CN111694878A - Government affair subject matter co-processing method and system based on matter association network - Google Patents
Government affair subject matter co-processing method and system based on matter association network Download PDFInfo
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Abstract
The invention provides a government affair theme affair co-processing method based on an affair 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 an item correlation network; 5) mining subject matters; 6) and optimizing the subject matters together. The method fully considers the strength of the association relationship between the government departments, performs association clustering on the government departments, improves the cooperative connection between the departments, and provides a theoretical basis for joint handling of the departments. And topic matters are mined in the department virtual community with close association relationship, and the topic matters are generated and output, so that the work efficiency of government departments is effectively improved.
Description
Technical Field
The present invention relates to information processing technology, and more particularly, to a technology for identifying a co-office event.
Background
Government matters generally refer to administrative services of various governments. Subject matter refers to government matters having a higher relevance, such as the same or similar primary objectives. The item co-handling means that a plurality of originally independent and separated items are jointly handled, and the materials are uniformly received, the items are handled in the background, and the items are output from the window. The significance of the event co-handling is that the government affairs to be dealt with by the user to the government usually involve a plurality of sub-events, and the events are logically connected but physically independent from each other, so that a complete event is split into a plurality of events, and the user is easy to make mistakes and run legs for a plurality of times in the dealing process.
Under the call of 'department information sharing, information island breaking, data running more and people running less' promoted by the state, all parts of the country actively respond, develop 'one-affair-associated' construction, namely, integrate the business of related functional departments, integrate the business process of multiple departments related to the handling target matters through an informatization means, set up a comprehensive handling window in a government affair service hall and the like, uniformly handle related handling matters, collect all required materials at one time, carry out one-time input and automatically distribute all related departments, and realize 'one-window handling and parallel handling' supported by the informatization technology.
The existing developed 'one-thing-to-many-affair' construction is based on common knowledge or life experience, for example, a series of registration and registration are needed to be conducted when children are born, so that one-thing-to-many-evidence-to-many-affair is developed in most areas of the country, and newborns born in hospitals with obstetrical departments and conforming to policies such as provincial residents, insurance registration and the like can be handled at one time in hospitals or administrative service centers for birth items such as vaccination, birth medicine certification, residents, insurance registration, social insurance card application and the like.
With the continuous promotion of the reform of 'internet + government affair service', governments in various regions respond to calls, develop and deeply optimize the government affair service flow, continuously improve the service quality of government departments and furthest improve the livelihood of people mainly by means of material reduction, link reduction, time limit reduction, evidence reduction and the like.
The currently proposed feasible way for the event co-handling is to classify the events of the same type together, then eliminate the events with low association degree according to the association degree between the events, and establish the co-handling of the remaining events. For example, in a restaurant, a market administration, a health care organization, a fire department, and the like are all collected together with matters related to the business of the restaurant, and government staff manually screen out process matters necessary for the restaurant to be made by a user, thereby developing an event-by-event.
In the process of the joint handling of the provinces and the matters, the adopted method is based on the life experience, the common knowledge and the experience accumulated by the government affair personnel handling the matters, and the joint handling matters are manually searched in a manual mode. The clerk needs to face massive transaction data, department data, material data and historical transaction data, and select a proper transaction set from the transaction data as a trial-and-error object of joint office by reasonably considering the relationship between the transaction and the department and related laws and regulations.
Because the existing methods are mostly realized mainly by manpower, a large amount of labor is required to be consumed, the office time is greatly prolonged, particularly, in the face of increasing electronic government affair data, only manual analysis and mining are required, the items are jointly transacted by manpower, the complete topic item association relationship is difficult to dig out, the existing 'one item' mostly lacks corresponding theoretical basis support, the time and labor are wasted, the automatic flow is difficult to form, and the number of constructed co-office items is limited.
Traditional government affairs are complex and scattered, and the following problems are usually involved in handling target affairs of the masses: the masses do not know that several licenses need to be handled for handling target items, 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 need to be submitted repeatedly during the transaction and government agencies need to review repeatedly. These problems all result in multiple leg runs when people transact items, which is time consuming and resource consuming. Therefore, a plurality of government departments have developed 'one-affair-handling' construction, namely, a plurality of matters are accepted at one time in one window, and the main purposes of the construction are to reduce the handling complexity for the public unfamiliar with the business and improve the handling efficiency of the government service department.
Therefore, how to provide a more reasonable and clear item co-office construction method, and deeply dig the items capable of being co-office, so that the government departments can reasonably and effectively construct an 'one-item co-office' for internal government items, 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 the departments with close contact based on the item correlation network according to the correlation among the departments, to mine the subject items in the departments with close contact, to construct the subject item co-office so as to realize the government affair co-office 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 co-handling method based on the matter association network comprises the following steps:
1) constructing a government affair knowledge base: constructing a government affair knowledge base according to the government affair data, wherein the knowledge base comprises three entities of departments, affairs and materials and the dependency relationship among the entities;
the attributes of the department entities comprise department IDs, department names and the like, the attributes of the matter entities comprise matter IDs, matter names and matter transaction time limits, and the attributes of the material entities comprise material IDs and material names;
the dependency relationships include: (1) department D → item I, meaning that department D can handle item I; (2) material M → item I, which indicates that material M is needed to handle item I; (3) item I → material M, which shows that the item I will generate material M after successfully transacting; wherein A → B indicates that B depends on A;
2) constructing a department associated network: based on the government affairs knowledge base, three types of entities are used as three types of nodes in the network: department, item and material nodes; forming edges among nodes in the network according to the dependency relationship among the nodes, and establishing an association relationship between department nodes according to the edges among the nodes; the association relationship between the department nodes and 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 the association relationship between two department nodes as the weight of the department association relationship;
3) dividing a department virtual community: clustering department nodes according to the department association relation weight to form different virtual communities, wherein the association degree of the nodes in the communities is high, and the association degree between the communities is low;
4) and (3) constructing an item correlation 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 incidence 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 and back sequence of the nodes, and the direction of the dependence relation A → B indicates that A is arranged in front of B and B is arranged in back of A; the association relationship between the transaction nodes is the condition that the two transaction nodes are connected with the same material in the network; taking the number of materials with an association relationship between two item nodes as item association relationship weight;
5) mining subject matters: traversing all event nodes in the event correlation network, taking the event nodes with correlation relations with more than k other post-event nodes as subject event nodes, wherein k is a preset threshold value, and taking the events associated with each subject event as the co-office events of the subject event; collecting service data of each theme item node, determining the priority of the theme items according to the service data, and forming and outputting the theme items into a list according to the priority from high to low; in the subject matter list, the related content of each subject matter includes the co-office matter, the attribute of the matter entity and the required material.
Further, in step 5), before outputting the list from high to low according to the priority of the subject matters, performing a step 6) of optimizing the subject matters together: for each topic, merging it with the respective required material list for the co-office, removing duplicate materials, and removing materials that may result from the topic or the co-office, resulting in a co-office optimized required material list.
In addition, a system for realizing the method is provided, and comprises 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 theme item mining module which are respectively corresponding to the steps. Meanwhile, in order to realize theme event simultaneous optimization, a theme event simultaneous optimization module is arranged in the system.
The method and the system construct the government affair knowledge base with clear dependency relationship based on the government affair data, are beneficial to managing huge government affair data, and mine more potential knowledge. The topological structure characteristic of the government department association diagram fully considers the strength of the association relationship between the government departments, performs association clustering on the government departments, tries to eliminate the problem of information isolated island between the departments, improves the cooperative connection between the departments, and provides a theoretical basis for joint handling of the departments. And mining the theme matters in the department virtual community with close association relationship, and generating theme matter output. The method replaces the original time-consuming and labor-consuming operation of manual searching, has certain interpretability, and provides theoretical basis support for topic affair co-operation.
The method has the advantages that the process of mining the interoperable events among departments is automated, theoretical basis support is provided through data analysis, the interoperable events are mined, and decision support is provided for government affair personnel. The method has the advantages that the influence of the network topology characteristics and the incidence relation among the government matters is considered, the association clustering is carried out on the government departments, the departments with close contact are found, the theme matters are mined in the departments, the theme-matter joint construction is carried out, the government affair flow optimization method is provided for government personnel, the handling efficiency of the government departments is effectively improved, and the service quality of the government departments and the handling experience of the citizens can be improved.
Drawings
FIG. 1 is a flow chart of an embodiment method.
Detailed Description
The system comprises 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, a theme item mining module and a theme item co-processing optimization module:
a specific embodiment of the system method is shown in figure 1,
1) the government affair item knowledge base building module is used for building a government affair item knowledge base: constructing a government affair item knowledge base according to the government affair item data, wherein the knowledge base comprises three entities of departments, items and materials and the dependency relationship among the entities;
the attributes of the department entities comprise department IDs, department names and the like, the attributes of the matter entities comprise matter IDs, matter names and matter transaction time limits, and the attributes of the material entities comprise material IDs and material names;
the dependency relationships include: (1) department D → item I, meaning that department D can handle item I; (2) material M → item I, which indicates that material M is needed to handle item I; (3) item I → material M, which shows that the item I will generate material M after successfully transacting; wherein A → B indicates that B depends on A.
2) The department associated network construction module is used for constructing a department associated network:
based on the government affair knowledge base, according to the existing relations, if the department 1 can accept the affair 1, the affair 1 is processed to generate the material 1, the material 1 can be used for processing the affair 2, and the affair 2 is processed by the department 2, thereby establishing the association relation between the departments.
Taking three types of entities as three types of nodes in the network: department, item and material nodes; forming edges among nodes in the network according to the dependency relationship among the nodes, and establishing an association relationship between department nodes according to the edges among the nodes to form an item association network; the association relationship between the department nodes and 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 closeness of the association between different departments, the number of materials with the association between two department nodes is used as the weight of the department association.
3) The department virtual community dividing module is used for dividing the department virtual communities: clustering the department nodes according to the department association relation weight to form different virtual communities, wherein the association degree of the nodes in the communities is high, and the association degree between the communities is low, so that a department cluster set with high contact is generated, and then, the matters in the communities are analyzed to mine and form the subject matters of the joint work.
Specifically, the department nodes may be clustered by using a Fast Unfolding community partitioning algorithm. The modularity is also called a modularization metric value, and is a commonly used method for measuring the strength of a network community structure, and the closer the value is to 1, the stronger the strength of the community structure divided by the network is, that is, the better the dividing quality is. Optimal network community partitioning can be achieved by maximizing the modularity Q.
The specific steps of the Fast Unfolding community partitioning algorithm are as follows:
A) an initial stage: each department node is divided into a community independently;
B) and (3) circulating step:
B1) taking out department nodes with the 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 Qc：
Wherein A isi,jRepresenting department association relation weight between the i department gate node and the j department node;
represents the sum of the association relation weights of all department nodes in the department association network,
representing the association relation weight sum of department nodes i; c. CiA community ID value representing the affiliation of the department node i; c. CjIndicates department nodeThe community ID value to which j belongs; (c)i,cj) To indicate the function, when department node i is in the same community as department node j, (c)i,cj) Is 1, otherwise is 0;
B2) taking a community as a current community;
B3) community division updating: taking out one of the department nodes not belonging 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 to be the ID value of the current community;
B4) calculating the updated modularity Q of the community division as an updated modularity Qu, comparing Qc with Qu, if Qu is larger than Qc, receiving the current update, and using the updated modularity Qu as the modularity of the current community division, otherwise, not receiving the update; judging whether all department nodes which do not belong to the current community are traversed or not, if so, executing the step B5), otherwise, returning to the step B3);
B5) judging whether all communities are traversed or not, if so, executing the step B6 if one cycle updating is completed), and otherwise, returning to the step B2);
B6) if all the updates are not accepted in the cycle update, the current community division is indicated to be stable, the community division algorithm is quitted, the current community division is taken as a final clustering result, and otherwise, the step B1) is returned.
4) The item association network-free construction module is used for constructing an item 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 incidence 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 and back sequence of the nodes, and the direction of the dependence relation A → B indicates that A is arranged in front of B and B is arranged in back of A; the association relationship between the event node and the event node is the case that two event nodes are connected with the same material in the network, for example, if there is an event a → material → event B, the event a is the leading event of the event B, and the event B is the trailing event of the event a; taking the number of materials with an association relationship between two item nodes as item association relationship weight;
5) the theme item mining module is used for mining theme items: traversing all event nodes in the event correlation network, taking the event nodes which have correlation with more than k other post-event nodes as subject event nodes, taking k as a preset threshold value and taking the value as 2, and taking the events correlated with each subject event as the co-office events of the subject event; collecting service data of each theme item node, determining the priority of the theme items according to the service data, and forming and outputting the theme items into a list according to the priority from high to low; in the subject matter list, the related content of each subject matter includes the co-office matter, the attribute of the matter entity and the required material.
Specifically, a breadth-first search mode is adopted to traverse all item nodes in the item correlation network; in the traversing process, when the number of the postitems connected with a certain item node is more than k, the current search is terminated, and the search of the next item node is started.
The business data of the subject matter node comprises: connecting two items, namely co-occurrence times and item handling times;
the specific method for determining the co-handling priority of the item-related network subject items according to the service data is that the sum of the handling times of each subject item and the co-handling items thereof is used as an influence degree value, and the size of the influence degree value is positively correlated with the priority level;
6) the item co-doing optimization module is used for topic item co-doing optimization processing, and list output is carried out after the topic items are arranged from high to low according to the priority, and the co-doing optimization comprises the following steps:
for each topic, merging it with the respective required material list for the co-office, removing duplicate materials, and removing materials that may result from the topic or the co-office, resulting in a co-office optimized required material list.
The main effects of the co-office subject matter are as follows:
1) merging the repeated materials related in the subject matter list, and only handing over one part of materials coordinated and shared by related personnel;
2) merging a plurality of material table entries related in the subject matter list to avoid the repeated filling of the same entry by a user;
3) for the theme event string rebuilding flow, uniformly receiving the transaction materials of all the events;
4) the parallel transaction of the simultaneous transaction with the dependency relationship is changed from serial transaction to parallel transaction, the prepositive transaction and the postpositive transaction can be simultaneously handled, and whether the postpositive transaction is effective or not is determined by the prepositive transaction condition, so that the whole transaction time limit can be shortened;
5) uniformly checking and accepting materials;
6) the background coordinates the front and back links, and the user only needs to submit necessary materials without concerning the intermediate process.
Originally, a plurality of separated matters are presented to a user, the user submits materials uniformly in a window in a government hall, the materials are distributed and processed by a government background system, and finally the handling result is notified uniformly or a handling certificate is issued uniformly by a window.
Claims (9)
1. A government affair theme affair co-handling method based on an affair association network is characterized by comprising the following steps:
1) constructing a government affair knowledge base: constructing a government affair knowledge base according to the government affair data, wherein the knowledge base comprises three entities of departments, affairs and materials and the dependency relationship among the entities;
the attributes of the department entities comprise attributes such as department IDs, department names and the like, the attributes of the matter entities comprise matter IDs, matter names, matter transaction time limits and the like, and the attributes of the material entities comprise material IDs, material names and the like;
the dependency relationships include: (1) department D → item I, meaning that department D can handle item I; (2) material M → item I, which indicates that material M is needed to handle item I; (3) item I → material M, which shows that the item I will generate material M after successfully transacting; wherein A → B indicates that B depends on A;
2) constructing a department associated network: based on the government affairs knowledge base, three types of entities are used as three types of nodes in the network: department, item and material nodes; forming edges among nodes in the network according to the dependency relationship among the nodes, establishing an incidence 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 and back sequence of the nodes, and the direction of the dependency relationship A → B indicates that A is arranged in front of B and B is arranged behind A; the association relationship between the department nodes and 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 the association relationship between two department nodes as the weight of the department association relationship;
3) dividing a department virtual community: clustering department nodes according to the department association relation weight to form different virtual communities, wherein the association degree of the nodes in the communities is high, and the association degree between the communities is low;
4) and (3) constructing an item correlation network: traversing all department nodes in the virtual community, inquiring all item nodes connected with all the department nodes and material nodes connected with the item nodes, and establishing an association relationship between the item nodes and the item nodes according to edges among the nodes to form an item association network in the department community; the association relationship between the transaction nodes is the condition that the two transaction nodes are connected with the same material in the network; taking the number of materials with an association relationship between two item nodes as item association relationship weight;
5) mining subject matters: traversing all event nodes in the event correlation network, taking the event nodes with correlation relations with more than k other post-event nodes as subject event nodes, wherein k is a preset threshold value, and taking the events associated with each subject event as the co-office events of the subject event; collecting service data of each theme item node, determining the priority of the theme items according to the service data, and forming and outputting the theme items into a list according to the priority from high to low; in the subject matter list, the related content of each subject matter includes the co-office matter, the attribute of the matter entity and the required material.
2. The method as claimed in claim 1, further comprising, before the output is sorted from high to low according to the subject matter priority in step 5), performing the subject matter co-handling optimization operation in step 6) to obtain a final subject matter list:
for each topic, merging it with the respective required material list for the co-office, removing duplicate materials, and removing materials that may result from the topic or the co-office, resulting in a co-office optimized required material list.
3. The method as claimed in claim 1, wherein the step 3) of clustering the department nodes by using Fast Unfolding community partitioning algorithm comprises the following specific steps:
A) an initial stage: each department node is divided into a community independently;
B) and (3) circulating step:
B1) taking out department nodes with the 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 Qc：
Wherein A isi,jRepresenting department association relation weight between the i department gate node and the j department node;
represents the sum of the association relation weights of all department nodes in the department association network,
representing the association relation weight sum of department nodes i; c. CiA community ID value representing the affiliation of the department node i; c. CjIndicating a community ID value to which the department node j belongs; (c)i,cj) To indicate the function, when department node i is in the same community as department node j, (c)i,cj) Is 1, otherwise is 0;
B2) taking a community as a current community;
B3) community division updating: taking out one of the department nodes not belonging 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 to be the ID value of the current community;
B4) calculating the updated modularity Q of the community division as an updated modularity Qu, comparing Qc with Qu, if Qu is larger than Qc, receiving the current update, and using the updated modularity Qu as the modularity of the current community division, otherwise, not receiving the update; judging whether all department nodes which do not belong to the current community are traversed or not, if so, executing the step B5), and otherwise, returning to the step B3);
B5) judging whether all communities are traversed or not, if so, executing the step B6 if one cycle updating is completed), and otherwise, returning to the step B2);
B6) and if all the updates are not accepted in the cycle update, taking the current community division as a final clustering result, and otherwise, returning to the step B1).
4. The method of claim 1, wherein the join transaction threshold k takes a value of 2.
5. The method as recited in claim 1, wherein in step 5), a breadth first search is employed to traverse all transaction nodes in the transaction correlation network.
6. The method as claimed in claim 1, wherein the service data of the subject matter node in step 5) comprises: connecting two items, namely co-occurrence times and item handling times;
the specific method for determining the item co-handling priority of the item-related network subject item according to the service data is that the sum of the handling times of each subject item and the co-handling item thereof is used as an influence degree value, and the size of the influence degree value is positively correlated with the priority level.
7. A government affair theme affair co-handling system based on a theme association network is characterized by comprising a government affair theme knowledge base building module, a department association network building module, a department virtual community dividing module, a theme association network building module and a theme affair mining module;
the government affair item knowledge base construction module is used for constructing a government affair item knowledge base according to government affair item data, and 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 IDs, department names and the like, the attributes of the matter entities comprise matter IDs, matter names, matter transaction time limits and the like, and the attributes of the material entities comprise material IDs, material names and the like; the dependency relationships include: (1) department D → item I, meaning that department D can handle item I; (2) material M → item I, which indicates that material M is needed to handle item I; (3) item I → material M, which shows that the item I will generate material M after successfully transacting; wherein A → B indicates that B depends on A;
the department associated network construction module is used for taking three types of entities as three types of nodes in the network based on the government affair item knowledge base: department, item and material nodes; forming edges among nodes in the network according to the dependency relationship among the nodes, and establishing an association relationship between department nodes according to the edges among the nodes; the association relationship between the department nodes and 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 the association relationship between two department nodes as the weight of the department association relationship;
the department virtual community dividing module is used for clustering department nodes according to the department association relation weight to form different virtual communities, the association degree of the nodes in the communities is high, and the association degree between the communities is low;
the item correlation network building 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, building the correlation 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 and back sequence of the nodes, and the direction of the dependency relationship A → B indicates that A is arranged in front of B and B is arranged in back of A; the association relationship between the transaction nodes is the condition that the two transaction nodes are connected with the same material in the network; taking the number of materials with an association relationship between two item nodes as item association relationship weight;
the theme event mining module is used for traversing all event nodes in the event correlation network, taking the event nodes which have correlation with more than k other post-event nodes as theme event nodes, wherein k is a preset threshold value, and taking the events correlated with the theme events as the co-handling events of the theme events; collecting service data of each theme item node, determining the priority of the theme items according to the service data, and forming and outputting the theme items into a list according to the priority from high to low; in the subject matter list, the related content of each subject matter includes the co-office matter, the attribute of the matter entity and the required material.
8. The system of claim 7, further comprising a topic association optimization module for performing topic association optimization before the topic association network construction module outputs the topic list: for each topic, merging it with the respective required material list for the co-office, removing duplicate materials, and removing materials that may result from the topic or the co-office, resulting in a co-office optimized required material list.
9. The system of claim 7, wherein the department virtual community partitioning module clusters the department nodes using a Fast Unfolding community partitioning algorithm, and is specifically implemented as follows:
A) the department virtual community dividing module divides each department node into a community;
B) the department virtual community division module circularly updates community division:
B1) taking out department nodes with the 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 Qc：
Wherein A isi,jRepresenting department association relation weight between the i department gate node and the j department node;
represents the sum of the association relation weights of all department nodes in the department association network,
representing the association relation weight sum of department nodes i; c. CiA community ID value representing the affiliation of the department node i; c. CjIndicating a community ID value to which the department node j belongs; (c)i,cj) To indicate the function, when department node i is in the same community as department node j, (c)i,cj) Is 1, otherwise is 0;
B2) taking a community as a current community;
B3) community division updating: taking out one of the department nodes not belonging 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 to be the ID value of the current community;
B4) calculating the updated modularity Q of the community division as an updated modularity Qu, comparing Qc with Qu, if Qu is larger than Qc, receiving the current update, and using the updated modularity Qu as the modularity of the current community division, otherwise, not receiving the update; judging whether all department nodes which do not belong to the current community are traversed or not, if so, executing the step B5), otherwise, returning to the step B3);
B5) judging whether all communities are traversed or not, if so, executing the step B6 if one cycle updating is completed), and otherwise, returning to the step B2);
B6) and if all the updates are not accepted in the cycle update, taking the current community division as a final clustering result, and otherwise, returning to the step B1).
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112241420A (en) * | 2020-10-26 | 2021-01-19 | 浪潮云信息技术股份公司 | Government affair service item recommendation method based on association rule algorithm |
| CN112241876A (en) * | 2020-10-26 | 2021-01-19 | 浪潮云信息技术股份公司 | All-element dynamic combing method for government affair service affairs |
| CN112862445A (en) * | 2021-02-22 | 2021-05-28 | 浪潮云信息技术股份公司 | High-concurrency data exchange processing method and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010139167A1 (en) * | 2009-06-05 | 2010-12-09 | 深圳市脑库计算机系统有限公司 | Expert support application system platform for government affair and business affair decision-making and its construction method |
| CN105117437A (en) * | 2015-08-10 | 2015-12-02 | 陈飞 | Artificial intelligent platform based auxiliary business handling administrative examination and approval method |
| CN109255586A (en) * | 2018-08-24 | 2019-01-22 | 安徽讯飞智能科技有限公司 | A kind of online personalized recommendation method that E-Governance Oriented is handled affairs |
| CN109582714A (en) * | 2018-12-03 | 2019-04-05 | 甘肃万维信息技术有限责任公司 | A kind of government affairs item data processing method based on time fading correlation |
-
2020
- 2020-05-11 CN CN202010391288.5A patent/CN111694878B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010139167A1 (en) * | 2009-06-05 | 2010-12-09 | 深圳市脑库计算机系统有限公司 | Expert support application system platform for government affair and business affair decision-making and its construction method |
| CN105117437A (en) * | 2015-08-10 | 2015-12-02 | 陈飞 | Artificial intelligent platform based auxiliary business handling administrative examination and approval method |
| CN109255586A (en) * | 2018-08-24 | 2019-01-22 | 安徽讯飞智能科技有限公司 | A kind of online personalized recommendation method that E-Governance Oriented is handled affairs |
| CN109582714A (en) * | 2018-12-03 | 2019-04-05 | 甘肃万维信息技术有限责任公司 | A kind of government affairs item data processing method based on time fading correlation |
Non-Patent Citations (4)
| Title |
|---|
| 冯健;史丹丹;罗香玉;叶鸥;: "利用节点重要度和社团接近度发现社团结构" * |
| 刘峤;钟云;李杨;刘瑶;秦志光;: "基于图的中文集成实体链接算法" * |
| 刘瑶;康晓慧;高红;刘峤;吴祖峰;秦志光;: "基于节点亲密度和度的社会网络社团发现方法" * |
| 叶鑫;董路安;宋禺;: "基于大数据与知识的"互联网+政务服务"云平台的构建与服务策略研究" * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112241420A (en) * | 2020-10-26 | 2021-01-19 | 浪潮云信息技术股份公司 | Government affair service item recommendation method based on association rule algorithm |
| CN112241876A (en) * | 2020-10-26 | 2021-01-19 | 浪潮云信息技术股份公司 | All-element dynamic combing method for government affair service affairs |
| CN112862445A (en) * | 2021-02-22 | 2021-05-28 | 浪潮云信息技术股份公司 | High-concurrency data exchange processing method and storage medium |
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