CN113901332B - Tenure history information mining method and device, storage medium and electronic equipment - Google Patents

Tenure history information mining method and device, storage medium and electronic equipment Download PDF

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CN113901332B
CN113901332B CN202111144809.8A CN202111144809A CN113901332B CN 113901332 B CN113901332 B CN 113901332B CN 202111144809 A CN202111144809 A CN 202111144809A CN 113901332 B CN113901332 B CN 113901332B
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CN113901332A (en
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张鹏飞
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Yancheng Tianyanchawei Technology Co ltd
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Abstract

The invention discloses a method and a device for mining tenninal history information, a storage medium and electronic equipment, wherein the method comprises the following steps: acquiring first arbitrary history information corresponding to preset object information; clustering the first optional history information to determine an optional history information sub-cluster; and determining second tenninal history information corresponding to the target object corresponding to the preset object information based on the tenninal history information sub-cluster. The method can rapidly and accurately determine the tenure history information of the target object of the target unit, can provide convenience for solving the tenure history of the target object, provides powerful basis for personnel capability judgment and background investigation of a company, and solves the problem that the tenure history information of the target object cannot be accurately analyzed from mass data.

Description

Tenure history information mining method and device, storage medium and electronic equipment
Technical Field
The present invention relates to the field of computer information processing technology, and more particularly, to a method and apparatus for mining tenure information, and a storage medium and an electronic device.
Background
Corporate target objects often experience multiple job sites during the course of a job, where job positions may also change multiple times. Particularly for some excellent talents, multiple positions may be served in different units at the same time. The tenninal history information of the target object plays an important role in the capability assessment thereof. However, there is currently no way to obtain tenninal history information of a target object.
Therefore, how to mine from a huge amount of change records, so as to determine the tenability history information of the target object of the target unit is an urgent problem to be solved.
Disclosure of Invention
The problem to be solved by the invention comprises how to mine the tenure information of the target object from mass data.
The invention is proposed to solve the above-mentioned technical problems such as how to mine the tenure information associated with the preset object information from the mass data, and determine the tenure information of the specific target object according to the mined tenure information.
The embodiment of the invention provides a tenure history information mining method, which comprises the following steps:
acquiring first arbitrary history information corresponding to preset object information;
Clustering the first optional history information to determine an optional history information sub-cluster;
and determining second tenninal history information corresponding to the target object corresponding to the preset object information based on the tenninal history information sub-cluster.
Preferably, the acquiring the first arbitrary role history information corresponding to the preset object information includes:
searching information in a data source based on the preset object information to acquire a change record item corresponding to the preset object information;
and acquiring first arbitrary history information corresponding to the preset object information according to the change record item and a preset strategy.
Preferably, wherein the method further comprises:
analyzing the historical change content corresponding to the preset change item, obtaining a change record item corresponding to any object information, and determining the data source according to the obtained change record item corresponding to any object information.
Preferably, the obtaining, according to the change record item and a preset policy, the first arbitrary role history information corresponding to the preset object information includes:
classifying the change record items based on the attribution information in the change record items, and acquiring the change record items corresponding to any attribution information;
Determining the arbitrary time information corresponding to any attribution information according to the change time information, the first change identifier and the second change identifier in the change record item corresponding to any attribution information;
and acquiring first optional history information corresponding to the preset object information according to optional time information corresponding to any attribution information.
Preferably, the determining the time-of-job information corresponding to the arbitrary attribution information according to the change time information, the first change identifier and the second change identifier in the change record item corresponding to the arbitrary attribution information includes:
determining the optional job ending time corresponding to any attribution information according to the first change identifier and the change time information in the change record item;
and determining the optional job starting time corresponding to any piece of attribution information according to the second change identification and the change time information in the change record item.
Preferably, the acquiring the first arbitrary role history information corresponding to the preset object information according to the arbitrary role time information corresponding to the arbitrary attribution information includes:
and merging time intervals according to the tenure starting time and the tenure ending time corresponding to any attribution information to acquire first tenure history information corresponding to preset object information.
Preferably, wherein the method further comprises:
judging whether the first attribution information and the second attribution information corresponding to any two adjacent time periods are consistent or not, and determining a judging result;
and when the judging result indicates that the first attribution information and the second attribution information are consistent, combining the two arbitrary adjacent time periods.
Preferably, the determining whether the first attribution information and the second attribution information corresponding to any two adjacent time periods are consistent, determining a determination result includes:
deleting preset characters in the first attribution information and the second attribution information to acquire first attribution processing information and second attribution processing information;
and when the first attribution processing information is consistent with the second attribution processing information, determining that the judging result is consistent.
Preferably, wherein the method further comprises:
when the first attribution processing information and the second attribution processing information are inconsistent, format conversion is carried out on the first attribution processing information and the second attribution processing information according to a preset conversion rule so as to obtain first format information and second format information;
and when the first format information and the second format information are consistent, determining that the judging result is consistent.
Preferably, the performing format conversion on the first home processing information and the second home processing information according to a preset conversion rule to obtain first format information and second format information includes:
converting letters in the first format information and the second format information into a first preset format; and/or converting the text in the first format information and the second format information into a second preset format so as to acquire the first format information and the second format information.
Preferably, the clustering the first tenninal lineage information to determine a tenninal lineage information sub-cluster includes:
determining attribution information corresponding to the preset object information according to the first arbitrary role history information;
determining an associated object corresponding to any attribution information according to the arbitrary attribution information;
constructing a relation network according to the preset object information, any attribution information and the corresponding relation between the associated objects;
clustering is carried out according to the structure of the relation network after the preset object information is removed, so that the tenure history information sub-clusters are determined.
Preferably, the determining, based on the sub-cluster of tenninal history information, second tenninal history information corresponding to the target object corresponding to the preset object information includes:
Determining target tenure course information sub-clusters to which preset attribution information belongs;
and carrying out data merging processing based on the first tenninal process information in the target tenninal process information sub-cluster, and determining second tenninal process information corresponding to the target object corresponding to the preset object information.
Preferably, the optional history information includes: object information, attribution information corresponding to each time period and position information corresponding to each time period.
According to another aspect of the embodiment of the present invention, there is provided a tenninal history information mining apparatus, the apparatus including:
the first arbitrary history information acquisition module is used for acquiring first arbitrary history information corresponding to the preset object information;
the clustering module is used for clustering the first optional history information to determine an optional history information sub-cluster;
and the second tenninal history information determining module is used for determining second tenninal history information corresponding to the target object corresponding to the preset object information based on the tenninal history information sub-cluster.
According to still another aspect of an embodiment of the present invention, there is provided an electronic apparatus including: a processor and a memory; wherein,
The memory is configured to store the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any of the foregoing embodiments of the present invention.
According to a further aspect of an embodiment of the present invention, there is provided a computer readable storage medium, characterized in that the computer readable storage medium stores a computer program for executing the method according to any of the above embodiments.
According to the method and the device for mining the optional history information, the storage medium and the electronic equipment provided by the embodiment of the invention, the first optional history information corresponding to the preset object information is obtained; clustering the first optional history information to determine an optional history information sub-cluster; and determining second tenninal history information corresponding to the target object corresponding to the preset object information based on the tenninal history information sub-cluster. The method can rapidly and accurately determine the tenure history information of the target object of the target unit, can provide convenience for solving the tenure history of the target object, provides powerful basis for personnel capability judgment and background investigation of a company, and solves the problem that the tenure history of the target object cannot be accurately analyzed from mass data.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
Exemplary embodiments of the present invention may be more completely understood in consideration of the following drawings:
FIG. 1 is a flowchart of a method 100 for mining tenninal history information provided in accordance with an exemplary embodiment of the present invention;
FIG. 2 is an exemplary diagram of clustering first tenninal lineage information to determine tenninal lineage information sub-clusters according to an exemplary embodiment of the invention;
fig. 3 is a schematic structural diagram of an optional history information mining apparatus 300 according to an exemplary embodiment of the present invention;
fig. 4 is a structure of an electronic device 40 provided in an exemplary embodiment of the present invention.
Detailed Description
Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some embodiments of the present invention and not all embodiments of the present invention, and it should be understood that the present invention is not limited by the example embodiments described herein.
It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
It will be appreciated by those of skill in the art that the terms "first," "second," etc. in embodiments of the present invention are used merely to distinguish between different steps, devices or modules, etc., and do not represent any particular technical meaning nor necessarily logical order between them.
It should also be understood that in embodiments of the present invention, "plurality" may refer to two or more, and "at least one" may refer to one, two or more.
It should also be appreciated that any component, data, or structure referred to in an embodiment of the invention may be generally understood as one or more without explicit limitation or the contrary in the context.
In addition, the term "and/or" in the present invention is merely an association relationship describing the association object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In the present invention, the character "/" generally indicates that the front and rear related objects are an or relationship.
It should also be understood that the description of the embodiments of the present invention emphasizes the differences between the embodiments, and that the same or similar features may be referred to each other, and for brevity, will not be described in detail.
Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods, and apparatus should be considered part of the specification.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
Embodiments of the invention are operational with numerous other general purpose or special purpose computing system environments or configurations with electronic devices, such as terminal devices, computer systems, servers, etc. Examples of well known terminal devices, computing systems, environments, and/or configurations that may be suitable for use with the terminal device, computer system, server, or other electronic device include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, mainframe computer systems, and distributed cloud computing technology environments that include any of the foregoing, and the like.
Electronic devices such as terminal devices, computer systems, servers, etc. may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc., that perform particular tasks or implement particular abstract data types. The computer system/server may be implemented in a distributed cloud computing environment in which tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computing system storage media including memory storage devices.
Exemplary method
Fig. 1 is a flowchart of a tenure information mining method 100 provided by an exemplary embodiment of the present invention. The embodiment can be applied to an electronic device, as shown in fig. 1, and includes the following steps:
step 101, obtaining first arbitrary history information corresponding to preset object information.
Preferably, the acquiring the first arbitrary role history information corresponding to the preset object information includes:
searching information in a data source based on the preset object information to acquire a change record item corresponding to the preset object information;
And acquiring first arbitrary history information corresponding to the preset object information according to the change record item and a preset strategy.
Preferably, the optional history information includes: object information, attribution information corresponding to each time period and position information corresponding to each time period.
In an embodiment of the present invention, the data source includes a change record item corresponding to each object information. The change record item comprises object information, attribution information, change time, change item, a first change identifier and a second change identifier. Wherein the object information indicates a name of a person who is assigned in the unit; the attribution information represents a unit name of the tenure unit; the change project represents personnel changes such as legal persons/stakeholders/directors, and the change project corresponds to position information; the first change identifier is used for indicating whether the change is in progress or not, and the second change identifier is used for indicating whether the change is in progress or not. The change identifier can be set according to requirements, for example, the optional values of the change identifier are set to be 1 and 0, when the change identifier is 1, the change identifier is in incumbent, and when the change identifier is 0, the change identifier is out of incumbent. For example, if the change entry is [ Zhang San, company A, french, 2021/8/1,0,1], then it is indicated that company A adds Zhang San to day 1 of 8 in 2021. For example, if the change record is [ Lifour, company A, legal person, 2021/8/1, 0], then this indicates that company A withdraws Lifour from legal person at 8 months 1 of 2021.
In the embodiment of the invention, information searching is firstly carried out in a data source according to the required preset object information, the change record items of which the object information is the preset object information in all change records are obtained, and then the first arbitrary history information corresponding to the preset object information is determined according to a preset strategy according to the obtained change record items. For example, if the preset object information is Zhang San, all change record items including Zhang San in the data source are extracted, and then the first arbitrary history information is determined according to the extracted change record items. Wherein the tenninal history information includes: the object information, the attribution information corresponding to each time period and the position information time period corresponding to each time period are determined according to the change time.
Preferably, wherein the method further comprises:
analyzing the historical change content corresponding to the preset change item, obtaining a change record item corresponding to any object information, and determining the data source according to the obtained change record item corresponding to any object information.
In the embodiment of the present invention, the history change content includes changes of various items, and the change content is in text form, where: first, a preset change catalog (for example, a legal person, a stakeholder, etc.) is used as a keyword, and matching is performed based on the set keyword and the history change content to determine the history change content corresponding to the stakeholder change, the legal person change, etc.; then, the acquired history change contents are analyzed, and information extraction is performed based on the contents in the change record item, thereby determining the change record item.
Preferably, the obtaining, according to the change record item and a preset policy, the first arbitrary role history information corresponding to the preset object information includes:
classifying the change record items based on the attribution information in the change record items, and acquiring the change record items corresponding to any attribution information;
determining the arbitrary time information corresponding to any attribution information according to the change time information, the first change identifier and the second change identifier in the change record item corresponding to any attribution information;
and acquiring first optional history information corresponding to the preset object information according to optional time information corresponding to any attribution information.
Preferably, the determining the time-of-job information corresponding to the arbitrary attribution information according to the change time information, the first change identifier and the second change identifier in the change record item corresponding to the arbitrary attribution information includes:
determining the optional job ending time corresponding to any attribution information according to the first change identifier and the change time information in the change record item;
and determining the optional job starting time corresponding to any piece of attribution information according to the second change identification and the change time information in the change record item.
Preferably, the acquiring the first arbitrary role history information corresponding to the preset object information according to the arbitrary role time information corresponding to the arbitrary attribution information includes:
and merging time intervals according to the tenure starting time and the tenure ending time corresponding to any attribution information to acquire first tenure history information corresponding to preset object information.
In the embodiment of the present invention, after determining the change record item corresponding to the preset object information, first, the change record item is classified according to the attribution information (i.e., optional unit information) in the change record item.
For example, if the preset object information is Zhang San, the change record item corresponding to Zhang San includes: DATA 1= [ Zhang San, A company, legal, 2020/8/1,0,1], DATA 2= [ Zhang San, A company, legal, 2021/6/1, 0], DATA 3= [ Zhang San, B company, stockholder, 2021/1, 0,1], DATA 4= [ Zhang San, B company, stockholder, 2021/9/1, 0], then DATA1 and DATA2 can be classified as a type according to the attribution information, as change record items corresponding to "A company"; DATA3 and DATA4 are classified as change records corresponding to "company B".
In the embodiment of the present invention, after determining a change record item corresponding to any attribution information, for a person in the change record item (the object information of the person is the same because the extraction of the change record is performed according to the preset object information), the change time corresponding to the person when the person appears at the earliest time in the post-change content is determined to be the time of start of job, the change time corresponding to the person when the person appears at the latest time in the pre-change content is determined to be the time of end of job, otherwise, the person is in the job state, so that the time information of job corresponding to each attribution information is determined.
Specifically, determining the optional job ending time corresponding to any attribution information according to the latest change time information in the change record item with the first change identifier as the preset change identifier; and determining the optional job starting time corresponding to any attribution information according to the earliest change time information in the change record item with the second change identifier as the preset change identifier. Wherein, the preset change table may be set to 1. And determining the end time of the tenure according to the change time information of the latest occurrence of 1 in the first change mark, and determining the end time of the tenure according to the change time information of the earliest occurrence of 1 in the second change mark.
For example, continuing with the above example, from the change identification, it may be determined that the start time of the job at company a is 2020, 8, 1, and the end time of the job is 2021, 6, 1.
In the embodiment of the invention, by extracting the personnel wilting change record item from the wilting units and then reversely looking up all units of the personnel wilting from the personnel perspective, the wilting start time and the wilting end time of each unit. The time intervals are combined to obtain attribution information corresponding to each time period and position information corresponding to each time period corresponding to personnel. Specifically, after determining the time of beginning of the job and the time of ending of the job corresponding to the same attribution information, merging time intervals to obtain attribution information corresponding to each time period and position information corresponding to each time period corresponding to preset object information, so as to form complete time line data.
For example, in 2020, with Zhang Sanat 1-6 months at A, which is a timeline, 5-8 months at B, and another timeline is generated, then at 5-6 months at both A and B, the two timelines are merged into a complete timeline, and the person would have 4 time nodes 1 month, 5 months, 6 months, 8 months, start at 1 month, start at 5 months at A, end at two 5 months, end at B, end at 6 months at A, end at B, end at 8 months at B, and end at all are legal, so that it can be determined that the first time history information corresponding to Zhang Santa includes: [ < Zhang three, A company, legal person, 2020.1.1,2020.5.1, >, < Zhang three, A company, legal person, 2020.5.1,2020.6.1>, < Zhang three, B company, legal person, 2020.5.1,2020.6.1>, < Zhang three, B company, legal person, 2020.6.1,2020.8.1> ].
Preferably, wherein the method further comprises:
judging whether the first attribution information and the second attribution information corresponding to any two adjacent time periods are consistent or not, and determining a judging result;
and when the judging result indicates that the first attribution information and the second attribution information are consistent, combining the two arbitrary adjacent time periods.
Preferably, the determining whether the first attribution information and the second attribution information corresponding to any two adjacent time periods are consistent, determining a determination result includes:
deleting preset characters in the first attribution information and the second attribution information to acquire first attribution processing information and second attribution processing information;
and when the first attribution processing information is consistent with the second attribution processing information, determining that the judging result is consistent.
Preferably, wherein the method further comprises:
when the first attribution processing information and the second attribution processing information are inconsistent, format conversion is carried out on the first attribution processing information and the second attribution processing information according to a preset conversion rule so as to obtain first format information and second format information;
and when the first format information and the second format information are consistent, determining that the judging result is consistent.
Preferably, the performing format conversion on the first home processing information and the second home processing information according to a preset conversion rule to obtain first format information and second format information includes:
converting letters in the first format information and the second format information into a first preset format; and/or converting the text in the first format information and the second format information into a second preset format so as to acquire the first format information and the second format information.
The extracted time line data has adjacent time periods which are the same unit, but due to the fact that the content for describing the attribution information may have a small error, the extracted time line data is mistakenly regarded as not being a company, and therefore the analysis of the optional history information is inaccurate. Therefore, it is necessary to determine the attribution information, and to combine the first arbitrary history information corresponding to the same attribution information.
Therefore, in the embodiment of the present invention, it is necessary to determine whether the first attribution information and the second attribution information corresponding to any two adjacent time periods are identical, and when it is determined that the first attribution information and the second attribution information are identical, the two adjacent time periods are combined.
Specifically, in the embodiment of the invention, deleting preset characters such as various symbols, punctuations and the like in the first attribution information and the second attribution information to acquire first attribution processing information and second attribution processing information; and when the first attribution processing information is consistent with the second attribution processing information, determining that the judging result is consistent. For example, if the first attribution information is "a/company", and the second attribution information is "a/, company", the preset character is deleted, and then the first attribution processing information is "a company", and the second attribution information is "a company", and at this time, the time periods are combined, and the attribution information is unified.
When the first attribution processing information and the second attribution processing information are inconsistent, format conversion is carried out on the first attribution processing information and the second attribution processing information according to a preset conversion rule so as to acquire first format information and second format information, and letters in the first format information and the second format information are specifically converted into a first preset format; and/or converting the text in the first format information and the second format information into a second preset format to acquire the first format information and the second format information, and determining that the judging result is consistent when the first format information and the second format information are consistent. At this time, the two pieces of attribution information are considered to be one entity, so that the time periods are combined, and the attribution information is unified. Otherwise, the processing is not performed. For example, converting two pieces of attribution processing information into lower case/upper case, and/or converting two pieces of attribution processing information into pinyin, if the two pieces of attribution processing information are consistent after conversion, the two pieces of attribution processing information are regarded as the same attribution information, and the judgment result is consistent; converting the two contents into pinyin, and if the two contents are consistent, considering the two contents as the same entity. For example, if the attribution information is "Zhang Sana company", "Zhang Sana company" or "Zhang San A company", the attribution information is determined to be the same entity by case-to-case conversion and/or letter conversion.
Step 102, clustering the first optional history information to determine an optional history information sub-cluster.
Preferably, the clustering the first tenninal lineage information to determine a tenninal lineage information sub-cluster includes:
determining attribution information corresponding to the preset object information according to the first arbitrary role history information;
determining an associated object corresponding to any attribution information according to the arbitrary attribution information;
constructing a relation network according to the preset object information, any attribution information and the corresponding relation between the associated objects;
clustering is carried out according to the structure of the relation network after the preset object information is removed, so that the tenure history information sub-clusters are determined.
In real life, many persons with the same name exist, so that the uniqueness of the persons with the same name is also needed to be judged so as to remove any job history information corresponding to repeated person names.
In the embodiment of the invention, the clustering of the assigned enterprises is performed according to the attribution information in the first assigned history information corresponding to the preset object information, and then, the number of people with the same name is determined according to the number of finally-split clusters. Specifically, the clustering method is as follows: firstly, acquiring all attribution information in first arbitrary history information corresponding to preset object information as associated attribution information, and then acquiring associated objects (personnel in a change record item) which are externally associated by all units according to the associated attribution information; then, a relation network is built by taking preset object information as a center according to the corresponding relation among the preset object information, the associated attribution information and the associated objects, then the objects corresponding to the preset object information are removed from the relation network, and clustering is carried out according to the structure of the relation network after the preset object information is removed, so that the optional history information sub-clusters are determined. After clustering, the tenninal history information of the same person belongs to the same cluster. Wherein, according to the number of the independent networks divided by the relation network after the object of the center is removed, the number of people with the same name can be determined.
For example, as shown in fig. 2, a person with object information a is at the discretion of a company a, B, C, D, f, wherein it is determined that company a, B is associated with person B, C, D is associated with person C, f is associated with person D, after a relational network is constructed, if a is removed from the network and can be divided into three networks that are not associated with each other, the company with object information a is considered to be divided into 3 groups, and object information a corresponds to three different persons.
And step 103, determining second tenninal history information corresponding to the target object corresponding to the preset object information based on the tenninal history information sub-cluster.
Preferably, the determining, based on the sub-cluster of tenninal history information, second tenninal history information corresponding to the target object corresponding to the preset object information includes:
determining target tenure course information sub-clusters to which preset attribution information belongs;
and carrying out data merging processing based on the first tenninal process information in the target tenninal process information sub-cluster, and determining second tenninal process information corresponding to the target object corresponding to the preset object information.
In determining the tenninal history information, it is generally necessary to know the name of a person and a unit where the person was tenninal.
Therefore, in the embodiment of the present invention, first, it is necessary to determine preset object information and one preset attribution information corresponding to the preset object information; then, determining target tenure course information sub-clusters to which the target tenure course information sub-clusters belong according to preset attribution information; then, according to the tenninal history information in the target tenninal history information sub-cluster, determining second tenninal history information corresponding to the target object corresponding to the preset object information, including: the name of the person, the wilt company information corresponding to each time period and the position information corresponding to each time period.
According to the method provided by the embodiment of the invention, from the perspective of personnel, personnel are taken as main bodies to excavate the wilting histories of the personnel, wherein a clustering and grouping processing mode is provided for the problem of the renaming, and the judgment of the personnel can be rapidly and accurately carried out; the system and the method can provide convenience for users to know the job history of personnel, provide powerful basis for the personnel capacity judgment and background investigation of the company, and solve the problem that the job history information of the personnel cannot be analyzed from mass data.
Exemplary apparatus
Fig. 3 is a schematic structural diagram of an optional history information mining apparatus 300 according to an exemplary embodiment of the present invention. As shown in fig. 3, the tenninal history information mining apparatus 300 of the present embodiment includes:
The first role history information obtaining module 301 is configured to obtain first role history information corresponding to preset object information.
Preferably, the first arbitrary role history information obtaining module 301 obtains first arbitrary role history information corresponding to the preset object information, including:
searching information in a data source based on the preset object information to acquire a change record item corresponding to the preset object information;
and acquiring first arbitrary history information corresponding to the preset object information according to the change record item and a preset strategy.
Preferably, the first arbitrary role history information acquiring module 301 further includes:
analyzing the historical change content corresponding to the preset change item, obtaining a change record item corresponding to any object information, and determining the data source according to the obtained change record item corresponding to any object information.
Preferably, the first arbitrary role history information obtaining module 301 obtains, according to a preset policy and the change record item, first arbitrary role history information corresponding to the preset object information, including:
classifying the change record items based on the attribution information in the change record items, and acquiring the change record items corresponding to any attribution information;
Determining the arbitrary time information corresponding to any attribution information according to the change time information, the first change identifier and the second change identifier in the change record item corresponding to any attribution information;
and acquiring first optional history information corresponding to the preset object information according to optional time information corresponding to any attribution information.
Preferably, the first optional process information obtaining module 301 determines optional time information corresponding to the optional attribution information according to the change time information, the first change identifier and the second change identifier in the change record corresponding to the optional attribution information, and includes:
determining the optional job ending time corresponding to any attribution information according to the first change identifier and the change time information in the change record item;
and determining the optional job starting time corresponding to any piece of attribution information according to the second change identification and the change time information in the change record item.
Preferably, the first optional history information obtaining module 301 obtains first optional history information corresponding to preset object information according to optional time information corresponding to the optional attribution information, including:
And merging time intervals according to the tenure starting time and the tenure ending time corresponding to any attribution information to acquire first tenure history information corresponding to preset object information.
Preferably, the first arbitrary role history information acquiring module 301 further includes:
judging whether the first attribution information and the second attribution information corresponding to any two adjacent time periods are consistent or not, and determining a judging result;
and when the judging result indicates that the first attribution information and the second attribution information are consistent, combining the two arbitrary adjacent time periods.
Preferably, the first arbitrary history information acquiring module 301 determines whether the first attribution information and the second attribution information corresponding to any two adjacent time periods are consistent, and determines a determination result, including:
deleting preset characters in the first attribution information and the second attribution information to acquire first attribution processing information and second attribution processing information;
and when the first attribution processing information is consistent with the second attribution processing information, determining that the judging result is consistent.
Preferably, the first arbitrary role history information acquiring module 301 further includes:
When the first attribution processing information and the second attribution processing information are inconsistent, format conversion is carried out on the first attribution processing information and the second attribution processing information according to a preset conversion rule so as to obtain first format information and second format information;
and when the first format information and the second format information are consistent, determining that the judging result is consistent.
Preferably, the first arbitrary history information obtaining module 301 performs format conversion on the first home processing information and the second home processing information according to a preset conversion rule to obtain first format information and second format information, including:
converting letters in the first format information and the second format information into a first preset format; and/or converting the text in the first format information and the second format information into a second preset format so as to acquire the first format information and the second format information.
The clustering module 302 is configured to cluster the first tenninal lineage information to determine a tenninal lineage information sub-cluster.
Preferably, the clustering module 302 clusters the first arbitrary calendar information to determine an arbitrary calendar information sub-cluster, including:
Determining attribution information corresponding to the preset object information according to the first arbitrary role history information;
determining an associated object corresponding to any attribution information according to the arbitrary attribution information;
constructing a relation network according to the preset object information, any attribution information and the corresponding relation between the associated objects;
clustering is carried out according to the structure of the relation network after the preset object information is removed, so that the tenure history information sub-clusters are determined.
And a second tenninal history information determining module 303, configured to determine second tenninal history information corresponding to the target object corresponding to the preset object information based on the tenninal history information sub-cluster.
Preferably, the second tenninal history information determining module 303 determines second tenninal history information corresponding to a target object corresponding to the preset object information based on the tenninal history information sub-cluster, including:
determining target tenure course information sub-clusters to which preset attribution information belongs;
and carrying out data merging processing based on the first tenninal process information in the target tenninal process information sub-cluster, and determining second tenninal process information corresponding to the target object corresponding to the preset object information.
Preferably, the optional history information includes: object information, attribution information corresponding to each time period and position information corresponding to each time period.
The tenure information mining apparatus 300 according to the embodiment of the present invention corresponds to the tenure information mining method 100 according to another embodiment of the present invention, and is not described herein.
Exemplary electronic device
Fig. 4 is a structure of an electronic device provided in an exemplary embodiment of the present invention. The electronic device may be either or both of the first device and the second device, or a stand-alone device independent thereof, which may communicate with the first device and the second device to receive the acquired input signals therefrom. Fig. 4 illustrates a block diagram of an electronic device according to an embodiment of the disclosure. As shown in fig. 4, the electronic device 40 includes one or more processors 41 and memory 42.
The processor 41 may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.
Memory 42 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium that may be executed by the processor 41 to implement the method of mining arbitrary history information and/or other desired functions of the software program of the various embodiments of the present disclosure described above. In one example, the electronic device may further include: an input device 43 and an output device 44, which are interconnected by a bus system and/or other forms of connection mechanisms (not shown).
In addition, the input device 43 may also include, for example, a keyboard, a mouse, and the like.
The output device 44 can output various information to the outside. The output device 44 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.
Of course, only some of the components of the electronic device relevant to the present disclosure are shown in fig. 4 for simplicity, components such as buses, input/output interfaces, etc. being omitted. In addition, the electronic device may include any other suitable components depending on the particular application.
Exemplary embodimentsComputer program product and computer readable storage medium
In addition to the methods and apparatus described above, embodiments of the present disclosure may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps in the method of mining arbitrary history information according to various embodiments of the present disclosure described in the "exemplary methods" section of this specification.
The computer program product may write program code for performing the operations of embodiments of the present disclosure in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present disclosure may also be a computer-readable storage medium, having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform steps in a method of mining arbitrary history information according to various embodiments of the present disclosure described in the above-described "exemplary methods" section of the present disclosure.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present disclosure have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present disclosure are merely examples and not limiting, and these advantages, benefits, effects, etc. are not to be considered as necessarily possessed by the various embodiments of the present disclosure. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, since the disclosure is not necessarily limited to practice with the specific details described.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different manner from other embodiments, so that the same or similar parts between the embodiments are mutually referred to. For system embodiments, the description is relatively simple as it essentially corresponds to method embodiments, and reference should be made to the description of method embodiments for relevant points.
The block diagrams of the devices, apparatuses, devices, systems referred to in this disclosure are merely illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, apparatuses, devices, systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.
The methods and apparatus of the present disclosure may be implemented in a number of ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above-described sequence of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present disclosure may also be implemented as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
It is also noted that in the apparatus, devices and methods of the present disclosure, components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered equivalent to the present disclosure. The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the disclosure to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.

Claims (12)

1. A method of mining tenninal history information, the method comprising:
acquiring first arbitrary history information corresponding to preset object information;
clustering the first optional history information to determine an optional history information sub-cluster;
determining second tenninal history information corresponding to a target object corresponding to the preset object information based on the tenninal history information sub-cluster;
the obtaining the first arbitrary role history information corresponding to the preset object information includes:
searching information in a data source based on the preset object information to acquire a change record item corresponding to the preset object information;
according to the change record item and a preset strategy, acquiring first arbitrary history information corresponding to the preset object information, wherein the first arbitrary history information comprises: classifying the change record items based on the attribution information in the change record items, and acquiring the change record items corresponding to any attribution information; determining the arbitrary time information corresponding to any attribution information according to the change time information, the first change identifier and the second change identifier in the change record item corresponding to any attribution information; acquiring first optional time history information corresponding to the preset object information according to optional time information corresponding to any attribution information;
The clustering the first optional history information to determine an optional history information sub-cluster includes:
determining attribution information corresponding to the preset object information according to the first arbitrary role history information;
determining an associated object corresponding to any attribution information according to the arbitrary attribution information;
constructing a relation network according to the preset object information, any attribution information and the corresponding relation between the associated objects;
clustering according to the structure of the relation network after the preset object information is removed, so as to determine the wilful history information sub-clusters;
the determining, based on the sub-cluster of the tenninal history information, second tenninal history information corresponding to a target object corresponding to the preset object information includes:
determining target tenure course information sub-clusters to which preset attribution information belongs;
and carrying out data merging processing based on the first tenninal process information in the target tenninal process information sub-cluster, and determining second tenninal process information corresponding to the target object corresponding to the preset object information.
2. The method according to claim 1, wherein the method further comprises:
analyzing the historical change content corresponding to the preset change item, obtaining a change record item corresponding to any object information, and determining the data source according to the obtained change record item corresponding to any object information.
3. The method of claim 1, wherein the determining the arbitrary time information corresponding to the arbitrary attribution information according to the change time information, the first change identifier, and the second change identifier in the change record corresponding to the arbitrary attribution information includes:
determining the optional job ending time corresponding to any attribution information according to the first change identifier and the change time information in the change record item;
and determining the optional job starting time corresponding to any piece of attribution information according to the second change identification and the change time information in the change record item.
4. The method of claim 3, wherein the obtaining the first arbitrary role history information corresponding to the preset object information according to the arbitrary role time information corresponding to the arbitrary home information includes:
and merging time intervals according to the tenure starting time and the tenure ending time corresponding to any attribution information, and obtaining first tenure history information corresponding to the preset object information.
5. The method according to claim 1, wherein the method further comprises:
judging whether the first attribution information and the second attribution information corresponding to any two adjacent time periods are consistent or not, and determining a judging result;
And when the judging result indicates that the first attribution information and the second attribution information are consistent, combining the two arbitrary adjacent time periods.
6. The method of claim 5, wherein determining whether the first attribution information and the second attribution information corresponding to any two adjacent time periods are identical, determining a determination result, comprises:
deleting preset characters in the first attribution information and the second attribution information to acquire first attribution processing information and second attribution processing information;
and when the first attribution processing information is consistent with the second attribution processing information, determining that the judging result is consistent.
7. The method of claim 6, wherein the method further comprises:
when the first attribution processing information and the second attribution processing information are inconsistent, format conversion is carried out on the first attribution processing information and the second attribution processing information according to a preset conversion rule so as to obtain first format information and second format information;
and when the first format information and the second format information are consistent, determining that the judging result is consistent.
8. The method of claim 7, wherein the performing format conversion on the first home processing information and the second home processing information according to a preset conversion rule to obtain first format information and second format information comprises:
Converting letters in the first format information and the second format information into a first preset format; and/or converting the text in the first format information and the second format information into a second preset format so as to acquire the first format information and the second format information.
9. The method of any of claims 1-8, wherein the tenninal history information comprises: object information, attribution information corresponding to each time period and position information corresponding to each time period.
10. A penmanship process information mining apparatus, the apparatus comprising:
the first arbitrary history information acquisition module is used for acquiring first arbitrary history information corresponding to the preset object information;
the clustering module is used for clustering the first optional history information to determine an optional history information sub-cluster;
the second tenninal history information determining module is used for determining second tenninal history information corresponding to the target object corresponding to the preset object information based on the tenninal history information sub-cluster;
the first arbitrary role history information acquisition module acquires first arbitrary role history information corresponding to preset object information, and includes:
Searching information in a data source based on the preset object information to acquire a change record item corresponding to the preset object information;
according to the change record item and a preset strategy, acquiring first arbitrary history information corresponding to the preset object information, wherein the first arbitrary history information comprises: classifying the change record items based on the attribution information in the change record items, and acquiring the change record items corresponding to any attribution information; determining the arbitrary time information corresponding to any attribution information according to the change time information, the first change identifier and the second change identifier in the change record item corresponding to any attribution information; acquiring first optional time history information corresponding to the preset object information according to optional time information corresponding to any attribution information;
the clustering module clusters the first arbitrary history information to determine an arbitrary history information sub-cluster, including:
determining attribution information corresponding to the preset object information according to the first arbitrary role history information;
determining an associated object corresponding to any attribution information according to the arbitrary attribution information;
constructing a relation network according to the preset object information, any attribution information and the corresponding relation between the associated objects;
Clustering according to the structure of the relation network after the preset object information is removed, so as to determine the wilful history information sub-clusters;
the second tenninal history information determining module determines second tenninal history information corresponding to a target object corresponding to the preset object information based on the tenninal history information sub-cluster, including:
determining target tenure course information sub-clusters to which preset attribution information belongs;
and carrying out data merging processing based on the first tenninal process information in the target tenninal process information sub-cluster, and determining second tenninal process information corresponding to the target object corresponding to the preset object information.
11. An electronic device, the electronic device comprising: a processor and a memory; wherein,
the memory is configured to store the processor-executable instructions;
the processor being configured to read the executable instructions from the memory and execute the instructions to implement the method of any of the preceding claims 1-9.
12. A computer readable storage medium, characterized in that the storage medium stores a computer program for executing the method of any of the preceding claims 1-9.
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