CN111241160A - Method and device for determining hidden relation of people - Google Patents
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Abstract
The application provides a method and a device for determining a person implicit relationship, which relate to the technical field of information processing, and the method comprises the following steps: acquiring a related person filled by each person in a plurality of persons, and generating a relationship data cluster of each person according to the related person filled by each person; merging a plurality of relational data clusters comprising the same relational persons by adopting a merging algorithm to obtain a merged relational data cluster; and acquiring the recessive relation person of each person according to the combined relation data cluster and the relation person filled by each person. The multiple relation data clusters comprising the same relation person can be accurately merged by adopting the merging algorithm, so that the recessive relation person of the person can be found according to the merged relation data cluster, the work can be reasonably distributed according to an avoidance system in subsequent work arrangement, the work progress is ensured not to be interfered by the recessive relation between the person and the person, and the effective development of the work is ensured.
Description
Technical Field
The application relates to the technical field of information processing, in particular to a method and a device for determining a hidden relation of people.
Background
In China, people always pay attention to relationship between blood relationship and relationship between relatives and friends, which have penetrated into various fields of social life, and if there is a certain relationship between people, such as relationship between relatives and friends, the normal development of work can be affected, so the interference of various relationships to normal work can be generally avoided through an avoidance system.
Disclosure of Invention
The embodiment of the application aims to provide a method and a device for determining a hidden relation between persons, so as to solve the problem that the hidden relation between persons cannot be determined in the prior art.
In a first aspect, an embodiment of the present application provides a method for determining a human implicit relationship, where the method includes: acquiring a related person filled by each person in a plurality of persons, and generating a relationship data cluster of each person according to the related person filled by each person; merging a plurality of relational data clusters comprising the same relational persons by adopting a merging algorithm to obtain a merged relational data cluster; and acquiring the recessive relation person of each person according to the combined relation data cluster and the relation person filled by each person.
In the implementation process, the relation data cluster is directly generated according to the relation persons filled by the persons, then the combination algorithm is adopted to accurately combine a plurality of relation data clusters comprising the same relation persons to obtain the combination relation data cluster, so that the recessive relation persons of the persons can be found according to the combination relation data cluster, the work can be reasonably distributed according to the avoidance system in the subsequent work arrangement, the work progress is guaranteed not to be interfered by the recessive relation between the persons, and the effective development of the work is guaranteed.
Optionally, the generating of the relationship data cluster of each person according to the related person filled in by each person includes: generating a first relation data cluster of each person according to the relation person with the identity label filled in by each person; judging whether at least two related unidentified relatives in all the unidentified relatives filled by all the personnel have a relationship according to a preset relativity rule; if so, generating a second relation data cluster according to two different persons corresponding to two related persons without the identity identifiers; the merging of a plurality of relational data clusters including the same relational person by adopting a merging algorithm to obtain a merged relational data cluster comprises the following steps: merging a plurality of first relational data clusters comprising the same relational persons by adopting a merging algorithm to obtain a first merged relational data cluster; merging a plurality of second relation data clusters comprising the same relation person by adopting a merging algorithm to obtain a second merging relation data cluster; and merging the first merging relation data cluster and the second merging relation data cluster which comprise the same relation person to obtain the merging relation data cluster.
In the implementation process, the identity is unique, and one person corresponds to one identity, so that the recessive relationship of the person can be accurately analyzed according to the relationship person with the identity, and the relationship between the relationship persons without the identity can be deduced according to the preset relative rule, so that whether the relationship exists between the relationship persons without the identity is deduced according to the known information, and the recessive relationship of the person can be accurately found according to the deduced relationship.
Optionally, the obtaining the implicit relatives of each person according to the merged relationship data cluster and the relatives filled by each person includes: removing the relation persons filled by each person from the merged relation data cluster to obtain an indirect relation data cluster; and analyzing and obtaining the recessive relation person of each person according to the indirect relation data cluster.
In the implementation process, in order to visually find out the recessive relation person of each person, the relation person filled by the persons can be removed from the merged relation data cluster, and the indirect relation data cluster is analyzed to obtain the recessive relation person of the persons, so that reference can be provided for arrangement of personnel work, an avoidance system can be effectively implemented for the arrangement of the personnel work, and normal development of the work is ensured.
Optionally, the merging, by using a merging algorithm, a plurality of relationship data clusters including the same relationship person includes: selecting each relation data cluster which is not combined as an initial relation data cluster from a plurality of relation data clusters; traversing relation data clusters which are not combined except the initial relation data cluster in the plurality of relation data clusters, searching a target relation data cluster which comprises the same relation person with the initial relation data cluster, and combining the initial relation data cluster and the target relation data cluster; wherein, the same relation is the node for merging the data clusters.
In the implementation process, two or more relationship data clusters comprising the same relationship person can be accurately combined into one relationship data cluster by adopting a combination algorithm, or two relationship data clusters are combined into one relationship data cluster by an intermediate relationship person, and meanwhile, the calculation amount can be reduced, the calculation efficiency is improved, and thus the implicit relationship between the persons can be more accurately determined.
In a second aspect, an embodiment of the present application provides an apparatus for determining a hidden relationship of a person, where the apparatus includes: the system comprises a relational data cluster generating module, a relation data cluster generating module and a relation data cluster generating module, wherein the relational data cluster generating module is used for acquiring the related persons filled by each person in a plurality of persons and generating a relational data cluster of each person according to the related persons filled by each person; a merged relation data cluster obtaining module, configured to merge a plurality of relation data clusters including the same relation person by using a merging algorithm, so as to obtain a merged relation data cluster; and the implicit relation person acquisition module is used for acquiring the implicit relation person of each person according to the combined relation data cluster and the relation person filled by each person.
Optionally, the relationship persons reported by each person include a relationship person with an identity and a relationship person without an identity, and the relationship data cluster generating module includes: the first relational data cluster generating unit is used for generating a first relational data cluster of each person according to the related person with the identity label filled in by each person; the preset relative rule judging unit is used for judging whether at least two related unidentified relatives in all the unidentified relatives filled by all the personnel exist according to the preset relative rule; the second relational data cluster generating unit is used for generating a second relational data cluster according to two different persons corresponding to the related two unidentified relatives if judging that the related at least two unidentified relatives exist in all unidentified relatives filled by all persons according to a preset membership rule; the merged relation data cluster obtaining module comprises: a first merging relation data cluster obtaining unit, configured to merge a plurality of first relation data clusters including the same relation person by using a merging algorithm, and obtain a first merging relation data cluster; a second merged relational data cluster obtaining unit configured to merge a plurality of second relational data clusters including the same related person by using a merging algorithm, and obtain a second merged relational data cluster; and the merging relation data cluster acquiring unit is used for merging the first merging relation data cluster and the second merging relation data cluster which comprise the same relation person to acquire a merging relation data cluster.
Optionally, the implicit relationship obtaining module includes: an indirect relation data cluster obtaining unit, configured to remove the related person filled by each person from the merged relation data cluster, and obtain an indirect relation data cluster; and the implicit relation person acquisition unit is used for analyzing and acquiring the implicit relation person of each person according to the indirect relation data cluster.
Optionally, the merge relationship data cluster obtaining module includes: an initial relationship data cluster obtaining unit, configured to select each relationship data cluster that is not merged yet as an initial relationship data cluster from the multiple relationship data clusters; a relation data cluster merging unit, configured to traverse relation data clusters that are not merged in the plurality of relation data clusters except the initial relation data cluster, search for a target relation data cluster including a person having the same relation as the initial relation data cluster, and merge the initial relation data cluster and the target relation data cluster; wherein, the same relation is the node for merging the data clusters.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor and a memory, where the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the electronic device executes the method provided in the first aspect.
In a fourth aspect, embodiments of the present application provide a readable storage medium, on which a computer program is stored, where the computer program runs the method provided in the first aspect as described above when being executed by a processor.
Additional features and advantages of the present application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the present application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a flowchart of a method for determining a human implicit relationship according to an embodiment of the present application;
fig. 2 is a schematic diagram of a relational data cluster according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of relational data cluster merging according to an embodiment of the present disclosure;
fig. 4 is a schematic diagram of another relational data cluster merging provided in the embodiment of the present application;
fig. 5 is a block diagram illustrating a structure of a device for determining a human implicit relationship according to an embodiment of the present application;
fig. 6 is a block diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
The relationships mentioned herein include not only direct relatives and relatives with blood relationship, but also distant relatives and marriage relatives.
If there is a certain relationship between persons, such as a relationship between relatives and friends, and if there is a certain relationship between two persons in the same department, the normal development of the work will be affected, for example, the cadres of a certain department, the persons related to a certain legal case, etc., and if there is a relationship between persons in the relatives, contradiction will often occur in relation to personal interests or the authority will be abused due to common interests, etc., so that the overall coordination inside the department will be destroyed, and the inter-personal relationship will be complicated. Generally, interference of various relationships on normal work can be avoided through an avoidance system, at present, in order to implement the avoidance system, people are required to declare relatives, but the requirements on the declaration of the relatives of the people in different levels and different areas are different, so the perfection degree of information is different, for example, the relatives generally declared comprise a couple relationship and an orthodox relationship, and the collateral relationship and the close relation within the third generation do not require declaration, so that the implicit relationship between the people cannot be determined, and a great number of disadvantages are brought to normal work.
In order to solve the above problem and ensure the normal development of work, an embodiment of the present application provides a method for determining a hidden relationship of a person, please refer to fig. 1, where the method includes the following steps:
step S110: and acquiring the related persons filled by each person in the plurality of persons, and generating a relationship data cluster of each person according to the related persons filled by each person.
For example, if it is required to know whether a plurality of cadres with implicit relations exist among all cadres of a certain department, the cadres generally fill in basic information of family situations or personal declaration materials, then obtain relatives filled by the cadres according to the basic information of the family situations filled by the cadres and the personal declaration materials, and then generate a relation data cluster for representing the relation according to the relation between the cadres and the filling relatives, where the relation data cluster includes information such as all the relatives and the relations between the relatives and the cadres.
As an embodiment, when the cadre fills in the basic information of family conditions, the name information and relationship type of the family members will be filled in the basic information item of the cadre, and the family members in the zhang filling can be shown in the following table 1:
TABLE 1
| Name information | Type of relationship |
| One piece of Zhang | Father and father |
| Li two | Mother |
| WangSi tea | Spouse |
| Zhang Wu | Child-woman |
Wherein, the name of Zhang three father is Zhang one, the name of Zhang three mother is Lidi, the name of Zhang three spouse is WangIV, and the name of Zhang three child is Zhang five. At this time, the generated relational data cluster is: zhang III refers to Zhang I as a father, Zhang III refers to mother plum, Zhang III refers to spouse Wang, and Zhang III refers to child Zhang V. In order to clearly display the relationship between the relatives of Zhang III and Zhang III, the recording can be performed by taking Zhang III as the center, and the recording can also be performed according to different requirements and other modes, such as Zhang III of children with Zhang III, Zhang III of children with Lidi, Wang IV of spouses with Zhang III, and Zhang V of children with Zhang III according to the order of generation.
As another embodiment, when the cadre fills in the personal declaration material, the relatives that are generally filled in include the unique identification codes of the children and spouses, the names, identification numbers, passport information and the like of the children and spouses, and the personal declaration material filled in the zhang-san report can be shown in the following table 2:
TABLE 2
| Name of relationship person | Relationships between | Identity recognition code |
| WangSi tea | Spouse | 1234567 |
| Zhang Wu | Child-woman | 7654321 |
Wherein, the name of Zhang San spouse is Wang Si, the identification code is 1234567, the name of Zhang Sanzi girl is Zhang Wu, and the identification code is 7654321. At this time, the generated relational data cluster is: the identity code of the spouse of Zhang III is 1234567, the identity code of the Wang IV is 1234567, the identity code of the child of Zhang III is Zhang Wu, and the identity code of the Zhang Wu is 7654321.
In addition, the cadre can also fill in basic information of family conditions and personal declaration materials at the same time so as to obtain more comprehensive relatives with the cadre. For example, if Zhang three reports the above-mentioned tables 1 and 2 at the same time, the relatives that can be obtained include Zhang one father, Liyi mother, Wanyi spouse and Wuyi son. The generated relational data cluster is as follows: the id of the father of zhang san, the mother of zhang san, the spouse of zhang san, the id of wang xi is 1234567, the child of zhang san, the id of zhang wu is 7654321, please refer to fig. 2, and fig. 2 is a schematic diagram of a relationship data cluster provided in the embodiment of the present application.
Step S120: and merging a plurality of relation data clusters comprising the same relation person by adopting a merging algorithm to obtain a merged relation data cluster.
The merging algorithm can be that two relation data clusters containing the same relation person are directly merged into a merging relation data cluster, or a recursion is utilized to search for an intermediate person for the relation persons in the relation data cluster except for the relation person of the middle person, if the intermediate person shows that two relations have intersection, the two relations can be known mutually among members through the intermediate person, so that the two relations are merged, the merged set is stored as a new set, the merged set can be removed, repeated traversal work is avoided, and the step is repeated until all sets have no intersection with the intermediate person except for the relation person.
Adopting a merging algorithm to merge a plurality of relational data clusters comprising the same relational persons, wherein the step of acquiring the merged relational data cluster comprises the following steps of firstly, selecting each non-merged relational data cluster from the plurality of relational data clusters as an initial relational data cluster; traversing relation data clusters which are not combined except the initial relation data cluster in the plurality of relation data clusters, searching a target relation data cluster which comprises the same relation person with the initial relation data cluster, and combining the initial relation data cluster and the target relation data cluster; wherein, the same relation is the node for merging the data clusters.
Specifically, please refer to fig. 3, where fig. 3 is a schematic diagram of a relationship data cluster merging method provided in the embodiment of the present application, if a plurality of relationship data clusters are: data cluster A (Zhao three, Wang four, Zhang five), data cluster B (Zhao six, Zhao seven, ZhouBa), data cluster C (Wu nine, ZhouBa), wherein Zhao three, Zhao six and Wu nine are all cadres, for convenience of description, the above-mentioned relational data cluster only writes out the names of the related persons, actually, because the duplicate name condition easily appears, each person in the data cluster has a corresponding unique number or identification, such as identity card number, whether the same related persons exist in the relational data cluster can be judged through the identity card number, for example, data cluster A (Zhao three-1234568, Wang four-1234567, Zhao five-7654321), data cluster B (Zhao six-2345678, Zhao five-7654321, Zhao seven-2345679, Zhoueight-2345670), because the same identity identification 7654321 is included in data cluster A and data cluster B, it can be judged that the same related persons Zhao five are included in data cluster A and data cluster B, the embodiment provided by the application has the advantages that the unique identity is used for judging whether two relatives are the same relatives or not except the description.
The initial relational data cluster A can be obtained, then the non-merged relational data clusters except the initial relational data cluster in the plurality of relational data clusters are traversed, namely the data cluster B and the data cluster C are traversed, the data cluster B is accessed firstly, the fact that the data cluster A and the data cluster B both comprise Zhang Wu can be found, therefore, the data cluster A and the data cluster B can be merged into one data cluster by taking the Zhang Wu as a junction point for merging the data cluster A and the data cluster B, so that a first merged data cluster (Zhang three, Wang four, Zhang five, Zhao seven and Zhang eight) is obtained, then the data cluster C is accessed, the data cluster C and the first merged data cluster are both found to comprise Zhang eight, therefore, the data cluster C and the first merged data cluster can be merged into a second merged data cluster (Zhang three, Wang four, Zhang five, Zhao six, seven, Zhou eight and Zhao nine), after the data cluster C is accessed, no data cluster can be accessed, and all the data clusters are merged into one merged data cluster, so that the merging algorithm is ended, at this time, implicit relations exist among Zhang three, Zhao six and Wu nine which are all cadres, and therefore, when work arrangement is carried out, an avoidance system is followed, and work tasks are allocated to the three cadres. If the data cluster D is included in the above example, the data cluster C can be accessed and then the data cluster D can be continuously accessed, wherein the data cluster D and the data cluster merged for the second time do not include the same related person, and then the merging algorithm can be ended.
After the relational data clusters shown in fig. 3 are merged, the obtained merged data clusters can display the relationship between each related person, and the method can more intuitively see whether the two related persons have a relationship.
Fig. 4 is a schematic diagram of another relationship data cluster merging provided in the embodiment of the present application, where a merging process of the merged data cluster in fig. 4 is the same as the merging process shown in fig. 3, where the merged relationship data cluster in fig. 4 may show a degree of closeness between any two correspondents, for example, zhangsan and wangtong are a direct relationship, and zhangsan and wujiu are a four-layer junction relationship, so that whether the relationship between the two correspondents is valid or not may be determined according to the degree of closeness. For example, if the indirect relationship threshold is 9 layers, then if the indirect relationship between two related persons is 10 layers, it can be said that the implicit relationship between the two related persons does not belong to an intimate relationship, and therefore it can be determined that there is no valid implicit relationship between the two related persons.
By adopting the merging algorithm, two or more relational data clusters comprising the same relational person can be accurately merged into one relational data cluster, or two relational data clusters are merged into one relational data cluster through an intermediate relational person, and meanwhile, the calculation amount can be reduced, the calculation efficiency is improved, and the recessive relation between the cadre part and the cadre part can be more accurately found out.
Step S130: and acquiring the recessive relation person of each person according to the combined relation data cluster and the relation person filled by each person.
The method for acquiring the implicit relation person of each cadre according to the consolidated relation data cluster and the relation person filled by each cadre comprises the following steps of removing the relation person filled by each cadre from the consolidated relation data cluster to acquire an indirect relation data cluster, and finally analyzing and acquiring the implicit relation person of each cadre according to the indirect relation data cluster.
Following the above example, the plurality of relational data clusters are: data cluster a (zhang san, wang si, zhao qi, zhou ba), data cluster C (wu ji, zhou ba), the resulting merged relational data cluster is (zhang san, wang si, zhao xi, zhao qi, zhou ba, wu ji), regarding zhu san, wang si, zhao wu and zhang i self filling them are removed from the merged relational data cluster, and the obtained indirect relational data cluster is (zhao xi, zhao qi, zhou ba, wu ji), the recessive relations between zhang san and zhao, zhao qi, zhou ba and wu ji can be analyzed, for example, if zhang wu is the spouse of zhao xi, zhang wu is the daughter of zhao san, zhao xi is daughter of zhao san, zhou ba is mother of zhao, zhou eight is daughter of wu ji, and further, the grandma is family friend of zhao, finally, it can be concluded that wujie is a brother of Zhangjia, i.e., a recessive relationship between wujie cadre and Zhangi can be finally discovered.
In order to find out the recessive relation person of each cadre, the relation person filled by the cadres can be removed from the merged relation data cluster, and the indirect relation data cluster is analyzed, so that the recessive relation person of the cadres can be obtained, reference can be provided for arrangement of cadre work, an avoidance system can be effectively implemented for the arrangement of the cadre work, and normal development of the cadre work is ensured.
In the implementation process, the relation data cluster is directly generated according to the relation person filled by the cadre, then the plurality of relation data clusters comprising the same relation person are accurately combined by adopting a combination algorithm to obtain the combination relation data cluster, so that the recessive relation person of the cadre can be found according to the combination relation data cluster, the work can be reasonably distributed according to an avoidance system in the subsequent work arrangement, the work progress is ensured not to be interfered by the recessive relation between the cadre and the cadre, and the effective development of the work is ensured.
As an implementation manner, if only the relation persons with the identification are included in the relation persons filled in by the persons, the relation data cluster of each person can be directly generated according to the relation between the persons and the relation persons, so that two relation data clusters including the same relation persons can be accurately found, and the two relation data clusters are combined.
As another embodiment, if the related persons in the staff report include related persons with identification marks and related persons without identification marks, when generating the related data cluster of each staff according to the related persons in the staff report, the first related data cluster of each staff may be generated according to the related persons with identification marks in the staff report, and then it is determined according to the preset membership rules whether there are at least two related persons without identification marks among all related persons without identification marks in the staff report, at this time, the related persons without identification marks may be numbered according to certain rules, so as to avoid the situation of duplicate names, for example, the family of the cadre a has three persons, two persons have identification marks, another person has no identification mark, the identification mark of the family a1 is 123, the identification mark of the family a2 is 124, the identification mark of the family a3 is not, but with information such as name, home address, native place, etc. One of two persons in the family of the cadre b has an identification, the other person has no identification, the identification of the family b1 is 126, and the family b2 also has information such as name, family address, native place and the like. A first relationship data cluster for each cadre can be generated from the identified relationship person, e.g., the first relationship data cluster for cadre a is ((a, 121), (a1, 123), (a2, 124)), and the first relationship data cluster for cadre b is ((b, 122), (b1, 123)).
The preset relative rule can be judged according to a method commonly used for judging whether the family is a relative or not, and can be judged according to a commonly used relative naming rule, for example, if the names of the family a3 of the cadre a are one-to-one, and the last name of the family B2 of the cadre B is one-to-two, because the middle characters or the last characters of sibling names of siblings are the same, the relationship of siblings between a3 and B2 can be inferred, and also the relationship between the same family names can be inferred according to the relationship between the same family, for example, if the names of the family a3 of the cadre a are one-to-one, the relationship between the family B2 of the cadre B is one-to-two, the relationship between the family a3 and the family B2 can be inferred. It can be understood that after the relationship is determined to be possible, further investigation and confirmation may be performed on the relationship that may exist, for example, identity information of two related persons that may exist in relationship may be obtained in a proper manner, for example, account information and medical insurance information provided by police, and further investigation and inference may be performed to ensure that the recessive related person of the cadre is finally and accurately found.
Then, after judging that there are at least two related unidentified relatives among all the unidentified relatives filled by all the cadres according to the preset relativity rule, a second relationship data cluster can be generated according to two different cadres corresponding to the related two unidentified relatives, for example, after a relationship is determined between the family a3 of the cadre a and the family b2 of the cadre b, a second relationship data cluster is generated, and the second relationship data cluster can be represented as (a, a3, b2, b) or directly represented as (a3, b 2).
Then, a merging algorithm is used to merge a plurality of relationship data clusters including the same relationship person, and a merged relationship data cluster is obtained, where the merging algorithm used is the same as the merging algorithm described in the above embodiments. Specifically, a merging algorithm may be used to merge a plurality of first relationship data clusters including the same relationship person, to obtain a first merged relationship data cluster, where the plurality of first relationship data clusters obtained in the above example are ((a, 121), (a1, 123), (a2, 124)) and ((b, 122), (b1, 123)), and since the identities of a1 and b1 are the same and unique, a1 and b1 are the same person, at this time, the first merged relationship data cluster may be obtained as ((a, 121), (a1, 123), (a2, 124), (b, 122)) and then the merging algorithm is used to merge a plurality of second relationship data clusters including the same relationship person, to obtain a second merged relationship data cluster, and the second relationship data cluster found above (a, a3, b2, b) is continuously used, at this time, there is only one second relationship data cluster, the second relational data cluster may be directly used as a second merged relational data cluster (a, a3, b2, b), and if a plurality of second relational data clusters are obtained in the above steps, the second relational data clusters including the same related person may be merged by using a merging algorithm to obtain the second merged relational data cluster. Finally, the first merge relationship data cluster and the second merge relationship data cluster including the same relationship person are merged to obtain a merge relationship data cluster, for example, the first merge relationship data cluster is ((a, 121), (a1, 123), (a2, 124), (b, 122)), the second merge relationship data cluster is (a, a3, b2, b), and the finally obtained merge relationship data cluster is ((a, 121), (a1, 123), (a2, 124), a3, (b, 122), b2), wherein a1 and b1 are the same person, and only a1 is written here.
In the implementation process, the identity is unique, and one person corresponds to one identity, so that the recessive relation of the cadres can be accurately analyzed according to the relation person with the identity, and the relation between the relation persons without the identity can be deduced according to the preset relativity rule, so that whether the relation exists between the relation persons without the identity is deduced according to the known information, and the recessive relation of the cadres is accurately found according to the deduced relation.
It is to be understood that the persons mentioned in the embodiments of the present application may be understood as positions related to the same work arrangement, for example, all the responsible persons of a certain project, the trial officers, the bookkeeping personnel, the accompanying officers, the counsel officers, the inspection officers, and the like related to a certain court case. By adopting the method and the device, whether the implicit relation exists between the personnel can be confirmed, so that the personnel can carry out work scheduling according to an avoidance system when carrying out work scheduling.
Based on the same inventive concept, an apparatus 100 for determining a hidden relation of people is also provided in the embodiment of the present application, please refer to fig. 5. The apparatus may be a module, a program segment, or code on an electronic device. It should be understood that the human implicit relationship determination apparatus 100 corresponds to the above-mentioned embodiment of the method of fig. 1, and can perform the steps related to the embodiment of the method of fig. 1, and the specific functions of the human implicit relationship determination apparatus 100 may be referred to the above description, and the detailed description is appropriately omitted here to avoid repetition.
Optionally, the apparatus 100 for determining a human implicit relationship includes:
the relational data cluster generating module 110 is configured to obtain a related person filled by each person in the plurality of persons, and generate a relational data cluster for each person according to the related person filled by each person;
a merged relation data cluster obtaining module 120, configured to merge a plurality of relation data clusters including the same relation person by using a merging algorithm, so as to obtain a merged relation data cluster;
and the implicit relationship person obtaining module 130 is configured to obtain the implicit relationship person of each person according to the merged relationship data cluster and the relationship person filled by each person.
Optionally, the relationship persons filled by each person include a relationship person with an identity and a relationship person without an identity, and the relationship data cluster generating module 110 includes:
the first relational data cluster generating unit is used for generating a first relational data cluster of each person according to the related persons with the identity marks filled in by each person;
the preset relative rule judging unit is used for judging whether at least two related unidentified relatives in all the unidentified relatives filled by all the personnel exist according to the preset relative rule;
the second relational data cluster generating unit is used for generating a second relational data cluster according to two different persons corresponding to the related two unidentified relatives if judging that the related at least two unidentified relatives exist in all unidentified relatives filled by all persons according to a preset membership rule;
the merged relationship data cluster obtaining module 120 includes:
a first merging relation data cluster obtaining unit, configured to merge a plurality of first relation data clusters including the same relation person by using a merging algorithm, and obtain a first merging relation data cluster;
a second merged relational data cluster obtaining unit configured to merge a plurality of second relational data clusters including the same related person by using a merging algorithm, and obtain a second merged relational data cluster;
and the merging relation data cluster acquiring unit is used for merging the first merging relation data cluster and the second merging relation data cluster which comprise the same relation person to acquire a merging relation data cluster.
Optionally, the implicit relationship obtaining module 130 includes:
the indirect relation data cluster acquisition unit is used for removing the relation persons filled by each person from the merged relation data cluster to acquire an indirect relation data cluster;
and the implicit relation person acquisition unit is used for acquiring the implicit relation person of each person according to the indirect relation data cluster analysis.
Optionally, the merged relational data cluster obtaining module 120 includes:
an initial relationship data cluster obtaining unit, configured to select each relationship data cluster that is not merged yet as an initial relationship data cluster from the multiple relationship data clusters;
the relation data cluster merging unit is used for traversing relation data clusters which are not merged in the plurality of relation data clusters except the initial relation data cluster, searching a target relation data cluster which comprises the same relation person with the initial relation data cluster, and merging the initial relation data cluster and the target relation data cluster; wherein, the same relation is the node for merging the data clusters.
Referring to fig. 6, fig. 6 is a block diagram of an electronic device according to an embodiment of the present disclosure, where the electronic device includes: at least one processor 601, at least one communication interface 602, at least one memory 603, and at least one communication bus 604. Wherein the communication bus 604 is used for implementing direct connection communication of these components, the communication interface 602 is used for communicating signaling or data with other node devices, and the memory 603 stores machine-readable instructions executable by the processor 601. When the electronic device is in operation, the processor 601 communicates with the memory 603 via the communication bus 604, and the machine-readable instructions when called by the processor 601 perform the methods described above.
The processor 601 may be an integrated circuit chip having signal processing capabilities. The processor 601 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. Which may implement or perform the various methods, steps, and logic blocks disclosed in the embodiments of the present application. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The Memory 603 may include, 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.
It will be appreciated that the configuration shown in fig. 6 is merely illustrative and that the electronic device may include more or fewer components than shown in fig. 6 or have a different configuration than shown in fig. 6. The components shown in fig. 6 may be implemented in hardware, software, or a combination thereof. In this embodiment of the application, the electronic device may be, but is not limited to, a dedicated detection device, a desktop computer, a notebook computer, a smart phone, an intelligent wearable device, and other physical devices, and may also be a virtual device such as a virtual machine. In addition, the electronic device is not necessarily a single device, but may also be a combination of multiple devices, such as a server cluster, and the like.
The present application provides a readable storage medium, and when being executed by a processor, a computer program performs the method processes performed by an electronic device in the method embodiment shown in fig. 1.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method, and will not be described in too much detail herein.
To sum up, the embodiment of the present application provides a method and an apparatus for determining a hidden relationship of a person, where the method includes: acquiring a related person filled by each person in a plurality of persons, and generating a relationship data cluster of each person according to the related person filled by each person; merging a plurality of relational data clusters comprising the same relational persons by adopting a merging algorithm to obtain a merged relational data cluster; and acquiring the recessive relation person of each person according to the combined relation data cluster and the relation person filled by each person. The relation data clusters are directly generated according to the relation persons filled by the persons, then a plurality of relation data clusters comprising the same relation persons are accurately combined by adopting a combination algorithm to obtain the combination relation data clusters, so that the recessive relation persons of the persons can be found according to the combination relation data clusters, the work can be reasonably distributed according to an avoidance system in subsequent work arrangement, the work progress is guaranteed not to be interfered by the recessive relation between the persons, and the effective development of the work is guaranteed.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A method for determining a human implicit relationship, the method comprising:
acquiring a related person filled by each person in a plurality of persons, and generating a relationship data cluster of each person according to the related person filled by each person;
merging a plurality of relational data clusters comprising the same relational persons by adopting a merging algorithm to obtain a merged relational data cluster;
and acquiring the recessive relation person of each person according to the combined relation data cluster and the relation person filled by each person.
2. The method according to claim 1, wherein the related persons filled in by each person include a related person with an identification and a related person without an identification, and the generating of the relationship data cluster of each person according to the related person filled in by each person comprises:
generating a first relation data cluster of each person according to the relation person with the identity label filled in by each person;
judging whether at least two related unidentified relatives in all the unidentified relatives filled by all the personnel have a relationship according to a preset relativity rule;
if the relationship data exists, generating a second relationship data cluster according to two different persons corresponding to the two related relationship persons without the identity identifiers;
the merging of a plurality of relational data clusters including the same relational person by adopting a merging algorithm to obtain a merged relational data cluster comprises the following steps:
merging a plurality of first relational data clusters comprising the same relational persons by adopting a merging algorithm to obtain a first merged relational data cluster;
merging a plurality of second relation data clusters comprising the same relation person by adopting a merging algorithm to obtain a second merging relation data cluster;
and merging the first merging relation data cluster and the second merging relation data cluster which comprise the same relation person to obtain a merging relation data cluster.
3. The method according to claim 1, wherein the obtaining the implicit relatives of each person according to the consolidated relationship data cluster and the relatives filled in by each person comprises:
removing the relation persons filled by each person from the merged relation data cluster to obtain an indirect relation data cluster;
and analyzing and obtaining the recessive relation person of each person according to the indirect relation data cluster.
4. The method of claim 1, wherein merging a plurality of relational data clusters comprising the same relational person using a merging algorithm comprises:
selecting each relation data cluster which is not combined as an initial relation data cluster from a plurality of relation data clusters;
traversing relation data clusters which are not combined except the initial relation data cluster in the plurality of relation data clusters, searching a target relation data cluster which comprises the same relation person with the initial relation data cluster, and combining the initial relation data cluster and the target relation data cluster; wherein, the same relation is the node for merging the data clusters.
5. An apparatus for determining a hidden relationship between persons, the apparatus comprising:
the system comprises a relational data cluster generating module, a relation data cluster generating module and a relation data cluster generating module, wherein the relational data cluster generating module is used for acquiring the related persons filled by each person in a plurality of persons and generating a relational data cluster of each person according to the related persons filled by each person;
a merged relation data cluster obtaining module, configured to merge a plurality of relation data clusters including the same relation person by using a merging algorithm, so as to obtain a merged relation data cluster;
and the implicit relation person acquisition module is used for acquiring the implicit relation person of each person according to the combined relation data cluster and the relation person filled by each person.
6. The apparatus of claim 5, wherein the stakeholders filled by each person comprise identified stakeholders and unidentified stakeholders, and the relationship data clustering module comprises:
the first relational data cluster generating unit is used for generating a first relational data cluster of each person according to the related person with the identity label filled in by each person;
the preset relative rule judging unit is used for judging whether at least two related unidentified relatives in all the unidentified relatives filled by all the personnel exist according to the preset relative rule;
the second relational data cluster generating unit is used for generating a second relational data cluster according to two different persons corresponding to the related two unidentified relatives if judging that the related at least two unidentified relatives exist in all unidentified relatives filled by all persons according to a preset membership rule;
the merged relation data cluster obtaining module comprises:
a first merging relation data cluster obtaining unit, configured to merge a plurality of first relation data clusters including the same relation person by using a merging algorithm, and obtain a first merging relation data cluster;
a second merged relational data cluster obtaining unit configured to merge a plurality of second relational data clusters including the same related person by using a merging algorithm, and obtain a second merged relational data cluster;
and the merging relation data cluster acquiring unit is used for merging the first merging relation data cluster and the second merging relation data cluster which comprise the same relation person to acquire a merging relation data cluster.
7. The apparatus of claim 5, wherein the implicit contacts obtaining module comprises:
an indirect relation data cluster obtaining unit, configured to remove the related person filled by each person from the merged relation data cluster, and obtain an indirect relation data cluster;
and the implicit relation person acquisition unit is used for analyzing and acquiring the implicit relation person of each person according to the indirect relation data cluster.
8. The apparatus of claim 5, wherein the merge relationship data cluster obtaining module comprises:
an initial relationship data cluster obtaining unit, configured to select each relationship data cluster that is not merged yet as an initial relationship data cluster from the multiple relationship data clusters;
a relation data cluster merging unit, configured to traverse relation data clusters that are not merged in the plurality of relation data clusters except the initial relation data cluster, search for a target relation data cluster including a person having the same relation as the initial relation data cluster, and merge the initial relation data cluster and the target relation data cluster; wherein, the same relation is the node for merging the data clusters.
9. An electronic device comprising a processor and a memory, the memory storing computer readable instructions that, when executed by the processor, perform the method of any of claims 1 to 4.
10. A readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1 to 4.
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