WO2020248378A1

WO2020248378A1 - Service query method and apparatus, and storage medium and computer device

Info

Publication number: WO2020248378A1
Application number: PCT/CN2019/103032
Authority: WO
Inventors: 石明川; 姚飞
Original assignee: 平安科技（深圳）有限公司
Priority date: 2019-06-12
Filing date: 2019-08-28
Publication date: 2020-12-17
Also published as: CN110347788A

Abstract

Provided in the present application are a service query method and apparatus, and a storage medium and a computer device. The method comprises: reading a search text input by a user and used for a service query; extracting participles in the search text, and obtaining, according to the extracted participles, a participle group; performing semantic analysis on each participle in the participle group by using a semantic analysis method, and determining a search condition of the search text; and extracting target service data from a data cube according to the search condition, and feeding the target service data back to the user. The method can simplify the logic of a service query, and improve the efficiency of a service query.

Description

Business inquiry method, device, storage medium and computer equipment

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on June 12, 2019, the application number is 201910506767.4, and the invention title is "business query methods, devices and storage media, computer equipment", the entire content of which is incorporated by reference In this application.

Technical field

This application relates to the technical field of business query. Specifically, the application relates to a business query method, device, storage medium, and computer equipment.

Background technique

The development of natural language processing systems occupies an important position in the current field of database intelligence. The traditional query system is based on a specific business, and the corresponding data logic query is realized by professional analysis and coding. Specifically, R&D personnel need to create corresponding tables and define business logic queries. The inventor realizes that for different businesses, R&D personnel need to repeatedly build tables and define business logic queries, and perform corresponding coding, resulting in low development efficiency and low front-end query efficiency.

For example, when the front-end query "the institution with the highest life insurance net profit in 2017", the traditional query system first predefines a function that supports similar query queries. Developers first create a table containing business indicators "net profit" and corresponding institutions classified by time, and then write SQL query statements, passing in "life insurance" and "net profit" as keywords. Finally, the front end selects the label that the user most wants to know through the linkage drop-down menu, and queries the displayed data. For a different business index, the front-end and back-end meter construction needs to be considered. When there are many business indicators, users can only select the query results they want through a long drop-down menu or multi-level menu. The query operation is very troublesome and the query efficiency is very low.

Summary of the invention

This application proposes a business query method, device, storage medium, and computer equipment, which can simplify the logic of business query and improve the efficiency of business query.

This application provides the following solutions:

A business query method includes: reading a search text input by a user for business query; extracting word segmentation in the search text, and obtaining a phrase group based on the extracted word segmentation; using a semantic analysis method to analyze the word segmentation Semantic analysis is performed on each word segmentation to determine the retrieval condition of the search text; the target business data is extracted from the data cube according to the retrieval condition, and the target business data is fed back to the user.

A business query device includes: a reading module for reading search text input by a user for business query; an extraction module for extracting word segmentation in the search text, and obtaining a word segmentation group based on the extracted word segmentation Obtaining module, used for semantic analysis of each word segmentation of the phrase group using semantic analysis, to determine the retrieval conditions of the search text; feedback module, used for extracting target business data from the data cube according to the retrieval conditions To feed back the target service data to the user.

A non-volatile computer-readable storage medium having a computer program stored thereon; the computer program is suitable for being loaded by a processor and executing the business query method described in any of the above embodiments.

A computer device includes: one or more processors; a memory; one or more application programs, wherein the one or more application programs are stored in the memory and configured to be used by the one or more The processor executes, and the one or more application programs are configured to execute the service query method according to any one of the foregoing embodiments.

The business query method provided by the foregoing embodiment does not require a separate table creation and business query logic definition for the target business retrieved by the user. This method can simplify the business query logic and improve the efficiency of the business query.

Description of the drawings

The above and/or additional aspects and advantages of the present application will become obvious and easy to understand from the following description of the embodiments in conjunction with the accompanying drawings, in which:

FIG. 1 is a method flowchart in an embodiment of a service query method provided by this application;

FIG. 2 is a flowchart of a part of the method in another embodiment of a service query method provided by this application;

Fig. 3 is a flowchart of some methods in another embodiment of a service query method provided by this application;

FIG. 4 is a structural block diagram in an embodiment of a service query device provided by this application;

FIG. 5 is a schematic structural diagram in an embodiment of a computer device provided by this application.

Detailed ways

Those skilled in the art can understand that the term "terminal" and "terminal equipment" used herein include both wireless signal receiver equipment, equipment that only has wireless signal receivers without transmitting capability, and equipment receiving and transmitting hardware. A device, which has a device capable of performing two-way communication receiving and transmitting hardware on a two-way communication link. Such equipment may include: cellular or other communication equipment, which has a single-line display or multi-line display or cellular or other communication equipment without a multi-line display; PCS (Personal Communications Service, personal communication system), which can combine voice and data Processing, fax and/or data communication capabilities; PDA (Personal Digital Assistant), which can include radio frequency receivers, pagers, Internet/Intranet access, web browsers, notebooks, calendars and/or GPS (Global Positioning System (Global Positioning System) receiver; a conventional laptop and/or palmtop computer or other device, which has and/or includes a radio frequency receiver, a conventional laptop and/or palmtop computer or other device. The "terminal" and "terminal equipment" used here may be portable, transportable, installed in vehicles (aviation, sea and/or land), or suitable and/or configured to operate locally, and/or In a distributed form, it runs on the earth and/or any other location in space. The "terminal" and "terminal device" used here can also be communication terminals, Internet terminals, music/video playback terminals, such as PDA, MID (Mobile Internet Device, mobile Internet device) and/or music/video playback Functional mobile phones can also be devices such as smart TVs and set-top boxes.

This application provides a service query method, which is used to perform a service query based on the search text after receiving a search text of a service query input by a front-end user, and feed back relevant data of the queried service to the front end. In an embodiment, as shown in FIG. 1, the service query method includes the following steps:

S100: Read the search text input by the user for the business query.

In this implementation, the business system provides a search box to the user's front end to receive the search text entered during the user's business search. Here, the search text is the text of natural language, and the business system can parse natural language (including business professional related words) into report data display that the user wants to obtain.

S200: Extract word segmentation in the search text, and obtain a word segmentation group according to the extracted word segmentation.

In this embodiment, after the business system obtains the search text input by the user during search from the front end, it performs word segmentation processing on the search text, extracts each word segmentation of the search text, and combines each word segmentation to obtain a word segmentation group. For example, when a user enters "the institution with the highest net profit of life insurance in 2017" on the front end, the search text is cut into words and the result is "2017", "life insurance", "net profit", "highest", "of", " mechanism". All the word segmentation constitute the word segmentation group of the search text.

In one embodiment, step S200 includes: segmenting the search text, filtering out the target word segmentation according to the part of speech of each word segmentation after word segmentation, and obtaining the word segmentation group according to the filtered target word segmentation.

In this embodiment, after the business system performs word segmentation on the search text, some word segmentation obtained does not have substantial search meaning. Such as the auxiliary word "的", the conjunction "和" and so on. Here, the word segmentation that has no substantive meaning for the search can be filtered out to obtain multiple target word segments, and the word segmentation group can be formed according to the multiple target words. Therefore, subsequent semantic analysis and search matching based on the word segmentation in the phrase group can reduce the analysis and search pressure of the system server.

In one embodiment, the screening of the target participles according to the part of speech of each participle after the word segmentation includes: obtaining the part of speech of each participle after the word segmentation, removing the part of speech as conjunctions and auxiliary word participles, and the removed participles As the target participle.

Specifically, the unrepresentative content such as conjunctions and auxiliary words in the word segmentation can be eliminated, and the subsequent system server does not need to perform search operations based on the conjunctions and auxiliary words in the search text, which can reduce the pressure on the server and improve the search efficiency.

S300: Use a semantic analysis method to perform semantic analysis on each word segmentation of the word segmentation group, and determine the retrieval condition of the search text.

In this embodiment, after obtaining the word segmentation of the search text, the system performs semantic analysis on each word segmentation in the word segmentation group. The semantic analysis method may be to identify the word segmentation text, match the word segmentation with the words in the system corpus, and determine the semantics of the word segmentation. Furthermore, according to the semantics of each word segmentation, the retrieval conditions of the target business searched by the user are determined. Among them, the business system can read the target business searched by the user from the database according to the search condition. For example, the sub-phrases include "2017", "life insurance", "net profit", "highest", "of", and "institution". Through semantic analysis, it is understood that the purpose of user search is to query organizations, and the others are all limited conditions of query organizations. Here, the query organization and the limited conditions of the query organization are the retrieval conditions.

In one embodiment, step S300 includes: performing semantic analysis on each of the word segmentation, filtering out the keywords of the search text according to the semantics of each word segmentation, and determining the retrieval conditions of the search text according to the keywords .

If the number of characters in the search text entered by the user is too large, or the language logic and words used in the entered search text are not standardized, it is more difficult to calculate the target business based on the semantics of each word segmentation, which adds too much pressure to the server. In this embodiment, keyword filtering is performed according to the meaning of each segmented word, the keywords are filtered out, and then the retrieval conditions of the search text are determined according to the keywords, which can reduce the computing pressure of the server.

S400: Extract target service data from the data cube according to the search condition, and feed back the target service data to the user.

In this embodiment, after the business system determines the search condition, it searches for the corresponding target business data in the data cube of the database according to the search condition. Among them, the data cube is a multidimensional data model that is determined according to multiple query conditions and stores multiple business data. The multiple query conditions include the retrieval condition, and the multiple business data includes the target business data. In one embodiment, the query condition includes N (N is an integer greater than or equal to 1) limiting conditions, each limiting condition determines a piece of dimensional information of the data cube, and the N limiting conditions determine N in the data cube. Dimensions of information. One or more business data can be determined through a query condition. Specifically, a query condition determines a piece of business data stored in the data cube. The search condition belongs to one of the search conditions, and certain business data in the data cube can be determined according to the search condition, that is, the target business data.

After the business system obtains the target business data, the target business data is fed back to the user front end. Target business data is related data information associated with the target business in the business system. For example, according to the search text "Institution with the highest net profit of life insurance in 2017", the target business of the user's query is determined as "Institution", and the condition is limited to "The highest net profit of life insurance in 2017". The system stores various life insurance in 2017 Get the net profit of the life insurance institution with the highest net profit, and use the institution’s data information as the search data for users.

The service query method provided by the foregoing embodiment extracts the word segmentation of the search text input by the front-end user, performs semantic analysis on each word segmentation, and determines the retrieval condition of the search text. Further, directly according to the retrieval conditions, extract the target business data searched by the user from the data cube, and feed back the target business data to the front-end user. Therefore, there is no need to create a separate table and define business query logic for the target business retrieved by the user, and the corresponding target business data can be read from the data cube after directly analyzing the search text entered during the user retrieval. This method can simplify the logic of business query and improve the efficiency of business query.

In an embodiment, the retrieval condition in step S300 includes the retrieved target business object and the restriction condition of the target business object. At this time, as shown in FIG. 2, steps S300 and S400 respectively include:

S310: Perform semantic analysis on each of the word segmentation to obtain the semantics corresponding to each word segmentation.

S320: Determine the target business object and the limiting conditions of the target business object by analyzing the semantics corresponding to all the word segmentation and the sentence structure of the search text.

S410: Extract target business data from the data cube according to the target business object and the defined conditions of the target business object.

In this embodiment, when the business system reads the search text input by the front-end user, it analyzes the sentence structure of the search text. After semantic analysis is performed on each word segmentation and the corresponding semantics of each word segmentation is obtained, the semantics corresponding to all the word segments and the sentence structure of the search text are combined to determine the target business object and the target business object's limiting conditions in the retrieval conditions.

In one embodiment, step S320 includes: determining the position of each word segmentation in the search text according to the sentence structure of the search text, and determining the position of each word segmentation in the search text and the semantics of each word segmentation. The target business object and the limiting conditions of the target business object. Specifically, the sentence structure of the search text can be determined by subject, predicate, and object.

In an embodiment, the business system may create a data cube in advance based on the business data of the system. As shown in Figure 3, the data cube is determined in the following manner:

S401: Acquire business objects of multiple businesses and the limiting conditions of each business object.

S403. Use the business objects of the multiple services as a first index condition, and the limited conditions of the business objects as a second index condition, and determine the size of the data cube according to the first index condition and the second index condition. The number of dimensions and the retrieval information of each dimension; wherein the second index condition determines one or more dimensions, and the first index condition determines one dimension.

S405: Determine the data cube according to the number of dimensions and the retrieval information of each dimension.

In this embodiment, multiple business data are stored in the business system. The target business data is contained in the multiple business data. The business system obtains the business object of each business and the restriction conditions of each business object, uses the business object as the first index condition, and the restriction condition of the business object as the second index condition, and builds a data cube. Wherein, the first index condition determines one dimension information of the data cube, and the second index condition determines one or more dimension information of the data cube. Each dimension information corresponds to the retrieval information during retrieval by the system. The data cube is established according to the number of dimensions of the data cube and the retrieval information of each dimension.

At this time, step S400 includes: filtering first dimension information from the first index condition of the data cube according to the target business object; in the data cube, the first dimension information corresponds to multiple Business data, multiple limited conditions corresponding to each business data; according to the limited conditions of the target business object, the target business data is filtered out from the multiple business data corresponding to the first dimensional information.

Specifically, after the business system establishes the data cube based on the business data, the target business object is used as the first index condition to filter out multiple business data corresponding to the first dimensional information. Furthermore, the limited condition of the target object is used as the second index condition to filter out the target business data from multiple business data.

Further, there are multiple limiting conditions for the target business object. The filtering out the target business data from the plurality of business data corresponding to the first dimensional information according to the limited conditions of the target business object includes: performing n times to select one from the plurality of limited conditions Or multiple restricted conditions are combined, and a condition set is obtained after each combination; where n is an integer greater than 1; each condition set is selected from the multiple business data corresponding to the first dimension information For one target business data, multiple target business data are obtained. At this time, the feeding back the target service data to the user includes: prioritizing the multiple target service data according to the limited conditions in the condition set; and feeding back the sorted multiple service data to the user. State the target business data.

Specifically, there are multiple limiting conditions for the target object, which is assumed to be M here. In the data cube, there are S pieces of dimensional information determined by the limited condition of the business object as the second index condition. Among them, M>S. At this time, the business system obtains one or more (less than or equal to S) of the M restricted conditions and combines them. According to the combined restrictive conditions, all the information corresponding to the first dimension in the data cube is obtained. One target business data is filtered out of the multiple business data. In this embodiment, the business system obtains one or more restriction conditions from M restriction conditions and performs a combined operation, executes it n times, and filters out one target business data each time, so that n target business data can be obtained. Finally, when feeding back the target business data to the user, prioritize the n target business data according to the priority of the restricted conditions after each combination, and feed back the sorted multiple target business data to the user .

In an embodiment, the prioritizing the multiple target business data according to the restriction conditions in the condition set includes: obtaining the priority of each restriction condition in each condition set; each restriction The priority of the conditions is determined by the system according to preset rules; the priorities of the restricted conditions in each of the condition sets are compared respectively, and the target service data is prioritized according to the priority ranking of the restricted conditions.

Specifically, the system limits the priority of each restricted condition in each condition set according to preset rules, and determines the priority of each restricted condition. Among them, the preset rule may be prioritized according to the matching degree between the limited conditions and the target business data from high to low. Therefore, after the system determines the priority of the restricted conditions of each condition set, the priority of each restricted condition in the condition set is compared, and the final ranking is the priority ranking of multiple target business data. Therefore, multiple target business data can be provided to the user, so that the feedback target business data is more oriented toward the user's needs.

This application also provides a service query device. In an embodiment, as shown in FIG. 4, the service query device includes a reading module 10, an extraction module 20, an acquisition module 30, and a feedback module 40.

The reading module 10 is used for reading the search text input by the user for business query. In this implementation, the business system provides a search box to the user's front end for receiving the search text input by the user during business search. Here, the search text is the text of natural language, and the business system can parse natural language (including business professional related words) into report data display that the user wants to obtain.

The extraction module 20 is used to extract word segmentation in the search text, and obtain a word segmentation group according to the extracted word segmentation. In this embodiment, after the business system obtains the search text input by the user during search from the front end, it performs word segmentation processing on the search text, extracts each word segmentation of the search text, and combines each word segmentation to obtain a word segmentation group. For example, when a user enters "the institution with the highest net profit of life insurance in 2017" on the front end, the search text is cut into words and the result is "2017", "life insurance", "net profit", "highest", "of", " mechanism". All the word segmentation constitute the word segmentation group of the search text.

The acquiring module 30 is configured to use a semantic analysis method to perform semantic analysis on each word segment of the word segmentation group, and determine the retrieval condition of the search text. In this embodiment, after obtaining the word segmentation of the search text, the system performs semantic analysis on each word segmentation in the word segmentation group. The semantic analysis method may be to identify the word segmentation text, match the word segmentation with the words in the system corpus, and determine the semantics of the word segmentation. Furthermore, according to the semantics of each word segmentation, the retrieval conditions of the target business searched by the user are determined. Among them, the business system can read the target business searched by the user from the database according to the search condition. For example, the sub-phrases include "2017", "life insurance", "net profit", "highest", "of", "institution". Through semantic analysis, it is understood that the purpose of user search is to query organizations, and the others are all limited conditions of query organizations. Here, the query organization and the limited conditions of the query organization are the retrieval conditions.

The feedback module 40 is configured to extract target service data from the data cube according to the search condition, and feed back the target service data to the user. In this embodiment, after the business system determines the search condition, it searches for the corresponding target business data in the data cube of the database according to the search condition. Among them, the data cube is a multidimensional data model that is determined according to multiple query conditions and stores multiple business data. The multiple query conditions include the retrieval condition, and the multiple business data includes the target business data. In one embodiment, the query condition includes N (N is an integer greater than or equal to 1) limiting conditions, each limiting condition determines a piece of dimensional information of the data cube, and the N limiting conditions determine N in the data cube. Dimensions of information. One or more business data can be determined through a query condition. Specifically, a query condition determines a piece of business data stored in the data cube. The search condition belongs to one of the search conditions, and certain business data in the data cube can be determined according to the search condition, that is, the target business data.

In other embodiments, each module in the service query device provided in this application is also used to perform operations corresponding to each step in the service query method described in this application, and detailed descriptions are omitted here.

The application also provides a non-volatile computer-readable storage medium. A computer program is stored on the non-volatile computer readable storage medium; when the computer program is executed by a processor, the service query method described in any of the above embodiments is implemented. The non-volatile computer-readable storage medium may be a memory. For example, internal memory or external memory, or both internal memory and external memory. The internal memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or random access memory. External storage can include hard disks, floppy disks, ZIP disks, U disks, tapes, etc. The non-volatile computer-readable storage medium disclosed in this application includes but is not limited to these types of memories. The memory disclosed in this application is only an example and not a limitation.

This application also provides a computer device. A computer device includes: one or more processors; memory; and one or more application programs. Wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, and the one or more application programs are configured to execute the one described in any of the above embodiments Business query method.

FIG. 5 is a schematic structural diagram of a computer device in an embodiment of the application. The computer device described in this embodiment may be a server, a personal computer, and a network device. As shown in FIG. 5, the device includes a processor 503, a memory 505, an input unit 507, a display unit 509 and other devices. Those skilled in the art can understand that the device structure shown in FIG. 5 does not constitute a limitation on all devices, and may include more or less components than those shown in the figure, or combine certain components. The memory 505 may be used to store the application program 501 and various functional modules, and the processor 503 runs the application program 501 stored in the memory 505 to execute various functional applications and data processing of the device. The memory may be internal memory or external memory, or include both internal memory and external memory. The internal memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or random access memory. External storage can include hard disks, floppy disks, ZIP disks, U disks, tapes, etc. The memory disclosed in this application includes but is not limited to these types of memory. The memory disclosed in this application is only an example and not a limitation.

The input unit 507 is used for receiving signal input and receiving keywords input by the user. The input unit 507 may include a touch panel and other input devices. The touch panel can collect the user's touch operations on or near it (for example, the user uses any suitable objects or accessories such as fingers, stylus, etc., to operate on the touch panel or near the touch panel), and according to preset The program drives the corresponding connection device; other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (such as playback control buttons, switch buttons, etc.), trackball, mouse, and joystick. The display unit 509 can be used to display information input by the user or information provided to the user and various menus of the computer device. The display unit 509 can take the form of a liquid crystal display, an organic light emitting diode, or the like. The processor 503 is the control center of the computer equipment. It uses various interfaces and lines to connect the various parts of the entire computer. By running or executing the software programs and/or modules stored in the memory 505, and calling the data stored in the memory, execute Various functions and processing data.

In an embodiment, the device includes one or more processors 503, one or more memories 505, and one or more application programs 501. The one or more application programs 501 are stored in the memory 505 and configured to be executed by the one or more processors 503, and the one or more application programs 501 are configured to execute the above-mentioned embodiments. Business query method.

In addition, the functional units in the various embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units may be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or software functional modules. If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it may also be stored in a computer-readable non-volatile computer-readable storage medium.

A person of ordinary skill in the art can understand that all or part of the steps in the above embodiments can be implemented by hardware, or by a program to instruct relevant hardware, and the program can be stored in a computer-readable non-volatile computer-readable storage. Among the media, non-volatile computer-readable storage media may include memory, magnetic disks, or optical disks.

The above are only part of the implementation of this application. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of this application, several improvements and modifications can be made, and these improvements and modifications are also Should be regarded as the scope of protection of this application.

It should be understood that the functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units may be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or software functional modules.

Claims

A business query method, including:

Read the search text entered by the user for business query;

Extracting word segmentation in the search text, and obtaining word segmentation groups according to the extracted word segmentation;

Use a semantic analysis method to perform semantic analysis on each word segmentation of the word segmentation group to determine the retrieval conditions of the search text;

Extracting target business data from the data cube according to the retrieval conditions, and feeding back the target business data to the user.
The method according to claim 1, wherein the retrieval conditions include the retrieved target business object and the limited conditions of the target business object; the semantic analysis method is used to perform semantic analysis on each segment of the phrase group to determine the location The retrieval conditions of the search text and the extraction of target business data from the data cube according to the retrieval conditions include:

Perform semantic analysis on each of the word segmentation to obtain the semantics corresponding to each word segmentation;

Determine the target business object and the limited conditions of the target business object by analyzing the semantics corresponding to all the word segmentation and the sentence structure of the search text;

Extract target business data from the data cube according to the target business object and the defined conditions of the target business object.
According to the method of claim 2, the data cube is determined in the following manner:

Obtain business objects of multiple businesses and the qualifications of each business object;

The business objects of the multiple services are used as a first index condition, and the limited conditions of the business objects are used as a second index condition, and the number of dimensions of the data cube is determined according to the first index condition and the second index condition And retrieval information of each dimension; wherein the second index condition determines one or more dimensions, and the first index condition determines one dimension;

The data cube is determined according to the number of dimensions and the retrieval information of each dimension.
The method according to claim 3, said extracting target business data from a data cube according to the target business object and the defined conditions of the target business object, comprising:

The first dimensional information is filtered from the first index condition of the data cube according to the target business object; in the data cube, the first dimensional information corresponds to multiple business data, and each business data corresponds to Multiple qualifications;

The target business data is filtered out from the plurality of business data corresponding to the first dimensional information according to the limiting condition of the target business object.
The method according to claim 4, wherein the target business object has a plurality of limited conditions; and the plurality of business data corresponding to the first dimensional information is filtered out according to the limited conditions of the target business object The target business data includes:

Perform n times to select one or more restricted conditions from the plurality of restricted conditions to combine, and obtain a condition set after each combination; where n is an integer greater than 1;

Filtering out one target business data from the multiple business data corresponding to the first dimensional information according to each condition set to obtain multiple target business data;

The feeding back the target service data to the user includes:

Prioritize the multiple target business data according to the restricted conditions in the condition set;

Feeding back to the user the sorted plurality of target service data.
The method according to claim 5, wherein the prioritizing the multiple target service data according to the restriction conditions in the condition set comprises:

Acquire the priority of each restricted condition in each of the condition sets; the priority of each restricted condition is determined by the system according to a preset rule;

The priorities of the restricted conditions in each of the condition sets are respectively compared, and the target service data is prioritized according to the priority ranking of the restricted conditions.
The method according to claim 1, said adopting a semantic analysis method to perform semantic analysis on each word segmentation of the word segmentation group to determine the retrieval conditions of the search text, comprising:

Semantic analysis is performed on the respective word segmentation, the keywords of the search text are filtered according to the semantics of each word segmentation, and the retrieval conditions of the search text are determined according to the keywords.
A business query device, including:

The reading module is used to read the search text input by the user for business query;

An extraction module for extracting word segmentation in the search text, and obtaining word segmentation groups according to the extracted word segmentation;

The acquisition module is used to perform semantic analysis on each word segmentation of the word segmentation group by using a semantic analysis method to determine the retrieval conditions of the search text;

The feedback module is used to extract target business data from the data cube according to the retrieval conditions, and feed back the target business data to the user.
A non-volatile computer-readable storage medium having a computer program stored thereon; the computer program is adapted to be loaded by a processor and execute a business query method, the business query method comprising:

Read the search text entered by the user for business query;

Extracting word segmentation in the search text, and obtaining word segmentation groups according to the extracted word segmentation;

Use a semantic analysis method to perform semantic analysis on each word segmentation of the word segmentation group to determine the retrieval conditions of the search text;

Extracting target business data from the data cube according to the retrieval conditions, and feeding back the target business data to the user.
The non-volatile computer-readable storage medium according to claim 9, wherein the retrieval conditions include the retrieved target business object and the limited conditions of the target business object; and the semantic analysis method is used to perform segmentation on each segment of the phrase group Semantic analysis determines the retrieval conditions of the search text, and extracts target business data from the data cube according to the retrieval conditions, including:

Perform semantic analysis on each of the word segmentation to obtain the semantics corresponding to each word segmentation;

Determine the target business object and the limited conditions of the target business object by analyzing the semantics corresponding to all the word segmentation and the sentence structure of the search text;

Extract target business data from the data cube according to the target business object and the defined conditions of the target business object.
According to the non-volatile computer-readable storage medium of claim 10, the data cube is determined in the following manner:

Obtain business objects of multiple businesses and the qualifications of each business object;

The business objects of the multiple services are used as a first index condition, and the limited conditions of the business objects are used as a second index condition, and the number of dimensions of the data cube is determined according to the first index condition and the second index condition And retrieval information of each dimension; wherein the second index condition determines one or more dimensions, and the first index condition determines one dimension;

The data cube is determined according to the number of dimensions and the retrieval information of each dimension.
According to the non-volatile computer-readable storage medium of claim 11, the data cube is determined in the following manner:

The extracting target business data from the data cube according to the target business object and the defined conditions of the target business object includes:

The first dimensional information is filtered out from the first index condition of the data cube according to the target business object; in the data cube, the first dimensional information corresponds to multiple business data, and each business data corresponds to Multiple qualifications;

The target business data is filtered out from the plurality of business data corresponding to the first dimensional information according to the limiting condition of the target business object.
The non-volatile computer-readable storage medium according to claim 12, wherein the limitation conditions of the target business object are multiple; and the limitation conditions of the target business object are obtained from the multiple information corresponding to the first dimension information. The target business data is filtered out of three business data, including:

Perform n times to select one or more restricted conditions from the plurality of restricted conditions to combine, and obtain a condition set after each combination; where n is an integer greater than 1;

Filtering out one target business data from the multiple business data corresponding to the first dimensional information according to each condition set to obtain multiple target business data;

The feeding back the target service data to the user includes:

Prioritize the multiple target business data according to the restricted conditions in the condition set;

Feeding back to the user the sorted plurality of target service data.
The non-volatile computer-readable storage medium according to claim 13, wherein the prioritizing the plurality of target service data according to the limited conditions in the condition set comprises:

Acquire the priority of each restricted condition in each of the condition sets; the priority of each restricted condition is determined by the system according to a preset rule;

The priorities of the restricted conditions in each of the condition sets are respectively compared, and the target service data is prioritized according to the priority ranking of the restricted conditions.
According to the non-volatile computer-readable storage medium of claim 9, said adopting a semantic analysis method to perform semantic analysis on each word segmentation of said word segmentation group to determine the retrieval condition of said search text includes:

Semantic analysis is performed on the respective word segmentation, the keywords of the search text are filtered according to the semantics of each word segmentation, and the retrieval conditions of the search text are determined according to the keywords.
A computer device including:

One or more processors;

Memory

One or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, and the one or more application programs are configured to execute A business query method, the business query method includes:

Read the search text entered by the user for business query;

Extracting word segmentation in the search text, and obtaining word segmentation groups according to the extracted word segmentation;

Use a semantic analysis method to perform semantic analysis on each word segmentation of the word segmentation group to determine the retrieval conditions of the search text;

Extracting target business data from the data cube according to the retrieval conditions, and feeding back the target business data to the user.
The computer device according to claim 16, wherein the retrieval conditions include the retrieved target business object and the limited conditions of the target business object; the semantic analysis method is used to perform semantic analysis on each word segmentation of the word segmentation group to determine The retrieval conditions of the search text, extracting target business data from the data cube according to the retrieval conditions, include:

Perform semantic analysis on each of the word segmentation to obtain the semantics corresponding to each word segmentation;

Determine the target business object and the limited conditions of the target business object by analyzing the semantics corresponding to all the word segmentation and the sentence structure of the search text;

Extract target business data from the data cube according to the target business object and the defined conditions of the target business object.
The computer device according to claim 17, wherein the data cube is determined in the following manner:

Obtain business objects of multiple businesses and the qualifications of each business object;

The business objects of the multiple services are used as a first index condition, and the limited conditions of the business objects are used as a second index condition, and the number of dimensions of the data cube is determined according to the first index condition and the second index condition And retrieval information of each dimension; wherein the second index condition determines one or more dimensions, and the first index condition determines one dimension;

The data cube is determined according to the number of dimensions and the retrieval information of each dimension.
The computer device according to claim 18, wherein the data cube is determined in the following manner:

The extracting target business data from the data cube according to the target business object and the defined conditions of the target business object includes:

The first dimensional information is filtered out from the first index condition of the data cube according to the target business object; in the data cube, the first dimensional information corresponds to multiple business data, and each business data corresponds to Multiple qualifications;

The target business data is filtered out from the plurality of business data corresponding to the first dimensional information according to the limiting condition of the target business object.
The computer device according to claim 19, wherein the target business object has a plurality of limited conditions; the filter is selected from the plurality of business data corresponding to the first dimensional information according to the limited conditions of the target business object Output the target business data, including:

Perform n times to select one or more restricted conditions from the plurality of restricted conditions to combine, and obtain a condition set after each combination; where n is an integer greater than 1;

Filtering out one target business data from the multiple business data corresponding to the first dimensional information according to each condition set to obtain multiple target business data;

The feeding back the target service data to the user includes:

Prioritize the multiple target business data according to the restricted conditions in the condition set;

Feeding back to the user the sorted plurality of target service data.