Description:The following complete specification particularly describes and ascertains the nature of this invention and the manner in which it is to be performed: -
Field of the invention
The application pertains to big data and human resource management, namely to a data-driven technique and system for organising and managing human resources.
Background of the invention
The creation of new businesses in India is driven by the country's rapid technological advancement, which raises the bar for talent strategy implementation. The soft strength of the individual talent, such as the self-soft strength of behaviour patterns, interpersonal cooperation, personal development, organisation cooperation, and the like, must also be continuously improved. In addition, the enterprise leader must pay attention to the soft strength at all times, such as team cooperation, effective conflict avoidance, master management content, and staff character traits. Indian businesses must realise business informatisation as well as the intelligentisation of organisation management activities in order to compete in the future digital and intelligent society. Additionally, the entire value chain from strategy to organisation to customer experience must be transformed by intelligent informatisation.
According to the perspective of enterprise informationisation, intelligent operation, and management, the enterprise needs to be created with the capacity for intelligent organisation and management; the interior of the enterprise should be transformed from administrative management and authority drive to intelligent information drive and cultural drive; and a leader needs to change from power lead. The organisation form and structure of an enterprise should be represented and presented in informational form, and the connection, interaction, and drive between an organisation and a person should be based on digital reconstruction, connection, and driving digitally.
Therefore, a problem that needs to be solved immediately is how to use information technology and intelligent technology to help business employees recognise their own values and potential. This will improve the ability of business department organisation management to make quick decisions and will also increase team productivity.

Description of the invention
One of the technical issues stated above will hopefully be partially resolved by the current application.
The first goal of the current application is to create a data-driven system for organising and managing human resources in order to achieve this. The system makes use of statistical machine learning analysis and visualisation technology, which not only enables the enterprise to help employees recognise their own worth and potential, but also enhances the organisation and management of the enterprise's departments and the effectiveness of teams.
The provision of a data-driven strategy for managing and organising human resources is the second goal of the current application.
An embodiment of the present application's data-driven human resource organisation management system contains the following to accomplish the aforementioned goal:
The micro service system architecture is used to generate and adjust the capability evaluation indexes of the talents and organisations of the enterprise based on the human resource organisation management data generated by the human resource communication management module. The data management module is used to carry out the capability evaluation of the human resource organisation management data generated by the human resource communication management module.
Based on machine learning technology, the data analysis module performs cluster analysis, classification analysis, and abnormal point analysis on the multidimensional data collected from enterprise employees to produce a data analysis result. The visual display module then uses the data analysis result to produce configurable data visual charts, visual analysis reports, and capability evaluation reports.
A human resource organisation and management method based on data driving is provided by the embodiment of the second feature of the present application, which includes: producing data for human resource organisation management based on the window theory;
Data acquisition on employee information through multiple dimensions to obtain multi-dimensional acquisition data of enterprise employees; performing cluster analysis, classification analysis, and abnormal point analysis on the multi-dimensional collected data of the enterprise employees based on a machine learning technology to obtain a data analysis result; and generating data analysis results according to the ability evaluation indexes of the enterprise talents and organisations.
A theory in the area of human resource organisation management is combined with intelligent analysis and statistical machine learning to perform multidimensional and multi-mode data collection for different types of enterprises, time sequence data accumulation, data storage and migration of large-scale structured and unstructured data, and data mining are all made possible by the data-driven human resource organisation management system and method. The window theory of Harry is therefore combined with artificial intelligence, big data analysis, and data visualisation technology to create an information system that serves organisation management and continuously improves team productivity based on data driving.
The system helps the workforce realise their own potential, the manager improve the organization's digital management capabilities, and the company become competitive and viable in a digital society. The procedure entails building a sizable talent database by collecting and storing data on different employee types, performing data analysis and decision-suggesting using statistical machine learning models, and offering visual display-based decision support for organisational management for different roles.
The invention aims to create an information system supporting organisation management and continuously enhancing team efficiency based on data driving by fusing the window theory of Harry Potter with artificial intelligence, big data analysis, and data visualisation technology.
The invention provides a big data artificial intelligence based data analysis visualisation system that addresses the issues that the current human resource information system is primarily used for enterprise internal management, while research institutions primarily adopt a sampling investigation mode for researching human resource requirements and posts of specific administrative divisions, lack of collection, cleaning, mining, and analysis of internet-based human resource data, and lack of data collection, cleaning, mining, and analysis.
The following steps are included in the big data artificial intelligence-based data analysis visualisation system:
Step1: Creating a metadata base in accordance with the index standard and defining an index standard for the skills needed for big data in online human resources;
Step2, gathering information from job postings on the internet in accordance with the skill demand index standard defined in step1 and storing the information using a big data platform;
Step 3: Organising the data from the retrieved WebPages into structured data with a standardised format;
Step 4 involves creating a machine learning algorithm to mine data,
Step 5 involves creating an interactive real-time data visualisation webpage to show the analysis's findings.
By obtaining information from network-published job postings. After data collection is complete, the data is cleaned to create structured data in a standard format; it is then mined for information using a machine learning algorithm, necessary fields and records are sorted out, and the analysis result is displayed in a webpage mode using various types of graphic icons; the analysis result of the demand for human resource talent is displayed in real time in accordance with the demand's specified standard, allowing for decision-making.
The system addresses issues such as the fact that the current human resource information system is primarily used for enterprise internal management, and that research institutions typically adopt a sampling investigation mode for researching human resource requirements and posts of specific administrative divisions, lack of collection, cleaning, mining, and analysis of large human resource data based on the Internet, and lack of an interactive real-time access data visualisation display.
The Step1 criteria for the big data talent demand index also include criteria for industry categories and/or geographic location nodes. There are several collection duties designed for different company sectors, regions, and even posts. In order to ensure mutual influence between the tasks, these jobs are distributed and completed. As a result, numerous goal standards are defined in accordance with diverse needs, and the server's workload is significantly decreased.
The metadata bases in step1 also consist of metadata bases that follow the geographic location node standard or the industrial category standard. The workload on the database can be reduced, the hardware requirements for the database can be reduced, the database has a stronger data storage target performance, the metadata bases with various purposes cannot influence one another, and even if one database is destroyed, other data can still be accessed, increasing practicability.
The analysis of the talent demand data for various posts based on a geographic location and the talent demand standard is also included in step4 of building a machine learning algorithm to analyse and mine the data; the talent requirement criteria include, but are not limited to, talent age and scholarly calendar.
The data analysis visualisation system based on big data artificial intelligence consists of a data acquisition module, a big data storage platform, a data analysis module, and a display module. The data acquisition module collects talent information and recruitment information from a network terminal and transmits the information to the big data storage platform for storage.
Additionally, the data analysis module includes a scoring vector creation module. This module is used to generate score vectors based on the information gathered about talent demand and applicant resumes.
FIG. 1 is a schematic flow diagram of the process of the present invention.
Drawing describes the invention
Step1: Creating a metadata base in accordance with the index standard and defining an index standard for the skills needed for big data in online human resources;
Step2, gathering information from job postings on the internet in accordance with the skill demand index standard defined in step1 and storing the information using a big data platform;
Step 3: Organising the data from the retrieved WebPages into structured data with a standardised format;
Step 4 involves creating a machine learning algorithm to mine data,
Step 5 involves creating an interactive real-time data visualisation webpage to show the analysis's findings.
AI based program based on the non linear SVM theorem
The SVM converts the mapped high-dimensional inner product into a function of the low-dimensional space by introducing a kernel function, where F is a mapping from the low-dimensional feature space to the high-dimensional feature space, and if there is a function K (x, z), for any low-dimensional feature vectors x and z, there are

The function K (x, z) is called the kernel function.
Useful kernel functions include polynomial kernel functions:
K(x,y)=(yx*z+r)d
wherein y, r and d are super paramteres;
the available kernel functions may also include a gaussian kernel function:

wherein, sigma is the only super parameter, | | x-y | | | represents the norm of the vector, namely the modulus of the vector;
substituting the kernel function into an objective function in a generalized SVM learning algorithm to obtain an optimization problem of the nonlinear SVM, wherein the data set T { (x)1,y1),(x2,y2),…(xN,yN)},

0=ai=C,i=1,2,....,N
An optimal solution can be obtained

A specific implementation comprises using geographic location node criteria and/or industry category criteria in step1 of the big-data talent demand index.
One proposed implementation calls for the metadata bases in step1 to include metadata bases that adhere to the industry category standard and/or the geo-location node standard.
The process of building a machine learning algorithm to analyse and mine the data in step4 in one conceivable embodiment comprises analysing the talent demand data of various posts based on a geographic location and the talent demand criteria; the talent requirement criteria include, but are not restricted to, talent age and scholarly calendar.
In one possible completion, step2 entails gathering information from job postings on the internet in accordance with the talent demand index standards defined in step1 and storing the information using a big data platform.
Collecting data from network-published job postings, cleaning the data using the acquired data to create structured data with a consistent format after the data acquisition is complete; creating a machine learning algorithm to analyse and mine the data, sorting out relevant fields and records, and displaying the analysis's findings in webpage mode using a variety of graphic icons; The issue that an existing human resource information system is primarily used for enterprise internal management, the analysis result of the human resource talent demand is shown in real time according to the specified standard of the demand, decision support is carried out on pertinent departments and research institutions, and enterprises are helped to learn about the talent dynamics of the industry.
A data analysis visualisation system based on big data artificial intelligence is provided in a second aspect of the present embodiment. The system has four components: a data acquisition module, a big data storage platform, a data analysis module, and a display module. The data acquisition module collects talent and recruitment information from network terminals and sends it to the big data storage platform for storage.
The data analysis module includes a scoring vector generating module that is used to score gathered talent demand information and applicant resume information during implementation.
An apparatus for data analysis and visualisation based on big data artificial intelligence is provided in a third aspect of this embodiment. The apparatus includes a memory, a processor, and a transceiver that are sequentially connected. The memory stores a computer programme, the transceiver transmits and receives messages, and the processor reads the computer programme and executes the programme of the data analysis and visualisation method based on big data artificial intelligence.
A fourth feature of the current embodiment is the provision of a computer-readable storage medium that has instructions that, when run on a computer, carry out the technique described in the first feature or any of its potential implementations. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer-readable storage medium refers to a carrier for storing data and may include, but is not limited to, floppy discs, optical discs, hard discs, flash memories, flash discs and/or Memory sticks (Memory sticks), etc.
A computer programme product according to a fifth aspect of the present invention comprises instructions for causing a computer to carry out the method described in the first aspect of the embodiments or any of its possible implementations. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable apparatus.
Those knowledgeable in the art will be able to tell from the preceding descriptions of the embodiments that each embodiment can be implemented both by hardware and software, along with the requisite general hardware platform. In light of this, the aforementioned technical solutions may take the form of a software product that is stored in a computer-readable storage medium, such as ROM/RAM, magnetic disc, optical disc, etc., and contains instructions for instructing a computer device to carry out all or a portion of the methods described in the aforementioned embodiments.
, Claims:We Claim
1. A data-driven human resources organization and management system, comprising:
Step1: Creating a metadata base in accordance with the index standard and defining an index standard for the skills needed for big data in online human resources;
Step2, gathering information from job postings on the internet in accordance with the skill demand index standard defined in step1 and storing the information using a big data platform;
Step 3: Organising the data from the retrieved webpages into structured data with a standardised format;
Step 4 involves creating a machine learning algorithm to mine data,
Step 5 involves creating an interactive real-time data visualisation webpage to show the analysis's findings.
Artificial Intelligence program based on the non linear SVM theorem.
2. The data-driven human resources organization and management system as claimed in claim 1 wherein step1, the big data talent criteria comprise location, Industry and work experience.
3. The data-driven human resources organization and management system as claimed in claim 1 wherein Step4, the process of building a machine learning algorithm to analyse and mine the data, whose analysis includes analysing talent demand data and talent demand standards of various posts based on the same administrative region; the talent demand criteria include talent age and/or scholars.