WO2020042583A1 - 潜力绩优人员类型识别方法、系统、计算机装置及介质 - Google Patents

潜力绩优人员类型识别方法、系统、计算机装置及介质 Download PDF

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WO2020042583A1
WO2020042583A1 PCT/CN2019/077509 CN2019077509W WO2020042583A1 WO 2020042583 A1 WO2020042583 A1 WO 2020042583A1 CN 2019077509 W CN2019077509 W CN 2019077509W WO 2020042583 A1 WO2020042583 A1 WO 2020042583A1
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potential
type
model
neural network
performance
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French (fr)
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邓悦
金戈
徐亮
肖京
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Ping An Technology Shenzhen Co Ltd
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Ping An Technology Shenzhen Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06398—Performance of employee with respect to a job function

Definitions

  • the present application relates to the field of data processing, and in particular, to a method, a system, a computer device, and a medium for identifying a type of a potential performer.
  • Big data is a feature of the development of the Internet to the present stage. Under the background of technological innovation represented by cloud computing, data that was difficult to collect and use began to be easily collected and used. The current technology's potential development direction for non-outstanding personnel is generally judged and judged subjectively based on the daily performance of non-outstanding personnel. The accuracy is not high, and it lacks a scientific and effective identification method.
  • the present application provides a method, system, computer device, and storage medium for identifying the types of potential performers, so as to accurately locate the types of performers whose growth is not excellent.
  • An embodiment of the present application provides a method for identifying a type of potential performers, the method includes:
  • a maximum probability value is selected from the probability values greater than the preset probability, and the type of excellence corresponding to the maximum probability value is selected Develop a type of merit as a potential for said non- merit person.
  • An embodiment of the present application provides a type system for identifying potential performers, and the system includes:
  • An acquisition module configured to obtain sample data of a plurality of high performing personnel, wherein a plurality of the high performing personnel are classified into a plurality of high performing types, and each of the high performing personnel is classified into a high performing type;
  • a calculation module configured to input sample data of non-high performing personnel into the potential high performing identification model and calculate a probability value that the non high performing personnel grows into each high performing type
  • a judging module for judging whether each said probability value is greater than a preset probability
  • a selection module configured to select a maximum probability value from the probability values greater than the preset probability when the non-outstanding person includes one or more probability values greater than the preset probability, and set the maximum probability
  • the type of merit corresponding to the value is regarded as the type of merit of potential development of the non-outstanding person.
  • An embodiment of the present application provides a computer device.
  • the computer device includes a processor and a memory.
  • the memory stores a plurality of computer-readable instructions.
  • the processor is configured to execute the computer-readable instructions stored in the memory, such as The steps of the method of identifying the potential performers described earlier.
  • An embodiment of the present application provides a non-volatile readable storage medium on which computer-readable instructions are stored.
  • the computer-readable instructions are executed by a processor, the method for identifying a type of a potential high-performance person as described above is implemented. step.
  • the above-mentioned method, system, computer device and non-volatile readable storage medium for identifying potential performers based on the neural network and sample data of the performers are used to establish and train a potential performer identification model, and use the model to identify non-excellent performers.
  • FIG. 1 is a flowchart of steps in a method for identifying a type of a potential performer according to an embodiment of the present application.
  • FIG. 2 is a flowchart of steps in a method for identifying a type of a potential performer in another embodiment of the present application.
  • FIG. 3 is a functional block diagram of a type system for identifying potential performers in an embodiment of the present application.
  • FIG. 4 is a schematic diagram of a computer device according to an embodiment of the present application.
  • the method for identifying potential talented persons of the present application is applied in one or more computer devices.
  • the computer device is a device capable of automatically performing numerical calculations and / or information processing in accordance with instructions set or stored in advance.
  • the hardware includes, but is not limited to, a microprocessor and an Application Specific Integrated Circuit (ASIC). , Programmable Gate Array (Field-Programmable Gate Array, FPGA), Digital Processor (Digital Signal Processor, DSP), Embedded Equipment, etc.
  • ASIC Application Specific Integrated Circuit
  • the computer device may be a computing device such as a desktop computer, a notebook computer, a tablet computer, or a server.
  • the computer device can perform human-computer interaction with a user through a keyboard, a mouse, a remote control, a touch pad, or a voice control device.
  • FIG. 1 is a flowchart of steps in a preferred embodiment of a method for identifying a type of potential and excellent performer in the present application. According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.
  • the method for identifying a type of a potential high performer specifically includes the following steps.
  • Step S11 Obtain sample data of a plurality of high-performing persons, where a plurality of the high-performing persons are classified into a plurality of high-performing types, and each of the high-performing persons is classified as a high-performing type.
  • the sample data of the top performers stored in the top performer sample database may be obtained by connecting to a top performer sample database through an access network.
  • the high-performance personnel sample database may collect sample data of multiple high-performance personnel by means of big data, or may receive sample data of multiple high-performance personnel entered by humans.
  • a plurality of the high-performance personnel belong to multiple types of high-performance, but each of the high-performance personnel belongs to only one high-performance type, that is, each of the high-performance personnel cannot belong to multiple high-performance types simultaneously.
  • the multiple types of performance may include three types of performance, which are resource-based, learning-based, and hard-working.
  • the resource type can refer to people with strong business ability and working ability
  • the learning type can refer to people with strong learning ability and strong growth ability
  • the diligent type can refer to people with long learning time and long working hours per day.
  • the following uses the example of high-performing and non-high-performing personnel as employees of Company A as an example.
  • Company A includes 1,000 employees.
  • the 1,000 employees can be classified into two types: high-performing and non-high-performing.
  • Each employee can only be classified as high-performing or non-high-performing.
  • the division of non-high performers can be divided according to the division rules corresponding to multiple high performers.
  • each employee's learning ability and growth ability can be used to determine whether an employee is a high performer of learning type.
  • resource-based performance excellence you can determine whether an employee belongs to resource-based performance excellence according to the business capabilities of each employee.
  • the types of top performers include resource, learning, and hard work.
  • 200 of them were classified as high-performing personnel, and the remaining 800 employees were classified as non-high-performing personnel.
  • each high-performing person belongs to a type of high-performing
  • resource-based high-performing personnel includes 80
  • learning high-performing personnel includes 50
  • hard-working high-performing personnel includes 70.
  • the high-performing person and the non-high-performing person each have multiple dimensional information, and each dimensional information corresponds to a dimensional value, and the dimensional value is used to quantify the corresponding dimensional information.
  • the multi-dimensional information includes behavior trajectory, APP active status, business expansion status, consumption status, hobbies, training status, attendance status, education background, and so on.
  • Each dimension of information can also be further subdivided.
  • the behavior trajectory can further include the scope of activities, the frequency of entertainment venues, etc.
  • the educational background can further include the academic level, the comprehensive strength of graduated colleges, and the majors studied. Assuming that one of the dimensions is age, the corresponding dimension value is the corresponding age.
  • the age dimension value of is 30; if one dimension information is the business expansion situation, the dimension value of the business expansion situation can be scored according to the employee's business situation (based on a preset scoring standard) to obtain a dimension value.
  • obtaining the sample data of multiple high-performance personnel is to obtain each dimension information of the multiple high-performance personnel and the dimensional value corresponding to each dimension information.
  • the dimensional information of each high-performing person is the same as that of each non-high-performing person. That is, if each high-performing person includes 10 dimensional information, they are behavior trajectory, APP activity, business expansion, consumption, and interest. Hobbies, training, attendance, education, KPI scores, years of work; then each non-outstanding person also includes 10 dimensions of information, which are behavior trajectory, APP activity, business expansion, consumption, hobbies, training , Attendance, education, KPI score, working years.
  • Step S12 Establish and train a potential performance recognition model based on sample data of a plurality of performance performers.
  • the potential performance recognition model may be a classification model trained based on a neural network model and sample data of a plurality of performance talents. Specifically, a neural network model may be established first, and the neural network model includes an input layer, multiple hidden layers, and an output layer, and then the neural network model is trained by using multiple sample data of the performers to obtain the neural network model. Potential performance recognition model.
  • the input layer of the neural network model is used to receive a plurality of sample data of the top performers.
  • Each hidden layer includes multiple nodes (neurons), and each node in each hidden layer is configured to pair the data from the model.
  • the output of at least one node of an adjacent lower layer in the performs a linear or non-linear transformation.
  • the input of the nodes in the upper hidden layer can be based on the output of one node or several nodes in the adjacent lower layer, and each hidden layer has a corresponding weight, which is obtained based on the training sample data.
  • the model can be trained by using a supervised learning process to obtain the initial weights of each hidden layer.
  • the backpropagation (BP) algorithm can be used to adjust the weight of each hidden layer.
  • the output layer of the neural network model is used to receive the output signal from the last hidden layer.
  • step S12 may specifically include:
  • model prediction accuracy rate is not less than the preset threshold, use the neural network model that has been trained as the potential performance recognition model.
  • model prediction accuracy rate is less than the preset threshold, adjust the parameters of the neural network model, and use the training set to retrain the adjusted neural network model until the model prediction obtained by the validation set is verified
  • accuracy rate is not less than the preset threshold, wherein the parameters of the neural network model include the total number of layers, the number of neurons in each layer, and the like.
  • the parameter for adjusting the neural network model may be adjusting the total number of layers of the neural network model and / or the number of neurons in each layer.
  • the training set is used to train a neural network model
  • the validation set is used to verify a trained neural network model.
  • the neural network model may be trained by using the training set to obtain an intermediate model, and then the data of the high-performing personnel in the verification set may be input into the intermediate model to perform classification verification of the type of excellence, and according to each A verification result can statistically obtain a model prediction accuracy rate; and then determine whether the prediction accuracy rate of the intermediate model is less than a preset threshold; if the prediction accuracy rate of the intermediate model is not less than the preset threshold, it indicates that the classification effect of the intermediate model is better.
  • the intermediate model can be directly used as the potential performance recognition model; if the model prediction accuracy is less than the preset threshold, it indicates that the classification of the intermediate model is not effective and needs to be improved.
  • the preset threshold may be set according to actual usage requirements. For example, the preset threshold is set to 95%, that is, the model prediction accuracy rate needs to be not less than 95%.
  • Step S13 The sample data of the non-outstanding personnel are input into the potential performance identification model to calculate a probability value that the non-outstanding personnel will grow into each type of excellent performance.
  • the potential performance recognition model can identify potential performance excellence types of non-high-performing personnel.
  • sample data of non-high-performance personnel can be used as the potential performance recognition model.
  • the output of the potential performance recognition model is regarded as the probability that non-high performers will grow into each type of performance.
  • each of the probability values represents a probability value that the non-outstanding person grows into an outstanding person of each excellent type.
  • the judgment may be implemented.
  • the preset probability can also be set according to actual application requirements. For example, if the preset probability is set to 60%, then determining whether each of the probability values is greater than a preset probability is determining each of the probability values. Whether it is greater than 60%.
  • the non-outstanding person includes one or more probability values greater than the preset probability, select a maximum probability value from the probability values greater than the preset probability, and compare the maximum probability value with
  • the high performance type serves as a potential development high performance type of the non-high performance person.
  • a non-high performing person when a non-high performing person includes a probability value higher than a preset probability, it indicates that the non-high performing person can be regarded as a potential high performing person.
  • the preset probability of a high-performance type is set to 60%.
  • the probability that the non-high-performance person grows into the high-performance type is not less than 60%, the non-high-performance person is determined to be a potential high-performance person who can grow into the high-performance type.
  • the probability that the non-high performing person grows into the high performing type is less than 60%, it is determined that the non high performing person is not a potential high performing person of the high performing type.
  • a non-outstanding person may be multiple excellent types of potential excellent performers at the same time, that is, a non-outstanding person includes multiple probability values greater than the preset probability.
  • a maximum probability value is selected, and the type of merit corresponding to the maximum probability value is used as the type of merit of the most potential development of the non-performing person.
  • the preset probability is 60%.
  • the probability that non-outstanding personnel A1 will grow into resource-based high-performing personnel is 0.8
  • the probability of growing into learning-based high-performing personnel is 0.7
  • growing into hard-working excellent The probability of personnel is 0.9. Since the probability of growing into a hard-working high-performance person is the largest, the hard-working type is determined as the potential high-performance type of the non-high-performance person A1.
  • a performance type corresponding to a maximum probability value may be randomly selected as the potential development performance type of the non-highly qualified person. For example, according to the potential performance recognition model, it is obtained that the probability of non-outperformer A2 growing into a resource excellent performer is 0.8, the probability of growing into a learning excellent performer is 0.7, and the probability of growing into a hardworking outstanding performer is 0.8.
  • the probability value of hard-working high-performance personnel and growing into resource-based high-performance personnel are both 0.8, you can choose to judge hard-working type as the potential high-performance type of the non-high-performance person A2, or you can determine resource-based high-performance person A2 as the Potential development performance type.
  • a type of merit corresponding to the maximum probability value that matches a preset requirement may also be selected as the type of potential development merit of the non-performing person.
  • the preset demand may be a company's current critical staffing type requirements. If the company has the largest demand for resource-based high-performance personnel, the demand for learning-based high-performance personnel is the second, and the demand for hard-working high-performance personnel is the lowest. At this time, if the potential high performance recognition model is used to obtain the non-high performing person A2, the probability of growing into a resource high performing person is 0.8, the probability of growing into a learning high performing person is 0.7, and the probability of growing into a hardworking high performing person is 0.8. For both the hard-working high-performance personnel and the resource-based high-performance personnel, the probability value is 0.8 and the company has the greatest demand for resource-based high-performance personnel.
  • the method for identifying potential performers as shown in FIG. 2 further includes step S16.
  • step S16 when the probability value that the non-excellent person grows into each type of superiority is less than the preset probability, it is determined that the non-excellent person is a person without potential.
  • the non-outstanding person may be determined to be a person without potential.
  • the preset probability is 0.6
  • the probability that non-outstanding personnel A3 will grow into resource-type high performing personnel through the potential performance recognition model is 0.5
  • the probability of growing into learning high performing personnel is 0.48
  • the probability of growing into hardworking high performing personnel is 0.55
  • the non-outstanding person A3 is judged as a person without potential.
  • FIG. 3 is a functional module diagram of a preferred embodiment of a type identification system for potential performers.
  • the potential and excellent personnel type identification system 10 may include an acquisition module 101, a establishment module 102, a calculation module 103, a determination module 104, and a selection module 105.
  • the obtaining module 101 is configured to obtain sample data of a plurality of high-performing persons, wherein a plurality of the high-performing persons are classified into a plurality of high-performing types, and each of the high-performing persons is classified into a high-performing type.
  • the obtaining module 101 may be connected to a sample database of high performing personnel through an access network, and then obtain sample data of high performing personnel stored in the sample database of high performing personnel.
  • the high-performance personnel sample database may collect sample data of multiple high-performance personnel by means of big data, or may receive sample data of multiple high-performance personnel entered by humans.
  • a plurality of the high-performance personnel belong to multiple types of high-performance, but each of the high-performance personnel belongs to only one high-performance type, that is, each of the high-performance personnel cannot belong to multiple high-performance types at the same time.
  • the multiple types of performance may include three types of performance, which are resource-based, learning-based, and hard-working.
  • the resource type can refer to people with strong business ability and working ability
  • the learning type can refer to people with strong learning ability and strong growth ability
  • the diligent type can refer to people with long learning time and long working hours per day.
  • the following uses the example of high-performing and non-high-performing personnel as employees of Company A as an example.
  • Company A includes 1,000 employees.
  • the 1,000 employees can be classified into two types: high-performing and non-high-performing.
  • Each employee can only be classified as high-performing or non-high-performing.
  • the division of non-high performers can be divided according to the division rules corresponding to multiple high performers.
  • each employee's learning ability and growth ability can be used to determine whether an employee is a high performer of learning type.
  • resource-based performance excellence you can determine whether an employee belongs to resource-based performance excellence according to the business capabilities of each employee.
  • the types of top performers include resource, learning, and hard work.
  • 200 of them were classified as high-performing personnel, and the remaining 800 employees were classified as non-high-performing personnel.
  • each high-performing person belongs to a type of high-performing
  • resource-based high-performing personnel includes 80
  • learning high-performing personnel includes 50
  • hard-working high-performing personnel includes 70.
  • the high-performance personnel and the non-high-performance personnel have multiple dimensional information, and each of the dimensional information corresponds to a dimensional value, and the dimensional value is used to quantify the corresponding dimensional information.
  • the multi-dimensional information includes behavior trajectory, APP active status, business expansion status, consumption status, hobbies, training status, attendance status, education background, and so on.
  • Each dimension of information can also be further subdivided.
  • the behavior trajectory can further include the scope of activities, the frequency of entertainment venues, etc.
  • the educational background can further include the academic level, the comprehensive strength of graduated colleges, and the majors studied. Assuming that one of the dimensions is age, the corresponding dimension value is the corresponding age.
  • the age dimension value of is 30; if one dimension information is the business expansion situation, the dimension value of the business expansion situation can be scored according to the employee's business situation (based on a preset scoring standard) to obtain a dimension value.
  • the obtaining module 101 obtains sample data of a plurality of high-performing persons, that is, obtains each dimension information and a dimensional value corresponding to each dimension of the plurality of high-performing persons.
  • the dimensional information of each high-performing person is the same as that of each non-high-performing person. That is, if each high-performing person includes 10 dimensional information, they are behavior trajectory, APP activity, business expansion, consumption, and interest. Hobbies, training, attendance, education, KPI scores, years of work; then each non-outstanding person also includes 10 dimensions of information, which are behavior trajectory, APP activity, business expansion, consumption, hobbies, training , Attendance, education, KPI score, working years.
  • the establishing module 102 is configured to establish and train a potential high performance recognition model based on sample data of a plurality of high performance personnel.
  • the potential performance recognition model may be a classification model trained based on a neural network model and sample data of a plurality of performance talents.
  • the establishment module 102 may first establish a neural network model, the neural network model includes an input layer, a plurality of hidden layers, and an output layer, and then use a plurality of sample data of the outstanding personnel to compare the neural network model with the neural network model. Training is performed to obtain the potential performance recognition model.
  • the input layer of the neural network model is used to receive a plurality of sample data of the top performers.
  • Each hidden layer includes multiple nodes (neurons), and each node in each hidden layer is configured to pair the data from the model.
  • the output of at least one node of an adjacent lower layer in the performs a linear or non-linear transformation.
  • the input of the nodes in the upper hidden layer can be based on the output of one node or several nodes in the adjacent lower layer, and each hidden layer has a corresponding weight, which is obtained based on the training sample data.
  • the model can be trained by using a supervised learning process to obtain the initial weights of each hidden layer.
  • the backpropagation (BP) algorithm can be used to adjust the weight of each hidden layer.
  • the output layer of the neural network model is used to receive the output signal from the last hidden layer.
  • the manner in which the establishment module 102 establishes and trains a potential performance recognition model based on sample data of a plurality of excellent performers may specifically include:
  • model prediction accuracy rate is not less than the preset threshold, use the neural network model that has been trained as the potential performance recognition model.
  • model prediction accuracy rate is less than the preset threshold, adjust the parameters of the neural network model, and use the training set to retrain the adjusted neural network model until the model prediction obtained by the validation set is verified
  • accuracy rate is not less than the preset threshold, wherein the parameters of the neural network model include the total number of layers, the number of neurons in each layer, and the like.
  • the parameter for adjusting the neural network model may be adjusting the total number of layers of the neural network model and / or the number of neurons in each layer.
  • the training set is used to train a neural network model
  • the validation set is used to verify a trained neural network model.
  • the establishment module 102 may first use the training set to train a neural network model to obtain an intermediate model, and then input the high-performance personnel data in the verification set into the intermediate model to perform the classification of the high-performance type. Verify, and according to each verification result, a model prediction accuracy rate can be statistically obtained; then determine whether the intermediate model prediction accuracy rate is less than a preset threshold; if the intermediate model prediction accuracy rate is not less than the preset threshold, indicating that The classification effect of the intermediate model is good, and the use of the intermediate model can be directly used as the identification model of potential performance. If the prediction accuracy of the model is less than the preset threshold, it indicates that the classification effect of the intermediate model is not good.
  • the neural network model Need to improve, at this time, you can adjust the parameters of the neural network model, and use the training set to retrain the adjusted neural network model to obtain a new intermediate model, and then use the validation set to retrieve the newly obtained
  • the intermediate model is verified to obtain a new model prediction accuracy, and then the new model is judged. Whether the prediction accuracy is less than a preset threshold. If the prediction accuracy of the new model is not less than the preset threshold, it indicates that the recovered intermediate model has a better classification effect and meets the needs of use.
  • the recovered intermediate model can be used as the Potential performance recognition model; if the prediction accuracy of the new model is still less than the preset threshold, the above steps need to be repeated again until the model prediction accuracy obtained through the verification set is not less than the preset threshold.
  • the preset threshold may be set according to actual usage requirements. For example, the preset threshold is set to 95%, that is, the model prediction accuracy rate needs to be not less than 95%.
  • the calculation module 103 is configured to input sample data of non-outstanding personnel into the potential performance identification model and calculate a probability value that the non-outstanding personnel grows into each type of excellent performance.
  • the potential performance recognition model can identify potential performance excellence types of non-high performing personnel. At this time, the sample data of non-high performance personnel can be used as the input of the potential performance recognition model and the potential performance excellent can be identified. The output of the recognition model is considered as the probability that non-high performers will grow into each high performer type.
  • the determining module 104 is configured to determine whether each of the probability values is greater than a preset probability.
  • each of the probability values represents a probability value that the non-outstanding person grows into an outstanding person of each excellent type.
  • the judgment may be implemented.
  • the preset probability can also be set according to actual application requirements. For example, if the preset probability is set to 60%, then determining whether each of the probability values is greater than a preset probability is determining each of the probability values. Whether it is greater than 60%.
  • the selecting module 105 is configured to select a maximum probability value from the probability values greater than the preset probability, and set the maximum probability
  • the type of merit corresponding to the value is regarded as the type of merit of potential development of the non-outstanding person.
  • a non-high performer when a non-high performer includes a probability value higher than a preset probability, it indicates that the non-high performer can be regarded as a potential high performer.
  • the preset probability of a high-performance type is set to 60%.
  • the probability that the non-high-performance person grows into the high-performance type is not less than 60%, the non-high-performance person is determined to be a potential high-performance person who can grow into the high-performance type.
  • the probability that the non-high performing person grows into the high performing type is less than 60%, it is determined that the non high performing person is not a potential high performing person of the high performing type.
  • a non-outstanding person may be multiple excellent types of potential excellent performers at the same time, that is, a non-outstanding person includes multiple probability values greater than the preset probability.
  • a maximum probability value is selected, and the type of merit corresponding to the maximum probability value is used as the type of merit of the most potential development of the non-performing person.
  • the preset probability is 60%.
  • the probability that non-outstanding personnel A1 will grow into resource-based high-performing personnel is 0.8
  • the probability of growing into learning-based high-performing personnel is 0.7
  • growing into hard-working excellent The probability of personnel is 0.9. Since the probability of growing into a hard-working high-performance person is the largest, the hard-working type is determined as the potential high-performance type of the non-high-performance person A1.
  • the selection module 105 may randomly select a performance type corresponding to a maximum probability value as the potential development performance type of the non-high performing person. For example, according to the potential performance recognition model, it is obtained that the probability of non-outperformer A2 growing into a resource excellent performer is 0.8, the probability of growing into a learning excellent performer is 0.7, and the probability of growing into a hardworking outstanding performer is 0.8. The probability value of hard-working high-performance personnel and growing into resource-based high-performance personnel are both 0.8, you can choose to judge hard-working type as the potential high-performance type of the non-high-performance person A2, or you can determine resource-based high-performance person A2 as the Potential development performance type.
  • the selection module 105 may also select a type of merit corresponding to the maximum probability value that matches a preset demand as the type of potential development merit of the non-excellent personnel.
  • the preset demand may be a company's current critical staffing type requirements. If the company has the largest demand for resource-based high-performance personnel, the demand for learning-based high-performance personnel is the second, and the demand for hard-working high-performance personnel is the lowest.
  • the probability of growing into a resource high performing person is 0.8
  • the probability of growing into a learning high performing person is 0.7
  • the probability of growing into a hardworking high performing person is 0.8.
  • the probability value is 0.8 and the company has the greatest demand for resource-based high-performance personnel, then the resource type can be judged as the potential high-performance type of the non-high-performance personnel A2.
  • the potential high performance person type identification system 10 further includes a determination module 106.
  • the determining module 106 is used to determine the non-outstanding person as a person without potential.
  • the determination module 106 determines that the non-outstanding person is a person without potential.
  • the preset probability is 0.6
  • the probability that non-outstanding personnel A3 will grow into resource-based high-performing personnel through the potential performance recognition model is 0.5
  • the probability of growing into a learning-based high-performing person is 0.48
  • the probability of growing into a hard-working high-performing person is 0.55
  • the non-outstanding person A3 is judged as a person without potential.
  • FIG. 4 is a schematic diagram of a preferred embodiment of a computer device of the present application.
  • the computer device 1 includes a memory 20, a processor 30, and computer-readable instructions 40 stored in the memory 20 and executable on the processor 30, such as a potential high-performance personnel type identification program.
  • the processor 30 executes the computer-readable instructions 40
  • the steps in the embodiment of the method for identifying potential and excellent performers are implemented, for example, steps S11 to S15 shown in FIG. 1 and steps S11 to S16 shown in FIG. 2.
  • the processor 30 executes the computer-readable instructions 40
  • the functions of the modules in the embodiment of the type identification system for potential performers are performed, for example, modules 101 to 106 in FIG. 3.
  • the computer-readable instructions 40 may be divided into one or more modules / units, the one or more modules / units are stored in the memory 20 and executed by the processor 30, To complete this application.
  • the one or more modules / units may be a series of computer-readable instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer-readable instructions 40 in the computer device 1.
  • the computer-readable instructions 40 may be divided into an acquisition module 101, a establishment module 102, a calculation module 103, a determination module 104, a selection module 105, and a determination module 106 in FIG. 3.
  • the second embodiment For specific functions of each module, refer to the second embodiment.
  • the computer device 1 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
  • a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
  • the schematic diagram is only an example of the computer device 1, and does not constitute a limitation on the computer device 1. It may include more or fewer components than shown in the figure, or combine some components, or different Components, for example, the computer apparatus 1 may further include an input-output device, a network access device, a bus, and the like.
  • the so-called processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), Ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor, or the processor 30 may be any conventional processor, etc.
  • the processor 30 is a control center of the computer device 1 and uses various interfaces and lines to connect the entire computer device 1 The various parts.
  • the memory 20 may be configured to store the computer-readable instructions 40 and / or modules / units, and the processor 30 may execute or execute the computer-readable instructions and / or modules / units stored in the memory 20, and
  • the data stored in the memory 20 is called to implement various functions of the computer device 1.
  • the memory 20 may mainly include a storage program area and a storage data area, where the storage program area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.), etc .; the storage data area may Data (such as audio data, phone book, etc.) created according to the use of the computer device 1 are stored.
  • the memory 20 may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, an internal memory, a plug-in hard disk, a Smart Memory Card (SMC), and a Secure Digital (SD).
  • a non-volatile memory such as a hard disk, an internal memory, a plug-in hard disk, a Smart Memory Card (SMC), and a Secure Digital (SD).
  • SSD Secure Digital
  • flash memory card Flash card
  • flash memory device at least one disk storage device, flash memory device, or other volatile solid-state storage device.
  • the modules / units integrated in the computer device 1 When the modules / units integrated in the computer device 1 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile readable storage medium. Based on this understanding, this application implements all or part of the processes in the methods of the above embodiments, and can also be completed by computer-readable instructions instructing related hardware.
  • the computer-readable instructions can be stored in a non-volatile memory. In the read storage medium, when the computer-readable instructions are executed by a processor, the steps of the foregoing method embodiments can be implemented.
  • the computer-readable instructions include computer-readable instruction codes, and the computer-readable instruction codes may be in a source code form, an object code form, an executable file, or some intermediate form.
  • the non-volatile readable medium may include: any entity or device capable of carrying the computer-readable instruction code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), electric carrier signals, telecommunication signals, and software distribution media.
  • ROM Read-Only Memory
  • RAM Random Access Memory
  • electric carrier signals telecommunication signals
  • telecommunication signals and software distribution media.
  • the content contained in the non-volatile readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdictions. For example, in some jurisdictions, according to legislation and patent practices, non- Volatile readable media does not include electrical carrier signals and telecommunication signals.
  • each functional unit in each embodiment of the present application may be integrated in the same processing unit, or each unit may exist separately physically, or two or more units may be integrated in the same unit.
  • the integrated unit can be implemented in the form of hardware, or in the form of hardware plus software functional modules.

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Abstract

本申请提供一种潜力绩优人员类型识别方法、系统、计算机装置及介质。所述潜力绩优人员类型识别方法包括：获取多个绩优人员的样本数据；根据多个所述绩优人员的样本数据建立并训练得到一潜力绩优识别模型；将非绩优人员的样本数据输入至所述潜力绩优识别模型计算得出所述非绩优人员成长为每一绩优类型的概率值；判断每一所述概率值是否大于一预设概率；及从大于预设概率的概率值中选取一最大概率值，并将所述最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型。本申请基于神经网络及绩优人员样本数据建立并训练得到潜力绩优识别模型，并根据该模型准确定位出非绩优人员成长的的绩优类型，以进行定向培养

Description

潜力绩优人员类型识别方法、系统、计算机装置及介质
本申请要求于2018年08月27日提交中国专利局，申请号为201810982686.7发明名称为“潜力绩优人员类型识别方法、系统、终端及计算机可存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及数据处理领域，尤其涉及一种潜力绩优人员类型识别方法、系统、计算机装置及介质。
背景技术
本部分旨在为权利要求书及具体实施方式中陈述的本申请的实施方式提供背景或上下文。此处的描述不因为包括在本部分中就承认是现有技术。
大数据是互联网发展到现今阶段的一种特征，在以云计算为代表的技术创新的衬托下，原本很难收集和使用的数据开始容易采集以及被利用起来。现有技术对于非绩优人员的潜能发展方向，一般是评判人根据非绩优人员的日常表现主观地进行判断与认定，准确性不高，缺乏一种科学、有效地认定方法。
有基于此，需要提出一种基于多个绩优人员样本信息实现针对非绩优人员的潜能发展方向的评估方法。
发明内容
鉴于上述，本申请提供一种潜力绩优人员类型识别方法、系统、计算机装置及存储介质，以准确定位出非绩优人员成长的的绩优类型。
本申请一实施方式提供一种潜力绩优人员类型识别方法，所述方法包括：
获取多个绩优人员的样本数据，其中多个所述绩优人员分属于多种绩优类型，每一所述绩优人员分属于一种绩优类型；
根据多个所述绩优人员的样本数据建立并训练得到一潜力绩优识别模型；
将非绩优人员的样本数据输入至所述潜力绩优识别模型计算得出所述非绩优人员成长为每一绩优类型的概率值；
判断每一所述概率值是否大于一预设概率；及
当所述非绩优人员包含一个或多个大于所述预设概率的概率值时，从大于所述预设概率的概率值中选取一最大概率值，并将所述最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型。
本申请一实施方式提供一种潜力绩优人员类型识别系统，所述系统包括：
获取模块，用于获取多个绩优人员的样本数据，其中多个所述绩优人员 分属于多种绩优类型，每一所述绩优人员分属于一种绩优类型；
建立模块，用于根据多个所述绩优人员的样本数据建立并训练得到一潜力绩优识别模型；
计算模块，用于将非绩优人员的样本数据输入至所述潜力绩优识别模型计算得出所述非绩优人员成长为每一绩优类型的概率值；
判断模块，用于判断每一所述概率值是否大于一预设概率；及
选取模块，用于在所述非绩优人员包含一个或多个大于所述预设概率的概率值时，从大于所述预设概率的概率值中选取一最大概率值，并将所述最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型。
本申请一实施方式提供一种计算机装置，所述计算机装置包括处理器及存储器，所述存储器上存储有若干计算机可读指令，所述处理器用于执行存储器中存储的计算机可读指令时实现如前面所述的潜力绩优人员类型识别方法的步骤。
本申请一实施方式提供一种非易失性可读存储介质，其上存储有计算机可读指令，所述计算机可读指令被处理器执行时实现如前面所述的潜力绩优人员类型识别方法的步骤。
上述潜力绩优人员类型识别方法、系统、计算机装置及非易失性可读存储介质，基于神经网络与绩优人员的样本数据来建立并训练得到潜力绩优识别模型，并利用该模型实现识别出非绩优人员成长的最具潜力的绩优类型，训练样本均衡且识别准确性高，进而可以实现对潜力绩优人员进行定向培养，提升人员培养效果。
附图说明
图1是本申请一实施例中潜力绩优人员类型识别方法的步骤流程图。
图2是本申请另一实施例中潜力绩优人员类型识别方法的步骤流程图。
图3为本申请一实施例中潜力绩优人员类型识别系统的功能模块图。
图4为本申请一实施例中计算机装置示意图。
具体实施方式
为了能够更清楚地理解本申请的上述目的、特征和优点，下面结合附图和具体实施方式对本申请进行详细描述。需要说明的是，在不冲突的情况下，本申请的实施方式及实施方式中的特征可以相互组合。
在下面的描述中阐述了很多具体细节以便于充分理解本申请，所描述的实施方式仅仅是本申请一部分实施方式，而不是全部的实施方式。基于本申请中的实施方式，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施方式，都属于本申请保护的范围。
除非另有定义，本文所使用的所有的技术和科学术语与属于本申请的技术领域的技术人员通常理解的含义相同。本文中在本申请的说明书中所使用的术语只是为了描述具体的实施方式的目的，不是旨在于限制本申请。
优选地，本申请的潜力绩优人员类型识别方法应用在一个或者多个计算机装置中。所述计算机装置是一种能够按照事先设定或存储的指令，自动进行数值计算和/或信息处理的设备，其硬件包括但不限于微处理器、专用集成电路(Application Specific Integrated Circuit，ASIC)、可编程门阵列(Field－Programmable Gate Array，FPGA)、数字处理器(Digital Signal Processor，DSP)、嵌入式设备等。
所述计算机装置可以是桌上型计算机、笔记本电脑、平板电脑、服务器等计算设备。所述计算机装置可以与用户通过键盘、鼠标、遥控器、触摸板或声控设备等方式进行人机交互。
实施例一：
图1是本申请潜力绩优人员类型识别方法较佳实施例的步骤流程图。根据不同的需求，所述流程图中步骤的顺序可以改变，某些步骤可以省略。
参阅图1所示，所述潜力绩优人员类型识别方法具体包括以下步骤。
步骤S11、获取多个绩优人员的样本数据，其中多个所述绩优人员分属于多种绩优类型，每一所述绩优人员分属于一种绩优类型。
在一实施方式中，可以通过接入网络来连接至一绩优人员样本库，进而来获取所述绩优人员样本库存储的绩优人员的样本数据。所述绩优人员样本库可以通过大数据方式搜集多个绩优人员的样本数据，也可以通过接收人为录入的多个绩优人员的样本数据。
在一实施方式中，多个所述绩优人员分属于多种绩优类型，但每一所述绩优人员只分属于一个绩优类型，即每一所述绩优人员不能同时属于多种绩优类型。
举例而言，该多个绩优类型可以包括三种绩优类型，分别为资源型、学习型及勤奋型。资源型可以是指业务能力强、工作能力强的人员，学习型可以是指学习能力强、成长能力强的人员，勤奋型可以是指学习时间长、每天工作时间长的人员。对于每一绩优人员来说，其只能被归类到三种绩优类型中的一种。以下以绩优人员和非绩优人员是A公司员工为例进行举例说明。
举例而言，A公司包括1000名员工，该1000名员工可以被划分为绩优人员和非绩优人员两种类型，每一员工只能被划分为绩优人员或者被划分为非绩优人员，绩优人员和非绩优人员的划分可以根据多个绩优类型对应的划分规则进行划分，比如对于学习型绩优类型，可以根据每一员工的学习能力、成长能力来判定某一员工是否为学习型的绩优人员，再比如对应资源型绩优类型，可以根据每一员工的业务能力来判定某一员工是否属于资源型的绩优人员。绩优人员的类型包括资源型、学习型及勤奋型。比如根据每一员工的日常表现，其中200名员工被划分为绩优人员，其余800名员工被划分为非绩优人员。在200名绩优人员中，每一绩优人员属于一种绩优类型，资源型绩优人员包括80人，学习型绩优人员包括50人，勤奋型绩优人员包括70人。
在一实施方式中，绩优人员和非绩优人员均具有多个维度信息，每一维度 信息对应有一维度值，该维度值用于对对应的维度信息进行量化。比如该多个维度信息包括行为轨迹、APP活跃情况、业务扩展情况、消费情况、兴趣爱好、培训情况、出勤情况、学历等。每一维度信息也可以进行进一步细分，比如行为轨迹可以进一步包括活动范围、娱乐场所频次等，学历可以进一步包括学历等级、毕业院校综合实力、所学专业等。假设其中一维度信息为年龄，则对应的维度值为对应的年龄大小，若绩优人员a1的年龄为27，绩优人员a2的年龄为30，则绩优人员a1的年龄维度值为27，绩优人员a2的年龄维度值为30；若一维度信息为业务扩展情况，该业务扩展情况的维度值可以根据员工的业务情况进行评分(可以根据一预设评分标准)得到一维度值。在步骤S11中，所述获取多个绩优人员的样本数据即获取多个绩优人员的每一维度信息及每一维度信息对应的维度值。
在一实施方式中，每一绩优人员与每一非绩优人员的维度信息相同，即若每一绩优人员包括10个维度信息，分别是行为轨迹、APP活跃情况、业务扩展情况、消费情况、兴趣爱好、培训情况、出勤情况、学历、KPI评分、工作年限；则每一非绩优人员同样包括10个维度信息，分别是行为轨迹、APP活跃情况、业务扩展情况、消费情况、兴趣爱好、培训情况、出勤情况、学历、KPI评分、工作年限。
步骤S12、根据多个所述绩优人员的样本数据建立并训练得到一潜力绩优识别模型。
在一实施方式中，所述潜力绩优识别模型可以是基于神经网络模型和多个所述绩优人员的样本数据训练出来的分类模型。具体地，可以先建立一神经网络模型，所述神经网络模型包括输入层、多个隐藏层及输出层，再利用多个所述绩优人员的样本数据对所述神经网络模型进行训练得到所述潜力绩优识别模型。
所述神经网络模型的输入层用于接收多个所述绩优人员的样本数据，每一隐藏层包括多个节点(神经元)，每一隐藏层中的每一节点被配置成对来自该模型中的相邻下层的至少一个节点的输出执行线性或非线性变换。其中，上层隐藏层的节点的输入可以基于相邻下层中的一个节点或若干节点的输出，每个隐藏层具有对应的权值，该权值是基于训练样本数据获得的。在对该模型进行训练时，可以通过利用有监督的学习过程来进行模型的训练，得到各个隐藏层的初始权值。可以通过向后传播(Back propagation，BP)算法来对各隐藏层的权值的进行调节，所述神经网络模型的输出层用于接收来自最后一层隐藏层的输出信号。
在一实施方式中，所述步骤S12可以具体包括：
a.将多个所述绩优人员的样本数据划分为训练集及验证集；
b.建立一神经网络模型，并利用所述训练集对所述神经网络模型进行训练；
c.利用所述验证集对训练后的神经网络模型进行验证，并根据每一验证结果统计得到一模型预测准确率；
d.判断所述模型预测准确率是否小于预设阈值；
e.若所述模型预测准确率不小于所述预设阈值，将训练完成的所述神经网络模型作为所述潜力绩优识别模型。
f.若所述模型预测准确率小于所述预设阈值，调整所述神经网络模型的参数，并利用所述训练集重新对调整后的神经网络模型进行训练，直到验证集验证得到的模型预测准确率不小于所述预设阈值，其中所述神经网络模型的参数包括总层数、每一层的神经元数等。
在一实施方式中，所述调整所述神经网络模型的参数可以是调整所述神经网络模型的总层数和/或每一层的神经元数。所述训练集用于对神经网络模型进行训练，所述验证集用于对训练后的神经网络模型进行验证。
在一实施方式中，可以先利用所述训练集对神经网络模型进行训练得到一中间模型，再将所述验证集中的绩优人员数据输入至所述中间模型中进行绩优类型分类验证，并根据每一验证结果可以统计得到一模型预测准确率；再判断所述中间模型预测准确率是否小于预设阈值；若所述中间模型预测准确率不小于所述预设阈值，表明此中间模型分类效果较好，满足使用需求，可以直接将所述中间模型作为所述潜力绩优识别模型；若所述模型预测准确率小于所述预设阈值时，表明此中间模型分类效果不好，需要进行改善，此时可以调整所述神经网络模型的参数，并利用所述训练集重新对调整后的神经网络模型进行训练得到一新的中间模型，然后再次利用所述验证集对重新得到的中间模型进行验证得到一新的模型预测准确率，再判断该新的模型预测准确率是否小于预设阈值，若该新的模型预测准确率不小于所述预设阈值，表明重新得到的中间模型分类效果较好，满足使用需求，可以将重新得到的中间模型作为所述潜力绩优识别模型；如果该新的模型预测准确率仍然小于所述预设阈值，需要再次重复上述步骤直至通过验证集得到的模型预测准确率不小于所述预设阈值。
在一实施方式中，所述预设阈值可以根据实际使用需求进行设定，例如所述预设阈值设置为95％，即模型预测准确率需不小于95％。
步骤S13、将非绩优人员的样本数据输入至所述潜力绩优识别模型计算得出所述非绩优人员成长为每一绩优类型的概率值。
在一实施方式中，通过步骤S12的训练与验证，所述潜力绩优识别模型可以实现对非绩优人员的潜力绩优类型进行识别，此时可以将非绩优人员的样本数据作为所述潜力绩优识别模型的输入，将所述潜力绩优识别模型的输出视为非绩优人员成长为各绩优类型的概率。
S14、判断每一所述概率值是否大于一预设概率。
在一实施方式中，每一所述概率值代表着所述非绩优人员成长为每一绩优类型的绩优人员的概率值，通过判断每一所述概率值是否大于一预设概率可以实现判断该非绩优人员成长为每一绩优类型的绩优人员的概率。所述预设概率同样可以根据实际应用需求进行设定，例如所述预设概率设为60％，则所述判断每一所述概率值是否大于一预设概率为判断每一所述概率值是否 大于60％。
S15、当所述非绩优人员包含一个或多个大于所述预设概率的概率值时，从大于所述预设概率的概率值中选取一最大概率值，并将所述最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型。
在一实施方式中，非绩优人员包含有高于预设概率的概率值时，表明该非绩优人员可被视为潜力绩优人员。比如，一绩优类型的预设概率设为60％，当该非绩优人员成长为该绩优类型的概率不小于60％时，判定该非绩优人员为可以成长为该绩优类型的潜力绩优人员，当该非绩优人员成长为该绩优类型的概率小于60％时，判定该非绩优人员不是该绩优类型的潜力绩优人员。
在一实施方式中，某个非绩优人员可能同时是多种绩优类型的潜力绩优人员，即非绩优人员包含多个大于所述预设概率的概率值，此时从大于所述预设概率的概率值中选取一最大概率值，并将所述最大概率值对应的绩优类型作为该非绩优人员的最有潜力发展的绩优类型。举例而言，预设概率为60％，通过所述潜力绩优识别模型得到非绩优人员A1成长为资源型绩优人员的概率为0.8，成长为学习型绩优人员的概率为0.7，成长为勤奋型绩优人员的概率为0.9，由于成长为勤奋型绩优人员的概率值最大，则将勤奋型判定为该非绩优人员A1的潜力发展绩优类型。
在一实施方式中，当存在多个最大概率值时，可以随机选择一最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型。比如，通过所述潜力绩优识别模型得到非绩优人员A2成长为资源型绩优人员的概率为0.8，成长为学习型绩优人员的概率为0.7，成长为勤奋型绩优人员的概率为0.8，由于成长为勤奋型绩优人员与成长为资源型绩优人员的概率值均为0.8，则可以选择将勤奋型判定为该非绩优人员A2的潜力发展绩优类型，也可以将资源型判定为该非绩优人员A2的潜力发展绩优类型。
在一实施方式中，当存在多个最大概率值时，还可以选择一与预设需求匹配的最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型。比如，所述预设需求可以是公司当前紧要人员类型需求，若公司对于资源型绩优人员需求最大，对学习型绩优人员需求次之，对勤奋型绩优人员需求最低。此时若通过所述潜力绩优识别模型得到非绩优人员A2成长为资源型绩优人员的概率为0.8，成长为学习型绩优人员的概率为0.7，成长为勤奋型绩优人员的概率为0.8，由于成长为勤奋型绩优人员与成长为资源型绩优人员的概率值均为0.8且公司对于资源型绩优人员需求最大，则可以将资源型判定为该非绩优人员A2的潜力发展绩优类型。
请同时参阅图2，与图1示出的潜力绩优人员类型识别方法相比，图2示处的潜力绩优人员类型识别方法还包括步骤S16。
步骤S16，当所述非绩优人员成长为每一绩优类型的概率值均小于所述预设概率时，判定所述非绩优人员为无潜力人员。
在一实施方式中，若一非绩优人员成长为每一绩优类型的概率值均小于预设概率时，则可以判定所述非绩优人员为无潜力人员。比如预设概率为0.6， 通过潜力绩优识别模型得到非绩优人员A3成长为资源型绩优人员的概率为0.5，成长为学习型绩优人员的概率为0.48，成长为勤奋型绩优人员的概率为0.55，则该非绩优人员A3被判定为无潜力人员。
实施例二：
图3为本申请潜力绩优人员类型识别系统较佳实施例的功能模块图。
参阅图2所示，所述潜力绩优人员类型识别系统10可以包括获取模块101、建立模块102、计算模块103、判断模块104、选取模块105。
获取模块101用于获取多个绩优人员的样本数据，其中多个所述绩优人员分属于多种绩优类型，每一所述绩优人员分属于一种绩优类型。
在一实施方式中，所述获取模块101可以通过接入网络来连接至一绩优人员样本库，进而来获取所述绩优人员样本库存储的绩优人员的样本数据。所述绩优人员样本库可以通过大数据方式搜集多个绩优人员的样本数据，也可以通过接收人为录入的多个绩优人员的样本数据。
在一实施方式中，多个所述绩优人员分属于多种绩优类型，但每一所述绩优人员只分属于一个绩优类型，即每一所述绩优人员不能同时属于多种绩优类型。
举例而言，该多个绩优类型可以包括三种绩优类型，分别为资源型、学习型及勤奋型。资源型可以是指业务能力强、工作能力强的人员，学习型可以是指学习能力强、成长能力强的人员，勤奋型可以是指学习时间长、每天工作时间长的人员。对于每一绩优人员来说，其只能被归类到三种绩优类型中的一种。以下以绩优人员和非绩优人员是A公司员工为例进行举例说明。
举例而言，A公司包括1000名员工，该1000名员工可以被划分为绩优人员和非绩优人员两种类型，每一员工只能被划分为绩优人员或者被划分为非绩优人员，绩优人员和非绩优人员的划分可以根据多个绩优类型对应的划分规则进行划分，比如对于学习型绩优类型，可以根据每一员工的学习能力、成长能力来判定某一员工是否为学习型的绩优人员，再比如对应资源型绩优类型，可以根据每一员工的业务能力来判定某一员工是否属于资源型的绩优人员。绩优人员的类型包括资源型、学习型及勤奋型。比如根据每一员工的日常表现，其中200名员工被划分为绩优人员，其余800名员工被划分为非绩优人员。在200名绩优人员中，每一绩优人员属于一种绩优类型，资源型绩优人员包括80人，学习型绩优人员包括50人，勤奋型绩优人员包括70人。
在一实施方式中，绩优人员和非绩优人员均具有多个维度信息，每一维度信息对应有一维度值，该维度值用于对对应的维度信息进行量化。比如该多个维度信息包括行为轨迹、APP活跃情况、业务扩展情况、消费情况、兴趣爱好、培训情况、出勤情况、学历等。每一维度信息也可以进行进一步细分，比如行为轨迹可以进一步包括活动范围、娱乐场所频次等，学历可以进一步包括学历等级、毕业院校综合实力、所学专业等。假设其中一维度信息为年龄，则对应的维度值为对应的年龄大小，若绩优人员a1的年龄为27，绩优 人员a2的年龄为30，则绩优人员a1的年龄维度值为27，绩优人员a2的年龄维度值为30；若一维度信息为业务扩展情况，该业务扩展情况的维度值可以根据员工的业务情况进行评分(可以根据一预设评分标准)得到一维度值。所述获取模块101获取多个绩优人员的样本数据即获取多个绩优人员的每一维度信息及每一维度信息对应的维度值。
在一实施方式中，每一绩优人员与每一非绩优人员的维度信息相同，即若每一绩优人员包括10个维度信息，分别是行为轨迹、APP活跃情况、业务扩展情况、消费情况、兴趣爱好、培训情况、出勤情况、学历、KPI评分、工作年限；则每一非绩优人员同样包括10个维度信息，分别是行为轨迹、APP活跃情况、业务扩展情况、消费情况、兴趣爱好、培训情况、出勤情况、学历、KPI评分、工作年限。
建立模块102用于根据多个所述绩优人员的样本数据建立并训练得到一潜力绩优识别模型。
在一实施方式中，所述潜力绩优识别模型可以是基于神经网络模型和多个所述绩优人员的样本数据训练出来的分类模型。具体地，所述建立模块102可以先建立一神经网络模型，所述神经网络模型包括输入层、多个隐藏层及输出层，再利用多个所述绩优人员的样本数据对所述神经网络模型进行训练得到所述潜力绩优识别模型。
所述神经网络模型的输入层用于接收多个所述绩优人员的样本数据，每一隐藏层包括多个节点(神经元)，每一隐藏层中的每一节点被配置成对来自该模型中的相邻下层的至少一个节点的输出执行线性或非线性变换。其中，上层隐藏层的节点的输入可以基于相邻下层中的一个节点或若干节点的输出，每个隐藏层具有对应的权值，该权值是基于训练样本数据获得的。在对该模型进行训练时，可以通过利用有监督的学习过程来进行模型的训练，得到各个隐藏层的初始权值。可以通过向后传播(Back propagation，BP)算法来对各隐藏层的权值的进行调节，所述神经网络模型的输出层用于接收来自最后一层隐藏层的输出信号。
在一实施方式中，所述建立模块102根据多个所述绩优人员的样本数据建立并训练得到一潜力绩优识别模型的方式可以具体包括：
a.将多个所述绩优人员的样本数据划分为训练集及验证集；
b.建立一神经网络模型，并利用所述训练集对所述神经网络模型进行训练；
c.利用所述验证集对训练后的神经网络模型进行验证，并根据每一验证结果统计得到一模型预测准确率；
d.判断所述模型预测准确率是否小于预设阈值；
e.若所述模型预测准确率不小于所述预设阈值，将训练完成的所述神经网络模型作为所述潜力绩优识别模型。
f.若所述模型预测准确率小于所述预设阈值，调整所述神经网络模型的参数，并利用所述训练集重新对调整后的神经网络模型进行训练，直到验证 集验证得到的模型预测准确率不小于所述预设阈值，其中所述神经网络模型的参数包括总层数、每一层的神经元数等。
在一实施方式中，所述调整所述神经网络模型的参数可以是调整所述神经网络模型的总层数和/或每一层的神经元数。所述训练集用于对神经网络模型进行训练，所述验证集用于对训练后的神经网络模型进行验证。
在一实施方式中，所述建立模块102可以先利用所述训练集对神经网络模型进行训练得到一中间模型，再将所述验证集中的绩优人员数据输入至所述中间模型中进行绩优类型分类验证，并根据每一验证结果可以统计得到一模型预测准确率；再判断所述中间模型预测准确率是否小于预设阈值；若所述中间模型预测准确率不小于所述预设阈值，表明此中间模型分类效果较好，满足使用需求，可以直接将所述中间模型作为所述潜力绩优识别模型；若所述模型预测准确率小于所述预设阈值时，表明此中间模型分类效果不好，需要进行改善，此时可以调整所述神经网络模型的参数，并利用所述训练集重新对调整后的神经网络模型进行训练得到一新的中间模型，然后再次利用所述验证集对重新得到的中间模型进行验证得到一新的模型预测准确率，再判断该新的模型预测准确率是否小于预设阈值，若该新的模型预测准确率不小于所述预设阈值，表明重新得到的中间模型分类效果较好，满足使用需求，可以将重新得到的中间模型作为所述潜力绩优识别模型；如果该新的模型预测准确率仍然小于所述预设阈值，需要再次重复上述步骤直至通过验证集得到的模型预测准确率不小于所述预设阈值。
在一实施方式中，所述预设阈值可以根据实际使用需求进行设定，例如所述预设阈值设置为95％，即模型预测准确率需不小于95％。
计算模块103用于将非绩优人员的样本数据输入至所述潜力绩优识别模型计算得出所述非绩优人员成长为每一绩优类型的概率值。
在一实施方式中，所述潜力绩优识别模型可以实现对非绩优人员的潜力绩优类型进行识别，此时可以将非绩优人员的样本数据作为所述潜力绩优识别模型的输入，将所述潜力绩优识别模型的输出视为非绩优人员成长为各绩优类型的概率。
判断模块104用于判断每一所述概率值是否大于一预设概率。
在一实施方式中，每一所述概率值代表着所述非绩优人员成长为每一绩优类型的绩优人员的概率值，通过判断每一所述概率值是否大于一预设概率可以实现判断该非绩优人员成长为每一绩优类型的绩优人员的概率。所述预设概率同样可以根据实际应用需求进行设定，例如所述预设概率设为60％，则所述判断每一所述概率值是否大于一预设概率为判断每一所述概率值是否大于60％。
当所述非绩优人员包含一个或多个大于所述预设概率的概率值时，选取模块105用于从大于所述预设概率的概率值中选取一最大概率值，并将所述最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型。
在一实施方式中，非绩优人员包含有高于预设概率的概率值时，表明该非 绩优人员可被视为潜力绩优人员。比如，一绩优类型的预设概率设为60％，当该非绩优人员成长为该绩优类型的概率不小于60％时，判定该非绩优人员为可以成长为该绩优类型的潜力绩优人员，当该非绩优人员成长为该绩优类型的概率小于60％时，判定该非绩优人员不是该绩优类型的潜力绩优人员。
在一实施方式中，某个非绩优人员可能同时是多种绩优类型的潜力绩优人员，即非绩优人员包含多个大于所述预设概率的概率值，此时从大于所述预设概率的概率值中选取一最大概率值，并将所述最大概率值对应的绩优类型作为该非绩优人员的最有潜力发展的绩优类型。举例而言，预设概率为60％，通过所述潜力绩优识别模型得到非绩优人员A1成长为资源型绩优人员的概率为0.8，成长为学习型绩优人员的概率为0.7，成长为勤奋型绩优人员的概率为0.9，由于成长为勤奋型绩优人员的概率值最大，则将勤奋型判定为该非绩优人员A1的潜力发展绩优类型。
在一实施方式中，当存在多个最大概率值时，所述选取模块105可以随机选择一最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型。比如，通过所述潜力绩优识别模型得到非绩优人员A2成长为资源型绩优人员的概率为0.8，成长为学习型绩优人员的概率为0.7，成长为勤奋型绩优人员的概率为0.8，由于成长为勤奋型绩优人员与成长为资源型绩优人员的概率值均为0.8，则可以选择将勤奋型判定为该非绩优人员A2的潜力发展绩优类型，也可以将资源型判定为该非绩优人员A2的潜力发展绩优类型。
在一实施方式中，当存在多个最大概率值时，所述选取模块105还可以选择一与预设需求匹配的最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型。比如，所述预设需求可以是公司当前紧要人员类型需求，若公司对于资源型绩优人员需求最大，对学习型绩优人员需求次之，对勤奋型绩优人员需求最低。此时若通过所述潜力绩优识别模型得到非绩优人员A2成长为资源型绩优人员的概率为0.8，成长为学习型绩优人员的概率为0.7，成长为勤奋型绩优人员的概率为0.8，由于成长为勤奋型绩优人员与成长为资源型绩优人员的概率值均为0.8且公司对于资源型绩优人员需求最大，则可以将资源型判定为该非绩优人员A2的潜力发展绩优类型。
在一实施方式中，所述潜力绩优人员类型识别系统10还包括判定模块106。
当所述非绩优人员成长为每一绩优类型的概率值均小于所述预设概率时，所述判定模块106用于判定所述非绩优人员为无潜力人员。
在一实施方式中，若一非绩优人员成长为每一绩优类型的概率值均小于预设概率时，所述判定模块106判定所述非绩优人员为无潜力人员。比如预设概率为0.6，通过潜力绩优识别模型得到非绩优人员A3成长为资源型绩优人员的概率为0.5，成长为学习型绩优人员的概率为0.48，成长为勤奋型绩优人员的概率为0.55，则该非绩优人员A3被判定为无潜力人员。
图4为本申请计算机装置较佳实施例的示意图。
所述计算机装置1包括存储器20、处理器30以及存储在所述存储器20 中并可在所述处理器30上运行的计算机可读指令40，例如潜力绩优人员类型识别程序。所述处理器30执行所述计算机可读指令40时实现上述潜力绩优人员类型识别方法实施例中的步骤，例如图1所示的步骤S11～S15、图2所示的步骤S11～S16。或者，所述处理器30执行所述计算机可读指令40时实现上述潜力绩优人员类型识别系统实施例中各模块的功能，例如图3中的模块101～106。
示例性的，所述计算机可读指令40可以被分割成一个或多个模块/单元，所述一个或者多个模块/单元被存储在所述存储器20中，并由所述处理器30执行，以完成本申请。所述一个或多个模块/单元可以是能够完成特定功能的一系列计算机可读指令段，所述指令段用于描述所述计算机可读指令40在所述计算机装置1中的执行过程。例如，所述计算机可读指令40可以被分割成图3中的获取模块101、建立模块102、计算模块103、判断模块104、选取模块105及判定模块106。各模块具体功能参见实施例二。
所述计算机装置1可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。本领域技术人员可以理解，所述示意图仅仅是计算机装置1的示例，并不构成对计算机装置1的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件，例如所述计算机装置1还可以包括输入输出设备、网络接入设备、总线等。
所称处理器30可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者所述处理器30也可以是任何常规的处理器等，所述处理器30是所述计算机装置1的控制中心，利用各种接口和线路连接整个计算机装置1的各个部分。
所述存储器20可用于存储所述计算机可读指令40和/或模块/单元，所述处理器30通过运行或执行存储在所述存储器20内的计算机可读指令和/或模块/单元，以及调用存储在存储器20内的数据，实现所述计算机装置1的各种功能。所述存储器20可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序(比如声音播放功能、图像播放功能等)等；存储数据区可存储根据计算机装置1的使用所创建的数据(比如音频数据、电话本等)等。此外，存储器20可以包括高速随机存取存储器，还可以包括非易失性存储器，例如硬盘、内存、插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)、至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。
所述计算机装置1集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个非易失性可读取存储介质中。 基于这样的理解，本申请实现上述实施例方法中的全部或部分流程，也可以通过计算机可读指令来指令相关的硬件来完成，所述的计算机可读指令可存储于一非易失性可读存储介质中，所述计算机可读指令在被处理器执行时，可实现上述各个方法实施例的步骤。其中，所述计算机可读指令包括计算机可读指令代码，所述计算机可读指令代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述非易失性可读介质可以包括：能够携带所述计算机可读指令代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述非易失性可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，非易失性可读介质不包括电载波信号和电信信号。
在本申请所提供的几个实施例中，应该理解到，所揭露的计算机装置和方法，可以通过其它的方式实现。例如，以上所描述的计算机装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式。
另外，在本申请各个实施例中的各功能单元可以集成在相同处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在相同单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用硬件加软件功能模块的形式实现。
对于本领域技术人员而言，显然本申请不限于上述示范性实施例的细节，而且在不背离本申请的精神或基本特征的情况下，能够以其他的具体形式实现本申请。因此，无论从哪一点来看，均应将实施例看作是示范性的，而且是非限制性的，本申请的范围由所附权利要求而不是上述说明限定，因此旨在将落在权利要求的等同要件的含义和范围内的所有变化涵括在本申请内。不应将权利要求中的任何附图标记视为限制所涉及的权利要求。此外，显然“包括”一词不排除其他单元或步骤，单数不排除复数。计算机装置权利要求中陈述的多个单元或计算机装置也可以由同一个单元或计算机装置通过软件或者硬件来实现。第一，第二等词语用来表示名称，而并不表示任何特定的顺序。
最后应说明的是，以上实施例仅用以说明本申请的技术方案而非限制，尽管参照较佳实施例对本申请进行了详细说明，本领域的普通技术人员应当理解，可以对本申请的技术方案进行修改或等同替换，而不脱离本申请技术方案的精神和范围。

Claims (20)

  1. 一种潜力绩优人员类型识别方法，其特征在于，所述方法包括：
    获取多个绩优人员的样本数据，其中多个所述绩优人员分属于多种绩优类型，每一所述绩优人员分属于一种绩优类型；
    根据多个所述绩优人员的样本数据建立并训练得到一潜力绩优识别模型；
    将非绩优人员的样本数据输入至所述潜力绩优识别模型计算得出所述非绩优人员成长为每一绩优类型的概率值；
    判断每一所述概率值是否大于一预设概率；及
    当所述非绩优人员包含一个或多个大于所述预设概率的概率值时，从大于所述预设概率的概率值中选取一最大概率值，并将所述最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型。
  2. 如权利要求1所述的潜力绩优人员类型识别方法，其特征在于，所述绩优人员的样本数据和所述非绩优人员的样本数据均包括多个维度信息，每一所述维度信息对应有一维度值，且所述绩优人员与所述非绩优人员具有相同的维度信息。
  3. 如权利要求1或2所述的潜力绩优人员类型识别方法，其特征在于，所述根据多个所述绩优人员的样本数据建立并训练得到一潜力绩优识别模型的步骤包括：
    建立一神经网络模型，所述神经网络模型包括输入层、多个隐藏层及输出层；及
    利用多个所述绩优人员的样本数据对所述神经网络模型进行训练得到所述潜力绩优识别模型。
  4. 如权利要求1或2所述的潜力绩优人员类型识别方法，其特征在于，所述根据多个所述绩优人员的样本数据建立并训练得到一潜力绩优识别模型的步骤包括：
    将多个所述绩优人员的样本数据划分为训练集及验证集；
    建立一神经网络模型，并利用所述训练集对所述神经网络模型进行训练；
    利用所述验证集对训练后的神经网络模型进行验证，并根据每一验证结果统计得到一模型预测准确率；
    判断所述模型预测准确率是否小于预设阈值；
    若所述模型预测准确率不小于所述预设阈值，将训练完成的所述神经网络模型作为所述潜力绩优识别模型。
  5. 如权利要求4所述的潜力绩优人员类型识别方法，其特征在于，所述判断所述模型预测准确率是否小于预设阈值的步骤之后还包括：
    若所述模型预测准确率小于所述预设阈值，调整所述神经网络模型的参数，并利用所述训练集重新对调整后的神经网络模型进行训练；
    利用所述验证集对重新训练的神经网络模型进行验证，并根据每一验证结果重新统计得到一模型预测准确率，并判断重新统计得到的模型预测准确 率是否小于预设阈值；
    若所述重新统计得到的模型预测准确率不小于所述预设阈值，将所述重新训练得到的神经网络模型作为所述潜力绩优识别模型；及
    若所述重新统计得到的模型预测准确率小于所述预设阈值，重复上述步骤直至通过所述验证集验证得到的模型预测准确率不小于所述预设阈值；
    其中，所述神经网络模型的参数包括总层数、每一层的神经元数。
  6. 如权利要求1所述的潜力绩优人员类型识别方法，其特征在于，所述判断每一所述概率值是否大于一预设概率的步骤之后还包括：
    当所述非绩优人员成长为每一绩优类型的概率值均小于所述预设概率时，判定所述非绩优人员为无潜力人员。
  7. 如权利要求1所述的潜力绩优人员类型识别方法，其特征在于，所述将所述最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型的步骤包括：
    当存在多个最大概率值时，随机选择一最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型；或
    当存在多个最大概率值时，选择一与预设需求匹配的最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型。
  8. 一种潜力绩优人员类型识别系统，其特征在于，所述系统包括：
    获取模块，用于获取多个绩优人员的样本数据，其中多个所述绩优人员分属于多种绩优类型，每一所述绩优人员分属于一种绩优类型；
    建立模块，用于根据多个所述绩优人员的样本数据建立并训练得到一潜力绩优识别模型；
    计算模块，用于将非绩优人员的样本数据输入至所述潜力绩优识别模型计算得出所述非绩优人员成长为每一绩优类型的概率值；
    判断模块，用于判断每一所述概率值是否大于一预设概率；及
    选取模块，用于在所述非绩优人员包含一个或多个大于所述预设概率的概率值时，从大于所述预设概率的概率值中选取一最大概率值，并将所述最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型。
  9. 一种计算机装置，所述计算机装置包括处理器及存储器，所述存储器上存储有若干计算机可读指令，其特征在于，所述处理器用于执行存储器中存储的计算机可读指令时实现以下步骤：
    获取多个绩优人员的样本数据，其中多个所述绩优人员分属于多种绩优类型，每一所述绩优人员分属于一种绩优类型；
    根据多个所述绩优人员的样本数据建立并训练得到一潜力绩优识别模型；
    将非绩优人员的样本数据输入至所述潜力绩优识别模型计算得出所述非绩优人员成长为每一绩优类型的概率值；
    判断每一所述概率值是否大于一预设概率；及
    当所述非绩优人员包含一个或多个大于所述预设概率的概率值时，从大于所述预设概率的概率值中选取一最大概率值，并将所述最大概率值对应的 绩优类型作为所述非绩优人员的潜力发展绩优类型。
  10. 如权利要求9所述的计算机装置，其特征在于，所述处理器根据多个所述绩优人员的样本数据建立并训练得到一潜力绩优识别模型时，执行所述计算机可读指令以实现以下步骤：
    建立一神经网络模型，所述神经网络模型包括输入层、多个隐藏层及输出层；及
    利用多个所述绩优人员的样本数据对所述神经网络模型进行训练得到所述潜力绩优识别模型。
  11. 如权利要求9所述的计算机装置，其特征在于，所述处理器根据多个所述绩优人员的样本数据建立并训练得到一潜力绩优识别模型时，执行所述计算机可读指令以实现以下步骤：
    将多个所述绩优人员的样本数据划分为训练集及验证集；
    建立一神经网络模型，并利用所述训练集对所述神经网络模型进行训练；
    利用所述验证集对训练后的神经网络模型进行验证，并根据每一验证结果统计得到一模型预测准确率；
    判断所述模型预测准确率是否小于预设阈值；
    若所述模型预测准确率不小于所述预设阈值，将训练完成的所述神经网络模型作为所述潜力绩优识别模型。
  12. 如权利要求11所述的计算机装置，其特征在于，在所述判断所述模型预测准确率是否小于预设阈值的步骤之后，所述处理器执行所述计算机可读指令还用以实现以下步骤：
    若所述模型预测准确率小于所述预设阈值，调整所述神经网络模型的参数，并利用所述训练集重新对调整后的神经网络模型进行训练；
    利用所述验证集对重新训练的神经网络模型进行验证，并根据每一验证结果重新统计得到一模型预测准确率，并判断重新统计得到的模型预测准确率是否小于预设阈值；
    若所述重新统计得到的模型预测准确率不小于所述预设阈值，将所述重新训练得到的神经网络模型作为所述潜力绩优识别模型；及
    若所述重新统计得到的模型预测准确率小于所述预设阈值，重复上述步骤直至通过所述验证集验证得到的模型预测准确率不小于所述预设阈值；
    其中，所述神经网络模型的参数包括总层数、每一层的神经元数。
  13. 如权利要求9所述的计算机装置，其特征在于，在所述判断每一所述概率值是否大于一预设概率的步骤之后，所述处理器执行所述计算机可读指令还用以实现以下步骤：
    当所述非绩优人员成长为每一绩优类型的概率值均小于所述预设概率时，判定所述非绩优人员为无潜力人员。
  14. 如权利要求9所述的计算机装置，其特征在于，所述处理器在将所述最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型时，执行所述计算机可读指令以实现以下步骤：
    当存在多个最大概率值时，随机选择一最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型；或
    当存在多个最大概率值时，选择一与预设需求匹配的最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型。
  15. 一种非易失性可读存储介质，其上存储有计算机可读指令，其特征在于，所述计算机可读指令被处理器执行时实现以下步骤步骤：
    获取多个绩优人员的样本数据，其中多个所述绩优人员分属于多种绩优类型，每一所述绩优人员分属于一种绩优类型；
    根据多个所述绩优人员的样本数据建立并训练得到一潜力绩优识别模型；
    将非绩优人员的样本数据输入至所述潜力绩优识别模型计算得出所述非绩优人员成长为每一绩优类型的概率值；
    判断每一所述概率值是否大于一预设概率；及
    当所述非绩优人员包含一个或多个大于所述预设概率的概率值时，从大于所述预设概率的概率值中选取一最大概率值，并将所述最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型。
  16. 如权利要求15所述的存储介质，其特征在于，所述根据多个所述绩优人员的样本数据建立并训练得到一潜力绩优识别模型时，所述计算机可读指令被所述处理器执行以实现以下步骤：
    建立一神经网络模型，所述神经网络模型包括输入层、多个隐藏层及输出层；及
    利用多个所述绩优人员的样本数据对所述神经网络模型进行训练得到所述潜力绩优识别模型。
  17. 如权利要求15所述的存储介质，其特征在于，所述根据多个所述绩优人员的样本数据建立并训练得到一潜力绩优识别模型时，所述计算机可读指令被所述处理器执行以实现以下步骤：
    将多个所述绩优人员的样本数据划分为训练集及验证集；
    建立一神经网络模型，并利用所述训练集对所述神经网络模型进行训练；
    利用所述验证集对训练后的神经网络模型进行验证，并根据每一验证结果统计得到一模型预测准确率；
    判断所述模型预测准确率是否小于预设阈值；
    若所述模型预测准确率不小于所述预设阈值，将训练完成的所述神经网络模型作为所述潜力绩优识别模型。
  18. 如权利要求17所述的存储介质，其特征在于，在所述判断所述模型预测准确率是否小于预设阈值的步骤之后，所述计算机可读指令被所述处理器执行还用以实现以下步骤：
    若所述模型预测准确率小于所述预设阈值，调整所述神经网络模型的参数，并利用所述训练集重新对调整后的神经网络模型进行训练；
    利用所述验证集对重新训练的神经网络模型进行验证，并根据每一验证结果重新统计得到一模型预测准确率，并判断重新统计得到的模型预测准确 率是否小于预设阈值；
    若所述重新统计得到的模型预测准确率不小于所述预设阈值，将所述重新训练得到的神经网络模型作为所述潜力绩优识别模型；及
    若所述重新统计得到的模型预测准确率小于所述预设阈值，重复上述步骤直至通过所述验证集验证得到的模型预测准确率不小于所述预设阈值；
    其中，所述神经网络模型的参数包括总层数、每一层的神经元数。
  19. 如权利要求15所述的存储介质，其特征在于，在所述判断每一所述概率值是否大于一预设概率的步骤之后，所述所述计算机可读指令被所述处理器执行还用以实现以下步骤：
    当所述非绩优人员成长为每一绩优类型的概率值均小于所述预设概率时，判定所述非绩优人员为无潜力人员。
  20. 如权利要求15所述的存储介质，其特征在于，所述将所述最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型时，所述计算机可读指令被所述处理器执行以实现以下步骤：
    当存在多个最大概率值时，随机选择一最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型；或
    当存在多个最大概率值时，选择一与预设需求匹配的最大概率值对应的绩优类型作为所述非绩优人员的潜力发展绩优类型。
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