WO2016101818A1 - 一种数据处理方法、装置及系统 - Google Patents

一种数据处理方法、装置及系统 Download PDF

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WO2016101818A1
WO2016101818A1 PCT/CN2015/097487 CN2015097487W WO2016101818A1 WO 2016101818 A1 WO2016101818 A1 WO 2016101818A1 CN 2015097487 W CN2015097487 W CN 2015097487W WO 2016101818 A1 WO2016101818 A1 WO 2016101818A1
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data
information
candidate information
attribute data
attribute
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PCT/CN2015/097487
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English (en)
French (fr)
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闵万里
汤开智
刘军宁
王瑜
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阿里巴巴集团控股有限公司
闵万里
汤开智
刘军宁
王瑜
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Publication of WO2016101818A1 publication Critical patent/WO2016101818A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor

Definitions

  • the present application belongs to the field of information data processing, and in particular, to a data processing method, device and system.
  • the talent information analysis management system commonly used in the market includes systems such as Beishen and Mordern HR in Cloud.
  • Most of the information analysis systems mentioned above adopt the result data processing method of the data table, and face the complicated ETL (extract, transform, load, extraction, transposition, loading) processing process in the data processing process.
  • the processing of data information analysis by the system is too complicated and the execution efficiency is low.
  • the information data acquired by the system generally includes only attribute information such as academic qualifications, age, occupation, working years, and expected salary.
  • the analysis of talents is limited by the limited data obtained and fixed analysis methods. It does not involve the analysis of other data that affects the flow of talents. The flexibility of system data analysis is poor and the accuracy of output results is low.
  • the purpose of the present application is to provide a data processing method, device and system, which make the data analysis and processing of the relevant person information more accurate and flexible, and can effectively and quantitatively analyze the talent flow.
  • a data processing method, apparatus and system provided by the present application are implemented as follows:
  • a data processing method comprising:
  • a data processing device comprising:
  • An information acquiring module configured to acquire first information including first attribute data and second attribute data
  • a matching module configured to store a preset matching condition, and select, from the first information, the first candidate information that the first attribute data meets a preset matching condition
  • a relationship identifying module configured to store a first association relationship of the second attribute, and select, from the second attribute data of the first candidate information, the first associated data that the second attribute data meets the first association relationship ;
  • An assignment module configured to assign the first attribute data and the first associated data
  • an output module configured to calculate an output value of the first candidate information according to the first attribute data of the first candidate information and the value of the first associated data.
  • a data processing system configured to include:
  • a processing unit that selects first associated data having a first association relationship from the first candidate information
  • a processing unit that assigns data of the first candidate information and the first associated data
  • a processing unit that derives an output value of the first candidate information is calculated based on the assignment of the data.
  • the data processing method, device and system provided by the present application analyzes the self-professional attributes related to talents and the big data of interpersonal social interaction, and can analyze the factors affecting the turnover of talents and the flow of talents.
  • the degree of influence assigns the factors, and then quantitatively calculates the output value of the talent flow.
  • the data analyzed and processed in the application is added to the second attribute data of the professional social information of the talent, comprehensively considering the work and living environment in which the talent is located to effectively measure the risk and possibility of the job hopping, and can be based on the candidate node Relevant data assignment quantitatively calculates the output value of candidates, which can analyze the talent data more comprehensively and accurately, so that talent managers can effectively discover the trend of talent flow, formulate recruitment or treatment programs that meet the needs of talents, and improve the management efficiency of talents. .
  • FIG. 1 is a schematic flow chart of a method for processing an embodiment of a data processing method according to the present application
  • FIG. 2 is a schematic diagram of a relationship network of candidates for heterogeneous relationships
  • FIG. 3 is a schematic flow chart of a method for another embodiment of a data processing method according to the present application.
  • FIG. 4 is a schematic flow chart of a method for processing another method of data processing according to the present application.
  • FIG. 5 is a schematic structural diagram of a module of an embodiment of a data processing apparatus according to the present application.
  • FIG. 6 is a schematic structural diagram of a module of another embodiment of a data processing apparatus according to the present application.
  • FIG. 7 is a schematic structural diagram of a module of another embodiment of a data processing apparatus according to the present application.
  • FIG. 8 is a schematic structural diagram of a module of another embodiment of a data processing apparatus according to the present application.
  • FIG. 9 is a schematic block diagram showing another embodiment of a data processing apparatus according to the present application.
  • the data processing method described in the present application is not limited to analyzing the matching degree and flow possibility of other companies according to the acquired information data related to talents, and can also be used for analyzing and controlling the flow tendency of talents within the company.
  • Based on the analysis of big data related to talents we can refer to the analysis of the factors affecting the turnover of talents and the impact of such factors on the flow of talents.
  • the application provides a data processing method, which can effectively measure the risk and possibility of talent change based on the big data analysis of the talent information, so that the manager can effectively discover the trend of the talent flow and formulate a recruitment or treatment plan that meets the talent demand.
  • FIG. 1 is a flowchart of a method of an embodiment of a data processing method according to the present application. As shown in FIG. 1, the method may include:
  • S1 Acquire first attribute data and second attribute data of the first information.
  • the first information may include multiple pieces of information data related to the talent, or may include information data acquired in a plurality of ways.
  • the first information may include first attribute data, and the first attribute data may generally include related information that directly reflects the matching of talents with occupations or positions, such as the age of the talent, the number of job hops, the working years, and the work. Unit, industry, position, place of work, expected salary, etc.
  • the first information may include a partnership based on the alliance.
  • Obtaining the second attribute data related to the professional occupational social network, and the specific second attribute data may include at least one of information such as professional communication, interpersonal social networking, and recruitment website.
  • the second attribute data of the first information described in the present application may include information data of the recruitment website, such as data acquired from a website server such as 51job or the street network.
  • the second attribute data provided by these recruitment websites can be more intuitive The basic information and conditions of talents should be required.
  • the second attribute data described in the present application may be provided by information data provided by various professional exchange websites having a cooperative relationship with the talent recruitment management, such as linkin in a comprehensive field, information data of a website, or a certain professional field, for example Information data provided by the Mobile Communications Forum (MSCBSC), the CSDN Chinese IT community, etc.
  • MSCBSC Mobile Communications Forum
  • CSDN Chinese IT community etc.
  • the source of the second attribute data in the embodiment may further include information data related to talent interpersonal social interaction.
  • it may include information such as Weibo, Renren, Century Jiayuan, Douban, and QQ Space.
  • the obtained second attribute data may reflect the relative change of the number of logins and residence times of the talent A on certain recruitment websites, the job location of the job search, etc., or obtain the recent Weibo information from the public Weibo information and need to be stable. Work and other information. In this way, it is possible to obtain multi-faceted information data related to talents, and in the subsequent data processing, talents can be obtained according to accurate analysis.
  • the first information may be classified and integrated according to the preset primary key information, and the classified information data is stored.
  • talents can use different media account information in different social media, but some information filled in account registration or security verification, such as email or mobile phone number, is usually the same, so the mailbox or mobile phone number can be used as the
  • the preset primary key information classifies and integrates the acquired first information data, and stores the classified information data to form an enhanced talent information database.
  • the first information is classified and integrated based on the preset primary key information, so that the first information of the talents of different information structures and different information fields can be integrated, so that the talent management personnel can view and use the information more concisely and conveniently.
  • the enhanced talent information database that can be formed after classification and integration includes not only the traditional first attribute data related to the talents' own occupation, but also the professional social interaction with talents, such as the frequency of recruitment of talent recruitment websites, occupation information sharing, and individuals. Information such as career planning sharing can reflect the recent trends of talents more comprehensively and accurately.
  • S2 Select, from the first information, the first candidate information that the first attribute data meets a preset matching condition.
  • the first attribute data may include information related to the professional attribute of the talent.
  • the first information that meets the preset matching condition may be selected from the first information.
  • the first candidate information may include data of the first information of the candidate that meets the recruitment requirement or other preset matching conditions.
  • the preset matching condition may be selected according to a post or occupation requirement, has a certain working life, and has participated in a certain type of engineering.
  • the project may pass the qualification of a professional qualification, or you can use the headhunting matching mode to select candidates who need to be recruited or managed.
  • the candidate matching conditions of the candidate can be preset according to the recruitment or management requirements.
  • a candidate who meets the requirements can be selected.
  • the first candidate information of the candidate can be selected according to the first attribute data of the talent, and the plurality of first information that meets the preset matching condition can be selected as the first candidate information, that is, the candidate information can be selected.
  • a number of talents are selected as the candidate.
  • the selected candidate is willing to leave the original company to quit to another company, or whether the employee of the company can be stably in the matched position for a long time, and needs to combine the second information in the first information in this embodiment.
  • the attribute data is further analyzed. Therefore, the data processing method described in this application may further include:
  • S3 Select, from the second attribute data of the first candidate information, the first associated data that the second attribute data meets the first association relationship.
  • the acquired first information includes not only the first attribute data, but also second attribute data of professional social interactions related to talents, social, media, etc., the data not only reflecting the matching information of the talents themselves and the posts, but also It reflects the relationship between talents and talents, talents and units, talents and qualifications, and other different levels and types.
  • the heterogeneous relationship of the candidate may be established by using the first information including the first attribute data, the second attribute data, and the like as the node, and each single node in the heterogeneous relationship may use the first
  • the attribute data is described by individual characteristics such as age, position, working years, and the like.
  • the second attribute data including work experience, work unit status, job information, qualification qualification, and the like may then be used in the heterogeneous relationship to describe the occupational and social characteristics.
  • the second attribute data may include information data related to the status of the work unit, such as the company's business market share of the company in which the candidate is located, annual turnover, number of employees, salary (third party statistics), and the like.
  • Information data that may also be related to job information may include, for example, comfort, travel frequency, salary, market demand, decision influence, and the like.
  • the second attribute data may further include information data related to qualification qualifications, for example, may include participating vocational training, industry organizations or associations, qualification certification, and the like.
  • the information content included in the first attribute data and the second attribute data of each node may be set according to requirements.
  • the edge between the node and the node can reflect the interaction between talents and talents, talents and units, talents and positions.
  • a certain management relationship may exist between different nodes.
  • the first association relationship of the second attribute data may be preset, and the second attribute data that meets the first association relationship may be associated with each other.
  • the first association relationship may be set in advance, and may be expressed as (1) whether it is in the same company; (2) whether it is graduated from the same school; (3) whether it is a subordinate relationship or the like.
  • the specific first association relationship may be set to be the same as the second attribute data or the second attribute data conforms to a preset affiliation, a subordinate relationship, or the like.
  • the second attribute data of the first candidate information may be selected.
  • the first attribute data in which the second attribute data meets the first association relationship for example, belongs to the same company.
  • the heterogeneous relationship of the candidate established by the first information may be represented by a relational network diagram, such as a heterogeneous relationship network diagram of a candidate described in FIG. 2.
  • the A-F nodes are respectively the first candidate information, and each candidate includes corresponding attribute data.
  • the first candidate information having the first associated data in the heterogeneous relationship graph may be used to represent the association between different candidates.
  • the first attribute data and the first associated data of the first information in the heterogeneous relationship network may be assigned, for example, the talents in the heterogeneous relationship network established above may be The position, working years, qualifications, frequency of travel, and annual financial statements of the company are assigned.
  • the specific value can be assigned according to the empirical value obtained by analyzing the flow of talents or the self-defined evaluation rules.
  • the assignment of the first attribute data and the first associated data may be expressed as the influence of the assigned data information on the talent hopping.
  • the positions of different industries can be assigned with reference to the nature of the job work.
  • the geographical mobility with strong travel time and long period of time related to the construction project can be assigned 40, and the office A relatively stable office position can be assigned a value of 10.
  • an important qualification for an industry can be assigned a value of 5, which indicates that the candidate reduces the candidate's job-hopping risk after obtaining the qualification, and increases the possibility that the candidate will flow.
  • S5 Calculate an output value of the first candidate information according to the first attribute data of the first candidate information and the value of the first associated data.
  • an output value of the first candidate information may be calculated according to the first attribute data of the first candidate information and the value of the first associated data in the heterogeneous relationship network.
  • the output value may include calculating a total score of the first attribute data of the candidate and the value of the first associated data, and may be used to quantitatively evaluate the position stability of the candidate. For example, it is known from the social information data of the talent personal homepage that the candidate A working in a medium-sized city C1 is ready to purchase the property in another large city C2, and the candidate A can be known from the information data of the talent circle of the talent friend.
  • the candidate A can be calculated to have poor stability and high probability of job hopping, thereby utilizing the multi-dimensional second attribute data to more accurately and effectively assess the flow of talents.
  • the output value of the first candidate may be obtained by using the data assignment total score normalization to obtain an interval score of 0 to 100, or may be directly added by using the relevant data assignment without the upper limit. In this way, the application does not limit this.
  • the data processing method provided by the present application can calculate the output value of the candidate according to the value of the multi-dimensional data of each node in the candidate heterogeneous network.
  • the heterogeneous relationship network includes not only the first attribute data related to the professional attributes of the talents, but also the working environment, the industry environment, the interpersonal social information, and the professional communication with the talents.
  • a multi-dimensional second attribute data which may affect the flow of talents, and may quantitatively calculate the output value of the candidate based on the data assignment associated with the candidate node, so that the analysis of the talent data can be performed more comprehensively and accurately. Intuitive and effective access to information on talent mobility.
  • the obtained first information may further include information related to consumption of talents on the Internet, such as online shopping data of various online shopping websites.
  • the online shopping data of these talents can often reflect the changes in the working conditions of talents, changes in living conditions, purchasing power, and changes in working and living conditions.
  • the reference data can be extracted from online shopping data, and the flow tendency of talents can be reflected through analysis. For example, if the default harvest address of a certain shopping website of a target talent changes, it may indicate that the work unit or work place of the target talent has changed.
  • a target person's shopping on the shopping site or the product of interest is more inclined to maternal and child supplies, which may indicate that the family structure of the target talent has changed. For example, if a new baby is added, it may indicate that the target talent may find a higher salary. Work or a work location closer to the family.
  • the reference data of different online shopping data may correspond to different attribute tags of the talents. Therefore, in this embodiment, reference data reflecting the flow of talents may also be extracted from the obtained online shopping data, and the reference data in the online shopping data may also be performed.
  • Assignment. 3 is a schematic flow chart of a method for another embodiment of a data processing method according to the present application. As shown in FIG. 3, in another embodiment, the method may further include:
  • S6 acquiring online shopping data of the first candidate information, extracting reference data of the online shopping data, and assigning a value to the reference data;
  • the calculating, according to the first attribute data of the first candidate information and the value of the first associated data, the output value of the first candidate information comprises: first attribute data according to the first candidate information
  • the output value of the first candidate information is calculated from the values of the first associated data and the reference data of the first candidate information.
  • the established heterogeneous relationship network may include reference data of the online shopping data, such as a harvest address, a consumption quota, a consumption type, etc., and the reference data of the online shopping data may be assigned, and the candidate is calculated.
  • the reference data of the online shopping data is taken into account when outputting the value.
  • the data processing method described in this embodiment not only processes the professional social information of the talent, but also adds the online shopping data of the talent, analyzes the online shopping behavior and habits of the talent, and further determines the flow tendency of the candidate.
  • Recruitment and management provide strong support.
  • reference events may also be set in the established heterogeneous relationship network, and the reference events may include industry information, company actions, and government that can affect the flow of talents. Policies, emergencies, etc.
  • the data processing method may further include:
  • the output value of the first candidate information is calculated according to the value of the first attribute data of the first candidate information and the reference data of the first associated data and the first candidate information, including:
  • the output of the first candidate information is obtained according to the first attribute data of the first candidate information and the first associated data and the reference data adjusted by the reference data of the first candidate information. value.
  • the reference event described in this embodiment may be pre-defined set event information that may occur, or may be acquired real-time input event information.
  • the reference event may be given a corresponding weight according to the influence degree of the event on the talent flow, and is used for quantitatively evaluating the influence of the reference event on the influence of the talent flow. Then, the assignment of the variable information associated with the reference event in the heterogeneous relationship may be adjusted according to the weight of the reference event.
  • the reference event may generally affect a plurality of data information associated with the reference event, such as salary increase or decrease, future development prospects or disadvantages, etc., which may affect the mobility of talents, and may be used in this application.
  • a data assignment score that affects the first candidate information. In this way, the reference event has been added, which greatly improves the flexibility of the analysis and processing of information data about talents.
  • a large e-commerce company AL announced that the application phase has been completed and will be listed on a national exchange.
  • the reference event of the company AL to be listed is a good news for talents, which usually affects all employees under the company AL and the company or its subsidiaries that have a cooperative relationship with the company AL, and even affects the future development of the entire industry. Foresight, then the probability that the candidate of the company AL will flow will be reduced, the weight of the reference event may be set to a negative value, or similarly, the right to assign data associated with the reference event may be reduced. value.
  • the reference event usually affects all nodes related to the company, and may be set in the heterogeneous relationship network related to the reference event.
  • the data assignment of the node is increased by 30 points or by 30%, indicating that the company's BD talent mobility is increasing.
  • the assignment of the data associated with the reference event in the heterogeneous relationship can be adjusted based on the weight assigned to the reference event, thereby enabling a more real-time and accurate quantitative analysis of the talent flow.
  • the weight value given to the reference event in the embodiment may be a scalar value, for example, 10 points or 30 points, or a relative value, as shown in the above-mentioned increase of 30%. Set according to your needs.
  • the weight of the reference event may be set according to the influence of the reference event. For example, in the application scenario of the company BD, if the financial statement is falsified and causes huge compensation, the corresponding reference event weight corresponds. Increase, for example, can be set to 50 or 80, etc., increasing the possibility of company BD talent hopping.
  • a plurality of said reference events may be included in the same heterogeneous network, for example, bilateral trade agreements with certain countries, government incentives Policy, company mergers and acquisitions and other good news, these multiple reference events can affect the assignment of data related to the heterogeneous relationship with different weights, affecting the final talent change and the output value of the flow.
  • the data processing method described above performs the classification and integration of the first information according to the preset primary key information, stores the classified and integrated information, forms an enhanced talent database, and can also perform feature extraction on the classified information. And storing the information after the feature extraction, and constructing a headhunting knowledge base.
  • the feature extraction of the classified information may include talent feature extraction, headhunting feature extraction, talent category and headhunting category association, feature extraction, etc., storing the information data after the feature extraction, and constructing a headhunting knowledge base, which may be used
  • the formation of the required talent trends report can also be used for knowledge transfer and sharing of talent discovery capabilities.
  • the characteristics extracted by constructing the headhunting knowledge base may include talent sources, talent tracking flow characteristics (contact cycle, number of interviews, expectations and actual salary, etc.), inherent characteristics (shared or shared by a certain category of talents) Features) and so on.
  • the established headhunting knowledge base can be used to obtain the trend report of the recruited or managed talents based on the acquired talent information data, and can help the talent management personnel grasp the talent trend more accurately, timely and efficiently.
  • the headhunting knowledge base can also be used to cultivate or improve the ability of talent managers to discover talents. Through the headhunting knowledge base, talent managers can obtain more comprehensive and accurate factors and influences on the flow of talents, what are the possibilities of talent flow, and how to meet the needs of talents. It can greatly improve the talent identification and management ability of talent management personnel.
  • FIG. 5 is a schematic structural diagram of a module of a data processing apparatus according to the present application. As shown in FIG. 5, the apparatus may include:
  • the information obtaining module 101 may be configured to acquire first information including the first attribute data and the second attribute data;
  • the matching module 102 may be configured to store a preset matching condition, and select, from the first information, the first candidate information that the first attribute data meets a preset matching condition;
  • the relationship identifying module 103 may be configured to store a first association relationship of the second attribute, and select, from the second attribute data of the first candidate information, the first attribute data that meets the first association relationship. Associated data;
  • the assignment module 104 can be configured to assign the first attribute data and the first associated data.
  • the output module 105 is configured to calculate an output value of the first candidate information according to the first attribute data of the first candidate information and the value of the first associated data.
  • the first attribute data may generally include relevant information that directly reflects the matching of talents with occupations or positions, such as the age of the talent, the number of job hops, the working years, the work unit, the industry, the position, the work place, the expected salary, etc.
  • the second attribute data may include at least one of professional communication, interpersonal social, recruitment website, and the like.
  • the first association relationship may be set to be the same as the second attribute data or the second attribute data conforms to a preset affiliation, a subordinate relationship, or the like.
  • the first attribute data and the first associated data of the first information in the heterogeneous relationship network may be assigned, for example, the talents in the heterogeneous relationship network established above may be The position, working years, qualifications, frequency of travel, and annual financial statements of the company are assigned.
  • the specific value can be assigned according to the empirical value obtained by analyzing the flow of talents or the self-defined evaluation rules.
  • FIG. 6 is a schematic structural diagram of another embodiment of a data processing apparatus according to the present application. As shown in FIG. 6, the apparatus may further include:
  • the online shopping data module 1011 may be configured to acquire online shopping data of the first candidate information, extract reference data of the online shopping data, and assign a value to the reference data.
  • the evaluation module 105 calculates, according to the first attribute data of the first candidate information and the value of the first associated data, that the output value of the first candidate information comprises: according to the first candidate information An attribute data and first associated data and values of reference data of the first candidate information are used to calculate an output value of the first candidate information.
  • FIG. 7 is a block diagram of another embodiment of a data processing apparatus according to the present application. As shown in FIG. 7, the apparatus may further include:
  • the event module 106 can be configured to set a reference event, and assign a weight to the reference event
  • the adjusting module 107 is configured to: when the reference event is triggered, adjust an assignment of data associated with the reference event according to a weight of the reference event;
  • the output value of the first candidate information is calculated according to the value of the first attribute data of the first candidate information and the reference data of the first associated data and the first candidate information, including:
  • the output of the first candidate information is obtained according to the first attribute data of the first candidate information and the first associated data and the reference data adjusted by the reference data of the first candidate information. value.
  • the influence analysis of the reference event on the flow of the candidate is added, and the dynamic analysis of the candidate flow can be dynamically based on the latest event dynamics, and the accuracy of the real-time analysis of the talent information data is increased.
  • the setting reference event described in the foregoing may be a preset reference event stored in a preset manner, or may be a reference event of the acquired real-time input, and then assign a weight to the reference event.
  • FIG. 8 is a block diagram of another embodiment of a data processing apparatus according to the present application. As shown in FIG. 8, the apparatus may further include:
  • the enhanced information database 108 can be configured to perform classification and integration on the first information according to preset primary key information, and store the classified integrated information data.
  • the enhanced information database 108 described in this embodiment may include second attribute data or online shopping data related to talent professional socialization, in addition to the traditional first attribute data related to the talent occupation, such as talent recruitment network.
  • Information such as station registration frequency, circle of friends, occupation information, online shopping receipt address, etc., can enrich the information source of the enhanced information database 108, and provide more and more comprehensive information data for other data analysis based on the enhanced information database. .
  • FIG. 9 is a block diagram of another embodiment of a data processing apparatus according to the present application. As shown in FIG. 9, the apparatus may further include:
  • the headhunting knowledge base 109 can be configured to perform feature extraction on the information integrated by the classification, and store the information data after the feature extraction.
  • the feature extraction of the classified information may include talent feature extraction, headhunting feature extraction, talent category and headhunting category association, feature extraction, etc., storing the information data after the feature extraction, and constructing a headhunting knowledge base 109, It can be used to generate the required talent trend report, and can also be used for knowledge transfer and sharing of talent discovery capabilities.
  • the knowledge base 109 can be divided according to the work category.
  • a record in the knowledge base can include characteristics of successful cases of recruiting or managing talents, for example, specific talent sources, talent tracking flow characteristics (contact cycle, number of interviews, expectations) And actual salary, etc., inherent characteristics (shared or shared by a certain class of talent).
  • the established headhunting knowledge base 109 can be used for the knowledge information recruited or managed by the acquired talents, and can help the talent management personnel to manage more accurately, timely and efficiently.
  • the headhunting knowledge base 109 can also be used to cultivate or improve the ability of talent managers to discover talents. Through the headhunting knowledge base 109, the talent management personnel can learn more comprehensively and accurately the factors affecting the flow of talents, the influence of the possibility, the possibility of the flow of talents, and how to meet the needs of the required talents. It can greatly improve the talent identification and management ability of talent management personnel.
  • the application may include an enhanced information database established based on preset primary key information and a headhunting knowledge database established by extracting feature information
  • the two talent information databases are established, and the purpose of using the two talent information databases is Can be different.
  • the enhanced information database is mainly for integrating multi-dimensional and multi-domain information lines related to talents, extracting the related information of the same natural person, and forming an information base about the talents themselves.
  • the headhunting knowledge base is mainly used for the analysis and talent inheritance of the talent management, such as the analysis of talent trends, how to recruit talents, how to recruit talents, and the source of talent information. Therefore, the application can include two kinds of talent information databases to complete their respective uses, which is more in line with the actual application design requirements.
  • the data processing method or device described in the present application can be applied to a talent recruitment or management system, and provides powerful analysis and support for talent turnover and talent identification.
  • the present application provides a data processing system, which can be configured to include:
  • a processing unit that selects first associated data having a first association relationship from the first candidate information
  • a processing unit that assigns data of the first candidate information and the first associated data
  • a processing unit that derives an output value of the first candidate information is calculated based on the assignment of the data.
  • the data processing system described in the present application may include an information management system implemented based on a cloud technology platform, where each functional module in the system may be located in a dedicated server, or may be located in a distributed different server that can implement the same function. in.
  • the data processing system described in the present application integrates multi-dimensional, multi-domain and talent-related information data, can be used for resource sources of talent information, calculates the job matching degree of talents, and can also be used for quantitative evaluation of talent occupational stability. It has provided a powerful help to talent recruitment and management personnel.
  • the unit, module or device set forth in the above embodiments may be implemented by a computer chip or an entity, or by a product having a certain function.
  • the above devices are described as being separately divided into various modules by function.
  • the functions of each module may be implemented in the same software or software and/or hardware, or the modules that implement the same function may be implemented by multiple sub-modules or a combination of sub-units, for example, the device may be
  • the event module 106 and the adjustment module 107 are arranged as a function module to implement adjustment of reference events and data assignments.
  • the controller can be logically programmed by means of logic gates, switches, ASICs, programmable logic controllers, and embedding.
  • the application can be described in the general context of computer-executable instructions executed by a computer, such as a program module.
  • program modules include routines, programs, objects, components, data structures, classes, and the like that perform particular tasks or implement particular abstract data types.
  • the present application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are connected through a communication network.
  • program modules can be located in both local and remote computer storage media including storage devices.
  • the present application can be implemented by means of software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present application may be embodied in the form of a software product in essence or in the form of a software product, which may be stored in a storage medium such as a ROM/RAM or a disk. , an optical disk, etc., includes instructions for causing a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to perform the methods described in various embodiments of the present application or portions of the embodiments.
  • a computer device which may be a personal computer, mobile terminal, server, or network device, etc.

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Abstract

一种数据处理方法、装置及系统。所述方法包括：获取第一信息的第一属性数据和第二属性数据(S1)；从所述第一信息中选出所述第一属性数据符合预置匹配条件的第一候选信息(S2)；从所述第一候选信息的第二属性数据中选出所述第二属性数据符合第一关联关系的第一关联数据(S3)；对所述第一属性数据和第一关联数据进行赋值(S4)；根据所述第一候选信息的第一属性数据和第一关联数据的值计算得出所述第一候选信息的输出值(S5)。可以实现与人才相关的多维度信息数据的分析处理，使人才流向分析更具有准确性和灵活性。

Description

一种数据处理方法、装置及系统 技术领域
本申请属于信息数据处理领域，尤其涉及一种数据处理方法、装置及系统。
背景技术
随着现代社会的发展，人才流动，特别是优秀人才的快速流动成为一个普遍现象。如何在这样一个人才快速流动的环境中发现人才，并积极主动迎合人才的需求，保持自己的人才竞争优势已经成为各大公司人力资源管理所面临的一个非常重要的问题。
目前市场上常用的人才信息分析管理系统包括Beishen(北森)、Mordern HR in Cloud等系统。上述所述信息分析系统大多数采用数据表格的结果化数据处理方式，在数据处理过程中面临复杂的ETL(extract、transform、load，萃取、转置、加载)的处理过程。一方面，所述系统进行数据信息分析的处理过程过于复杂，执行效率低，另一方面所述系统获取的信息数据通常仅包括例如学历、年龄、职业、工作年限、期望薪资等基于属性信息，对人才的分析受到获取的有限数据和固定分析方法的限制，没有涉及到其他影响人才流向的数据的分析，系统数据分析的灵活性较差、输出结果准确性较低。
发明内容
本申请目的在于提供一种数据处理方法、装置及系统，使对有关人信息才的数据分析、处理更具有准确性和灵活性，能有效、定量的分析人才流向。
本申请提供的一种数据处理方法、装置及系统是这样实现的：
一种数据处理方法，所述方法包括：
获取第一信息的第一属性数据和第二属性数据；
从所述第一信息中选出所述第一属性数据符合预置匹配条件的第一候选信息；
从所述第一候选信息的第二属性数据中选出所述第二属性数据符合第一关联关系的第一关联数据；
对所述第一属性数据和第一关联数据进行赋值；
根据所述第一候选信息的第一属性数据和第一关联数据的值计算得出所述第一候选信息的输出值。
一种数据处理装置，所述装置包括：
信息获取模块，用于获取包括第一属性数据、第二属性数据的第一信息；
匹配模块，用于存储预置匹配条件，并从所述第一信息中选出所述第一属性数据符合预置匹配条件的第一候选信息；
关系识别模块，用于存储所述第二属性的第一关联关系，并从所述第一候选信息的第二属性数据中选出所述第二属性数据符合第一关联关系的第一关联数据；
赋值模块，用于对所述第一属性数据和第一关联数据进行赋值；
输出模块，用于根据所述第一候选信息的第一属性数据和第一关联数据的值计算得出所述第一候选信息的输出值。
一种数据处理系统，所述系统被设置成，包括：
存储包括职业社交信息的第二属性数据、网购数据中的至少一种信息的数据库，以及，从所述数据库中选出符合预置匹配条件的第一候选信息的处理单元，以及
从所述第一候选信息中选出具有第一关联关系的第一关联数据的处理单元，以及，
对所述第一候选信息的数据及所述第一关联数据进行赋值的处理单元，以及，
根据所述数据的赋值计算得出所述第一候选信息的输出值的处理单元。
本申请提供的一种数据处理方法、装置及系统，对与人才相关的自身职业属性、人际社交的大数据进行分析，可以根据分析得到的影响人才跳槽流动的因素，以及这样因素对人才流动的影响程度对所述因素进行赋值，然后定量计算得出人才流动的输出值。本申请中分析处理的数据加入了人才的职业社交信息的第二属性数据，综合考虑人才所处的工作和生活环境来有效评测人才的跳槽风险及可能性，并可以根据与所述候选人才节点相关的数据赋值定量计算候选人才的输出值，可以更加全面、准确的进行人才数据的分析，以便人才管理人员可以有效发现人才流向动向，制定迎合人才需求的招聘或处理方案，提高人才的管理效率。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请中记载的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1是本申请一种数据处理方法一种实施例的方法流程示意图；
图2是本申请一种候选人异构关系的关系网络示意图；
图3是本申请一种数据处理方法另一种实施例的方法流程示意图；
图4是本申请一种数据处理方法另一种实施例的方法流程示意图
图5是本申请一种数据处理装置一种实施例的模块结构示意图；
图6是本申请一种数据处理装置另一种实施例的模块结构示意图；
图7是本申请一种数据处理装置另一种实施例的模块结构示意图；
图8是本申请一种数据处理装置另一种实施例的模块结构示意图；
图9是本申请一种数据处理装置另一种实施例的模块结构示意图。
具体实施方式
为了使本技术领域的人员更好地理解本申请中的技术方案，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都应当属于本申请保护的范围。
本申请中所述的数据处理方法不限于根据获取的与人才有关的信息数据分析其他公司人才匹配程度和流动可能性，还可以用于分析掌握自己公司内部人才的流动倾向。通过基于与人才有关的大数据分析，可以参考分析影响人才跳槽流动的因素都有哪些，以及这样因素对人才流动的影响程度。本申请提供一种数据处理方法，可以基于人才信息的大数据分析，来有效评测人才的跳槽风险及可能性，以便管理人员可以有效发现人才流向动向，制定迎合人才需求的招聘或处理方案。
下面是本申请提供的一种数据处理方法的一个实施例。图1是本申请所述一种数据处理方法的一个实施例的方法流程图，如图1所述，所述方法可以包括：
S1：获取第一信息的第一属性数据和第二属性数据。
所述的第一信息可以包括与人才相关多种信息数据，或者可以包括通过多种方式获取的信息数据。例如通过接收简历信息获取的信息数据，或者自身公司的人才登记管理系统获取的信息数据等。所述的第一信息中可以包括第一属性数据，所述的第一属性数据通常可以包括可以直接反映人才与职业或职位匹配性的相关信息，例如人才的年龄、跳槽次数、工作年限、工作单位、从事行业、所在职位、工作地点、期望薪资等等。
在本实施例中，为了更广泛的获取关于与人才有关的多维度的信息，丰富信息数据源，提高信息数据处理的准确度和可靠性，所述的第一信息可以包括基于友盟合作关系获取的合法的、授权的与人才职业社交相关的第二属性数据，具体的所述第二属性数据可以包括职业交流、人际社交、招聘网站等信息数据中的至少一种。
本申请中所述第一信息的第二属性数据可以包括招聘网站的信息数据，例如从51job、大街网等网站服务器中获取的数据。这些招聘网站的提供的第二属性数据可以更加直观的反 应人才的基本信息和条件需求。
人才的流向变动常常会受到行业领域咨询或者行业发展周期的影响。因此本申请所述的第二属性数据可以通过与人才招聘管理方有合作关系的各个职业交流网站提供的信息数据，例如综合性领域的linkin、知乎网站的信息数据，或者某一专业领域例如移动通信论坛(MSCBSC)、CSDN中文IT社区等提供的信息数据。
人才的流向变动信息还经常会体现在人才的社交圈中。例如人才关于本职业的最新看法、新的职业规划、婚姻及亲属关系变动等引起的人才流向变动。因此，本实施例中所述第二属性数据的来源还可以包括与人才人际社交有关的信息数据。例如可以包括人才的微博、人人网、世纪佳缘、豆瓣、QQ空间说说等相关的信息数据。
具体的例如某一领域的高等专业人才A因婚姻问题需要在老家H城市寻找一份和原工作领域相关且工作地点相对稳定的工作，则人才A很有可能会更加频繁登陆某一个或者多个招聘网站，在招聘网站上有多个求职信息发布。获取的第二属性数据中可以反映出人才A在某些招聘网站的登陆次数和驻留时间的相对变化、求职的工作地点等，或者从公开的微博信息中获取近期将要结婚，需要稳定的工作等信息。这样，可以获取与人才有关的多方面的信息数据，在后续数据处理时可以根据准确的进行分析得到人才动向。
当然，在获取多方面的包括第一属性数据和第二属性数据的第一信息后，还可以按照预置主键信息对所述第一信息进行分类整合，存储所述分类整合后的信息数据。
例如人才在不同的社交媒体中可以使用不同的媒体账号信息，但在账号注册或安全验证时填写的某些信息例如邮箱或者手机号码通常是相同的，因此可以以所述邮箱或者手机号码等作为预置主键信息对获取的第一信息数据进行分类整合，存储所述分类整合后的信息数据，可以形成增强型人才信息数据库。基于预置主键信息对所述第一信息进行分类整合，这样可以整合不同信息结构、不同信息领域的人才的第一信息，可以使人才管理人员更加简洁、方便的查看、使用信息。经过分类整合后可以形成的增强型人才信息数据库除了包括传统的与人才自身职业相关的第一属性数据数据外，还包括了与人才的职业社交，如人才招聘网站登录频率、职业信息分享、个人职业规划分享等信息数据，可以更加全面、准确的反映人才近期动向。
S2：从所述第一信息中选出所述第一属性数据符合预置匹配条件的第一候选信息。
上述所述第一属性数据可以包括与人才自身职业属性相关的信息，在获取所述第一信息的第一属性数据后，可以从所述第一信息中选出符合预置匹配条件的第一候选信息，所述第一候选信息可以包括符合招聘需求或者其他预置匹配条件的候选人才的第一信息的数据。所述的预置匹配条件，例如可以根据岗位或职业要求选取具有一定工作年限、参与过某类工程 项目或者通过某项专业资格认证的人才，或者可以采用猎头匹配模式选出需要招聘或者管理的候选人才，具体的可以根据招聘或者管理需求预先设置候选人才的预置匹配条件。一般的，基于所述第一信息的第一属性数据可以筛选出符合要求的候选人才。本实施例中可以根据人才的第一属性数据选出候选人才的第一候选信息，通常的可以选取符合所述预置匹配条件的多个第一信息作为所述第一候选信息，即可以选出多个人才作为所述候选人才。
通常情况下，与所述预置匹配条件匹配度越高的人才越能满足招聘岗位的需求。但是，所述选取的候选人才是否愿意离开原公司到跳槽到另一公司，或者自身公司的员工是否可以长期稳定的处于所匹配的岗位上，还需要结合本实施例中第一信息的第二属性数据进一步进行分析。因此，本申请所述的数据处理方法进一步的还可以包括：
S3：从所述第一候选信息的第二属性数据中选出所述第二属性数据符合第一关联关系的第一关联数据。
所述获取的第一信息不仅包括第一属性数据，还可以包括与人才相关的人际、社交、媒体等的职业社交的第二属性数据，这些数据不仅反映人才自身与岗位的匹配信息，还可以反映人才与人才、人才与单位、人才与资历认证等不同层级、不同类型之间的关系。可以基于获取的包括第一属性数据、第二属性数据等的第一信息以所述候选人才为节点建立所述候选人才的异构关系，所属异构关系中的每个单节点可以用第一属性数据如年龄、职位、工作年限等进行个体特征描述。然后可以在所述异构关系中用包括工作经历、工作单位状况、岗位信息、资历资格等第二属性数据进行职业、社交特征的描述。
例如所述第二属性数据可以包括与工作单位状况相关的信息数据，如候选人才所在公司的公司业务市场占有率、年度营业额、员工数目、薪资待遇(第三方统计数据)等。也可以与岗位信息相关的信息数据，例如可以包括舒适度、出差频率、薪资、市场需求度、决策影响力等。当然所述第二属性数据还可以包括与资历资格相关的信息数据，例如可以包括参加的职业培训、行业组织或协会、资格认证等。在具体的实施过程中，每个节点具体的所述第一属性数据、第二属性数据包括的信息内容可以根据需求进行设置。
在所述异构关系中，所述节点与节点之间的边可以反映人才与人才、人才与单位、人才与职位等之间的互动关系。不同的节点之间可以存在一定的管理关系，本实施例中，可以预先设置所述第二属性数据的第一关联关系，将符合所述第一关联关系的第二属性数据关联起来，建立所述候选人才的异构关系网。所述的第一关联关系可以预先进行设置，可以表示为如(1)是否在同一家公司；(2)是否同一个学校毕业；(3)曾经是否为上下级关系等等。具体的所述第一关联关系可以设置为第二属性数据相同或者第二属性数据符合预置的从属关系、上下级关系等。在所述异构关系网中，可以从所述第一候选信息的第二属性数据中选 出所述第二属性数据符合第一关联关系的第一关联数据，例如属于同一家公司。
所述通过第一信息建立的候选人才的异构关系可以通过关系网络图进行体现，例如图2所述的一种候选人才的异构关系网络示意图。在图2中，A-F节点分别为第一候选信息，每个候选人才都包括相应属性数据。可以在所述异构关系图中具有所述第一关联数据的第一候选信息接起来，可以用于表述不同候选人才之间的关联性。
S4：对所述第一属性数据和第一关联数据进行赋值。
在建立所述候选人才的异构关系网后，可以对所述异构关系网中第一信息的第一属性数据和第一关联数据进行赋值，例如可以对上述建立的异构关系网中人才的职位、工作年限、资格认证、出差频率、公司年度财务报表等数据进行赋值，具体的可以根据分析人才流动状况得到的经验值，或者自行定义的赋值规则进行赋值。所述对第一属性数据和第一个关联数据进行的赋值，可以表示为被赋值的数据信息对人才跳槽的影响力。具体的实施过程中，例如可以参考职位工作性质对不同行业的职位进行赋值，例如与建筑工程项目相关的地域流动性较强、出差频率和周期较长的职位可以将其赋值为40，而办公地点相对较为为稳定的办公室职位可以赋值为10。又如可以对某一行业的重要资格认证赋值为5，可以表示候选人才在获取所述资格认证后减小了候选人的跳槽风险，增加了所述候选人才流动的可能性。
S5：根据所述第一候选信息的第一属性数据和第一关联数据的值计算得出所述第一候选信息的输出值。
进一步的，可以在所述异构关系网中根据所述第一候选信息的第一属性数据和第一关联数据的值计算得出所述第一候选信息的输出值。所述的输出值可以包括计算人所述候选人才的第一属性数据和第一关联数据的值的总分值，可以用于定量评价所述候选人才的职位稳定性。例如从人才个人主页的社交信息数据中获知工作在某中等城市C1的候选人A在另一大城市C2中准备购买房产，进一步的从人才朋友交际圈的信息数据中可以得知该候选人A与城市C2的多个同学和朋友近期的联系频率较为频繁，而且从候选人A职业社交的个人资料中可以得知A个人能力较强，已获取多项行业资格认证，但所处公司整体业绩却是一般。综合上述，可以根据本申请所述的方法计算得到所述候选人A职位稳定性较差，跳槽可能性高，从而利用多维度的第二属性数据更加准确、有效评定人才的流向。在具体的实施过程中所述第一候选人的输出值可以采用数据赋值总分归一化的方式得到0～100的区间分值，也可以采用不设上限的各个相关数据赋值直接相加的方式，本申请对此不做限制。
本申请提供的所述一种数据处理方法，可以根据候选人才异构关系网中各个节点的多维度数据的值计算所述候选人才的输出值。所述的异构关系网中，不仅包括了与人才自身职业属性相关的第一属性数据，还包括了与人才的工作环境、行业环境、人际社交信息、职业交 流等可能对人才流动造成影响的多维度的第二属性数据，并可以根据与所述候选人才节点相关的数据赋值定量计算候选人才的输出值，可以更加全面、准确的进行人才数据的分析，直观、有效的获取人才流动信息。
随着现在电子商务的迅猛发展，网上购物成为越来越多人的消费方式。本申请的另一种优选实施例中，所述获取的第一信息还可以包括与人才在网上的消费有关的信息，例如各个网络购物网站的网购数据。这些人才的网购数据往往可以反映出人才的工作状况变化、生活状况变化、购买力以及工作生活状态的变化等，可以从网购数据提取参考数据，通过分析可以反映出人才的流动倾向。例如某目标人才的某一购物网站的默认收获地址发生变化，可能预示着该目标人才的工作单位或工作地点发生了变化。或者某目标人才在购物网站上的购物或者关注商品更加倾向于母婴用品，可以表示该目标人才的家庭结构发生变化，如增加了新生婴儿，则可能预示着该目标人才可能会寻找薪资更高的工作或者距离家庭更近的工作地点。不同网购数据的参考数据可以对应着人才的不同属性标签，因此，本实施例中还可以从获取的网购数据中提取出反映人才流动的参考数据，同样可以对所述网购数据中的参考数据进行赋值。图3是本申请所述一种数据处理方法另一种实施例的方法流程示意图。如图3所示，另一种实施例中，所述方法还可以包括：
S6：获取所述第一候选信息的网购数据，提取所述网购数据的参考数据，并为所述参考数据赋值；
相应的，所述根据所述第一候选信息的第一属性数据和第一关联数据的值计算得出所述第一候选信息的输出值包括：根据所述第一候选信息的第一属性数据和第一关联数据以及所述第一候选信息的参考数据的值计算得出所述第一候选信息的输出值。
当然，所述建立的异构关系网中可以包括网购数据的参考数据，例如收获地址、消费额度、消费类型等，可以对所述网购数据的参考数据进行赋值，并在计算所述候选人才的输出值时将所述网购数据的参考数据考虑在内。
本实施例中所述的数据处理方法不仅对人才的职业社交信息进行处理，还加入了人才的网购数据，对人才的网购消费行为和习惯进行分析，进一步用来测定候选人才的流动倾向。采用对包括人才的网购数据的信息数据对人才进行分析，符合现代消费习惯的发展趋势，可以更加全面、多维度的对人才的生活、工作环境状态进行分析，及时捕捉人才的流动倾向，为人才招聘和管理提供了有力支持。
图4是本申请所述一种数据处理方法另一种实施例的方法流程示意图。如图4所示，所述一种数据处理方法中，还可以在所述建立的异构关系网中设置参考事件，所述的参考事件可以包括可以影响人才流动的行业资讯、公司动作、政府政策、突发事件等。这些参考事件 都可以影响人才的流向，因此，本申请的另一种实施例中，所述数据处理方法还可以包括：
S7：设置参考事件，对所述参考事件赋予权值；
在所述参考事件触发时，根据所述参考事件的权值调整与所述参考事件有关联的数据的赋值；
相应的，所述根据所述第一候选信息的第一属性数据和第一关联数据/和所述第一候选信息的参考数据的值计算得出所述第一候选信息的输出值包括：
所述参考事件触发时，根据所述第一候选信息的第一属性数据和第一关联数据/和所述第一候选信息的参考数据调整后的赋值计算得出所述第一候选信息的输出值。
本实施例中所述的参考事件可以为预先定义设置的可能发生的事件信息，也可以为获取的实时输入的事件信息。所述的参考事件可以根据事件对人才流向的影响程度赋予相应的权值，用于定量评测该参考事件对影响人才流动的影响性。然后，可以根据所述参考事件的权值调整所述异构关系中与所述参考事件有关联的变量信息的赋值。在所述参考事件发生时，通常可以影响多个与所述参考事件有关联的数据信息，例如工资增长或降低、未来发展前景利好或不利等都会影响人才的流动可能性，在本申请中可以表现为影响所述第一候选信息的数据赋值分值。这样，加入了参考事件，大大提高了关于人才的信息数据分析处理的灵活性。
具体的一个应用场景中，某大型电商公司AL宣布申报阶段已经完成，即将在某国交易所上市。该公司AL即将上市的参考事件属于对人才的利好消息，通常会影响该公司AL下的所有员工以及与所述公司AL有合作关系的友盟公司或旗下子公司，甚至会影响整个行业未来发展前景，那么在所述公司AL的候选人才流动的可能性就会降低，可以将所述参考事件的权值设置为负值，或者类似的可以降低与所述参考事件有关联的数据赋值的权值。
另一种场景中，例如某公司BD因财务报表作假问题引发重大负面新闻，那么该参考事件通常会影响与该公司有关的所有节点，可以在所述异构关系网中设置与该参考事件有关的节点的数据赋值增加30个点或者增加30％，表示该公司BD人才流动的可能性增加。这样，可以基于赋予所述参考事件的权值调整所述异构关系中与所述参考事件有关联的数据的赋值，进而更加实时、准确的对人才流动的定量分析。
需要说明的是，本实施例中所述对所述参考事件赋予的权值可以为标量值，例如增加10分或30分，也可以为相对值，如上述的增加30％，具体的可以根据需求进行设置。所述参考事件的权值大小可以根据参考事件影响力进行设定，例如上述公司BD的应用场景中，如果所述财务报表作假还引起面临巨额赔偿等问题，那么相应的参考事件的权值相应增加，例如可以设置为50或80等，增大公司BD人才跳槽的可能性。当然，在本申请中在同一所述异构关系网中可以包括多个所述参考事件，例如与某些国家达成双边贸易协议、政府激励性 政策、公司并购等多个利好消息，这些多个参考事件都可以以不同的权重影响所述异构关系中与之相关的数据的赋值，对最终人才的变动和流向的输出值产生影响。
上述中所述数据处理方法按照预置主键信息对所述第一信息进行分类整合，存储所述分类整合后的信息，形成增强型人才数据库，还可以对所述分类整合后的信息进行特征提取，存储所述特征提取后的信息，构建猎头知识库。所述对分类整合后的信息进行特征提取可以包括人才特征提取、猎头特征提取、人才类别与猎头类别联和特征提取等，存储所述特征提取后的信息数据，构建形成猎头知识库，可以用于形成所需的人才动向报告，也可以用于人才发现能力的知识传承与分享。构建所述猎头知识库所提取的特征具体的可以包括人才来源、人才追踪流水特征(联系周期、面试个数、期望与实际薪水等)、固有特征(某一分类人才共有或者绝大部分共有的特征)等。所述建立的猎头知识库，可以用于基于获取的人才信息数据获取所招聘或者管理的人才的动向报告，可以更准确、及时、高效的帮助人才管理人员把握人才动向。同时，所述猎头知识库，还可以用于培养或提高人才管理人员发现人才的能力。通过所述猎头知识库，人才管理人员可以更加全面、准确的获取到影响人才流动的因素及影响力有多大、人才流动的可能性体现在哪些方面，以及如何迎合所需人才的需求等信息数据，可以大大提高人才管理人员人才识别和管理能力。
基于本申请所述人才处理方法，本申请提供一种数据处理装置，所述装置可以实现与人才相关的多维度信息数据的获取，并对所述多维度的信息数据进行分析处理，定量计算得出人才关于职业稳定性的输出值。图5是本申请所述一种数据处理装置的模块结构示意图，如图5所示，所述装置可以包括：
信息获取模块101，可以用于获取包括第一属性数据、第二属性数据的第一信息；
匹配模块102，可以用于存储预置匹配条件，并从所述第一信息中选出所述第一属性数据符合预置匹配条件的第一候选信息；
关系识别模块103，可以用于存储所述第二属性的第一关联关系，并从所述第一候选信息的第二属性数据中选出所述第二属性数据符合第一关联关系的第一关联数据；
赋值模块104，可以用于对所述第一属性数据和第一关联数据进行赋值；
输出模块105，可以用于根据所述第一候选信息的第一属性数据和第一关联数据的值计算得出所述第一候选信息的输出值。
所述的第一属性数据通常可以包括可以直接反映人才与职业或职位匹配性的相关信息，例如人才的年龄、跳槽次数、工作年限、工作单位、从事行业、所在职位、工作地点、期望薪资等等，所述第二属性数据可以包括职业交流、人际社交、招聘网站等信息数据中的至少一种。
所述第一关联关系可以设置为第二属性数据相同或者第二属性数据符合预置的从属关系、上下级关系等。在建立所述候选人才的异构关系网后，可以对所述异构关系网中第一信息的第一属性数据和第一关联数据进行赋值，例如可以对上述建立的异构关系网中人才的职位、工作年限、资格认证、出差频率、公司年度财务报表等数据进行赋值，具体的可以根据分析人才流动状况得到的经验值，或者自行定义的赋值规则进行赋值。
图6是本申请所述一种数据处理装置另一种实施例的模块结构示意图，如图6所示，所装置还可以包括：
网购数据模块1011，可以用于获取所述第一候选信息的网购数据，提取所述网购数据的参考数据，并为所述参考数据赋值。
相应的，所述赋值模块105根据所述第一候选信息的第一属性数据和第一关联数据的值计算得出所述第一候选信息的输出值包括：根据所述第一候选信息的第一属性数据和第一关联数据以及所述第一候选信息的参考数据的值计算得出所述第一候选信息的输出值。
图7是本申请所述一种数据处理装置另一种实施例的模块结构示意图，如图7所示，所述装置还可以包括：
事件模块106，可以用于设置参考事件，对所述参考事件赋予权值；
调整模块107，可以用于在所述参考事件触发时，根据所述参考事件的权值调整与所述参考事件有关联的数据的赋值；
相应的，所述根据所述第一候选信息的第一属性数据和第一关联数据/和所述第一候选信息的参考数据的值计算得出所述第一候选信息的输出值包括：
所述参考事件触发时，根据所述第一候选信息的第一属性数据和第一关联数据/和所述第一候选信息的参考数据调整后的赋值计算得出所述第一候选信息的输出值。
本实施例中增加了参考事件对候选人才流动的影响分析处理，可以根据最新事件动态的对候选人才流向动态分析，增加人才信息数据实时分析的准确性。上述中所述的设置参考事件，可以为预先设置存储的参考事件，也可以为获取的实时输入的参考事件，然后对所述参考事件赋予权值。
图8是本申请所述一种数据处理装置另一种实施例的模块结构示意图，如图8所示，所述装置还可以包括：
增强型信息数据库108，可以用于按照预置主键信息对所述第一信息进行分类整合，存储所述分类整合后的信息数据。
本实施例中所述的增强型信息数据库108，除了包括传统的与人才职业相关的第一属性数据数据外，还可以包括与人才职业社交相关的第二属性数据或者网购数据，如人才招聘网 站登录频率、朋友圈、职业信息、网购收货地址等信息，可以丰富所述增强型信息数据库108信息源，为基于所述增强型信息数据库的其他数据分析提供更多、更全面的信息数据。
图9是本申请所述一种数据处理装置另一种实施例的模块结构示意图，如图9所示，所述装置还可以包括：
猎头知识库109，可以用于对所述分类整合后的信息进行特征提取，存储所述特征提取后的信息数据。
所述对分类整合后的信息进行特征提取，可以包括人才特征提取、猎头特征提取、人才类别与猎头类别联和特征提取等，存储所述特征提取后的信息数据，构建形成猎头知识库109，可以用于形成所需的人才动向报告，也可以用于人才发现能力的知识传承与分享。所述知识库109可以按照工作类别进行划分，知识库中一条记录可以包含招聘或者管理人才的成功案例的特征，例如具体的可以包括人才来源、人才追踪流水特征(联系周期、面试个数、期望与实际薪水等)、固有特征(某一分类人才共有或者绝大部分共有的特征)等。所述建立的猎头知识库109，可以用于获取的人才所招聘或者管理的知识信息，可以更准确、及时、高效的帮助人才管理人员管理。所述猎头知识库109，还可以用于培养或提高人才管理人员发现人才的能力。通过所述猎头知识库109，人才管理人员可以更加全面、准确的学习到影响人才流动的因素及影响力有多大、人才流动的可能性体现在哪些方面，以及如何迎合所需人才的需求等，可以大大提高人才管理人员人才识别和管理能力。
需要说明的是，本申请中虽然可以包括基于预置主键信息建立的增强型信息数据库和提取特征信息建立的猎头知识库两种人才信息数据库，但该两种人才信息数据库的建立方法和使用目的可以不相同。所述增强型信息数据库主要是对与人才相关的多维度、多领域的信息行整合，提取出相同自然人的关联信息，形成的关于人才自身的信息库。而所述猎头知识库主要是用于人才动向的分析、如何发现人才、如何招聘到需要的人才、人才信息来源等人才管理能力的知识的分享和传承。因此，本申请中可以包括两种人才信息数据库，完成各自的用途，这也更加符合实际应用设计需求。
本申请所述的数据处理方法或装置可以应用于人才招聘或者管理系统中，对人才的跳槽流动性、人才识别等提供有力的分析和支持。具体的本申请提供一种数据处理系统，所述系统可以被设置成包括：
存储包括职业社交信息的第二属性数据、网购数据中的至少一种信息的数据库，以及，从所述数据库中选出符合预置匹配条件的第一候选信息的处理单元，以及
从所述第一候选信息中选出具有第一关联关系的第一关联数据的处理单元，以及，
对所述第一候选信息的数据及所述第一关联数据进行赋值的处理单元，以及，
根据所述数据的赋值计算得出所述第一候选信息的输出值的处理单元。
本申请所述的数据处理系统可以包括基于云技术平台实现的信息管理系统，所述系统中的各个功能模块可以位于专用的服务器中，也可以位于分布式的不同服务器的可以实现相同功能的设备中。本申请所述的数据处理系统整合多维度、多个领域与人才相关的信息数据，可以用于人才信息的资源来源，计算人才的岗位匹配度，还可以用于人才职业稳定性的定量评测，对人才招聘和管理人员提供了有力帮助。
上述实施例阐明的单元、模块或者装置，具体可以由计算机芯片或实体实现，或者由具有某种功能的产品来实现。为了描述的方便，描述以上装置时以功能分为各种模块分别描述。当然，在实施本申请时可以把各模块的功能在同一个或多个软件和/或硬件中实现，也可以将实现同一功能的模块由多个子模块或子单元的组合实现，例如可以将装置中的事件模块106和调整模块107设置成一个功能模块来实现对参考事件及数据赋值的调整。
本领域技术人员也知道，除了以纯计算机可读程序代码方式实现控制器以外，完全可以通过将方法步骤进行逻辑编程来使得控制器以逻辑门、开关、专用集成电路、可编程逻辑控制器和嵌入微控制器等的形式来实现相同功能。因此这种控制器可以被认为是一种硬件部件，而对其内部包括的用于实现各种功能的装置也可以视为硬件部件内的结构。或者甚至，可以将用于实现各种功能的装置视为既可以是实现方法的软件模块又可以是硬件部件内的结构。
本申请可以在由计算机执行的计算机可执行指令的一般上下文中描述，例如程序模块。一般地，程序模块包括执行特定任务或实现特定抽象数据类型的例程、程序、对象、组件、数据结构、类等等。也可以在分布式计算环境中实践本申请，在这些分布式计算环境中，由通过通信网络而被连接的远程处理设备来执行任务。在分布式计算环境中，程序模块可以位于包括存储设备在内的本地和远程计算机存储介质中。
通过以上的实施方式的描述可知，本领域的技术人员可以清楚地了解到本申请可借助软件加必需的通用硬件平台的方式来实现。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在存储介质中，如ROM/RAM、磁碟、光盘等，包括若干指令用以使得一台计算机设备(可以是个人计算机，移动终端，服务器，或者网络设备等)执行本申请各个实施例或者实施例的某些部分所述的方法。
本说明书中的各个实施例采用递进的方式描述，各个实施例之间相同或相似的部分互相参见即可，每个实施例重点说明的都是与其他实施例的不同之处。本申请可用于众多通用或 专用的计算机系统环境或配置中。例如：个人计算机、服务器计算机、手持设备或便携式设备、平板型设备、多处理器系统、基于微处理器的系统、可编程的电子设备、网络PC、小型计算机、大型计算机、包括以上任何系统或设备的分布式计算环境等等。
虽然通过实施例描绘了本申请，本领域普通技术人员知道，本申请有许多变形和变化而不脱离本申请的精神，希望所附的权利要求包括这些变形和变化而不脱离本申请的精神。

Claims (12)

  1. 一种数据处理方法，其特征在于，所述方法包括：
    获取第一信息的第一属性数据和第二属性数据；
    从所述第一信息中选出所述第一属性数据符合预置匹配条件的第一候选信息；
    从所述第一候选信息的第二属性数据中选出所述第二属性数据符合第一关联关系的第一关联数据；
    对所述第一属性数据和第一关联数据进行赋值；
    根据所述第一候选信息的第一属性数据和第一关联数据的值计算得出所述第一候选信息的输出值。
  2. 如权利要求1所述的一种数据处理方法，其特征在于，所述第二属性数据包括职业交流、人际社交、招聘网站信息数据中的至少一种。
  3. 如权利要求1所述的一种数据处理方法，其特征在于，所述方法还包括：
    获取所述第一候选信息的网购数据，提取所述网购数据的参考数据，并为所述参考数据赋值；
    相应的，所述根据所述第一候选信息的第一属性数据和第一关联数据的值计算得出所述第一候选信息的输出值包括：根据所述第一候选信息的第一属性数据和第一关联数据以及所述第一候选信息的参考数据的值计算得出所述第一候选信息的输出值。
  4. 如权利要求1-3中任意一项所述的一种数据处理方法，其特征在于，所述方法还包括：
    设置参考事件，对所述参考事件赋予权值；
    在所述参考事件触发时，根据所述参考事件的权值调整与所述参考事件有关联的数据的赋值；
    相应的，所述根据所述第一候选信息的第一属性数据和第一关联数据/和所述第一候选信息的参考数据的值计算得出所述第一候选信息的输出值包括：
    所述参考事件触发时，根据所述第一候选信息的第一属性数据和第一关联数据/和所述第一候选信息的参考数据调整后的赋值计算得出所述第一候选信息的输出值。
  5. 如权利要求1-3中任意一项所述的一种数据处理方法，其特征在于，所述方法还包括：
    按照预置主键信息对所述第一信息进行分类整合，存储所述分类整合后的信息数据。
  6. 如权利要求5所述的一种数据处理方法，其特征在于，所述方法还包括：
    对所述分类整合后的信息进行特征提取，存储所述特征提取后的信息数据。
  7. 一种数据处理装置，其特征在于，所述装置包括：
    信息获取模块，用于获取包括第一属性数据、第二属性数据的第一信息；
    匹配模块，用于存储预置匹配条件，并从所述第一信息中选出所述第一属性数据符合预置匹配条件的第一候选信息；
    关系识别模块，用于存储所述第二属性的第一关联关系，并从所述第一候选信息的第二属性数据中选出所述第二属性数据符合第一关联关系的第一关联数据；
    赋值模块，用于对所述第一属性数据和第一关联数据进行赋值；
    输出模块，用于根据所述第一候选信息的第一属性数据和第一关联数据的值计算得出所述第一候选信息的输出值。
  8. 如权利要求7所述的一种数据处理装置，其特征在于，所述装置还包括：
    网购数据模块，用于获取所述第一候选信息的网购数据，提取所述网购数据的参考数据，并为所述参考数据赋值；
    相应的，所述赋值模块根据所述第一候选信息的第一属性数据和第一关联数据的值计算得出所述第一候选信息的输出值包括：根据所述第一候选信息的第一属性数据和第一关联数据以及所述第一候选信息的参考数据的值计算得出所述第一候选信息的输出值。
  9. 如权利要求7或8所述的一种数据处理装置，其特征在于，所述装置还包括：
    事件模块，用于设置参考事件，对所述参考事件赋予权值；
    调整模块，用于在所述参考事件触发时，根据所述参考事件的权值调整与所述参考事件有关联的数据的赋值；
    相应的，所述根据所述第一候选信息的第一属性数据和第一关联数据/和所述第一候选信息的参考数据的值计算得出所述第一候选信息的输出值包括：
    所述参考事件触发时，根据所述第一候选信息的第一属性数据和第一关联数据/和所述第一候选信息的参考数据调整后的赋值计算得出所述第一候选信息的输出值。
  10. 如权利要求7或8所述的一种数据处理装置，其特征在于，所述装置还包括：
    增强型信息数据库，用于按照预置主键信息对所述第一信息进行分类整合，存储所述分类整合后的信息数据。
  11. 如权利要求10所述的一种数据处理装置，其特征在于，所述装置还包括：
    猎头知识库，用于对所述分类整合后的信息进行特征提取，存储所述特征提取后的信息数据。
  12. 一种数据处理系统，其特征在于，所述系统被设置成，包括：
    存储包括职业社交信息的第二属性数据、网购数据中的至少一种信息的数据库，以及，从所述数据库中选出符合预置匹配条件的第一候选信息的处理单元，以及
    从所述第一候选信息中选出具有第一关联关系的第一关联数据的处理单元，以及，
    对所述第一候选信息的数据及所述第一关联数据进行赋值的处理单元，以及，
    根据所述数据的赋值计算得出所述第一候选信息的输出值的处理单元。
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018032867A1 (zh) * 2016-08-15 2018-02-22 广州招商壹零壹网络科技股份有限公司 一种基于物业信息的数据处理方法及装置
CN111125639A (zh) * 2019-12-23 2020-05-08 中国电子科技集团公司第二十八研究所 基于数值回归的双边关系量化分析方法及计算机存储介质
CN114676117A (zh) * 2022-05-27 2022-06-28 成都明途科技有限公司 一种岗位数据存储方法、装置及岗位机器人

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108550027A (zh) * 2018-05-02 2018-09-18 同道精英（天津）信息技术有限公司 基于猎头资源和行为的人职匹配方法
CN109597695B (zh) * 2018-09-30 2020-08-21 阿里巴巴集团控股有限公司 一种数据处理方法、装置及设备
CN110069570B (zh) * 2018-11-16 2022-04-05 北京微播视界科技有限公司 数据处理方法和装置
CN110610267B (zh) * 2019-09-10 2021-06-29 北京京东智能城市大数据研究院 人才信息的处理方法及装置、计算机存储介质、电子设备
CN114492737B (zh) 2021-12-31 2022-12-09 北京百度网讯科技有限公司 数据处理方法、装置及电子设备、存储介质及程序产品
CN114971366B (zh) * 2022-06-14 2023-07-07 杭州市高层次人才发展服务中心 基于区域分析的人才流动评价方法、存储介质及电子设备

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100223267A1 (en) * 2009-02-27 2010-09-02 Accenture Global Services Gmbh Matching tools for use in attribute-based performance systems
CN103309885A (zh) * 2012-03-13 2013-09-18 阿里巴巴集团控股有限公司 一种在电子交易平台中识别特征用户的方法及装置和搜索方法及装置
CN103514288A (zh) * 2013-09-30 2014-01-15 广州品唯软件有限公司 客户端类别识别方法和系统
CN103975326A (zh) * 2011-12-06 2014-08-06 大陆汽车有限责任公司 用于从相关数据库中选择至少一个数据记录的方法和系统

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7958120B2 (en) * 2005-05-10 2011-06-07 Netseer, Inc. Method and apparatus for distributed community finding
CN102117323A (zh) * 2011-02-21 2011-07-06 深圳埃斯欧纳信息咨询有限公司 一种推荐求职简历的处理方法和系统
CN102799593B (zh) * 2011-05-24 2015-09-09 一零四资讯科技股份有限公司 个人化搜寻排序方法以及系统
CN104834668B (zh) * 2015-03-13 2018-10-02 陈文� 基于知识库的职位推荐系统

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100223267A1 (en) * 2009-02-27 2010-09-02 Accenture Global Services Gmbh Matching tools for use in attribute-based performance systems
CN103975326A (zh) * 2011-12-06 2014-08-06 大陆汽车有限责任公司 用于从相关数据库中选择至少一个数据记录的方法和系统
CN103309885A (zh) * 2012-03-13 2013-09-18 阿里巴巴集团控股有限公司 一种在电子交易平台中识别特征用户的方法及装置和搜索方法及装置
CN103514288A (zh) * 2013-09-30 2014-01-15 广州品唯软件有限公司 客户端类别识别方法和系统

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018032867A1 (zh) * 2016-08-15 2018-02-22 广州招商壹零壹网络科技股份有限公司 一种基于物业信息的数据处理方法及装置
CN111125639A (zh) * 2019-12-23 2020-05-08 中国电子科技集团公司第二十八研究所 基于数值回归的双边关系量化分析方法及计算机存储介质
CN114676117A (zh) * 2022-05-27 2022-06-28 成都明途科技有限公司 一种岗位数据存储方法、装置及岗位机器人
CN114676117B (zh) * 2022-05-27 2022-08-16 成都明途科技有限公司 一种岗位数据存储方法、装置及岗位机器人

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