CN109214446A - Potentiality good performance personnel kind identification method, system, terminal and computer readable storage medium - Google Patents
Potentiality good performance personnel kind identification method, system, terminal and computer readable storage medium Download PDFInfo
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Abstract
The present invention provides a kind of potentiality good performance personnel kind identification method, system, terminal and computer readable storage medium.Potentiality good performance personnel's kind identification method includes: the sample data for obtaining multiple good performance personnel；It is established according to the sample data of multiple good performance personnel and trains to obtain a potentiality good performance identification model；The sample data of non-good performance personnel is input to the potentiality good performance identification model, the probability value that the non-good performance personnel grow into each good performance type is calculated；Judge whether each probability value is greater than a predetermined probabilities；And a most probable value is chosen from the probability value for being greater than predetermined probabilities, and develop good performance type for the corresponding good performance type of the most probable value as the potentiality of the non-good performance personnel.Potentiality good performance identification model is established and trained to obtain the present invention is based on neural network and good performance personnel's sample data, and the good performance type of non-good performance personnel growth is accurately positioned out according to the model, to be oriented culture.
Description
Technical field
The present invention relates to data processing field more particularly to a kind of potentiality good performance personnel kind identification methods, system, terminal
And computer readable storage medium.
Background technique
This part intends to provides background for the embodiments of the present invention stated in claims and specific embodiment
Or context.Description herein recognizes it is the prior art not because not being included in this section.
Big data is a kind of feature of the internet development to the stage now, using cloud computing as the lining of the technological innovation of representative
Under support, the data that is difficult to collect originally and use start easy to collect and are utilized.The prior art is for non-good performance people
The potential developing direction of member, usually umpire subjectively judge and assert according to the daily performance of non-good performance personnel, quasi-
True property is not high, lacks a kind of science, effectively identification.
Based on this, needing to propose a kind of potential realized based on multiple good performance personnel sample informations and be directed to non-good performance personnel
The appraisal procedure of developing direction.
Summary of the invention
In view of above-mentioned, the present invention provides a kind of potentiality good performance personnel kind identification method, system, terminal and computer-readable
Storage medium, the good performance type of non-good performance personnel growth is accurately positioned out.
One embodiment of the application provides a kind of potentiality good performance personnel's kind identification method, which comprises
The sample data of multiple good performance personnel is obtained, the plurality of good performance personnel belong to a variety of good performance types, often
The one good performance personnel belong to a kind of good performance type；
It is established according to the sample data of multiple good performance personnel and trains to obtain a potentiality good performance identification model；
The sample data of non-good performance personnel is input to the potentiality good performance identification model, the non-good performance people is calculated
Member grows into the probability value of each good performance type；
Judge whether each probability value is greater than a predetermined probabilities；And
It is described pre- from being greater than when the non-good performance personnel include one or more probability values for being greater than the predetermined probabilities
If choosing a most probable value in the probability value of probability, and using the corresponding good performance type of the most probable value as the non-achievement
The potentiality of excellent personnel develop good performance type.
Preferably, the sample data of the good performance personnel and the sample data of the non-good performance personnel include multiple dimensions
Information, each dimensional information is corresponding with one-dimensional angle value, and the good performance personnel and the non-good performance personnel are having the same
Dimensional information.
Preferably, the sample data according to multiple good performance personnel is established and trains to obtain potentiality good performance identification
The step of model includes:
A neural network model is established, the neural network model includes input layer, multiple hidden layers and output layer；And
The neural network model is trained to obtain the potentiality using the sample data of multiple good performance personnel
Good performance identification model.
Preferably, the sample data according to multiple good performance personnel is established and trains to obtain potentiality good performance identification
The step of model includes:
The sample data of multiple good performance personnel is divided into training set and verifying collection；
A neural network model is established, and the neural network model is trained using the training set；
The neural network model after training is verified using verifying collection, and is counted according to each verification result
To a model prediction accuracy rate；
Judge whether the model prediction accuracy rate is less than preset threshold；
If the model prediction accuracy rate is not less than the preset threshold, the neural network model that training is completed is made
For the potentiality good performance identification model.
Preferably, it is described judge the step of whether the model prediction accuracy rate is less than preset threshold after further include:
If the model prediction accuracy rate is less than the preset threshold, the parameter of the neural network model, and benefit are adjusted
Neural network model adjusted is trained again with the training set；
The neural network model of re -training is verified using verifying collection, and again according to each verification result
Statistics obtains a model prediction accuracy rate, and judges whether the model prediction accuracy rate counted again is less than preset threshold；
If the model prediction accuracy rate counted again is not less than the preset threshold, the re -training is obtained
The neural network model arrived is as the potentiality good performance identification model；And
If the model prediction accuracy rate counted again is less than the preset threshold, repeat the above steps until logical
It crosses the model prediction accuracy rate that the verifying collection verifying obtains and is not less than the preset threshold；
Wherein, the parameter of the neural network model includes the neuron number of total number of plies, each layer.
Preferably, it is described judge the step of whether each probability value is greater than a predetermined probabilities after further include:
When the probability value that the non-good performance personnel grow into each good performance type is respectively less than the predetermined probabilities, institute is determined
Non- good performance personnel are stated as no potentiality personnel.
Preferably, described to develop the corresponding good performance type of the most probable value as the potentiality of the non-good performance personnel
The step of good performance type includes:
When there are multiple most probable values, the corresponding good performance type of one most probable value of random selection is as the non-achievement
The potentiality of excellent personnel develop good performance type；Or
When there are multiple most probable values, a good performance type corresponding with the matched most probable value of preset need is selected
Potentiality as the non-good performance personnel develop good performance type.
One embodiment of the application provides a kind of potentiality good performance personnel's identification system, the system comprises:
Module is obtained, for obtaining the sample data of multiple good performance personnel, the plurality of good performance personnel belong to more
Kind good performance type, each good performance personnel belong to a kind of good performance type；
Module is established, obtains potentiality good performance knowledge for establishing and training according to the sample data of multiple good performance personnel
Other model；
Computing module is calculated for the sample data of non-good performance personnel to be input to the potentiality good performance identification model
The non-good performance personnel grow into the probability value of each good performance type；
Judgment module, for judging whether each probability value is greater than a predetermined probabilities；And
Module is chosen, for including one or more probability values for being greater than the predetermined probabilities in the non-good performance personnel
When, a most probable value is chosen from the probability value for being greater than the predetermined probabilities, and by the corresponding good performance of the most probable value
Type develops good performance type as the potentiality of the non-good performance personnel.
One embodiment of the application provides a kind of terminal, and the terminal includes processor and memory, on the memory
Several computer programs are stored with, are realized when the processor is for executing the computer program stored in memory such as front institute
The step of potentiality good performance personnel's kind identification method stated.
One embodiment of the application provides a kind of computer readable storage medium, is stored thereon with computer program, described
The step of potentiality good performance personnel kind identification method as elucidated before is realized when computer program is executed by processor.
Above-mentioned potentiality good performance personnel kind identification method, system, terminal and computer readable storage medium are based on nerve net
The sample data of network and good performance personnel are identified to establish and train to obtain potentiality good performance identification model using the model realization
The most potential good performance type of non-good performance personnel growth, training sample is balanced and identification accuracy is high, and then may be implemented pair
Potentiality good performance personnel are oriented culture, promote personnel education effect.
Detailed description of the invention
It, below will be to required in embodiment description in order to illustrate more clearly of the technical solution of embodiment of the present invention
The attached drawing used is briefly described, it should be apparent that, the accompanying drawings in the following description is some embodiments of the present invention, for
For those of ordinary skill in the art, without creative efforts, it can also be obtained according to these attached drawings other
Attached drawing.
Fig. 1 is the step flow chart of potentiality good performance personnel's kind identification method in one embodiment of the invention.
Fig. 2 is the step flow chart of potentiality good performance personnel's kind identification method in another embodiment of the present invention.
Fig. 3 is the functional block diagram of potentiality good performance personnel's identification system in one embodiment of the invention.
Fig. 4 is computer schematic device in one embodiment of the invention.
Specific embodiment
To better understand the objects, features and advantages of the present invention, with reference to the accompanying drawing and specific real
Applying mode, the present invention will be described in detail.It should be noted that in the absence of conflict, presently filed embodiment and reality
The feature applied in mode can be combined with each other.
In the following description, numerous specific details are set forth in order to facilitate a full understanding of the present invention, described embodiment
Only some embodiments of the invention, rather than whole embodiments.Based on the embodiment in the present invention, this field
Those of ordinary skill's every other embodiment obtained without making creative work, belongs to guarantor of the present invention
The range of shield.
Unless otherwise defined, all technical and scientific terms used herein and belong to technical field of the invention
The normally understood meaning of technical staff is identical.Term as used herein in the specification of the present invention is intended merely to description tool
The purpose of the embodiment of body, it is not intended that in the limitation present invention.
Preferably, potentiality good performance personnel's kind identification method of the invention is applied in one or more computer installation
In.The computer installation is that one kind can be automatic to carry out numerical value calculating and/or information according to the instruction for being previously set or storing
The equipment of processing, hardware include but is not limited to microprocessor, specific integrated circuit (Application Specific
Integrated Circuit, ASIC), programmable gate array (Field-Programmable Gate Array, FPGA), number
Word processing device (Digital Signal Processor, DSP), embedded device etc..
The computer installation can be the calculating such as desktop PC, laptop, tablet computer, server and set
It is standby.The computer installation can carry out people by modes such as keyboard, mouse, remote controler, touch tablet or voice-operated devices with user
Machine interaction.
Embodiment one:
Fig. 1 is the step flow chart of potentiality good performance personnel kind identification method preferred embodiment of the present invention.According to different
Demand, the sequence of step can change in the flow chart, and certain steps can be omitted.
As shown in fig.1, potentiality good performance personnel's kind identification method specifically includes following steps.
Step S11, the sample data of multiple good performance personnel is obtained, the plurality of good performance personnel belong to a variety of good performances
Type, each good performance personnel belong to a kind of good performance type.
In one embodiment, good performance personnel's sample database can be connected to by access network, and then to obtain
State the sample data of the good performance personnel of good performance personnel's sample database storage.The good performance personnel sample database can pass through big data mode
The sample data of multiple good performance personnel is collected, the sample data of multiple good performance personnel of the artificial typing of reception can also be passed through.
In one embodiment, multiple good performance personnel belong to a variety of good performance types, but each good performance personnel
Only belong to a good performance type, i.e., each good performance personnel cannot belong to a variety of good performance types simultaneously.
For example, multiple good performance type may include three kinds of good performance types, respectively resource-type, learning-oriented and diligent
Type.Resource-type can refer to the personnel that professional ability is strong, ability to work is strong, learning-oriented to refer to that learning ability is strong, energy of growing up
The strong personnel of power, diligent type can refer to the personnel of learning time long, daily longevity of service.Each good performance personnel are come
It says, one of three kinds of good performance types can only be classified into.It is below that company A employee is with good performance personnel and non-good performance personnel
Example is illustrated.
For example, company A includes 1000 employees, which can be divided into good performance personnel and non-good performance
Personnel's two types, each employee can only be divided into good performance personnel or be divided into non-good performance personnel, good performance personnel and non-
The division of good performance personnel can be divided according to the corresponding division rule of multiple good performance types, such as learning-oriented good performance class
Type can determine whether a certain employee is learning-oriented good performance personnel according to the learning ability of each employee, business growth ability, then
Such as corresponding resource type good performance type, it can determine whether a certain employee belongs to resource-type according to each employee professional ability
Good performance personnel.The type of good performance personnel includes resource-type, learning-oriented and diligent type.Such as the daily table according to each employee
Existing, wherein 200 employees are divided into good performance personnel, remaining 800 employee is divided into non-good performance personnel.In 200 good performances
In personnel, each good performance personnel belong to a kind of good performance type, and resource-type good performance personnel include 80 people, and learning-oriented good performance personnel include
50 people, diligent type good performance personnel include 70 people.
In one embodiment, good performance personnel and non-good performance personnel all have multiple dimensional informations, each dimensional information pair
There should be one-dimensional angle value, the dimension values are for quantifying corresponding dimensional information.For example multiple dimensional information includes behavior
Situation, operation expanding situation, consumption, hobby, training situation, attendance, educational background etc. are enlivened in track, APP.It is each
Dimensional information can also be segmented further, for example action trail may further include scope of activities, the public place of entertainment frequency
Deng educational background may further include academic grade, graduated school comprehensive strength, learned profession etc..Assuming that wherein dimension information
For the age, then corresponding dimension values are corresponding age size, if the age of good performance personnel a1 is 27, the age of good performance personnel a2
It is 30, then the age dimension values of good performance personnel a1 are 27, and the age dimension values of good performance personnel a2 are 30；If dimension information is industry
Business spread scenarios, the dimension values of the operation expanding situation can be scored according to the service conditions of employee (can be pre- according to one
If standards of grading) obtain one-dimensional angle value.In step s 11, the sample data for obtaining multiple good performance personnel obtains multiple
The each dimensional information and the corresponding dimension values of each dimensional information of good performance personnel.
In one embodiment, each good performance personnel are identical as the dimensional information of each non-good performance personnel, even each achievement
Excellent personnel include 10 dimensional informations, are that action trail, APP enliven situation, operation expanding situation, consumption, interest respectively
Hobby, training situation, attendance, educational background, KPI scoring, length of service；Then each non-good performance personnel equally include 10 dimensions
Information is that action trail, APP enliven situation, operation expanding situation, consumption, hobby, training situation, turn out for work respectively
Situation, educational background, KPI scoring, length of service.
Step S12, it is established according to the sample data of multiple good performance personnel and trains to obtain potentiality good performance identification mould
Type.
In one embodiment, the potentiality good performance identification model can be based on neural network model and multiple achievements
The sample data of excellent personnel trains the disaggregated model come.Specifically, a neural network model, the nerve net can first be established
Network model includes input layer, multiple hidden layers and output layer, recycles the sample data of multiple good performance personnel to the mind
It is trained to obtain the potentiality good performance identification model through network model.
The input layer of the neural network model is used to receive the sample data of multiple good performance personnel, each hidden layer
Including multiple nodes (neuron), each node in each hidden layer is configured to the adjacent lower in the model
The output execution of at least one node linearly or nonlinearly converts.Wherein, the input of the node of upper layer hidden layer can be based on phase
The output of a node or several nodes in adjacent lower layer, each hidden layer have corresponding weight, which is based on training
What sample data obtained.It, can be by carrying out model using there is the learning process of supervision when being trained to the model
Training, obtains the initial weight of each hidden layer.It can be come by back-propagation (Back propagation, BP) algorithm to each
The weight of hidden layer is adjusted, and the output layer of the neural network model is for receiving from the defeated of the last layer hidden layer
Signal out.
In one embodiment, the step S12 can be specifically included:
A. the sample data of multiple good performance personnel is divided into training set and verifying collects；
B. a neural network model is established, and the neural network model is trained using the training set；
C. the neural network model after training is verified using verifying collection, and is counted according to each verification result
Obtain a model prediction accuracy rate；
D. judge whether the model prediction accuracy rate is less than preset threshold；
If e. the model prediction accuracy rate is not less than the preset threshold, the neural network model that training is completed
As the potentiality good performance identification model.
If f. the model prediction accuracy rate is less than the preset threshold, the parameter of the neural network model is adjusted, and
Neural network model adjusted is trained again using the training set, until the model prediction that verifying collection verifying obtains
Accuracy rate is not less than the preset threshold, wherein the parameter of the neural network model includes the neuron of total number of plies, each layer
Number etc..
In one embodiment, the parameter of the adjustment neural network model can be the adjustment neural network mould
The total number of plies and/or each layer of neuron number of type.The training set is for being trained neural network model, the verifying
Collection is for verifying the neural network model after training.
In one embodiment, neural network model can be trained first with the training set to obtain an intermediate die
Type, then the good performance demographic data that the verifying is concentrated is input to progress good performance classification of type verifying in the mid-module, and
It can be counted to obtain a model prediction accuracy rate according to each verification result；The mid-module predictablity rate is judged again whether
Less than preset threshold；If the mid-module predictablity rate is not less than the preset threshold, show this mid-module classification effect
Fruit is preferable, satisfies the use demand, can be directly using the mid-module as the potentiality good performance identification model；If the model
When predictablity rate is less than the preset threshold, shows that this mid-module classifying quality is bad, improved, it at this time can be with
The parameter of the neural network model is adjusted, and neural network model adjusted is trained again using the training set
A new mid-module is obtained, is then collected again using the verifying and is verified to obtain one newly to the mid-module retrieved
Model prediction accuracy rate, then judge whether the new model prediction accuracy rate is less than preset threshold, if the new model prediction
Accuracy rate is not less than the preset threshold, shows that the mid-module classifying quality retrieved is preferable, satisfies the use demand, can be with
Using the mid-module retrieved as the potentiality good performance identification model；If the new model prediction accuracy rate still less than
The preset threshold needs to repeat the above steps again until collecting obtained model prediction accuracy rate not less than described by verifying
Preset threshold.
In one embodiment, the preset threshold can be set according to actual use demand, such as described default
Threshold value is set as 95%, i.e. model prediction accuracy rate need to be not less than 95%.
Step S13, by the sample data of non-good performance personnel be input to the potentiality good performance identification model be calculated it is described
Non- good performance personnel grow into the probability value of each good performance type.
In one embodiment, by the training and verifying of step S12, the potentiality good performance identification model be may be implemented pair
The potentiality good performance type of non-good performance personnel identifies, at this time can be using the sample data of non-good performance personnel as the potentiality achievement
The output of the potentiality good performance identification model is considered as non-good performance personnel and grows into each good performance type by the input of excellent identification model
Probability.
S14, judge whether each probability value is greater than a predetermined probabilities.
In one embodiment, each probability value represents the non-good performance personnel and grows into each good performance type
The probability value of good performance personnel may be implemented to judge the non-good performance by judging whether each probability value is greater than a predetermined probabilities
Personnel grow into the probability of the good performance personnel of each good performance type.The predetermined probabilities equally can according to practical application request into
Row setting, such as the predetermined probabilities are set as 60%, then described to judge whether each probability value is greater than a predetermined probabilities and is
Judge whether each probability value is greater than 60%.
S15, when the non-good performance personnel include one or more probability value for being greater than the predetermined probabilities, from being greater than institute
It states and chooses a most probable value in the probability value of predetermined probabilities, and using the corresponding good performance type of the most probable value as described in
The potentiality of non-good performance personnel develop good performance type.
In one embodiment, when non-good performance personnel include the probability value higher than predetermined probabilities, show the non-good performance people
Member can be considered as potentiality good performance personnel.For example, the predetermined probabilities of a good performance type are set as 60%, when the non-good performance personnel grow into
When the probability of the good performance type is not less than 60%, determine that the non-good performance personnel are the potentiality good performance that can grow into the good performance type
Personnel determine that the non-good performance personnel are not the achievements when the non-good performance personnel grow into the probability of the good performance type less than 60%
The potentiality good performance personnel of excellent type.
In one embodiment, some non-good performance personnel may be the potentiality good performance personnel of a variety of good performance types simultaneously, i.e.,
Non- good performance personnel include multiple probability values greater than the predetermined probabilities, are selected from the probability value for being greater than the predetermined probabilities at this time
A most probable value is taken, and using the corresponding good performance type of the most probable value as the most potential development of the non-good performance personnel
Good performance type.For example, predetermined probabilities 60%, by the potentiality good performance identification model obtain non-good performance personnel A1 at
The probability of a length of resource-type good performance personnel is 0.8, and the probability for growing into learning-oriented good performance personnel is 0.7, grows into diligent type achievement
The probability of excellent personnel is 0.9, since the probability value for growing into diligent type good performance personnel is maximum, then diligent type is determined as the non-achievement
The potentiality of excellent personnel A1 develop good performance type.
In one embodiment, when there are multiple most probable values, it is corresponding that a most probable value can be randomly selected
Good performance type develops good performance type as the potentiality of the non-good performance personnel.For example, being obtained by the potentiality good performance identification model
The probability that resource-type good performance personnel are grown into non-good performance personnel A2 is 0.8, and the probability for growing into learning-oriented good performance personnel is
0.7, the probability for growing into diligent type good performance personnel is 0.8, due to growing into diligent type good performance personnel and growing into resource-type achievement
The probability value of excellent personnel is 0.8, then can choose diligent type being determined as that the potentiality of the non-good performance personnel A2 develop good performance class
Resource-type can also be determined as that the potentiality of the non-good performance personnel A2 develop good performance type by type.
In one embodiment, when there are multiple most probable values, it is also an option that one is matched most with preset need
The corresponding good performance type of greatest develops good performance type as the potentiality of the non-good performance personnel.For example, the preset need
It can be company's currently critical personnel's type demand, if company is maximum for resource-type good performance personnel demand, to learning-oriented good performance
Personnel demand is taken second place, minimum to diligent type good performance personnel demand.If obtaining non-achievement by the potentiality good performance identification model at this time
The probability that excellent personnel A2 grows into resource-type good performance personnel is 0.8, and the probability for growing into learning-oriented good performance personnel is 0.7, growth
Probability for diligent type good performance personnel is 0.8, due to growing into diligent type good performance personnel and growing into resource-type good performance personnel's
Probability value is 0.8 and company is maximum for resource-type good performance personnel demand, then resource-type can be determined as to the non-good performance people
The potentiality of member A2 develop good performance type.
Please refer to Fig. 2, compared with potentiality good performance personnel's kind identification method shown in fig. 1, Fig. 2 shows the potentiality achievement at place
Excellent personnel's kind identification method further includes step S16.
Step S16, when the probability value that the non-good performance personnel grow into each good performance type is respectively less than the predetermined probabilities
When, determine that the non-good performance personnel are no potentiality personnel.
In one embodiment, if the probability value that a non-good performance personnel grow into each good performance type is respectively less than predetermined probabilities
When, then it can be determined that the non-good performance personnel for no potentiality personnel.For example predetermined probabilities are 0.6, identify mould by potentiality good performance
Type obtains non-good performance personnel A3 and grows into the probability of resource-type good performance personnel to be 0.5, grows into the probability of learning-oriented good performance personnel
It is 0.48, the probability for growing into diligent type good performance personnel is 0.55, then the non-good performance personnel A3 is judged as no potentiality personnel.
Embodiment two:
Fig. 3 is the functional block diagram of potentiality good performance personnel identification system preferred embodiment of the present invention.
As shown in fig.2, the potentiality good performance personnel identification system 10 may include obtaining module 101, establishing mould
Block 102, judgment module 104, chooses module 105 at computing module 103.
The sample data that module 101 is used to obtain multiple good performance personnel is obtained, the plurality of good performance personnel belong to
A variety of good performance types, each good performance personnel belong to a kind of good performance type.
In one embodiment, the acquisition module 101 can be connected to good performance personnel's sample by access network
Library, and then to obtain the sample data of the good performance personnel of the good performance personnel sample database storage.The good performance personnel sample database can
The sample data of multiple good performance personnel is collected in a manner of through big data, can also pass through multiple good performance people of the artificial typing of reception
The sample data of member.
In one embodiment, multiple good performance personnel belong to a variety of good performance types, but each good performance personnel
Only belong to a good performance type, i.e., each good performance personnel cannot belong to a variety of good performance types simultaneously.
For example, multiple good performance type may include three kinds of good performance types, respectively resource-type, learning-oriented and diligent
Type.Resource-type can refer to the personnel that professional ability is strong, ability to work is strong, learning-oriented to refer to that learning ability is strong, energy of growing up
The strong personnel of power, diligent type can refer to the personnel of learning time long, daily longevity of service.Each good performance personnel are come
It says, one of three kinds of good performance types can only be classified into.It is below that company A employee is with good performance personnel and non-good performance personnel
Example is illustrated.
For example, company A includes 1000 employees, which can be divided into good performance personnel and non-good performance
Personnel's two types, each employee can only be divided into good performance personnel or be divided into non-good performance personnel, good performance personnel and non-
The division of good performance personnel can be divided according to the corresponding division rule of multiple good performance types, such as learning-oriented good performance class
Type can determine whether a certain employee is learning-oriented good performance personnel according to the learning ability of each employee, business growth ability, then
Such as corresponding resource type good performance type, it can determine whether a certain employee belongs to resource-type according to each employee professional ability
Good performance personnel.The type of good performance personnel includes resource-type, learning-oriented and diligent type.Such as the daily table according to each employee
Existing, wherein 200 employees are divided into good performance personnel, remaining 800 employee is divided into non-good performance personnel.In 200 good performances
In personnel, each good performance personnel belong to a kind of good performance type, and resource-type good performance personnel include 80 people, and learning-oriented good performance personnel include
50 people, diligent type good performance personnel include 70 people.
In one embodiment, good performance personnel and non-good performance personnel all have multiple dimensional informations, each dimensional information pair
There should be one-dimensional angle value, the dimension values are for quantifying corresponding dimensional information.For example multiple dimensional information includes behavior
Situation, operation expanding situation, consumption, hobby, training situation, attendance, educational background etc. are enlivened in track, APP.It is each
Dimensional information can also be segmented further, for example action trail may further include scope of activities, the public place of entertainment frequency
Deng educational background may further include academic grade, graduated school comprehensive strength, learned profession etc..Assuming that wherein dimension information
For the age, then corresponding dimension values are corresponding age size, if the age of good performance personnel a1 is 27, the age of good performance personnel a2
It is 30, then the age dimension values of good performance personnel a1 are 27, and the age dimension values of good performance personnel a2 are 30；If dimension information is industry
Business spread scenarios, the dimension values of the operation expanding situation can be scored according to the service conditions of employee (can be pre- according to one
If standards of grading) obtain one-dimensional angle value.In step s 11, the sample data for obtaining multiple good performance personnel obtains multiple
The each dimensional information and the corresponding dimension values of each dimensional information of good performance personnel.
In one embodiment, each good performance personnel are identical as the dimensional information of each non-good performance personnel, even each achievement
Excellent personnel include 10 dimensional informations, are that action trail, APP enliven situation, operation expanding situation, consumption, interest respectively
Hobby, training situation, attendance, educational background, KPI scoring, length of service；Then each non-good performance personnel equally include 10 dimensions
Information is that action trail, APP enliven situation, operation expanding situation, consumption, hobby, training situation, turn out for work respectively
Situation, educational background, KPI scoring, length of service.
Module 102 is established for establishing according to the sample data of multiple good performance personnel and training to obtain a potentiality good performance
Identification model.
In one embodiment, the potentiality good performance identification model can be based on neural network model and multiple achievements
The sample data of excellent personnel trains the disaggregated model come.Specifically, the module 102 of establishing can first establish a neural network
Model, the neural network model include input layer, multiple hidden layers and output layer, recycle the sample of multiple good performance personnel
Notebook data is trained the neural network model to obtain the potentiality good performance identification model.
The input layer of the neural network model is used to receive the sample data of multiple good performance personnel, each hidden layer
Including multiple nodes (neuron), each node in each hidden layer is configured to the adjacent lower in the model
The output execution of at least one node linearly or nonlinearly converts.Wherein, the input of the node of upper layer hidden layer can be based on phase
The output of a node or several nodes in adjacent lower layer, each hidden layer have corresponding weight, which is based on training
What sample data obtained.It, can be by carrying out model using there is the learning process of supervision when being trained to the model
Training, obtains the initial weight of each hidden layer.It can be come by back-propagation (Back propagation, BP) algorithm to each
The weight of hidden layer is adjusted, and the output layer of the neural network model is for receiving from the defeated of the last layer hidden layer
Signal out.
In one embodiment, described to establish module 102 and establish and instruct according to the sample data of multiple good performance personnel
The mode got to a potentiality good performance identification model can specifically include:
A. the sample data of multiple good performance personnel is divided into training set and verifying collects；
B. a neural network model is established, and the neural network model is trained using the training set；
C. the neural network model after training is verified using verifying collection, and is counted according to each verification result
Obtain a model prediction accuracy rate；
D. judge whether the model prediction accuracy rate is less than preset threshold；
If e. the model prediction accuracy rate is not less than the preset threshold, the neural network model that training is completed
As the potentiality good performance identification model.
If f. the model prediction accuracy rate is less than the preset threshold, the parameter of the neural network model is adjusted, and
Neural network model adjusted is trained again using the training set, until the model prediction that verifying collection verifying obtains
Accuracy rate is not less than the preset threshold, wherein the parameter of the neural network model includes the neuron of total number of plies, each layer
Number etc..
In one embodiment, the parameter of the adjustment neural network model can be the adjustment neural network mould
The total number of plies and/or each layer of neuron number of type.The training set is for being trained neural network model, the verifying
Collection is for verifying the neural network model after training.
In one embodiment, the module 102 of establishing can carry out neural network model first with the training set
Training obtains a mid-module, then the good performance demographic data that the verifying is concentrated is input in the mid-module and carries out good performance
Classification of type verifying, and can be counted to obtain a model prediction accuracy rate according to each verification result；The intermediate die is judged again
Whether type predictablity rate is less than preset threshold；If the mid-module predictablity rate is not less than the preset threshold, show
This mid-module classifying quality is preferable, satisfies the use demand, and can directly know the mid-module as the potentiality good performance
Other model；If the model prediction accuracy rate is less than the preset threshold, shows that this mid-module classifying quality is bad, need
Improved, the parameter of the neural network model adjustable at this time, and using the training set again to mind adjusted
It is trained to obtain a new mid-module through network model, then be collected again using the verifying to the intermediate die retrieved
Type is verified to obtain a new model prediction accuracy rate, then judges whether the new model prediction accuracy rate is less than default threshold
Value, if the new model prediction accuracy rate be not less than the preset threshold, show the mid-module classifying quality retrieved compared with
It is good, it satisfies the use demand, it can be using the mid-module retrieved as the potentiality good performance identification model；If the new mould
Type predictablity rate needs to repeat the above steps again until the model obtained by verifying collection still less than the preset threshold
Predictablity rate is not less than the preset threshold.
In one embodiment, the preset threshold can be set according to actual use demand, such as described default
Threshold value is set as 95%, i.e. model prediction accuracy rate need to be not less than 95%.
Computing module 103 is calculated for the sample data of non-good performance personnel to be input to the potentiality good performance identification model
The non-good performance personnel grow into the probability value of each good performance type out.
In one embodiment, the potentiality good performance type to non-good performance personnel may be implemented in the potentiality good performance identification model
It is identified, at this time can be using the sample data of non-good performance personnel as the input of the potentiality good performance identification model, it will be described
The output of potentiality good performance identification model is considered as the probability that non-good performance personnel grow into each good performance type.
Judgment module 104 is for judging whether each probability value is greater than a predetermined probabilities.
In one embodiment, each probability value represents the non-good performance personnel and grows into each good performance type
The probability value of good performance personnel may be implemented to judge the non-good performance by judging whether each probability value is greater than a predetermined probabilities
Personnel grow into the probability of the good performance personnel of each good performance type.The predetermined probabilities equally can according to practical application request into
Row setting, such as the predetermined probabilities are set as 60%, then described to judge whether each probability value is greater than a predetermined probabilities and is
Judge whether each probability value is greater than 60%.
When the non-good performance personnel include one or more probability values for being greater than the predetermined probabilities, module 105 is chosen
For choosing a most probable value from the probability value for being greater than the predetermined probabilities, and by the corresponding good performance of the most probable value
Type develops good performance type as the potentiality of the non-good performance personnel.
In one embodiment, when non-good performance personnel include the probability value higher than predetermined probabilities, show the non-good performance people
Member can be considered as potentiality good performance personnel.For example, the predetermined probabilities of a good performance type are set as 60%, when the non-good performance personnel grow into
When the probability of the good performance type is not less than 60%, determine that the non-good performance personnel are the potentiality good performance that can grow into the good performance type
Personnel determine that the non-good performance personnel are not the achievements when the non-good performance personnel grow into the probability of the good performance type less than 60%
The potentiality good performance personnel of excellent type.
In one embodiment, some non-good performance personnel may be the potentiality good performance personnel of a variety of good performance types simultaneously, i.e.,
Non- good performance personnel include multiple probability values greater than the predetermined probabilities, are selected from the probability value for being greater than the predetermined probabilities at this time
A most probable value is taken, and using the corresponding good performance type of the most probable value as the most potential development of the non-good performance personnel
Good performance type.For example, predetermined probabilities 60%, by the potentiality good performance identification model obtain non-good performance personnel A1 at
The probability of a length of resource-type good performance personnel is 0.8, and the probability for growing into learning-oriented good performance personnel is 0.7, grows into diligent type achievement
The probability of excellent personnel is 0.9, since the probability value for growing into diligent type good performance personnel is maximum, then diligent type is determined as the non-achievement
The potentiality of excellent personnel A1 develop good performance type.
In one embodiment, when there are multiple most probable values, the selection module 105 can be randomly selected one most
The corresponding good performance type of greatest develops good performance type as the potentiality of the non-good performance personnel.For example, passing through the potentiality
Good performance identification model obtains non-good performance personnel A2 and grows into the probability of resource-type good performance personnel to be 0.8, grows into learning-oriented good performance
The probability of personnel be 0.7, grow into diligent type good performance personnel probability be 0.8, due to grow into diligent type good performance personnel at
The probability value of a length of resource-type good performance personnel is 0.8, then can choose diligent type being determined as that the non-good performance personnel A2's is latent
Power develops good performance type, and resource-type can also being determined as to, the potentiality of the non-good performance personnel A2 develop good performance type.
In one embodiment, when there are multiple most probable values, the selection module 105 it is also an option that one with it is pre-
If the corresponding good performance type of the matched most probable value of demand develops good performance type as the potentiality of the non-good performance personnel.Than
Such as, the preset need can be company's currently critical personnel's type demand, if company for resource-type good performance personnel demand most
Greatly, take second place to learning-oriented good performance personnel demand, it is minimum to diligent type good performance personnel demand.If being known at this time by the potentiality good performance
Other model obtains non-good performance personnel A2 and grows into the probability of resource-type good performance personnel to be 0.8, grows into learning-oriented good performance personnel's
Probability is 0.7, and the probability for growing into diligent type good performance personnel is 0.8, due to growing into diligent type good performance personnel and growing into money
The probability value of source type good performance personnel is 0.8 and company is maximum for resource-type good performance personnel demand, then can sentence resource-type
The potentiality for being set to the non-good performance personnel A2 develop good performance type.
In one embodiment, the potentiality good performance personnel identification system 10 further includes determination module 106.
It is described to sentence when the probability value that the non-good performance personnel grow into each good performance type is respectively less than the predetermined probabilities
Cover half block 106 is for determining the non-good performance personnel for no potentiality personnel.
In one embodiment, if the probability value that a non-good performance personnel grow into each good performance type is respectively less than predetermined probabilities
When, the determination module 106 determines the non-good performance personnel for no potentiality personnel.For example predetermined probabilities are 0.6, pass through potentiality achievement
Excellent identification model obtains non-good performance personnel A3 and grows into the probability of resource-type good performance personnel to be 0.5, grows into learning-oriented good performance people
The probability of member is 0.48, and the probability for growing into diligent type good performance personnel is 0.55, then the non-good performance personnel A3 is judged as without latent
Power personnel.
Fig. 4 is the schematic diagram of computer installation preferred embodiment of the present invention.
The computer installation 1 includes memory 20, processor 30 and is stored in the memory 20 and can be in institute
State the computer program 40 run on processor 30, such as potentiality good performance personnel's type recognition procedure.The processor 30 executes
The step in above-mentioned potentiality good performance personnel kind identification method embodiment is realized when the computer program 40, such as shown in Fig. 1
Step S11~S15, step S11~S16 shown in Fig. 2.Alternatively, when the processor 30 executes the computer program 40
Realize the function of each module in above-mentioned potentiality good performance personnel identification system embodiment, such as the module 101~106 in Fig. 3.
Illustratively, the computer program 40 can be divided into one or more module/units, it is one or
Multiple module/units are stored in the memory 20, and are executed by the processor 30, to complete the present invention.Described one
A or multiple module/units can be the series of computation machine program instruction section that can complete specific function, and described instruction section is used
In implementation procedure of the description computer program 40 in the computer installation 1.For example, the computer program 40 can be with
Be divided into the acquisition module 101 in Fig. 3, establish module 102, computing module 103, judgment module 104, choose module 105 and
Determination module 106.Each module concrete function is referring to embodiment two.
The computer installation 1 can be the calculating such as desktop PC, notebook, palm PC and cloud server and set
It is standby.It will be understood by those skilled in the art that the schematic diagram is only the example of computer installation 1, do not constitute to computer
The restriction of device 1 may include perhaps combining certain components or different components, example than illustrating more or fewer components
Such as described computer installation 1 can also include input-output equipment, network access equipment, bus.
Alleged processor 30 can be central processing unit (Central Processing Unit, CPU), can also be
Other general processors, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor 30 is also possible to any conventional processing
Device etc., the processor 30 are the control centres of the computer installation 1, utilize various interfaces and the entire computer of connection
The various pieces of device 1.
The memory 20 can be used for storing the computer program 40 and/or module/unit, and the processor 30 passes through
Operation executes the computer program and/or module/unit being stored in the memory 20, and calls and be stored in memory
Data in 20 realize the various functions of the computer installation 1.The memory 20 can mainly include storing program area and deposit
Store up data field, wherein storing program area can application program needed for storage program area, at least one function (for example sound is broadcast
Playing function, image player function etc.) etc.；Storage data area, which can be stored, uses created data (ratio according to computer installation 1
Such as audio data, phone directory) etc..In addition, memory 20 may include high-speed random access memory, it can also include non-easy
The property lost memory, such as hard disk, memory, plug-in type hard disk, intelligent memory card (Smart Media Card, SMC), secure digital
(Secure Digital, SD) card, flash card (Flash Card), at least one disk memory, flush memory device or other
Volatile solid-state part.
If the integrated module/unit of the computer installation 1 is realized in the form of SFU software functional unit and as independence
Product when selling or using, can store in a computer readable storage medium.Based on this understanding, of the invention
It realizes all or part of the process in above-described embodiment method, can also instruct relevant hardware come complete by computer program
At the computer program can be stored in a computer readable storage medium, and the computer program is held by processor
When row, it can be achieved that the step of above-mentioned each embodiment of the method.Wherein, the computer program includes computer program code, institute
Stating computer program code can be source code form, object identification code form, executable file or certain intermediate forms etc..It is described
Computer-readable medium may include: any entity or device, recording medium, U that can carry the computer program code
Disk, mobile hard disk, magnetic disk, CD, computer storage, read-only memory (ROM, Read-Only Memory), arbitrary access
Memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium etc..It needs
It is bright, the content that the computer-readable medium includes can according in jurisdiction make laws and patent practice requirement into
Row increase and decrease appropriate, such as do not include electric load according to legislation and patent practice, computer-readable medium in certain jurisdictions
Wave signal and telecommunication signal.
In several embodiments provided by the present invention, it should be understood that disclosed computer installation and method, it can be with
It realizes by another way.For example, computer installation embodiment described above is only schematical, for example, described
The division of unit, only a kind of logical function partition, there may be another division manner in actual implementation.
It, can also be in addition, each functional unit in each embodiment of the present invention can integrate in same treatment unit
It is that each unit physically exists alone, can also be integrated in same unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of hardware adds software function module.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included in the present invention.Any reference signs in the claims should not be construed as limiting the involved claims.This
Outside, it is clear that one word of " comprising " does not exclude other units or steps, and odd number is not excluded for plural number.It is stated in computer installation claim
Multiple units or computer installation can also be implemented through software or hardware by the same unit or computer installation.The
One, the second equal words are used to indicate names, and are not indicated any particular order.
Finally it should be noted that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although reference
Preferred embodiment describes the invention in detail, those skilled in the art should understand that, it can be to of the invention
Technical solution is modified or equivalent replacement, without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. a kind of potentiality good performance personnel's kind identification method, which is characterized in that the described method includes:
The sample data of multiple good performance personnel is obtained, the plurality of good performance personnel belong to a variety of good performance types, Mei Yisuo
It states good performance personnel and belongs to a kind of good performance type；
It is established according to the sample data of multiple good performance personnel and trains to obtain a potentiality good performance identification model；
By the sample data of non-good performance personnel be input to the potentiality good performance identification model be calculated the non-good performance personnel at
The probability value of a length of each good performance type；
Judge whether each probability value is greater than a predetermined probabilities；And
It is described default general from being greater than when the non-good performance personnel include one or more probability values for being greater than the predetermined probabilities
A most probable value is chosen in the probability value of rate, and using the corresponding good performance type of the most probable value as the non-good performance people
The potentiality of member develop good performance type.
2. potentiality good performance personnel's kind identification method as described in claim 1, which is characterized in that the sample of the good performance personnel
Data and the sample data of the non-good performance personnel include multiple dimensional informations, and each dimensional information is corresponding with dimension
Value, and the good performance personnel and the non-good performance personnel dimensional information having the same.
3. potentiality good performance personnel's kind identification method as claimed in claim 1 or 2, which is characterized in that described according to multiple institutes
The sample data for stating good performance personnel is established and trains the step of obtaining a potentiality good performance identification model
A neural network model is established, the neural network model includes input layer, multiple hidden layers and output layer；And
The neural network model is trained to obtain the potentiality good performance using the sample data of multiple good performance personnel
Identification model.
4. potentiality good performance personnel's kind identification method as claimed in claim 1 or 2, which is characterized in that described according to multiple institutes
The sample data for stating good performance personnel is established and trains the step of obtaining a potentiality good performance identification model
The sample data of multiple good performance personnel is divided into training set and verifying collection；
A neural network model is established, and the neural network model is trained using the training set；
The neural network model after training is verified using verifying collection, and counts to obtain one according to each verification result
Model prediction accuracy rate；
Judge whether the model prediction accuracy rate is less than preset threshold；
If the model prediction accuracy rate is not less than the preset threshold, the neural network model that training is completed is as institute
State potentiality good performance identification model.
5. potentiality good performance personnel's kind identification method as claimed in claim 4, which is characterized in that the judgement model is pre-
It surveys after the step of whether accuracy rate is less than preset threshold further include:
If the model prediction accuracy rate is less than the preset threshold, the parameter of the neural network model is adjusted, and utilize institute
Training set is stated again to be trained neural network model adjusted；
The neural network model of re -training is verified using verifying collection, and is counted again according to each verification result
A model prediction accuracy rate is obtained, and judges whether the model prediction accuracy rate counted again is less than preset threshold；
If the model prediction accuracy rate counted again is not less than the preset threshold, the re -training is obtained
Neural network model is as the potentiality good performance identification model；And
If the model prediction accuracy rate counted again is less than the preset threshold, repeat the above steps until passing through institute
It states the model prediction accuracy rate that verifying collection verifying obtains and is not less than the preset threshold；
Wherein, the parameter of the neural network model includes the neuron number of total number of plies, each layer.
6. potentiality good performance personnel's kind identification method as described in claim 1, which is characterized in that the judgement is each described general
After the step of whether rate value is greater than a predetermined probabilities further include:
When the probability value that the non-good performance personnel grow into each good performance type is respectively less than the predetermined probabilities, determine described non-
Good performance personnel are no potentiality personnel.
7. potentiality good performance personnel's kind identification method as described in claim 1, which is characterized in that described by the maximum probability
Being worth the step of corresponding good performance type develops good performance type as the potentiality of the non-good performance personnel includes:
When there are multiple most probable values, the corresponding good performance type of one most probable value of random selection is as the non-good performance people
The potentiality of member develop good performance type；Or
When there are multiple most probable values, select a good performance type corresponding with the matched most probable value of preset need as
The potentiality of the non-good performance personnel develop good performance type.
8. a kind of potentiality good performance personnel's identification system, which is characterized in that the system comprises:
Module is obtained, for obtaining the sample data of multiple good performance personnel, the plurality of good performance personnel belong to a variety of achievements
Excellent type, each good performance personnel belong to a kind of good performance type；
Module is established, obtains potentiality good performance identification mould for establishing and training according to the sample data of multiple good performance personnel
Type；
Computing module, for by the sample data of non-good performance personnel be input to the potentiality good performance identification model be calculated it is described
Non- good performance personnel grow into the probability value of each good performance type；
Judgment module, for judging whether each probability value is greater than a predetermined probabilities；And
Module is chosen, for when the non-good performance personnel include one or more probability value for being greater than the predetermined probabilities, from
A most probable value is chosen in probability value greater than the predetermined probabilities, and the corresponding good performance type of the most probable value is made
Develop good performance type for the potentiality of the non-good performance personnel.
9. a kind of terminal, the terminal includes processor and memory, and several computer programs are stored on the memory,
It is characterized in that, is realized when the processor is for executing the computer program stored in memory as any in claim 1-7
Described in one the step of potentiality good performance personnel's kind identification method.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
The step of potentiality good performance personnel's kind identification method as described in any one of claim 1-7 is realized when being executed by processor
Suddenly.
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| PCT/CN2019/077509 WO2020042583A1 (en) | 2018-08-27 | 2019-03-08 | Method and system for type identification of potential outstanding personnel, and computer device and medium |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109886641A (en) * | 2019-01-24 | 2019-06-14 | 平安科技（深圳）有限公司 | A kind of post portrait setting method, post portrait setting device and terminal device |
| CN110033160A (en) * | 2019-02-27 | 2019-07-19 | 贵州力创科技发展有限公司 | A kind of performance appraisal system and method |
| CN110134722A (en) * | 2019-05-22 | 2019-08-16 | 北京小度信息科技有限公司 | Target user determines method, apparatus, equipment and storage medium |
| CN110188780A (en) * | 2019-06-03 | 2019-08-30 | 电子科技大学中山学院 | Method and device for constructing deep learning model for positioning multi-target feature points |
| WO2020042583A1 (en) * | 2018-08-27 | 2020-03-05 | 平安科技（深圳）有限公司 | Method and system for type identification of potential outstanding personnel, and computer device and medium |
| CN111428963A (en) * | 2020-02-21 | 2020-07-17 | 贝壳技术有限公司 | Data processing method and device |
| EP3690658A1 (en) * | 2019-01-30 | 2020-08-05 | Hongfujin Precision Electronics (Tianjin) Co., Ltd. | Method for detecting repair-necessary motherboards and device using the method |
| CN112308749A (en) * | 2020-10-29 | 2021-02-02 | 泰康保险集团股份有限公司 | Device and method for generating culture plan, electronic device, and readable storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105893641A (en) * | 2016-07-01 | 2016-08-24 | 中国传媒大学 | Job recommending method |
| CN108062657A (en) * | 2017-11-30 | 2018-05-22 | 朱学松 | Method and system are interviewed in personnel recruitment |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105590175A (en) * | 2016-02-15 | 2016-05-18 | 云南电网有限责任公司 | Skilled talent evaluation method based on factor analysis and BP neural networks |
| CN108053136A (en) * | 2017-12-27 | 2018-05-18 | 上海储翔信息科技有限公司 | A kind of car dealer's analysis on Achievements expert system based on neutral net |
| CN108399502A (en) * | 2018-03-06 | 2018-08-14 | 江苏中企教育科技股份有限公司 | A kind of foreign trade industry talent ability evaluation method of covering skills for occupation at different levels |
| CN109214446B (en) * | 2018-08-27 | 2023-09-15 | 平安科技（深圳）有限公司 | Potential performance personnel type identification method, system, terminal and computer readable storage medium |
-
2018
- 2018-08-27 CN CN201810982686.7A patent/CN109214446B/en active Active
-
2019
- 2019-03-08 WO PCT/CN2019/077509 patent/WO2020042583A1/en active Application Filing
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105893641A (en) * | 2016-07-01 | 2016-08-24 | 中国传媒大学 | Job recommending method |
| CN108062657A (en) * | 2017-11-30 | 2018-05-22 | 朱学松 | Method and system are interviewed in personnel recruitment |
Non-Patent Citations (1)
| Title |
|---|
| 王庆;刘琨;张志超;: "基于BP神经网络的知识员工―岗位匹配测评研究", vol. 29, no. 10, pages 294 - 295 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020042583A1 (en) * | 2018-08-27 | 2020-03-05 | 平安科技（深圳）有限公司 | Method and system for type identification of potential outstanding personnel, and computer device and medium |
| CN109886641A (en) * | 2019-01-24 | 2019-06-14 | 平安科技（深圳）有限公司 | A kind of post portrait setting method, post portrait setting device and terminal device |
| EP3690658A1 (en) * | 2019-01-30 | 2020-08-05 | Hongfujin Precision Electronics (Tianjin) Co., Ltd. | Method for detecting repair-necessary motherboards and device using the method |
| US11113165B2 (en) | 2019-01-30 | 2021-09-07 | Hongfujin Precision Electronics (Tianjin) Co., Ltd. | Method for detecting repair-necessary motherboards and device using the method |
| CN110033160A (en) * | 2019-02-27 | 2019-07-19 | 贵州力创科技发展有限公司 | A kind of performance appraisal system and method |
| CN110134722A (en) * | 2019-05-22 | 2019-08-16 | 北京小度信息科技有限公司 | Target user determines method, apparatus, equipment and storage medium |
| CN110188780A (en) * | 2019-06-03 | 2019-08-30 | 电子科技大学中山学院 | Method and device for constructing deep learning model for positioning multi-target feature points |
| CN111428963A (en) * | 2020-02-21 | 2020-07-17 | 贝壳技术有限公司 | Data processing method and device |
| CN111428963B (en) * | 2020-02-21 | 2023-12-19 | 贝壳技术有限公司 | Data processing method and device |
| CN112308749A (en) * | 2020-10-29 | 2021-02-02 | 泰康保险集团股份有限公司 | Device and method for generating culture plan, electronic device, and readable storage medium |
| CN112308749B (en) * | 2020-10-29 | 2023-10-31 | 泰康保险集团股份有限公司 | Culture plan generation device, method, electronic device, and readable storage medium |
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| CN109214446B (en) | 2023-09-15 |
| WO2020042583A1 (en) | 2020-03-05 |
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