CN109584094B - Interpersonal path rapid positioning system, method and medium - Google Patents
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Abstract
A interpersonal path fast positioning system, method and medium, face to the specific demand of users, while forming the personal portrait information, introduce the method of the cluster analysis to catch the interest attribute of users; interpersonal contact data such as personal work relationship, family relationship, social relationship and the like are fully utilized, and a dynamic adjustment mechanism is introduced for the setting of interpersonal relationship weight; the system supports the system to search and screen the target personnel according to the fuzzy requirement matching of the user under the condition that the user does not know the name, the mobile phone number and other clear information of the target personnel, realizes the quick positioning of the target personnel, and completes the planning and recommendation of the optimal contact path.
Description
Technical Field
The invention relates to a system, a method and a medium for quickly positioning interpersonal paths, and belongs to the technical field of computers.
Background
The interpersonal network is a relationship network for exchanging information between people to achieve a specific purpose. At present, enterprises and individuals have urgent needs for application to human networks. For enterprises, experts are precious implicit knowledge resources, but the search and the positioning of the experts facing the targeted requirements are not easy, so that many employees have problems in working, do not know who to ask for the education, or even know who to ask for the education, do not know how to draw the distance to the experts by using the interpersonal relationship of the employees, and the problems greatly limit the mining, circulation and sharing of expert knowledge; for individuals, social contact is an important requirement for people's life, and everyone wants to acquire more human resources by using the personal relationship of the individual, so that the personal relationship is widened and extended. In the past, the construction and application of the interpersonal relationship network are relatively conservative due to the technical difficulty, in recent years, with the coming of a big data era, the interpersonal relationship data of people are easier to capture and acquire, and the interpersonal relationship network is more comprehensive and complete than before.
Based on the above requirements, many scholars begin to study the construction and application methods of interpersonal relationship networks. Some scholars propose some methods for constructing interpersonal relationship networks, but in the aspect of interpersonal relationship weight setting, preset relationship strength is adopted, dynamic adjustment of relationship weight is not realized, and a user needs to know the name or the mobile phone number of a target user and inputs the conditions for searching, so that fuzzy matching search of the user under the condition that the specific attribute of the target person is not known is not supported; the learners provide competition information acquisition based on the interpersonal network, but the learners known by the users are mainly used for acquiring the information, and the learners do not search and locate the information of the interpersonal network which is required by the users but not familiar with the users.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the defects of the prior art are overcome, and a system, a method and a medium for quickly positioning the interpersonal path are provided. The method is oriented to specific requirements of users, and when personal portrait information is formed, a clustering analysis method is introduced to capture interest and hobby attributes of the users; interpersonal contact data such as personal work relationship, family relationship, social relationship and the like are fully utilized, and a dynamic adjustment mechanism is introduced for the setting of interpersonal relationship weight; the system supports the system to search and screen the target personnel according to the fuzzy requirement matching of the user under the condition that the user does not know the name, the mobile phone number and other clear information of the target personnel, realizes the quick positioning of the target personnel, and completes the planning and recommendation of the optimal contact path.
The purpose of the invention is realized by the following technical scheme:
a interpersonal path rapid positioning system comprises an individual portrait generating module, an individual portrait perfecting module, an individual relationship map generating module, an interpersonal relationship distance dynamic configuration module, an interpersonal relationship interconnection network building module, a target person positioning module and an interpersonal relationship path planning module;
the personal portrait generating module generates all personal portraits by using a data capturing method and a clustering method based on a user communication data source; the personal portrait perfecting module perfects all personal portraits according to user feedback; the personal relationship map generation module establishes a personal relationship map of the user by using all the completed personal portraits; the interpersonal relationship distance dynamic configuration module is used for calculating the relationship distance between the user and all personal portraits according to a preset period; the interpersonal relationship internet construction module establishes an interpersonal relationship network according to the personal relationship map of the user and the relationship distance; the target person positioning module is used for positioning a target person; and the interpersonal relationship path planning module calculates the optimal interpersonal relationship path between the user and the target person according to the interpersonal relationship network and the positioning of the target person.
According to the interpersonal path rapid positioning system, the user interpersonal data source comprises personal social platform data and personal work service platform data.
In the interpersonal path rapid positioning system, the method for the personal portrait generation module to generate all the personal portraits by using the data capture method and the clustering method based on the user interpersonal data source comprises the following steps:
obtaining preliminary personal portrait information based on a user social data source, analyzing personal social platform data and personal work service platform data, predicting personal interest points, and obtaining an interest point set of a user, wherein the interest point set and the preliminary personal portrait information form a personal portrait.
The interpersonal path quick positioning system utilizes all personal portraits which are completed by the personal portraits perfecting module to comprise personal basic condition data, working information data and interest information data.
In the interpersonal path rapid positioning system, the relationship between any two personal portraits in the personal relationship map is one of family relationship, work relationship, social relationship and classmate relationship;
the relationship distance D of the contactrelaThe calculation method comprises the following steps:
step (5a), calculating the total contact time T _ F between any two personal portraits according to the contact frequency and each contact time between any two personal portraits; acquiring a relation distance value D _ InAsp between any two personal portraits according to the total contact time T _ F between any two personal portraits;
step (5b), according to the relation between any two personal portraits, presetting the weight of family relation as D _ AspfamiThe weight of the working relation is D _ AspworkThe weight of the social relation is D _ AspsocialThe weight of the relation of classmates is D _ Aspclass；
Step (5c), calculating the relation distance D between any two personal portraitsrela，DrelaD _ Asp _ InAsp; wherein the weight value D _ Asp is determined according to the type of relationship between the two personal portraits.
In the interpersonal path rapid positioning system, the larger the total contact time length T _ F between any two personal portraits in the step (4a), the smaller the relationship distance value D _ InAsp between the two personal portraits.
In the interpersonal path rapid positioning system, in the step (4a), the total contact time T _ F between any two personal portraits is calculated according to the contact frequency and each contact time between any two personal portraits and the weight of a preset contact way;
the contact way comprises a voice instant communication way, a text instant communication way, a voice non-instant communication way and a text non-instant communication way;
the weight of the contact way is the weight of the voice instant communication way, the weight of the text instant communication way, the weight of the voice non-instant communication way and the weight of the text non-instant communication way in turn from big to small.
In the interpersonal path fast positioning system, the method for establishing the interpersonal relationship network comprises the following steps:
(8a) randomly selecting person PiAs a network start node, with PiCentered on, construct PiA first-level human network map Netpi _ l _1 which is a center;
(8b) traversing the personnel in the first-level human network map Netpi _ l _1, and aiming at any personnel P in the first-level human network map Netpi _ l _1jCalculate PjAnd PiA distance D betweenrela(i,j)；
(8c) With PjBy repeating (8a) to (8b) as a center, P is constructedjThe central interpersonal network map is taken as PiThe second-level interpersonal network map Netpi _ l _2 with the center calculated as PjAny person P in the interpersonal network map as the centerkAnd PjThe distance between them;
(8d) is represented by PkCenter, repeat (8a) - (8b) and construct with PkThe central interpersonal network map is taken as PiThree-level interpersonal network diagram Netpi _ l _4 with P as center and calculationkAny person and P in central interpersonal network diagramkThe distance between them.
A interpersonal path fast positioning method adopts the interpersonal path fast positioning system, and the interpersonal path fast positioning method of the system comprises the following steps:
step (9a), determining the user P who puts forward the demandoriginAnd the target person PobjWith PobjSearching the ID in the interpersonal path quick positioning system, and when one record number is obtained, selecting the record and transferring to the step (9 b); otherwise, selecting the record with the minimum value of the distance between the record and the central node from the plurality of records, if only one record is counted, selecting the record to step (9b), otherwise, selecting the record with the earliest value of the distance between the record and the central node to step (9b) when the distance between the record and the central node of the plurality of records is equal;
step (9b), obtaining the target person P according to the record selected in the step (9a)objAnd the distance value of the center person Pobj _ c _1 corresponding to the center person;
step (9c), repeating the above method until the central person corresponding to step (9b) is Porigin(ii) a I.e. to obtain the requesting user PoriginAnd the target person PobjThe path between and the distance value.
A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned method for fast locating an interpersonal path.
Compared with the prior art, the invention has the following beneficial effects:
(1) when the personal portrait information is formed, the method of cluster analysis is introduced to capture the interest and hobby attributes of the user, so that the extra workload caused by the user defining the interest and hobby is reduced, and the problems of inconsistent information description, inaccuracy, definition ignorance and the like caused by depending on the description of the user are avoided;
(2) according to the invention, a dynamic adjustment mechanism is introduced for setting interpersonal relationship weight, the frequency, time and the like of contact between the user and each contact in the period are analyzed according to a preset period, the degree of closeness of the relationship is calculated and analyzed, a distance value is set, and the distance value is updated regularly, so that the interpersonal relationship distance can be ensured to be accurate and objective;
(3) the invention simultaneously supports the receiving of the definite requirements of the user and the analysis of the fuzzy requirements of the user, realizes the matching screening and filtering of the target personnel, and solves the problem that the user knows what the user wants to find but does not know who the user is;
(4) according to the demand of the user, the target person is positioned in the user interpersonal relationship network, the connection path between the user and the target person is planned by utilizing the interpersonal relationship network extending layer by layer, and the problem that the user wants to find a certain target person but does not know how to find the person by utilizing the interpersonal relationship of the user is solved;
(5) the method provided by the invention can recommend people similar to the field of the user, or with repeated interpersonal relationships to the user besides planning the interpersonal relationship path, thereby expanding the social circle of the user.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a flow chart of the present invention for personal portrait generation based on data capture and clustering;
FIG. 3 is a process for constructing a personal relationship graph according to the present invention;
FIG. 4 is a flow chart of dynamic configuration of interpersonal relationship distances according to the present invention;
FIG. 5 is a process of constructing an interpersonal relationship interconnection network according to the present invention;
FIG. 6 is a process of locating a target person according to the present invention for the user's explicit needs;
FIG. 7 is a process of locating a target person according to the present invention for the fuzzy requirement of the user;
fig. 8 is a flow of interpersonal relationship path planning of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
A interpersonal path quick positioning system is characterized in that: the system comprises a personal portrait generating module, a personal portrait perfecting module, a personal relation map generating module, a interpersonal relation distance dynamic configuration module, an interpersonal relation interconnected network constructing module, a target person positioning module and an interpersonal relation path planning module;
the personal portrait generating module generates all personal portraits by using a data capturing method and a clustering method based on a user communication data source, wherein the user communication data source comprises personal social platform data and personal work service platform data; the personal portrait perfecting module perfects all personal portraits according to user feedback; the personal relationship map generation module establishes a personal relationship map of the user by using all the completed personal portraits; the interpersonal relationship distance dynamic configuration module is used for calculating the relationship distance between the user and all personal portraits according to a preset period; the interpersonal relationship internet construction module establishes an interpersonal relationship network according to the personal relationship map of the user and the relationship distance; the target person positioning module is used for positioning a target person; and the interpersonal relationship path planning module calculates the optimal interpersonal relationship path between the user and the target person according to the interpersonal relationship network and the positioning of the target person.
The method for generating all the personal portraits by the personal portraits generating module based on the user communication data source by using the data capturing method and the clustering method comprises the following steps:
obtaining preliminary personal portrait information based on a user social data source, analyzing personal social platform data and personal work service platform data, predicting personal interest points, and obtaining an interest point set of a user, wherein the interest point set and the preliminary personal portrait information form a personal portrait.
All the personal portraits which are completed by the personal portraits perfecting module comprise personal basic situation data, working information data and hobby information data.
The relationship between any two personal portraits in the personal relationship map is one of family relationship, work relationship, social relationship and classmate relationship;
the relationship distance D of the contactrelaThe calculation method comprises the following steps:
step (5a), according to the contact frequency and each contact time between any two personal portraits, presetting the weight of a contact way, and calculating the total contact time T _ F between any two personal portraits; acquiring a relation distance value D _ InAsp between any two personal portraits according to the total contact time T _ F between any two personal portraits; the larger the total contact time length T _ F between any two personal portraits is, the smaller the relationship distance value D _ InAsp between the two personal portraits is;
the contact way comprises a voice instant communication way, a text instant communication way, a voice non-instant communication way and a text non-instant communication way; the weight of the contact way is the weight of the voice instant communication way, the weight of the text instant communication way, the weight of the voice non-instant communication way and the weight of the text non-instant communication way in turn from big to small.
Step (5b), according to the relation between any two personal portraits, presetting the weight of family relation as D _ AspfamiThe weight of the working relation is D _ AspworkThe weight of the social relation is D _ AspsocialThe weight of the relation of classmates is D _ Aspclass；
Step (5c), calculating the relation distance D between any two personal portraitsrela，DrelaD _ Asp _ InAsp; wherein the weight value D _ Asp is determined according to the type of relationship between the two personal portraits.
The method for establishing the interpersonal relationship network comprises the following steps:
(8a) randomly selecting person PiAs a network start node, with PiCentered on, construct PiA first-level human network map Netpi _ l _1 which is a center;
(8b) traversing the personnel in the first-level human network map Netpi _ l _1, and aiming at any personnel P in the first-level human network map Netpi _ l _1jCalculate PjAnd PiA distance D betweenrela(i,j)；
(8c) With PjBy repeating (8a) to (8b) as a center, P is constructedjThe central interpersonal network map is taken as PiThe second-level interpersonal network map Netpi _ l _2 with the center calculated as PjAny person P in the interpersonal network map as the centerkAnd PjThe distance between them;
(8d) is represented by PkCenter, repeat (8a) - (8b) and construct with PkThe central interpersonal network map is taken as PiThree-level interpersonal network diagram Netpi _ l _4 with P as center and calculationkAny person and P in central interpersonal network diagramkThe distance between them.
A interpersonal path rapid positioning method adopts the interpersonal path rapid positioning system and comprises the following steps:
step (9a), determining the user P who puts forward the demandoriginAnd the target person PobjWith PobjSearching the ID in the interpersonal path quick positioning system, and when one record number is obtained, selecting the record and transferring to the step (9 b); otherwise, selecting the record with the minimum value of the distance between the record and the central node from the plurality of records, if only one record is counted, selecting the record to step (9b), otherwise, selecting the record with the earliest value of the distance between the record and the central node to step (9b) when the distance between the record and the central node of the plurality of records is equal;
step (9b), obtaining the target person P according to the record selected in the step (9a)objAnd the distance value of the center person Pobj _ c _1 corresponding to the center person;
step (9c), repeating the above method until the central person corresponding to step (9b) is Porigin(ii) a I.e. to obtain the requesting user PoriginAnd the target person PobjThe path between and the distance value.
A computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-mentioned interpersonal path fast positioning method.
Example (b):
a method for quickly positioning interpersonal paths based on a personnel relationship network is disclosed, as shown in figure 1, and comprises the following steps:
step (1) preliminary personal portrait information is formed based on data source capture and clustering, as shown in fig. 2, the specific processing procedure is as follows:
(1a) identifying attribute items such as personal age, gender, working unit, field, specific post, job title and the like by taking personal social platform data and business working platform data as data sources, and extracting attribute values corresponding to the attribute items to form personal basic information;
(1b) taking a personal service working platform and a social platform as data sources, analyzing data, predicting interest points of individuals, capturing browsing, commenting, forwarding and downloading knowledge and daily processed files of the individuals in the service working platform, capturing articles and the like which are concerned, commented, forwarded and published by the individuals in the social platforms such as WeChat, microblog and forum, performing word segmentation, feature word extraction and other operations, and extracting typical text features of the articles;
(1c) calculating to form an interest category cluster by using the typical text characteristics of each article as a data source and applying a clustering algorithm, providing characteristic words corresponding to central vectors of each category cluster, using the characteristic words as labels of the interest category cluster to form an interest point set of a user, wherein the interest point set and the personal information in the step (1a) form a personal portrait based on system capture;
and (2) confirming and improving personal portrait information by the user, wherein the specific processing process is as follows: the user supplements or adjusts the personal portrait formed in the step (1) to form perfect personal portrait information, wherein the perfect personal portrait information comprises three categories of personal basic situation, work information and interest and hobby information, the personal basic situation comprises personal name, sex, age and the like, the work information comprises personal specialty, position, duty, title and the like, meanwhile, an entrance for deeply knowing related results is provided, the user can know information of papers, patents, participation projects and the like published by the person through triggering the entrance, the interest and hobby information comprises personal interest set and speciality set and the like, meanwhile, an entrance for deeply knowing related results is provided, and the user can know information sources, published information, acquired honor and the like of the person concerned about in the interest speciality aspect through triggering the entrance;
and (3) constructing a personal relationship map of the user, as shown in fig. 3, wherein the specific processing process is as follows:
(3a) the system takes personal human resource archive information, workflow processing information, mail incoming and outgoing information, social platform contact information, address list contact information and the like as data sources, extracts personal daily contacts to form a daily contact set of a user, records the daily contact set as C _ person, extracts data such as classification, labels and remarks marked by the user for each contact and information such as common contact time, contact flow, contact scene and the like, judges personnel categories and roles, and forms a primary personnel relationship MappreSaid MappreIn order to avoid the excessively large staff relation map, the system selects staff of each dimension from high to low according to the contact frequency, and the number of the staff does not exceed a preset threshold N _ Limit.
(3b) Personal Map-basedpreCompleting information, adding or removing a personnel list, adjusting personnel types and supplementing personnel information details of a relationship network according to personal actual conditions, if the quantity of personnel set by an information perfector in a certain dimension exceeds a preset threshold value N _ Limit, reminding and asking the information perfector to reset by the system, and forming a perfect personnel relationship Map by the system based on a personal perfection resultmatSaid MapmatAt least comprises four dimensions of working relationship, family relationship, social relationship and classmate relationship, wherein the working relationship dimension AspworkThe relation of business to business and the relation of co-workers are included, the relation of business to business refers to the contact persons whose working time goes through mails or public telephone, the relation of co-workers is subdivided into leadership, level and subordinate, and the dimension of family relation is AspfamiThe method comprises direct family relationship and collateral family relationship, wherein the direct family relationship is subdivided into parents, spouses, children and brothers and sisters, the collateral family relationship is subdivided into Gu (tertiary) table parent and aunt (Jiu) table parent, and the social relationship dimension Asp issocialIncluding social platform friends, forum common fans, training class common participants, the classmate relationship AspclassRefers to students in the individual study experience, including elementary school students, junior middle school students, high school students, college students, and researchersAnd studying, and the number of people in each dimension is lower than a preset threshold value N _ Limit.
Step (4) dynamically calculating the relationship distance between the user and each contact in the personal relationship graph, as shown in fig. 4, the relationship distance is marked as DrelaThe shorter the relationship distance, the closer the relationship intimacy. DrelaThe calculation process of (2) is as follows:
(4a) traversing each contact person in C _ person of the user in the period according to a preset updating period, calculating the contact frequency and each contact time of the user and each contact person, wherein the contact way is voice interaction, the contact time is actually spent time, the contact way is text interaction, the interaction efficiency is uncontrollable due to the fact that non-instant communication is involved, a fixed interaction duration can be preset for each information sending action, the interaction duration is taken as each contact time, the contact frequency and each contact time of the user and each contact person in the period are multiplied by each contact time, the product is recorded as a total contact duration T _ F, and the total contact duration of the user and the ith contact person is T _ F;
(4b) counting the number of people in each contact person belonging to family relation, work relation, social relation and classmate relation, comprehensively considering the number of actual people in each relation dimension and the T _ F value corresponding to each contact person, distributing the weight of four types of relations, and respectively recording the distance weights corresponding to the family relation, work relation, classmate relation and social relation as D _ Aspfami，D_Aspwork,D_Aspclass,D_AspsocialThe weight value range is 1-10;
(4c) distributing a dimensional internal relation distance value of each contact according to the value of T _ F under each relation dimension, marking as D _ InAsp, wherein the range is also 1-10, and the higher the T _ frq _ time is, the smaller the D _ InAsp is;
(4d) calculating the final relationship distance D between the user and each contactrela，DrelaD _ Asp _ InAsp; (ii) a Wherein the weight value D _ Asp is determined according to the type of relationship between the two personal portraits.
(4e) Relationship distance D between user and each contactrelaUpdating the calculation according to the preset updating periodAnd after each update, prompting the user whether to recalculate the target interpersonal path.
Step (5) constructing an interpersonal relationship interconnection network, as shown in fig. 5, the specific processing procedure is as follows:
(5a) randomly selecting person PiAs a network start node, with PiCentered on the construction of P according to the steps (3a) to (3b)iComplete personnel relationship MapmatI.e. Mapmat_pi；
(5b) Extracting Mapmat_piThe contact persons with different dimensions are respectively connected with the contact persons and the PiMarking different dimension relations by connecting lines with different colors, marking specific relations on the connecting lines, such as leaders, ranks and subordinates in the leadership dimension, college classmates and students classmates in the classmate dimension, and adding PiStoring related contact relation data into a data Table Table _ Cpi, wherein fields of Table _ Cpi comprise personnel ID, name, gender, age, work field, interest point, position, center node ID, relation with the center node, distance from the center node and association time established with the center node, wherein the information of the contact such as name, gender, age, work field, interest point, position and the like is obtained by analyzing and calculating by the method in the step (1);
(5c) extracting P in Table _ CpiiRelated personal relationship data formed by PiA first-level human network map Netpi _ l _1 which is a center;
(5d) traversing P in Table _ CpiiRelated person, for the jth person PjAnd (4) calculating P according to the preset distance values corresponding to different dimensions and person subcategories in the step (4)jAnd PiA distance D betweenrela(i, j) and marking on the connecting line between two nodes, and simultaneously, Drela(i, j) writing into a field of distance from the central node in Table _ Cpi;
(5e) with PjCentered on, according to the step (5b), P is formedjAnd P, andjthe person relationship data of (1) is continuously written into Table _ Cpi, wherein the field value of the 'central node ID' is a person PjFor already ID ofBy a person present in Table _ Cpi, i.e. PiIf it is also PjThen P is being establishedjAdding the personnel relationship data into a Table _ Cpi Table;
(5f) according to P in Table _ CpijRelated personnel relationship data, constructed with PjA first-level human network diagram Netpj _ l _1 serving as a center;
(5g) repeating the step (5e) until P in Table _ CpiiThe traversal of the related person ends, now formed with PiAnd (5) calculating and labeling the central node P of the Netpj _ l _1 according to the step (5d) of the central secondary human network diagram Netpi _ l _2jWith any one node PkD of (A)rela(j, k) writing the corresponding data into a field of distance from the central node in Table _ Cpi;
(5h) reading a preset network stage number NLevel, and reading the NLevel when the NLevel is in a preset state<3, no operation is performed, when NLevel is larger than or equal to 3, the steps (5e) - (5f) NLevel-2 times are repeated, complete data are formed in Table _ Cpi, and P is constructediAn N-level human network map Netpi _ l _ N which is used as a center;
step (6) for a user with clear requirements on target personnel, as shown in fig. 6, collecting requirement information and positioning the target personnel, wherein the clear requirements refer to requirements of known names of the target users, and the specific processing process is as follows:
(6a) the system receives a name In _ name of a target user as a data source, and searches matched personnel In Table _ Cpi by taking the In _ name as a keyword;
(6b) for the matched result, a personnel result List _ spec is formed, the number of personnel meeting the search condition is recorded as N _ spec, the List _ spec comprises N _ spec pieces of data, each piece of data comprises basic information such as personnel name, gender, age, working field, interest point, position and the like, and the system feeds back the List _ spec to the user for the user to select and confirm;
(6c) the user selects 1 in List _ spec as the target person to be searched for, the system receives the selection of the user, and marks the record meeting the corresponding condition in List _ spec as Recobj_specSimultaneously in NetpMarking the corresponding person in i _ l _ N in a highlighted way, and marking the person as Pobj_spec；
Step (7), for a user with fuzzy target person requirements, as shown in fig. 7, acquiring requirement information and positioning the target person, where the fuzzy requirement is a requirement that the name of the target user is unknown but part of characteristics of the target user can be described, and the specific processing procedure is as follows:
(7a) the system analyzes and mines the interpersonal relationship network data of the user from the age, the working field and the interest and hobbies respectively, and forms a cluster of each dimension by a clustering method;
(7b) providing cluster data sources with a plurality of dimensions for a user to select, and selecting the requirement information of the target person, such as gender, age range, working field, interest and the like by the user according to a system;
(7c) after the user finishes filling in the demand information, the system receives the demand information input by the user, the information is simultaneously used as a search condition, matched or approximately matched personnel are searched in Table _ Cpi, a personnel result List List _ blu is formed for the search result, the number of personnel meeting the search condition is recorded as N _ blu, then the List _ blu comprises N _ blu pieces of data, and each piece of data contains basic information of personnel name, gender, age, work field, interest point, position and the like for the user to select and confirm;
(7d) the user selects 1 in List _ blu as the target person to be searched for, the system receives the selection of the user, and marks the record meeting the corresponding condition in the List _ blu as Recobj_blurAnd simultaneously, identifying the corresponding person in the Netpi _ l _ N in a highlighted way, and marking the person as Pobj_blur；
And (8) planning an optimal interpersonal relationship path between the user and the target person, as shown in fig. 8, wherein the specific processing process is as follows:
(8a) let the user who proposes the demand be PoriginA 1 is to PoriginThe target person to be searched is marked as PobjLet users and PobjThe total distance between them is recorded as DtotalSetting up DtotalThe initial value is 0;
(8b) in Table _ Cpi with PobjIs a search condition, search PobjAnd if Nc _ obj is 1, taking the person corresponding to the 'central node ID' field in the only record in the Listc _ obj as PobjIf Nc _ obj is more than or equal to 2, comparing the fields of 'distance to center node' of each record in Listc _ obj, and taking the person corresponding to the field of 'center node ID' in the record with the smallest field value as PobjFor the corresponding central personnel, for the records with the same field value, the personnel corresponding to the record with the earliest value of 'establishing association time with the central node' is taken as PobjCorresponding center person, said PobjThe corresponding center personnel are marked as Pobj _ c _1, and P is recordedobjThe distance from the Pobj _ c _1 is recorded as Dcenter_1Setting up Dtotal＝Dtotal+Dcenter_1；
(8c) And (4) repeating the step (8b) by taking the ID of the searched Pobj _ c as a search condition, regarding the nth search (n is more than or equal to 2), recording the central node as the search condition as Pobj _ c _ n, recording the upper-level central node to be searched as Pobj _ c _ n +1, and recording the distance between the Pobj _ c _ n and the Pobj _ c _ n +1 as Dcenter_nSetting D for each search completedtotal＝Dtotal+Dcenter_nUntil Pobj _ c _ n +1 is the requesting user PoriginWhen the search is finished, the search is ended;
(8d) recording the search result into a data Table Table _ Ri, wherein the data field comprises a required user ID, a target personnel ID, a path personnel node and a total path distance, and the required user ID field stores PoriginThe "target person ID" field stores PobjThe "path personnel node" field stores a list of ID values from Pobj _ c _1 to Pobj _ c _ n, with "separation" between ID values in the format of (Pobj _ c _1, Pobj _ c _2, … … Pobj _ c _ n), and the "total path distance" field stores Dtotal；
(8e) Querying Table _ Ri, and in Netpi _ l _ N, matching personnel nodes with Table _ Ri records andhighlighting the connecting lines between the nodes, including the user P who proposes the demandoriginTarget person PobjAnd all Pobj _ c _ i, i of the way 1,2 … … n, forming the user and the target person PobjThe connection path between the target person and the user provides a contact path of the target person for the user.
The interpersonal path rapid positioning method based on the personnel relationship network provided by the invention is applied to the construction of an expert network, and specifically comprises the following steps:
(1) personal portrait generation module based on data capture and clustering
Firstly, the system captures the personal social platform data and the business working platform data of a user, identifies attribute items such as personal age, gender, working unit, field, specific post, job title and the like, extracts attribute values corresponding to the attribute items and forms personal basic information.
And then, taking the personal service working platform and the social platform as data sources, analyzing the data, predicting interest points of the person, capturing knowledge, commenting, forwarding and downloading of the person in the service working platform, daily processing files, capturing articles and the like which the person pays attention to, commenting, forwarding and publishing in the social platforms such as WeChat, microblog and forum, performing operations such as word segmentation and feature vector extraction, and extracting typical text feature vectors of the articles. The specific implementation process of word segmentation and feature vector extraction is as follows:
(a) performing word segmentation on a read text data full text by adopting a word segmentation algorithm based on a Markov model or maximum information entropy, then checking stop words in the text data full text after word segmentation by adopting a rule-based stop word recognition method, and replacing the stop words by using spaces, so that each word segmentation is segmented by using the spaces as segmentation characters, and then extracting each word by using the segmentation characters as identifiers to form a word segmentation set WordSplit;
(b) processing the word segmentation set WordSplit by adopting a feature word extraction algorithm, extracting feature words of the text data, and calculating the weight corresponding to each feature word by adopting a feature weight calculation method; and then, the feature vector of the text data is formed by the feature words of the text data and the weights of the feature words.
Wherein, information gain method and χ method can be adopted2The statistical method or the mutual information method is used for processing the word segmentation set corresponding to each text data, extracting the feature words of each text data, and calculating the feature weights of the feature words by adopting a Boolean weight algorithm, an absolute word frequency (TF) algorithm, an Inverted Document Frequency (IDF) algorithm, a TF-IDF algorithm or a TFC algorithm, and specifically refers to 'statistical natural language processing' compiled by Zongqing university Press in 2008.
And finally, taking the typical text features of the articles as a data source, applying a clustering algorithm to calculate and form an interest category cluster, proposing a feature word corresponding to the center vector of each category cluster, taking the feature word as a label of the interest category cluster, and forming an interest point set of the user, wherein the interest point set and the personal information in the step (1a) form a personal portrait captured based on the system. The specific implementation process of the cluster analysis is as follows:
(a) recording the total number of the text feature vectors as M;
(b) and (4) performing correction operation of the text characteristic vector, namely adding and averaging the lengths of the characteristic vectors of all the preprocessed text data, and taking the value as the uniform length of the characteristic vector of the text data, and marking the value as L. Intercepting all the preprocessed text data feature vectors, if the length is greater than L, keeping L values, and if the length is less than L, performing zero filling operation to make the feature vectors of all the text data have L lengths.
(c) If M is>1, and pair (log)10M)2And taking the integer K to be more than or equal to 2 after the integer is rounded, and taking K as the number of the clusters.
(d) Randomly selecting K text data as initial clustering centers from M text data subjected to preprocessing, namely taking K feature vectors corresponding to the K text data as initial cluster center vectors; wherein the K central vectors are recorded as T1′、T2′、…、T′K(ii) a Recording the feature vectors of M-K text data except the clustering center as T'K+1、T′K+2、…、T′M；
(e) Clustering and dividing feature vectors of M-K text data, and dividing T'K+1、T′K+2、…、T′MIs divided into T1′、T2′、…、T′KIn the cluster class of the central vector, the specific division process is as follows:
(e-1), calculating feature vectors T 'of M-K text data'K+1、T′K+2、…、T′MAnd K central vectors T1′、T2′、…、T′KThe similarity distance between them; wherein the m-th feature vector T'K+mWith the nth central vector Tn' similarity distance between them
m＝1、2、…、M-K，n＝1、2、…、K；
(e-2) according to M-K feature vectors T'K+1、T′K+2、…、T′MAnd K central vectors T1′、T2′、…、T′KThe similarity distance between them, carry on the clustering and divide, wherein:
if the m-th feature vector T'K+mAnd n 'th center vector T'n′Distance of similarity of (S)m,n′Minimum, i.e. Sm,n′＝min(Sm,1,Sm,2,…,Sm,k) Then m 'th feature vector T'K+mIs divided into T'n′In a cluster of classes that are central vectors; m ═ 1,2, …, M-K, n ═ 1,2, …, or K;
(e-3) respectively calculating the average value of the feature vectors in the K cluster classes, and taking the average value as the central vector of the cluster classes; i.e. the centre vector T of the nth clusternUpdating the average value of all the feature vectors in the nth cluster class;
(e-4), if the similarity distance between the updated cluster center vector and the cluster center vector before updating is less than or equal to the set error threshold, judging that the cluster division is finished, recording the center vectors of K clusters,are respectively marked as F1、F2、…、FK(ii) a If the similarity distance between the updated cluster center vector and the cluster center vector before updating is larger than the set error threshold, returning to the step (e-1);
(2) interpersonal relationship distance dynamic configuration module
The interpersonal relationship distance dynamic configuration process has the following specific implementation mode:
firstly, traversing each contact in a daily contact set C _ person of a user in the period according to a preset updating period, calculating the contact frequency and each contact time of the user and each contact, wherein the contact is voice interaction, the contact time is actually spent time, the contact is text interaction, the interaction efficiency is uncontrollable due to non-instant communication, a fixed interaction time length can be preset for each information sending action, the interaction time length is taken as each contact time, the contact frequency and each contact time of the user and each contact in the period are multiplied, the product is recorded as a total contact time length T _ F, and the total contact time length of the user and the ith contact is T _ F;
secondly, counting how many people belong to family relations, work relations, social relations and classmate relations among all the contacts, comprehensively considering the number of actual people contained in all the relation dimensions and the T _ F value corresponding to all the contacts, distributing weights of four types of relations, and recording distance weights corresponding to the four types of dimensional relations of the family relations, the work relations, the classmate relations and the social relations as D _ Aspfami，D_Aspwork,D_Aspclass,D_AspsocialThe weight value range is 1-10;
then, according to the value of T _ F under each relation dimension, distributing a relation distance value in the dimension of each contact, wherein the range of the relation distance value is also 1-10, and the higher the T _ frq _ time is, the smaller the D _ InAsp is;
and finally, calculating the final relationship distance between the user and each contact person, wherein the calculation method comprises the following steps:
Drela，Drela＝D_Asp*D_InAsp。
interpersonal path quick positioning system
The invention relates to a interpersonal path rapid positioning method based on a personnel relationship network, which can be based on an interpersonal path rapid positioning system.
The system comprises a data capturing and clustering-based personal portrait generating module, a user-confirmed personal portrait perfecting module, a personal relation map generating module, a interpersonal relation distance dynamic configuration module, an interpersonal relation interconnection network building module, a target person positioning module and an interpersonal relation path planning module, wherein the data capturing and clustering-based personal portrait generating module is used for generating the personal portrait in the step (1), the user-confirmed personal portrait perfecting module is used for perfecting the personal portrait in the step (2), the personal relation map generating module is used for building the personal relation map in the step (3), the interpersonal relation distance dynamic configuration module is used for dynamically calculating and updating the relation distance in the step (4), the interpersonal relation interconnection network building module is used for building the interpersonal relation network in the step (5), the target person positioning module is used for positioning the target person facing to the clear requirement in the step (6) and matching and screening the target person facing the fuzzy requirement in the step (7), and the interpersonal relation path planning module is used for planning and displaying the optimal interpersonal relation path in the step (8).
In the embodiment, a human-based path rapid positioning method based on a personnel relationship network is applied in the construction of an expert network, a system of the method consists of a server and a client, wherein a database server adopts a Xeon2.8 dual-core processor, a 16G memory and a 2TB hard disk and is responsible for storing all data information, and a tape library and backup software are configured to be used for backup and recovery of historical data; the application server adopts a Linux operating system and data management software more than Oracle11g, is used for realizing personal portrait generation, personal relationship map generation, interpersonal relationship Internet construction, target person positioning and interpersonal relationship path planning, and is responsible for the back-end analysis and processing work of data transmitted by the client; the client host computer adopts a 3.7GHZ CPU, an 8G memory and a 2T hard disk, uses a Windows8/7/XP operating system, interacts with the server in a B/S mode, and has the main functions of front-end display and submission of data required by the server.
The system and the method are successfully applied to the expert network construction of the knowledge management system of the first research institute of the aerospace science and technology group company, and for the expert to be searched by the staff, the system and the method not only can quickly locate the specific staff, but also can plan the interpersonal relationship path between the user and the expert, thereby solving the problem that the staff wants to search for the expert in a certain field but does not know who the expert is or how to search, greatly promoting the exertion of the function of the expert, further promoting the inheritance and reuse of organization intelligence assets, and proving the practicability of the system and the method.
Those skilled in the art will appreciate that those matters not described in detail in the present specification are well known in the art.
Claims (5)
1. A interpersonal path quick positioning system is characterized in that: the system comprises a personal portrait generating module, a personal portrait perfecting module, a personal relation map generating module, a interpersonal relation distance dynamic configuration module, an interpersonal relation interconnected network constructing module, a target person positioning module and an interpersonal relation path planning module;
the personal portrait generating module generates all personal portraits by using a data capturing method and a clustering method based on a user communication data source; the personal portrait perfecting module perfects all personal portraits according to user feedback; the personal relationship map generation module establishes a personal relationship map of the user by using all the completed personal portraits; the interpersonal relationship distance dynamic configuration module is used for calculating the relationship distance between the user and all personal portraits according to a preset period; the interpersonal relationship internet construction module establishes an interpersonal relationship network according to the personal relationship map of the user and the relationship distance; the target person positioning module is used for positioning a target person; the interpersonal relationship path planning module calculates an optimal interpersonal relationship path between the user and the target person according to the interpersonal relationship network and the positioning of the target person;
the relationship between any two personal portraits in the personal relationship map is one of family relationship, work relationship, social relationship and classmate relationship;
relationship distance D of contactrelaThe calculation method comprises the following steps:
step (5a), calculating the total contact time T _ F between any two personal portraits according to the contact frequency and each contact time between any two personal portraits; acquiring a relation distance value D _ InAsp between any two personal portraits according to the total contact time T _ F between any two personal portraits;
step (5b), according to the relation between any two personal portraits, presetting the weight of family relation as D _ AspfamiThe weight of the working relation is D _ AspworkThe weight of the social relation is D _ AspsocialThe weight of the relation of classmates is D _ Aspclass；
Step (5c), calculating the relation distance D between any two personal portraitsrela，DrelaD _ Asp _ InAsp; wherein the weight value D _ Asp is determined according to the type of the relationship between the two personal portraits;
step (5a), calculating the total contact time length T _ F between any two personal portraits according to the contact frequency and each contact time between any two personal portraits and the weight of a preset contact way;
the contact way comprises a voice instant communication way, a text instant communication way, a voice non-instant communication way and a text non-instant communication way;
the weight of the contact way is sequentially the weight of the voice instant communication way, the weight of the text instant communication way, the weight of the voice non-instant communication way and the weight of the text non-instant communication way from large to small;
the target person positioning module realizes the positioning of the target person by adopting the following modes:
(7a) analyzing and mining the interpersonal relationship network data of the user from three dimensions of age, working field and interest, and forming a cluster of each dimension by a clustering method;
(7b) providing the cluster data sources of the three dimensions for a user to select, wherein the user selects the information of target personnel;
(7c) obtaining a search result according to the information of the target personnel as a search condition, forming a personnel result list, wherein each piece of data in the list covers the basic information of the personnel for the user to select and confirm;
(7d) the user selects 1 item as a target person to be searched for self-confirmation;
the method for generating all the personal portraits by the personal portraits generating module based on the user communication data source by using the data capturing method and the clustering method comprises the following steps:
obtaining preliminary personal portrait information based on a user social data source, analyzing personal social platform data and personal work service platform data, predicting personal interest points and then obtaining an interest point set of a user, wherein the interest point set and the preliminary personal portrait information form a personal portrait;
the method for predicting the interest points of the individual and then obtaining the interest point set of the user comprises the following steps:
performing word segmentation processing on a user communication data source by adopting a word segmentation algorithm based on a Markov model or the maximum information entropy, and then extracting each word by adopting a rule-based stop word recognition method to form a word segmentation set WordSplit;
extracting feature words from the word segmentation set WordSplit by adopting a feature word extraction algorithm, and calculating the weight corresponding to each feature word by adopting a feature weight calculation method; forming a feature vector by the feature words and the weights of the feature words; calculating to form an interest category cluster by applying a clustering algorithm, and providing a characteristic word corresponding to a central vector of each category cluster as a label of the interest category cluster to form an interest point set of a user;
wherein, the total number of the feature vectors is recorded as M; correcting the characteristic vectors in a unified length; determining the number K of the clusters according to M; randomly selecting K eigenvectors from M as initial cluster-like central vectors; performing cluster division on the M-K characteristic vectors, updating and randomly selecting the K characteristic vectors, and judging that the cluster division is finished until the similarity distance of the cluster center vectors after two clusters is less than or equal to a set error threshold;
the larger the total contact time length T _ F between any two personal portraits in the step (5a), the smaller the relationship distance value D _ InAsp between the two personal portraits;
the method for establishing the interpersonal relationship network comprises the following steps:
(8a) randomly selecting person PiAs a network start node, with PiCentered on, construct PiA first-level human network map Netpi _ l _1 which is a center;
(8b) traversing the personnel in the first-level human network map Netpi _ l _1, and aiming at any personnel P in the first-level human network map Netpi _ l _1jCalculate PjAnd PiA distance D betweenrela(i,j)；
(8c) With PjBy repeating (8a) to (8b) as a center, P is constructedjThe central interpersonal network map is taken as PiThe second-level interpersonal network map Netpi _ l _2 with the center calculated as PjAny person P in the interpersonal network map as the centerkAnd PjThe distance between them;
(8d) with PkBy repeating (8a) to (8b) as a center, P is constructedkThe central interpersonal network map is taken as PiThree-level interpersonal network diagram Netpi _ l _4 with P as center and calculationkAny person and P in central interpersonal network diagramkThe distance between them.
2. The interpersonal path rapid positioning system of claim 1, characterized in that: the user interaction data source comprises personal social platform data and personal work service platform data.
3. The interpersonal path rapid positioning system of claim 1, characterized in that: all the personal portraits which are completed by the personal portraits perfecting module comprise personal basic situation data, working information data and hobby information data.
4. A interpersonal path rapid positioning method is characterized in that: the interpersonal path rapid positioning system of claim 1 is adopted, comprising the following steps:
step (9a), determining and proposingUser P of demandoriginAnd the target person PobjWith PobjSearching the ID in the interpersonal path quick positioning system, and when one record number is obtained, selecting the record and transferring to the step (9 b); otherwise, selecting the record with the minimum value of the distance between the record and the central node from the plurality of records, if only one record is counted, selecting the record to step (9b), otherwise, selecting the record with the earliest value of the distance between the record and the central node to step (9b) when the distance between the record and the central node of the plurality of records is equal;
step (9b), obtaining the target person P according to the record selected in the step (9a)objAnd the distance value of the center person Pobj _ c _1 corresponding to the center person;
step (9c), repeating the above method until the central person corresponding to step (9b) is Porigin(ii) a I.e. to obtain the requesting user PoriginAnd the target person PobjThe path between and the distance value.
5. A computer-readable storage medium having stored thereon a computer program, characterized in that: which program, when being executed by a processor, carries out the steps of the method as claimed in claim 4.
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