the current website;
step two: if the data information of the talents to be analyzed is not searched in the current website library, searching in the website library where the latest work or practice experience of the talents to be analyzed is located, and if the data information of the talents to be analyzed is searched, comparing the searched data information with the history information provided by the talents to be analyzed;
step three: if the data information of the talents to be analyzed is not retrieved in the site where the latest work or practice experience of the talents to be analyzed is located, sequentially retrieving the data information of the talents to be analyzed from large to small according to the year time of the work or practice experience of the site where the work or practice experience of the talents to be analyzed is located, and if the data information of the talents to be analyzed is retrieved, comparing the retrieved data information with the history information provided by the talents to be analyzed;
step four: if the data information of the talents to be analyzed is not retrieved from the site where the working experience of the talents to be analyzed is located, sequentially retrieving the talents to be analyzed from near to far in the sites which are not retrieved until the retrieval of all the sites is completed or the data information of the talents to be analyzed is retrieved, if the data information of the talents to be analyzed is retrieved, comparing the retrieved data information with the history information provided by the talents to be analyzed, and if the data information of the talents to be analyzed is not retrieved in all the sites, newly creating the data information of the talents to be analyzed in the current site;
step five: when the retrieved data information of the talents to be analyzed is compared with the history information provided by the talents to be analyzed, firstly, secondarily determining the identity information of the talents to be analyzed, if the secondary determination results are the same, then, comparing, if the comparison result is in an error range, generating a talent curve in real time by using the retrieved data information of the talents to be analyzed in the site library, if the comparison result is not in the error range, generating a talent curve in real time by using the history information provided by the talents to be analyzed, and comparing the talent curve generated in real time with a standard talent curve;
step six: determining the type of the talents to be analyzed according to the difference range of the talent curve generated in real time in the standard talent curve;
the analysis method comprises the steps that after a data processing module preliminarily determines identity information of talents to be analyzed, the preliminarily determined talent identity information is searched in a current website library, whether the data information of the talents to be analyzed exists in the current website library is confirmed, if the data information of the talents to be analyzed exists in the current website library, the searched data information of the talents to be analyzed is compared with history information provided by the talents to be analyzed, if the data information of the talents to be analyzed does not exist in the current website library, the searched data information is preferentially searched in the website library where the talents to be analyzed work or practice experience is located, then the sites where the talents to be analyzed work or practice experience are located are sequentially searched from large to small according to the annual time of the work or practice experience, and then the sites which are not searched are sequentially searched from near to far from the current site, and stopping searching until the searching of all the sites is finished or the data information of the talents to be analyzed is searched, newly building the data information of the talents to be analyzed in the current site if the data information of the talents to be analyzed is not searched in all the sites, secondarily determining the identity information of the talents to be analyzed if the data information of the talents to be analyzed is searched, comparing the searched data information with the historical information provided by the talents to be analyzed, generating a real-time talent curve by using the data information of different talents to be analyzed according to different comparison results, and determining the type of the talents to be analyzed if the real-time generated talent curve is in the difference range of the standard talent curve.
Furthermore, an algorithm is arranged in the data processing module to quantize the related data information of the talents, and a talent data information matrix F (N, L, W, A, B) is preset in the data processing module, wherein N represents the age of the current talents in the website library, L represents the academic history of the current talents in the website library, W represents the working years of the current talents in the website library, A represents the professional degree of the current talents in the website library, and B represents other skills of the current talents in the website library;
the data processing module constructs a talent real-time data information matrix Fs (Ns, Ls, Ws, As and Bs) to be analyzed according to history information of talents to be analyzed, wherein the Ns represents the age of the talents to be analyzed in the history, the Ls represents the academic history of the talents to be analyzed in the history, the Ws represents the working age of the talents to be analyzed in the history, the As represents the professional degree of the talents to be analyzed in the history, and the Bs represents other skills of the talents to be analyzed in the history;
in the fifth step, when the data processing module compares the retrieved data information of the talents to be analyzed with the history information provided by the talents to be analyzed, the data module compares parameters in the talent data information matrix F and the talent real-time data information matrix Fs to be analyzed respectively, sets the age error as Nc which is not less than 0, the academic error as Lc, as Lc which is not less than 0, the working age error as Wc, as Wc which is not less than 0, the professional error as Ac, the other skill error as Bc, and the total error reference value as Fc,
if Ns-N is less than or equal to Nc, determining that the age of the talent to be analyzed is within the error range, otherwise, determining that the age of the talent to be analyzed is not within the error range;
if Ls-L is less than or equal to Lc, determining that the academic history of the talents to be analyzed is within the error range, otherwise, determining that the academic history is not within the error range;
if Ws-W is less than or equal to Wc, determining that the working life of the talent to be analyzed is within the error range, otherwise, not determining the working life of the talent to be analyzed is within the error range;
if As-A is less than or equal to Ac, determining that the specialty of the talent to be analyzed is within the error range, otherwise, determining that the specialty of the talent is not within the error range;
and if Bs-B is less than or equal to Bc, determining that other skills of the talents to be analyzed are in the error range, otherwise, determining that the skills are not in the error range.
Further, if the comparison results of the retrieved data information of the talents to be analyzed and the historical information provided by the talents to be analyzed are within the error range, generating talent curves in real time according to the retrieved data information of the talents to be analyzed in the site library;
if the retrieved data information of the talents to be analyzed and the comparison result of the history information provided by the talents to be analyzed are not within the error range, generating talent curves in real time according to the history information provided by the talents to be analyzed;
if the comparison result part of the retrieved data information of the talents to be analyzed and the historical information provided by the talents to be analyzed is within the error range, judging whether the sum of the parameter values which are not within the error range is larger than the total error reference value Fc, if so, generating a talent curve in real time by the historical information provided by the talents to be analyzed, otherwise, generating a talent curve in real time by the data information of the talents to be analyzed retrieved in the site library.
Further, in the first step, when the identity information of the talent to be analyzed is preliminarily determined, the preliminary determination mode includes face recognition, the face information of the talent to be analyzed is collected in real time, the collected face information of the talent to be analyzed is compared with certificate information provided by the talent to be analyzed, the identity information of the talent to be analyzed is preliminarily determined, if the identity information of the talent to be analyzed is preliminarily determined to be different from the certificate information, the identity information of the talent to be analyzed is prompted to be inconsistent, and if the identity information of the talent to be analyzed is preliminarily determined to be the same as the certificate information, the preliminarily determined identity information of the talent to be analyzed is retrieved in the current website library;
when the preliminary determination is carried out, acquiring face information of the talent to be analyzed in real time through a camera shooting module, acquiring position information of face characteristic points of the talent to be analyzed, and comparing the distance and the slope between the positions, wherein the camera shooting module acquires the face position information in real time, and a real-time face information matrix P (a1, a2, a3, a4, a5, a6, a7 and a8) is obtained by taking the midpoint position of the connecting line of the centers of the nostrils and the nostrils as a coordinate origin o, wherein a1 represents first endpoint position information of eyes, a2 represents second endpoint position information of the eyes, a3 represents third endpoint position information of the eyes, a4 represents fourth endpoint position information of the eyes, a5 represents first endpoint position information of the lips, a6 represents second endpoint position information of the lips, a7 represents first endpoint position information of the nasal wings, a8 represents second endpoint position information of the nasal wings, and acquiring a real-time segment information matrix Y (k1, k2 and k3 … kn) formed by every two end points through the position information among the end points, wherein k1 represents the slope of a preset first segment, k2 represents the slope of a preset second segment, k3 represents the slope of a preset third segment, and kn represents the slope of a preset nth segment.
Further, the camera shooting module collects the face position information of the photo in the certificate information, and takes the midpoint position of the central connecting line of the nostrils of the nose as a coordinate origin o ', a preset face information matrix P' and a preset line segment information matrix Y 'are obtained, and for a preset face information matrix P' (a '1, a'2, a '3, a'4, a '5, a'6, a '7 and a'8), wherein a '1 represents first endpoint position information of the eyes, a'2 represents second endpoint position information of the eyes, a '3 represents third endpoint position information of the eyes, a'4 represents fourth endpoint position information of the eyes, a '5 represents first endpoint position information of the lips, a'6 represents second endpoint position information of the lips, a '7 represents first endpoint position information of the nasal wings, and a'8 represents second endpoint position information of the nasal wings; for the preset line segment information matrix Y '(k'1, r '1, k'2, r '2, k'3 … k 'n, r' n), where k '1 represents the slope of the preset first line segment, r'1 represents the length of the preset first line segment, k '2 represents the slope of the preset second line segment, r'2 represents the length of the preset second line segment, k '3 represents the slope of the preset third line segment, r'3 represents the length of the preset third line segment, k 'n represents the slope of the preset nth line segment, and r' n represents the length of the preset nth line segment.
Further, according to the comparison between ki in the collected real-time segment information matrix Y of the face of the talent to be analyzed and k ' i and r ' i in the preset segment information matrix Y ',
when ki is less than or equal to (beta multiplied by r 'i)/alpha multiplied by k' i, the fact that the face information acquired by the talent to be analyzed in real time is the same as the certificate information is judged;
when ki is larger than (beta multiplied by r 'i)/alpha multiplied by k' i, the fact that the human talent to be analyzed collects the face information in real time is judged to be different from the certificate information.
Further, in the fifth step, the data processing module performs secondary determination on the talent identity information to be analyzed before comparing the retrieved data information of the talent to be analyzed with the history information provided by the talent to be analyzed, when performing secondary determination on the talent identity information to be analyzed, the certificate information included in the talent history to be analyzed is retrieved on a related website, and the retrieval result is compared with the face information of the talent to be analyzed acquired in real time, the identity information of the talent to be analyzed is determined secondarily, if the comparison results are the same, the identity information of the talent to be analyzed is determined to be consistent, and if the comparison results are different, the identity information of the talent to be analyzed is prompted to be inconsistent.
Furthermore, the data processing module compares the talent curve generated in real time with a standard talent curve, sets the talent curve correlation value as c, when the talent curve is generated in real time according to the history information provided by the talents to be analyzed, the talent curve correlation value c is,
c＝0.1×Ns/N0+0.3×Ls/L0+0.5×Ws/W0+0.7×As/A0+0.2×Bs/B0
when the talent curve is generated in real time by using the data information of the talents to be analyzed retrieved from the site library, the associated value c of the talent curve is,
c＝0.1×N/N0+0.3×L/L0+0.5×W/W0+0.7×A/A0+0.2×B/B0
in the formula, N represents the age of the current talent in the site library, L represents the academic history of the current talent in the site library, W represents the working age of the current talent in the site library, a represents the professional degree of the current talent in the site library, B represents other skills of the current talent in the site library, Ns represents the academic history of the talent to be analyzed in the history, Ws represents the working age of the talent to be analyzed in the history, As represents the professional degree of the talent to be analyzed in the history, Bs represents other skills of the talent to be analyzed in the history, N0 represents the age of the current talent of the standard talent, L0 represents the academic history of the current talent in the site library, W0 represents the working age of the current talent in the site library, a0 represents the professional degree of the current talent in the site library, and B0 represents other skills of the current talent in the site library.
Further, the data processing module is preset with a curve difference matrix C (C1, C2, C3 … Cn), where C1 represents a first preset curve difference, C2 represents a second preset curve difference, C3 represents a third preset curve difference, and Cn represents an nth preset curve difference, and the type of the talent to be analyzed is determined to be the type of the talent to be analyzed according to the talent curve correlation value C
If C is less than or equal to C1, determining that the talents to be analyzed are talents of the first type;
if C is more than C1 and less than or equal to C2, determining that the talents to be analyzed are talents of a second type;
if C is more than C2 and less than or equal to C3, determining that the talents to be analyzed are talents of a third type;
and if C is more than C3 and less than or equal to Cn, determining that the talent to be analyzed is the nth type talent.
Further, a talent database analysis system based on big data, comprising:
the enterprise database module is used for displaying enterprise related data information;
the database module is used for displaying talent related data information;
the flexible job place module is used for integrating and displaying the professional information issued by the enterprise library and the visitor library;
the puzzlement brocade module is used for integrating and displaying the problem information published in the enterprise library and the visitor library;
the image shooting module is used for calling the image shooting function of the equipment where the system is located to collect the face information of the talent to be analyzed and transmitting the collected image information to the data processing module;
and the data processing module is respectively connected with the enterprise library module, the visitor library module, the flexible workplace module, the confusion and sincere module and the camera shooting module, is used for processing the data information of the enterprise library module, the visitor library module, the flexible workplace module, the confusion and sincere module and the camera shooting module, and determines and divides the type of the identity information of the talents to be analyzed.
Compared with the prior art, the invention has the advantages that the invention provides the talent database analysis method based on big data, the identity information of the talents to be analyzed is preliminarily confirmed by collecting the face information of the talents to be analyzed, the data information of the talents to be analyzed is searched in the current website library, if the data information of the talents to be analyzed is not searched in the current website, the latest work or practice experience of the talents to be analyzed is searched in the website library, the work or practice experience of the talents to be analyzed is searched in the order of the annual time of the work experience of the websites from big to small, and the sites which are not searched are searched in the order of the distance from the current website from near to far in sequence until the data information of the talents to be analyzed is searched or is searched until the data information of the talents to be analyzed is completed, and if the data information of the talents to be analyzed is not searched in all the websites, newly building data information of talents to be analyzed in the current site; and if the retrieved data information of the talents to be analyzed is the same, comparing the retrieved data information with the history information provided by the talents to be analyzed, secondarily determining the identity information of the talents to be analyzed, comparing the identity information of the talents to be analyzed, determining to generate a real-time talent curve by using the data information of different talents to be analyzed according to different comparison results, and determining the type of the talents to be analyzed within the difference range of the standard talent curve of the talent curve generated in real time. The identity information of the talents to be analyzed is determined primarily and secondarily, so that the identity information of the talents to be analyzed is determined more accurately, after the identity information of the talents to be analyzed is determined, when the records provided by the talents to be analyzed are compared with the data information retrieved by the system, the error range of data analysis is determined, so that the accuracy of the talent data to be analyzed is determined more accurately, the type of the talents to be analyzed is judged, the authenticity of the data of the talents in the past is improved, the situation of large deviation of the talents to be analyzed is reduced, and the accuracy of system analysis is improved.
Furthermore, the invention quantizes the talent related data information through the algorithm arranged in the data processing module, respectively compares the information of each parameter when the talents to be analyzed are analyzed, respectively judges the errors of the comparison results, and determines the data sources of different generated curves of the talents to be analyzed according to different results of the errors, thereby further reducing the errors of artificially judged data.
Particularly, when the identity information of the talent to be analyzed is primarily determined, the identity information can be compared with the record information and the certificate information of the talent to be analyzed in a face recognition mode, when the primary determination is the same, the talent to be analyzed is retrieved, errors of data are reduced, when the talent data to be analyzed is retrieved, the talent to be analyzed is secondarily determined, the talent to be analyzed can be secondarily determined by the related information of the website where the certificate is located in the record, and the talent to be analyzed is compared after the secondary determination is the same, so that the identity accuracy is further improved, and meanwhile, unnecessary calculation and system loss are reduced.
Furthermore, the invention greatly improves the retrieval time and further improves the retrieval efficiency by the retrieval in the current site, the retrieval sequence of the site where the latest work experience of the talent to be analyzed is located, the sites with the work experiences from long to short, and other sites.
Furthermore, the invention sets a talent curve correlation value c, determines the type of the talents to be analyzed through the comparison of the talent curve generated in real time and the standard talent curve, determines different progress types for the growth of the talents to be analyzed in the aspect of determining the accuracy of the identity information and the past information of the talents to be analyzed, further improves the accuracy of system analysis, and ensures the real referential property of data.
Drawings
FIG. 1 is a schematic flow chart of a big data-based talent database analysis method according to the present invention;
FIG. 2 is a functional framework diagram of the big data based talent database analysis system according to the present invention.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described below with reference to examples; it should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and do not limit the scope of the present invention.
It should be noted that in the description of the present invention, the terms of direction or positional relationship indicated by the terms "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, which are only for convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Referring to fig. 1, the present invention provides a system and a method for analyzing a talent database based on big data, wherein the method for analyzing a talent database based on big data comprises:
the method comprises the following steps: the data processing module preliminarily determines the identity information of the talents to be analyzed according to the current site, searches in the current site according to the identity information of the talents after preliminary determination, and compares the searched data information of the talents to be analyzed with the history information provided by the talents to be analyzed when the data information of the talents to be analyzed is searched in the current site;
step two: if the data information of the talents to be analyzed is not searched in the current website library, searching the website library in which the latest work or practice experience of the talents to be analyzed is located, and if the data information of the talents to be analyzed is searched, comparing the searched data information with the history information provided by the talents to be analyzed;
step three: if the data information of the talents to be analyzed is not retrieved in the site where the latest work or practice experience of the talents to be analyzed is located, sequentially retrieving the talents to be analyzed from large to small according to the years of the work experience of the site where the work or practice experience of the talents to be analyzed is located, and if the data information of the talents to be analyzed is retrieved, comparing the retrieved data information with the history information provided by the talents to be analyzed;
step four: if the data information of the talents to be analyzed is not retrieved in the site where the working experience of the talents to be analyzed is located, sequentially retrieving the talents to be analyzed from near to far in the sites which are not retrieved until the retrieval of all the sites is completed or the data information of the talents to be analyzed is retrieved, if the data information of the talents to be analyzed is retrieved, comparing the retrieved data information with the history information provided by the talents to be analyzed, and if the data information of the talents to be analyzed is not retrieved in all the sites, newly creating the data information of the talents to be analyzed in the current site;
step five: when the retrieved data information of the talents to be analyzed is compared with the history information provided by the talents to be analyzed, the identity information of the talents to be analyzed is determined secondarily, if the secondary determination results are the same, then the comparison is carried out, if the comparison result is in the error range, the talent curve is generated in real time by using the retrieved data information of the talents to be analyzed in the site library, if the comparison result is not in the error range, the talent curve is generated in real time by using the history information provided by the talents to be analyzed, and the talent curve generated in real time is compared with the standard talent curve;
step six: and determining the type of the talents to be analyzed according to the difference range of the talent curve generated in real time in the standard talent curve.
Specifically, in the first step of the present invention, when the identity information of the talent to be analyzed is preliminarily determined, the preliminary determination method includes face recognition, and the identity information of the talent to be analyzed is preliminarily determined by acquiring the face information of the talent to be analyzed in real time, and comparing the acquired face information of the talent to be analyzed with certificate information provided by the talent to be analyzed. If the identity information of the talents to be analyzed is preliminarily determined to be different from the certificate information, prompting that the identity information of the talents to be analyzed does not accord with the certificate information; and if the identity information of the talents to be analyzed is preliminarily determined to be the same as the certificate information, searching the identity information of the talents to be analyzed in the current website library by using the preliminarily determined identity information of the talents to be analyzed, and comparing the searched data information of the talents to be analyzed with the historical information provided by the talents to be analyzed.
Specifically, in the embodiment of the present invention, when performing the preliminary determination, the image capturing module acquires face information of the talent to be analyzed in real time, acquires position information of facial feature points of the talent to be analyzed, and compares the distance and slope between the positions, the image capturing module acquires the facial position information in real time, and acquires a real-time facial information matrix P (a1, a2, a3, a4, a5, a6, a7, a8) with the midpoint of the central connecting line of the nostrils as the origin o, where a1 represents first endpoint position information of the eyes, a2 represents second endpoint position information of the eyes, a3 represents third endpoint position information of the eyes, a4 represents fourth endpoint position information of the eyes, a5 represents first endpoint position information of the lips, a6 represents second endpoint position information of the lips, a7 represents first endpoint position information of the nasal wings, a8 represents the position information of the second end points of the nose, and a real-time segment information matrix Y (k1, k2, k3 … kn) formed by every two end points is obtained through the position information among the end points, wherein k1 represents the slope of a preset first segment, k2 represents the slope of a preset second segment, k3 represents the slope of a preset third segment, and kn represents the slope of a preset nth segment;
the camera shooting module collects the face position information of the photo in the certificate information, takes the midpoint position of the connecting line of the centers of the nostrils of the nose as a coordinate origin o ', obtains a preset face information matrix P' and a preset line segment information matrix Y ', and for a preset face information matrix P' (a '1, a'2, a '3, a'4, a '5, a'6, a '7 and a'8), wherein a '1 represents first endpoint position information of the eyes, a'2 represents second endpoint position information of the eyes, a '3 represents third endpoint position information of the eyes, a'4 represents fourth endpoint position information of the eyes, a '5 represents first endpoint position information of the lips, a'6 represents second endpoint position information of the lips, a '7 represents first endpoint position information of the nasal wings, and a'8 represents second endpoint position information of the nasal wings; for a preset line segment information matrix Y '(k'1, r '1, k'2, r '2, k'3 … k 'n, r' n), wherein k '1 represents the slope of a preset first line segment, r'1 represents the length of the preset first line segment, k '2 represents the slope of a preset second line segment, r'2 represents the length of the preset second line segment, k '3 represents the slope of a preset third line segment, r'3 represents the length of the preset third line segment, k 'n represents the slope of a preset nth line segment, and r' n represents the length of the preset nth line segment;
comparing ki in the collected real-time segment information matrix Y of the face of the talent to be analyzed with k ' i and r ' i in the preset segment information matrix Y ',
when ki is less than or equal to (beta multiplied by r 'i)/alpha multiplied by k' i, the fact that the face information acquired by the talent to be analyzed in real time is the same as the certificate information is judged;
when ki is larger than (beta multiplied by r 'i)/alpha multiplied by k' i, the fact that the human talent to be analyzed collects the face information in real time is judged to be different from the certificate information.
Specifically, in the embodiment of the present invention, the history information may be written information provided by the talent to be analyzed, electronic information, or other information dictated by the talent to be analyzed.
Specifically, in the embodiment of the present invention, the site library may be different site libraries in different regions, or may be a site library in each province, or may be a site library in each city, or may be a site library in each area.
Specifically, in the second step of the present invention, if the data information of the talents to be analyzed is not retrieved from the current site library, the data information is retrieved from the site library in which the place where the latest work or practice experience is located among the history information provided by the talents to be analyzed, if there is no work or practice experience in the history information provided by the talents to be analyzed, the data information is retrieved from the near side to the far side in the sites which are not retrieved, and if the data information of the talents to be analyzed is retrieved from the site library in which the place where the latest work or practice experience is located, the retrieved data information is compared with the history information provided by the talents to be analyzed.
Specifically, in the third step of the present invention, if the data information of the talent to be analyzed is not retrieved from the site library in which the place where the latest work or practice experience of the talent to be analyzed is located, the data information of the talent to be analyzed is retrieved from the site library in which the work or practice experience of the talent to be analyzed is located in the order of longest to shortest from the year time of the work or practice experience of the talent to be analyzed, until the data information of the talent to be analyzed is retrieved from the site library in which the place where the work or practice experience is located or all the work or practice experiences provided by the history of the talent to be analyzed are retrieved, the retrieved data information is compared with the history information provided by the talent to be analyzed if the data information of the talent to be analyzed is retrieved from the site library in which the work or practice experience provided by the history of the talent to be analyzed is located.
Specifically, in the fourth step of the present invention, if the data information of the talents to be analyzed is not retrieved in the site library in which the work or practice experience provided by the talent to be analyzed is located, the sites that are not retrieved are sequentially retrieved in the order of closest distance to the current site and farthest distance from the current site, with the distance from the current site as a standard, until the data information of the talents to be analyzed is retrieved, or the retrieval is stopped when the retrieval of all the sites is completed, if the data information of the talents to be analyzed is not retrieved after the retrieval of all the sites is completed, the data information of the talents to be analyzed is recorded according to the registration program, and if the data information of the talents to be analyzed is retrieved, the retrieved data information is compared with the history information provided by the talents to be analyzed.
Specifically, in the embodiment of the present invention, if the data information of the talent to be analyzed is not retrieved after the retrieval of all the sites is completed, the talent to be analyzed is determined according to the registration condition of the current site, if the registration condition is satisfied, the data information of the talent to be analyzed is newly created and collected, and is transmitted to the current site library, and if the registration condition is not satisfied, the identity of the talent to be analyzed is determined to be inconsistent.
Specifically, in the embodiment of the present invention, when newly registering data information of a talent to be analyzed, the registration program may perform entry after verifying the authenticity of data of the talent to be analyzed again, or may perform verification on information such as whether the talent to be analyzed has a crime condition, whether the information belongs to a local persistent population, and the like, and perform entry of the information on the talent to be analyzed after meeting the local registration admission standard.
Specifically, in the fifth step, before comparing the retrieved data information of the talents to be analyzed with the history information provided by the talents to be analyzed, the data processing module performs secondary determination on the identity information of the talents to be analyzed, and when performing secondary determination on the identity information of the talents to be analyzed, the certificate information included in the history of the talents to be analyzed is retrieved on the related website, and the retrieval result is compared with the face information of the talents to be analyzed, which is acquired in real time, and the identity information of the talents to be analyzed is determined secondarily, if the comparison results are the same, the identity information of the talents to be analyzed is determined to be consistent, and if the comparison results are different, the identity information of the talents to be analyzed is prompted to be inconsistent.
Specifically, in the embodiment of the invention, when the identity information of the talent to be analyzed is secondarily determined, the certificate information and the collected image information on the related website are respectively compared through a primary determination to-be-determined comparison method, so that the talent to be analyzed is secondarily determined.
Specifically, in the fifth step, when the identity of the talent to be analyzed is determined to be the same for the second time, the retrieved data information of the talent to be analyzed is compared with the history information provided by the talent to be analyzed, and whether the comparison result is within the error range is determined.
The data processing module is internally provided with an algorithm for quantizing the relevant data information of talents, wherein the age, the academic history, the working age, the professional degree and other skills in the talent data information matrix belong to parameters, the parameters can also be other parameters, such as relevant working experience, matching degree of the professional and the work and other factors, and the parameters can be set according to different weight proportions during quantization and can also be set according to different data.
Specifically, in the embodiment of the present invention, a talent data information matrix F (N, L, W, A, B) is prestored in the data processing module, where N represents the age of the current talent in the site library, L represents the academic history of the current talent in the site library, W represents the working year of the current talent in the site library, a represents the professional degree of the current talent in the site library, and B represents other skills of the current talent in the site library.
Specifically, in the embodiment of the present invention, the data processing module constructs, from history information of talents to be analyzed collected in real time, a real-time data information matrix Fs (Ns, Ls, Ws, As, Bs) of talents to be analyzed, where Ns represents an age of talents to be analyzed in the history, Ls represents a academic history of talents to be analyzed in the history, Ws represents a working age of talents to be analyzed in the history, As represents a professional degree of talents to be analyzed in the history, and Bs represents other skills of talents to be analyzed in the history.
The data module respectively compares the parameters in the talent data information matrix F and the talent real-time data information matrix Fs to be analyzed, namely N is compared with Ns, L is compared with Ls, W is compared with Ws, A is compared with As, B is compared with Bs,
setting age error as Nc, Nc being more than or equal to 0, academic error as Lc, Lc being more than or equal to 0, working age error as Wc, Wc being more than or equal to 0, professional error as Ac, other skill error as Bc, and total error reference value as Fc.
If Ns-N is less than or equal to Nc, determining that the age of the talent to be analyzed is within the error range, otherwise, determining that the age of the talent to be analyzed is not within the error range;
if Ls-L is less than or equal to Lc, determining that the academic history of the talents to be analyzed is within the error range, otherwise, determining that the academic history is not within the error range;
if Ws-W is less than or equal to Wc, determining that the working life of the talent to be analyzed is within the error range, otherwise, not determining the working life of the talent to be analyzed is within the error range;
if As-A is less than or equal to Ac, determining that the specialty of the talent to be analyzed is within the error range, otherwise, determining that the specialty of the talent is not within the error range;
if Bs-B is less than or equal to Bc, determining that other skills of the talents to be analyzed are within the error range, otherwise, determining that the skills are not within the error range;
if the retrieved data information of the talents to be analyzed and the comparison result of the historical information provided by the talents to be analyzed are within the error range, generating talent curves in real time by using the retrieved data information of the talents to be analyzed in the site library;
if the retrieved data information of the talents to be analyzed and the comparison result of the history information provided by the talents to be analyzed are not within the error range, generating talent curves in real time according to the history information provided by the talents to be analyzed;
if the comparison result part of the retrieved data information of the talents to be analyzed and the historical information provided by the talents to be analyzed is within the error range, judging whether the sum of the parameter values which are not within the error range is larger than the total error reference value Fc, if so, generating a talent curve in real time by the historical information provided by the talents to be analyzed, otherwise, generating a talent curve in real time by the data information of the talents to be analyzed retrieved in the site library.
The data processing module compares the talent curve generated in real time with a standard talent curve, sets a talent curve correlation value as c, and when the talent curve is generated in real time according to the history information provided by the talents to be analyzed, the talent curve correlation value c is
c＝0.1×Ns/N0+0.3×Ls/L0+0.5×Ws/W0+0.7×As/A0+0.2×Bs/B0
When the talent curve is generated in real time by using the data information of the talents to be analyzed retrieved from the site library, the associated value c of the talent curve is
c＝0.1×N/N0+0.3×L/L0+0.5×W/W0+0.7×A/A0+0.2×B/B0
In the formula, N represents the age of the current talent in the site library, L represents the academic history of the current talent in the site library, W represents the working age of the current talent in the site library, a represents the professional degree of the current talent in the site library, B represents other skills of the current talent in the site library, Ns represents the academic history of the talent to be analyzed in the history, Ws represents the working age of the talent to be analyzed in the history, As represents the professional degree of the talent to be analyzed in the history, Bs represents other skills of the talent to be analyzed in the history, N0 represents the age of the current talent of the standard talent, L0 represents the academic history of the current talent in the site library, W0 represents the working age of the current talent in the site library, a0 represents the professional degree of the current talent in the site library, and B0 represents other skills of the current talent in the site library.
The data processing module establishes a curve difference matrix C (C1, C2 and C3 … Cn), wherein C1 represents a first preset curve difference, C2 represents a second preset curve difference, C3 represents a third preset curve difference, and Cn represents an nth preset curve difference, and the type of the talent to be analyzed is determined to be the type of the talent to be analyzed according to the talent curve correlation value C
If C is less than or equal to C1, determining that the talents to be analyzed are talents of the first type;
if C is more than C1 and less than or equal to C2, determining that the talents to be analyzed are talents of a second type;
if C is more than C2 and less than or equal to C3, determining that the talents to be analyzed are talents of a third type;
and if C is more than C3 and less than or equal to Cn, determining that the talent to be analyzed is the nth type talent.
Referring to fig. 2, the present invention further provides a talent database analysis system based on big data, including:
the enterprise database module is used for displaying enterprise related data information;
the database module is used for displaying talent related data information;
the flexible job place module is used for integrating and displaying the professional information issued by the enterprise library and the visitor library;
the puzzlement brocade module is used for integrating and displaying the problem information published in the enterprise library and the visitor library;
the image shooting module is used for calling the image shooting function of the equipment where the system is located to collect the face information of the talent to be analyzed and transmitting the collected image information to the data processing module;
and the data processing module is respectively connected with the enterprise library module, the visitor library module, the flexible workplace module, the confusion and sincere module and the camera shooting module, is used for processing the data information of the enterprise library module, the visitor library module, the flexible workplace module, the confusion and sincere module and the camera shooting module, and determines and divides the type of the identity information of the talents to be analyzed.
Specifically, in the embodiment of the present invention, the enterprise database module includes an enterprise data unit, an audit management unit, an authentication management unit, an enterprise management unit, and an authentication setting unit, where the enterprise data unit includes audit data, enterprise data, and authentication data; the audit management unit comprises claim audit and audit rejection; the authentication management unit comprises authentication audit, audit rejection and authentication records; the enterprise management unit comprises all enterprises, authenticated expired and unauthenticated enterprises; the authentication setting unit comprises package setting and advertisement setting, wherein the package setting comprises common authentication, advanced authentication and VI P authentication.
Specifically, in the embodiment of the invention, the talent base module comprises a talent entrance unit and a personal information unit, wherein talent entrance comprises directly entering real-name authentication, entering a talent base personal interface after the real name and obtaining data during the real-name authentication; the personal information unit comprises personal information and personal rights and interests, and the personal information comprises basic materials, work experiences, education experiences, self introduction, success cases, honor, post requirements and problems for release; the basic data in the personal information displayed in the personal information unit can include information such as head portrait, name, ethnicity, gender, birth date, household address, current identity, industry, occupation, work address and the like for displaying.
Specifically, in the embodiment of the present invention, the confusion solution module includes a question unit, a confusion solution unit, and a search unit.
Specifically, in the embodiment of the invention, the flexible workplace module comprises a post demand unit published by an enterprise library and a job hunting demand unit published by a person arriving library.
Specifically, in the embodiment of the present invention, the analysis system collects the face information of the talent to be analyzed, preliminarily confirms the identity information of the talent to be analyzed, searches the data information of the talent to be analyzed in the current site library, searches the site library in which the latest work or practice experience of the talent to be analyzed is located if the data information of the talent to be analyzed is not searched in the current site, sequentially searches the sites in which the work or practice experience of the talent to be analyzed is located from large to small according to the annual time of the work experience if the data information of the talent to be analyzed is not searched in the site in which the latest work or practice experience of the talent to be analyzed is located, and sequentially searches the sites in which the talent to be analyzed is not searched in the distance from the current site from near to far, until the retrieval of all the sites is completed or the data information of talents to be analyzed is retrieved, if the data information of talents to be analyzed is not retrieved in all the sites, newly building the data information of talents to be analyzed in the current site; and if the retrieved data information of the talents to be analyzed is the same, comparing the retrieved data information with the history information provided by the talents to be analyzed, secondarily determining the identity information of the talents to be analyzed, comparing the identity information of the talents to be analyzed, determining to generate a real-time talent curve by using the data information of different talents to be analyzed according to different comparison results, and determining the type of the talents to be analyzed within the difference range of the standard talent curve of the talent curve generated in real time.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention; various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A talent database analysis method based on big data is characterized by comprising the following steps:
the method comprises the following steps: the data processing module preliminarily determines the identity information of the talents to be analyzed according to the current website, searches in the current website according to the identity information of the talents after preliminary determination, and compares the searched data information of the talents to be analyzed with the history information provided by the talents to be analyzed if the data information of the talents to be analyzed is searched in the current website;
step two: if the data information of the talents to be analyzed is not searched in the current website library, searching in the website library where the latest work or practice experience of the talents to be analyzed is located, and if the data information of the talents to be analyzed is searched, comparing the searched data information with the history information provided by the talents to be analyzed;
step three: if the data information of the talents to be analyzed is not retrieved in the site where the latest work or practice experience of the talents to be analyzed is located, sequentially retrieving the data information of the talents to be analyzed from large to small according to the year time of the work or practice experience of the site where the work or practice experience of the talents to be analyzed is located, and if the data information of the talents to be analyzed is retrieved, comparing the retrieved data information with the history information provided by the talents to be analyzed;
step four: if the data information of the talents to be analyzed is not retrieved from the site where the working experience of the talents to be analyzed is located, sequentially retrieving the talents to be analyzed from near to far in the sites which are not retrieved until the retrieval of all the sites is completed or the data information of the talents to be analyzed is retrieved, if the data information of the talents to be analyzed is retrieved, comparing the retrieved data information with the history information provided by the talents to be analyzed, and if the data information of the talents to be analyzed is not retrieved in all the sites, newly creating the data information of the talents to be analyzed in the current site;
step five: when the retrieved data information of the talents to be analyzed is compared with the history information provided by the talents to be analyzed, firstly, secondarily determining the identity information of the talents to be analyzed, if the secondary determination results are the same, then, comparing, if the comparison result is in an error range, generating a talent curve in real time by using the retrieved data information of the talents to be analyzed in the site library, if the comparison result is not in the error range, generating a talent curve in real time by using the history information provided by the talents to be analyzed, and comparing the talent curve generated in real time with a standard talent curve;
step six: determining the type of the talents to be analyzed according to the difference range of the talent curve generated in real time in the standard talent curve;
the analysis method comprises the steps that after a data processing module preliminarily determines identity information of talents to be analyzed, the preliminarily determined talent identity information is searched in a current website library, whether the data information of the talents to be analyzed exists in the current website library is confirmed, if the data information of the talents to be analyzed exists in the current website library, the searched data information of the talents to be analyzed is compared with history information provided by the talents to be analyzed, if the data information of the talents to be analyzed does not exist in the current website library, the searched data information is preferentially searched in the website library where the talents to be analyzed work or practice experience is located, then the sites where the talents to be analyzed work or practice experience are located are sequentially searched from large to small according to the annual time of the work or practice experience, and then the sites which are not searched are sequentially searched from near to far from the current site, and stopping searching until the searching of all the sites is finished or the data information of the talents to be analyzed is searched, newly building the data information of the talents to be analyzed in the current site if the data information of the talents to be analyzed is not searched in all the sites, secondarily determining the identity information of the talents to be analyzed if the data information of the talents to be analyzed is searched, comparing the searched data information with the historical information provided by the talents to be analyzed, generating a real-time talent curve by using the data information of different talents to be analyzed according to different comparison results, and determining the type of the talents to be analyzed if the real-time generated talent curve is in the difference range of the standard talent curve.
2. The talent database analysis method based on big data according to claim 1, wherein an algorithm is arranged in the data processing module to quantify relevant data information of talents, and a talent data information matrix F (N, L, W, A, B) is preset in the data processing module, wherein N represents the age of the current talent in the site library, L represents the academic history of the current talent in the site library, W represents the working age of the current talent in the site library, A represents the specialty of the current talent in the site library, and B represents other skills of the current talent in the site library;
the data processing module constructs a talent real-time data information matrix Fs (Ns, Ls, Ws, As and Bs) to be analyzed according to history information of talents to be analyzed, wherein the Ns represents the age of the talents to be analyzed in the history, the Ls represents the academic history of the talents to be analyzed in the history, the Ws represents the working age of the talents to be analyzed in the history, the As represents the professional degree of the talents to be analyzed in the history, and the Bs represents other skills of the talents to be analyzed in the history;
in the fifth step, when the data processing module compares the retrieved data information of the talents to be analyzed with the history information provided by the talents to be analyzed, the data module compares parameters in the talent data information matrix F and the talent real-time data information matrix Fs to be analyzed respectively, sets the age error as Nc which is not less than 0, the academic error as Lc, as Lc which is not less than 0, the working age error as Wc, as Wc which is not less than 0, the professional error as Ac, the other skill error as Bc, and the total error reference value as Fc,
if Ns-N is less than or equal to Nc, determining that the age of the talent to be analyzed is within the error range, otherwise, determining that the age of the talent to be analyzed is not within the error range;
if Ls-L is less than or equal to Lc, determining that the academic history of the talents to be analyzed is within the error range, otherwise, determining that the academic history is not within the error range;
if Ws-W is less than or equal to Wc, determining that the working life of the talent to be analyzed is within the error range, otherwise, not determining the working life of the talent to be analyzed is within the error range;
if As-A is less than or equal to Ac, determining that the specialty of the talent to be analyzed is within the error range, otherwise, determining that the specialty of the talent is not within the error range;
and if Bs-B is less than or equal to Bc, determining that other skills of the talents to be analyzed are in the error range, otherwise, determining that the skills are not in the error range.
3. The talent database analysis method based on big data according to claim 2, wherein if the comparison result between the retrieved data information of the talents to be analyzed and the historical information provided by the talents to be analyzed is within the error range, a talent curve is generated in real time by using the retrieved data information of the talents to be analyzed in the site library;
if the retrieved data information of the talents to be analyzed and the comparison result of the history information provided by the talents to be analyzed are not within the error range, generating talent curves in real time according to the history information provided by the talents to be analyzed;
if the comparison result part of the retrieved data information of the talents to be analyzed and the historical information provided by the talents to be analyzed is within the error range, judging whether the sum of the parameter values which are not within the error range is larger than the total error reference value Fc, if so, generating a talent curve in real time by the historical information provided by the talents to be analyzed, otherwise, generating a talent curve in real time by the data information of the talents to be analyzed retrieved in the site library.
4. The talent database analysis method based on big data according to claim 1, wherein in the first step, when the identity information of the talent to be analyzed is preliminarily determined, the preliminary determination mode includes face recognition, the face information of the talent to be analyzed is collected in real time, the collected face information of the talent to be analyzed is compared with certificate information provided by the talent to be analyzed, the identity information of the talent to be analyzed is preliminarily determined, if the identity information of the talent to be analyzed is preliminarily determined to be different from the certificate information, it is prompted that the identity information of the talent to be analyzed is not in accordance, and if the identity information of the talent to be analyzed is preliminarily determined to be the same as the certificate information, the identity information of the talent to be analyzed is retrieved in the current website library;
when the preliminary determination is carried out, acquiring face information of the talent to be analyzed in real time through a camera shooting module, acquiring position information of face feature points of the talent to be analyzed, and comparing the distance and the slope between the positions, wherein the camera shooting module acquires the face position information in real time, and a real-time face information matrix P (a1, a2, a3, a4, a5, a6, a7 and a8) is obtained by taking the midpoint of a connecting line of centers of nostrils as a coordinate origin o, wherein a1 represents first endpoint position information of eyes, a2 represents second endpoint position information of eyes, a3 represents third endpoint position information of eyes, a4 represents fourth endpoint position information of eyes, a5 represents first endpoint position information of lips, a6 represents second endpoint position information of lips, a7 represents first endpoint position information of nasal wings, a8 represents second endpoint position information of nasal wings, and acquiring a real-time segment information matrix Y (k1, k2 and k3 … kn) formed by every two end points through the position information among the end points, wherein k1 represents the slope of a preset first segment, k2 represents the slope of a preset second segment, k3 represents the slope of a preset third segment, and kn represents the slope of a preset nth segment.
5. The talent database analysis method based on big data according to claim 4, wherein the camera shooting module collects face position information of a photo in certificate information, and obtains a preset face information matrix P ' and a preset line segment information matrix Y ' with a midpoint position of a central connecting line of nostrils as a coordinate origin o ', wherein for the preset face information matrix P ' (a '1, a '2, a '3, a '4, a '5, a '6, a '7, a '8), a '1 represents first endpoint position information of eyes, a '2 represents second endpoint position information of eyes, a '3 represents third endpoint position information of eyes, a '4 represents fourth endpoint position information of eyes, a '5 represents first endpoint position information of lips, a '6 represents second endpoint position information of lips, a '7 represents first endpoint position information of nasal wings, a'8 represents second end point position information of the alar nose; for the preset line segment information matrix Y '(k'1, r '1, k'2, r '2, k'3 … k 'n, r' n), where k '1 represents the slope of the preset first line segment, r'1 represents the length of the preset first line segment, k '2 represents the slope of the preset second line segment, r'2 represents the length of the preset second line segment, k '3 represents the slope of the preset third line segment, r'3 represents the length of the preset third line segment, k 'n represents the slope of the preset nth line segment, and r' n represents the length of the preset nth line segment.
6. The talent database analysis method based on big data according to claim 5, wherein based on the ki in the collected real-time segment information matrix Y of the face of talent to be analyzed and k ' i and r ' i in the preset segment information matrix Y ',
when ki is less than or equal to (beta multiplied by r 'i)/alpha multiplied by k' i, the fact that the face information acquired by the talent to be analyzed in real time is the same as the certificate information is judged;
when ki is larger than (beta multiplied by r 'i)/alpha multiplied by k' i, the fact that the human talent to be analyzed collects the face information in real time is judged to be different from the certificate information.
7. The talent database analysis method based on big data according to claim 3, wherein in the fifth step, the data processing module performs secondary determination on the identity information of the talent to be analyzed before comparing the retrieved data information of the talent to be analyzed with the history information provided by the talent to be analyzed, and when performing secondary determination on the identity information of the talent to be analyzed, the certificate information included in the history of the talent to be analyzed is retrieved on the relevant website, and the retrieval result is compared with the face information of the talent to be analyzed, and the identity information of the talent to be analyzed is determined secondarily, if the comparison results are the same, it is determined that the identity information of the talent to be analyzed matches, and if the comparison results are different, it is prompted that the identity information of the talent to be analyzed does not match.
8. The talent database analysis method based on big data according to claim 7, wherein the data processing module compares the talent curve generated in real time with a standard talent curve, sets a talent curve correlation value c, when the talent curve is generated in real time based on the historical information provided by the talents to be analyzed, the talent curve correlation value c is,
c＝0.1×Ns/N0+0.3×Ls/L0+0.5×Ws/W0+0.7×As/A0+0.2×Bs/B0
when the talent curve is generated in real time by using the data information of the talents to be analyzed retrieved from the site library, the associated value c of the talent curve is,
c＝0.1×N/N0+0.3×L/L0+0.5×W/W0+0.7×A/A0+0.2×B/B0
in the formula, N represents the age of the current talent in the site library, L represents the academic history of the current talent in the site library, W represents the working age of the current talent in the site library, a represents the professional degree of the current talent in the site library, B represents other skills of the current talent in the site library, Ns represents the academic history of the talent to be analyzed in the history, Ws represents the working age of the talent to be analyzed in the history, As represents the professional degree of the talent to be analyzed in the history, Bs represents other skills of the talent to be analyzed in the history, N0 represents the age of the current talent of the standard talent, L0 represents the academic history of the current talent in the site library, W0 represents the working age of the current talent in the site library, a0 represents the professional degree of the current talent in the site library, and B0 represents other skills of the current talent in the site library.
9. The talent database analysis method based on big data according to claim 8, wherein the data processing module is pre-configured with a curve difference matrix C (C1, C2, C3 … Cn), wherein C1 represents a first pre-configured curve difference, C2 represents a second pre-configured curve difference, C3 represents a third pre-configured curve difference, Cn represents an nth pre-configured curve difference, and the type of talent to be analyzed is determined according to the talent curve correlation value C
If C is less than or equal to C1, determining that the talents to be analyzed are talents of the first type;
if C is more than C1 and less than or equal to C2, determining that the talents to be analyzed are talents of a second type;
if C is more than C2 and less than or equal to C3, determining that the talents to be analyzed are talents of a third type;
and if C is more than C3 and less than or equal to Cn, determining that the talent to be analyzed is the nth type talent.
10. A talent database analysis system based on big data, comprising:
the enterprise database module is used for displaying enterprise related data information;
the database module is used for displaying talent related data information;
the flexible job place module is used for integrating and displaying the professional information issued by the enterprise library and the visitor library;
the puzzlement brocade module is used for integrating and displaying the problem information published in the enterprise library and the visitor library;
the image shooting module is used for calling the image shooting function of the equipment where the system is located to collect the face information of the talent to be analyzed and transmitting the collected image information to the data processing module;
and the data processing module is respectively connected with the enterprise library module, the visitor library module, the flexible workplace module, the confusion and sincere module and the camera shooting module, is used for processing the data information of the enterprise library module, the visitor library module, the flexible workplace module, the confusion and sincere module and the camera shooting module, and determines and divides the type of the identity information of the talents to be analyzed.
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