CN111694878B - Government affair subject matter joint office method and system based on matter correlation network - Google Patents
Government affair subject matter joint office method and system based on matter correlation network Download PDFInfo
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Abstract
The invention provides a government affair theme item joint handling method based on an item association network, which comprises the following steps: 1) Constructing a government affair item knowledge base; 2) Constructing a department association network; 3) Dividing a department virtual community; 4) Constructing a matter association network; 5) Excavating subject matter; 6) Topic joint optimization. The invention fully considers the strength of the association relationship between government departments, clusters the association relationship of the government departments, improves the cooperation relationship between the departments, and provides a theoretical basis for the joint transaction of the departments. Subject matters are mined in the department virtual communities with close association, and subject matter output is generated, so that the office efficiency of government departments is effectively improved.
Description
Technical Field
The present invention relates to information processing technology, and more particularly to a backlog recognition technology.
Background
Government matters refer generally to administrative services for governments throughout. Subject matter refers to government matters with higher relevance, such as the same or similar main targets. And the item joint handling means that a plurality of items which are originally independently separated are combined and handled, the material receiving is uniformly carried out, the back office is handled, and the window is exported. The meaning of the transaction joint office is that the government affairs to be transacted by the user usually involve a plurality of sub-matters, and the matters are logically connected but are physically independent, so that a complete matter is split into a plurality of small matters, and the user is easy to make mistakes and run legs for many times in the transacting process.
Under the call of the national release of 'department information sharing, breaking information island, making data run more, masses run less', developing 'one-event joint office' construction, namely integrating related functional department business, integrating a plurality of departments related to handling target matters through informatization means, setting up comprehensive acceptance windows in government service halls and the like, uniformly accepting related handling matters, collecting all required materials at one time, carrying out one-time input, automatically distributing each related department, and realizing 'one-window acceptance and parallel handling' of informatization technical support.
The existing construction of 'one-thing joint office' is mostly based on common sense or life experience, for example, a series of registration needs to be handled when a child is born, so that the one-thing joint office is developed in most regions of the country, and the newborn who is born in a hospital with obstetrics and accords with policies such as in-province drop, participation registration and the like can handle the birth matters such as preventive inoculation, birth medical evidence, drop, participation registration, social insurance card application and the like in the hospital or administrative service center at one time.
With the continuous promotion of the innovation of the Internet and government service, governments in various areas respond to calls to develop deep optimization of government service flows, and the service quality of government departments is continuously improved and the folk life is improved to the greatest extent mainly through the measures of material reduction, link reduction, time limit reduction, evidence reduction and the like.
Currently, a feasible item joint-handling mode is proposed, wherein items of the same type are classified together, then items with low association degree are removed according to the association degree between the items, and the rest items are established to be joint-handled. For example, in the case of a restaurant, all the matters related to the business of the restaurant in the departments such as the market administration, the health and wellness agency, and the fire control team are collected, and the government staff manually screens out the flow matters necessary for the user to make the restaurant, thereby performing a single event.
In the process of joint handling of all provinces, the adopted method is mostly based on life experience, common sense and accumulated experience of government staff acceptance matters, and the joint handling matters are manually searched in a manual mode. The clerks need to face massive item data, department data, material data and historical handling data, and through reasonable consideration of relations between items and departments and relevant laws and regulations, a proper item set is screened out from the clerks and used as a test point object of the joint office.
The existing method is mainly realized by manpower, so that a great deal of labor is required, office time is greatly increased, particularly, the ever-increasing electronic government affair data is faced, only manual analysis and excavation are used for realizing the joint handling among matters by manpower, the complete topic matter association relation is difficult to excavate, the existing 'one-thing' lacks corresponding theoretical basis support, the time and the labor are wasted, an automatic process is difficult to form, and the quantity of the constructed joint handling matters is limited.
Traditional government matters are complex and scattered, and the objective matters for the transacted masses generally involve the following problems: the masses are not aware of the need of several licenses for handling target matters, which functional departments are involved, what the sequence of application among the departments is, which standard conditions are permitted to be handled by each department, and the like; some materials require repeated submissions during the process and repeated audits by government authorities. These problems all result in multiple leg runs while transacting business for the masses, which is time consuming and wasteful of resources. Therefore, a plurality of government departments have developed a 'one-thing joint office' construction, namely, a plurality of matters are accepted at one time in one window, and the main purpose of the construction is to reduce the handling complexity for the masses unfamiliar with the business and improve the handling efficiency of government service departments.
Therefore, how to provide a more reasonable and clear transaction joint-handling construction method and deeply mine the transaction which can be conducted in joint-handling, so that government departments can reasonably and effectively develop internal government matters to construct a 'one-transaction joint-handling' construction, and the method is a technical problem to be solved at present.
Disclosure of Invention
The technical problem to be solved by the invention is to automatically find out the tightly connected departments according to the relevance among the departments based on the item association network, mine the subject matters in the tightly connected departments, and construct the subject matters joint-backlog so as to realize the method and the system for realizing the method.
The technical scheme adopted by the invention for realizing the technical problems is that the government affair subject matter joint handling method based on the matter correlation network comprises the following steps:
1) Constructing a government affair item knowledge base: constructing a government affair item knowledge base according to government affair item data, wherein the knowledge base comprises three types of entities of departments, items and materials and the dependency relationship among the entities;
the attributes of the department entity comprise department ID, department name and the like, the attributes of the event entity comprise event ID, event name and event handling time limit, and the attributes of the material entity comprise material ID and material name;
the dependency relationship includes: (1) department d→item I, indicating that department D can handle item I; (2) Material m→item I, indicating that material M is required for handling item I; (3) Item I→material M, which means that material M will be generated after item I is handled successfully; wherein A.fwdarw.B represents that B depends on A;
2) Constructing a department association network: based on a government matter knowledge base, three types of entities are used as three types of nodes in a network: department, item and material node; forming edges among nodes in a network according to the dependency relationship among the nodes, and establishing an association relationship between department nodes and department nodes according to the edges among the nodes; the association relation between the department nodes is the condition that the two department nodes are connected with the same item or material in the network; taking the number of materials with association relations between two department nodes as department association relation weight;
3) Dividing department virtual communities: clustering department nodes according to the department association relation weight to form different virtual communities, wherein the association degree of nodes in communities is higher, and the association degree between communities is lower;
4) Building a matter association network: traversing all department nodes in the virtual community, inquiring all item nodes connected with all department nodes and material nodes connected with the item nodes, establishing an association relation between the item nodes and the item nodes according to edges among the nodes, wherein the directivity of the edges is used for distinguishing the front-back sequence of the nodes, and the direction of the dependency relation A-B indicates that A is arranged in front of B, and B is arranged behind A; the association relation between the item nodes is the condition that the two item nodes are connected with the same material in the network; taking the number of materials with association relation between two item nodes as item association relation weight;
5) Mining subject matter: traversing all item nodes in an item association network, taking item nodes with association relation with more than k other post item nodes as subject item nodes, wherein k is a preset threshold value, and taking items associated with each subject item as the backlog items of the subject item; collecting service data of each subject matter node, determining the priority of the subject matter according to the service data, forming a list of the subject matter from high to low according to the priority, and outputting the list; the related content of each subject matter in the subject matter list comprises a backlog, attributes of a subject matter entity and required materials.
Further, step 6) of performing topic joint optimization before outputting the list from high to low according to topic priority in step 5): for each topic, it is combined with the respective required list of materials for the backlog, the duplicate material is removed, and the material that may be generated by the topic or backlog is removed, resulting in a list of materials required after backlog optimization.
In addition, a system for realizing the method is provided, which comprises a government affair knowledge base construction module, a department association network construction module, a department virtual community division module, a matter association network construction module and a subject matter mining module which respectively correspond to the realization of the steps. Meanwhile, in order to realize topic joint optimization, a topic joint optimization module is arranged in the system.
The invention constructs the government affair knowledge base with definite dependency relationship based on the government affair data, which is helpful for managing huge government affair data and excavating more potential knowledge. The topological structure characteristic of the government department association diagram fully considers the strength of association relation between government departments and departments, carries out association clustering on the government departments, tries to eliminate the problem of 'information island' among the departments, improves the cooperative connection among the departments, and provides theoretical basis for the joint handling of the departments. Excavating the subject matters in the department virtual communities with close association, and generating subject matters output. Replaces the original time-consuming and labor-consuming operation of manual searching, has a certain interpretation, and provides theoretical basis support for topic joint office.
The method has the advantages that the process of digging a piece of things which can be done in a joint way among departments is automated, theoretical basis support is provided through data analysis, the joint-way things are dug out, and decision support is provided for government staff. The influence of the network topology characteristics and the association relation between government matters is considered, the government departments are subjected to association clustering, the tightly-connected departments are found, the subject matters are mined, the subject matters joint-work construction is carried out, a government flow optimization method is provided for government staff, the office efficiency of the government departments is effectively improved, and the service quality of the government departments and the office experience of citizens can be improved.
Drawings
FIG. 1 is a flow chart of an embodiment method.
Detailed Description
The system comprises a government affair knowledge base construction module, a department association network construction module, a department virtual community division module, a matter association network construction module, a subject matter mining module and a subject matter joint optimization module:
a method of an embodiment of the system is shown in figure 1,
1) The government affair knowledge base construction module is used for constructing a government affair knowledge base: constructing a government affair item knowledge base according to government affair item data, wherein the knowledge base comprises three types of entities of departments, items and materials and the dependency relationship among the entities;
the attributes of the department entity comprise department ID, department name and the like, the attributes of the event entity comprise event ID, event name and event handling time limit, and the attributes of the material entity comprise material ID and material name;
the dependency relationship includes: (1) department d→item I, indicating that department D can handle item I; (2) Material m→item I, indicating that material M is required for handling item I; (3) Item I→material M, which means that material M will be generated after item I is handled successfully; wherein A.fwdarw.B means that B depends on A.
2) The department association network construction module is used for constructing a department association network:
based on the government matters knowledge base, according to the existing relation, if the department 1 can accept the matters 1, the matters 1 are processed to generate the materials 1, the materials 1 can be used for processing the matters 2, and the matters 2 are accepted by the department 2, so that the association relation between the departments is established.
Three types of entities are used as three types of nodes in the network: department, item and material node; forming edges among nodes in the network according to the dependency relationship among the nodes, and establishing association relationship between department nodes and department nodes according to the edges among the nodes to form an item association network; the association relation between the department nodes is the condition that the two department nodes are connected with the same item or material in the network; in order to measure the tightness degree of the association relationship between different departments, the number of materials with the association relationship between the nodes of the two departments is used as the association relationship weight of the departments.
3) The department virtual community dividing module is used for dividing department virtual communities: the method comprises the steps of clustering department nodes according to department association relation weights to form different virtual communities, wherein node association degrees in communities are higher, association degrees among communities are lower, so that a department cluster set with high association is generated, further, matters in communities are analyzed, and the topic matters of joint backlog are mined.
Specifically, a Fast Unfolding community partitioning algorithm may be used to cluster the department nodes. The modularity is also called a modularized metric, and is a commonly used method for measuring the strength of a network community structure, wherein the closer the value is to 1, the stronger the strength of the community structure divided by the network is, namely the better the dividing quality is. An optimal web community partitioning can be obtained by maximizing the modularity Q.
The specific steps of the Fast Unfolding community division algorithm are as follows:
a) The initial stage: each department node is divided into a community independently;
b) The circulation steps are as follows:
b1 Extracting department nodes with association relation with each department node and corresponding department association relation weight from the department association network, and calculating the modularity Q under the current community division as the current modularity Q c ：
Wherein A is i,j Representing department association relation weight between the i department node and the j department node;representing the sum of all department node association weights in the department association network, +.>Representing the association relation weight sum of the department node i; c i A community ID value to which the department node i belongs is represented; c j A community ID value to which the department node j belongs is represented; delta (c) i ,c j ) To indicate a function, when the department node i is in the same community as the department node j, δ (c) i ,c j ) 1, otherwise 0;
b2 Taking out a community as a current community;
b3 Community partition update): taking out one of the department nodes which do not belong to the current community for updating, separating the department node from the original community, and adding the department node into the current community, namely updating the community ID value of the department node into the ID value of the current community;
b4 Calculating the updated module degree Q of community division as updated module degree Qu, comparing Qc with Qu, if the Qu is larger than Qc, receiving current update, and taking the updated module degree Qu as the module degree of the current community division, otherwise, not receiving update; judging whether all department nodes which do not belong to the current community are traversed, if yes, executing the step B5), otherwise returning to the step B3);
b5 Judging whether all communities are traversed, if yes, executing the step B6) if one-time cyclic updating is completed, otherwise, returning to the step B2);
b6 If all the updates are not accepted in the current circulation update, indicating that the current community division is in a steady state, exiting the community division algorithm, taking the current community division as a final clustering result, and otherwise, returning to the step B1).
4) The event association network-free construction module is used for constructing an event association network: traversing all department nodes in the virtual community, inquiring all item nodes connected with all department nodes and material nodes connected with the item nodes, establishing an association relation between the item nodes and the item nodes according to edges among the nodes, wherein the directivity of the edges is used for distinguishing the front-back sequence of the nodes, and the direction of the dependency relation A-B indicates that A is arranged in front of B, and B is arranged behind A; the association relationship between the event node and the event node is a case that two event nodes are connected with the same material in the network, for example, there is event a→material→event B, event a→event B, event a is a preceding event of event B, and event B is a following event of event a; taking the number of materials with association relation between two item nodes as item association relation weight;
5) The topic mining module is used for mining topic: traversing all item nodes in an item association network, taking item nodes which have association relations with more than k other post item nodes as subject item nodes, taking k as a preset threshold value, taking the value as 2, and taking items associated with each subject item as the combined items of the subject items; collecting service data of each subject matter node, determining the priority of the subject matter according to the service data, forming a list of the subject matter from high to low according to the priority, and outputting the list; the related content of each subject matter in the subject matter list comprises a backlog, attributes of a subject matter entity and required materials.
Specifically, traversing all item nodes in the item association network by adopting a breadth-first search mode; in the traversal process, when the number of postitems connected with a certain item node is larger than k, the current search is terminated, and the search of the next item node is entered.
The service data of the subject matter node comprises: the number of co-occurrence times and the number of transaction times of the two connected transactions;
the specific method for determining the joint-handling priority of the network subject matters related to the matters according to the service data is to take the sum of the handling times of each subject matter and the joint-handling matters as an influence degree value, wherein the magnitude of the influence degree value is positively related to the priority;
6) The item joint optimization module is used for the joint optimization processing of the theme items, and the list output after the joint optimization is carried out on the theme items arranged from high to low according to the priority, and the joint optimization comprises the following steps:
for each topic, it is combined with the respective required list of materials for the backlog, the duplicate material is removed, and the material that may be generated by the topic or backlog is removed, resulting in a list of materials required after backlog optimization.
The main effects of the joint topic are as follows:
1) Combining the repeated materials related to the subject matter list, and only needing to hand over one part of material to be shared in coordination by related personnel;
2) Combining a plurality of material list items related in the subject matter list, and avoiding the user from repeatedly filling the same item;
3) For the serial reconstruction flow of the subject matter, collecting the handling materials of all matters uniformly;
4) The method has the advantages that the joint transaction with the dependency relationship is changed from serial transaction to parallel transaction, the pre-transaction and the post-transaction can be simultaneously transacted, and whether the post-transaction is effective or not is determined by the pre-transaction transacting condition, so that the whole transacting time limit can be shortened;
5) Uniformly inspecting and accepting materials;
6) The background performs front-back link coordination, and a user does not need to care about an intermediate flow and only needs to submit necessary materials.
The user presents a matter to the user, the user submits materials uniformly in one window of the government hall, the government background system distributes the materials, and finally the window uniformly notifies the transacting result or issues the transacting certificate.
Claims (7)
1. A government affair theme item joint handling method based on item association network is characterized by comprising the following steps:
1) Constructing a government affair item knowledge base: constructing a government affair item knowledge base according to government affair item data, wherein the knowledge base comprises three types of entities of departments, items and materials and the dependency relationship among the entities;
the attributes of the department entities comprise attributes such as department ID, department name and the like, the attributes of the event entities comprise event ID, event name, event handling time limit and the like, and the attributes of the material entities comprise material ID, material name and the like;
the dependency relationship includes: (1) department d→item I, indicating that department D can handle item I; (2) Material m→item I, indicating that material M is required for handling item I; (3) Item I→material M, which means that material M will be generated after item I is handled successfully; wherein A.fwdarw.B represents that B depends on A;
2) Constructing a department association network: based on a government matter knowledge base, three types of entities are used as three types of nodes in a network: department, item and material node; forming edges among nodes in a network according to the dependency relationship among the nodes, establishing an association relationship between department nodes and department nodes according to the edges among the nodes, wherein the directivity of the edges is used for distinguishing the front-back sequence of the nodes, and the direction of the dependency relationship A-B indicates that the front part of the A is arranged at the front part of the B, and the rear part of the A is arranged at the rear part of the B; the association relation between the department nodes is the condition that the two department nodes are connected with the same item or material in the network; taking the number of materials with association relations between two department nodes as department association relation weight;
3) Dividing department virtual communities: clustering department nodes according to the department association relation weight to form different virtual communities, wherein the association degree of nodes in communities is higher, and the association degree between communities is lower;
4) Building a matter association network: traversing all department nodes in the virtual community, inquiring all item nodes connected with all department nodes and material nodes connected with the item nodes, and establishing association relations between the item nodes according to edges among the nodes to form an item association network in the department community; the association relation between the item nodes is the condition that the two item nodes are connected with the same material in the network; taking the number of materials with association relation between two item nodes as item association relation weight;
5) Mining subject matter: traversing all item nodes in an item association network, taking item nodes with association relation with more than k other post item nodes as subject item nodes, wherein k is a preset threshold value, and taking items associated with each subject item as the backlog items of the subject item; collecting service data of each subject matter node, determining the priority of the subject matter according to the service data, forming a list of the subject matter from high to low according to the priority, and outputting the list; the related content of each subject matter in the subject matter list comprises a backlog, attributes of a subject matter entity and required materials;
the step 3) adopts Fast Unfolding community division algorithm to cluster the department nodes, and the specific steps are as follows:
a) The initial stage: each department node is divided into a community independently;
b) The circulation steps are as follows:
b1 Extracting department nodes with association relation with each department node and corresponding department association relation weight from the department association network, and calculating the modularity Q under the current community division as the current modularity Q c ：
Wherein A is i,j Representing department association relation weight between the i department node and the j department node;representing the sum of all department node association weights in the department association network, +.>Representing the association relation weight sum of the department node i; c i A community ID value to which the department node i belongs is represented; c j A community ID value to which the department node j belongs is represented; delta (c) i ,c j ) To indicate a function, when the department node i is in the same community as the department node j, δ (c) i ,c j ) 1, otherwise 0;
b2 Taking out a community as a current community;
b3 Community partition update): taking out one of the department nodes which do not belong to the current community for updating, separating the department node from the original community, and adding the department node into the current community, namely updating the community ID value of the department node into the ID value of the current community;
b4 Calculating the updated module degree Q of community division as updated module degree Qu, comparing Qc with Qu, if the Qu is larger than Qc, receiving current update, and taking the updated module degree Qu as the module degree of the current community division, otherwise, not receiving update; judging whether all department nodes which do not belong to the current community are traversed, if yes, executing the step B5), otherwise returning to the step B3);
b5 Judging whether all communities are traversed, if yes, executing the step B6) if one-time cyclic updating is completed, otherwise, returning to the step B2);
b6 If all the updates are not accepted in the current circulation update, taking the current community division as a final clustering result, otherwise, returning to the step B1).
2. The method of claim 1, wherein the topic joint optimization operation of step 6) is further performed to obtain a final topic list before the output is ordered from high to low according to topic priority in step 5):
for each topic, it is combined with the respective required list of materials for the backlog, the duplicate material is removed, and the material that may be generated by the topic or backlog is removed, resulting in a list of materials required after backlog optimization.
3. The method of claim 1, wherein the backlog threshold k has a value of 2.
4. The method of claim 1, wherein step 5) traverses all transaction nodes in the transaction-related network using breadth-first search.
5. The method of claim 1, wherein the service data of the subject node in step 5) includes: the number of co-occurrence times and the number of transaction times of the two connected transactions;
the specific method for determining the transaction priority of the transaction related network subject matters according to the service data is to take the sum of the transaction times of each subject matter and the transaction related matters as an influence degree value, wherein the magnitude of the influence degree value is positively related to the priority.
6. The government affair topic and item joint office system based on the item association network is characterized by comprising a government affair item knowledge base construction module, a department association network construction module, a department virtual community division module, an item association network construction module and a topic item mining module;
the government affair knowledge base construction module is used for constructing a government affair knowledge base according to government affair data, wherein the knowledge base comprises departments, matters, materials and dependency relations among the entities; the attributes of the department entities comprise attributes such as department ID, department name and the like, the attributes of the event entities comprise event ID, event name, event handling time limit and the like, and the attributes of the material entities comprise material ID, material name and the like; the dependency relationship includes: (1) department d→item I, indicating that department D can handle item I; (2) Material m→item I, indicating that material M is required for handling item I; (3) Item I→material M, which means that material M will be generated after item I is handled successfully; wherein A.fwdarw.B represents that B depends on A;
the department association network construction module is used for taking three types of entities as three types of nodes in a network based on a government matters knowledge base: department, item and material node; forming edges among nodes in a network according to the dependency relationship among the nodes, and establishing an association relationship between department nodes and department nodes according to the edges among the nodes; the association relation between the department nodes is the condition that the two department nodes are connected with the same item or material in the network; taking the number of materials with association relations between two department nodes as department association relation weight;
the department virtual community dividing module is used for clustering department nodes according to department association relation weights to form different virtual communities, wherein the association degree of nodes in communities is higher, and the association degree between communities is lower;
the item association network construction module is used for traversing all department nodes in the virtual community, inquiring all item nodes connected with all department nodes and material nodes connected with the item nodes, establishing association relations between the item nodes and the item nodes according to edges among the nodes, wherein the directivity of the edges is used for distinguishing the front-back sequence of the nodes, and the direction of the dependency relation A-B indicates that A is arranged in front of B, and B is arranged behind A; the association relation between the item nodes is the condition that the two item nodes are connected with the same material in the network; taking the number of materials with association relation between two item nodes as item association relation weight;
the topic mining module is used for traversing all the topic nodes in the topic association network, taking the topic nodes which have association relation with more than k other posttopic nodes as topic nodes, taking k as a preset threshold value, and taking the topic associated with each topic as the joint backlog of the topic; collecting service data of each subject matter node, determining the priority of the subject matter according to the service data, forming a list of the subject matter from high to low according to the priority, and outputting the list; the related content of each subject matter in the subject matter list comprises a backlog, attributes of a subject matter entity and required materials;
the division virtual community division module adopts Fast Unfolding community division algorithm to cluster the division nodes, and is specifically realized as follows:
a) The department virtual community dividing module divides each department node into a community independently;
b) The department virtual community division module circularly updates community division:
b1 Extracting department nodes with association relation with each department node and corresponding department association relation weight from the department association network, and calculating the modularity Q under the current community division as the current modularity Q c ：
Wherein A is i,j Representing department association relation weight between the i department node and the j department node;representing the sum of all department node association weights in the department association network, +.>Representing the association relation weight sum of the department node i; c i A community ID value to which the department node i belongs is represented; c j A community ID value to which the department node j belongs is represented; delta (c) i ,c j ) To indicate a function, when the department node i is in the same community as the department node j, δ (c) i ,c j ) 1, otherwise 0;
b2 Taking out a community as a current community;
b3 Community partition update): taking out one of the department nodes which do not belong to the current community for updating, separating the department node from the original community, and adding the department node into the current community, namely updating the community ID value of the department node into the ID value of the current community;
b4 Calculating the updated module degree Q of community division as updated module degree Qu, comparing Qc with Qu, if the Qu is larger than Qc, receiving current update, and taking the updated module degree Qu as the module degree of the current community division, otherwise, not receiving update; judging whether all department nodes which do not belong to the current community are traversed, if yes, executing the step B5), otherwise returning to the step B3);
b5 Judging whether all communities are traversed, if yes, executing the step B6) if one-time cyclic updating is completed, otherwise, returning to the step B2);
b6 If all the updates are not accepted in the current circulation update, taking the current community division as a final clustering result, otherwise, returning to the step B1).
7. The system of claim 6, further comprising a topic joint optimization module for performing topic joint optimization before the topic list is output by the topic association network construction module: for each topic, it is combined with the respective required list of materials for the backlog, the duplicate material is removed, and the material that may be generated by the topic or backlog is removed, resulting in a list of materials required after backlog optimization.
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