CN115456413A - Method, device and equipment for matching personnel with posts and storage medium - Google Patents
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Abstract
The application belongs to the field of finance and computers, and particularly relates to a method, a device, equipment and a storage medium for matching personnel with posts. Generating first description information of different data sources facing to the personnel nodes according to the personnel attribute data extracted from the source data, and generating second description information of different data sources facing to the post nodes according to the post attribute data extracted from the source data; determining an association relation between data respectively corresponding to the personnel node and the post node according to the first description information and the second description information; determining a first knowledge graph according to the association relation; extracting first target data from the personnel attribute data, and extracting second target data from the post attribute data; generating a second knowledge-graph based on the first knowledge-graph; and outputting data of the matching relation set of the personnel and the posts based on the second knowledge graph. This application can improve the efficiency of matcing personnel and post.
Description
Technical Field
The present application relates to the field of finance and the field of computers, and in particular, to a method, an apparatus, a device, and a storage medium for matching a person with a post.
Background
With the continuous development of computer technology, the needs of enterprises for talents are continuously increased, and therefore, how to effectively identify talents and put proper personnel on the right posts becomes very important.
In the related art, when matching between a person and a post, the matching is usually realized by writing a large number of service system codes, but in the matching process between the person and the post, each index or rule is complex and variable, and when the index or rule changes, a large number of modifications need to be performed on the service system codes, so that the matching efficiency between the person and the post is low.
Disclosure of Invention
The application provides a method, a device, equipment and a storage medium for matching personnel with posts, which are used for achieving the purpose of improving the efficiency of matching the personnel with the posts.
In a first aspect, the present application provides a method for matching a person with a post, including: generating first description information of different data sources facing to the personnel nodes according to the personnel attribute data extracted from the source data, and generating second description information of different data sources facing to the post nodes according to the post attribute data extracted from the source data; determining an association relation between data respectively corresponding to the personnel node and the post node according to the first description information and the second description information; determining a first knowledge graph containing personnel nodes and post nodes according to the association relationship; extracting first target data from the personnel attribute data and second target data from the post attribute data, wherein the first target data are used for describing target personnel nodes, the second target data are used for describing target post nodes, the personnel nodes comprise target personnel nodes, and the post nodes comprise target post nodes; generating a second knowledge graph containing target personnel nodes and target post nodes based on the first knowledge graph; and outputting data of the matching relation set of the personnel and the posts based on the second knowledge graph.
In a possible implementation manner, before generating first description information of different data sources facing to the person node according to the person attribute data extracted from the source data and generating second description information of different data sources facing to the position node according to the position attribute data extracted from the source data, the method further includes: acquiring source data from a source system; and preprocessing the source data to obtain preprocessed data meeting preset standards.
In a possible embodiment, preprocessing the source data to obtain preprocessed data that meets a preset criterion includes: filtering the source data to obtain filtered data; and carrying out format conversion processing and/or integration processing on the filtered data to obtain preprocessed data.
In a possible implementation manner, generating first description information of different data sources facing to the person node according to the person attribute data extracted from the source data, and generating second description information of different data sources facing to the position node according to the position attribute data extracted from the source data includes: generating first description information according to personnel attribute data extracted from the preprocessed data; and generating second description information according to the post attribute data extracted from the preprocessed data.
In one possible implementation, outputting data of the matching relationship set of the person and the position based on the second knowledge-graph comprises: determining rules between data respectively corresponding to the target person node and the target post node in the second knowledge graph by adopting a graph mining algorithm and a rule reasoning algorithm; based on the rule, adjusting the matching relation between the personnel and the post contained in the second knowledge graph to obtain a target knowledge graph; and outputting the matching relation set data based on the target knowledge graph.
In one possible embodiment, outputting the data of the matching relationship set includes: and outputting the matching relation set data according to a preset data structure.
In a possible implementation manner, after determining, according to the first description information and the second description information, an association relationship between data corresponding to the person node and the post node, further includes: and performing associated storage on the personnel nodes and the post nodes through a node linking technology.
In a second aspect, the present application provides a person and post matching device, comprising: the first generation module is used for generating first description information of different data sources facing to the personnel nodes according to the personnel attribute data extracted from the source data and generating second description information of different data sources facing to the position nodes according to the position attribute data extracted from the source data; the first determining module is used for determining the association relationship between the data corresponding to the personnel node and the post node according to the first description information and the second description information; the second determining module is used for determining a first knowledge graph containing the personnel nodes and the post nodes according to the association relation; the extraction module is used for extracting first target data from the personnel attribute data and second target data from the post attribute data, wherein the first target data are used for describing target personnel nodes, the second target data are used for describing target post nodes, the personnel nodes comprise target personnel nodes, and the post nodes comprise target post nodes; the second generation module is used for generating a second knowledge graph containing target personnel nodes and target post nodes based on the first knowledge graph; and the output module is used for outputting the data of the matching relationship set of the personnel and the post based on the second knowledge graph.
In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively coupled to the processor; the memory stores computer-executable instructions; the processor executes computer-executable instructions stored in the memory to implement the person-to-post matching method of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and the computer-executable instructions are executed by a processor to implement the people and post matching method according to the first aspect.
In a fifth aspect, an embodiment of the present application provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the method for matching a person with a post of the first aspect is implemented.
According to the matching method, device, equipment and storage medium for the personnel and the posts, knowledge modeling is carried out by utilizing a data structure of a knowledge map based on a map, a proper algorithm is combined, analysis processing is carried out on source data, an optimal relationship set between entities is finally determined, a source system directly forms a visual page according to the result of the relationship set between the entities, the complexity and the coupling degree of all services realized in the same system are greatly reduced, the code research and development efficiency is improved, and therefore the matching efficiency of the personnel and the posts is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic structural diagram of a personnel and station matching system according to an embodiment of the present application;
FIG. 2 is a flowchart of a method for matching a person with a post according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a person and station matching device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and the description are not intended to limit the scope of the present inventive concept in any way, but rather to illustrate it by reference to a specific embodiment for a person skilled in the art, from which further drawings may be derived, without inventive faculty, for a person skilled in the art.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The terms referred to in the present application are explained first:
knowledge Graph (Knowledge Graph): the data structure based on the graph is composed of nodes (points) and edges (edges), wherein the nodes are used for representing entities, and the edges are used for representing the relationships between the entities.
Entity: may be used to represent a person or thing in the real world.
The relationship is as follows: for representing connections between different entities.
The post specification: a summary indicating what the enterprise expects the employee to do, specifying what the employee should do, what should do, and where to perform duties.
In the related art provided in the background art, at least the following technical problems exist:
with the continuous improvement of the demand degree of the enterprise for talents, it becomes crucial to effectively identify talents and place proper talents on the right post, so that effectively recommending proper and post competent personnel for the interior of the enterprise becomes the focus of increasing attention of the enterprise.
At present, when matching between personnel and posts is realized, the following methods are generally adopted: firstly, various post specifications, post competency and the like are manually maintained through an offline excel table, huge and complex operations are performed through complex excel calculation, association of a plurality of sheet pages and the like, and the efficiency is low and the error-prone rate is high; secondly, by compiling a large amount of business system codes (such as java codes), complex and variable indexes, rules and the like are realized, and research and development work caused by changes cannot be efficiently completed; thirdly, reading various data prepared in the early stage by a BI tool, and drawing a related report so as to realize the output of personnel post matching, but the efficiency is still low due to data change.
In addition, in the second method, the coupling degree between the codes is high, and all models matched with the personnel at the post are required to be logically written through a large number of service codes, so that the overall logic is too complex, in addition, all rules are relatively solidified, and when the rules or the models are changed, the complicated codes are required to be modified for solving the problem, so the efficiency is low; in addition, algorithm promotion and model optimization are integrated in one system, code is difficult to achieve, post and personnel matching is business work in the field of manpower specialty, all business fields and business scenes are achieved through codes, various data algorithms, algorithm tuning and other work are achieved only through code compiling, the effect is poor, and meanwhile code compiling cannot be well fed back to model optimization through the process and the result.
Aiming at the problems in the related technology, the application provides a method for matching the personnel with the posts, the matching between the personnel and the posts is realized through a knowledge graph technology, and the corresponding relation between the changed data and the personnel is established through a corresponding algorithm on the basis of continuously perfecting the models of the posts and the personnel, so that the dynamic matching between the posts and the personnel is completed. Knowledge modeling is carried out by utilizing a data structure of a knowledge map based on a map, and a proper algorithm is combined to analyze and process source data, so that an optimal relationship set between entities is finally determined, a source system directly forms a visual page according to the result of the relationship set between the entities, the complexity and the coupling degree of all services realized in the same system are greatly reduced, the code research and development efficiency is improved, and the efficiency of matching personnel and posts is improved.
In one embodiment, the person-to-post matching method may be applied in an application scenario. Fig. 1 is a schematic structural diagram of a person and post matching system according to an embodiment of the present disclosure, and as shown in fig. 1, the person and post matching system may include a source system, a system database, a source data obtaining and cleaning module, an entity relationship algorithm and storage module, a graph database, and a data response module. The system comprises a source system, a system database, a post-personnel matching relation database and a database, wherein the source system is used for outputting source data, and the system database is used for storing the source data and the post-personnel matching relation data; the source data acquisition and cleaning module is used for cleaning source data acquired from a source system; the entity relation algorithm and storage module is used for acquiring and cleaning data output by the module according to the source data, determining an analysis strategy and a related algorithm of entity result relation data so as to generate a knowledge graph, and storing the knowledge graph into a graph database; and the data response module is used for determining corresponding post-personnel matching relation data according to the entity relation algorithm and the knowledge graph generated by the storage module and returning the post-personnel matching relation data to the source system.
With reference to the above scenario, the following describes in detail the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
The application provides a method for matching personnel with posts. Fig. 2 is a flowchart of a person and station matching method provided in an embodiment of the present application, and as shown in fig. 2, the method includes the following steps:
s201: according to the personnel attribute data extracted from the source data, first description information of different data sources facing to the personnel nodes is generated, and according to the post attribute data extracted from the source data, second description information of different data sources facing to the post nodes is generated.
In this step, the source data may be acquired from a plurality of source systems, and the source data included in the plurality of source systems may be different. The source data may include data corresponding to two main fields, the two main fields may be a person field and a post field, the data corresponding to the person field may include person attribute data, and the data corresponding to the post field may include post attribute data.
Specifically, the person attribute data may be used to describe a corresponding person node, and the position attribute data may be used to describe a corresponding position node.
Optionally, before generating the first description information and the second description information, mapping and merging may also be performed on different source data, that is, source data from different source systems are mapped and merged into one source system.
S202: and determining the association relationship between the data corresponding to the personnel node and the post node respectively according to the first description information and the second description information.
In this step, after the first description information and the second description information are determined, the relationship between the person and the post may be determined by using the first description information and the second description information, so that the association relationship between the data corresponding to the person node and the post node may be determined according to the relationship between the person and the post.
S203: and determining a first knowledge graph containing the personnel nodes and the post nodes according to the association relationship.
In this step, after determining the association relationship between the data corresponding to the good person node and the post node, the person node and the post node having the association relationship may be connected to form the first knowledge graph.
S204: and extracting first target data from the personnel attribute data and extracting second target data from the position attribute data.
In this step, the first target data is used to describe target person nodes, the second target data is used to describe target post nodes, the person nodes include target person nodes, and the post nodes include target post nodes.
Specifically, after the first knowledge graph is formed, because there are many people nodes and post nodes in the first knowledge graph, and the matching relationship between people and posts in the first knowledge graph is relatively comprehensive, detailed, but also relatively complex, when a user wants to determine data of a certain source or a certain structure, the user can extract first target data from the people attribute data and extract second target data from the post attribute data to extract a desired target people node and a desired target post node.
S205: and generating a second knowledge-graph containing the target personnel nodes and the target post nodes based on the first knowledge-graph.
In this step, after the first target data and the second target data are extracted, the target person node described by the first target data and the target post node described by the second target data may be determined, so that the relationship between the target person node and the target post node may be determined according to the relationship between the determined person node and the determined post node in the first knowledge graph, and thus, the second knowledge graph may be generated.
S206: and outputting data of the matching relation set of the personnel and the posts based on the second knowledge graph.
In this step, after the second knowledge graph is generated, the matching relationship set data between the people and the post may be output according to the relationship between the target people node and the target post node corresponding to the second knowledge graph.
According to the matching method of the personnel and the posts, the source data are analyzed and processed, the incidence relation between the data corresponding to the personnel nodes and the data corresponding to the post nodes are determined, so that a first knowledge graph containing all the personnel and the posts in the source data is generated, then the first target data is extracted from the personnel attribute data according to needs, and the second target data is extracted from the post attribute data to generate a second knowledge graph containing the target personnel nodes and the target post nodes, so that the matching relation set data of the personnel and the posts can be output, the problem of low efficiency caused by the fact that codes need to be modified is solved, and the efficiency of matching the personnel and the posts is improved.
In one embodiment, before generating first description information of different data sources facing to the person node according to the person attribute data extracted from the source data and generating second description information of different data sources facing to the position node according to the position attribute data extracted from the source data, the method further includes: acquiring source data from a source system; and preprocessing the source data to obtain preprocessed data meeting a preset standard.
In the scheme, the source data can be acquired from the source system, but because the source data in the source system is huge in quantity and not all the source data accord with the preset standard, the source data can be preprocessed to screen out preprocessed data which accord with the preset standard, so that the complexity of the source data is reduced, the efficiency of constructing the knowledge graph can be improved, and the efficiency of matching personnel and posts can be improved.
Alternatively, the preset criterion may be a calculation criterion of a preset target data algorithm.
In one embodiment, preprocessing the source data to obtain preprocessed data that meets a preset criterion includes: filtering the source data to obtain filtered data; and carrying out format conversion processing and/or integration processing on the filtered data to obtain preprocessed data.
In the scheme, when the source data is preprocessed, the source data can be filtered firstly to screen invalid data in the source data, and only valid data is reserved, so that the accuracy of the constructed knowledge graph is improved, and the accuracy of matching between personnel and posts can be further improved.
In the above scheme, after the filtered data is obtained, data format conversion processing can be performed on the data which is not in the target format in the filtered data, so that the uniform format of all the obtained data is ensured, the data calculation efficiency is improved, the efficiency of constructing the knowledge graph is improved, and the efficiency of matching the personnel with the posts can be improved.
In the scheme, after the filtering data are obtained, data integration processing can be carried out on data which are not integrated in the filtering data, so that the data calculation efficiency is improved, the efficiency of constructing the knowledge graph is improved, and the efficiency of matching personnel and posts can be improved.
Alternatively, the source data may be pre-processed as required by the target data algorithm.
In one embodiment, generating first description information of different data sources facing to the personnel nodes according to the personnel attribute data extracted from the source data, and generating second description information of different data sources facing to the position nodes according to the position attribute data extracted from the source data includes: generating first description information according to the personnel attribute data extracted from the preprocessed data; and generating second description information according to the post attribute data extracted from the preprocessed data.
In this scheme, after the source data is preprocessed to obtain preprocessed data, in order to improve the efficiency of data calculation and improve the accuracy of matching between people and positions, first description information may be generated according to people attribute data extracted from the preprocessed data, and second description information may be generated according to position attribute data extracted from the preprocessed data, so that the efficiency and the accuracy of generating the first description information and the second description information are high.
In one embodiment, outputting the data of the matching relationship set of the person and the position based on the second knowledge-graph comprises: determining rules between data corresponding to the target person nodes and the target post nodes in the second knowledge graph by adopting a graph mining algorithm and a rule reasoning algorithm; based on the rules, the matching relation between the personnel and the posts contained in the second knowledge graph is adjusted to obtain a target knowledge graph; and outputting the matching relation set data based on the target knowledge graph.
In the scheme, the second knowledge graph comprises explicit or implicit rules or modes between data corresponding to target personnel nodes and target post nodes respectively, so that the explicit or implicit rules or modes in the second knowledge graph can be found out by adopting a graph mining algorithm and a rule reasoning algorithm, and then the found rules or modes are utilized to continuously improve the second knowledge graph, so that the accuracy of the matching relation between personnel and posts in the target knowledge graph can be greatly improved, and the accuracy of matching between the personnel and the posts is improved.
In one embodiment, outputting the data of the set of matching relationships comprises: and outputting the matching relation set data according to a preset data structure.
In the scheme, when the matching relationship set data is output, the data format can be converted according to a predetermined data structure, and then the matching relationship set data is responded to the source system, so that the source system can identify the matching relationship set data, the source system can determine the matching relationship between personnel and posts, and the problem that the matching relationship between the personnel and the posts cannot be determined because the source system cannot identify the matching relationship set data is avoided.
Alternatively, the matching relationship set data may be responsive to the source system outputting the source data, or may be responsive to other systems that require the use of the matching relationship set data.
In an embodiment, after determining the association relationship between the data corresponding to the person node and the station node according to the first description information and the second description information, the method further includes: and through a node linking technology, the person nodes and the post nodes are stored in an associated manner.
In the scheme, the relevance storage of various types of data surrounding each person node and each post node can be realized through a node linking technology, so that the relation error between the person node and each post node caused by accidents is avoided, the accuracy of constructing the knowledge graph can be improved, and the accuracy of matching the person and the post can be improved.
It should be noted that the method, apparatus, device, and storage medium for matching a person with a post according to the present application can be used in the financial field and the computer field, and can also be used in any field other than the financial field and the computer field. The application fields of the method, the device, the equipment and the storage medium for matching the personnel and the posts are not limited.
The method for matching the personnel with the post provided by the embodiment avoids the problem that the matching efficiency of the personnel with the post is low because a service system realizes a complex service scene of matching the post with the personnel through a large number of complex codes; through knowledge graph technology, the calculation of relation set data between posts and personnel is efficiently completed, and meanwhile, services are decoupled; the expandability is strong, the function requirements of other similar service scenes can be realized by using the knowledge graph, and the service system only needs to pay attention to the processing of the source data and the response data.
On the whole, the technical scheme that this application provided is one kind and both can improve the rate of accuracy of matching personnel and post, can also improve the technical scheme who matches the efficiency of personnel and post.
The embodiment of the application also provides a matching device for the personnel and the posts. Fig. 3 is a schematic structural diagram of a matching device for people and posts according to an embodiment of the present application, and as shown in fig. 3, the matching device 300 for people and posts includes:
the first generating module 301 is configured to generate first description information of different data sources facing to the person nodes according to the person attribute data extracted from the source data, and generate second description information of different data sources facing to the position nodes according to the position attribute data extracted from the source data;
a first determining module 302, configured to determine, according to the first description information and the second description information, an association relationship between data corresponding to the person node and the post node;
a second determining module 303, configured to determine, according to the association relationship, a first knowledge graph including the person node and the post node;
an extracting module 304, configured to extract first target data from the person attribute data, and extract second target data from the post attribute data, where the first target data is used to describe a target person node, the second target data is used to describe a target post node, the person node includes a target person node, and the post node includes a target post node;
a second generation module 305, configured to generate a second knowledge-graph including the target person node and the target post node based on the first knowledge-graph;
and the output module 306 is used for outputting the data of the matching relationship set of the personnel and the posts based on the second knowledge graph.
Optionally, the person and station matching device 300 further comprises a processing module (not shown) for: generating first description information of different data sources facing to personnel nodes according to personnel attribute data extracted from source data, and acquiring source data from a source system before generating second description information of different data sources facing to station nodes according to station attribute data extracted from the source data; and preprocessing the source data to obtain preprocessed data meeting preset standards.
Optionally, when the processing module performs preprocessing on the source data to obtain preprocessed data meeting a preset standard, the processing module is specifically configured to: filtering the source data to obtain filtered data; and carrying out format conversion processing and/or integration processing on the filtered data to obtain preprocessed data.
Optionally, when the first generating module 301 generates first description information of different data sources facing to the staff nodes according to the staff attribute data extracted from the source data, and generates second description information of different data sources facing to the post nodes according to the post attribute data extracted from the source data, the first generating module is specifically configured to: generating first description information according to the personnel attribute data extracted from the preprocessed data; and generating second description information according to the post attribute data extracted from the preprocessed data.
Optionally, the output module 306 is specifically configured to, when outputting the data of the matching relationship set between the person and the post based on the second knowledge graph: determining rules between data corresponding to the target person nodes and the target post nodes in the second knowledge graph by adopting a graph mining algorithm and a rule reasoning algorithm; based on the rule, adjusting the matching relation between the personnel and the post contained in the second knowledge graph to obtain a target knowledge graph; and outputting the matching relation set data based on the target knowledge graph.
Optionally, when the output module 306 outputs the data of the matching relationship set between the person and the post, the output module is specifically configured to: and outputting the matching relation set data according to a preset data structure.
Optionally, the person and post matching device 300 further comprises a storage module (not shown) for: and after determining the association relationship between the data respectively corresponding to the personnel node and the post node according to the first description information and the second description information, performing association storage on the personnel node and the post node through a node linking technology.
The matching device for people and posts provided in this embodiment is used for implementing the technical scheme of the matching method for people and posts in the foregoing method embodiment, and the implementation principle and technical effect are similar, and are not described herein again.
The embodiment of the application further provides the electronic equipment. Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 4, the electronic device 400 includes:
a processor 411, a memory 412 communicatively coupled to processor 411, and an interactive interface 413;
memory 412 is used to store computer-executable instructions that are executable by processor 411;
the processor 411 is configured to execute the instructions via a computer stored in the execution memory 412, so as to implement the technical solution of the above-mentioned people and station matching method.
In the above electronic device 400, the memory 412, the processor 411, and the interaction interface 413 are electrically connected directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines, such as a bus. The memory 412 stores computer-executable instructions for implementing the method for matching a person with a station, and includes at least one software functional module which can be stored in the memory in the form of software or firmware, and the processor 411 executes various functional applications and data processing by running the software programs and modules stored in the memory 412.
The Memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory is used for storing programs, and the processor executes the programs after receiving the execution instructions. Further, the software programs and modules within the aforementioned memories may also include an operating system, which may include various software components and/or drivers for managing system tasks (e.g., memory management, storage device control, power management, etc.) and may communicate with various hardware or software components to provide an operating environment for other software components.
The processor may be an integrated circuit chip having signal processing capabilities. The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The embodiment of the present application further provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, the computer-executable instructions are used to implement the technical solution of the method for matching people and posts provided in the foregoing method embodiment.
The embodiment of the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the computer program is used to implement the technical solution of the method for matching people and posts provided in the foregoing method embodiment.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (11)
1. A personnel and post matching method is characterized by comprising the following steps:
generating first description information of different data sources facing to personnel nodes according to personnel attribute data extracted from source data, and generating second description information of different data sources facing to post nodes according to post attribute data extracted from the source data;
determining an association relationship between data corresponding to the personnel node and the post node respectively according to the first description information and the second description information;
determining a first knowledge graph containing the personnel nodes and the post nodes according to the incidence relation;
extracting first target data from the personnel attribute data, and extracting second target data from the post attribute data, wherein the first target data is used for describing target personnel nodes, the second target data is used for describing target post nodes, the personnel nodes comprise the target personnel nodes, and the post nodes comprise the target post nodes;
generating a second knowledge-graph comprising the target person nodes and the target post nodes based on the first knowledge-graph;
and outputting data of the matching relation set of the personnel and the posts based on the second knowledge graph.
2. The matching method according to claim 1, wherein before generating first description information of different data sources facing to the person node according to the person attribute data extracted from the source data and generating second description information of different data sources facing to the position node according to the position attribute data extracted from the source data, the method further comprises:
acquiring the source data from a source system;
and preprocessing the source data to obtain preprocessed data meeting preset standards.
3. The matching method according to claim 2, wherein the preprocessing the source data to obtain preprocessed data that meets a preset criterion includes:
filtering the source data to obtain filtered data;
and carrying out format conversion processing and/or integration processing on the filtered data to obtain the preprocessed data.
4. The matching method according to claim 2 or 3, wherein the generating of the first description information of the different data sources facing the personnel nodes according to the personnel attribute data extracted from the source data and the generating of the second description information of the different data sources facing the position nodes according to the position attribute data extracted from the source data comprises:
generating the first description information according to the personnel attribute data extracted from the preprocessed data;
and generating the second description information according to the post attribute data extracted from the preprocessed data.
5. The matching method according to any one of claims 1 to 3, wherein outputting data of a person-to-station matching relationship set based on the second knowledge graph comprises:
determining rules between data respectively corresponding to the target person nodes and the target post nodes in the second knowledge graph by adopting a graph mining algorithm and a rule reasoning algorithm;
based on the rule, adjusting the matching relation between the personnel and the post contained in the second knowledge graph to obtain a target knowledge graph;
and outputting the matching relation set data based on the target knowledge graph.
6. The matching method according to claim 5, wherein the outputting the matching relationship set data includes:
and outputting the matching relation set data according to a preset data structure.
7. The matching method according to any one of claims 1 to 3, wherein after determining the association relationship between the data corresponding to the person node and the station node according to the first description information and the second description information, the method further comprises:
and performing associated storage on the personnel nodes and the post nodes through a node linking technology.
8. A personnel and post matching device, comprising:
the first generation module is used for generating first description information of different data sources facing to the personnel nodes according to the personnel attribute data extracted from the source data and generating second description information of different data sources facing to the station nodes according to the station attribute data extracted from the source data;
a first determining module, configured to determine, according to the first description information and the second description information, an association relationship between data corresponding to the person node and the post node, respectively;
a second determining module, configured to determine, according to the association relationship, a first knowledge graph including the person node and the post node;
an extraction module, configured to extract first target data from the person attribute data, and extract second target data from the post attribute data, where the first target data is used to describe a target person node, the second target data is used to describe a target post node, the person node includes the target person node, and the post node includes the target post node;
a second generation module for generating a second knowledge-graph comprising the target person nodes and the target post nodes based on the first knowledge-graph;
and the output module is used for outputting the data of the matching relationship set of the personnel and the posts based on the second knowledge graph.
9. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored by the memory to implement the person-to-post matching method of any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon computer-executable instructions for implementing the person and post matching method of any one of claims 1 to 7 when executed by a processor.
11. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, is adapted to carry out the person-to-post matching method according to any one of claims 1 to 7.
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| CN117196165A (en) * | 2023-04-21 | 2023-12-08 | 山东浪潮爱购云链信息科技有限公司 | Recommendation method and device for labor outsourcing personnel |
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| CN117196165A (en) * | 2023-04-21 | 2023-12-08 | 山东浪潮爱购云链信息科技有限公司 | Recommendation method and device for labor outsourcing personnel |
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