A design method, device and medium of a big data intelligent retrieval platform

Abstract

The application discloses a design method, equipment and medium of a big data intelligent retrieval platform, to solve the problem that the correlation between each retrieval word and retrieval information is poor due to the splitting of retrieval information in the prior art, resulting in a large deviation between the obtained retrieval content and the actual desired retrieval content. It includes: determining the target retrieval information of a plurality of retrieval sample data and binding to generate corresponding data indexes for storage; determining the dimension type of a plurality of retrieval sample data and determining the associated retrieval information of the dimension type for binding to generate corresponding dimension indexes for storage; analyzing a plurality of retrieval sample data to determine a plurality of retrieval contents and establishing a plurality of full-text indexes corresponding to the plurality of full-text indexes for storage; obtaining a big data intelligent retrieval platform based on the data indexes, dimension indexes and full-text indexes corresponding to the plurality of retrieval sample data, to retrieve the data to be retrieved through the big data intelligent retrieval platform.

Description

Technical Field

[0001] This application relates to the field of big data intelligent retrieval technology, and in particular to a design method, device and medium for a big data intelligent retrieval platform. Background Technology

[0002] With the development of information technology, big data has become an indispensable technology in the information age. Big data is generally used to describe the large amounts of unstructured and semi-structured data created by a company, enterprise, or individual. Structured data, also known as row data, is data logically expressed and implemented using a two-dimensional table structure, strictly adhering to data format and length specifications, and is primarily stored and managed through relational databases. Unstructured data is not suitable for representation by two-dimensional database tables, including all formats of office documents, XML, HTML, various reports, images, and audio / video information. Existing technologies apply big data to intelligent data retrieval, gradually shifting towards processing data through big data technologies.

[0003] Existing retrieval platforms typically break down retrieval information into multiple search terms and then crawl the browser to retrieve the relevant retrieval content for each term. However, this method of retrieving retrieval content for each term can easily lead to poor correlation between each term and the retrieval information due to the breakdown of retrieval information. This results in a significant discrepancy between the final retrieval content and the actual desired retrieval content, and also lowers retrieval efficiency. Summary of the Invention

[0004] This application provides a design method, device, and medium for a big data intelligent retrieval platform to solve the technical problem in the prior art where the splitting of retrieval information results in poor correlation between each retrieval term and the retrieval information, leading to a large deviation between the final retrieval content and the actual desired retrieval content, and low retrieval efficiency.

[0005] On the one hand, embodiments of this application provide a design method for a big data intelligent retrieval platform, including:

[0006] Based on several retrieval sample data, several target retrieval information are determined, and the several target retrieval information are bound to the several retrieval sample data respectively to generate corresponding data indexes for storage;

[0007] And determine the dimension types corresponding to the plurality of retrieval sample data, and determine the associated retrieval information associated with each dimension type, so as to bind the associated retrieval information with the corresponding dimension type and generate the corresponding dimension index for storage; the dimension types include enterprise dimension, period dimension and personnel dimension;

[0008] The system parses the several retrieval sample data to determine the corresponding several retrieval contents, and builds several corresponding full-text indexes for the several retrieval sample data based on the several retrieval contents, and stores the several full-text indexes.

[0009] Based on the data index, dimension index, and full-text index corresponding to the aforementioned sample data, a corresponding big data intelligent retrieval platform is obtained, which is then used to retrieve the data to be retrieved.

[0010] In one implementation of this application, the step of determining several target retrieval information based on several retrieval sample data specifically includes:

[0011] The system receives several search sample data uploaded by users in a preset manner, performs cluster analysis on the search sample data, and obtains multiple search information corresponding to the search sample data based on the analysis results.

[0012] The multiple search information is sent to the service terminal corresponding to the search sample data, and based on the service terminal, the feedback data of the corresponding user is obtained; the feedback data is used to represent the user's operation data on the received multiple search information within a unit of time.

[0013] Based on the feedback data, the target retrieval information corresponding to the retrieval sample data is determined from the plurality of retrieval information.

[0014] In one implementation of this application, determining the dimension type corresponding to the plurality of retrieval sample data specifically includes:

[0015] Semantic analysis is performed on the aforementioned retrieval sample data, and based on the analysis results, the dimensional parameters in the retrieval sample data are determined; the dimensional parameters include one of numerical and textual information.

[0016] When the dimension parameter is a number, the dimension type of the retrieved sample data is determined to be a period dimension;

[0017] When the dimension parameter is text information, the dimension parameter is compared with a preset enterprise dictionary and a preset personnel dictionary, and based on the comparison result, the dimension type of the retrieved sample data is determined to be either an enterprise dimension or a personnel dimension.

[0018] In one implementation of this application, after determining that the dimension type of the retrieved sample data is a period dimension when the dimension parameter is a number, the method further includes:

[0019] Determine the number of digits in the dimension parameter, and determine the period type corresponding to the number of digits; the period type includes three types: year type, month type, and date type;

[0020] When the number of digits is 8, the period type of the retrieved sample data is determined to be date type;

[0021] When the number of digits is 6, the period type of the retrieved sample data is determined to be monthly.

[0022] If the number has 4 digits and starts with 19 or 20, the period type of the retrieved sample data is determined to be annual.

[0023] In one implementation of this application, the step of determining the associated retrieval information related to each dimension type specifically includes:

[0024] Based on the dimension type corresponding to the retrieved sample data, multiple retrieval information of the dimension type is obtained, and the multiple retrieval information is compared with the retrieved sample data to obtain multiple corresponding similarities;

[0025] Among the multiple similarities, the search information with similarity greater than a preset threshold is identified, and the search information with similarity greater than the preset threshold is used as the associated search information of the dimension type.

[0026] In one implementation of this application, the step of parsing the plurality of search sample data to determine the corresponding plurality of search contents specifically includes:

[0027] The various search sample data are identified, and multiple keywords in the search sample data are determined.

[0028] The retrieved sample data is parsed to obtain corresponding parsing results, and the specific content in the retrieved sample data is determined based on the parsing results; the specific content includes at least: title, link, content description, query conditions, and data dimensions;

[0029] Based on multiple keywords and specific content in the search sample data, the search content corresponding to the search sample data is determined.

[0030] In one implementation of this application, after obtaining the corresponding big data intelligent retrieval platform based on the data index, dimensional index, and full-text index corresponding to the plurality of retrieval sample data, the method further includes:

[0031] Several validation sample data are identified, and the data index, dimension index, and full-text index corresponding to several pre-stored retrieval sample data are obtained from the database;

[0032] Based on the data index, dimension index, and full-text index, determine whether the big data intelligent retrieval platform includes the data index corresponding to the verification sample data. If the big data intelligent retrieval platform does not include the data index of the verification sample data, then continue to determine whether the big data intelligent retrieval platform includes the dimension index corresponding to the verification sample data.

[0033] If the big data intelligent retrieval platform does not include the dimension index corresponding to the verification sample data, then determine whether the big data intelligent retrieval platform includes the full-text index corresponding to the verification sample data.

[0034] In one implementation of this application, after obtaining the corresponding big data intelligent retrieval platform based on the data index, dimensional index, and full-text index corresponding to the plurality of retrieval sample data, the method further includes:

[0035] In the case that the big data intelligent retrieval platform includes a data index corresponding to the verification sample data, the target retrieval information corresponding to the verification sample data is obtained based on the data index, and the basic information of the target retrieval information is returned.

[0036] In the case where the big data intelligent retrieval platform does not include the data index corresponding to the verification sample data, but includes the dimension index corresponding to the verification sample data, the platform obtains the associated retrieval information corresponding to the verification sample data based on the dimension index, and returns the basic information of the associated retrieval information.

[0037] In the case where the big data intelligent retrieval platform does not include the data index and dimensions corresponding to the verification sample data, but includes the full-text index corresponding to the verification sample data, the retrieval content corresponding to the verification sample data is obtained based on the full-text index, and the basic information of the retrieval content is returned to complete the verification of the big data intelligent retrieval platform.

[0038] On the other hand, embodiments of this application also provide a design device for a big data intelligent retrieval platform, the device comprising:

[0039] At least one processor;

[0040] And, a memory communicatively connected to the at least one processor;

[0041] The memory stores instructions that can be executed by the at least one processor, which are then executed by the at least one processor to enable the at least one processor to perform the design method of a big data intelligent retrieval platform as described above.

[0042] On the other hand, embodiments of this application also provide a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured as follows:

[0043] The above describes a design method for a big data intelligent retrieval platform.

[0044] This application provides a design method, device, and medium for a big data intelligent retrieval platform, which has at least the following beneficial effects:

[0045] By identifying the corresponding target retrieval information from several received retrieval sample data sets and binding them together, a data index corresponding to the retrieval sample data can be generated. Storing the data index facilitates finding the retrieval content corresponding to the same retrieval request as the retrieval sample data set. By determining the dimension types corresponding to several retrieval sample data sets, the associated retrieval information of the dimension types can be found and bound together to generate a dimension index. This facilitates finding the basic information in the associated retrieval information based on the dimension index even without a data index. By parsing the retrieval sample data to determine the corresponding retrieval content and building a full-text index between the retrieval content and the retrieval sample data, the basic information in the corresponding retrieval content can be found through the full-text index even without a data index or dimension index. The big data intelligent retrieval platform built based on data indexes, dimension indexes, and full-text indexes enables users to retrieve the content they need more quickly and easily, improving retrieval response efficiency and enhancing the user experience. Attached Figure Description

[0046] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0047] Figure 1 A flowchart illustrating a design method for a big data intelligent retrieval platform provided in this application embodiment;

[0048] Figure 2 This is a schematic diagram of the internal structure of a design device for a big data intelligent retrieval platform provided in an embodiment of this application. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0050] This application provides a design method, device, and medium for a big data intelligent retrieval platform. By receiving several retrieval sample data sets, the corresponding target retrieval information is determined. Binding these samples generates a data index corresponding to the retrieval sample data. Storing the data index facilitates finding the retrieval content corresponding to the same retrieval request as the retrieval sample data. By determining the dimension types corresponding to several retrieval sample data sets, the associated retrieval information of each dimension type is found and bound to generate a dimension index. This facilitates finding basic information in the associated retrieval information based on the dimension index even without a data index. By parsing the retrieval sample data to determine the corresponding retrieval content and establishing a full-text index between the retrieval content and the retrieval sample data, basic information in the corresponding retrieval content can be found through the full-text index even without a data index or dimension index. The big data intelligent retrieval platform built based on the data index, dimension index, and full-text index enables users to retrieve the required content more quickly and easily, improving retrieval response efficiency and enhancing the user experience. It solves the technical problem in existing technologies where the splitting of retrieval information results in poor correlation between each retrieval term and the retrieval information, leading to a large deviation between the final obtained retrieval content and the actual desired retrieval content, and low retrieval efficiency.

[0051] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0052] Figure 1 This is a flowchart illustrating a design method for a big data intelligent retrieval platform provided in an embodiment of this application. Figure 1 As shown in the embodiment of this application, a design method for a big data intelligent retrieval platform includes:

[0053] 101. Based on several retrieval sample data, determine several corresponding target retrieval information, and bind the several target retrieval information with several retrieval sample data respectively to generate corresponding data indexes for storage.

[0054] To address the issue that splitting retrieval information results in poor relevance between each search term and the retrieval information, this application trains on a large amount of retrieval sample data to determine the target retrieval information corresponding to each retrieval sample data. Then, the determined target retrieval information is bound together with the corresponding retrieval sample data, thus forming a corresponding data index. The generated data indexes are then stored for subsequent use.

[0055] Specifically, the server receives several search sample data uploaded by the user through a preset method, performs cluster analysis on the search sample data, and obtains multiple search information corresponding to each search sample data based on the analysis results. Then, the multiple search information are sent to the service terminal corresponding to the search sample data, and feedback data from the corresponding user is obtained based on the service terminal. It should be noted that the feedback data in this embodiment represents the user's operation data on the received multiple search information within a unit of time.

[0056] Based on the feedback data, the server determines the target retrieval information corresponding to the retrieval sample data from multiple retrieval information.

[0057] 102. And determine the dimension types corresponding to several retrieval sample data, and determine the associated retrieval information associated with each dimension type, so as to bind the associated retrieval information with the corresponding dimension type and generate the corresponding dimension index for storage.

[0058] The server also needs to determine the dimension type corresponding to each retrieved sample data, and then further determine the associated retrieval information associated with each dimension type. The determined associated retrieval information is then bound together with the corresponding dimension type, thus forming the corresponding dimension index. The generated dimension indexes are then stored. Subsequently, as long as the dimension type of the data to be retrieved is known, the associated retrieval information and retrieval content of this dimension type can be found based on the dimension index.

[0059] Specifically, the server performs semantic analysis on several retrieval sample data sets and determines the dimensional parameters in the retrieval sample data based on the analysis results. It should be noted that the dimensional parameters in this embodiment include either numerical or textual information.

[0060] When the dimension parameter is a number, the server can determine that the dimension type of the retrieved sample data is a period dimension. When the dimension parameter is text information, the server also needs to compare the text information in the dimension parameter with the pre-set enterprise dictionary and personnel dictionary respectively, and obtain the comparison results of the text information with each enterprise information in the enterprise dictionary and the comparison results of the text information with each personnel information in the personnel dictionary. Based on the comparison results, the server can determine whether the dimension type of the retrieved sample data is an enterprise dimension or a personnel dimension.

[0061] The server obtains multiple search information for the dimension type corresponding to the search sample data, compares the multiple search information with the search sample data to obtain multiple similarities, and then determines the search information with a similarity greater than a preset threshold from the multiple similarities and uses the search information with a similarity greater than the preset threshold as the associated search information for the dimension type.

[0062] In one embodiment of this application, when the dimension parameter is numeric, after determining that the dimension type of the retrieved sample data is a period dimension, the server can determine the number of digits in the dimension parameter and the corresponding period type. It should be noted that the period types in this embodiment include three types: year type, month type, and date type.

[0063] When the number of digits is 8, the server can determine that the period type of the retrieved sample data is date type. When the number of digits is 6, the server can determine that the period type of the retrieved sample data is month type. When the number of digits is 4 and the number starts with 19 or 20, the server can determine that the period type of the retrieved sample data is year type.

[0064] 103. The system parses several retrieval sample data to determine the corresponding retrieval content, and based on the retrieval content, it builds several full-text indexes for the several retrieval sample data, and stores the several full-text indexes.

[0065] The server also needs to parse each retrieval sample data to determine the retrieval content corresponding to each retrieval sample data. Then, based on the determined retrieval content, it will build a corresponding full-text index for the retrieval sample data. Finally, it will store the built full-text indexes for easy use later, thereby improving retrieval efficiency.

[0066] Specifically, the server identifies several search sample data sets, determines multiple keywords within them, parses the data to obtain corresponding analysis results, and then determines the specific content within the data sets based on these results. It should be noted that the specific content in this embodiment includes at least: title, link, content description, query conditions, and data dimensions.

[0067] Based on multiple keywords and specific content in the search sample data, the server can determine the search content corresponding to the search sample data.

[0068] 104. Based on the data index, dimensional index, and full-text index corresponding to several retrieval sample data, obtain the corresponding big data intelligent retrieval platform, and use the big data intelligent retrieval platform to retrieve the data to be retrieved.

[0069] Based on the data index, dimensional index, and full-text index determined by the server using several retrieval sample data, a big data intelligent retrieval platform is obtained, enabling users to quickly retrieve the content they need through the designed big data intelligent retrieval platform.

[0070] In one embodiment of this application, after obtaining the corresponding big data intelligent retrieval platform based on the data index, dimension index, and full-text index corresponding to several retrieval sample data, the server determines several verification sample data and retrieves the data index, dimension index, and full-text index corresponding to several pre-stored retrieval sample data from the database. Then, based on the data index, dimension index, and full-text index, it determines whether the big data intelligent retrieval platform includes the data index corresponding to the verification sample data. If the big data intelligent retrieval platform does not include the data index corresponding to the verification sample data, it continues to determine whether the big data intelligent retrieval platform includes the dimension index corresponding to the verification sample data. If the big data intelligent retrieval platform does not include the dimension index corresponding to the verification sample data, it determines whether the big data intelligent retrieval platform includes the full-text index corresponding to the verification sample data.

[0071] In one embodiment of this application, after the server obtains the corresponding big data intelligent retrieval platform based on the data index, dimension index, and full-text index corresponding to several retrieval sample data, and if the big data intelligent retrieval platform includes the data index corresponding to the verification sample data, the server obtains the target retrieval information corresponding to the verification sample data based on the data index and returns the basic information of the target retrieval information.

[0072] In the case where the big data intelligent retrieval platform does not include the data index corresponding to the verification sample data, but includes the dimension index corresponding to the verification sample data, the server obtains the associated retrieval information corresponding to the verification sample data based on the dimension index and returns the basic information of the associated retrieval information.

[0073] In the case where the big data intelligent retrieval platform does not include the data index and dimensions corresponding to the verification sample data, but includes the full-text index corresponding to the verification sample data, the server obtains the retrieval content corresponding to the verification sample data based on the full-text index and returns the basic information of the retrieval content, thus completing the verification of the big data intelligent retrieval platform.

[0074] The above are embodiments of the method proposed in this application. Based on the same inventive concept, embodiments of this application also provide a design device for a big data intelligent retrieval platform, the structure of which is as follows: Figure 2 As shown.

[0075] Figure 2 This is a schematic diagram of the internal structure of a design device for a big data intelligent retrieval platform provided in an embodiment of this application. Figure 2 As shown, the device includes:

[0076] At least one processor;

[0077] And, a memory that is communicatively connected to at least one processor;

[0078] The memory stores instructions that can be executed by at least one processor, and the instructions, when executed by at least one processor, enable at least one processor to:

[0079] Based on several retrieval sample data, several target retrieval information are determined, and the several target retrieval information are bound to several retrieval sample data respectively to generate corresponding data indexes for storage;

[0080] In addition, it determines the dimension types corresponding to several retrieval sample data, and determines the associated retrieval information associated with each dimension type, so as to bind the associated retrieval information with the corresponding dimension type and generate the corresponding dimension index for storage; the dimension types include enterprise dimension, period dimension and personnel dimension;

[0081] And the system parses several retrieval sample data separately to determine the corresponding retrieval content, and based on the retrieval content, it builds several corresponding full-text indexes for the several retrieval sample data, and stores the several full-text indexes.

[0082] Based on the data index, dimensional index, and full-text index corresponding to several retrieval sample data, a corresponding big data intelligent retrieval platform is obtained, which can be used to retrieve the data to be retrieved.

[0083] This application embodiment also provides a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured as follows:

[0084] Based on several retrieval sample data, several target retrieval information are determined, and the several target retrieval information are bound to several retrieval sample data respectively to generate corresponding data indexes for storage;

[0085] In addition, it determines the dimension types corresponding to several retrieval sample data, and determines the associated retrieval information associated with each dimension type, so as to bind the associated retrieval information with the corresponding dimension type and generate the corresponding dimension index for storage; the dimension types include enterprise dimension, period dimension and personnel dimension;

[0086] And the system parses several retrieval sample data separately to determine the corresponding retrieval content, and based on the retrieval content, it builds several corresponding full-text indexes for the several retrieval sample data, and stores the several full-text indexes.

[0087] Based on the data index, dimensional index, and full-text index corresponding to several retrieval sample data, a corresponding big data intelligent retrieval platform is obtained, which can be used to retrieve the data to be retrieved.

[0088] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.

[0089] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0090] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0091] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0092] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0093] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0094] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0095] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0096] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0097] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0098] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A design method of a big data intelligent retrieval platform, characterized in that, The method comprises: determining a plurality of target search information corresponding to a plurality of search sample data, and binding the plurality of target search information with the plurality of search sample data respectively to generate corresponding data indexes for storage; determining the dimension types corresponding to the plurality of search sample data, and determining the associated search information associated with each dimension type respectively to bind the associated search information with the corresponding dimension type to generate corresponding dimension indexes for storage; the dimension types include enterprise dimension, period dimension and personnel dimension; parsing the plurality of search sample data respectively to determine a plurality of search contents corresponding to the plurality of search sample data, and establishing a plurality of full-text indexes corresponding to the plurality of search sample data respectively according to the plurality of search contents to store the plurality of full-text indexes; obtaining a big data intelligent search platform corresponding to the data indexes, the dimension indexes and the full-text indexes of the plurality of search sample data to search the data to be searched through the big data intelligent search platform; The method comprises: receiving a plurality of search sample data uploaded by a user through a preset mode, and performing cluster analysis on the search sample data to obtain a plurality of search information corresponding to the search sample data according to the analysis result; sending the plurality of search information to a service terminal corresponding to the search sample data, and obtaining feedback data of the user based on the service terminal; the feedback data is used to represent the operation data of the user within a unit time after receiving the plurality of search information; determining the target search information corresponding to the search sample data from the plurality of search information according to the feedback data; The method comprises: obtaining a plurality of search information of the dimension type according to the dimension type corresponding to the search sample data, and comparing the plurality of search information with the search sample data to obtain a plurality of similarities; determining the search information greater than a preset threshold value from the plurality of similarities, and taking the search information greater than the preset threshold value as the associated search information of the dimension type; The method comprises: identifying the plurality of search sample data respectively, and determining a plurality of keywords in the search sample data; parsing the search sample data to obtain a parsing result, and determining specific content in the search sample data according to the parsing result; the specific content at least includes title, link, content description, query condition and data dimension; determining the search content corresponding to the search sample data based on the plurality of keywords and the specific content in the search sample data; The method comprises: The semantic analysis is performed on the plurality of search sample data, and a dimension parameter in the search sample data is determined according to an analysis result; the dimension parameter includes one of a number and text information; In a case where the dimension parameter is a number, it is determined that a dimension type of the search sample data is a period dimension; In a case where the dimension parameter is text information, the dimension parameter is compared with a preset enterprise dictionary and a preset personnel dictionary, and a dimension type of the search sample data is determined according to a comparison result, the dimension type being an enterprise dimension or a personnel dimension; After the determination that the dimension type of the search sample data is the period dimension in the case where the dimension parameter is the number, the method further includes: A number of digits in the dimension parameter is determined, and a period type corresponding to the number of digits is determined; the period type includes three types of an annual type, a monthly type and a date type; In a case where the number of digits is 8, it is determined that the period type of the search sample data is the date type; In a case where the number of digits is 6, it is determined that the period type of the search sample data is the monthly type; In a case where the number of digits is 4 and the number starts with 19 or 20, it is determined that the period type of the search sample data is the annual type.

2. The method of claim 1, wherein, After the obtaining of the big data intelligent search platform corresponding to the plurality of search sample data according to the data index, the dimension index and the full-text index, the method further includes: A plurality of check sample data are determined, and the data index, the dimension index and the full-text index corresponding to the plurality of search sample data pre-stored in a database are acquired; Based on the data index, the dimension index and the full-text index, it is determined whether the big data intelligent search platform includes a data index corresponding to the check sample data, if the big data intelligent search platform does not include the data index of the check sample data, it is further determined whether the big data intelligent search platform includes a dimension index corresponding to the check sample data; If the big data intelligent search platform does not include the dimension index corresponding to the check sample data, it is determined whether the big data intelligent search platform includes a full-text index corresponding to the check sample data.

3. The method of claim 2, wherein, After the obtaining of the big data intelligent search platform corresponding to the plurality of search sample data according to the data index, the dimension index and the full-text index, the method further includes: In a case where the big data intelligent search platform includes the data index corresponding to the check sample data, target search information corresponding to the check sample data is acquired based on the data index, and basic information of the target search information is returned; In a case where the big data intelligent search platform does not include the data index corresponding to the check sample data, but includes a dimension index corresponding to the check sample data, associated search information corresponding to the check sample data is acquired based on the dimension index, and basic information of the associated search information is returned; In the big data intelligent retrieval platform, the data index and dimension corresponding to the verification sample data are not included, and in the case that the full-text index corresponding to the verification sample data is included, the retrieval content corresponding to the verification sample data is acquired based on the full-text index, and the basic information of the retrieval content is returned to complete the verification of the big data intelligent retrieval platform.

4. A design device of a big data intelligent retrieval platform, characterized in that, The device comprises: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the design method of the big data intelligent retrieval platform according to any one of claims 1-3.

5. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer executable instructions are configured to: perform the design method of the big data intelligent retrieval platform according to any one of claims 1-3.

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