CN115409297B - Government affair service flow optimization method and system and electronic equipment - Google Patents
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Abstract
The invention provides a government affair service flow optimization method, a government affair service flow optimization system and electronic equipment. The method comprises the steps of collecting service data to form a big data storage structure, sorting, screening and combining the data, mining the data by using a frequent pattern mining algorithm to obtain association rules among the data, generating a front piece set D1 and a back piece set D2, drawing a directed graph by using the D1 and the D2, calculating the directed graph by using a graph theory algorithm, and calculating the node relation. Compared with the prior art, the method and the system have the advantages that the government affair services are analyzed by using the frequent pattern mining and graph theory algorithm, the association relationship among the service data can be fully mined, the government affair handling process can be optimized through the association relationship, and the government affair handling efficiency is improved.
Description
Technical Field
The invention relates to the field of big data mining, in particular to a government affair service flow optimization method, a government affair service flow optimization system and electronic equipment.
Background
At the present stage, the services needed to be handled by the masses are mainly combed in a manual mode, other related services are recommended according to the services handled by the masses, professional knowledge and service familiarity of workers are mainly relied on, the subjective influence of the workers is large, and all the related services are difficult to screen manually. The problem that needs to be solved currently is how to mine relevant information of a large amount of people's business information and provide a reasonable and effective flow scheme for people to handle business.
Disclosure of Invention
The invention aims to overcome at least one defect of the prior art and provides a government affair service flow optimization method, a system and an electronic device, which are used for optimizing the government affair service flow and providing an effective and reasonable service flow.
The technical scheme adopted by the invention is as follows:
the invention provides a government affair service flow optimization method, which comprises the following steps:
s1: collecting service data, forming a big data storage structure by the service data, and then sorting the service data in the big data storage structure;
s2: screening and filtering the service data in the S1;
s3: merging the service data screened in the S2;
s4: mining association rules of the merged service data, and generating a front piece set D1 and a back piece set D2 according to the association rules;
s5: calculating the Cartesian product of each association relation of the front piece set D1 and the back piece set D2 of the association rule obtained in the S4 to generate a two-dimensional table, and longitudinally combining all the two-dimensional tables to generate a relationship table;
s6: according to the relation table generated in S5, the front piece item and the back piece item are nodes, the front piece item points to the back piece item and is directed edges, the item relation directed graph is drawn, and the item relation directed graph is generated;
s7: and (3) calculating the relation of each node in the transaction relation directed graph in the S6 by using an algorithm of graph theory, and analyzing the application of the relation in the actual business process.
Because the number of the government affair service data is very large, the large data storage structure is adopted to store the service data, so that the service data can be better stored, and meanwhile, a plurality of data sorting tools compatible with the large data storage structure can be conveniently used for sorting, extracting and screening the service data meeting the requirements; then, the business data are mined, association rules among the business data are mined, and the association among the business data can be simply known from the association rules, so that the association between the business data and other business data can be simply reflected, and the business data can be recommended to the masses for handling at the same time; after the association rules are obtained, a directed graph is drawn according to the association rules, and then a graph theory algorithm is used for calculation, so that the actual physical significance between businesses, namely the priority and strong association of the businesses, can be intuitively known. Through the mining analysis of the data, the incidence relation between the businesses is more direct and clear, the government affairs can be better optimized according to the analysis result, and the government affair business handling efficiency is improved.
Further, the collecting the service data and forming the service data into a big data storage structure in step S1 specifically includes:
collecting traffic data using Hive and storing the traffic data in the HDFS.
Hive is a warehouse management tool constructed based on Hadoop, can be well compatible with a big data storage structure, provides a rich SQL mode to sort and query a Hadoop Distributed File System (DFS), and can use a library in a big data analysis engine to analyze data, so Hive is used to collect business data and collect and store the data in an HDFS, and corresponding tools can be conveniently used to sort and analyze the data.
Further, the sorting of the service data in the big data storage structure in step S1 specifically includes:
dividing data into 'personal transaction records' and 'enterprise transaction records' according to 'types of transactants';
the personal transaction record takes the personal identification number as the personal main code;
the enterprise business record takes an enterprise code as an enterprise main code;
respectively generating a corresponding personal transaction record table and an enterprise transaction record table, and respectively recording transaction items of all personal master codes and enterprise master codes and corresponding transaction time.
The government affair handling business is mainly divided into two types, namely personal business and enterprise business, so that the type of handling human is also divided into 'personal handling record' and 'enterprise handling record' during analysis, and a personal master code and an enterprise master code are correspondingly generated and used for establishing two corresponding data sets of 'personal handling record table' and 'enterprise handling record table' according to the master code, and data can be better managed after the data sets are generated.
Further, the filtering of the service data in step S2 specifically includes:
counting transaction items and corresponding transaction time of each main code in a time window by the service data in the S1 through main code aggregation, and deleting the service data of which the number of the transaction items is not more than 1;
the time window is a time period within the last year from the current time point.
An association rule is a description of the association between two things. If the number of office events within a time window for a person is less than or equal to 1, then the association between events cannot be discussed on that person. The transaction records of the user do not contribute to the subsequent frequent pattern mining, and the calculation efficiency of the subsequent algorithm can be improved by deleting the transaction records in advance.
Further, the step S3 merges the service data, specifically:
and (3) merging the data subjected to the main code aggregation in the S2 into a transaction set by taking the main code as a unit.
And grouping by using the group by in the Hive by taking the main code in the data set as a key, and aggregating the service records of 'transaction items' in the same group into one item set by using a collect _ set () function.
Further, the mining of the association rule of the merged service data in the step S4, and the generating of the front piece set D1 and the back piece set D2 according to the association rule specifically include:
mining association rules of the service data by using an FP-growth frequent pattern mining algorithm of an MLlib library in Spark, and generating an association rule table according to the association rules;
and the association rule table records a front piece set D1, a back piece set D2, confidence degrees and promotion degrees of each association rule.
Further, the minimum support degree of FP-growth frequent pattern mining is set to be 0.002, and the minimum confidence coefficient is set to be 0.5.
The Spark calculation engine and the hive both use HDFS as a storage basis, so that association rules among business data can be mined by directly using FP-growth frequent pattern mining in an MLlib library, the minimum support degree is set to be 0.002, the minimum confidence coefficient is 0.5, namely in the mining analysis process, the item relation with the support degree smaller than 0.002 or the execution degree smaller than 0.5 is screened out, items with higher association degree are extracted, wherein the minimum support degree and the minimum confidence coefficient are set according to the actual situation, and the actual situation is the region or time for collecting data and other factors influencing data quantity and content; the association rule describes a correlation relationship between several business items, and specifically may be understood as applying business in the back item set D2 as a percentage probability based on the confidence after applying business in the front item set D1. The relation between the services can be intuitively known, and the handling of the related services is recommended for the masses.
Further, in S7, the relationship between each node in the transaction relationship directed graph in S6 is calculated by using an algorithm of graph theory, specifically:
calculating the central value of each node in the item relation directed graph in the S6 by using a PageRank algorithm in the SparkGraphX;
calculating the strong connected component of each node in the item relation directed graph in the S6 by using a strong connected component algorithm in the spark GraphX;
and calculating the aggregation communities in the item relation directed graph in the S6 by using a Louvain modularization algorithm in the Neo4 j.
Specifically, the center value of each node is calculated by using a PageRank algorithm, and the influence degree of the service item represented by each node can be expressed according to the center value, that is, the higher center value corresponds to the item with higher influence degree, so that the influence of the service corresponding to the item on other services is larger, and important attention needs to be paid to optimization; using a strong connected component algorithm to obtain a strong connected component between each node, wherein each node in the strong connected component has higher correlation and can be transacted simultaneously during service transaction; the method comprises the steps of obtaining an aggregation community in a directed graph by using a Loovain algorithm of Neo4j, specifically, adjusting the training 'round number' hyper-parameter of a Louvain modular algorithm, adjusting the training 'round number', adjusting the granularity, namely the density, of the aggregation community, then combining professional knowledge, selecting the aggregation community with a proper granularity, and setting service item nodes under the same aggregation community to be handled together under the same actual scene due to higher similarity.
The invention also provides a government affair service system based on the optimization method, which comprises the following steps:
the system comprises a data acquisition module, a data storage module and a data analysis module;
the data acquisition module is used for acquiring service data;
the data storage module is used for generating a big data storage structure and storing the data in the data acquisition module;
the data analysis module is used for analyzing the service data in the data storage module.
The data acquisition module can acquire enough government affair business data in history, can also collect business data updated in real time, and stores the data in the data storage module, and the data storage module is internally provided with a big data engine unit which adopts a big data frame in the prior art, preferably a Spark frame; the device is also provided with a graph calculation unit which adopts the existing graph theory algorithm to analyze the data; the data analysis module adopts a data warehouse tool, preferably a Hive data warehouse tool, frequently mines the service data and analyzes the graph theory, and generates a corresponding analysis result.
The present invention also provides an electronic device comprising:
a processor and a memory;
the memory has stored thereon computer readable instructions which, when executed by the processor, cause the apparatus to perform a method of government service process optimization or a system of government service as described above.
Compared with the prior art, the invention has the beneficial effects that: the method for optimizing the government affair process is characterized in that business data are collected, a frequent mining mode and graph theory analysis are used for obtaining the correlation rule of business matters, the process of government affair service is adjusted and optimized according to the correlation rule, the government affair service process is simplified, the government affair handling efficiency is improved, the problem of handling is reasonably solved for people, and the satisfaction degree of people is improved.
Drawings
Fig. 1 is a diagram illustrating the steps of the government affairs service flow optimization method according to the present invention.
Detailed Description
The drawings are only for purposes of illustration and are not to be construed as limiting the invention. For a better understanding of the following embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
Example 1
The embodiment provides a government affair service flow optimization method, as shown in fig. 1, the optimization method includes the following specific steps:
s1: collecting service data, forming a big data storage structure by the service data, and then sorting the service data in the big data storage structure;
the method is characterized in that enough business data are collected by using Hive and stored in the HDFS, the Hive is a data warehouse tool and is based on Hadoop, so that data in a big data storage structure can be managed, and a library under a big data frame is used, so that the data can be conveniently sorted and analyzed;
in a specific implementation process, data is collected by using Hive, office record data of all government affairs in the last year is collected, a time period for collecting the data is specifically set according to actual conditions including factors affecting the data quantity and the data content such as the region where the data is collected, the data is stored in HDFS, then the data is divided into personal office records and enterprise office records according to types of clerks, the personal master code and the enterprise master code are respectively formed, the personal identity number is used as the personal master code, the enterprise code is used as the enterprise master code, the personal master code and the enterprise master code are respectively used for establishing corresponding index tables, the "office matters" corresponding to the master code and the "office time" of the office matters are recorded, two data sets of the "personal office record table" and the "enterprise office record table" are respectively formed, and a specific example of the office record table is as follows:
the table above is partial content of the personal transaction record table, the contents of the enterprise transaction record table are similar, the master code is used for aggregation, and then the contents of the record table are screened.
After the data is screened, the main codes are grouped by using Hive, the same main code is divided into a group, and the data in the "transaction items" under the same main code is aggregated into an item set by using a collect _ set () function, wherein a specific form is as follows:
after merging the item sets, mining association rules of business data in the item sets by using an FP-growth frequent pattern mining algorithm of an MLlib library in Spark, specifically, setting the minimum support degree of the FP-growth frequent pattern mining algorithm to be 0.002 and the minimum confidence degree to be 0.5, namely deleting data with the support degree of less than 0.002 or the confidence degree of less than 0.5, further removing redundant data, improving the calculation efficiency of the algorithm, and simultaneously extracting data with higher association degree, wherein the minimum support degree and the minimum confidence degree are set according to an actual situation, and the actual situation is an area or time for collecting data and other factors influencing data quantity and content; after data mining is carried out by using FP-growth, the item set is divided into a front piece set D1 and a back piece set D2, and association rules between services of the front piece set D1 and the back piece set D2 are obtained, specifically shownFor example, the following:
from the above table it is understood that: the people transacting the matters of 'talent entrance qualification' and 'rent room collection' (house accumulation) and the probability of 73.5 percent (confidence coefficient) transacts the matters of 'outside emigration-talent introduction', and the association degree of the matters between the front-part matters and the back-part matters is known through the confidence coefficient.
And then drawing directed icon tables according to the front piece set D1 and the back piece set D2, specifically, calculating the Cartesian product of each incidence relation in the front piece set D1 and the back piece set D2, generating a two-dimensional table, then combining all the two-dimensional tables longitudinally to generate a relation table, drawing an item relation directed graph and generating an item relation directed graph by taking the front piece items and the back piece items in the relation table as nodes and the front piece items and the back piece items as directed edges.
And analyzing the directed graph in the directed graph by using a graph theory algorithm. Specifically, the central value of each node is calculated by using a PageRank algorithm, and the influence degree of the service item represented by each node can be expressed according to the central value, that is, the item corresponding to the higher central value has higher influence degree, so that the influence of the service corresponding to the item on other services is greater, and in the service flow, the optimization of the handling flow can be performed on the services with greater influence, for example, a fast handling channel is opened to facilitate the handling of the services, and the handling process of the related services is optimized; using a strong connected component algorithm to obtain strong connected components among all nodes, wherein all nodes in the strong connected components have correlation with each other, and can be packaged and transacted simultaneously during service transaction in a specific implementation process; the method comprises the steps of obtaining an aggregation community in a directed graph by using a Louvain algorithm of Neo4j, specifically, adjusting a training 'round number' hyper parameter of a Louvain modular algorithm, adjusting the training 'round number', adjusting the granularity, namely the density, of the aggregation community, selecting the aggregation community under the appropriate granularity by combining professional knowledge, setting business item nodes under the same aggregation community to be handled together under the same actual scene due to higher similarity, integrating the business items under the same aggregation community into a transaction service in a specific implementation process, for example, if the combination of A, B and the item B is under the same aggregation community, considering whether the combination of the two items is a new scene requirement, and if so, integrating the A, B item scene.
Example 2
The embodiment provides a government affair service system, based on the optimization method in embodiment 1, which specifically includes:
the system comprises a data acquisition module, a data storage module and a data analysis module;
the data acquisition module is used for acquiring service data, specifically, a Hive tool is used for collecting the data, and the service data in a long enough time in history is collected and can be specifically set according to contents; the latest business data can be collected in real time, new government affair business can be handled every day due to the fact that social life is continuously changed, the new government affair business is continuously released, the data are collected together, the service flow of government affairs can be better optimized, and the handling efficiency of the government affair business is improved.
The data storage module is used for generating a big data storage structure and storing data in the data acquisition module, and particularly, the data storage structure can be managed by using data provided by Hive and can well arrange the data;
the data analysis module is used for analyzing the service data in the data storage module, and specifically, the data analysis module is provided with a big data engine unit and a graph calculation unit, the big data engine unit adopts a Spark frame, and can use an FP-growth frequent pattern mining algorithm of an MLlib library in Spark to perform relevance mining on the service data in the data storage module and generate a directed graph; the graph calculation unit calculates the directed graph generated by the big data engine unit by adopting a PageRank algorithm, a strong connectivity component algorithm and a Louvain modular algorithm, respectively calculates the central value of each node, the strong connectivity relation among the nodes and the aggregation community, and outputs the central value, the strong connectivity relation and the aggregation community.
Example 3
The present embodiment provides an electronic device, including:
a processor and a memory;
the memory stores computer readable instructions that when executed by the processor cause the processor to perform a method for government affairs service process optimization according to embodiment 1 or a government affairs service system according to embodiment 2.
It should be understood that the above-mentioned embodiments of the present invention are only examples for clearly illustrating the technical solutions of the present invention, and are not intended to limit the specific embodiments of the present invention. Any modification, equivalent replacement, and improvement made within the spirit and principle of the claims of the present invention should be included in the protection scope of the claims of the present invention.
Claims (9)
1. A government affair service flow optimization method is characterized by comprising the following steps:
s1: collecting service data, forming a big data storage structure by the service data, and then sorting the service data in the big data storage structure;
s2: screening and filtering the service data in the S1;
s3: merging the service data screened in the S2;
s4: mining association rules of the merged service data, and generating a front piece set D1 and a back piece set D2 according to the association rules;
s5: calculating the Cartesian product of each association relation of the front piece set D1 and the back piece set D2 of the association rule obtained in the S4 to generate a two-dimensional table, and longitudinally combining all the two-dimensional tables to generate a relation table;
s6: according to the relation table generated in S5, the front piece item and the back piece item are nodes, the front piece item points to the back piece item and is directed edges, the item relation directed graph is drawn, and the item relation directed graph is generated;
s7: calculating the relationship of each node in the item relationship directed graph in the S6 by using an algorithm of a graph theory, and analyzing the application of the relationship in the actual business process;
in S7, the relationship between each node in the item relationship directed graph in S6 is calculated by using an algorithm of graph theory, specifically:
calculating the central value of each node in the item relation directed graph in the S6 by using a PageRank algorithm in the SparkGraphX;
calculating the strong connected component of each node in the item relation directed graph in the S6 by using a strong connected component algorithm in the spark GraphX;
and calculating the aggregation communities in the item relation directed graph in the S6 by using a Louvain modularization algorithm in the Neo4 j.
2. The government affair service flow optimizing method according to claim 1, wherein the collecting of the business data and the forming of the business data into a big data storage structure in step S1 specifically comprises:
collecting traffic data using Hive and storing the traffic data in the HDFS.
3. The government affair service flow optimizing method according to claim 1, wherein the step S1 of sorting the business data in the big data storage structure comprises:
dividing data into 'personal transaction records' and 'enterprise transaction records' according to 'types of transactants';
the personal transaction record takes the personal identification number as the personal main code;
the enterprise business record takes an enterprise code as an enterprise main code;
respectively generating a corresponding personal transaction record table and an enterprise transaction record table, and respectively recording transaction items of all personal master codes and enterprise master codes and corresponding transaction time.
4. The government affair service flow optimizing method according to claim 3, wherein the filtering of the business data in step S2 is specifically:
counting transaction items and corresponding transaction time of each main code in a time window by the service data in the S1 through main code aggregation, and deleting the service data of which the number of the transaction items is not more than 1;
the time window is a time period within the last year from the current time point.
5. The government affair service flow optimization method according to claim 4, wherein in the step S3, the service data are merged, specifically:
and (3) merging the data subjected to the main code aggregation in the S2 into a transaction set by taking the main code as a unit.
6. The government affair service flow optimizing method according to claim 5, wherein association rules of the merged service data are mined in step S4, and a front-piece set D1 and a back-piece set D2 are generated according to the association rules, specifically:
mining association rules of the service data by using an FP-growth frequent pattern mining algorithm of an MLlib library in Spark, and generating an association rule table according to the association rules;
and the association rule table records a front piece set D1, a back piece set D2, confidence degrees and promotion degrees of each association rule.
7. A government service flow optimization method according to claim 6, wherein,
the minimum support degree of FP-growth frequent pattern mining is set to be 0.002, and the minimum confidence coefficient is set to be 0.5.
8. A government affairs service system for implementing a government affairs service flow optimization method according to any one of claims 1 to 7, comprising:
the system comprises a data acquisition module, a data storage module and a data analysis module;
the data acquisition module is used for acquiring service data;
the data storage module is used for generating a big data storage structure and storing the data in the data acquisition module;
the data analysis module is used for analyzing the service data in the data storage module.
9. An electronic device, comprising:
a processor and a memory;
the memory has stored thereon computer readable instructions which, when executed by the processor, implement a government service process optimization method according to any one of claims 1-7 or a government service system according to claim 8.
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