Data search method and device, equipment and storage medium
By using the method of word segmentation processing of search content and searching for multiple words in the database, the problem of inaccurate search of occupational names in the database is solved, and higher search accuracy and user experience are achieved.
Patent Information
- Application Number
- CN202411525243.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-29
- Publication Date
- 2025-04-04
AI Technical Summary
When searching for occupational names from databases, the search results are inaccurate and incomplete due to inconsistent descriptions of occupational names of different companies and the various writing methods of abbreviation.
By performing word segmentation processing on the content to be searched, multiple words are obtained, and the second target content corresponding to these words is searched in the preset database, thereby determining the third target content corresponding to the content to be searched, and improving the accuracy of data search.
Through word segmentation processing and multi-word search, the accuracy of data search is improved, and the search problems due to inconsistent descriptions of occupational names and diverse writing methods are solved, providing more accurate and complete search results.
Smart Images
Figure CN119760104A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of big data technology, and in particular to a data search method, device, equipment and storage medium. Background Art
[0002] A database is a container for storing data. The storage capacity of the database itself is very large and can store a large amount of data. Therefore, in practical applications, it is usually necessary to search for the required data from the large amount of data stored in the database.
[0003] Taking the insurance sector as an example, insurance companies’ databases generally store various data related to insurance business, such as occupational names in different industries. The user’s occupational risk level will affect whether he or she can purchase a certain insurance product and bear the corresponding premium. Therefore, it is crucial to choose the correct occupation based on the occupational information filled in by the user. When a user purchases insurance at an insurance company, it is necessary to search the database for the corresponding occupational name based on the information entered by the user to determine his or her occupational risk level. However, there are currently the following situations: (1) The descriptions of occupational names in different companies are inconsistent. For example, an “insurance agent” may be described as an “insurance broker”, “insurance consultant” or “financial planner” in different companies. (2) Due to the existence of abbreviations and abbreviations, occupational names may be written in multiple ways. These problems result in inaccurate and incomplete search results when users choose their own occupations, which brings inconvenience to users. Summary of the invention
[0004] The main purpose of the present application is to provide a data search method, device, equipment and storage medium, which increases the data search method by performing word segmentation processing on the content to be searched to obtain multiple words, and then searching for corresponding second target content in a preset database based on these words; then, the third target content corresponding to the content to be searched is determined from the searched second target content, thereby improving the accuracy of data search.
[0005] In a first aspect, the present application provides a data search method, comprising:
[0006] Get the content to be searched entered by the user;
[0007] In a preset database, obtaining first target content corresponding to the content to be searched;
[0008] When the first target content corresponding to the to-be-searched content is not obtained, performing word segmentation processing on the to-be-searched content to obtain a plurality of words;
[0009] In the preset database, obtaining second target content corresponding to the multiple words;
[0010] According to the second target contents corresponding to the multiple words, the third target contents corresponding to the to-be-searched contents are determined.
[0011] In a second aspect, the present application also provides a data search device, comprising:
[0012] A first acquisition module is used to acquire the content to be searched input by the user;
[0013] A second acquisition module, used to acquire first target content corresponding to the content to be searched in a preset database;
[0014] A word segmentation module, used for performing word segmentation processing on the content to be searched to obtain a plurality of words when the first target content corresponding to the content to be searched is not obtained;
[0015] A third acquisition module, configured to acquire, in the preset database, second target content corresponding to the plurality of words;
[0016] The determination module is used to determine the third target content corresponding to the to-be-searched content according to the second target content corresponding to the multiple words.
[0017] In a third aspect, the present application further provides a computer device, the computer device comprising a memory and a processor;
[0018] The memory is used to store computer programs;
[0019] The processor is used to execute the computer program and implement the data search method as described above when executing the computer program.
[0020] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the data search method as described above are implemented.
[0021] The present application provides a data search method, device, equipment and storage medium, wherein the method includes: obtaining the content to be searched input by the user; obtaining the first target content corresponding to the content to be searched in a preset database; when the first target content corresponding to the content to be searched is not obtained, performing word segmentation processing on the content to be searched to obtain multiple words; obtaining the second target content corresponding to the multiple words in the preset database; and determining the third target content corresponding to the content to be searched based on the second target content corresponding to the multiple words. The present application first searches the preset database for the first target content corresponding to the content to be searched, and when the first target content is not obtained, obtains multiple words by performing word segmentation processing on the content to be searched, and then searches the preset database for the corresponding second target content based on these words, thereby increasing the data search method; then, the third target content corresponding to the content to be searched is determined from the searched second target content, thereby improving the accuracy of data search. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0023] Figure 1 A flowchart of a data search method provided in an embodiment of the present application;
[0024] Figure 2 A schematic diagram of the connection between the server and the terminal device provided in the embodiment of the present application;
[0025] Figure 3 A schematic block diagram of a data search device provided in an embodiment of the present application;
[0026] Figure 4 A schematic block diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0028] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.
[0029] The embodiments of the present application provide a data search method, device, equipment and storage medium. Among them, the data search method can be applied to a terminal device, which can be a mobile phone, a tablet computer, a laptop computer, a desktop computer and other devices. It can also be applied to a server, which can be a separate server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.
[0030] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.
[0031] See also Figure 1 , Figure 1 This is a flow chart of a data search method provided in an embodiment of the present application. It should be noted that the data search method provided in an embodiment of the present application can be used in a terminal device, and of course can also be used in a server.
[0032] like Figure 2 As shown, the data search method is applied to a server, the server and the terminal device are in communication connection, and the third target content obtained by the data search method can be sent to the terminal device through the server. Of course, it is not limited to this and is not limited here.
[0033] In specific implementation, the terminal device includes but is not limited to: any one of a mobile phone, a tablet computer, a laptop computer, and a desktop computer; the server can be a single server or a server cluster, or a cloud server that provides cloud computing services.
[0034] like Figure 1 As shown, the data search method includes steps S101 to S105.
[0035] Step S101: Obtain the content to be searched input by the user.
[0036] The content to be searched can be words or text. For example, the content to be searched in the embodiment of the present application can be a job name (for example, various full names of jobs and abbreviations of jobs), or a description of a job.
[0037] Specifically, in the insurance field, insurance companies generally need to collect the user's occupational information. However, each occupation generally has a different occupational name. For example, insurance agent, insurance broker, insurance consultant, insurance specialist, etc. In the medical field, doctors can be called physicians, medical staff, doctors, doctors, etc. Therefore, the content to be searched input by the user can be words such as insurance, doctor, or insurance agent. Of course, the content to be searched can also be a text describing the work content of an insurance agent or a medical staff.
[0038] Step S102: Obtain first target content corresponding to the content to be searched from a preset database.
[0039] In the embodiment of the present application, the first target content may include related content for describing the content to be searched. For example, the content to be searched may include occupation names, and the preset database may store occupation types of different industries and occupation names corresponding to each occupation type.
[0040] The first target content corresponding to the occupation name can be the work content related to the occupation name, or the occupation classification content related to the occupation name. For example, if the occupation name is insurance agent, the first target content corresponding to it can be the work content related to the insurance agent; or it can be the occupation classification content related to the insurance agent, for example, insurance agents are divided into professional agents, part-time agents and individual agents, etc.
[0041] In an exemplary implementation, step S102 may include step S201 and step S202.
[0042] Step S201: Determine the hash value corresponding to the content to be searched.
[0043] Step S202: Search the hash value in a preset database to obtain the first target content corresponding to the hash value.
[0044] For example, in the embodiment of the present application, a hash table may be constructed in a preset database. Then, according to the content to be searched, the hash table is used to search for the first target content corresponding to the content to be searched in the preset database.
[0045] A hash table is an array-based data structure that uses a hash function to map keywords to a certain position in the array, thereby achieving operations such as search, insertion, and deletion.
[0046] The key to building a hash table is to choose a suitable hash function. The hash function can convert the content to be searched into an array index, that is, a hash value. It should be noted that hash functions can include direct addressing, digital analysis, folding, division and remainder, random number method, etc.
[0047] In addition, since the mapping of the hash function is not unique, multiple keywords may be mapped to the same array index, i.e., conflicts. Solutions to resolve conflicts include open addressing and linked list. The open addressing method resolves conflicts by finding the next empty hash address. The linked list method maintains a linked list at each array index to store all keywords mapped to the array index.
[0048] The embodiment of the present application needs to load the data in the preset database into a hash table. Specifically, one or more keywords for searching in the hash table are extracted from each record in the preset database; the extracted keywords are used as the input of the hash function to calculate the corresponding array index, that is, the hash value; then, the record is inserted into the corresponding array index (hash value) or the corresponding linked list.
[0049] After determining the hash value corresponding to the content to be searched, the hash value can be searched in the hash table of the preset database to obtain the first target content corresponding to the hash value; if a match is found, the matched related content is returned, that is, the first target content corresponding to the content to be searched is obtained. The process of searching for data through the hash table in the embodiment of the present application does not require too many processing steps, so it can still maintain high search efficiency when the amount of data is large.
[0050] Step S103: When the first target content corresponding to the content to be searched is not obtained, word segmentation is performed on the content to be searched to obtain a plurality of words.
[0051] In the embodiment of the present application, when the first target content corresponding to the content to be searched is not obtained, that is, when the first search in the preset database fails, the content to be searched can be segmented to obtain multiple words.
[0052] It should be noted that the word segmentation methods for Chinese can be divided into three types of word segmentation. The first type of word segmentation is rule-based word segmentation, the second type of word segmentation is statistical-based word segmentation, and the third type of word segmentation is dictionary-based word segmentation. The first type of word segmentation can include word segmentation methods such as forward maximum matching, reverse maximum matching, and bidirectional maximum matching. The second type of word segmentation can include word segmentation methods such as hidden Markov model and neural network model.
[0053] In the embodiment of the present application, multiple words can be obtained by segmenting the content to be searched. Specifically, the content to be searched can be a job name or job profile input by a user. The job name or job profile can be segmented to obtain multiple words, so that the content to be searched input by the user can be divided into multiple independent words for easy retrieval.
[0054] Step S104: Obtain second target content corresponding to a plurality of words from a preset database.
[0055] After the content to be searched is segmented to obtain multiple words, the multiple words can be searched in the preset database, so as to obtain the second target content corresponding to the multiple words. For example, the content to be searched is "insurance broker", and after the word segmentation, the two words "insurance" and "broker" can be obtained; the text corresponding to the two words "insurance" and "broker" can be searched in the preset database, that is, the second target content. For example, "insurance agent of an insurance company...", "real estate broker..." and "media company broker..." and other text contents.
[0056] In an exemplary implementation, step S104 may include step S401 and step S402.
[0057] Step S401: Obtain documents corresponding to a plurality of words from a preset database; the document includes at least one word.
[0058] Step S402: Acquire second target content in the document according to the documents corresponding to the multiple words.
[0059] For example, the embodiment of the present application can search for data corresponding to the content to be searched in a preset database through an inverted index method. It should be noted that an inverted index is a data structure used to quickly search for keywords in a document collection, which is usually used in full-text search engines, mainly mapping each unique keyword in a document to one or more document lists containing the keyword.
[0060] Specifically, based on the inverted index method, the occupation name and its related information can be indexed, and then the constructed inverted index can be stored in a preset database. For example, an index table can be created in a preset database, where each entry in the index table represents a keyword and its corresponding document. The embodiment of the present application can quickly retrieve documents including specific keywords through the inverted index, thereby improving the efficiency of retrieval.
[0061] The preset database in the embodiment of the present application may store different documents, for example, documents recording occupational names corresponding to different industries. In addition, the embodiment of the present application may update the occupational names in the preset database according to the to-be-searched content input by different users at different times, so as to associate the occupational names corresponding to different to-be-searched content, so as to facilitate subsequent users to directly recommend the occupational name when inputting the same to-be-searched content, thereby improving the user's experience.
[0062] In the embodiment of the present application, after the search content is segmented to obtain multiple words, the documents corresponding to the multiple words can be searched in the preset database, and each document can include at least one word. Taking the financial field as an example, the search content is "financial planner", and after the segmentation process, the three words "financial management", "planning", and "teacher" can be obtained. Based on these three words, the preset database can be searched to obtain document 1 and document 2 corresponding to the word "financial management"; document 2 and document 4 corresponding to the word "planning", and document 1, document 2, document 3, document 4... document n corresponding to the word "teacher". Therefore, there are a total of n documents corresponding to the three words "financial management", "planning" and "teacher", which are document 1, document 2, document 3, document 4... document n.
[0063] After acquiring documents corresponding to a plurality of words in a preset database, the embodiment of the present application can acquire the content recorded in these documents, such as the second target content.
[0064] Step S105: Determine the third target content corresponding to the content to be searched according to the second target content corresponding to the multiple words.
[0065] After obtaining the second target content corresponding to multiple words, the third target content corresponding to the content to be searched can be determined based on the multiple words and the second target content. Specifically, the second target content in Document 2 and Document 4 corresponding to the word "planning" may include content related to career planning, and the second target content in Document 1, Document 2, Document 3, Document 4... Document n corresponding to the word "teacher" may include content related to teachers, doctors, lecturers, etc. Obviously, a word can generally correspond to documents including different occupational names or other content. Therefore, the embodiment of the present application needs to determine the third target content corresponding to the content to be searched as "financial planner" based on the second target content corresponding to the three words "financial management", "planning" and "teacher".
[0066] In an exemplary implementation, step S105 may include: step S501 and step S502.
[0067] Step S501: Determine a target document corresponding to the content to be searched according to the degree of association between the second target content in each document and a plurality of words.
[0068] Step S502: Determine the third target content according to the target document.
[0069] For example, the embodiment of the present application can determine the target document corresponding to the content to be searched according to the degree of association between the second target content and multiple words in each document. For example, according to the content in document 1, document 2, document 3, document 4, ... document n corresponding to the three words "financial management", "planning" and "teacher", that is, the second target content, only the content in document 2 includes the three words "financial management", "planning" and "teacher" at the same time, therefore, it can be determined that the target document corresponding to "financial planner" is document 2.
[0070] In an exemplary implementation, step S501 may include: step S5011 and step S5012.
[0071] Step S5011: Determine the degree of association between the second target content in each document and the multiple words according to the occurrence position and frequency of the words in each document.
[0072] Step S5012: determine the target document corresponding to the content to be searched according to the degree of association between the second target content and the multiple words in each document; the degree of association corresponding to the target document is higher than the degree of association between the second target content and the multiple words in each document.
[0073] Exemplarily, the embodiment of the present application can determine the degree of association between the second target content and multiple words in each document based on the position and frequency of occurrence of the words in each document. Specifically, if multiple words appear in the same document, and the frequency of occurrence of these multiple words in the document is very high, it means that the degree of association between the second target content in the document and the multiple words is high. If multiple documents include multiple words at the same time, and multiple words frequently appear in one of the documents, and these words also appear continuously in the title of the document, it can be said that the degree of association between the second target content in the document and the multiple words is very high.
[0074] After determining the degree of association between the second target content and multiple words in each document, the embodiment of the present application can calculate the score of the degree of association corresponding to each document, and then sort the score of each degree of association. Exemplarily, the higher the score, the higher the degree of association. The embodiment of the present application can use the document corresponding to the highest score as the target document. That is, the degree of association corresponding to the target document is higher than the degree of association between the second target content and multiple words in each other document.
[0075] In an exemplary implementation, step S502 may include: determining the third target content according to the occurrence positions of the plurality of words in the target document.
[0076] In practical applications, the embodiment of the present application can determine the third target content according to the positions where multiple words appear in the target document. Specifically, the content between the first position and the last position where at least two of the multiple words appear simultaneously in the target document can be determined as the third target content. In this way, unnecessary second target content can be screened out, and the third target content corresponding to the content to be searched can be quickly located, thereby improving the accuracy of the retrieval.
[0077] In an exemplary embodiment, the method may further include step S106 and step S107.
[0078] Step S106: When the third target content corresponding to the content to be searched is not obtained, the content to be searched is expanded to obtain expanded data.
[0079] Step S107: Acquire fourth target content corresponding to the extended data in a preset database.
[0080] If the third target content corresponding to the content to be searched is not obtained, it may be that there is a spelling error in the content to be searched. Based on this, the embodiment of the present application can perform lexical analysis and semantic analysis on the content to be searched based on natural language processing technology to expand the content to be searched and obtain more extended data with similar semantics to the content to be searched; then, continue to search for the fourth target content corresponding to the extended data in the preset database. It not only supports fuzzy search caused by spelling errors in the content to be searched, improves the fault tolerance of the data, but also increases the search conditions, thereby further improving the scope of retrieval.
[0081] The data search method provided in the above embodiment includes: obtaining the content to be searched input by the user; obtaining the first target content corresponding to the content to be searched in the preset database; when the first target content corresponding to the content to be searched is not obtained, segmenting the content to be searched to obtain multiple words; obtaining the second target content corresponding to the multiple words in the preset database; and determining the third target content corresponding to the content to be searched based on the second target content corresponding to the multiple words. The embodiment of the present application first searches the preset database for the first target content corresponding to the content to be searched, and when the first target content is not obtained, multiple words are obtained by segmenting the content to be searched, and then the corresponding second target content is searched in the preset database based on these words, thereby increasing the data search method; then, the third target content corresponding to the content to be searched is determined from the searched second target content, thereby improving the accuracy of the data search.
[0082] See also Figure 3 , Figure 3A schematic block diagram of a data search device provided in an embodiment of the present application. The data search device can be configured in a server or a terminal device to execute the aforementioned data search method.
[0083] like Figure 3 As shown, the data search device includes: a first acquisition module 110 , a second acquisition module 120 , a word segmentation module 130 , a third acquisition module 140 and a determination module 150 .
[0084] The first acquisition module 110 is used to acquire the content to be searched input by the user;
[0085] The second acquisition module 120 is used to acquire the first target content corresponding to the content to be searched in a preset database;
[0086] The word segmentation module 130 is used to perform word segmentation processing on the content to be searched to obtain multiple words when the first target content corresponding to the content to be searched is not obtained;
[0087] The third acquisition module 140 is used to acquire second target content corresponding to a plurality of words in a preset database;
[0088] The determination module 150 is used to determine the third target content corresponding to the content to be searched according to the second target content corresponding to the multiple words.
[0089] In an exemplary implementation, the second acquisition module 120 may include: a value determination submodule and a search submodule.
[0090] The value determination submodule is used to determine the hash value corresponding to the content to be searched.
[0091] The search submodule is used to search the hash value in a preset database to obtain the first target content corresponding to the hash value.
[0092] In an exemplary implementation, the third acquisition module 140 may include a first acquisition submodule and a second acquisition submodule.
[0093] The first acquisition submodule is used to acquire documents corresponding to a plurality of words in a preset database; the document includes at least one word.
[0094] The second acquisition submodule is used to acquire the second target content in the document according to the documents corresponding to the multiple words.
[0095] In an exemplary embodiment, the determination module 150 may include a first determination submodule and a second determination submodule.
[0096] The first determination submodule is used to determine the target document corresponding to the content to be searched according to the association degree between the second target content and the multiple words in each document.
[0097] The second determination submodule is used to determine the third target content according to the target document.
[0098] In an exemplary implementation, the first determination submodule may include: a relevance determination submodule and a document determination submodule.
[0099] The association degree determination submodule is used to determine the association degree between the second target content in each document and the multiple words according to the occurrence position and occurrence frequency of the words in each document.
[0100] The document determination submodule is used to determine the target document corresponding to the content to be searched according to the correlation degree between the second target content and multiple words in each document; the correlation degree corresponding to the target document is higher than the correlation degree between the second target content and multiple words in each document.
[0101] In an exemplary implementation, the second determination submodule may be used to determine the third target content according to the occurrence positions of the plurality of words in the target document.
[0102] In an exemplary implementation, the device may further include: an expansion module and a fourth acquisition module.
[0103] The expansion module is used to expand the content to be searched to obtain expansion data when the third target content corresponding to the content to be searched is not obtained.
[0104] The fourth acquisition module is used to acquire fourth target content corresponding to the extended data in a preset database.
[0105] It should be noted that technicians in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described devices and modules and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0106] The method of the present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.
[0107] Exemplarily, the above method and apparatus may be implemented in the form of a computer program, which may be run on a computer device.
[0108] See also Figure 4 , Figure 4 A schematic block diagram of the structure of a computer device provided in an embodiment of the present application. The computer device may be a server or a terminal device.
[0109] like Figure 4 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include a storage medium and an internal memory.
[0110] The storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute the steps of any data search method.
[0111] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.
[0112] The internal memory provides an environment for the operation of the computer program in the storage medium. When the computer program is executed by the processor, the processor can execute the steps of any data search method.
[0113] The network interface is used for network communication, such as sending assigned tasks.
[0114] Those skilled in the art will understand that Figure 4The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
[0115] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.
[0116] In one embodiment, the processor is used to execute a computer program and implement the following steps when executing the computer program:
[0117] Get the content to be searched entered by the user;
[0118] In a preset database, obtaining first target content corresponding to the content to be searched;
[0119] When the first target content corresponding to the to-be-searched content is not obtained, segmenting the to-be-searched content to obtain a plurality of words;
[0120] In a preset database, obtaining second target content corresponding to a plurality of words;
[0121] According to the second target contents corresponding to the multiple words, the third target contents corresponding to the content to be searched are determined.
[0122] In an exemplary implementation, obtaining first target content corresponding to the content to be searched in a preset database includes:
[0123] Determine the hash value corresponding to the content to be searched;
[0124] In a preset database, the hash value is searched to obtain the first target content corresponding to the hash value.
[0125] In an exemplary implementation, obtaining second target content corresponding to a plurality of words in a preset database includes:
[0126] In a preset database, documents corresponding to a plurality of words are obtained; the document includes at least one word;
[0127] According to the documents corresponding to the multiple words, second target content in the documents is obtained.
[0128] In an exemplary implementation, determining the third target content corresponding to the content to be searched according to the second target content corresponding to the plurality of words includes:
[0129] Determining a target document corresponding to the content to be searched according to the degree of association between the second target content in each document and the plurality of words;
[0130] According to the target document, determine the third target content.
[0131] In an exemplary implementation, determining the target document corresponding to the content to be searched according to the degree of association between the second target content in each document and a plurality of words includes:
[0132] Determining the degree of association between the second target content in each document and the plurality of words according to the occurrence position and the occurrence frequency of the words in each document;
[0133] According to the association degree between the second target content and multiple words in each document, the target document corresponding to the content to be searched is determined; the association degree corresponding to the target document is higher than the association degree between the second target content and multiple words in each document.
[0134] In an exemplary implementation, determining the third target content according to the target document includes:
[0135] The third target content is determined according to the occurrence positions of the plurality of words in the target document.
[0136] In an exemplary embodiment, it also includes:
[0137] When the third target content corresponding to the to-be-searched content is not obtained, the to-be-searched content is expanded to obtain expanded data;
[0138] In the preset database, fourth target content corresponding to the extended data is obtained.
[0139] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the data search described above can refer to the corresponding process in the embodiment of the aforementioned data search method, and will not be repeated here.
[0140] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. The method implemented when the computer program is executed by a processor can refer to the various embodiments of the data search method of the present application.
[0141] The computer-readable storage medium may be an internal storage unit of the computer device of the aforementioned embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device.
[0142] It should be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.
[0143] It should also be understood that the term "and / or" used in the specification of this application and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system including the element.
[0144] The serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments. The above description is only a specific implementation mode of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.
Claims
1. A data search method, characterized in that: include: Get the content to be searched entered by the user; In a preset database, obtaining first target content corresponding to the content to be searched; When the first target content corresponding to the to-be-searched content is not obtained, performing word segmentation processing on the to-be-searched content to obtain a plurality of words; In the preset database, obtaining second target content corresponding to the multiple words; According to the second target contents corresponding to the multiple words, the third target contents corresponding to the to-be-searched contents are determined.
2. The data search method according to claim 1, characterized in that: The step of obtaining, in a preset database, first target content corresponding to the content to be searched includes: Determine a hash value corresponding to the content to be searched; In the preset database, the hash value is searched to obtain the first target content corresponding to the hash value.
3. The data search method according to claim 1, characterized in that: The step of acquiring the second target content corresponding to the plurality of words in the preset database includes: In the preset database, obtaining documents corresponding to the multiple words; the document includes at least one of the words; According to the documents corresponding to the multiple words, second target content in the documents is acquired.
4. The data search method according to claim 3, characterized in that: The determining, based on the second target contents corresponding to the multiple words, the third target contents corresponding to the to-be-searched contents comprises: Determining a target document corresponding to the content to be searched according to the degree of association between the second target content in each of the documents and the multiple words; A third target content is determined according to the target document.
5. The data search method according to claim 4, characterized in that: The step of determining the target document corresponding to the content to be searched according to the degree of association between the second target content in each of the documents and the plurality of words includes: Determining the degree of association between the second target content in each of the documents and the plurality of words according to the occurrence position and the occurrence frequency of the words in each of the documents; The target document corresponding to the content to be searched is determined according to the degree of association between the second target content in each of the documents and the multiple words; the degree of association corresponding to the target document is higher than the degree of association between the second target content in each of the documents and the multiple words.
6. The data search method according to claim 4, characterized in that: The step of determining the third target content according to the target document includes: The third target content is determined according to the occurrence positions of the multiple words in the target document.
7. The data search method according to any one of claims 1 to 6, characterized in that: Also includes: When the third target content corresponding to the to-be-searched content is not obtained, the to-be-searched content is expanded to obtain expanded data; In the preset database, fourth target content corresponding to the extended data is obtained.
8. A data search device, characterized in that: include: A first acquisition module is used to acquire the content to be searched input by the user; A second acquisition module, used to acquire first target content corresponding to the content to be searched in a preset database; A word segmentation module, used for performing word segmentation processing on the content to be searched to obtain a plurality of words when the first target content corresponding to the content to be searched is not obtained; A third acquisition module, configured to acquire, in the preset database, second target content corresponding to the plurality of words; The determination module is used to determine the third target content corresponding to the to-be-searched content according to the second target content corresponding to the multiple words.
9. A computer device, characterized in that: The computer device includes a memory and a processor; The memory is used to store computer programs; The processor is used to execute the computer program and implement the data search method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the data search method according to any one of claims 1 to 7 are implemented.