Data search method and device, computer device, and storage medium

Abstract

The embodiment of the application belongs to the technical field of big data, is applied to the technical field of data search, and relates to a data search method and device, a computer device and a storage medium, which comprise the following steps: counting the total data amount of commonly used data cached in a preset search engine; acquiring a search key field input by a search user through a first search box or a second search box through a man-machine interaction mode; and performing data search in a target database according to the search key field by the search engine. At least two different search boxes are set in the search engine in advance, the amount of commonly used data cached in the search engine is judged in advance, the search user is provided with different search boxes, and search data is returned to the search user by selectively using a conventional search mode or an intelligent recommendation mode according to the specific type of the search box, so that the delay and lag during search are avoided, and the search experience of the search user is improved.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to a data search method, apparatus, computer equipment, and storage medium. Background Technology

[0002] In current search engines, retrieving large amounts of data through query statements is one of the most important means for users to obtain information. When a user enters a query into a search engine using keywords, the web search engine checks its index and provides a list of the most matching web pages according to its criteria. Most search engines support Boolean operators "AND", "OR", and "NOT", and some search engines also provide approximate searches that allow users to specify the distance between keywords to improve the accuracy of search data.

[0003] However, as search engines are used for a long time, the amount of cached data increases, which can easily cause search delays or page freezes, resulting in a very poor search experience for users. Summary of the Invention

[0004] The purpose of this application is to provide a data search method, apparatus, computer device, and storage medium to solve the problem in the prior art that as the amount of data in a search engine increases, it leads to delays and lags during data searches, resulting in a poor search experience for users.

[0005] To address the aforementioned technical problems, this application provides a data search method, employing the following technical solution:

[0006] A data search method includes the following steps:

[0007] Calculate the total amount of frequently used data cached within the preset search engine;

[0008] Determine whether the total data volume exceeds a preset data volume indicator;

[0009] If the total amount of data does not exceed the preset data amount index, the first search box will be rendered and displayed on the preset search interface;

[0010] If the total amount of data exceeds the preset data amount index, a second search box will be rendered and displayed on the search interface;

[0011] The search keywords are obtained by the search user through the first search box or the second search box via human-computer interaction.

[0012] The search engine performs a data search within the target database based on the search keyword fields.

[0013] Furthermore, before performing the step of determining whether the total data volume exceeds a preset data volume indicator, the method further includes:

[0014] Obtain two sets of front-end code blocks developed for the search engine to display the search box, wherein one set of front-end code blocks is used to render the first search box and the other set of front-end code blocks is used to render the second search box.

[0015] The two sets of front-end code blocks used for displaying the search box are respectively encapsulated into a first callable display object and a second callable display object;

[0016] Call the first callable display object or the second callable display object to perform initial search box rendering and display, and obtain initialization configuration information, wherein the initialization configuration information includes the display object called to perform initial search rendering and display;

[0017] The initialization configuration information is sent to the preset interface monitoring backend.

[0018] Furthermore, the step of rendering and displaying the first search box on the preset search interface specifically includes:

[0019] The initialization configuration information is obtained from the interface monitoring backend;

[0020] Based on the initialization configuration information, determine whether the called display object is the first callable display object;

[0021] If the called display object is the first callable display object, then send a prompt to the interface display monitoring backend that the first search box has been rendered and displayed on the search interface;

[0022] If the called display object is the second callable display object, it is determined whether the previously rendered display result is the first search box. If it is determined that it is not the first search box, the first callable display object is called to re-render the initial search box to replace the previously rendered display result. The first callable display object is used as the display object called for the initial search rendering display, and the initialization configuration information is updated.

[0023] Furthermore, the step of rendering and displaying the second search box on the search interface specifically includes:

[0024] The initialization configuration information is obtained from the interface monitoring backend;

[0025] Based on the initialization configuration information, determine whether the called display object is the second callable display object;

[0026] If the called display object is the second callable display object, then send a prompt to the interface display monitoring backend that the second search box has been rendered and displayed on the search interface;

[0027] If the called display object is the first callable display object, then it is determined whether the previously rendered display result is the second search box. If it is determined that it is not the second search box, the second callable display object is called to re-render the initial search box to replace the previously rendered display result. The second callable display object is used as the display object called for the initial search rendering display, and the initialization configuration information is updated.

[0028] Furthermore, the step of the search engine performing a data search from the target database based on the search keyword fields specifically includes:

[0029] Based on the monitoring backend and the updated configuration information displayed on the interface, identify the input search box used to obtain the key search fields;

[0030] If the input search box is the first search box, then the search component, index component and retrieval component built into the search engine are called in sequence, and the target database is searched through the search component, index component and retrieval component to obtain data search results;

[0031] If the input search box is the second search box, then the intelligent recommendation component trained within the search engine is directly invoked to predict the data search results.

[0032] Furthermore, the search engine also includes a search user interface, and the step of searching the target database through the search component, index component, and retrieval component to obtain data search results specifically includes:

[0033] The search key fields are sent to the search component through the search user interface;

[0034] Execute the search component and retrieve a preliminary dataset containing the search key field from the target database using the search key field as the search phrase;

[0035] Based on the index components and the search key fields, construct the index statement;

[0036] The retrieval component is executed, and secondary filtered data that meets the query conditions is selected from the initial screening dataset using the index statement as the query condition, which is then used as the data search result.

[0037] The search results are returned to the user through the search user interface as a data return value, thus completing the data search.

[0038] Furthermore, the search engine also includes a search user interface, and the step of predicting data search results through the intelligent recommendation component specifically includes:

[0039] Obtain the identification information of the search user, and send the search key fields and the identification information to the intelligent recommendation component through the search user interface, wherein the identification information refers to the feature tags that affect the search differences among users;

[0040] The intelligent recommendation component is executed, and the output result is predicted using the search key fields and the identification information as input parameters and the historical search results of all search users cached in the target database as the query domain.

[0041] The predicted output is used as the data search result, and the data search result is returned to the search user as a data return value through the search user interface.

[0042] To address the aforementioned technical problems, this application also provides a data search device, which employs the following technical solution:

[0043] A data search device, comprising:

[0044] The data volume statistics module is used to count the total amount of commonly used data cached within the preset search engine;

[0045] The comparison and judgment module is used to determine whether the total data volume exceeds a preset data volume indicator;

[0046] The first rendering module is used to render and display the first search box on the preset search interface if the total data volume does not exceed the preset data volume index.

[0047] The second rendering module is used to render and display a second search box on the search interface if the total data volume exceeds a preset data volume index.

[0048] The search field acquisition module is used to acquire the search key fields entered by the search user through the first search box or the second search box through human-computer interaction.

[0049] The data search module is used by the search engine to perform data searches from the target database based on the search key fields.

[0050] To address the aforementioned technical problems, this application also provides a computer device that employs the following technical solution:

[0051] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the data search method described above.

[0052] To address the aforementioned technical problems, this application also provides a computer-readable storage medium, employing the technical solution described below:

[0053] A computer-readable storage medium storing computer-readable instructions that, when executed by a processor, implement the steps of the data search method described above.

[0054] Compared with the prior art, the embodiments of this application have the following main advantages:

[0055] The data search method described in this application involves: statistically analyzing the total amount of frequently used data cached within a preset search engine; obtaining search keywords input by the user through a first or second search box via human-computer interaction; and then, using the search engine to search for data from a target database based on the search keywords. By pre-setting at least two different search boxes within the search engine, and by pre-judging the amount of frequently used data cached within the search engine, different search boxes are selected and provided to the user. Furthermore, depending on the specific type of search box, either a conventional search method or an intelligent recommendation method is selectively used to return search data to the user, thus avoiding delays and lag during the search process and improving the user's search experience. Attached Figure Description

[0056] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is an exemplary system architecture diagram to which this application can be applied;

[0058] Figure 2 A flowchart of an embodiment of the data search method according to this application;

[0059] Figure 3 yes Figure 2 A flowchart of a specific implementation of step 203 shown;

[0060] Figure 4 yes Figure 2 A flowchart of a specific implementation of step 204 shown;

[0061] Figure 5 yes Figure 2 A flowchart of a specific implementation of step 206 shown;

[0062] Figure 6 yes Figure 5 A flowchart of a specific implementation of step 502 shown;

[0063] Figure 7 yes Figure 5 A flowchart of a specific implementation of step 503 shown;

[0064] Figure 8 A schematic diagram of a structure of an embodiment of the data search device according to this application;

[0065] Figure 9 A schematic diagram of the structure of an embodiment of the computer device according to this application. Detailed Implementation

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0067] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0068] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0069] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0070] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0071] Terminal devices 101, 102, and 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc.

[0072] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0073] It should be noted that the data search method provided in this application embodiment is generally executed by a server / terminal device, and correspondingly, the data search device is generally set in the server / terminal device.

[0074] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0075] Continue to refer to Figure 2 A flowchart of an embodiment of the data search method according to this application is shown. The data search method includes the following steps:

[0076] Step 201: Calculate the total amount of commonly used data cached in the preset search engine.

[0077] Specifically, the search engine used is Elasticsearch (Alibaba Cloud). Elasticsearch is an open-source search engine based on the Lucene library, developed by Elastic. Elasticsearch can cache a small amount of search data, but it has a storage limit. Once a certain amount of data is stored, using conventional search methods can easily cause search engine latency, which is not conducive to ensuring a high user experience.

[0078] The total amount of frequently used data cached in the preset search engine can be continuously counted using a scheduled task during each search until the total amount of frequently used data cached in the search engine reaches the preset data volume indicator, at which point the scheduled task stops. When the frequently used data cached in the search engine is cleared, the scheduled task is triggered to be executed again.

[0079] Step 202: Determine whether the total data volume exceeds the preset data volume index.

[0080] Furthermore, before performing the step of determining whether the total data volume exceeds the preset data volume indicator, the method further includes: obtaining two sets of front-end code blocks developed for the search engine for displaying the search box; encapsulating the two sets of front-end code blocks for displaying the search box into a first callable display object and a second callable display object respectively; calling the first callable display object or the second callable display object to perform initial search box rendering and display, and obtaining initialization configuration information; and sending the initialization configuration information to the preset interface monitoring backend.

[0081] Specifically, of the two sets of front-end code blocks used to display the search box, one set of front-end code blocks is used to render the first search box, and the other set of front-end code blocks is used to render the second search box.

[0082] Specifically, the first search box is a search box that can combine multiple keywords. Functionally, it can construct a search keyword field by combining multiple key search terms using AND / OR / NOT methods. The first search box can also be called an advanced search box or an intelligent search box. The second search box is a search box that cannot combine multiple keywords. Functionally, it can only obtain a single search term as a search keyword field and has no selectable AND / OR / NOT combination methods. The first search box can also be called a simple search box or a regular search box.

[0083] By pre-setting different search boxes, users are not limited to relying on just one search combination, which better meets actual business needs.

[0084] Specifically, the initialization configuration includes rendering and displaying the initial search box on the search interface using a preset initialization object. The initial search box rendering and display can be completed by calling either the first callable display object or the second callable display object.

[0085] Specifically, the initialization configuration information includes the display object called for the initial search rendering display.

[0086] Step 203: If the total amount of data does not exceed the preset data amount index, then the first search box is rendered and displayed on the preset search interface.

[0087] Continue to refer to Figure 3 , Figure 3 yes Figure 2 A flowchart of a specific implementation of step 203 shown includes:

[0088] Step 301: Obtain the initialization configuration information from the interface monitoring backend;

[0089] Step 302: Based on the initialization configuration information, determine whether the called display object is the first callable display object;

[0090] Step 303: If the called display object is the first callable display object, then send a prompt to the interface display monitoring backend that the first search box has been rendered and displayed on the search interface.

[0091] Step 304: If the called display object is the second callable display object, then determine whether the previously rendered display result is the first search box. If it is determined that it is not the first search box, call the first callable display object to re-render the initial search box to replace the previously rendered display result. Use the first callable display object as the display object called for the initial search rendering display and update the initial configuration information.

[0092] In another embodiment, after step 203, the first callable object is directly invoked for re-rendering, and the first callable object is used as the display object invoked for the initial search rendering display to update the initialization configuration information.

[0093] When the total amount of data does not exceed the preset data volume index, that is, when the amount of commonly used data cached in the search engine is small, the first search box is rendered, so that the search user can construct search key fields in the form of AND / OR / NOT combination, and since the amount of commonly used data cached in the search engine is small, it will not cause the search engine to delay or lag.

[0094] Step 204: If the total amount of data exceeds the preset data amount index, then render and display the second search box on the search interface.

[0095] Continue to refer to Figure 4 , Figure 4 yes Figure 2 A flowchart of a specific implementation of step 204 shown includes:

[0096] Step 401: Obtain the initialization configuration information from the interface monitoring backend;

[0097] Step 402: Based on the initialization configuration information, determine whether the called display object is the second callable display object;

[0098] Step 403: If the called display object is the second callable display object, then send a prompt to the interface display monitoring backend that the second search box has been rendered and displayed on the search interface.

[0099] Step 404: If the called display object is the first callable display object, then determine whether the previously rendered display result is the second search box. If it is determined that it is not the second search box, call the second callable display object to re-render the initial search box to replace the previously rendered display result. Use the second callable display object as the display object called for the initial search rendering display and update the initial configuration information.

[0100] In another embodiment, after step 204, the second callable object is directly invoked for re-rendering, and the second callable object is used as the display object invoked for the initial search rendering display to update the initialization configuration information.

[0101] When the total amount of data exceeds the preset data volume index, that is, when the amount of commonly used data cached in the search engine is large, using the conventional first search box method is likely to cause search engine delays or lag. In this case, rendering the second search box replaces the first search box, so that search users cannot construct search keywords based on AND / OR / NOT combinations, thus avoiding the situation where the large amount of commonly used data cached in the search engine is likely to cause search engine delays or lag.

[0102] Step 205: Obtain the search key fields entered by the search user through the first search box or the second search box via human-computer interaction.

[0103] Step 206: The search engine performs a data search from the target database based on the search keyword fields.

[0104] Current search engines, such as Elasticsearch (Alibaba Cloud), include a search user interface, a search component, an indexing component, and a retrieval component. The search user interface sends search keywords to the search component, enabling it to initially filter data from the World Wide Web, cloud platforms, or target data warehouses based on these keywords, creating a preliminary dataset containing the keywords. The indexing component analyzes the relationships between the search keywords, constructs index statements, and uses these index statements and the retrieval component to filter target data from the preliminary dataset. Finally, the target data is returned to the search user through the search user interface, completing the entire data search process.

[0105] Continue to refer to Figure 5 , Figure 5 yes Figure 2 A flowchart of a specific implementation of step 206 shown includes:

[0106] Step 501: Based on the monitoring backend and the updated configuration information displayed on the interface, identify the input search box used to obtain the search key field;

[0107] Step 502: If the input search box is the first search box, then the search component, index component and retrieval component built into the search engine are called in sequence, and the target database is searched through the search component, index component and retrieval component to obtain data search results;

[0108] Continue to refer to Figure 6 , Figure 6 yes Figure 5 A flowchart of a specific implementation of step 502 shown includes:

[0109] Step 601: Send the search key fields to the search component through the search user interface;

[0110] Step 602: Execute the search component and retrieve the initial screening dataset containing the search key field from the target database using the search key field as the search phrase;

[0111] Step 603: Construct an index statement based on the index component and the search key field;

[0112] Step 604: Execute the retrieval component and use the index statement as the query condition to filter out secondary filtered data that meets the query condition from the initial screening dataset as the data search result;

[0113] Step 605: Return the data search results to the search user as a data return value through the search user interface to complete the data search.

[0114] When the amount of frequently used data cached within a search engine is relatively small, directly using the aforementioned search engine for data searching is more conducive to fast searching and makes full use of the search engine's powerful search function.

[0115] Step 503: If the input search box is the second search box, then the intelligent recommendation component trained within the search engine is directly invoked to predict the data search results.

[0116] Continue to refer to Figure 7 , Figure 7 yes Figure 5 A flowchart of a specific implementation of step 503 shown includes:

[0117] Step 701: Obtain the identification information of the search user, and send the search key fields and the identification information to the intelligent recommendation component through the search user interface, wherein the identification information refers to the feature tags that affect the search differences among users;

[0118] Furthermore, the identification information includes employee number or ID information for distinguishing search users, tag information for distinguishing differences in interests among search users, and distinguishing feature tags for distinguishing professional departments among search users.

[0119] Step 702: Execute the intelligent recommendation component, and use the search key field and the identification information as input parameters, and use the historical search results of all search users cached in the target database as the query domain to predict the output results;

[0120] Step 703: The predicted output result is used as the data search result, and the data search result is returned to the search user as a data return value through the search user interface.

[0121] Furthermore, after executing the step of the search engine performing a data search from the target database based on the search key fields, the method further includes: obtaining the data return values ​​corresponding to all historical search users; counting the data in the data return values ​​that have been returned a preset number of times, and caching the data that has reached the preset number of times as frequently used data in the search engine.

[0122] By caching frequently used data within the search engine, it is ensured that when performing data searches, the return values ​​are retrieved first from the search engine, rather than from the database, which reduces the direct exchange work between the front-end and back-end to some extent.

[0123] Furthermore, the search engine also includes an intelligent picking component and an intelligent training component. After performing the step of returning the data search results to the search user in the form of data return values ​​through the search user interface to complete the current data search, the method further includes: obtaining the identification information of each search user among all search users, wherein the identification information refers to feature tags that affect the search differences between users; obtaining the search key fields and search results of each search user each time they perform a search according to the intelligent picking component; and using the identification information, the search key fields, and the data search results as incremental training data, and feeding them into the intelligent training component for intelligent training to update the intelligent recommendation component used to predict the feature relationship between the identification information, the search key fields, and the data search results.

[0124] Since the same user, or all users in the same department, have corresponding search habits or commonalities when searching on a search engine, an artificial intelligence model is used to build a relationship between the identification information, the key search fields, and the search results. Specifically, using the identification information and the key search fields as feature information and the search results as prediction results, a common correspondence between the feature information and the prediction results is trained. When the amount of frequently used data cached in the search engine reaches a certain level, the corresponding prediction results can be directly called based on the feature information of the search user, reducing the search pressure on the search engine.

[0125] By adding intelligent picking and intelligent training components to the search engine, the feature relationships among user identification information, search keywords, and data search results are trained. When the amount of frequently used data cached in the search engine reaches a certain level, the search method is no longer used to obtain search data; instead, intelligent recommendation is used to recommend data as search results, avoiding delays and lag during the search process. An incremental training method is used to obtain the intelligent recommendation component. After each return value is obtained during the entire data search process, an incremental update is performed. Initially, the second search box is not enabled, but return values ​​are still obtained for training each time. This ensures that when the second search box is enabled later, a certain amount of intelligent recommendation training has already been completed in the early stages. Afterward, when the second search box is used, incremental training is also performed each time a return value is obtained to ensure that the updates of the intelligent recommendation component are more intelligent.

[0126] The data search method described in this embodiment calculates the total amount of frequently used data cached in a preset search engine; obtains the search keywords entered by the user through a first or second search box via human-computer interaction; and performs a data search from the target database based on the search keywords and the search engine. The search engine is pre-configured with intelligent recommendation, intelligent picking, and intelligent training components, providing at least two different search boxes. By pre-judging the amount of frequently used data cached in the search engine, different search boxes are selected and provided to the user. Then, depending on the specific type of search box, either a conventional search method or an intelligent recommendation method is selectively used to return search data to the user, avoiding delays and lag during the search and improving the user's search experience.

[0127] Further reference Figure 8 As a response to the above Figure 2 To implement the method shown, this application provides an embodiment of a data search device, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0128] like Figure 8 As shown, the data search device 800 described in this embodiment includes: a data volume statistics module 801, a comparison and judgment module 802, a first rendering module 803, a second rendering module 804, a search field acquisition module 805, and a data search module 806. Wherein:

[0129] The data volume statistics module 801 is used to count the total amount of commonly used data cached in the preset search engine;

[0130] The comparison and judgment module 802 is used to determine whether the total data volume exceeds a preset data volume index;

[0131] The first rendering module 803 is used to render and display the first search box on a preset search interface if the total data volume does not exceed the preset data volume index.

[0132] The second rendering module 804 is used to render and display a second search box on the search interface if the total data volume exceeds a preset data volume index.

[0133] The search field acquisition module 805 is used to acquire the search key fields entered by the search user through the first search box or the second search box through human-computer interaction.

[0134] The data search module 806 is used by the search engine to perform data search from the target database based on the search key fields.

[0135] This application calculates the total amount of frequently used data cached within a pre-defined search engine; it then obtains the search keywords entered by the user through a first or second search box via human-computer interaction; and finally, it performs a data search from the target database based on the search keywords and the search engine. A smart picking component and a smart training component are pre-set within the search engine, providing at least two different search boxes. By pre-judging the amount of frequently used data cached within the search engine, different search boxes are selected and provided to the user. Depending on the specific type of search box, either a conventional search method or a smart recommendation method is selectively used to return search data to the user, avoiding delays and lag during the search process and improving the user's search experience.

[0136] Furthermore, the data search module 806 includes a search box recognition submodule, a first search submodule, and a second search submodule, wherein:

[0137] The search box recognition submodule is used to identify the input search box used to obtain the search key field based on the monitoring backend and the updated configuration information displayed on the interface.

[0138] The first search submodule is used to sequentially call the search component, index component and retrieval component built into the search engine if the input search box is the first search box, and search the target database through the search component, index component and retrieval component to obtain data search results;

[0139] The second search submodule is used to directly call the intelligent recommendation component trained within the search engine if the input search box is the second search box, and predict the data search results through the intelligent recommendation component.

[0140] Furthermore, the first search submodule includes an interface transmission unit, a data initial screening unit, an index construction unit, a secondary screening unit, and a data return unit, wherein:

[0141] An interface transmission unit is used to send the search key fields to the search component through the search user interface;

[0142] The data screening unit is used to execute the search component and obtain a preliminary screening dataset containing the search key field from the target database using the search key field as the search phrase;

[0143] An index building unit is used to build an index statement based on the index component and the search key field;

[0144] A secondary filtering unit is used to execute the retrieval component and, using the index statement as the query condition, filter out secondary filtering data that meets the query condition from the primary filtering dataset as the data search result;

[0145] The data return unit is used to return the data search results to the search user in the form of a data return value through the search user interface, thereby completing the data search.

[0146] Furthermore, the second search submodule includes an interface transmission unit, a recommendation prediction unit, and a data return unit, wherein:

[0147] An interface transmission unit is used to obtain the identification information of the search user and send the search key fields and the identification information to the intelligent recommendation component through the search user interface, wherein the identification information refers to feature tags that affect the search differences between users;

[0148] The recommendation prediction unit is used to execute the intelligent recommendation component, and uses the search key field and the identification information as input parameters, and the historical search results of all search users cached in the target database as the query domain to predict the output result;

[0149] The data return unit is used to take the predicted output result as the data search result and return the data search result to the search user in the form of a data return value through the search user interface.

[0150] Furthermore, the data search device 800 also includes a frequently used data caching module, which is used to obtain the data return values ​​corresponding to all historical search users; it is also used to count the data in the data return values ​​that have been returned a preset number of times, and cache the data that have reached the preset number of times as frequently used data in the search engine.

[0151] Furthermore, the data search device 800 also includes an intelligent training module. The intelligent recommendation module is used to acquire the identification information of each search user among all search users, wherein the identification information refers to feature tags that affect the search differences between users; it is also used to acquire the search key fields and search results of all search users each time they search, based on the intelligent picking component; and it is also used to input the identification information, the search key fields, and the data search results as incremental training data into the intelligent training component for intelligent training, so as to update the intelligent recommendation component used to predict the feature relationship between the identification information, the search key fields, and the data search results.

[0152] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0153] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0154] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 9 , Figure 9 This is a basic structural block diagram of the computer device in this embodiment.

[0155] The computer device 9 includes a memory 9a, a processor 9b, and a network interface 9c that are interconnected via a system bus. It should be noted that only the computer device 9 with components 9a-9c is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0156] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0157] The memory 9a includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 9a may be an internal storage unit of the computer device 9, such as the hard disk or memory of the computer device 9. In other embodiments, the memory 9a may also be an external storage device of the computer device 9, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 9. Of course, the memory 9a may include both the internal storage unit and its external storage device of the computer device 9. In this embodiment, the memory 9a is typically used to store the operating system and various application software installed on the computer device 9, such as computer-readable instructions for data search methods. In addition, the memory 9a can also be used to temporarily store various types of data that have been output or will be output.

[0158] In some embodiments, the processor 9b may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 9b is typically used to control the overall operation of the computer device 9. In this embodiment, the processor 9b is used to execute computer-readable instructions stored in the memory 9a or to process data, such as executing computer-readable instructions for the data search method.

[0159] The network interface 9c may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 9 and other electronic devices.

[0160] The computer device proposed in this embodiment belongs to the field of research and development management technology. This application calculates the total amount of frequently used data cached in a preset search engine; obtains the search keywords entered by the user through a first or second search box via human-computer interaction; and performs a data search from the target database based on the search keywords and the search engine. An intelligent picking component and an intelligent training component are pre-set within the search engine, providing at least two different search boxes. By pre-judging the amount of frequently used data cached in the search engine, different search boxes are selected and provided to the user. Then, based on the specific type of search box, either a conventional search method or an intelligent recommendation method is selectively used to return search data to the user, avoiding delays and lag during the search and improving the user's search experience.

[0161] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by a processor to cause the processor to perform the steps of the data search method described above.

[0162] The computer-readable storage medium proposed in this embodiment belongs to the field of research and development management technology. This application calculates the total amount of frequently used data cached within a preset search engine; obtains the search keywords entered by the user through a first or second search box via human-computer interaction; and performs a data search from the target database based on the search keywords and the search engine. An intelligent picking component and an intelligent training component are pre-set within the search engine, providing at least two different search boxes. By pre-judging the amount of frequently used data cached within the search engine, different search boxes are selected and provided to the user. Then, based on the specific type of search box, either a conventional search method or an intelligent recommendation method is selectively used to return search data to the user, avoiding delays and lag during the search and improving the user's search experience.

[0163] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0164] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A data search method, characterized in that, Includes the following steps: Calculate the total amount of frequently used data cached within the preset search engine; Determine whether the total data volume exceeds a preset data volume indicator; If the total amount of data does not exceed the preset data amount index, the first search box is rendered and displayed on the preset search interface. The first search box is a search box that can realize the combination of multiple keywords. If the total data volume exceeds the preset data volume index, a second search box will be rendered and displayed on the search interface. The second search box is a search box that cannot realize the combination of multiple keywords, but functionally it can obtain a single search term as a search key field. The search keywords are obtained by the search user through the first search box or the second search box via human-computer interaction. The search engine performs a data search from the target database based on the search keyword fields; The search engine includes a user interface, a search component, an indexing component, and a retrieval component; the search engine performs data searches from the target database based on the search keywords, specifically including: The search user interface sends the search key fields to the search component; The search component initially filters out a preliminary dataset containing the search key field from the target database based on the search key field. The indexing component constructs index statements by analyzing the combined relationships between the search key fields; Target data is filtered from the initial screening dataset using the indexing statements and the retrieval components. The target data is returned to the search user as a return value through the search user interface.

2. The data search method according to claim 1, characterized in that, Before performing the step of determining whether the total data volume exceeds a preset data volume indicator, the method further includes: Obtain two sets of front-end code blocks developed for the search engine to display the search box, wherein one set of front-end code blocks is used to render the first search box and the other set of front-end code blocks is used to render the second search box. The two sets of front-end code blocks used for displaying the search box are respectively encapsulated into a first callable display object and a second callable display object; Call the first callable display object or the second callable display object to perform initial search box rendering and display, and obtain initialization configuration information, wherein the initialization configuration information includes the display object called to perform initial search rendering and display; The initialization configuration information is sent to the preset interface monitoring backend.

3. The data search method according to claim 2, characterized in that, The step of rendering and displaying the first search box on the preset search interface specifically includes: The initialization configuration information is obtained from the interface monitoring backend; Based on the initialization configuration information, determine whether the called display object is the first callable display object; If the called display object is the first callable display object, then send a prompt to the interface monitoring backend that the first search box has been rendered and displayed on the search interface. If the called display object is the second callable display object, it is determined whether the previously rendered display result is the first search box. If it is determined that it is not the first search box, the first callable display object is called to re-render the initial search box to replace the previously rendered display result. The first callable display object is used as the display object called for the initial search rendering display, and the initialization configuration information is updated.

4. The data search method according to claim 2, characterized in that, The step of rendering and displaying the second search box on the search interface specifically includes: The initialization configuration information is obtained from the interface monitoring backend; Based on the initialization configuration information, determine whether the called display object is the second callable display object; If the called display object is the second callable display object, then send a prompt to the interface monitoring backend that the second search box has been rendered and displayed on the search interface; If the called display object is the first callable display object, then it is determined whether the previously rendered display result is the second search box. If it is determined that it is not the second search box, the second callable display object is called to re-render the initial search box to replace the previously rendered display result. The second callable display object is used as the display object called for the initial search rendering display, and the initialization configuration information is updated.

5. The data search method according to claim 3 or 4, characterized in that, The steps of the search engine performing a data search from the target database based on the search keyword fields specifically include: Based on the interface monitoring backend and the updated configuration information, identify the input search box used to obtain the search key field; If the input search box is the first search box, then the search component, index component and retrieval component built into the search engine are called in sequence, and the target database is searched through the search component, index component and retrieval component to obtain data search results; If the input search box is the second search box, then the intelligent recommendation component trained within the search engine is directly invoked to predict the data search results.

6. The data search method according to claim 5, characterized in that, The search engine also includes a search user interface. The step of searching the target database through the search component, index component, and retrieval component to obtain data search results specifically includes: The search key fields are sent to the search component through the search user interface; Execute the search component and retrieve a preliminary dataset containing the search key field from the target database using the search key field as the search phrase; Based on the index components and the search key fields, construct the index statement; The retrieval component is executed, and secondary filtered data that meets the query conditions is selected from the initial screening dataset using the index statement as the query condition, which is then used as the data search result. The search results are returned to the search user as data return values ​​through the search user interface.

7. The data search method according to claim 5, characterized in that, The search engine also includes a search user interface, and the step of predicting data search results through the intelligent recommendation component specifically includes: Obtain the identification information of the search user, and send the search key fields and the identification information to the intelligent recommendation component through the search user interface, wherein the identification information refers to the feature tags that affect the search differences among users; The intelligent recommendation component is executed, and the output result is predicted using the search key fields and the identification information as input parameters and the historical search results of all search users cached in the target database as the query domain. The predicted output is used as the data search result, and the data search result is returned to the search user as a data return value through the search user interface.

8. A data search device, characterized in that, include: The data volume statistics module is used to count the total amount of commonly used data cached within the preset search engine; The comparison and judgment module is used to determine whether the total data volume exceeds a preset data volume indicator; The first rendering module is used to render and display a first search box on a preset search interface if the total data volume does not exceed a preset data volume index. The first search box is a search box that can realize multiple keyword combinations. The second rendering module is used to render and display a second search box on the search interface if the total data volume exceeds a preset data volume index. The second search box is a search box that cannot realize multiple keyword combinations, but functionally it can obtain a single search term as a search key field. The search field acquisition module is used to acquire the search key fields entered by the search user through the first search box or the second search box through human-computer interaction. A data search module is used by the search engine to perform data searches from the target database based on the search key fields; The search engine includes a search user interface, a search component, an indexing component, and a retrieval component; the data search module has the following functions: The search user interface sends the search key fields to the search component; The search component initially filters out a preliminary dataset containing the search key field from the target database based on the search key field. The indexing component constructs index statements by analyzing the combined relationships between the search key fields; Target data is filtered from the initial screening dataset using the indexing statements and the retrieval components. The target data is returned to the search user as a return value through the search user interface.

9. A computer device comprising a memory and a processor, the memory storing computer-readable instructions, wherein the processor, when executing the computer-readable instructions, implements the steps of the data search method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the data search method as described in any one of claims 1 to 7.

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