CN111861253A - Personnel capacity determining method and system - Google Patents
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Abstract
The application provides a method and a system for determining personnel capacity, which are used for acquiring personnel characteristic information of a person to be evaluated and a single evaluation result of the person to be evaluated under each evaluation dimension indicated by the personnel characteristic information; converting the personnel characteristic information into corresponding information characteristic vectors; inputting the information feature vectors into a trained feature weight matching model, and determining the evaluation weight of each evaluation dimension on the person to be evaluated; and determining a comprehensive evaluation result of the person to be evaluated based on the single evaluation result of the person to be evaluated in each evaluation dimension and the evaluation weight of each evaluation dimension. Therefore, different personnel characteristic information can be obtained for different personnel to be evaluated, and the personnel to be evaluated can be evaluated in a targeted manner, so that the evaluation efficiency of the personnel and the accuracy of the evaluation result are improved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and a system for determining a person ability.
Background
With the improvement of the living standard of people and the increasing enhancement of risk guarantee awareness, each person usually selects some required guarantee resources for the person under the condition of economic permission, in order to be capable of pertinently serving different customers, different enterprises can work out various guarantee resources according to the types or the situations of different customers so as to be selected by the user, and simultaneously monitor the service quality of each employee to different customers, so that the user experience of the user under different choices is pertinently improved.
At present, each enterprise generally employs an experienced assessment expert to assess the service quality of the staff to the clients according to the daily performance of the staff, but when the number of the staff owned by a large enterprise is large, the work efficiency of manual assessment is low, and the manual assessment usually has subjective impression, so that the assessment result is often not accurate enough.
Disclosure of Invention
In view of this, an object of the present application is to provide a method and a system for determining staff capabilities, which can obtain different staff characteristic information for different staff to be assessed, and further evaluate the staff to be assessed in a targeted manner, thereby facilitating improvement of staff assessment efficiency and accuracy of assessment results.
The embodiment of the application provides a method for determining personnel capacity, which comprises the following steps:
acquiring personnel characteristic information of a person to be evaluated and a single evaluation result of the person to be evaluated under each evaluation dimension indicated by the personnel characteristic information;
converting the personnel characteristic information into corresponding information characteristic vectors;
inputting the information feature vectors into a trained feature weight matching model, and determining the evaluation weight of each evaluation dimension on the person to be evaluated;
and determining a comprehensive evaluation result of the person to be evaluated based on the single evaluation result of the person to be evaluated in each evaluation dimension and the evaluation weight of each evaluation dimension.
Further, the converting the person feature information into a corresponding information feature vector includes:
carrying out data preprocessing on the personnel characteristic information to obtain processed conversion characteristic information;
and inputting the conversion characteristic information into a trained characteristic transformation model to obtain an information characteristic vector corresponding to the conversion characteristic information.
Further, the inputting the information feature vector into a trained feature weight matching model, and determining the evaluation weight of each evaluation dimension for the person to be evaluated includes:
inputting the information feature vector to a feature fusion layer in the feature weight matching model to obtain an evaluation feature vector of the person to be evaluated;
and inputting the evaluation feature vector to an evaluation layer in the feature weight matching model, and determining the evaluation weight of each evaluation dimension on the person to be evaluated.
Further, the inputting the information feature vector into a feature fusion layer in the feature weight matching model to obtain an evaluation feature vector of the person to be evaluated includes:
inputting the information characteristic vector to a vector conversion sublayer in the characteristic fusion layer, and determining a sparse characteristic vector corresponding to the information characteristic vector;
and splicing the information characteristic vector and the sparse characteristic vector through a vector splicing sublayer in the characteristic fusion layer to obtain an evaluation characteristic vector of the person to be evaluated.
Further, the feature weight matching model is trained by the following steps:
acquiring sample personnel characteristic information of each sample evaluating person from a sample database, and evaluating the sample evaluation weight of each evaluation dimension indicated by the sample personnel characteristic information on the sample evaluating person;
inputting the characteristic information of the sample personnel into the trained characteristic transformation model to obtain a sample information characteristic vector corresponding to the sample evaluating personnel;
and training the constructed logistic regression model by taking the sample information feature vector as an input feature and taking the sample evaluation weight of each evaluation dimension to the sample evaluation personnel as an output feature to obtain the weight matching model.
Further, the feature transformation model is trained by:
acquiring sample personnel characteristic information of each sample evaluating personnel and a real information characteristic vector corresponding to each sample evaluating personnel from a sample database;
and training the constructed gradient lifting iterative decision tree model by taking the characteristic information of the sample personnel as input characteristics and the real information characteristic vector corresponding to the sample evaluation personnel as output characteristics to obtain the characteristic transformation model.
Further, the determining a comprehensive assessment result of the person to be assessed based on the individual assessment result of the person to be assessed in each assessment dimension and the assessment weight of each assessment dimension includes:
calculating the total evaluation score of the personnel to be evaluated based on the single evaluation result corresponding to the personnel to be evaluated in each evaluation dimension and the evaluation weight of each evaluation dimension on the personnel to be evaluated;
and determining a comprehensive evaluation result of the personnel to be evaluated based on the total evaluation score and a preset evaluation grade.
The embodiment of the present application further provides a system for determining a person ability, where the system for determining a person ability includes:
the system comprises an acquisition module, a judgment module and a display module, wherein the acquisition module is used for acquiring personnel characteristic information of a person to be evaluated and a single evaluation result of the person to be evaluated under each evaluation dimension indicated by the personnel characteristic information;
the conversion module is used for converting the personnel characteristic information into corresponding information characteristic vectors;
the first determination module is used for inputting the information feature vector to a trained feature weight matching model and determining the evaluation weight of each evaluation dimension on the person to be evaluated;
and the second determination module is used for determining the comprehensive evaluation result of the person to be evaluated based on the single evaluation result of the person to be evaluated in each evaluation dimension and the evaluation weight of each evaluation dimension.
Further, when the conversion module is configured to convert the person feature information into a corresponding information feature vector, the conversion module is configured to:
carrying out data preprocessing on the personnel characteristic information to obtain processed conversion characteristic information;
and inputting the conversion characteristic information into a trained characteristic transformation model to obtain an information characteristic vector corresponding to the conversion characteristic information.
Further, when the first determining module is configured to input the information feature vector to a trained feature weight matching model and determine the evaluation weight of each evaluation dimension for the person to be evaluated, the first determining module is configured to:
inputting the information feature vector to a feature fusion layer in the feature weight matching model to obtain an evaluation feature vector of the person to be evaluated;
and inputting the evaluation feature vector to an evaluation layer in the feature weight matching model, and determining the evaluation weight of each evaluation dimension on the person to be evaluated.
Further, when the first determining module is configured to input the information feature vector to a feature fusion layer in the feature weight matching model to obtain an evaluation feature vector of the person to be evaluated, the first determining module is configured to:
inputting the information characteristic vector to a vector conversion sublayer in the characteristic fusion layer, and determining a sparse characteristic vector corresponding to the information characteristic vector;
and splicing the information characteristic vector and the sparse characteristic vector through a vector splicing sublayer in the characteristic fusion layer to obtain an evaluation characteristic vector of the person to be evaluated.
Further, the determination system further includes a first training module, and the first training module is configured to train the feature weight matching model by:
acquiring sample personnel characteristic information of each sample evaluating person from a sample database, and evaluating the sample evaluation weight of each evaluation dimension indicated by the sample personnel characteristic information on the sample evaluating person;
inputting the characteristic information of the sample personnel into the trained characteristic transformation model to obtain a sample information characteristic vector corresponding to the sample evaluating personnel;
and training the constructed logistic regression model by taking the sample information feature vector as an input feature and taking the sample evaluation weight of each evaluation dimension to the sample evaluation personnel as an output feature to obtain the weight matching model.
Further, the determination system further comprises a second training module, wherein the second training module is configured to train the feature transformation model by:
acquiring sample personnel characteristic information of each sample evaluating personnel and a real information characteristic vector corresponding to each sample evaluating personnel from a sample database;
and training the constructed gradient lifting iterative decision tree model by taking the characteristic information of the sample personnel as input characteristics and the real information characteristic vector corresponding to the sample evaluation personnel as output characteristics to obtain the characteristic transformation model.
Further, when the second determination module is configured to determine a comprehensive assessment result of the person to be assessed based on the individual assessment results of the person to be assessed in each assessment dimension and the assessment weight of each assessment dimension, the second determination module is configured to:
calculating the total evaluation score of the personnel to be evaluated based on the single evaluation result corresponding to the personnel to be evaluated in each evaluation dimension and the evaluation weight of each evaluation dimension on the personnel to be evaluated;
and determining a comprehensive evaluation result of the personnel to be evaluated based on the total evaluation score and a preset evaluation grade.
An embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the machine readable instructions when executed by the processor performing the steps of the method for determining human capabilities as described above.
The embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the method for determining human ability as described above.
The method and the system for determining the personnel capacity, provided by the embodiment of the application, are used for acquiring the personnel characteristic information of the personnel to be evaluated and a single evaluation result of the personnel to be evaluated under each evaluation dimension indicated by the personnel characteristic information; converting the personnel characteristic information into corresponding information characteristic vectors; inputting the information feature vectors into a trained feature weight matching model, and determining the evaluation weight of each evaluation dimension on the person to be evaluated; and determining a comprehensive evaluation result of the person to be evaluated based on the single evaluation result of the person to be evaluated in each evaluation dimension and the evaluation weight of each evaluation dimension.
Therefore, the method and the device have the advantages that the obtained personnel characteristic information of the personnel to be evaluated and the evaluation value corresponding to each evaluation dimension indicated by the personnel characteristic information are obtained, the obtained personnel characteristic information is converted into the corresponding information characteristic vector, the evaluation weight of each evaluation dimension on the personnel to be evaluated is determined through the trained characteristic weight matching model, finally, the comprehensive evaluation result of the personnel to be evaluated is determined based on the obtained single evaluation result of the personnel to be evaluated in each evaluation dimension, and the personnel to be evaluated can be evaluated based on the obtained personnel characteristic information of the personnel to be evaluated in different evaluation dimensions, so that the evaluation efficiency of the personnel and the accuracy of the evaluation result are improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a flowchart of a method for determining a person ability according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a method for determining human competency according to another embodiment of the present application;
fig. 3 is a schematic structural diagram of a system for determining human competency according to an embodiment of the present disclosure;
fig. 4 is a second schematic structural diagram of a system for determining human competency provided by an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. Every other embodiment that can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present application falls within the protection scope of the present application.
First, an application scenario to which the present application is applicable will be described. The method and the device can be applied to the technical field of computers, and the comprehensive evaluation result of the person to be evaluated is determined by acquiring the person characteristic information of the person to be evaluated in each evaluation dimension and the preset single evaluation result in each evaluation dimension. The determination system provided by the application can obtain the personnel characteristic information of the person to be evaluated and a single evaluation result corresponding to each evaluation dimension indicated by the personnel characteristic information of the person to be evaluated, convert the obtained plurality of personnel characteristic information into corresponding information characteristic vectors, and determine the evaluation weight of each evaluation dimension on the person to be evaluated through the trained characteristic weight matching model and the trained information characteristic vectors, so that the comprehensive evaluation result of the person to be evaluated is determined based on the obtained single evaluation result corresponding to each evaluation dimension.
According to research, currently, each enterprise generally employs an experienced assessment expert to assess the service quality of the staff to the client according to the daily performance of the staff, but when a large number of staff have, the work efficiency of manual assessment is low, and the result of the assessment is often not accurate due to the fact that subjective impression is usually generated during manual assessment.
Based on the method, the embodiment of the application provides a method for determining the ability of the person, which is helpful for improving the evaluation efficiency of the person and the accuracy of the evaluation result.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for determining a person ability according to an embodiment of the present disclosure. As shown in fig. 1, a method for determining a person capability provided in an embodiment of the present application includes:
s101, obtaining personnel characteristic information of a person to be evaluated, and obtaining a single evaluation result of the person to be evaluated under each evaluation dimension indicated by the personnel characteristic information.
In the step, before the person to be evaluated is evaluated, the person characteristic information of the person to be evaluated is acquired, and for the person to be evaluated, a preset single evaluation result corresponding to each evaluation dimension indicated by the person characteristic information is obtained.
Wherein assessing the dimensions may include: industry basic quality, individual productivity, service evaluation, platform cooperation degree and the like; and the industry base quality can also include: working years, industry qualifications, and the like; the platform cooperation degree may further include: client follow-up aggressiveness, client assistance aggressiveness, training completion rate, completion policy keeping management rate and the like.
Here, different assessment dimensions correspond to different individual assessment results among different persons to be assessed, for example, a person a to be assessed and a person B to be assessed, and the assessment dimension of the industry basis quality may be more important for the person a to be assessed than the person B to be assessed, so that the individual assessment result of the assessment dimension of the industry basis quality is higher for the person a to be assessed than for the person B to be assessed, for example, the individual assessment result of the assessment dimension of the industry basis quality for the person a to be assessed is 5 points, and the individual assessment result of the assessment dimension of the industry basis quality for the person B to be assessed is 4 points.
The evaluation dimensionalities indicated by the obtained personnel characteristic information are different for different personnel to be evaluated, for example, the personnel characteristic information obtained for the personnel to be evaluated A comprises an evaluation dimensionality a, an evaluation dimensionality b and an evaluation dimensionality c; and the person characteristic information acquired by the person B to be evaluated does not include the evaluation dimension B, and only includes the evaluation dimension a and the evaluation dimension c.
Therefore, different persons to be evaluated can be evaluated in a targeted manner, and the accuracy of the comprehensive evaluation result is improved.
And S102, converting the personnel characteristic information into corresponding information characteristic vectors.
In the step, after the personnel characteristic information of the personnel to be evaluated is obtained, the personnel characteristic information of the personnel to be evaluated is converted into a corresponding information characteristic vector for being used in a subsequent evaluation process.
S103, inputting the information feature vector into a trained feature weight matching model, and determining the evaluation weight of each evaluation dimension on the person to be evaluated.
In the step, the information feature vector corresponding to the person to be evaluated obtained through conversion is input into a trained feature weight matching model, and the evaluation weight of each evaluation dimension on the person to be evaluated is determined.
Corresponding to the above embodiment, for the person a to be evaluated and the person B to be evaluated, when the person a to be evaluated is evaluated, the evaluation dimension of the industry basis quality is more important, and compared with the person a to be evaluated, the evaluation dimension of the industry basis quality is slightly less important for the person B to be evaluated, so that after the person a to be evaluated passes through the feature weight matching model, the evaluation weight corresponding to the evaluation dimension of the industry basis quality is higher than the evaluation weight of the person B to be evaluated by the evaluation dimension of the industry basis quality.
And S104, determining a comprehensive evaluation result of the person to be evaluated based on the single evaluation result of the person to be evaluated in each evaluation dimension and the evaluation weight of each evaluation dimension.
In the step, a comprehensive evaluation result of the person to be evaluated is determined based on a single evaluation result which is obtained in advance and corresponds to the person to be evaluated in each evaluation dimension and the evaluation weight of each evaluation dimension on the person to be evaluated, which is determined through a characteristic weight matching model.
According to the method for determining the personnel capacity, the personnel characteristic information of the personnel to be evaluated is obtained, and the individual evaluation result of the personnel to be evaluated under each evaluation dimension indicated by the personnel characteristic information is obtained; converting the personnel characteristic information into corresponding information characteristic vectors; inputting the information feature vectors into a trained feature weight matching model, and determining the evaluation weight of each evaluation dimension on the person to be evaluated; and determining a comprehensive evaluation result of the person to be evaluated based on the single evaluation result of the person to be evaluated in each evaluation dimension and the evaluation weight of each evaluation dimension.
Therefore, the method and the device have the advantages that the obtained personnel characteristic information of the personnel to be evaluated and the evaluation value corresponding to each evaluation dimension indicated by the personnel characteristic information are obtained, the obtained personnel characteristic information is converted into the corresponding information characteristic vector, the evaluation weight of each evaluation dimension on the personnel to be evaluated is determined through the trained characteristic weight matching model, finally, the comprehensive evaluation result of the personnel to be evaluated is determined based on the obtained single evaluation result of the personnel to be evaluated in each evaluation dimension, and the personnel to be evaluated can be evaluated based on the obtained personnel characteristic information of the personnel to be evaluated in different evaluation dimensions, so that the evaluation efficiency of the personnel and the accuracy of the comprehensive evaluation result are improved.
Referring to fig. 2, fig. 2 is a flowchart illustrating a method for determining a person ability according to another embodiment of the present application. As shown in fig. 2, a method for determining a person capability provided in an embodiment of the present application includes:
s201, obtaining personnel feature information of a person to be evaluated, and obtaining a single evaluation result of the person to be evaluated under each evaluation dimension indicated by the personnel feature information.
S202, carrying out data preprocessing on the personnel characteristic information to obtain processed conversion characteristic information.
In the step, after the personnel characteristic information of the personnel to be evaluated is obtained, data preprocessing is carried out on the personnel characteristic information, data of unnecessary dimension fields such as abnormal information, missing data and secret data in the personnel characteristic information are filtered, and conversion characteristic information used for evaluating the personnel to be evaluated is determined.
Wherein, the data preprocessing comprises: at least one of data cleansing, data integration, data reduction, and data transformation.
The data cleaning is to correct the characteristic information such as noise information in the characteristic information of the person.
The data integration means that a plurality of pieces of information in the personnel characteristic information are combined into one piece of consistent information, and the combined information is stored in a corresponding data warehouse.
The data reduction means that redundant information in the personnel characteristic information is deleted or clustered to reduce the size of the data.
The data transformation refers to compressing information data in the personnel feature information to a smaller interval, such as normalization, standardization and the like, which can further improve the accuracy of the model and the operation efficiency of vector measurement in the model.
In this way, it is possible to help avoid a situation in which the processing time of data in the subsequent evaluation process is long due to a large amount of information.
S203, inputting the conversion characteristic information into a trained characteristic transformation model to obtain an information characteristic vector corresponding to the conversion characteristic information.
In the step, conversion characteristic information obtained after data preprocessing is input into a trained characteristic transformation model, and the conversion characteristic information is converted into corresponding information characteristic vectors, namely a plurality of conversion characteristic information are converted into a vector form.
And S204, inputting the information feature vector into a trained feature weight matching model, and determining the evaluation weight of each evaluation dimension on the person to be evaluated.
S205, determining a comprehensive assessment result of the person to be assessed based on the single assessment result of the person to be assessed in each assessment dimension and the assessment weight of each assessment dimension.
The descriptions of S201, S204 to S205 may refer to the descriptions of S101, S103 to S104, and the same technical effects can be achieved, which is not described in detail herein.
Further, step S204 includes: inputting the information feature vector to a feature fusion layer in the feature weight matching model to obtain an evaluation feature vector of the person to be evaluated; and inputting the evaluation feature vector to an evaluation layer in the feature weight matching model, and determining the evaluation weight of each evaluation dimension on the person to be evaluated.
In the step, the information feature vector obtained after the feature transformation model is input to a feature fusion layer in a trained feature weight matching model, so as to obtain an evaluation feature vector used in a subsequent evaluation process.
And inputting the obtained evaluation feature vector into an evaluation layer in a feature weight matching model, and determining the evaluation weight of each evaluation dimension on the person to be evaluated, wherein the evaluation weight can indicate the importance of the evaluation dimension on the person to be evaluated, the more important evaluation dimension has higher evaluation weight, and the less important evaluation dimension has lower evaluation weight.
Further, the inputting the information feature vector into a feature fusion layer in the feature weight matching model to obtain an evaluation feature vector of the person to be evaluated includes: inputting the information characteristic vector to a vector conversion sublayer in the characteristic fusion layer, and determining a sparse characteristic vector corresponding to the information characteristic vector; and splicing the information characteristic vector and the sparse characteristic vector through a vector splicing sublayer in the characteristic fusion layer to obtain an evaluation characteristic vector of the person to be evaluated.
Inputting the obtained information characteristic vector to a vector conversion sublayer in a characteristic fusion layer in a characteristic weight matching model, and determining a sparse characteristic vector corresponding to the information characteristic vector; and inputting the obtained information characteristic vector and the sparse characteristic vector into a vector splicing sublayer in the characteristic fusion layer, and splicing the information characteristic vector and the sparse characteristic vector together to obtain an evaluation characteristic vector corresponding to the person to be evaluated.
The sparse feature vector corresponding to the information feature vector can be obtained through a logistic regression algorithm, and the logistic regression algorithm is a linear model and cannot capture nonlinear information, so that the information feature vector and the sparse feature vector are spliced together through a vector splicing sublayer to ensure the integrity of the information.
Further, the feature weight matching model is trained by the following steps: acquiring sample personnel characteristic information of each sample evaluating person from a sample database, and evaluating the sample evaluation weight of each evaluation dimension indicated by the sample personnel characteristic information on the sample evaluating person; inputting the characteristic information of the sample personnel into the trained characteristic transformation model to obtain a sample information characteristic vector corresponding to the sample evaluating personnel; and training the constructed logistic regression model by taking the sample information feature vector as an input feature and taking the sample evaluation weight of each evaluation dimension to the sample evaluation personnel as an output feature to obtain the weight matching model.
In the step, the sample personnel information of each sample evaluating personnel is obtained from the sample database, and meanwhile, the sample evaluation weight of each evaluation dimension indicated by the sample personnel characteristic information for the sample evaluating personnel is obtained from the database.
And inputting the acquired sample personnel feature information into a trained feature transformation model, and converting the sample personnel feature information into a vector form to obtain a sample information feature vector corresponding to the sample evaluating personnel.
And training the constructed logistic regression model by taking the obtained sample information feature vector as an input feature and the obtained sample evaluation weight of each evaluation dimension to the sample evaluator as an output feature to obtain the trained weight ratio model.
The sample database stores personnel behavior data and business related data, including ETL, operational data, a data warehouse, a data mart and the like.
ETL, abbreviation of Extraction-Transformation-Loading, is responsible for extracting data in distributed and heterogeneous data sources, such as relational data, flat data files, etc., to a temporary intermediate layer, then cleaning, converting, integrating, and finally Loading to a data warehouse or a data mart, which becomes the basis of online analysis processing and data mining.
The Operational Data Store (ODS) forms an isolation between the service system and the Data Store, and the ODS directly stores the Data extracted from the service system, and the Data are consistent with the service system in structure and Data, so that the complexity of Data extraction is reduced. And transferring a detail query function of a part of service systems, wherein the data stored in the ODS is the same as the service systems, and reports of the original service systems can be generated from the ODS. Starting from the top in the data warehouse, the ODS stores detail data, the DW or the DM stores aggregated data, and the ODS provides a function of inquiring the detail.
The Data Warehouse (DW), a strategic set that provides all types of Data support for enterprise-level decision-making customization processes, is a universal set that contains all topics. The efficiency is high enough, and the entered data can be quickly processed; data quality, a data warehouse is support data for providing a plurality of decision systems, so that the data accuracy is very important; expansibility, enterprise business expansion and reduction of cost considerations for enterprises to construct data warehouses. The data warehouse is oriented to the topics, data in the data warehouse are organized according to a certain topic domain, each topic corresponds to a macroscopic analysis field, data which are useless for decision making are eliminated, and a concise view of a specific topic is provided.
Data mart (DataMart, DM), a local DW built with a certain business application as a starting point, wherein the DW only concerns data needed by the DW, but does not consider the whole data architecture and application of an enterprise, and each application has its own DM.
Further, the feature transformation model is trained by: acquiring sample personnel characteristic information of each sample evaluating personnel and a real information characteristic vector corresponding to each sample evaluating personnel from a sample database; and training the constructed gradient lifting iterative decision tree model by taking the characteristic information of the sample personnel as input characteristics and the real information characteristic vector corresponding to the sample evaluation personnel as output characteristics to obtain the characteristic transformation model.
In the step, sample personnel feature information of each sample evaluating personnel and a real information feature vector corresponding to each sample evaluating personnel are obtained from a sample database; and training the constructed gradient lifting iterative decision tree model to obtain a feature transformation model by taking the characteristic information of the sample personnel as the input feature of the constructed gradient lifting iterative decision tree model, taking the real information characteristic vector corresponding to the sample evaluation personnel as the output feature of the constructed gradient lifting iterative decision tree model.
Further, step S205 includes: calculating the total evaluation score of the personnel to be evaluated based on the single evaluation result corresponding to the personnel to be evaluated in each evaluation dimension and the evaluation weight of each evaluation dimension on the personnel to be evaluated; and determining a comprehensive evaluation result of the personnel to be evaluated based on the total evaluation score and a preset evaluation grade.
In the step, the total evaluation score of the person to be evaluated is calculated based on the obtained single evaluation result corresponding to the person to be evaluated in each evaluation dimension and the determined evaluation weight of each evaluation dimension on the person to be evaluated; and determining the comprehensive evaluation result of the person to be evaluated according to the calculated total evaluation score and the preset evaluation grade.
For example, a plurality of ratings may be preset, and a score value corresponding to each rating, for example, 1 to 5 are rated as good, 6 to 10 are rated as high, and when the total rating of the person to be rated is calculated to be 7, the person to be rated is determined to be "high".
According to the method for determining the personnel capacity, the personnel characteristic information of the personnel to be evaluated is obtained, and the individual evaluation result of the personnel to be evaluated under each evaluation dimension indicated by the personnel characteristic information is obtained; carrying out data preprocessing on the personnel characteristic information to obtain processed conversion characteristic information; inputting the conversion characteristic information into a trained characteristic transformation model to obtain an information characteristic vector corresponding to the conversion characteristic information; inputting the information feature vectors into a trained feature weight matching model, and determining the evaluation weight of each evaluation dimension on the person to be evaluated; and determining a comprehensive evaluation result of the person to be evaluated based on the single evaluation result of the person to be evaluated in each evaluation dimension and the evaluation weight of each evaluation dimension.
Therefore, the method comprises the steps of obtaining personnel characteristic information of a person to be evaluated and a single evaluation result corresponding to each evaluation dimension indicated by the personnel characteristic information, carrying out data preprocessing on the obtained plurality of personnel characteristic information, filtering out redundant and wrong personnel characteristic information, determining conversion characteristic information for a subsequent evaluation process, converting the obtained conversion characteristic information into corresponding information characteristic vectors, determining the evaluation weight of each evaluation dimension on the person to be evaluated through a trained characteristic weight matching model, finally determining a comprehensive evaluation result of the person to be evaluated based on the obtained single evaluation result of the person to be evaluated in each evaluation dimension, and further evaluating the person to be evaluated based on the obtained personnel characteristic information of the person to be evaluated in different evaluation dimensions, therefore, the evaluation efficiency of personnel and the accuracy of the comprehensive evaluation result are improved.
Referring to fig. 3 and 4, fig. 3 is a schematic structural diagram of a system for determining human competence provided in an embodiment of the present application, and fig. 4 is a second schematic structural diagram of a system for determining human competence provided in an embodiment of the present application. As shown in fig. 3, the determination system 300 includes:
the obtaining module 310 is configured to obtain staff feature information of a person to be evaluated, and a single evaluation result of the person to be evaluated in each evaluation dimension indicated by the staff feature information;
a conversion module 320, configured to convert the person feature information into a corresponding information feature vector;
the first determining module 330 is configured to input the information feature vector to a trained feature weight matching model, and determine an evaluation weight of each evaluation dimension for the person to be evaluated;
a second determining module 340, configured to determine a comprehensive assessment result of the person to be assessed based on the individual assessment results of the person to be assessed in each assessment dimension and the assessment weight of each assessment dimension.
Further, as shown in fig. 4, the determining system 300 further includes a first training module 350, where the first training module 350 is configured to train the feature weight matching model by:
acquiring sample personnel characteristic information of each sample evaluating person from a sample database, and evaluating the sample evaluation weight of each evaluation dimension indicated by the sample personnel characteristic information on the sample evaluating person;
inputting the characteristic information of the sample personnel into the trained characteristic transformation model to obtain a sample information characteristic vector corresponding to the sample evaluating personnel;
and training the constructed logistic regression model by taking the sample information feature vector as an input feature and taking the sample evaluation weight of each evaluation dimension to the sample evaluation personnel as an output feature to obtain the weight matching model.
Further, as shown in fig. 4, the determining system 300 further includes a second training module 360, and the second training module 360 is configured to train the feature transformation model by:
acquiring sample personnel characteristic information of each sample evaluating personnel and a real information characteristic vector corresponding to each sample evaluating personnel from a sample database;
and training the constructed gradient lifting iterative decision tree model by taking the characteristic information of the sample personnel as input characteristics and the real information characteristic vector corresponding to the sample evaluation personnel as output characteristics to obtain the characteristic transformation model.
Further, when the conversion module 320 is configured to convert the person feature information into a corresponding information feature vector, the conversion module 320 is configured to:
carrying out data preprocessing on the personnel characteristic information to obtain processed conversion characteristic information;
and inputting the conversion characteristic information into a trained characteristic transformation model to obtain an information characteristic vector corresponding to the conversion characteristic information.
Further, when the first determining module 330 is configured to input the information feature vector to a trained feature weight matching model to determine the rating weight of each rating dimension for the person to be rated, the first determining module 330 is configured to:
inputting the information feature vector to a feature fusion layer in the feature weight matching model to obtain an evaluation feature vector of the person to be evaluated;
and inputting the evaluation feature vector to an evaluation layer in the feature weight matching model, and determining the evaluation weight of each evaluation dimension on the person to be evaluated.
Further, when the first determining module 330 is configured to input the information feature vector to a feature fusion layer in the feature weight matching model to obtain an evaluation feature vector of the person to be evaluated, the first determining module 330 is configured to:
inputting the information characteristic vector to a vector conversion sublayer in the characteristic fusion layer, and determining a sparse characteristic vector corresponding to the information characteristic vector;
and splicing the information characteristic vector and the sparse characteristic vector through a vector splicing sublayer in the characteristic fusion layer to obtain an evaluation characteristic vector of the person to be evaluated.
Further, when the second determining module 340 is configured to determine the comprehensive assessment result of the person to be assessed based on the single assessment result of the person to be assessed in each assessment dimension and the assessment weight of each assessment dimension, the second determining module 340 is configured to:
calculating the total evaluation score of the personnel to be evaluated based on the single evaluation result corresponding to the personnel to be evaluated in each evaluation dimension and the evaluation weight of each evaluation dimension on the personnel to be evaluated;
and determining a comprehensive evaluation result of the personnel to be evaluated based on the total evaluation score and a preset evaluation grade.
The system for determining the staff capacity, provided by the embodiment of the application, is used for acquiring the staff characteristic information of the staff to be evaluated and a single evaluation result of the staff to be evaluated under each evaluation dimension indicated by the staff characteristic information; converting the personnel characteristic information into corresponding information characteristic vectors; inputting the information feature vectors into a trained feature weight matching model, and determining the evaluation weight of each evaluation dimension on the person to be evaluated; and determining a comprehensive evaluation result of the person to be evaluated based on the single evaluation result of the person to be evaluated in each evaluation dimension and the evaluation weight of each evaluation dimension.
Therefore, the method and the device have the advantages that the obtained personnel characteristic information of the personnel to be evaluated and the evaluation value corresponding to each evaluation dimension indicated by the personnel characteristic information are obtained, the obtained personnel characteristic information is converted into the corresponding information characteristic vector, the evaluation weight of each evaluation dimension on the personnel to be evaluated is determined through the trained characteristic weight matching model, finally, the comprehensive evaluation result of the personnel to be evaluated is determined based on the obtained single evaluation result of the personnel to be evaluated in each evaluation dimension, and the personnel to be evaluated can be evaluated based on the obtained personnel characteristic information of the personnel to be evaluated in different evaluation dimensions, so that the evaluation efficiency of the personnel and the accuracy of the comprehensive evaluation result are improved.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 5, the electronic device 500 includes a processor 510, a memory 520, and a bus 530.
The memory 520 stores machine-readable instructions executable by the processor 510, when the electronic device 500 runs, the processor 510 communicates with the memory 520 through the bus 530, and when the machine-readable instructions are executed by the processor 510, the steps of the method for determining the human capability in the method embodiments shown in fig. 1 and fig. 2 may be performed.
An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method for determining the human capability in the method embodiments shown in fig. 1 and fig. 2 may be executed.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and there may be other divisions in actual implementation, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the exemplary embodiments of the present application, and are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for determining a person's ability, the method comprising:
acquiring personnel characteristic information of a person to be evaluated and a single evaluation result of the person to be evaluated under each evaluation dimension indicated by the personnel characteristic information;
converting the personnel characteristic information into corresponding information characteristic vectors;
inputting the information feature vectors into a trained feature weight matching model, and determining the evaluation weight of each evaluation dimension on the person to be evaluated;
and determining a comprehensive evaluation result of the person to be evaluated based on the single evaluation result of the person to be evaluated in each evaluation dimension and the evaluation weight of each evaluation dimension.
2. The method of determining according to claim 1, wherein said converting the person feature information into a corresponding information feature vector comprises:
carrying out data preprocessing on the personnel characteristic information to obtain processed conversion characteristic information;
and inputting the conversion characteristic information into a trained characteristic transformation model to obtain an information characteristic vector corresponding to the conversion characteristic information.
3. The determination method according to claim 1, wherein the inputting the information feature vector into a trained feature weight matching model, and determining the evaluation weight of each evaluation dimension for the person to be evaluated comprises:
inputting the information feature vector to a feature fusion layer in the feature weight matching model to obtain an evaluation feature vector of the person to be evaluated;
and inputting the evaluation feature vector to an evaluation layer in the feature weight matching model, and determining the evaluation weight of each evaluation dimension on the person to be evaluated.
4. The determination method according to claim 3, wherein the inputting the information feature vector into a feature fusion layer in the feature weight matching model to obtain an assessment feature vector of the person to be assessed comprises:
inputting the information characteristic vector to a vector conversion sublayer in the characteristic fusion layer, and determining a sparse characteristic vector corresponding to the information characteristic vector;
and splicing the information characteristic vector and the sparse characteristic vector through a vector splicing sublayer in the characteristic fusion layer to obtain an evaluation characteristic vector of the person to be evaluated.
5. The method of claim 2, wherein the feature weight proportion model is trained by:
acquiring sample personnel characteristic information of each sample evaluating person from a sample database, and evaluating the sample evaluation weight of each evaluation dimension indicated by the sample personnel characteristic information on the sample evaluating person;
inputting the characteristic information of the sample personnel into the trained characteristic transformation model to obtain a sample information characteristic vector corresponding to the sample evaluating personnel;
and training the constructed logistic regression model by taking the sample information feature vector as an input feature and taking the sample evaluation weight of each evaluation dimension to the sample evaluation personnel as an output feature to obtain the weight matching model.
6. The method of determination according to claim 2, wherein the feature transformation model is trained by:
acquiring sample personnel characteristic information of each sample evaluating personnel and a real information characteristic vector corresponding to each sample evaluating personnel from a sample database;
and training the constructed gradient lifting iterative decision tree model by taking the characteristic information of the sample personnel as input characteristics and the real information characteristic vector corresponding to the sample evaluation personnel as output characteristics to obtain the characteristic transformation model.
7. The determination method according to claim 1, wherein the determining a comprehensive assessment result of the person to be assessed based on the individual assessment results of the person to be assessed in each assessment dimension and the assessment weight of each assessment dimension comprises:
calculating the total evaluation score of the personnel to be evaluated based on the single evaluation result corresponding to the personnel to be evaluated in each evaluation dimension and the evaluation weight of each evaluation dimension on the personnel to be evaluated;
and determining a comprehensive evaluation result of the personnel to be evaluated based on the total evaluation score and a preset evaluation grade.
8. A system for determining a person's ability, the system comprising:
the system comprises an acquisition module, a judgment module and a display module, wherein the acquisition module is used for acquiring personnel characteristic information of a person to be evaluated and a single evaluation result of the person to be evaluated under each evaluation dimension indicated by the personnel characteristic information;
the conversion module is used for converting the personnel characteristic information into corresponding information characteristic vectors;
the first determination module is used for inputting the information feature vector to a trained feature weight matching model and determining the evaluation weight of each evaluation dimension on the person to be evaluated;
and the second determination module is used for determining the comprehensive evaluation result of the person to be evaluated based on the single evaluation result of the person to be evaluated in each evaluation dimension and the evaluation weight of each evaluation dimension.
9. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is running, the machine-readable instructions when executed by the processor performing the steps of the method of determining human capabilities of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, performs the steps of the method for determining a person's ability as claimed in any one of claims 1 to 7.
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