CN114418023A - Post matching method, system, equipment and storage medium - Google Patents
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Abstract
The invention discloses a method, a system, equipment and a storage medium for post matching, wherein the method comprises the following steps: setting basic post information of a post to be recruited, wherein the basic post information comprises: the position requirement; acquiring birth information of the post job seeker and acquiring character characteristics of the post candidate based on the birth information; and calculating the matching degree of the post requirement and the character features of the job seeker. Therefore, the process of job recruitment by the human resource demander is simplified, the corresponding job is matched for the job seeker, the character portrait of the job seeker and the character portrait of the job waiting position are obtained by analyzing the full convolutional neural network model through the birth date character obtained by mass data training, the target candidate is visualized, the corresponding job seeker is matched more conveniently, the recruitment cost of the human resource demander is saved, and meanwhile, the corresponding job waiting position is matched for the job seeker accurately.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method, a system, a device, and a storage medium for post matching.
Background
Most of the existing techniques for post matching are to make a psychological test question for an applicant or a job seeker through subjective judgment of the applicant on the resume of the job seeker by the recruiter, and analyze the test result or score so as to give a suggestion on post matching degree for the recruiter or a human resource demand party. The current popular mode for knowing whether talents are suitable for the post in the enterprise recruitment process is mainly realized by analyzing resumes or doing a large number of psychological test questions and directly trying on the post first, but most resumes do not introduce or evaluate the talent characters of job seekers at present, and more people in the years can poll the face test passing rate by means of exaggeration of words, water addition and even counterfeiting of resumes. The manpower resource demander has difficulty or needs to spend larger cost to judge whether the job seeker is really matched with the post on the personality. Therefore, a method for matching job seekers with posts quickly and accurately is needed.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a post matching method, aiming at solving the technical problem that a human resource demander is difficult to judge whether the job seeker is really matched with a post on the personality or not or needs to spend a large cost.
In order to achieve the above object, the present invention provides a post matching method, comprising:
setting basic post information of a post to be recruited, wherein the basic post information comprises: the position requirement;
acquiring birth information of the post job seeker and acquiring character characteristics of the post candidate based on the birth information;
and calculating the matching degree of the post requirement and the character features of the job seeker.
In one embodiment, the step of obtaining the birth information of the position applicant and obtaining the character feature of the position applicant based on the birth information comprises:
acquiring birth information of a job seeker and preprocessing the birth information to obtain a character characteristic value of the job seeker;
and inputting the character features into a position matching model to obtain the character features of the job seeker.
In one embodiment, the step of inputting the character feature into the position matching model to obtain the character feature of the candidate comprises:
acquiring birth information of a plurality of users as a training sample set;
preprocessing the birth information throughout each birth information in the training sample set;
obtaining a character characteristic value of each user in the training sample set about the character to be analyzed;
and training the full convolution neural network model by taking the birth information of each user in the training sample set and the corresponding character characteristic value as input after each analysis character.
In one embodiment, the step of obtaining the birth information of the position applicant and obtaining the character feature of the position applicant based on the birth information further comprises:
and acquiring birth information of the job seeker and inputting the birth information into the full convolution neural network model to obtain a personality portrait of the job seeker.
In one embodiment, the step of setting basic post information of the post to be recruited comprises the following steps:
setting a corresponding target candidate character feature tag according to the post requirement of the post to be recruited;
and generating a target character portrait of the target candidate according to the target candidate character feature label.
In one embodiment, the method further comprises:
acquiring corresponding job seeker information according to the target character portrait of the target candidate;
and extracting the birth information of the job seeker to generate a corresponding character portrait.
In one embodiment, the method further comprises:
judging whether the matching degree of the character portrait of the job seeker and the character portrait of the target candidate reaches a preset threshold value or not;
and if so, taking the job seeker as a target candidate.
In addition, in order to achieve the above object, an embodiment of the present invention further provides a station matching system, including:
the device comprises a setting module and a display module, wherein the setting module is used for setting basic post information of a post to be recruited, and the basic post information comprises: the position requirement;
the acquisition module is used for acquiring the birth information of the post job seeker and acquiring the character characteristics of the post candidate based on the birth information;
and the matching module is used for calculating the matching degree of the post requirement and the character characteristics of the job seeker.
In addition, in order to achieve the above object, an embodiment of the present invention further provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the memory has stored thereon instructions executable by at least one processor, the instructions being executable by the at least one processor to enable the at least one processor, when executing, to implement the steps of post matching as defined in any one of the above.
In addition, in order to achieve the above object, an embodiment of the present invention further provides a computer storage medium, on which a station matching program is stored, and the station matching program, when executed by a processor, implements the step of station matching as described in any one of the above.
The post matching method and the post matching system provided by the invention have the following beneficial effects:
setting basic post information of a post to be recruited, wherein the basic post information comprises: the position requirement; acquiring birth information of the post job seeker and acquiring character characteristics of the post candidate based on the birth information; and calculating the matching degree of the post requirement and the character features of the job seeker. Therefore, the process of job recruitment by the human resource demander is simplified, the corresponding post is matched for the job seeker, the win-win effect of the human resource demander and the job seeker is achieved, the birth date character analysis full convolution neural network model obtained through mass data training is used for obtaining the character portrait of the job seeker and the job waiting position, the target candidate is visualized, the corresponding job seeker is matched more conveniently, the recruitment cost of the human resource demander is saved, and meanwhile, the corresponding job waiting position is matched for the job seeker accurately.
Drawings
FIG. 1 is a schematic diagram of a terminal \ device structure of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a method for post matching according to an embodiment of the present invention;
FIG. 3 is a detailed flowchart of step S20 in FIG. 2;
FIG. 4 is a detailed flowchart of step S202 in FIG. 3;
FIG. 5 is a detailed flowchart of step S10 in FIG. 2;
FIG. 6 is a schematic flow chart illustrating another embodiment of a method for post matching according to the present invention;
FIG. 7 is a detailed flowchart of step S50 in FIG. 6;
FIG. 8 is a block diagram of a station matching system according to an embodiment of the present invention;
fig. 9 is a block diagram of a station matching system according to another embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The main solution of the embodiment of the invention is as follows: setting basic post information of a post to be recruited, wherein the basic post information comprises: the position requirement; acquiring birth information of the post job seeker and acquiring character characteristics of the post candidate based on the birth information; and calculating the matching degree of the post requirement and the character features of the job seeker.
Because most of the resume in the prior art does not introduce or evaluate the talent of the job seeker, more and more people in the years can draw the face test passing rate by means of exaggerating words, adding water and even making fake through the resume. The manpower resource demander has difficulty or needs to spend larger cost to judge whether the job seeker is really matched with the post on the personality.
The invention provides a solution, so that the process of job recruitment by a human resource demander is simplified, the corresponding post is matched for a job seeker, the win-win of the human resource demander and the win-win of the job seeker is realized, the full convolutional neural network model is analyzed through the birth date character obtained by mass data training to obtain the job seeker and the character portrait of the job waiting position, the target candidate is visualized, the corresponding job seeker is matched more conveniently, the recruitment cost of the human resource demander is saved, and meanwhile, the corresponding job waiting position is matched for the job seeker accurately.
As shown in fig. 1, fig. 1 is a schematic terminal structure diagram of a hardware operating environment according to an embodiment of the present invention.
The terminal of the embodiment of the invention can be a PC, and can also be a mobile terminal device with a display function, such as a smart phone, a tablet computer, an electronic book reader, an MP3(Moving Picture Experts Group Audio Layer III, dynamic video Experts compress standard Audio Layer 3) player, an MP4(Moving Picture Experts Group Audio Layer IV, dynamic video Experts compress standard Audio Layer 3) player, a portable computer, and the like.
As shown in fig. 1, the terminal may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Optionally, the terminal may further include a camera, a Radio Frequency (RF) circuit, a sensor, an audio circuit, a WiFi module, and the like. Such as light sensors, motion sensors, and other sensors. Specifically, the light sensor may include an ambient light sensor that may adjust the brightness of the display screen according to the brightness of ambient light, and a proximity sensor that may turn off the display screen and/or the backlight when the mobile terminal is moved to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally, three axes), detect the magnitude and direction of gravity when the mobile terminal is stationary, and can be used for applications (such as horizontal and vertical screen switching, related games, magnetometer attitude calibration), vibration recognition related functions (such as pedometer and tapping) and the like for recognizing the attitude of the mobile terminal; of course, the mobile terminal may also be configured with other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which are not described herein again.
Those skilled in the art will appreciate that the terminal structure shown in fig. 1 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, memory 1005, which is one type of computer storage medium, may include an operating system, a network communication module, a user interface module, and a post matching application.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a backend server and performing data communication with the backend server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and processor 1001 may be configured to invoke the web site matching application stored in memory 1005 and perform the following operations:
setting basic post information of a post to be recruited, wherein the basic post information comprises: the position requirement;
acquiring birth information of the post job seeker and acquiring character characteristics of the post candidate based on the birth information;
and calculating the matching degree of the post requirement and the character features of the job seeker.
Referring to fig. 2, a first embodiment of a method for matching stations according to the present invention provides a method for matching stations, including:
step S10, basic post information of the post to be recruited is set, wherein the basic post information comprises: the position requirement;
step S20, acquiring birth information of the position job seeker and acquiring character characteristics of the position candidate based on the birth information;
and step S30, calculating the matching degree of the post requirement and the character feature of the job seeker.
Specifically, in this embodiment, when various human resource service organizations, such as professional intermediaries, hunting consultants, talent training units, professional training schools, talent markets, psychological counseling, human resource consultants, talent assessment, external human resource service packages, employment startup guidance service organizations, etc., recruit a post, a basic post requirement corresponding to the post needs to be obtained, and illustratively, the post of each category includes a market promotion category, a sales category, a human resource category, an operation category, a planning category, a framework category, a research and development category, a resource integration category, a system management category, a culture category, and a financial category, and after selection and confirmation, the human resource demander can perform a customized score/scale range according to an individualized requirement. And then, acquiring birth information in the resume submitted by the job seeker, inputting the birth information into a post matching model to obtain corresponding character characteristics of the job seeker according to the birth information, matching the character characteristics obtained by the job seeker with basic post information preset by an enterprise, and further calculating the matching degree of the job seeker and the post to be recruited. In this embodiment, the human resources demander includes: people and industry hunt and other human resource industry requirements. Therefore, the matching degree of the job seeker and the post to be recruited can be calculated by acquiring the birth information of the job seeker of the post to be recruited, judgment according to subjective contents on the resume is avoided, the recruitment time of an enterprise is reduced, and the recruitment work efficiency is improved.
In this embodiment, basic post information of a post to be recruited is set, where the basic post information includes: the position requirement; acquiring birth information of the post job seeker and acquiring character characteristics of the post candidate based on the birth information; and calculating the matching degree of the post requirement and the character features of the job seeker. Therefore, the matching degree of the job seeker and the post to be recruited can be calculated by acquiring the birth information of the job seeker of the post to be recruited, so that the judgment according to subjective contents on the resume is avoided, the recruitment time of a human resource demand party is reduced, and the recruitment work efficiency is improved.
Further, referring to fig. 3, in one embodiment of the method for post matching according to the present invention, the step S20 further includes:
step S201, acquiring birth information of job seekers, and preprocessing the birth information to obtain character characteristic values of the job seekers;
and S202, inputting the character features into a position matching model to obtain the character features of the job seeker.
Specifically, in this embodiment, the birth information of the job seeker corresponding to the job waiting position or the job seeker seeking a new job opportunity is acquired, and the processing is performed according to the birth year, the birth month and the gender of the job seeker and a preset algorithm, so as to finally obtain the character feature value of the character analysis model corresponding to the job seeker, where the character analysis model may be a character analysis fully convolutional neural network model trained according to the birth information of a large number of users and character features corresponding to the birth information and the character features obtained through psychological tests and the like. Illustratively, in the training of the full convolutional neural network model corresponding to the position matching, a corresponding birth feature value is calculated through a birth date of each user in a training sample set, illustratively, in this embodiment, a birth feature value corresponding to each user in the training sample set is calculated by obtaining the birth date of each user in the training sample set and according to a birth year and a birth month in the birth date of each user, a personality feature value of each personality to be analyzed in the training sample set is obtained according to a mapping relationship between obtained original feature values of each user corresponding to each personality to be analyzed, and further, a corresponding full convolutional neural network is trained through a personality analysis feature value of each personality to be analyzed in the training sample set. Therefore, the character characteristic values obtained through the relationship between the birth information and the character information of each user in the sample set are used for training the convolutional neural network, one character to be analyzed corresponds to one character characteristic value, and one character to be analyzed corresponds to one full convolutional neural network. Furthermore, the subjectivity and the randomness of the artificial answer are avoided, and the time consumed by character analysis is further simplified and the use experience of the user is improved.
In the embodiment, the birth information of the job seeker is acquired for preprocessing to obtain the character characteristic value of the job seeker; and inputting the character features into a position matching model to obtain the character features of the job seeker. The birth information of the job seeker is associated with the character characteristics of the job seeker, and the character characteristics of the job seeker can be obtained only by acquiring the birth information of the job seeker, so that the screening of the human resource demander on the job seeker is facilitated, the character testing process in recruitment is simplified, the recruitment efficiency of the human resource demander is further improved, and the recruitment cost of the human resource demander is saved.
Further, referring to fig. 4, based on the above embodiment, in one embodiment of the method for post matching provided by the present invention, the step S202 further includes:
step S2021, acquiring birth information of a plurality of users as a training sample set;
step S2022, preprocessing the birth information throughout each birth information in the training sample set;
step S2023, obtaining a character feature value of each user in the training sample set about the character to be analyzed;
step S2024, training the full convolution neural network model with the birth information of each user in the training sample set and the corresponding feature value of the character as input, throughout each analysis character.
Specifically, in the present embodiment, a full Convolutional neural Network (FCN) model corresponding to each character to be analyzed is obtained through deep learning, and, for example, by obtaining the birth dates of a plurality of users as a training sample set, and going through the birth dates of the plurality of users in the training sample set, and processing each birth date according to a preset algorithm to obtain a character characteristic value calculated by each birth date, and based on the personality characteristics corresponding to each user in the training sample set obtained in advance and the personality characteristic value obtained by the birth date of each user in the sample training set through a preset algorithm, the personality characteristics value is used as the personality characteristic value to be input and trained into the full convolution neural network model, and the full convolution neural network model of the character analysis model is obtained and applied to the position matching method in the embodiment. Therefore, the whole neural network model is analyzed through a large number of known character birth dates and character corresponding training characters, accuracy of results output through the whole neural network model is guaranteed, and use experience of a user is further improved.
In addition, in the embodiment, the five personality factors serve as a personality trait model recognized by the western psychology community, and researchers find that about five traits can cover all aspects of personality description through a lexical method. The five personality (OCEAN), also known as the personality OCEAN. Openness (openness): has characteristics of imagination, aesthetic feeling, rich emotion, differentiation, creation, intelligence and the like; responsibility (conscientiousness): displaying the characteristics of competence, justice, arrangement, full-time, achievement, self-discipline, caution, curbing and the like; extroversion (inversion): the special characteristics of enthusiasm, social contact, fruit break, activity, adventure, optimism and the like are presented; humanization (agreebleness) is preferred: has the characteristics of trust, profit, straightness, compliance, modesty, immigration and the like; neurogenic (neurootism): it is difficult to balance the emotional traits of anxiety, hostility, depression, self-consciousness, impulsivity, fragility, etc., i.e., it does not have the ability to maintain emotional stability. Five personality factors are employed in this embodiment to refine the different personality categories. Illustratively, five personality factors are obtained as the personality to be analyzed, a full convolution neural network model corresponding to each personality to be analyzed (namely five personality) is trained, the birth information of the personality to be analyzed is acquired, the birth information of the personality to be analyzed is preprocessed according to a preset algorithm model, the full convolution neural network model of the personality to be analyzed is input, and the personality password of the corresponding personality to be analyzed is output, wherein the personality password identifies the output value of the task person to be identified for the personality to be analyzed through the attention or other personality characteristic values. And obtaining the character of the person to be recognized according to the mapping of the analysis value in the character full convolution network model. Therefore, the character characteristics of the person to be identified can be obtained by simply inputting the birth information of the person to be identified, so that the character identification process is simple and convenient, the obtained character result is objective and real, and the user experience is further improved.
In the embodiment, birth information of a plurality of users is acquired as a training sample set; preprocessing the birth information throughout each birth information in the training sample set; obtaining a character characteristic value of each user in the training sample set about the character to be analyzed; and training the full convolution neural network model by taking the birth information of each user in the training sample set and the corresponding character characteristic value as input after each analysis character. Namely, the character analysis all-neural network model is trained according to the birth information of a large number of users and the characteristic values of the users on the five personalities, so that the truth and the objectivity of the character analysis result are ensured, the character analysis process of the job seeker is simplified, and the recruitment cost of a human resource demander is further saved.
Further, based on the above embodiment, in one embodiment of the method for matching positions provided by the present invention, the step S20 further includes:
step S201', obtaining birth information of the job seeker and inputting the birth information into the full convolutional neural network model to obtain a personality portrait of the job seeker.
Specifically, in this embodiment, according to the character analysis neural network model trained in the above embodiment, the birth information of the job seeker is obtained, and the birth date of the job seeker is processed according to a preset algorithm, which is mainly used to process the birth date of the job seeker to a type that matches the post matching fully convolutional neural network model input value, so that the job seeker corresponding character portrait is output through the post matching fully convolutional neural network model trained. Exemplarily, job seeker: zhang three, male, year and month of birth: on the year of 1995, on the year of 06, the character features obtained by inputting the birth information into the full convolution neural network model are: labeling: the big pattern of dryness-heat is dynamic; bovine analysis: communication capacity: certain communication ability exists, but the spleen qi is violent and needs to be accepted by people; sharing ability: not good at sharing, and prefers to do work behind the scenes; integration ability: the work has a pattern, and the systematicness is emphasized. Meanwhile, the person can enjoy love heart and like paying attention to other people; planning capability: planning ability and innovation ability are general, but financial aspects are more meticulous; the pushing capacity: the subjective motility is strong, the movement can be performed after deep thought and consideration before the movement, and the opposite sex margin is also relatively existed; limitation: the system is not easy to be bound by the rules, and the actions are easy to break the rules; professional suggestion: sales and market promotion. Therefore, the character portrait of the job seeker is obtained according to the birth information of the job seeker, and the manpower resource demander is simple and convenient, for example: the character testing time of various human resource service organizations, such as vocational agents, hunting consultants, talent training units, vocational training schools, talent markets, psychological counseling coaching, human resource consultants, talent assessment, human resource service outsourcing, employment and entrepreneurship guidance service organizations and the like, on job seekers is also avoided, the influence of subjective factors of the job seekers on the character analysis results is avoided, and the authenticity and objectivity of the character testing results are ensured.
In addition, in this embodiment, the obtained personality portrait of the job seeker further includes a personality analysis chart of the job seeker shown in a visual pattern such as a histogram or a sector graph according to the personality feature distribution map of the job seeker obtained in the full convolution neural network model, and the competence of each aspect of the job seeker can be represented by a specific score value according to the personality feature of the job seeker. Therefore, the system and the method help the human resource demander to more intuitively know the capability information of various aspects of the job seeker, and save the recruitment cost of the human resource demander.
Further, referring to fig. 5, based on the above embodiment, in one embodiment of the method for post matching according to the present invention, the step S10 further includes:
step S101, setting a corresponding target candidate character feature label according to the post requirement of the post to be recruited;
and step S102, generating a target character portrait of the target candidate according to the target candidate character feature label.
Specifically, in the present embodiment, the human resources demander includes but is not limited to various human resources services, such as job agents, hunting consultants, talent training units, school of job training, talent market, psychological counseling, human resource consultants, talent assessment, outsourcing of human resource services, employment startup guide service organizations, etc., according to the organization condition corresponding to the post to be recruited, the capability information required by the post to be recruited and the psychological information required by the post to be recruited, and according to actual post requirements, a human resource demander presets corresponding character feature labels, illustratively, the post of each category comprises a market promotion class, a sales class, a human resource class, an operation class, a planning class, a framework class, a research and development class, a resource integration class, a system management class, a culture class and a financial class, and after selection and confirmation, a user can define the score/scale range according to personalized requirements. For example, after the recruiting position is selected, the basic reference values or dimensions of the five large five-row personality positions corresponding to the position can be seen, and the user further confirms or adjusts the position according to the requirement of the user on the recruiting position on the basis, wherein each dimension can be divided into low, medium and high. The user can manually adjust the adjustment according to the situation. This version of the job matching highlights includes: and (4) classifying the posts, and meeting the requirements of five types of personality and capability of the posts. Accurate efficient intelligence is matched. Based on the prompt "what five personality traits you expect the other party to have" each index can then be selected according to the expected values of high, medium, and low, all default to low initially in the digitizing tool. And corresponding description introduction characters are remarked for each dimension selection, so that the user can know the action and the meaning during selection. High: 80-100 min, wherein: 60-70 points, low: 20-50 minutes. When the digitalized people's post matching method starts to be used, firstly, the post is selected to be hooked, then, the character traits of five people and the expected requirement of the post capability are required, and further, the character portrait of the target candidate is generated. Therefore, the character portrait of the corresponding target candidate is generated by selecting the post category, the expected capability, the character tag and the like by the human resource demander, the description of the information of the post to be recruited by the human resource demander is simplified, the recruitment cost of an enterprise is saved, and the satisfaction degree of the human resource demander to job seekers is improved.
In the embodiment, a corresponding target candidate character feature tag is set according to the post requirement of the post to be recruited; and generating a target character portrait of the target candidate according to the target candidate character feature label. Therefore, through the fact that the human resource demanders include but are not limited to various human resource service organizations, such as professional intermediaries, hunting consultants, talent training units, professional training schools, talent markets, psychological counseling coaching, human resource consultants, talent assessment, human resource service outsourcing, employment startup guidance service organizations and other demanders, the corresponding character portrait of the target candidate is generated by selecting the position category, the expected capacity, the character label and the like, the description of the to-be-hired position information of the human resource demanders is simplified, the hiring cost of an enterprise is saved, and the satisfaction degree of the human resource demanders on job seekers is improved.
Further, referring to fig. 6, based on the above embodiment, in one embodiment provided by a position matching method of the present invention, the method further includes:
step S40, acquiring corresponding job seeker information according to the target character portrait of the target candidate;
and step S50, extracting the birth information of the job seeker to generate a corresponding personality portrait.
Specifically, in the embodiment, with reference to the above embodiment, when the human resource demander sets the requirement for the job to be recruited, the character portrait of the target candidate is uploaded or collected based on the character portrait of the target candidate, and after the information is obtained, the information is quickly and intelligently analyzed, so that information such as analysis summary, comprehensive score result, ranking condition and the like is obtained. Specifically, birth information of numerous job seekers is collected in numerous job seekers based on the character portrait of the target candidate, the birth information is input into a trained full-convolution neural network model to obtain the character portrait of the job seeker, the character portrait of each job seeker is transversely compared with the character portrait of the target candidate of the human resource demander to calculate the similarity between the character portrait of each job seeker and the character portrait of the target candidate, namely the matching degree between each job seeker and a post to be recruited, and finally the target candidate is selected according to the matching degree of each job seeker. Therefore, the corresponding character portrait of the target candidate is generated through the selection of the human resource demander, the target candidate is selected from the candidate according to the similarity between the character portrait of each candidate and the character portrait of the target candidate, the recruitment flow of the human resource demander is simplified, the requirement of the human resource demander on the post to be recruited is specified and visualized, the searched target candidate is more accurate, and the use experience is further improved.
Further, referring to fig. 7, based on the above embodiment, in one embodiment of the method for post matching provided by the present invention, the method further includes:
step S501, judging whether the matching degree of the character portrait of the job seeker and the character portrait of the target candidate reaches a preset threshold value;
and step S502, if so, taking the job seeker as a target candidate.
Specifically, in the present embodiment, based on the above embodiment, the human resource demander selects the corresponding post category, the required character tag, the capability information, and the like according to the post required to be recruited, and sets them in advance to obtain the character portrait of the target candidate, extracts the birth information of the job seeker, inputs the birth information of the job seeker into the trained character analysis full convolutional neural network model based on the birth information of the job seeker to obtain the character portrait of the corresponding job seeker, and obtains the similarity between the character portrait of each job seeker and the character portrait of the target candidate, that is, the matching degree between each job seeker and the post to be recruited, compares the job seeker exceeding the target matching degree threshold according to the preset matching degree threshold, and uses the job seeker with the highest matching degree as the target candidate, although in some embodiments, the job seeker with the highest matching degree may be used as the target candidate, entering the next round of interview; of course, all job seekers reaching the target threshold may be used as target candidates to enter the next round of interview, which is not limited in this embodiment. Therefore, the process of job recruitment by the human resource demander is simplified, the corresponding post is matched for the job seeker, the win-win effect of the human resource demander and the job seeker is achieved, the birth date character analysis full convolution neural network model obtained through mass data training is used for obtaining the character portrait of the job seeker and the job waiting position, the target candidate is visualized, the corresponding job seeker is matched more conveniently, the recruitment cost of the human resource demander is saved, and meanwhile, the corresponding job waiting position is matched for the job seeker accurately.
In this embodiment, whether the matching degree of the character portrait of the job seeker and the character portrait of the target candidate reaches a preset threshold value is judged; and if so, taking the job seeker as a target candidate. Therefore, the process of recruiting the posts by the human resource demander is simplified, the corresponding posts are matched for the job seeker, the win-win of the human resource demander (comprising various human resource service mechanisms, such as vocational intermediaries, hunting consultants, talent training units, vocational training schools, talent markets, psychological consultation and guidance, human resource consultants, talent assessment, human resource service outsourcing, employment and creation guidance service mechanisms and the like) and the job seeker is realized, the whole convolutional neural network model is analyzed through the birth date characters obtained by mass data training to obtain the job seeker and the character representation of the post to be recruited, the target candidate is visualized, the corresponding job seeker is matched more conveniently, the recruitment cost of the human resource demander is saved, and the corresponding post to be recruited is matched for the job seeker accurately.
Further, referring to fig. 8, in an embodiment of the present invention, there is provided a station matching system 300, including:
a setting module 310, configured to set basic post information of a post to be recruited, where the basic post information includes: the position requirement;
the obtaining module 320 is used for obtaining the birth information of the position job seeker and obtaining the character characteristics of the position candidate based on the birth information;
and the matching module 330 is used for calculating the matching degree of the position requirement and the character characteristics of the job seeker.
Referring to fig. 9, one embodiment of the present invention provides a station matching system 400. The station matching system 400 includes:
a memory 420, a processor 410 and a computer program 440 stored on the memory 420 and executable on the processor 410, the computer program 440, when executed by the processor 410, implementing the steps of post matching as described in any of the above embodiments.
One embodiment of the present invention provides an electronic device, including: at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the memory has stored thereon instructions executable by at least one processor, the instructions being executable by the at least one processor to enable the at least one processor, when executing, to implement the steps of the station matching method as defined in any one of the above.
One embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the station matching method according to any one of the above embodiments.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method for post matching, comprising:
setting basic post information of a post to be recruited, wherein the basic post information comprises: the position requirement;
acquiring birth information of the post job seeker and acquiring character characteristics of the post candidate based on the birth information;
and calculating the matching degree of the post requirement and the character features of the job seeker.
2. The position matching method according to claim 1, wherein the step of obtaining birth information of the position job seeker and obtaining character features of the position job seeker based on the birth information comprises:
acquiring birth information of a job seeker and preprocessing the birth information to obtain a character characteristic value of the job seeker;
and inputting the character features into a position matching model to obtain the character features of the job seeker.
3. The job matching method according to claim 2, wherein the step of inputting the character features into a job matching model to obtain the character features of the candidate comprises:
acquiring birth information of a plurality of users as a training sample set;
preprocessing the birth information throughout each birth information in the training sample set;
obtaining a character characteristic value of each user in the training sample set about the character to be analyzed;
and training the full convolution neural network model by taking the birth information of each user in the training sample set and the corresponding character characteristic value as input after each analysis character.
4. The position matching method according to claim 3, wherein the step of obtaining birth information of the position applicant and obtaining the character feature of the position applicant based on the birth information further comprises:
and acquiring birth information of the job seeker and inputting the birth information into the full convolution neural network model to obtain a personality portrait of the job seeker.
5. The post matching method according to claim 1, wherein the step of setting basic post information of a post to be recruited comprises:
setting a corresponding target candidate character feature tag according to the post requirement of the post to be recruited;
and generating a target character portrait of the target candidate according to the target candidate character feature label.
6. The station matching method according to claim 5, characterized in that the method further comprises:
acquiring corresponding job seeker information according to the target character portrait of the target candidate;
and extracting the birth information of the job seeker to generate a corresponding character portrait.
7. The station matching method according to claim 6, characterized in that the method further comprises:
judging whether the matching degree of the character portrait of the job seeker and the character portrait of the target candidate reaches a preset threshold value or not;
and if so, taking the job seeker as a target candidate.
8. A post matching system, comprising:
the device comprises a setting module and a display module, wherein the setting module is used for setting basic post information of a post to be recruited, and the basic post information comprises: the position requirement;
the acquisition module is used for acquiring the birth information of the post job seeker and acquiring the character characteristics of the post candidate based on the birth information;
and the matching module is used for calculating the matching degree of the post requirement and the character characteristics of the job seeker.
9. An electronic device, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the memory has stored thereon instructions executable by at least one processor, the instructions being executable by the at least one processor to enable the at least one processor, when executing, to implement the steps of post matching as claimed in any one of claims 1-7.
10. A computer storage medium, characterized in that the computer storage medium has stored thereon a station matching program, which when executed by a processor implements the steps of station matching according to any one of claims 1-7.
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| CN202210090579.XA CN114418023A (en) | 2022-01-25 | 2022-01-25 | Post matching method, system, equipment and storage medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114936843A (en) * | 2022-05-20 | 2022-08-23 | 量心科技(深圳)有限公司 | Method and device for evaluating matching degree of personnel and post |
| CN115330142A (en) * | 2022-07-25 | 2022-11-11 | 北京百度网讯科技有限公司 | Training method of joint capacity model, capacity requirement matching method and device |
| CN116523268A (en) * | 2023-06-30 | 2023-08-01 | 广东中大管理咨询集团股份有限公司 | Person post matching analysis method and device based on big data portrait |
-
2022
- 2022-01-25 CN CN202210090579.XA patent/CN114418023A/en not_active Withdrawn
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114936843A (en) * | 2022-05-20 | 2022-08-23 | 量心科技(深圳)有限公司 | Method and device for evaluating matching degree of personnel and post |
| CN115330142A (en) * | 2022-07-25 | 2022-11-11 | 北京百度网讯科技有限公司 | Training method of joint capacity model, capacity requirement matching method and device |
| CN116523268A (en) * | 2023-06-30 | 2023-08-01 | 广东中大管理咨询集团股份有限公司 | Person post matching analysis method and device based on big data portrait |
| CN116523268B (en) * | 2023-06-30 | 2023-09-26 | 广东中大管理咨询集团股份有限公司 | Person post matching analysis method and device based on big data portrait |
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