CN112269816B - A Relevance Retrieval Method for Government Appointments - Google Patents

Abstract

The invention discloses a correlation retrieval method for government affairs reservation items, which belongs to the technical field of government affairs reservation. Based on user operation records, timing task induction is used to generate a correlation type index, and basic information maintenance generates a common type index; the correlation type index A statistical model in the form of scoring is generated, and a combined retrieval method is generated for keyword retrieval, related word retrieval, and ranking of relevance between keywords and reservation business for reservation business item retrieval. The present invention can accurately locate the service provider's needs and display relevant services to them when the public makes online reservations for business, and improves the efficiency of reservation service. At the same time, the statistical analysis method based on data can continuously optimize the query accuracy to improve performance and improve experience.

Description

A Relevance Retrieval Method for Government Appointment Items

technical field

The invention relates to the technical field of government affairs reservation, in particular to a correlation retrieval method for government affairs reservation items.

Background technique

With the continuous development and improvement of the field of government services and the development of the mobile Internet to a new stage, the online reservation process based on various channels such as web pages, apps and small programs provides the public with a simple, convenient and efficient experience in handling government affairs. The demand for handling affairs is becoming more and more urgent. More and more people need intelligent, personalized and accurate handling procedures. It is even more necessary to change the service mode, use the statistical analysis ability based on user data to use online services, improve the keyword hit rate, and improve the ability of government service governance.

Contents of the invention

The technical task of the present invention is to address the above deficiencies, and provide a method for retrieving the relevance of government affairs reservation items, which can accurately locate the needs of the service providers and display relevant services to them when the public makes online reservations for business, and improve the efficiency of appointment work. At the same time, data-based statistical analysis methods can continuously optimize query accuracy to improve performance and experience.

The technical solution adopted by the present invention to solve its technical problems is:

A method for retrieving the relevance of government affairs appointment items, based on user operation records, using timed task induction to generate a correlation type index, and basic information maintenance to generate a general type index;

The generation of the correlation type index uses a statistical model in the form of scoring, and generates a combined retrieval method of keyword retrieval, related word retrieval, and ranking of relevance between keywords and reservation business for the retrieval of reservation business items. In the multi-channel online reservation scenario, fast search for appointment business items and related business analysis and recommendation can be realized, and accurate prediction and intelligent analysis and recommendation of consumer demand for appointment items can be realized, so as to meet the requirements of personalization, intelligence and high accuracy , Reduce the pressure of database search and improve the efficiency of people's work.

This method can accurately locate the service provider's needs and display relevant services to them when the service people make online reservations for business, and improve the efficiency of reservation service. At the same time, the statistical analysis method based on data can continuously optimize the query accuracy to improve performance and improve experience.

Preferably, Elasticsearch search engine and Chinese IK word breaker are selected for retrieval. Elaticsearch is an open source and highly scalable distributed full-text search engine. It can store and retrieve data in near real-time, and its own scalability is very good. It occupies the first place in the open source search field. The Chinese IK tokenizer extracts keywords accurately. Therefore, a business analysis retrieval method can be proposed based on Elaticsearch.

Using a search engine instead of simple database retrieval, Elasticsearch is a good choice, it is an open source distributed, RESTful style search and data analysis engine, the bottom layer is the open source library Apache Lucene, as a distributed full-text search engine, It has good scalability, supports PB-level structured or unstructured data, and can fully adapt to the rapid positioning of appointment business with a large amount of data in the case of large-scale centralized deployment.

Elasticsearch has many high-quality tokenizers. The choice here is based on the Chinese IK tokenizer. She provides two word segmentation algorithms, ik_smart and ik_max_word. In order to locate the user's target data as much as possible, the finer-grained division method of ik_max_word can be used to divide a piece of text into semantics. With multi-level division, more indexes are created and positioning accuracy is higher.

Preferably, RocketMq is selected as the message queue to realize the decoupling of normal business and recorded results.

To achieve higher search accuracy, it is necessary to collect and summarize the user's target search terms and search results, wait for the subsequent scheduled tasks to pull data to the analysis model service, and choose to use the message queue for asynchronous solution without affecting the normal business process Coupled, Rokectmq is a good choice, with a transactional message solution to ensure the correct consumption and storage of each result set.

Preferably, the correlation type index includes three index types, that is, the retrieval result set is composed of three indexes and database SQL queries, and the three index types are respectively:

The index generated based on the keywords generated after the participatory processing of the name of the reservation business itself and the current reservation business is associated, which is denoted as N type (Normal),

The index of the correlation pattern based on user feedback and trigger behavior log timing inductive analysis, which is recorded as type C (Correlation),

and the business index of reservation business items carried by the related words based on keywords, which are recorded as R type (Related).

The importance of the three index types is different. The search results are sorted according to the degree of relevance of the C-type index results, the N-type index results and the R-type index results. The results are deduplicated, which can ensure that the appointment items are sorted and displayed according to the relevance. Increase retrieval reliability.

Further, the fields of each piece of information in the search result set include business item name, business item ID, business item department, keywords, index type and index ID, and the total result set also contains the UUID of this search, which is useful for subsequent user log collection and Recall records provide data.

Preferably, the basic information maintenance generates a common type index, and when the management service maintains the reserved business items, adding, modifying and deleting will have an impact on the basic index (that is, the N-type index),

After adding a reservation item, the business name is word-segmented, and each keyword and the current reservation business item ID form an index data and store it in the ES service;

After modifying the appointment item, delete the original basic type index and regenerate the new basic type index according to the item ID;

After deleting the appointment item, delete the original basic type index according to the item ID, and delete the other two types of indexes according to the item ID to ensure the accuracy of the data.

Preferably, log collection is performed on the user's operation records, including

After the client search request processing process is completed, each piece of data in the search result set is assembled and put into the message queue, and the log service records the log information as the consumer of the message, and puts it into the message queue; the assembled log information field includes the search term , keywords, Es index ID, item ID, index type and UUID of this search;

After the client user obtains the search results, he clicks to browse a piece of search information to form a click location recall log. The data is sent to the log service through the client for storage. Repeated clicks are only recorded once to prevent the analysis data from being distorted; the log information includes this Retrieve UUID, index ID, item ID, keyword, index type and search term;

After the client user obtains the search results, he clicks to browse a piece of search information and successfully handles the business, forming a successful recall log, and the data is sent to the log service through the client for storage; the log information includes this search UUID, index ID, Index type, item ID, keywords and search terms;

The above three types of log collections, which have the same retrieval UUID, are regarded as a set of retrieval process logs. After entering the log service respectively, wait for the scheduled task scan to put a set of retrieval process logs into the analysis model service for analysis and processing.

Preferably, a correlation index is generated:

The log service timing task scans the collected logs, packages and sends the three types of logs to the analysis model service according to the retrieval UUID, and generates a correlation index. The correlation calculation of the correlation index adopts numerical statistical rules, and according to the newly generated correlation The index itself contains a correlation field, which defaults to 100 and ranges from 0 to 1000.

A correlation index contains key information including keywords, corresponding base index IDs, and correlation values. The keywords and base indexes are mapped many-to-many and the correlation values are mounted. Retrieving this type of index will use the associated base index ID in the index. Finally point to the basic index; at the same time, set different step values according to different log types: browse recall (+1), process recall (+2) and miss recall (-1), the correlation value is set and changed in sequence, and the scan is scheduled Delete the index whose correlation value is 0, the purpose is to continuously correct the correlation value of each correlation index through the analysis service module, and improve the hit rate;

Although the type of index carried in the log is different, because the log will carry two information of keywords and basic index ID by default, the processing process is roughly the same; check whether there is a correlation index, if it exists, modify the value according to the above setp rule, if it does not exist Then generate a new index according to the above generation rules;

Through the continuous calibration of this model based on a large amount of data, the distribution of the correlation value of all the keywords of a reservation business item roughly conforms to the normal distribution, and the correlation value is used to determine the priority of display ranking.

Generate an index of associated words:

The fields contained in the associated vocabulary index are keywords and their associated word arrays. The data source is the reservation service search interface. After processing the search results, the IK word segmentation results are put into the message queue, and the analysis model service is processed as a consumer to generate an associated index. , the message queue implements asynchronous decoupling.

Only keywords and relevance are used to search, but the query results may still be inaccurate, so it is necessary to use the associated word index to achieve the function of inference and recommendation, and improve the accuracy of retrieval.

The retrieval method realizes the matching of keywords and related vocabulary results in fuzzy query of search vocabulary, and improves the hit rate of mass demand;

According to the log records of user browsing and successful processing after displaying the search results, the recall rate can be used to provide a correlation retrieval method, and the display will be displayed according to the correlation weight to improve the accuracy rate;

It can also improve the correlation accuracy of keywords and item information based on the correlation analysis model of recall rate;

Realize the correlation index of keywords and related words, and enhance the personalized recommendation function.

The present invention also claims to protect a device for retrieving the relevance of government appointment items, including: at least one memory and at least one processor;

said at least one memory for storing machine-readable programs;

The at least one processor is configured to call the machine-readable program to execute the above method.

The present invention also claims to protect a computer-readable medium, on which computer instructions are stored, and when the computer instructions are executed by a processor, the processor executes the above-mentioned method.

Compared with the prior art, a method for retrieving the relevance of government appointment items of the present invention has the following beneficial effects:

This method can reduce the pressure on the database in a concurrent environment and improve the concurrent ability when the system does not completely rely on database retrieval when performing business retrieval and keyword fuzzy search;

The data retrieval interface data comes from the Elasticsearch index and some database queries, which can provide millimeter-level response speed, reduce the waiting of the service people, and optimize the user experience.

Based on the statistical analysis of user operation log records and the induction and summary of timed tasks, the query results are more intelligent, accurate, and personalized, replacing the original fuzzy search based on the database, and optimizing the positioning accuracy of the service masses.

Description of drawings

Fig. 1 is a flow chart of a method for retrieving the relevance of government appointments provided by an embodiment of the present invention;

Fig. 2 is a schematic diagram of retrieval between the client and the reservation server provided by an embodiment of the present invention.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

In the current government service scenario, when the people who handle affairs accurately find the corresponding standard items and services according to their own needs, they often have low positioning efficiency and low accuracy when they are not familiar with them. This method based on database retrieval is becoming more and more It cannot meet the needs of the production environment. When the business query and retrieval process is based on database search, the required keywords can only be fuzzy searched using the database LIKE function. When the input keywords are slightly different, the query results will vary widely, and it is difficult to accurately locate the service business handling items corresponding to the needs. Therefore, to improve this situation, it is necessary for the business query and retrieval process to have word segmentation functions, fuzzy queries, and completely unambiguous Corresponding query and millisecond-level query features.

The embodiment of the present invention provides a correlation retrieval method for government appointment items, based on the basic function of Elasticsearch search engine and Chinese IK tokenizer index retrieval, based on user operation records, using timing task induction to generate correlation type index, and basic information maintenance to generate common type index;

The generation of the correlation type index uses a statistical model in the form of scoring, so as to generate a combined retrieval method of keyword retrieval, related word retrieval, and ranking of relevance between keywords and reservation business for the retrieval of reservation business items. In the multi-channel online reservation scenario, fast search for appointment business items and related business analysis and recommendation can be realized, and accurate prediction and intelligent analysis and recommendation of consumer demand for appointment items can be realized, so as to meet the requirements of personalization, intelligence and high accuracy , Reduce the pressure of database search and improve the efficiency of people's work.

This method can accurately locate the service provider's needs and display relevant services to them when the service people make online reservations for business, and improve the efficiency of reservation service. At the same time, the statistical analysis method based on data can continuously optimize the query accuracy to improve performance and improve experience.

Elaticsearch is an open source and highly scalable distributed full-text search engine. It can store and retrieve data in near real-time, and its own scalability is very good. It occupies the first place in the open source search field. The Chinese IK tokenizer extracts keywords accurately. Therefore, a business analysis retrieval method can be proposed based on Elaticsearch.

Using a search engine instead of simple database retrieval, Elasticsearch is a good choice, it is an open source distributed, RESTful style search and data analysis engine, the bottom layer is the open source library Apache Lucene, as a distributed full-text search engine, It has good scalability, supports PB-level structured or unstructured data, and can fully adapt to the rapid positioning of appointment business with a large amount of data in the case of large-scale centralized deployment.

Elasticsearch has many high-quality tokenizers. The choice here is based on the Chinese IK tokenizer. She provides two word segmentation algorithms, ik_smart and ik_max_word. In order to locate the user's target data as much as possible, the finer-grained division method of ik_max_word can be used to divide a piece of text into semantics. With multi-level division, more indexes are created and positioning accuracy is higher.

Choose RocketMq as the message queue to realize the decoupling of normal business and record results. To achieve higher search accuracy, it is necessary to collect and summarize the user's target search terms and search results, wait for the subsequent scheduled tasks to pull data to the analysis model service, and choose to use the message queue for asynchronous solution without affecting the normal business process Coupled, Rokectmq is a good choice, with a transactional message solution to ensure the correct consumption and storage of each result set.

Appeared in this article:

Search term: the search sentence or vocabulary entered by the user client;

Keywords: each word segmentation result processed by the IK tokenizer ik_max_word mode;

Associated Words: A keyword that has the same or similar search results as a keyword is called an associated word of the keyword.

The search result set consists of three indexes and database SQL queries. The three index types are:

Type N: "Normal" is an index generated based on the keywords generated after word segmentation of the appointment business name and the current appointment business;

Type C: "Correlation" is an index of the correlation pattern based on user feedback and trigger behavior log timing inductive analysis;

Type R: "Related" is based on the business index of reservation business items carried by the associated words of keywords.

The retrieval process between the client and the reservation server is briefly described as follows: the reservation server receives the search words entered by the user, processes them into multiple keywords using the Chinese IK tokenizer ik_max_word mode, and calls the ES container service for each keyword to obtain three types of keywords: Index results, obtain the primary key ID of the business item in the index, search the database to obtain the basic information of the business, form a result set and return it to the client. Refer to Figure 2.

The importance of the three index types is different. The search results are sorted according to the degree of relevance of the C-type index results, the N-type index results and the R-type index results. The results are deduplicated, which can ensure that the appointment items are sorted and displayed according to the relevance. Increase retrieval reliability.

The fields of each piece of information in the retrieval result set include business item name, business item ID, business item department, keywords, index type, and index ID. The total result set also contains the UUID of this search, which provides information for subsequent user log collection and recall records. data.

The analysis model service requires huge user behavior logs to support, so there are three parts of the log records about the search in the log link of the whole process of search analysis. The following are the generation process, information content and storage process:

After the client search request processing process is completed, each piece of data in the search result set is assembled and put into the message queue, and the log service records the log information as the consumer of the message. The assembled log information fields include search terms, keywords, Es index ID, item ID, index type and UUID of this search, and put them into the message queue.

After the client user obtains the retrieval results, he clicks to browse a certain piece of retrieval information to form a click-to-locate recall log, and the data is sent to the log service through the client for storage. The log information includes the retrieval UUID, index ID, item ID, keywords, index type, and search terms; repeated clicks are only recorded once to prevent the analysis data from being distorted.

After the client user obtains the search results, he clicks to browse a piece of search information and successfully handles the business, forming a successful recall log, and the data is sent to the log service through the client for storage; the log information includes this search UUID, index ID, Index Type, Item ID, Keyword and Search Term.

The above three types of log collections, which have the same retrieval UUID, are regarded as a set of retrieval process logs. After entering the log service respectively, wait for the scheduled task scan to put a set of retrieval process logs into the analysis model service for analysis and processing.

The management service maintains the base index:

When the management service maintains the reservation business items, adding, modifying and deleting will have an impact on the basic index (that is, the N-type index).

After adding a reservation item, the business name is word-segmented, and each keyword and the current reservation business item ID form an index data and store it in the ES service;

After modifying the appointment item, delete the original basic type index and regenerate the new basic type index according to the item ID;

After deleting the appointment item, delete the original basic type index according to the item ID, and delete the other two types of indexes according to the item ID to ensure the accuracy of the data.

Analytic Model Service generates a relevance index:

The log service timing task scans the collected logs, packages and sends the three types of logs to the analysis model service according to the retrieval UUID, and generates a correlation index. The correlation calculation of the correlation index adopts numerical statistical rules, and according to the newly generated correlation The index itself contains a correlation field, which defaults to 100 and ranges from 0 to 1000.

A correlation index contains key information including keywords, corresponding base index IDs, and correlation values. Its essence is to map keywords and base indexes with many-to-many associations and mount correlation values. Retrieving this type of index will use the associated The base index ID ultimately points to the base index. At the same time, set different step values according to different log types: browse recall (+1), process recall (+2) and miss recall (-1), the correlation value is set and changed in sequence, and the correlation value is deleted by regular scanning The index is 0, the purpose is to continuously correct the correlation value of each correlation index through the analysis service module, and improve the hit rate;

Although the types of indexes carried in the logs are different, the processing process is roughly the same because the logs carry keywords and basic index IDs by default. Check whether there is a correlation index, if it exists, modify the value according to the above setp rules, if not, generate a new index according to the above generation rules.

Through the continuous calibration of this model based on a large amount of data, the distribution of the correlation value of all the keywords of a reservation business item roughly conforms to the normal distribution, and the correlation value is used to determine the priority of display ranking.

Analyze the model to generate an index of associated words:

The fields contained in the associated vocabulary index are keywords and their associated word arrays. The data source is the reservation service search interface. After processing the search results, the IK word segmentation results are put into the message queue, and the analysis model service is processed as a consumer to generate an associated index. , the message queue implements asynchronous decoupling.

The IK word segmentation result includes a search word segmentation result. For example, when the search term is "vehicle annual inspection", the keyword segmentation is "vehicle", "annual inspection", "vehicle inspection", "vehicle annual inspection" and "vehicle". They are related words. Build the associated indexes for the above five keywords one by one. Each index contains its own keyword and its associated vocabulary array. When the subsequent search is triggered, its associated words will be found according to the keywords, and then the basic index will be searched based on the associated words. The associative vocabulary array of the special association type index will be continuously supplemented according to the IK word segmentation results, and the maximum number of words is 20 to prevent the efficiency of the basic index from being slowed down.

Only keywords and relevance are used to search, but the query results may still be inaccurate, so it is necessary to use the associated word index to achieve the function of inference and recommendation, and improve the accuracy of retrieval.

The retrieval method realizes the matching of keywords and related vocabulary results in fuzzy query of search vocabulary, and improves the hit rate of mass demand;

According to the log records of user browsing and successful processing after displaying the search results, the recall rate can be used to provide a correlation retrieval method, and the display will be displayed according to the correlation weight to improve the accuracy rate;

It can also improve the correlation accuracy of keywords and item information based on the correlation analysis model of recall rate;

Realize the correlation index of keywords and related words, and enhance the personalized recommendation function.

The present invention also claims to protect a device for retrieving the relevance of government appointment items, including: at least one memory and at least one processor;

said at least one memory for storing machine-readable programs;

The at least one processor is configured to call the machine-readable program to execute the above-mentioned method for retrieving the relevance of government appointment items.

An embodiment of the present invention also provides a computer-readable medium, where computer instructions are stored on the computer-readable medium, and when the computer instructions are executed by a processor, the processor executes the steps described in the above-mentioned embodiments of the present invention. The correlation retrieval method of the government affairs appointment items described above. Specifically, a system or device equipped with a storage medium may be provided, on which a software program code for realizing the functions of any of the above embodiments is stored, and the computer (or CPU or MPU of the system or device) ) to read and execute the program code stored in the storage medium.

In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, so the program code and the storage medium storing the program code constitute a part of the present invention.

Examples of storage media for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), Tape, non-volatile memory card, and ROM. Alternatively, the program code can be downloaded from a server computer via a communication network.

In addition, it should be clear that not only by executing the program code read by the computer, but also by making the operating system on the computer complete part or all of the actual operations through instructions based on the program code, so as to realize the function of any one of the embodiments.

In addition, it can be understood that the program code read from the storage medium is written into the memory provided in the expansion board inserted into the computer or written into the memory provided in the expansion unit connected to the computer, and then based on the program code The instruction causes the CPU installed on the expansion board or the expansion unit to perform some or all of the actual operations, so as to realize the functions of any one of the above-mentioned embodiments.

The present invention has been shown and described in detail through the accompanying drawings and preferred embodiments above, but the present invention is not limited to these disclosed embodiments, and those skilled in the art based on the above-mentioned multiple embodiments can know that the above-mentioned different embodiments can be combined More embodiments of the present invention can be obtained by means of code review in the present invention, and these embodiments are also within the protection scope of the present invention.

Claims (9)

1. A government affair appointment relativity retrieval method is characterized in that a relativity type index is generated by using timed task induction based on user operation records, and a common type index is generated by basic information maintenance;

generating a correlation type index by using a statistical model in a scoring form, and generating a combined retrieval mode of keyword retrieval, associated word retrieval and correlation ranking of keywords and reservation service for reservation service item retrieval;

collecting logs of operation records of users, and generating a relevance index:

the log service timing task scans the collected log, packages the log according to the search UUID, and sends the log to the analysis model service for processing, and generates a relevance index:

the relevance index comprises a keyword, a corresponding basic index ID and a relevance value, the keyword and the basic index are mapped in a many-to-many associated mode, the relevance value is mounted, and the basic index ID associated in the index is used for searching the type index to finally point to the basic index; meanwhile, different step values are set according to different log types: browse recall +1, transact recall +2 and miss recall-1, the correlation value presumes the change sequentially, scan and delete the index with 0 of correlation value regularly;

checking whether a correlation index exists, if so, modifying a numerical value according to the step rule, and if not, generating a new index according to the generation rule;

generating an associated word index:

the relevant vocabulary index contains fields which are keywords and relevant word arrays thereof, the reservation service search interface puts the IK word segmentation result into a message queue after processing the search result, and the analysis model service is used as a consumer to process and generate the relevant index, so that the message queue realizes asynchronous decoupling.

2. The method for searching relevance of government affairs appointment according to claim 1 wherein the search is performed by using an elastomer search engine and a chinese IK word segmentation device.

3. The method for searching relevance of government affair appointment according to claim 1 or 2, wherein the method is characterized in that RocketMq is selected as a message queue to realize decoupling of normal business and recording result.

4. The method for searching relevance of government affairs appointment according to claim 1, wherein the relevance type index comprises three index types, namely:

the index generated based on the association of the keyword generated after the name word segmentation processing of the appointment business and the current appointment business is marked as N type,

based on the user feedback and the trigger behavior log, the index of the analyzed relevance mode is summarized regularly and marked as C type,

and a reservation service item service index carried by the related words based on the keywords, recorded as R type,

and sequencing the search results sequentially according to the relevance of the C-type index results, the N-type index results and the R-type index results, and de-duplicating the results.

5. The method for searching relevance of government affair appointment according to claim 4 wherein the fields of each piece of information in the search result set contain business item names, business item IDs, business item departments, keywords, index types and index IDs, and the total result set also contains UUIDs of the search, so as to provide data for subsequent user log collection and recall records.

6. The method for searching relevance of government affairs appointment as set forth in claim 2 wherein the basic information maintenance generates a general type index,

after the reservation items are added, the business names are processed in word segmentation, and each keyword and the current reservation business item ID form index data to be stored in the ES service;

after the appointment is modified, deleting the original basic type index according to the appointment ID, and regenerating a new basic type index;

after the appointment is deleted, the original basic type index is deleted according to the appointment ID, and the other two types of indexes are deleted according to the appointment ID, so that the accuracy of data is ensured.

7. The method for searching for relevance of government affairs appointment as claimed in claim 1, 2, 4, 5 or 6, wherein the step of collecting the log of the operation record of the user comprises

After the client-side search request processing process is finished, each piece of data of the search result set is assembled and put into a message queue, and the log service is used as a consumption side of the message to record log information and put into the message queue; the assembled log information field comprises a search word, a keyword, an Es index ID, a matter ID, an index type and a current search UUID;

after the client user obtains the search result, clicking and browsing a certain search information to form a click positioning recall log, and sending the data to a log service for storage through the client, wherein repeated clicking is only recorded once; the log information comprises a current search UUID, an index ID, a item ID, a keyword, an index type and a search word;

after the client user obtains the search result, clicking and browsing a certain search information and successfully transacting business to form a successful transacting recall log, and sending the data to log service for storage through the client; the log information comprises a current search UUID, an index ID, an index type, a item ID, keywords and search words;

and collecting the three logs, namely regarding a group of search flow logs if the three logs have the same search UUID, waiting for the timing task scanning to put the group of search flow logs into the analysis model service for analysis processing after the search flow logs respectively enter the log service.

8. A government affair appointment correlation retrieval device, comprising: at least one memory and at least one processor;

the at least one memory for storing a machine readable program;

said at least one processor for invoking said machine readable program to perform the method of any of claims 1 to 7.

9. A computer readable medium having stored thereon computer instructions which, when executed by a processor, cause the processor to perform the method of any of claims 1 to 7.

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