Enterprise management system based on big data

Abstract

The invention relates to the technical field of information data analysis, and discloses an enterprise management system based on big data. Comprising a data acquisition module, a data preprocessing module, an enthusiasm index analysis module, an enterprise recognition degree analysis module, a discipline analysis module, an individual comprehensive analysis module, an overall comprehensive analysis module, an early warning judgment module and a man-machine interaction module. Through the arrangement of an enthusiasm index analysis module, an enterprise recognition degree analysis module, a discipline analysis module, an individual comprehensive analysis module and an overall comprehensive analysis module, the calculation of the personal comprehensive condition of the reserve cadre personnel through three evaluation indexes is facilitated, and the comprehensive condition of the reserve cadre personnel is converted into digital data. And early warning is carried out to serve as a basis for evaluating the reserve cadre personnel, so that an enterprise system can better carry out the management work of the reserve cadre personnel, and the management is enhanced to enable an enterprise to operate more efficiently.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise management, and more specifically to an enterprise management system based on big data. Background Art

[0002] Nowadays, the wave of big data has impacted the development of all walks of life. The relationship between the development of all walks of life and big data is getting closer and closer, and enterprise management is no exception. Talent management of enterprises is extremely important in enterprise management. Applying big data analysis technology to enterprise talent management can not only help enterprises to carry out talent management work in a timely manner, but also help to improve the efficiency of enterprise talent management work; enterprise reserve cadres are the backbone of the enterprise, and the management of reserve cadres is very important for the enterprise. The enterprise will publish the learning content and inspection content of reserve cadres on the management platform, and use the degree of completion as one of the criteria for judging whether the reserve cadres are qualified.

[0003] However, in the existing enterprise talent management system, there is a large amount of basic data about reserve cadres on the enterprise management platform that has not been fully utilized, and the comprehensive status of reserve cadres has not been analyzed based on this, so that the daily status of reserve cadres cannot be timely understood, and the reserve cadres management work cannot be carried out in a targeted manner. Through digital data, the comprehensive changes of reserve cadres can be intuitively seen, which can serve as a basis for evaluating reserve cadres; therefore, the existing enterprise management system has not analyzed the data on the enterprise management platform, so that the data cannot be fully utilized and the management effect of enterprise personnel cannot be improved. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides an enterprise management system based on big data to solve the problems existing in the above-mentioned background technology.

[0005] The present invention provides the following technical solutions: an enterprise management system based on big data, comprising a data acquisition module, a data preprocessing module, a positivity index analysis module, an enterprise recognition analysis module, a discipline analysis module, an individual comprehensive analysis module, an overall comprehensive analysis module, an early warning judgment module and a human-computer interaction module; The data acquisition module is used to collect target data of the target object; the data acquisition module includes a learning data acquisition unit, a network data acquisition unit and a discipline data acquisition unit; The data preprocessing module is used to preprocess the data of the data acquisition module to obtain target data that can be directly used; The enthusiasm index analysis module is used to receive data from the learning data collection unit, and calculate the learning data through the floating drop calculation formula to obtain the learning enthusiasm index α of the target object; The enterprise recognition analysis module is used to receive data from the network data collection unit, and perform type ratio analysis on the network data keywords of the target object based on the sentiment dictionary through natural language processing technology to obtain the recognition type of the target object, and then calculate the enterprise recognition β of the target object through the recognition calculation formula; The discipline analysis module is used to receive data from the discipline data collection unit, calculate the frequency of the target object's participation in reserve cadre meetings, the frequency of participating in training courses, and the frequency of participating in other corporate activities through a frequency calculation formula, and use this as an evaluation indicator to calculate the discipline index γ of the target object using a discipline index calculation formula; The individual comprehensive analysis module is used to receive data from the enthusiasm index analysis module, the enterprise recognition analysis module and the discipline analysis module, calculate the individual comprehensive status index ω of the target object through the individual comprehensive status index calculation formula, and transmit it to the overall comprehensive analysis module and the early warning judgment module; The overall comprehensive analysis module calculates the overall comprehensive status index ρ of the enterprise reserve cadres through the overall comprehensive status calculation formula and transmits it to the early warning judgment module; The early warning identification module is used to compare the data of the individual comprehensive analysis module with the data of the overall comprehensive analysis module, and issue corresponding early warnings according to the comparison results; The human-computer interaction module is used to perform human-computer interaction display on the data.

[0006] Preferably, the target objects are reserve cadres selected from within the enterprise, and are marked as 1, 2, 3...n in sequence; the target data includes learning data, network data and discipline data; the learning data collection unit is used to collect the learning data of the target object and transmit it to the enthusiasm index analysis module; the network data collection unit is used to collect the network data of the target object and transmit it to the enterprise recognition analysis module; the discipline data collection unit is used to collect the discipline data of the target object and transmit it to the discipline analysis module.

[0007] Preferably, the floating drop calculation formula is: , where t i is the learning time of the target object on the i-th day, t´ is the learning time scheduled by the enterprise system, is the average learning time of the target subjects, and n is the number of days.

[0008] Preferably, the recognition calculation formula is: , where E(z) is the text sentiment value of positive emotion, E(f) is the text sentiment value of negative emotion, E(l) is the text sentiment value of neutral emotion, n is the number of texts of positive recognition type, m is the number of texts of negative recognition type, and Q is the number of neutral recognition type.

[0009] Preferably, the frequency calculation formula is: , where i=1, 2, 3. When i=1, i represents the reserve cadre meeting, f1 is the frequency of the target object attending the reserve cadre meeting, a1 is the number of times the target object attends the reserve cadre meeting, and A1 is the total number of reserve cadre meetings held by the enterprise system. When i=2, i represents training courses, f2 is the frequency of the target object attending the training course, a2 is the number of times the target object attends the training course, and A2 is the total number of training courses conducted by the enterprise system. When i=3, i represents other enterprise activities, f3 is the frequency of the target object attending other enterprise activities, a3 is the number of times the target object attends other enterprise activities, and A3 is the total number of other enterprise activities conducted by the enterprise system.

[0010] Preferably, the discipline index calculation formula is: , where ε is the attendance of the target object. If the target object clocks in on time, ε is 0.1; if the target object clocks in on time, ε is -0.1.

[0011] Preferably, the individual comprehensive condition index calculation formula is: , where i is the target object 1, 2, 3...n, ω i is the individual comprehensive status index of the i-th reserve cadre personnel, and the overall comprehensive status calculation formula is: , where n is the number of target objects.

[0012] Preferably, the specific way in which the early warning identification module performs early warning is: If ω<ρ, a warning is issued, indicating that the individual comprehensive status index of the target object does not meet the overall comprehensive status index standard; if ω≥ρ, no warning is issued Technical effects and advantages of the present invention: The present invention is provided with an enthusiasm index analysis module, an enterprise recognition analysis module, a discipline analysis module, an individual comprehensive analysis module and an overall comprehensive analysis module, which is conducive to analyzing the learning data, network data words and discipline data of reserve cadres in the enterprise management platform, obtaining the learning enthusiasm index, enterprise recognition and discipline index of the reserve cadres, calculating the personal comprehensive situation of the reserve cadres as three evaluation indicators, converting the comprehensive situation of the reserve cadres into digital data, and issuing early warnings, which are used as the basis for evaluating the reserve cadres, so that the enterprise system can better manage the reserve cadres, and based on the analysis of the overall situation of the enterprise's reserve cadres based on the comprehensive personal situation of the reserve cadres, it can indirectly reflect the situation of the implementation of the management of the reserve cadres, timely understand the work status of the reserve cadres, better improve the overall comprehensive quality of the reserve cadre team, strengthen management and make the enterprise run more efficiently. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a structural diagram of the enterprise management system based on big data of the present invention. DETAILED DESCRIPTION

[0014] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. In addition, the forms of the various structures recorded in the following embodiments are only examples. The enterprise management system based on big data involved in the present invention is not limited to the various structures recorded in the following embodiments. All other implementations obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0015] The present invention provides an enterprise management system based on big data, including a data acquisition module, a data preprocessing module, a positive index analysis module, an enterprise recognition analysis module, a discipline analysis module, an individual comprehensive analysis module, an overall comprehensive analysis module, an early warning judgment module and a human-computer interaction module; The data acquisition module is used to collect target data of the target object; the target object is the reserve cadres selected from within the enterprise, marked as 1, 2, 3...n in sequence; the target data includes learning data, network data and discipline data; the data acquisition module includes a learning data acquisition unit, a network data acquisition unit and a discipline data acquisition unit; the learning data acquisition unit is used to collect the learning data of the target object and transmit it to the enthusiasm index analysis module; the network data acquisition unit is used to collect the network data of the target object and transmit it to the enterprise recognition analysis module; the discipline data acquisition unit is used to collect the discipline data of the target object and transmit it to the discipline analysis module; The learning data, network data keywords and daily data of each reserve cadre on the enterprise management platform are collected through web crawler technology; the enterprise management platform includes but is not limited to various learning platforms and management platforms or systems within the enterprise; the learning data is the learning time data of the target object, and the learning time data is specifically reflected in the time the reserve cadre learns enterprise knowledge on the enterprise management platform; the network data includes but is not limited to the comment and like data of the reserve cadre on the enterprise management platform; the discipline data includes but is not limited to the number of meetings attended by the reserve cadre, the number of training courses attended and the attendance; The data preprocessing module is used to preprocess the data of the data acquisition module to obtain target data that can be directly used; the preprocessing includes but is not limited to cleaning and denoising the data; The enthusiasm index analysis module is used to receive data from the learning data collection unit and calculate the learning data through the floating gap calculation formula to obtain the learning enthusiasm index α of the target object; it reflects the floating degree of the target object's corporate knowledge learning time. If there is a target object D i There is a huge fluctuation in the learning time of the target object D. The learning time is significantly reduced and continues to be lower than the learning time scheduled by the enterprise system. i The learning enthusiasm of enterprises has fluctuated significantly, and the enthusiasm for enterprise knowledge learning has declined significantly; The enterprise recognition analysis module is used to receive data from the network data collection unit, and perform type ratio analysis on the network data keywords of the target object based on the sentiment dictionary through natural language processing technology to obtain the recognition type of the target object, and then calculate the enterprise recognition β of the target object through the recognition calculation formula; the recognition type includes positive emotion, negative emotion and neutral emotion; The discipline analysis module is used to receive data from the discipline data collection unit, calculate the frequency of the target object's participation in reserve cadre meetings, the frequency of participating in training courses, and the frequency of participating in other corporate activities through a frequency calculation formula, and use this as an evaluation indicator to calculate the discipline index γ of the target object using a discipline index calculation formula; The individual comprehensive analysis module is used to receive data from the enthusiasm index analysis module, the enterprise recognition analysis module and the discipline analysis module, calculate the individual comprehensive status index ω of the target object through the individual comprehensive status index calculation formula, and transmit it to the overall comprehensive analysis module and the early warning judgment module; The overall comprehensive analysis module calculates the overall comprehensive status index ρ of the enterprise's reserve cadres through the overall comprehensive status calculation formula and transmits it to the early warning judgment module; it is used to indirectly reflect the enterprise's reserve cadres and personnel construction work; The warning discrimination module is used to compare the data of the individual comprehensive analysis module with the data of the overall comprehensive analysis module, and issue a corresponding warning according to the comparison result; if ω<ρ, a warning is issued, indicating that the individual comprehensive condition index of the target object does not meet the overall comprehensive condition index standard, and it is necessary to talk to the target object and understand the situation; if ω≥ρ, no warning is issued; The human-computer interaction module is used to perform human-computer interaction display on the data.

[0016] In this embodiment, it should be specifically explained that the floating height calculation formula is: , where t i is the learning time of the target object on the i-th day, t´ is the learning time scheduled by the enterprise system, is the average learning time of the target subjects, and n is the number of days.

[0017] In this embodiment, it should be specifically explained that the recognition calculation formula is: , where E(z) is the text sentiment value of positive emotion, E(f) is the text sentiment value of negative emotion, E(l) is the text sentiment value of neutral emotion, n is the number of texts of positive recognition type, m is the number of texts of negative recognition type, Q is the number of neutral recognition type, and E(z), E(f) and E(l) are all calculated by the existing sentiment value algorithm, which will not be described in detail in this embodiment.

[0018] In this embodiment, it should be specifically explained that the frequency calculation formula is: , where i=1, 2, 3. When i=1, i represents the reserve cadre meeting, f1 is the frequency of the target object attending the reserve cadre meeting, a1 is the number of times the target object attends the reserve cadre meeting, and A1 is the total number of reserve cadre meetings held by the enterprise system. When i=2, i represents training courses, f2 is the frequency of the target object attending the training course, a2 is the number of times the target object attends the training course, and A2 is the total number of training courses conducted by the enterprise system. When i=3, i represents other enterprise activities, f3 is the frequency of the target object attending other enterprise activities, a3 is the number of times the target object attends other enterprise activities, and A3 is the total number of other enterprise activities conducted by the enterprise system.

[0019] In this embodiment, it should be specifically explained that the discipline index calculation formula is: , where ε is the attendance of the target object. If the target object clocks in on time, ε is 0.1; if the target object clocks in on time, ε is -0.1.

[0020] In this embodiment, it should be specifically explained that the calculation formula of the individual comprehensive condition index is: , where i is the target object 1, 2, 3...n, ω i is the individual comprehensive status index of the i-th reserve cadre personnel, and the overall comprehensive status calculation formula is: , where n is the number of target objects.

[0021] In this embodiment, it should be specifically explained that the warning forms include but are not limited to WeChat public accounts, email addresses, and text messages.

[0022] In the present embodiment, it should be specifically explained that the difference between the present embodiment and the prior art lies mainly in that the present embodiment has an enthusiasm index analysis module, an enterprise recognition analysis module, a discipline analysis module, an individual comprehensive analysis module and an overall comprehensive analysis module, which analyze the learning data, network data words and discipline data of the reserve cadres in the enterprise management platform, and obtain the learning enthusiasm index, enterprise recognition and discipline index of the reserve cadres, which are used as three evaluation indicators to calculate the comprehensive personal situation of the reserve cadres, and the comprehensive situation of the reserve cadres is converted into digital data, and early warning is carried out, which is used as the basis for evaluating the reserve cadres, so that the enterprise system can better manage the reserve cadres, and based on the analysis of the overall situation of the enterprise's reserve cadres based on the comprehensive personal situation of the reserve cadres, it can indirectly reflect the situation in the implementation of the reserve cadres management work, timely understand the work status of the reserve cadres, better improve the overall comprehensive quality of the reserve cadres team, strengthen management and make the enterprise run more efficiently.

[0023] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention. The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An enterprise management system based on big data, characterized by: It includes data collection module, data preprocessing module, initiative index analysis module, enterprise recognition analysis module, discipline analysis module, individual comprehensive analysis module, overall comprehensive analysis module, early warning identification module and human-computer interaction module; The data acquisition module is used to collect target data of the target object; the data acquisition module includes a learning data acquisition unit, a network data acquisition unit and a discipline data acquisition unit; The data preprocessing module is used to preprocess the data of the data acquisition module to obtain target data that can be directly used; The enthusiasm index analysis module is used to receive data from the learning data acquisition unit, and calculate the learning data through the floating drop calculation formula to obtain the learning enthusiasm index α of the target object; The enterprise recognition analysis module is used to receive data from the network data collection unit, and perform type ratio analysis on the network data keywords of the target object based on the sentiment dictionary through natural language processing technology to obtain the recognition type of the target object, and then calculate the enterprise recognition β of the target object through the recognition calculation formula; The discipline analysis module is used to receive data from the discipline data collection unit, calculate the frequency of the target object's participation in reserve cadre meetings, the frequency of participating in training courses, and the frequency of participating in other corporate activities through a frequency calculation formula, and use this as an evaluation indicator to calculate the discipline index γ of the target object using a discipline index calculation formula; The individual comprehensive analysis module is used to receive data from the enthusiasm index analysis module, the enterprise recognition analysis module and the discipline analysis module, calculate the individual comprehensive status index ω of the target object through the individual comprehensive status index calculation formula, and transmit it to the overall comprehensive analysis module and the early warning judgment module; The overall comprehensive analysis module calculates the overall comprehensive status index ρ of the enterprise reserve cadres through the overall comprehensive status calculation formula and transmits it to the early warning judgment module; The early warning identification module is used to compare the data of the individual comprehensive analysis module with the data of the overall comprehensive analysis module, and issue corresponding early warnings according to the comparison results; The human-computer interaction module is used to perform human-computer interaction display on the data.

2. According to claim 1, the enterprise management system based on big data is characterized in that: The target objects are reserve cadres selected from within the enterprise, and are marked as 1, 2, 3...n in sequence; the target data includes learning data, network data and discipline data; the learning data acquisition unit is used to collect the learning data of the target object and transmit it to the enthusiasm index analysis module; the network data acquisition unit is used to collect the network data of the target object and transmit it to the enterprise recognition analysis module; the discipline data acquisition unit is used to collect the discipline data of the target object and transmit it to the discipline analysis module.

3. The enterprise management system based on big data according to claim 1, characterized in that: The floating drop calculation formula is: , where t i is the learning time of the target object on the i-th day, t´ is the learning time scheduled by the enterprise system, is the average learning time of the target subjects, and n is the number of days.

4. The enterprise management system based on big data according to claim 1, characterized in that: The recognition calculation formula is: , where E(z) is the text sentiment value of positive emotion, E(f) is the text sentiment value of negative emotion, E(l) is the text sentiment value of neutral emotion, n is the number of texts of positive recognition type, m is the number of texts of negative recognition type, and Q is the number of neutral recognition type.

5. The enterprise management system based on big data according to claim 1, characterized in that: The frequency calculation formula is: , where i=1, 2, 3. When i=1, i represents the reserve cadre meeting, f1 is the frequency of the target object attending the reserve cadre meeting, a1 is the number of times the target object attends the reserve cadre meeting, and A1 is the total number of reserve cadre meetings held by the enterprise system. When i=2, i represents training courses, f2 is the frequency of the target object attending the training course, a2 is the number of times the target object attends the training course, and A2 is the total number of training courses conducted by the enterprise system. When i=3, i represents other enterprise activities, f3 is the frequency of the target object attending other enterprise activities, a3 is the number of times the target object attends other enterprise activities, and A3 is the total number of other enterprise activities conducted by the enterprise system.

6. The enterprise management system based on big data according to claim 1, characterized in that: The discipline index calculation formula is: , where ε is the attendance of the target object. If the target object clocks in on time, ε is 0.1; if the target object clocks in on time, ε is -0.

1.

7. The enterprise management system based on big data according to claim 1, characterized in that: The calculation formula of the individual comprehensive condition index is: , where i is the target object 1, 2, 3...n, ω i is the individual comprehensive status index of the i-th reserve cadre personnel, and the overall comprehensive status calculation formula is: , where n is the number of target objects.

8. The enterprise management system based on big data according to claim 1, characterized in that: The specific way in which the early warning identification module performs early warning is: If ω<ρ, a warning is issued, indicating that the individual comprehensive status index of the target object does not meet the overall comprehensive status index standard; if ω≥ρ, no warning is issued.

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