CN113191569A

CN113191569A - Enterprise management method and system based on big data

Info

Publication number: CN113191569A
Application number: CN202110561859.XA
Authority: CN
Inventors: 吴秋玲
Original assignee: Shanghai Xiaozhui Electronic Technology Co ltd
Current assignee: Shanghai Xiaozhui Electronic Technology Co ltd
Priority date: 2021-05-23
Filing date: 2021-05-23
Publication date: 2021-07-30

Abstract

The invention discloses an enterprise management method and system based on big data, wherein the method comprises the following steps: inputting the working characteristics of a plurality of employees of a first enterprise into a first business capability evaluation model, obtaining a first business capability evaluation result and constructing a first business capability normal model library; extracting service reference convolution characteristics of the median employee according to a first service capability evaluation result of the median employee in the normal mode library; traversing and comparing the working characteristics of all the employees according to the service reference convolution characteristics to obtain a difference characteristic database; performing incremental learning on the first service capability evaluation model according to the difference characteristic data to obtain a second service capability evaluation model; and obtaining a first enterprise management scheme according to a second service capability evaluation result of the second service capability evaluation model. The system and the method solve the technical problems that in the prior art, effective data management is lacked in talent resources in enterprise management, talent waste is easy to occur, and benefit promotion degree is low.

Description

Enterprise management method and system based on big data

Technical Field

The invention relates to the field related to enterprise management, in particular to an enterprise management method and system based on big data.

Background

With the progress and development of society, the enterprise information management also has higher requirements, wherein the enterprise management is a general term for a series of activities such as planning, organizing, commanding, coordinating and controlling the production and operation activities of the enterprise and is an objective requirement of social mass production, and the enterprise management is to utilize resources such as manpower, material resources, financial resources, information and the like of the enterprise as far as possible, so that the aims of saving, rapidness, much and good are achieved, and the maximum economic benefit is obtained. How to obtain effective enterprise management benefits for mass data continuously updated by electronic information is a hot topic discussed at present.

However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:

the technical problems that the data management is insufficient and effective in the aspect of talent resources in enterprise management, talent waste is easy to occur and the benefit promotion degree is not high exist in the prior art.

Disclosure of Invention

The embodiment of the application provides an enterprise management method and system based on big data, solves the technical problems that in the prior art, effective data management is insufficient in talent resources in enterprise management, talent waste is easy to occur, and benefit promotion degree is not high, and achieves the technical effects that benchmark difference comparison is carried out on talent business capacity based on extracted convolution characteristics, and talent effective utilization rate and enterprise management quality are improved.

In view of the foregoing problems, embodiments of the present application provide a method and a system for enterprise management based on big data.

In a first aspect, an embodiment of the present application provides a big data-based enterprise management method, where the method includes: obtaining work characteristics of a plurality of employees of a first enterprise; inputting the working characteristics of the employees into a first business capability evaluation model to obtain a first business capability evaluation result of the employees; constructing a first business capability normal library according to the first business capability evaluation results of the plurality of employees; obtaining a median employee in the first business capability normative library; extracting service reference convolution characteristics of the median employee according to a first service capability evaluation result of the median employee in the normal model library; obtaining work characteristics of all employees of the first enterprise; traversing and comparing the working characteristics of all the employees according to the service reference convolution characteristics to obtain a difference characteristic database; performing incremental learning on the first service capability evaluation model according to the difference feature data in the difference feature database to obtain a second service capability evaluation model; obtaining a second service capability evaluation result of all the employees according to the second service capability evaluation model; and obtaining a first enterprise management scheme according to the second business capability evaluation result.

In another aspect, the present application further provides an enterprise management system based on big data, where the system includes: the system comprises a first obtaining unit, a second obtaining unit and a processing unit, wherein the first obtaining unit is used for obtaining working characteristics of a plurality of employees of a first enterprise; the first input unit is used for inputting the working characteristics of the employees into a first business capability evaluation model to obtain a first business capability evaluation result of the employees; the first construction unit is used for constructing a first business capability normative library according to the first business capability evaluation results of the employees; the second obtaining unit is used for obtaining the median staff in the first business capability normal library; the first extraction unit is used for extracting the business benchmark convolution characteristics of the median staff according to the first business capability evaluation result of the median staff in the normal model library; a third obtaining unit, configured to obtain work characteristics of all employees of the first enterprise; a fourth obtaining unit, configured to perform traversal comparison on the working features of all the employees according to the service reference convolution feature, and obtain a difference feature database; a fifth obtaining unit, configured to perform incremental learning on the first service capability evaluation model according to the difference feature data in the difference feature database, so as to obtain a second service capability evaluation model; a sixth obtaining unit, configured to obtain a second service capability evaluation result of all the employees according to the second service capability evaluation model; a seventh obtaining unit, configured to obtain the first enterprise management scheme according to the second service capability evaluation result.

In a third aspect, the present invention provides a big data based enterprise management system, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method according to the first aspect when executing the program.

One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

the method comprises the steps of obtaining working characteristics of a plurality of employees of a first enterprise, training a business capability evaluation model based on the working characteristics, performing first processing evaluation on the working characteristics of the employees to obtain a corresponding first business capability evaluation result, further constructing and obtaining a first business capability normal model base according to the business capabilities of all the employees processed by the model, sequencing all information in the database, screening the median employee with the median business capability, extracting and obtaining a business basic convolution characteristic of the median employee, traversing and comparing the characteristics of the screened employee with the characteristics of all the employees of the enterprise, performing incremental learning according to the difference characteristic, obtaining a second business capability evaluation model, and performing secondary business capability evaluation based on the level between the employee of the enterprise and a reference employee, the mode of obtaining the second service capability evaluation result can obtain a corresponding enterprise management scheme based on the second service capability evaluation result, and achieves the technical effects of performing benchmark difference comparison on talent service capabilities based on the extracted convolution characteristics, and improving the effective utilization rate of talents and the enterprise management quality.

The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.

Drawings

FIG. 1 is a schematic flow chart illustrating a big data-based enterprise management method according to an embodiment of the present disclosure;

FIG. 2 is a schematic structural diagram of an enterprise management system based on big data according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.

Description of reference numerals: a first obtaining unit 11, a first input unit 12, a first constructing unit 13, a second obtaining unit 14, a first extracting unit 15, a third obtaining unit 16, a fourth obtaining unit 17, a fifth obtaining unit 18, a sixth obtaining unit 19, a seventh obtaining unit 20, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 305.

Detailed Description

The embodiment of the application provides an enterprise management method and system based on big data, solves the technical problems that in the prior art, effective data management is insufficient in talent resources in enterprise management, talent waste is easy to occur, and benefit promotion degree is not high, and achieves the technical effects that benchmark difference comparison is carried out on talent service capacity based on extracted convolution characteristics, and talent effective utilization rate and enterprise management quality are improved. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.

Summary of the application

With the progress and development of society, the enterprise information management also has higher requirements, wherein the enterprise management is a general term for a series of activities such as planning, organizing, commanding, coordinating and controlling the production and operation activities of the enterprise and is an objective requirement of social mass production, and the enterprise management is to utilize resources such as manpower, material resources, financial resources, information and the like of the enterprise as far as possible, so that the aims of saving, rapidness, much and good are achieved, and the maximum economic benefit is obtained. How to obtain effective enterprise management benefits for mass data continuously updated by electronic information is a hot topic discussed at present. But the technical problems of insufficient effective data management on talent resources, easily-caused talent waste and low benefit improvement degree in enterprise management in the prior art exist.

In view of the above technical problems, the technical solution provided by the present application has the following general idea:

the embodiment of the application provides an enterprise management method based on big data, which comprises the following steps: obtaining work characteristics of a plurality of employees of a first enterprise; inputting the working characteristics of the employees into a first business capability evaluation model to obtain a first business capability evaluation result of the employees; constructing a first business capability normal library according to the first business capability evaluation results of the plurality of employees; obtaining a median employee in the first business capability normative library; extracting service reference convolution characteristics of the median employee according to a first service capability evaluation result of the median employee in the normal model library; obtaining work characteristics of all employees of the first enterprise; traversing and comparing the working characteristics of all the employees according to the service reference convolution characteristics to obtain a difference characteristic database; performing incremental learning on the first service capability evaluation model according to the difference feature data in the difference feature database to obtain a second service capability evaluation model; obtaining a second service capability evaluation result of all the employees according to the second service capability evaluation model; and obtaining a first enterprise management scheme according to the second business capability evaluation result.

Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.

Example one

As shown in fig. 1, an embodiment of the present application provides a big data based enterprise management method, where the method includes:

step S100: obtaining work characteristics of a plurality of employees of a first enterprise;

specifically, the employees of the first enterprise include representative employees with vivid work, wherein the employees are corresponding employees obtained by screening employees of each category, so as to obtain the work characteristics of the employees, further, the work characteristics refer to the value of the work itself, and typical work characteristic factors include interestingness, challenge, learnability, autonomy, achievement, recognition opportunity, talent exertion, promotion opportunity, skill diversity, work feedback, and the like. Therefore, the characteristic acquisition of the employee based on the working characteristic facilitates the further data feedback processing.

Step S200: inputting the working characteristics of the employees into a first business capability evaluation model to obtain a first business capability evaluation result of the employees;

specifically, the first service ability assessment model is a corresponding assessment model established on the basis of a neural network model, the working characteristics of the employees in the enterprise represent a representative characteristic set of the employees, the first service ability assessment model is established on the basis of the neural network model, the characteristics of the neural network model are provided, wherein the artificial neural network is an abstract mathematical model which is proposed and developed on the basis of modern neuroscience and aims to reflect the structure and the function of the brain, the neural network is an operation model and is formed by connecting a large number of neurons, each node represents a specific output function called an excitation function, the connection between every two nodes represents a weighted value of a signal passing through the connection, the weighted value is equivalent to the memory of the artificial neural network, the output of the network is in accordance with the connection mode of the network, the first service ability evaluation model established based on the neural network model can output an accurate first service ability evaluation result, so that the method has strong analysis and calculation ability and achieves the technical effect of intelligent and accurate evaluation.

Step S300: constructing a first business capability normal library according to the first business capability evaluation results of the plurality of employees;

specifically, the first service capability regular model library is constructed based on the first service capability evaluation result, wherein the first service capability evaluation result is an accurate evaluation result obtained by data supervised learning through a neural network model, so that the constructed first service capability regular model library has the characteristic of data accuracy, and further, the first service capability regular model library is a data set information library for comparison with a standard quantity, and the average number and the standard deviation of a certain standardized sample can be used for comparison.

Step S400: obtaining a median employee in the first business capability normative library;

step S500: extracting service reference convolution characteristics of the median employee according to a first service capability evaluation result of the median employee in the normal model library;

specifically, the median employee is a corresponding employee obtained by performing statistical analysis on information for comparison in the first business capability constant model library, wherein the median is obtained by arranging variable values in a statistical population according to a size sequence to form a number sequence, the variable value in the middle of the variable number sequence is called a median, and so on, the median employee sorts the employees in the first business capability constant model library according to business capabilities to obtain the median employee. And extracting convolution characteristics of the median staff, wherein the convolution characteristics are convolution extraction of the characteristics of the median staff, and further, the convolution can be used as a characteristic extractor in machine learning, so that the extracted characteristic information has centralization and representativeness, and further the service benchmark convolution characteristics of the median staff are obtained.

Step S600: obtaining work characteristics of all employees of the first enterprise;

specifically, the working characteristics of all employees of the first enterprise are a total employee characteristic set obtained by collecting the characteristics of all employees in the first enterprise, the working characteristics of a plurality of employees of the first enterprise are calculated for the business representing the employees, the amount of model input data is reduced, the process of extracting the characteristics of all employees is to collect all the employees of the enterprise uniformly, and the integrity of the collected data is improved.

Step S700: traversing and comparing the working characteristics of all the employees according to the service reference convolution characteristics to obtain a difference characteristic database;

specifically, the process of traversal comparison is to compare the business reference convolution features with all collected employee working features one by one, match the business reference convolution features with the features of all employees one by one with the business reference convolution features as a first reference standard, and store the matched difference feature data to obtain the difference feature database, wherein the process of feature traversal is to form the features of all employees into a first traversal route to perform traversal query to obtain traversal completion, and when the traversal completion meets the preset completion, the feature matching process is indicated to be complete, so that the difference feature database is more accurate and effective.

Step S800: performing incremental learning on the first service capability evaluation model according to the difference feature data in the difference feature database to obtain a second service capability evaluation model;

specifically, the first business ability assessment model is a corresponding assessment model obtained by machine learning based on the working characteristics of the plurality of employees, and because difference characteristic data needs to be combined with old training data of the first recurring risk assessment model to complete a comprehensive incremental learning result, after incremental learning is performed on the difference characteristic data, the basic performance of the first recurring risk assessment model can be retained, and corresponding incremental learning is completed, and further based on the second business ability assessment model, wherein the second recurring risk assessment model is an updated model after incremental learning, so that the technical effects of performing incremental learning on new incremental characteristics and improving the updating performance of the assessment model are achieved.

Step S900: obtaining a second service capability evaluation result of all the employees according to the second service capability evaluation model;

step S1000: and obtaining a first enterprise management scheme according to the second business capability evaluation result.

Specifically, through obtaining the difference data training, the second business capability evaluation model has the basic performance of the difference business capability evaluation model, and meanwhile, the basic data of the first business capability is kept, so that the second business capability evaluation result has accuracy and updating performance, the obtained first enterprise management scheme can effectively compare the special benefits of the employees in the first enterprise after completion, and a corresponding talent management adjustment scheme is generated according to the difference result, so that the technical effects of performing benchmarking difference comparison on the talent business capability based on the extracted convolution features and improving the effective talent utilization rate and the enterprise management quality are achieved.

Further, in the obtaining of the working characteristics of the multiple employees, step S100 in the embodiment of the present application further includes:

step S110: acquiring position information and work content information of the plurality of employees;

step S120: acquiring the function characteristics of the plurality of employees according to the position information and the work content information of the plurality of employees;

step S130: acquiring workload information and completion information of the plurality of employees;

step S140: obtaining efficiency characteristics of the plurality of employees according to the workload information and the completion degree information of the plurality of employees;

step S150: obtaining attendance characteristics of the plurality of employees;

step S160: and obtaining the working characteristics of the plurality of employees according to the functional characteristics, the efficiency characteristics and the attendance characteristics.

Specifically, the department corresponding to the employees is analyzed to obtain the corresponding department position grading and work attribute information, and then specific hierarchical feature analysis is carried out on the position information and the work content information of the employees to determine the function features; the working efficiency of the staff is determined according to the workload of the staff and the specific completion degree of the work, the obtained corresponding efficiency is used as the efficiency characteristics of the staff, and further, the attendance rates of the staff can be analyzed to determine the attendance characteristics of the staff. On the other hand, the functional characteristics, the efficiency characteristics and the attendance characteristics are corresponding working characteristics obtained by further matching the levels of the corresponding analysis indexes with preset target levels, so that the detailed working characteristics are achieved, and the analysis data are more specific.

Further, the step S700 in the embodiment of the present application further includes performing traversal comparison on the working features of all employees according to the service standard convolution features to obtain a difference feature database:

step S710: traversing, comparing and calculating the working characteristics of all the employees according to the service reference convolution characteristics to obtain difference characteristic data of all the employees;

step S720: storing the difference characteristic data and all the employees into different storage blocks correspondingly;

step S730: and identifying the different storage blocks according to the position information, classifying the identified different blocks, and constructing a difference characteristic database.

Specifically, the difference feature data is obtained by data extraction of data in the difference feature database, the traversal comparison calculation is a process of calculating corresponding difference data obtained based on one-to-one matching of reference convolution features, wherein the traversal comparison process can be self-checked through a traversal self-checking module, and when the traversal completion degree does not reach a preset traversal completion degree, the difference feature data is represented to be incomplete and the accuracy is not enough. And when the traversal completion degree reaches the preset target traversal completion degree, all the difference characteristic data are stored in blocks, wherein the labels are stored in a classified manner based on the marks of the position information in the process of block storage, so that the efficiency of data management is improved, and the technical effect of calling different types of characteristic data at any time is facilitated.

Further, the step S800 of the embodiment of the present application further includes performing incremental learning on the first service capability assessment model according to the difference feature data in the difference feature database to obtain a second service capability assessment model:

step S810: inputting the difference characteristic data in the difference characteristic database into the first service capability evaluation model to obtain a difference capability evaluation result;

step S820: obtaining first loss data by performing data loss analysis on the difference capability evaluation result;

step S830: and inputting the first loss data into the first service capability evaluation model for training to obtain the second service capability evaluation model.

Specifically, the difference capability assessment result is a corresponding prediction difference coefficient obtained by performing difference service assessment in the first service capability assessment model based on the difference feature data, and since the second service capability assessment model completes analysis of data loss based on an introduced loss function to obtain a new model, the first loss data is loss data representing knowledge related to the difference feature data by the first service capability assessment model, and then completes incremental learning of the first recurrence risk assessment model based on the first loss data, where incremental learning refers to a learning system that can continuously learn new knowledge from a new sample and can store most of previously learned knowledge. Incremental learning is very similar to the learning pattern of human beings themselves. With the rapid development and wide application of databases and internet technologies, a great deal of data is accumulated by various departments in the society. Furthermore, the first recurrence risk assessment model is obtained by forming a neural network by connecting a plurality of neurons, so that the second recurrence risk assessment model retains the basic functions of the first recurrence risk assessment model through the training of loss data, and maintains the continuous updating performance of the model, thereby improving the accuracy of business assessment and ensuring the technical effect of updating the accuracy of business capability assessment.

Further, in the obtaining a first enterprise management solution according to the second service capability evaluation result, in embodiment S1000 of the present application, further include:

step S1010: performing cluster analysis on the second service capability evaluation results of all the employees to obtain first cluster information;

step S1020: according to each category in the first clustering information, making a corresponding enterprise management scheme;

step S1030: and integrating all the corresponding enterprise management schemes to obtain the first enterprise management scheme.

Specifically, the second service ability evaluation result is a corresponding evaluation result obtained by predicting based on the first service ability evaluation model after incremental learning, and further, the first clustering information is obtained by performing cluster analysis on all evaluation data in the second service ability evaluation result, where the cluster analysis is to collect data on a similar basis for classification, in other words, the process of cluster analysis refers to an analysis process in which a set of abstract objects is grouped into multiple classes composed of similar objects. Therefore, the specific analysis of the evaluation results in categories is realized, the corresponding management scheme is made corresponding to the categories, the unified arrangement is completed, the dynamic adjustment of the subsequent management measures is facilitated, and the technical effect of improving the enterprise management quality is achieved.

Further, the step S200 of the embodiment of the present application further includes inputting the work characteristics of the multiple employees into a first business capability evaluation model to obtain a first business capability evaluation result of the multiple employees:

step S210: inputting the working characteristics of the plurality of employees into the first business capability assessment model respectively;

step S220: the first service capability evaluation model is obtained through training of multiple groups of training data, wherein each group of data in the multiple groups of training data comprises the working characteristics and identification information used for identifying the first service capability evaluation result;

step S230: and obtaining output information of the first business capability evaluation model, wherein the output information comprises a first business capability evaluation result of the plurality of employees.

Specifically, the first service ability assessment result is input into each group of training data as supervision data for supervision learning, the first service ability assessment model is a model established based on a neural network model, the neural network is an operation model formed by interconnection of a large number of neurons, and the output of the network is expressed according to a logic strategy of the connection mode of the network. Further, the training process is essentially a supervised learning process, each of the plurality of sets of training data includes the working characteristic and identification information for identifying the first service capability evaluation result, the first service capability evaluation model performs continuous self-correction and adjustment until the obtained output result is consistent with the identification information, the set of data supervised learning is ended, and the next set of data supervised learning is performed. When the output information of the first service capability evaluation model reaches the preset accuracy rate/reaches the convergence state, the supervised learning process is ended, the output of the first service capability evaluation result is more accurate through the training of the first service capability evaluation model, and the technical effect of intelligently analyzing data is achieved.

Further, step S1100 in the embodiment of the present application further includes:

step S1110: judging whether the first enterprise is a head office or not;

step S1120: if the first enterprise is not a head office, obtaining enterprise cultural characteristics of the head office of the first enterprise;

step S1130: obtaining an operation range characteristic of the first enterprise;

step S1140: obtaining a first adjusting parameter according to the enterprise culture characteristics of the head office and the operation range characteristics of the first enterprise;

step S1150: and adjusting the first enterprise management scheme according to the first adjustment parameter to obtain a second enterprise management scheme.

Specifically, when the first enterprise does not represent that the first enterprise is a branch enterprise for a head office, the first enterprise responds to enterprise culture characteristics and enterprise development concepts of the head office, such as culture atmosphere or working weather, and further adjusts specific parameters based on the operation range characteristics of the first enterprise, wherein the operation range characteristics of the first enterprise include specific characteristics of enterprise attributes, enterprise development fields, enterprise development levels, audience populations, docking companies, and the like, and then completes corresponding adjustment of the first enterprise management scheme.

To sum up, the enterprise management method and system based on big data provided by the embodiment of the application have the following technical effects:

1. the method comprises the steps of obtaining working characteristics of a plurality of employees of a first enterprise, training a business capability evaluation model based on the working characteristics, carrying out first processing evaluation on the models of the working characteristics of the employees, obtaining a corresponding first business capability evaluation result, further constructing and obtaining a first business capability normal library according to the business capabilities of all the employees processed by the models, sequencing all information in the database, screening the median employee with the obtained median business capability, extracting the basic business convolution features of the median employee, carrying out later evaluation, obtaining a corresponding enterprise management scheme according to the evaluation result, and achieving the technical effects of carrying out benchmark analysis on the business capability of the talents based on the extracted convolution features and improving the effective utilization rate of the talents and the enterprise management quality.

2. The second business capability assessment model is obtained by traversing and comparing the characteristics of the screened employees with the characteristics of all the employees of the enterprise and performing incremental learning according to the difference characteristics, so that the mode of performing secondary business capability incremental learning assessment based on the level between the employees of the enterprise and the reference employees is achieved, the business assessment accuracy is improved, and the technical effect of updating the business capability assessment accuracy is ensured.

3. The technical effects of flexible dynamic adjustment and improvement of management quality are achieved by adopting a mode of carrying out convolution feature capture on the working features of the staff, carrying out feature traversal comparison based on a mode of determining a median feature reference and carrying out classification storage of different categories on the evaluation result.

Example two

Based on the same inventive concept as the enterprise management method based on big data in the foregoing embodiment, the present invention further provides an enterprise management system based on big data, as shown in fig. 2, the system includes:

a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain work characteristics of a plurality of employees of a first enterprise;

a first input unit 12, where the first input unit 12 is configured to input the work characteristics of the multiple employees into a first business capability evaluation model, and obtain a first business capability evaluation result of the multiple employees;

a first constructing unit 13, where the first constructing unit 13 is configured to construct a first business capability normative library according to the first business capability evaluation result of the multiple employees;

a second obtaining unit 14, where the second obtaining unit 14 is configured to obtain a median employee in the first business capability normative library;

a first extraction unit 15, where the first extraction unit 15 is configured to extract a business benchmark convolution feature of the median employee according to a first business capability evaluation result of the median employee in the normal model library;

a third obtaining unit 16, wherein the third obtaining unit 16 is configured to obtain work characteristics of all employees of the first enterprise;

a fourth obtaining unit 17, where the fourth obtaining unit 17 is configured to perform traversal comparison on the working features of all the employees according to the service reference convolution feature to obtain a difference feature database;

a fifth obtaining unit 18, where the fifth obtaining unit 18 is configured to perform incremental learning on the first service capability evaluation model according to the difference feature data in the difference feature database to obtain a second service capability evaluation model;

a sixth obtaining unit 19, where the sixth obtaining unit 19 is configured to obtain a second service capability evaluation result of all employees according to the second service capability evaluation model;

a seventh obtaining unit 20, where the seventh obtaining unit 20 is configured to obtain the first enterprise management scheme according to the second service capability evaluation result.

Further, the system further comprises:

an eighth obtaining unit, configured to obtain position information and work content information of the plurality of employees;

a ninth obtaining unit, configured to obtain the function characteristics of the multiple employees according to the position information and the work content information of the multiple employees;

a tenth obtaining unit, configured to obtain workload information and completion information of the plurality of employees;

an eleventh obtaining unit, configured to obtain a first replacement indicator matrix according to the first replacement instruction;

a twelfth obtaining unit, configured to obtain efficiency characteristics of the multiple employees according to the workload information and the completion information of the multiple employees;

a thirteenth obtaining unit, configured to obtain attendance characteristics of the plurality of employees;

a fourteenth obtaining unit, configured to obtain work characteristics of the plurality of employees according to the duty characteristics, the efficiency characteristics, and the attendance characteristics.

Further, the system further comprises:

a fifteenth obtaining unit, configured to perform traversal, comparison and calculation on the working features of all the employees according to the service reference convolution feature, and obtain difference feature data of all the employees;

the first storage unit is used for storing the difference characteristic data and all the employees into different storage blocks in a corresponding mode;

and the second construction unit is used for identifying the different storage blocks according to the position information, classifying the identified different blocks and constructing a difference characteristic database.

Further, the system further comprises:

a sixteenth obtaining unit, configured to input the difference feature data in the difference feature database into the first service capability evaluation model, and obtain a difference capability evaluation result;

a seventeenth obtaining unit configured to obtain first loss data by performing data loss analysis on the difference capability evaluation result;

an eighteenth obtaining unit, configured to input the first loss data into the first service capability evaluation model for training, and obtain the second service capability evaluation model.

Further, the system further comprises:

a nineteenth obtaining unit, configured to perform cluster analysis on the second service capability evaluation results of all the employees to obtain first cluster information;

the first formulating unit is used for formulating a corresponding enterprise management scheme according to each category in the first clustering information;

a twentieth obtaining unit, configured to integrate all corresponding enterprise management solutions to obtain the first enterprise management solution.

Further, the system further comprises:

a second input unit, configured to input the working characteristics of the plurality of employees into the first business capability assessment model, respectively;

a twenty-first obtaining unit, configured to obtain, by training the first service capability assessment model through multiple sets of training data, where each set of data in the multiple sets of training data includes the working characteristic and identification information used to identify the first service capability assessment result;

a twenty-second obtaining unit, configured to obtain output information of the first business capability evaluation model, where the output information includes a first business capability evaluation result of the multiple employees.

Further, the system further comprises:

a first judging unit, configured to judge whether the first enterprise is a head office;

a twenty-second obtaining unit configured to obtain a corporate cultural characteristic of a head office of the first corporation if the first corporation is not a head office;

a twenty-third obtaining unit configured to obtain a second index information amount according to the first body performance index;

a twenty-fourth obtaining unit, configured to obtain a business segment characteristic of the first enterprise;

a twenty-fifth obtaining unit, configured to obtain a first adjustment parameter according to the corporate cultural characteristics of the head office and the operating range characteristics of the first enterprise;

a twenty-sixth obtaining unit, configured to adjust the first enterprise management scheme according to the first adjustment parameter, and obtain a second enterprise management scheme.

Various changes and specific examples of the big data based enterprise management method in the first embodiment of fig. 1 are also applicable to the big data based enterprise management system in the present embodiment, and a person skilled in the art can clearly know an implementation method of the big data based enterprise management system in the present embodiment through the foregoing detailed description of the big data based enterprise management method, so for the brevity of the description, detailed descriptions are omitted here.

Exemplary electronic device

The electronic device of the embodiment of the present application is described below with reference to fig. 3.

Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.

Based on the inventive concept of the big data based enterprise management method in the foregoing embodiments, the present invention further provides a big data based enterprise management system, on which a computer program is stored, which when executed by a processor implements the steps of any one of the foregoing big data based enterprise management methods.

Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium.

The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.

The embodiment of the invention provides an enterprise management method based on big data, which comprises the following steps: obtaining work characteristics of a plurality of employees of a first enterprise; inputting the working characteristics of the employees into a first business capability evaluation model to obtain a first business capability evaluation result of the employees; constructing a first business capability normal library according to the first business capability evaluation results of the plurality of employees; obtaining a median employee in the first business capability normative library; extracting service reference convolution characteristics of the median employee according to a first service capability evaluation result of the median employee in the normal model library; obtaining work characteristics of all employees of the first enterprise; traversing and comparing the working characteristics of all the employees according to the service reference convolution characteristics to obtain a difference characteristic database; performing incremental learning on the first service capability evaluation model according to the difference feature data in the difference feature database to obtain a second service capability evaluation model; obtaining a second service capability evaluation result of all the employees according to the second service capability evaluation model; and obtaining a first enterprise management scheme according to the second business capability evaluation result. The technical problems that talents are easy to waste and low in benefit improvement degree due to the fact that effective data management is insufficient in talent resources in enterprise management in the prior art are solved, and the technical effects that the talent service capacity is subjected to benchmark difference comparison based on the extracted convolution features, and the talent effective utilization rate and enterprise management quality are improved are achieved.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims

1. A big data based enterprise management method, wherein the method comprises:

obtaining work characteristics of a plurality of employees of a first enterprise;

inputting the working characteristics of the employees into a first business capability evaluation model to obtain a first business capability evaluation result of the employees;

constructing a first business capability normal library according to the first business capability evaluation results of the plurality of employees;

obtaining a median employee in the first business capability normative library;

extracting service reference convolution characteristics of the median employee according to a first service capability evaluation result of the median employee in the normal model library;

obtaining work characteristics of all employees of the first enterprise;

traversing and comparing the working characteristics of all the employees according to the service reference convolution characteristics to obtain a difference characteristic database;

performing incremental learning on the first service capability evaluation model according to the difference feature data in the difference feature database to obtain a second service capability evaluation model;

obtaining a second service capability evaluation result of all the employees according to the second service capability evaluation model;

and obtaining a first enterprise management scheme according to the second business capability evaluation result.

2. The method of claim 1, wherein the obtaining work characteristics of a plurality of employees comprises:

acquiring position information and work content information of the plurality of employees;

acquiring the function characteristics of the plurality of employees according to the position information and the work content information of the plurality of employees;

acquiring workload information and completion information of the plurality of employees;

obtaining efficiency characteristics of the plurality of employees according to the workload information and the completion degree information of the plurality of employees;

obtaining attendance characteristics of the plurality of employees;

and obtaining the working characteristics of the plurality of employees according to the functional characteristics, the efficiency characteristics and the attendance characteristics.

3. The method of claim 2, wherein the step of performing traversal comparison on the working features of all employees according to the business benchmark convolution features to obtain a difference feature database comprises:

traversing, comparing and calculating the working characteristics of all the employees according to the service reference convolution characteristics to obtain difference characteristic data of all the employees;

storing the difference characteristic data and all the employees into different storage blocks correspondingly;

and identifying the different storage blocks according to the position information, classifying the identified different blocks, and constructing a difference characteristic database.

4. The method of claim 1, wherein the incrementally learning the first business capability assessment model from the difference feature data in the difference feature database to obtain a second business capability assessment model comprises:

inputting the difference characteristic data in the difference characteristic database into the first service capability evaluation model to obtain a difference capability evaluation result;

obtaining first loss data by performing data loss analysis on the difference capability evaluation result;

and inputting the first loss data into the first service capability evaluation model for training to obtain the second service capability evaluation model.

5. The method of claim 1, wherein obtaining a first enterprise management scenario from the second business capability assessment result comprises:

performing cluster analysis on the second service capability evaluation results of all the employees to obtain first cluster information;

according to each category in the first clustering information, making a corresponding enterprise management scheme;

and integrating all the corresponding enterprise management schemes to obtain the first enterprise management scheme.

6. The method of claim 1, wherein said entering the work characteristics of the plurality of employees into a first business capability assessment model, obtaining a first business capability assessment result for the plurality of employees, comprises:

inputting the working characteristics of the plurality of employees into the first business capability assessment model respectively;

the first service capability evaluation model is obtained through training of multiple groups of training data, wherein each group of data in the multiple groups of training data comprises the working characteristics and identification information used for identifying the first service capability evaluation result;

and obtaining output information of the first business capability evaluation model, wherein the output information comprises a first business capability evaluation result of the plurality of employees.

7. The method of claim 1, wherein the method comprises:

judging whether the first enterprise is a head office or not;

if the first enterprise is not a head office, obtaining enterprise cultural characteristics of the head office of the first enterprise;

obtaining an operation range characteristic of the first enterprise;

obtaining a first adjusting parameter according to the enterprise culture characteristics of the head office and the operation range characteristics of the first enterprise;

and adjusting the first enterprise management scheme according to the first adjustment parameter to obtain a second enterprise management scheme.

8. A big-data based enterprise management system, wherein the system comprises:

the system comprises a first obtaining unit, a second obtaining unit and a processing unit, wherein the first obtaining unit is used for obtaining working characteristics of a plurality of employees of a first enterprise;

the first input unit is used for inputting the working characteristics of the employees into a first business capability evaluation model to obtain a first business capability evaluation result of the employees;

the first construction unit is used for constructing a first business capability normative library according to the first business capability evaluation results of the employees;

the second obtaining unit is used for obtaining the median staff in the first business capability normal library;

the first extraction unit is used for extracting the business benchmark convolution characteristics of the median staff according to the first business capability evaluation result of the median staff in the normal model library;

a third obtaining unit, configured to obtain work characteristics of all employees of the first enterprise;

a fourth obtaining unit, configured to perform traversal comparison on the working features of all the employees according to the service reference convolution feature, and obtain a difference feature database;

a fifth obtaining unit, configured to perform incremental learning on the first service capability evaluation model according to the difference feature data in the difference feature database, so as to obtain a second service capability evaluation model;

a sixth obtaining unit, configured to obtain a second service capability evaluation result of all the employees according to the second service capability evaluation model;

a seventh obtaining unit, configured to obtain the first enterprise management scheme according to the second service capability evaluation result.

9. A big-data based enterprise management system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any of claims 1-7 when executing the program.