CN117077972A - Matching degree detection method, device, equipment and storage medium - Google Patents
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Abstract
The invention belongs to the technical field of information detection, and discloses a matching degree detection method, a device, equipment and a storage medium. The office characteristic information of the target staff is obtained; analyzing the office characteristic information and determining work adaptation degree, work adaptation degree and work satisfaction degree; matching the work adaptation degree, the work adaptation degree and the work satisfaction degree with a preset matching degree dividing rule, and determining the matching degree between the target staff and the work post. The working adaptation degree, the working adaptation degree and the working satisfaction degree of the staff are determined by objectively reflecting office characteristic information of daily working characteristics of the staff, and the matching degree between the staff and the working positions is judged by integrating the working adaptation degree, the working adaptation degree and the working satisfaction degree, so that the automatic detection of the matching degree of the staff positions is realized, the efficiency is high, the influence of subjective images is avoided, and the actual requirements are met.
Description
Technical Field
The present invention relates to the field of information detection technologies, and in particular, to a method, an apparatus, a device, and a storage medium for detecting matching degree.
Background
In recent years, the economic development of the field is rapid, the gap to talents is also continuously increased, the excellent development of enterprises is not separated from the management of human resources, and reasonable matching of talents and posts is also an important factor for improving the competitiveness of enterprises in the market. The existing enterprises generally carry out a plurality of projects at the same time, collaborative management among the projects is a difficult problem facing the enterprises, and the human resources are scientifically arranged, so that higher benefits are brought to the enterprises, and meanwhile, the human cost of the enterprises is saved.
The matching of human resources today is based on the traditional mode to judge the matching degree of the personnel and the posts according to the requirements of the posts and the working experience and the existing skills of the personnel. The traditional personnel post allocation is more dependent on subjective consciousness, subjective judgment is carried out on the adaptation degree of staff and posts, the judgment standard is not comprehensive enough and objective enough, the accuracy is low, and the judgment efficiency is quite low when the staff with a large number of staff is required to face.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide a matching degree detection method, device, equipment and storage medium, and aims to solve the technical problems of insufficient accuracy and low efficiency in the prior art when judging the matching degree of staff and posts.
In order to achieve the above object, the present invention provides a matching degree detection method, which includes the following steps:
acquiring office characteristic information of a target employee;
analyzing the office characteristic information to determine work adaptation degree, work adaptation degree and work satisfaction degree;
and matching the work adaptation degree, the work adaptation degree and the work satisfaction degree with a preset matching degree dividing rule, and determining the matching degree between the target staff and the work post.
Optionally, the office characteristic information comprises software use time, mail reply intervals, the number of work speaking bars and facial expression images;
the step of analyzing the office characteristic information and determining the work adaptation degree, the work adaptation degree and the work satisfaction degree comprises the following steps:
determining the work adaptation degree according to the use time of the software;
determining the work fitness according to the mail reply interval and the number of the work speaking bars;
and determining the work satisfaction according to the facial expression image.
Optionally, the step of determining the work fitness according to the software usage time length includes:
acquiring the working time length corresponding to the target staff;
calculating the ratio of the software using time length to the working time length to obtain an adaptation degree score;
and matching the adaptation degree scores with score intervals corresponding to the preset adaptation degree levels, and determining the work adaptation degree corresponding to the target employee.
Optionally, the office feature information further includes employee attribute information;
the step of calculating the ratio of the software using time length to the working time length to obtain the fitness score comprises the following steps:
determining employee work categories according to the employee attribute information;
determining the type of the working software according to the employee working category;
extracting a working software use duration corresponding to the working software type from the software use duration;
and calculating the ratio of the using time length of the working software to the working time length to obtain an adaptation degree score.
Optionally, the step of determining the job fitness according to the mail reply interval and the number of the job utterances includes:
acquiring average mail reply intervals and average speaking number of staff in a trial period;
comparing the mail reply interval with the average mail reply interval to determine a first interestingness rating;
comparing the number of the working speech bars with the average number of the speech bars to determine a second interestingness rating;
and determining the work fitness corresponding to the target employee according to the first interestingness rating and the second interestingness rating.
Optionally, the step of determining the work satisfaction according to the facial expression image includes:
identifying the facial expression image through a preset expression identification model, and determining a satisfactory expression image;
calculating the ratio between the number of the satisfied expression images and the number of the facial expression images to obtain a satisfaction score;
and matching the satisfaction score with a score interval corresponding to each preset satisfaction grade, and determining the work satisfaction of the target staff.
Optionally, before the step of identifying the facial expression image through the preset expression identification model and determining the satisfactory expression image, the method further includes:
screening face pictures in a face picture library to obtain face non-shielding pictures;
performing specification adjustment on the face non-shielding picture, and constructing a model training set according to the adjusted picture;
training the initial expression recognition model based on the model training set to obtain a preset expression recognition model.
In addition, in order to achieve the above object, the present invention also provides a matching degree detection device, which includes the following modules:
the acquisition module is used for acquiring office characteristic information of the target staff;
the analysis module is used for analyzing the office characteristic information and determining work adaptation degree, work adaptation degree and work satisfaction degree;
the detection module is used for matching the work adaptation degree, the work adaptation degree and the work satisfaction degree with a preset matching degree dividing rule and determining the matching degree between the target staff and the work post.
In addition, in order to achieve the above object, the present invention also proposes a matching degree detection apparatus including: the matching degree detection device comprises a processor, a memory and a matching degree detection program which is stored in the memory and can run on the processor, wherein the matching degree detection program realizes the steps of the matching degree detection method when being executed by the processor.
In addition, in order to achieve the above object, the present invention also proposes a computer-readable storage medium having stored thereon a matching degree detection program which, when executed, implements the steps of the matching degree detection method as described above.
The office characteristic information of the target staff is obtained; analyzing the office characteristic information and determining work adaptation degree, work adaptation degree and work satisfaction degree; matching the work adaptation degree, the work adaptation degree and the work satisfaction degree with a preset matching degree dividing rule, and determining the matching degree between the target staff and the work post. The working adaptation degree, the working adaptation degree and the working satisfaction degree of the staff are determined by objectively reflecting office characteristic information of daily working characteristics of the staff, and the matching degree between the staff and the working positions is judged by integrating the working adaptation degree, the working adaptation degree and the working satisfaction degree, so that the automatic detection of the matching degree of the staff positions is realized, the efficiency is high, the influence of subjective images is avoided, and the actual requirements are met.
Drawings
FIG. 1 is a schematic diagram of an electronic device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart of a first embodiment of a matching degree detection method according to the present invention;
FIG. 3 is a schematic diagram of an information source device according to an embodiment of the invention;
FIG. 4 is a flowchart of a second embodiment of the matching degree detection method of the present invention;
fig. 5 is a block diagram of a first embodiment of a matching degree detection device according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a matching degree detection device of a hardware running environment according to an embodiment of the present invention.
As shown in fig. 1, the electronic device may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Those skilled in the art will appreciate that the structure shown in fig. 1 is not limiting of the electronic device and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a matching degree detection program may be included in the memory 1005 as one type of storage medium.
In the electronic device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the electronic device of the present invention may be provided in the matching degree detection device, where the electronic device invokes the matching degree detection program stored in the memory 1005 through the processor 1001, and executes the matching degree detection method provided by the embodiment of the present invention.
An embodiment of the present invention provides a matching degree detection method, referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the matching degree detection method of the present invention.
In this embodiment, the matching degree detection method includes the following steps:
step S10: and acquiring office characteristic information of the target staff.
The execution body of the embodiment may be the matching degree detection device, and the matching degree detection device may be an electronic device such as a personal computer, a server, or other devices capable of implementing the same or similar functions, which is not limited in this embodiment, and in the present embodiment and the following embodiments, the matching degree detection method of the present invention is described by taking the matching degree detection device as an example.
It should be noted that, the target employee may be an employee who needs to perform post matching degree detection, and may be specified by a manager (such as a human staff member of an enterprise) of the matching degree detection apparatus. The office characteristic information may include employee attribute information, a software use time, a mail reply interval, a work speaking number, facial expression images and other relevant data which can characterize the employee in daily work, and of course, other data may also be included.
The staff attribute information may include information such as staff name, staff identification, post type, post responsibility, post name, etc., the software use duration may include time of using various office software by staff in a specific period (such as about one month or three months in a practice period), the mail reply interval may be average interval time of feeding back after the staff receives a working mail in the specific period, the number of speaking bars of the working group may be total number of speaking bars of the staff in the working group (may be group chat in the office software) in the specific period, and the facial expression image may be an image including facial expression of the staff collected in the specific period (for example, a camera is placed on a place where the staff appears daily such as a company foreground, a tea room, a canteen, etc., to collect facial expression images of the staff, so as to ensure reliability of subsequent analysis, each person may grasp at least three facial expression images every day).
Further, for ease of understanding, the present embodiment will be described with reference to fig. 3, but this embodiment is not limited thereto, fig. 3 is a schematic diagram of an information source device in this embodiment, as shown in fig. 3, the office feature information (such as a working software use time period and a working time period of an employee) may be obtained by a tool installed locally on an employee terminal (employee PC), and the matching degree detection device (HR computer) may send a data obtaining request to the employee terminal. After the tool acquires the data, the data can be summarized and sent to the matching degree detection equipment. The facial expression image (face image of staff) contained therein may be collected by such a tool, or may be collected and then sent to the matching degree detection device by a camera (camera) installed in an office area.
Step S20: and analyzing the office characteristic information to determine the work adaptation degree, the work adaptation degree and the work satisfaction degree.
It should be noted that the work fitness may be used to characterize the fitness between the employee and the post, and the higher the work fitness, the higher the fitness between the employee and the post. The work fitness can be used for representing the fitness of staff to the post work, and the higher the work fitness is, the more the staff is suitable for the post work. The work satisfaction can be used for representing the satisfaction degree of staff on the post work, and the higher the work satisfaction degree is, the higher the satisfaction degree of the staff on the post work is.
In practical use, the office characteristic information contains relevant data which can characterize the characteristics of staff in daily work, and analysis of the office characteristic information can determine whether the staff is suitable for post work, is satisfied with the work, and is suitable for post work (i.e. whether work processing is good or not), so that the work adaptation degree, the work adaptation degree and the work satisfaction degree are determined.
Step S30: and matching the work adaptation degree, the work adaptation degree and the work satisfaction degree with a preset matching degree dividing rule, and determining the matching degree between the target staff and the work post.
The preset matching degree dividing rule may be preset by a manager of the matching degree detecting device, where the dividing rule may be set to comprehensively divide the employee and post work specific matching degree according to the work adaptation degree, the work adaptation degree and the work satisfaction degree.
In a specific implementation, the work adaptation degree and the work satisfaction degree may be represented in a level manner, and then the setting of the preset matching degree dividing rule may be as follows in table 1:
table 1 level matching degree dividing table
Of course, in a specific implementation, the preset matching degree dividing rule may also be set in the score interval corresponding to each level, and at this time, the preset matching degree dividing rule may be as shown in table 2 below:
TABLE 2 score match partitioning Table
The embodiment obtains office characteristic information of the target staff; analyzing the office characteristic information and determining work adaptation degree, work adaptation degree and work satisfaction degree; matching the work adaptation degree, the work adaptation degree and the work satisfaction degree with a preset matching degree dividing rule, and determining the matching degree between the target staff and the work post. The working adaptation degree, the working adaptation degree and the working satisfaction degree of the staff are determined by objectively reflecting office characteristic information of daily working characteristics of the staff, and the matching degree between the staff and the working positions is judged by integrating the working adaptation degree, the working adaptation degree and the working satisfaction degree, so that the automatic detection of the matching degree of the staff positions is realized, the efficiency is high, the influence of subjective images is avoided, and the actual requirements are met.
Referring to fig. 4, fig. 4 is a flowchart of a second embodiment of a matching degree detection method according to the present invention.
Based on the above first embodiment, the office feature information includes a software usage duration, a mail reply interval, a number of work utterances, and facial expression images, and at this time, the step S20 of the matching degree detection method of this embodiment includes:
step S201: and determining the work adaptation degree according to the use time of the software.
During the working period of the staff, the time length of the staff for specifically processing the work in the working process can be characterized according to the software use time length of the staff, so that the work adaptation degree of the target staff can be determined by analyzing the software use time length.
When the situation that the target staff can remotely office is considered, and the use time of various office software of the target staff (namely the software use time) is obtained, the time of using various office software at the computer (PC) end of the target staff (namely the time of using the office software at the company office) can be obtained, and the time of using the office Virtual Private Network (VPN) of the target staff (staff remote office) can be further obtained. That is, the duration of each type of office software of the target employee may include the sum of the duration of using each type of office software of the PC side of the target employee and the duration of using the office VPN of the target employee.
In a specific implementation, in a fixed total working time, the duration duty ratio of the processing work may be used to reflect the adapting degree of the staff to the job, that is, the job adapting degree, and at this time, step S201 in this embodiment may include:
acquiring the working time length corresponding to the target staff;
calculating the ratio of the software using time length to the working time length to obtain an adaptation degree score;
and matching the adaptation degree scores with score intervals corresponding to the preset adaptation degree levels, and determining the work adaptation degree corresponding to the target employee.
It should be noted that the working time length may be the total working time length of the target staff, and may be obtained by statistics of attendance records of the target staff in a specific period time (such as statistics of difference values of time stamps of on-duty and off-duty card stamping), or may be directly calculated according to a preset working time system, for example: assuming that the specific cycle time is approximately one month and calculated in the 8-hour system of working days, the working time length corresponding to the target employee=22 (the number of working days in one month) ×8=176.
In actual use, the ratio of the calculated software use duration to the working duration can be used as an adaptation score by comparing the calculated software use duration with the total working duration of the staff, namely, the time duty ratio of the real processing work of the staff in the working process can be determined, and the working adaptation of the staff can be judged.
In a specific implementation, after the fitness score is obtained, a score interval corresponding to each preset fitness level can be determined according to the fitness score, a corresponding target score interval is determined, and the preset fitness level corresponding to the target score interval is used as the corresponding work fitness of the target employee.
Different preset adaptation degree grades can correspond to different score intervals, and the number of the preset adaptation degree grades can be preset by a manager of the matching degree detection equipment according to actual needs.
For example: setting three preset adaptation degree grades of failed, normal and excellent, wherein the corresponding score intervals are respectively less than or equal to 60 percent, more than 60 percent, less than or equal to 80 percent and more than 80 percent.
Further, because different types of posts are different in the types of office software used in the office processing, in order to reasonably judge the work fitness at this time, the step of calculating the ratio of the software use duration to the work duration to obtain the fitness score in this embodiment may include:
determining employee work categories according to the employee attribute information;
determining the type of the working software according to the employee working category;
extracting a working software use duration corresponding to the working software type from the software use duration;
and calculating the ratio of the using time length of the working software to the working time length to obtain an adaptation degree score.
It should be noted that, the employee attribute information may be extracted from the office feature information, and determining the employee work category according to the employee attribute information may be reading a post type in the employee attribute information, and searching for the employee work category to which the post type belongs, so as to determine the employee work category corresponding to the target employee.
In actual use, different employee work categories may correspond to different work software types, and determining the work software type according to the employee work category may be searching for the corresponding work software type in a preset category software mapping table, where the preset category software mapping table may store a mapping relationship between the employee work category and the work software type, and the mapping relationship may be preset by a manager of the matching degree detection device.
In a specific implementation, the extracting the service duration of the working software corresponding to the type of the working software from the service duration of the software may be selecting the service duration of each office software included in the service duration of the software, obtaining the service duration corresponding to the type of the working software, and adding the service durations to obtain the service duration of the working software.
For example: assuming that the employee work category comprises a technical category and a non-technical category, wherein office software corresponding to the technical category is technical production category software, office software corresponding to the non-technical category is office communication category software, and if the employee work category corresponding to the target employee is technical category, the fitness score = long use time/working time of the technical production category software;
and if the staff work category corresponding to the target staff is a non-technical category, the fitness score=the service time length/work time length of the office communication software.
Step S202: and determining the work fitness according to the mail reply interval and the number of the work speaking bars.
It should be noted that, the mail reply interval and the number of the working speaking bars may be used to reflect the adaptation degree of the staff to the post work, and the higher the adaptation degree of the staff to the post work is, the shorter the mail reply interval is, and the higher the number of the working speaking bars is.
In a specific implementation, in order to reasonably determine the work fitness of the target employee, step S202 in this embodiment may include:
acquiring average mail reply intervals and average speaking number of staff in a trial period;
comparing the mail reply interval with the average mail reply interval to determine a first interestingness rating;
comparing the number of the working speech bars with the average number of the speech bars to determine a second interestingness rating;
and determining the work fitness corresponding to the target employee according to the first interestingness rating and the second interestingness rating.
The average mail reply interval may be an average value of mail reply intervals of all the staff members in the trial period, and the average number of speaking bars may be an average value of the number of working speaking bars of all the staff members in the trial period.
In actual use, the staff in the trial period just enters the enterprise, the familiarity degree of the staff in the post work is not too high, the average reply time interval of the staff in the trial period can represent the mail feedback speed in the normal contact post work, and similarly, the average speaking number of the staff in the trial period can represent the speaking number threshold in the normal contact post work, so that the average mail reply interval and the average speaking number of the staff in the trial period can be obtained to judge the adaptability of the target staff.
It can be appreciated that comparing the mail reply interval of the target employee with the average mail reply interval can determine the enthusiasm of the target employee to reply to the working mail, thereby obtaining a first interestingness rating; similarly, comparing the number of the target staff to the average number of the speaking bars can reflect the enthusiasm of the target staff to replying to the working problem, so as to obtain a second interestingness rating.
For example: if the mail reply interval of the target staff is greater than or equal to the average mail reply interval, the time consumption of the target staff for replying to the mail is short, and the high interest level of the target staff in the work is reflected, and at the moment, the first interest level rating can be high; otherwise, if the mail reply interval of the target employee is smaller than the average mail reply interval, the interest degree of the target employee in the work is low, and the first interest degree rating can be low;
similarly, if the number of the work speaking bars of the target staff is greater than or equal to the average number of the speaking bars, the interest level of the target staff in the work is indicated to be high, and the second interest level rating can be high at the moment; otherwise, if the number of work utterances of the target employee is less than the average number of utterances, then the second interestingness rating may be low.
In a specific implementation, if the first interest degree rating and the second interest degree rating are both high, the work fitness corresponding to the target employee is high; if the first interest degree rating and the second interest degree rating are both low, the work fitness corresponding to the target staff is low; and if one of the first interest degree rating and the second interest degree rating is high and the other is low, the work fitness corresponding to the target staff is medium.
Step S203: and determining the work satisfaction according to the facial expression image.
It should be noted that the facial expression may represent a specific satisfaction degree of the employee with respect to the work, so that the satisfaction degree of the work corresponding to the target employee may be determined by performing expression analysis on the facial expression image.
In a specific implementation, the operation satisfaction degree may be determined by performing expression analysis on the facial expression image and counting the occurrence ratio of the satisfied expression, and step S203 in this embodiment may include:
identifying the facial expression image through a preset expression identification model, and determining a satisfactory expression image;
calculating the ratio between the number of the satisfied expression images and the number of the facial expression images to obtain a satisfaction score;
and matching the satisfaction score with a score interval corresponding to each preset satisfaction grade, and determining the work satisfaction of the target staff.
It should be noted that the preset expression recognition model may be a pre-trained model for performing expression recognition, such as a convolutional neural network model or a deep learning model.
In actual use, facial expression images are identified through a preset expression identification model, if the identified expression is a preset satisfactory expression, the images can be used as satisfactory expression images, and all the satisfactory expression images can be obtained by respectively identifying each facial expression image.
The preset satisfaction expression may be preset by a manager of the matching degree detection device according to actual needs, for example: if staff is satisfied with the job, they will typically expose smile expressions, at this time, the manager of the matching degree detection device may set the smile expressions as preset satisfied expressions.
In a specific implementation, the ratio of the satisfied expression to all the collected images can characterize the satisfaction degree of staff on the post work, so the ratio between the number of the satisfied expression images and the number of the facial expression images can be calculated and used as a satisfaction degree score.
In actual use, after the satisfaction score is obtained, the satisfaction score can be matched with the score interval corresponding to each preset satisfaction grade, a target score interval is determined, and then the preset satisfaction grade corresponding to the target score interval is used as the work satisfaction of the target staff.
Different preset satisfaction levels can correspond to different score intervals, and the number of the preset satisfaction levels can be preset by a manager of the matching degree detection equipment according to actual needs.
For example: and setting a high satisfaction level and a low satisfaction level, wherein the corresponding score intervals can be respectively more than or equal to 0.6 and less than 0.6.
Further, in order to ensure the recognition accuracy of the expression recognition, the step of recognizing the facial expression image through the preset expression recognition model and determining the satisfactory expression image according to the embodiment may further include:
screening face pictures in a face picture library to obtain face non-shielding pictures;
performing specification adjustment on the face non-shielding picture, and constructing a model training set according to the adjusted picture;
training the initial expression recognition model based on the model training set to obtain a preset expression recognition model.
It should be noted that the face picture library may be a database for storing face pictures containing different expressions, where the face pictures contained in the face picture library may be obtained by crawling from a network through an image crawler technology.
It can be understood that, because the face pictures in the face picture library are obtained by crawling from the network, part defects (such as a part of a face is blocked or a part of the face is not clear) inevitably exist, and in order to ensure the expression recognition accuracy of the training preset expression recognition model, the pictures with the defects need to be removed, so that the face pictures in the face picture library can be screened, and the rest pictures can be used as face non-blocking pictures.
In practical use, as the face pictures are obtained by crawling from the network, the picture format and the resolution of each face picture may have differences, so that the data are unfavorable for model training, in order to improve the model training effect, the specification of the face non-shielding picture can be adjusted, and the corresponding expression labels are set for the adjusted picture, so that a plurality of training samples are obtained, and the obtained plurality of training samples are aggregated, so that a model training set can be obtained.
The specification adjustment of the face non-shielding picture may include adjustment of a picture format and resolution of the picture, for example: and uniformly adjusting the picture formats of all the face non-shielding pictures to be JPG formats, and uniformly scaling the resolution to 227 x 27.
In a specific implementation, the initial expression recognition model may be a convolutional network model built in advance, the initial expression recognition model is trained through a model training set, and the model can be used as a preset expression recognition model when the initial expression recognition model is trained to be converged.
In practical use, in order to ensure the expression recognition accuracy, when the initial expression recognition model is trained to be converged, the initial expression recognition model can be further subjected to accuracy verification through a verification set, when the accuracy reaches a preset threshold (such as 95%), the initial expression recognition model is used as the preset expression recognition model, if the accuracy does not reach the preset threshold, the initial expression recognition model can be further trained, and if necessary, a model training set can be further expanded. The construction mode of the verification set is consistent with that of the model training set, and will not be described herein.
The working adaptation degree is determined according to the software use time length; determining the work fitness according to the mail reply interval and the number of the work speaking bars; and determining the work satisfaction according to the facial expression image. Because the different information included in the office characteristic information is subjected to statistical analysis in different modes, the work adaptation degree and the work satisfaction degree for representing the specific work situation of the staff can be accurately calculated, and finally, the matching degree between the staff and the work post can be accurately determined.
In addition, the embodiment of the invention also provides a storage medium, wherein the storage medium stores a matching degree detection program, and the matching degree detection program realizes the steps of the matching degree detection method when being executed by a processor.
Referring to fig. 5, fig. 5 is a block diagram illustrating a first embodiment of a matching degree detecting device according to the present invention.
As shown in fig. 5, the matching degree detection device provided by the embodiment of the present invention includes:
an acquisition module 10, configured to acquire office feature information of a target employee;
the analysis module 20 is configured to analyze the office feature information and determine a work adaptation degree, and a work satisfaction degree;
and the detection module 30 is configured to match the work adaptability, and the work satisfaction with a preset matching degree dividing rule, and determine a matching degree between the target employee and the work post.
The embodiment obtains office characteristic information of the target staff; analyzing the office characteristic information and determining work adaptation degree, work adaptation degree and work satisfaction degree; matching the work adaptation degree, the work adaptation degree and the work satisfaction degree with a preset matching degree dividing rule, and determining the matching degree between the target staff and the work post. The working adaptation degree, the working adaptation degree and the working satisfaction degree of the staff are determined by objectively reflecting office characteristic information of daily working characteristics of the staff, and the matching degree between the staff and the working positions is judged by integrating the working adaptation degree, the working adaptation degree and the working satisfaction degree, so that the automatic detection of the matching degree of the staff positions is realized, the efficiency is high, the influence of subjective images is avoided, and the actual requirements are met.
Further, the office characteristic information comprises software use time, mail reply intervals, the number of work speaking bars and facial expression images;
the analysis module 20 is further configured to determine a work fitness according to the software usage time; determining the work fitness according to the mail reply interval and the number of the work speaking bars; and determining the work satisfaction according to the facial expression image.
Further, the analysis module 20 is further configured to obtain a working time length corresponding to the target employee; calculating the ratio of the software using time length to the working time length to obtain an adaptation degree score; and matching the adaptation degree scores with score intervals corresponding to the preset adaptation degree levels, and determining the work adaptation degree corresponding to the target employee.
Further, the office characteristic information further comprises employee attribute information;
the analysis module 20 is further configured to determine an employee work category according to the employee attribute information; determining the type of the working software according to the employee working category; extracting a working software use duration corresponding to the working software type from the software use duration; and calculating the ratio of the using time length of the working software to the working time length to obtain an adaptation degree score.
Further, the analysis module 20 is further configured to obtain an average mail reply interval and an average number of utterances of the staff in the trial period; comparing the mail reply interval with the average mail reply interval to determine a first interestingness rating; comparing the number of the working speech bars with the average number of the speech bars to determine a second interestingness rating; and determining the work fitness corresponding to the target employee according to the first interestingness rating and the second interestingness rating.
Further, the analysis module 20 is further configured to identify the facial expression image through a preset expression identification model, and determine a satisfactory expression image; calculating the ratio between the number of the satisfied expression images and the number of the facial expression images to obtain a satisfaction score; and matching the satisfaction score with a score interval corresponding to each preset satisfaction grade, and determining the work satisfaction of the target staff.
Further, the analysis module 20 is further configured to screen face pictures in the face picture library to obtain a face non-shielding picture; performing specification adjustment on the face non-shielding picture, and constructing a model training set according to the adjusted picture; training the initial expression recognition model based on the model training set to obtain a preset expression recognition model.
It should be understood that the foregoing is illustrative only and is not limiting, and that in specific applications, those skilled in the art may set the invention as desired, and the invention is not limited thereto.
It should be noted that the above-described working procedure is merely illustrative, and does not limit the scope of the present invention, and in practical application, a person skilled in the art may select part or all of them according to actual needs to achieve the purpose of the embodiment, which is not limited herein.
In addition, technical details not described in detail in the present embodiment may refer to the matching degree detection method provided in any embodiment of the present invention, which is not described herein.
Furthermore, 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 one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. Read Only Memory)/RAM, magnetic disk, optical disk) and including several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. The matching degree detection method is characterized by comprising the following steps of:
acquiring office characteristic information of a target employee;
analyzing the office characteristic information to determine work adaptation degree, work adaptation degree and work satisfaction degree;
and matching the work adaptation degree, the work adaptation degree and the work satisfaction degree with a preset matching degree dividing rule, and determining the matching degree between the target staff and the work post.
2. The matching degree detection method as set forth in claim 1, wherein the office characteristic information includes a software use time period, a mail reply interval, a number of work speaking pieces, and a facial expression image;
the step of analyzing the office characteristic information and determining the work adaptation degree, the work adaptation degree and the work satisfaction degree comprises the following steps:
determining the work adaptation degree according to the use time of the software;
determining the work fitness according to the mail reply interval and the number of the work speaking bars;
and determining the work satisfaction according to the facial expression image.
3. The matching degree detection method as claimed in claim 2, wherein the step of determining the work matching degree according to the software use time length comprises:
acquiring the working time length corresponding to the target staff;
calculating the ratio of the software using time length to the working time length to obtain an adaptation degree score;
and matching the adaptation degree scores with score intervals corresponding to the preset adaptation degree levels, and determining the work adaptation degree corresponding to the target employee.
4. The matching degree detection method as claimed in claim 3, wherein the office feature information further includes employee attribute information;
the step of calculating the ratio of the software using time length to the working time length to obtain the fitness score comprises the following steps:
determining employee work categories according to the employee attribute information;
determining the type of the working software according to the employee working category;
extracting a working software use duration corresponding to the working software type from the software use duration;
and calculating the ratio of the using time length of the working software to the working time length to obtain an adaptation degree score.
5. The matching degree detection method according to claim 2, wherein the step of determining the work fitness based on the mail reply interval and the number of work utterances includes:
acquiring average mail reply intervals and average speaking number of staff in a trial period;
comparing the mail reply interval with the average mail reply interval to determine a first interestingness rating;
comparing the number of the working speech bars with the average number of the speech bars to determine a second interestingness rating;
and determining the work fitness corresponding to the target employee according to the first interestingness rating and the second interestingness rating.
6. The matching degree detection method as claimed in claim 2, wherein the step of determining the work satisfaction degree from the facial expression image comprises:
identifying the facial expression image through a preset expression identification model, and determining a satisfactory expression image;
calculating the ratio between the number of the satisfied expression images and the number of the facial expression images to obtain a satisfaction score;
and matching the satisfaction score with a score interval corresponding to each preset satisfaction grade, and determining the work satisfaction of the target staff.
7. The matching degree detection method as set forth in claim 6, wherein the step of recognizing the facial expression image by a preset expression recognition model and determining a satisfactory expression image is preceded by the step of:
screening face pictures in a face picture library to obtain face non-shielding pictures;
performing specification adjustment on the face non-shielding picture, and constructing a model training set according to the adjusted picture;
training the initial expression recognition model based on the model training set to obtain a preset expression recognition model.
8. The matching degree detection device is characterized by comprising the following modules:
the acquisition module is used for acquiring office characteristic information of the target staff;
the analysis module is used for analyzing the office characteristic information and determining work adaptation degree, work adaptation degree and work satisfaction degree;
the detection module is used for matching the work adaptation degree, the work adaptation degree and the work satisfaction degree with a preset matching degree dividing rule and determining the matching degree between the target staff and the work post.
9. A matching degree detection device, characterized by comprising: a processor, a memory and a matching degree detection program stored on the memory and executable on the processor, which when executed by the processor implements the steps of the matching degree detection method according to any one of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a matching degree detection program which, when executed, implements the steps of the matching degree detection method according to any one of claims 1 to 7.
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