CN113326416A

CN113326416A - Method for retrieving data, method and device for sending retrieved data to client

Info

Publication number: CN113326416A
Application number: CN202110663571.3A
Authority: CN
Inventors: 武光蕊; 解珍
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2021-06-15
Filing date: 2021-06-15
Publication date: 2021-08-31

Abstract

The disclosure provides a method for retrieving data, a method for sending the retrieved data to a client, a device, an electronic device and a storage medium, and relates to the technical field of computers, in particular to the technical field of intelligent search. The specific implementation scheme is as follows: sending the acquired retrieval request aiming at the application data to a cloud end so that the cloud end determines at least one keyword according to the retrieval request; receiving at least one keyword from a cloud; and retrieving the data in the local database according to the at least one keyword to obtain a retrieval result.

Description

Method for retrieving data, method and device for sending retrieved data to client

Technical Field

The present disclosure relates to the field of computer technology, and more particularly, to the field of intelligent search technology.

Background

The amount of data generated by applications is increasing with the use of users. Data such as messages received in an IM (Instant Messaging) application, resources stored in a cloud disk application, and the like, become more and more as the usage time increases. Therefore, the need for retrieving data generated by an application program is becoming stronger.

Disclosure of Invention

The disclosure provides a method for retrieving data, a method for sending the retrieved data to a client, a device, equipment and a storage medium.

According to an aspect of the present disclosure, there is provided a method of retrieving data, including: sending an acquired retrieval request aiming at application data to a cloud end so that the cloud end determines at least one keyword according to the retrieval request; receiving the at least one keyword from the cloud; and retrieving data in the local database according to the at least one keyword to obtain a retrieval result.

According to another aspect of the present disclosure, there is provided a method of sending retrieval data to a client, including: acquiring a retrieval request from a client; determining at least one keyword according to the retrieval request; and sending the at least one keyword as retrieval data to the client.

According to another aspect of the present disclosure, there is provided an apparatus for retrieving data, including: the first sending module is used for sending the acquired retrieval request to a cloud end so that the cloud end can determine at least one keyword according to the retrieval request; a receiving module, configured to receive the at least one keyword from the cloud; and the retrieval module is used for retrieving the data in the local database according to the at least one keyword to obtain a retrieval result.

According to another aspect of the present disclosure, there is provided an apparatus for sending search data to a client, including: the acquisition module is used for acquiring a retrieval request from a client; the determining module is used for determining at least one keyword according to the retrieval request; and the second sending module is used for sending the at least one keyword as retrieval data to the client.

Another aspect of the present disclosure provides an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the embodiments of the present disclosure.

According to another aspect of the disclosed embodiments, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method shown in the disclosed embodiments.

According to another aspect of the embodiments of the present disclosure, there is provided a computer program product, a computer program, which when executed by a processor implements the method shown in the embodiments of the present disclosure.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 schematically illustrates an exemplary system architecture to which the methods and apparatus for retrieving data may be applied, according to an embodiment of the present disclosure;

FIG. 2 schematically shows a flow diagram of a method of retrieving data according to an embodiment of the present disclosure;

FIG. 3 schematically shows a flow diagram of a method of retrieving data according to another embodiment of the present disclosure;

FIG. 4 schematically shows a schematic diagram of a method of retrieving data according to an embodiment of the present disclosure;

FIG. 5 schematically shows a block diagram of an apparatus for retrieving data according to an embodiment of the present disclosure;

fig. 6 schematically shows a block diagram of an apparatus for sending retrieved data to a client according to another embodiment of the present disclosure; and

FIG. 7 illustrates a schematic block diagram of an example electronic device that can be used to implement embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

Fig. 1 schematically illustrates an exemplary system architecture 100 to which the methods and apparatus for retrieving data may be applied, according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.

As shown in fig. 1, the system architecture 100 according to this embodiment may include

clients

101, 102, 103, a network 104 and a cloud 105. Network 104 is the medium used to provide communication links between

clients

101, 102, 103 and cloud 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

A user may use

clients

101, 102, 103 to interact with cloud 105 over network 104 to receive or send messages, etc. Various applications may be installed on the

clients

101, 102, 103, such as a web-disk-like application, a shopping-like application, a web-browser application, a search-like application, an instant messaging application, a mailbox client, a social platform application, and so forth (by way of example only).

Clients

101, 102, 103 may be a variety of electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablets, laptop and desktop computers, and the like.

The cloud 105 may be a Server providing various services, and the Server may be a cloud Server, which is also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of large management difficulty and weak service extensibility in a conventional physical host and VPS service ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server incorporating a blockchain. . The cloud 105 may analyze and otherwise process data such as the received search request, and feed back a processing result (e.g., information or data obtained or generated according to the search request) to the terminal device.

It should be understood that the number of clients, networks, and clouds in fig. 1 is merely illustrative. Any number of clients, networks, and cloud-ends may be present, as desired for implementation.

It should be noted that, in the technical solution of the present disclosure, the acquisition, storage, application, and the like of the personal information of the related user all conform to the regulations of the relevant laws and regulations, and do not violate the common customs of the public order.

Fig. 2 schematically shows a flow chart of a method of retrieving data according to an embodiment of the present disclosure.

As shown in fig. 2, the method 200 includes operations S210 to S260.

In operation S210, the client sends the acquired search request for the application data to the cloud.

According to embodiments of the present disclosure, the application data may include data generated by an application program. The application programs include a web disk application, a shopping application, a web browser application, a search application, an instant messaging application, a mailbox client, a social platform application and the like. For example, for an instant messaging application, the application data may include a message record, and for a web disk application, the application data may include various information related to resources stored in the web disk, such as resource name, resource size, upload date, resource content, and the like.

According to the embodiment of the disclosure, when a user needs to retrieve application data, retrieval characters can be input through the client, and the client can generate a retrieval request (query) according to the retrieval characters input by the user and send the retrieval request to the cloud.

Then, in operation S220, the cloud acquires a retrieval request from the client.

In operation S230, the cloud determines at least one keyword according to the retrieval request.

According to the embodiment of the disclosure, the word segmentation processing can be performed on the search request to obtain at least one original word. And then, carrying out error correction processing on at least one original word to obtain at least one original word after error correction processing. And then, performing expansion processing on at least one original word after error correction processing to obtain at least one keyword.

According to an embodiment of the present disclosure, a search character in a search request is composed of a sequence of consecutive words. The word segmentation process may be used to reassemble the sequences of consecutive words in the search request into word sequences, i.e., to identify the words that these consecutive word sequences constitute.

According to the embodiment of the disclosure, due to a typing error and the like, the user may input the search character which does not conform to the user's intention, and accordingly, the word obtained after the word segmentation processing is incorrect. Based on this, the error correction process can be used to recognize the user's intention according to the search request and correct the wrong word to a correct word that more closely matches the user's intention according to the user's intention.

According to embodiments of the present disclosure, the same thing or concept may be expressed by a variety of different words. Based on this, the expansion process can be used to determine other words whose expressed things or concepts are the same as or similar to the original word, so that the original word can be expanded, and the expanded word can more fully cover the ideographical of the original word.

For example, if the search request is "Beijing Xiao Chi", two original words, i.e., "Beijing" and "Xiao Chi" can be obtained through the word segmentation process. The two original words are then subjected to an error correction process in which "small delays" are corrected to "snacks". Next, the expanding process is performed on "beijing" and "snacks", wherein "beijing" can be expanded to "beijing", "capital", "beijing", and the like, and "snacks" can be expanded to "snacks", "gourmets", "foods", and the like. The final keywords include "Beijing", "capital", "Beijing", "snack", "gourmet", "food", "foodstuff", etc.

In operation S240, the cloud sends at least one keyword as search data to the client.

In operation S250, the client receives at least one keyword from the cloud.

In operation S260, the client retrieves data in the local database according to the at least one keyword to obtain a retrieval result.

According to the embodiment of the disclosure, for each keyword in at least one keyword, the client may query, according to the inverted index information, feature data including the keyword in the local database as a retrieval result, where the feature data corresponds to the application data.

According to an embodiment of the present disclosure, reverse index information is set in advance. The inverted index information may include, for example, a word dictionary and an inverted list. Wherein the word dictionary may include all words that have appeared in the feature data. The word dictionary includes one or more index items, each of which may include a word and a pointer to the "posting list" to which the word corresponds. The posting list may indicate which feature data each word in the word dictionary appears in, each record in the posting list being referred to as a posting, each word in the word dictionary corresponding to a posting, each posting including all the feature data that includes the word. Based on this, in this embodiment, for each keyword, a word matching the keyword may be searched in the word dictionary, then a corresponding inverted item is determined according to the pointer corresponding to the word, and all feature data including the keyword are determined according to the inverted item.

According to the embodiment of the disclosure, when data is retrieved, the similarity between each word in the word dictionary and the keyword can be determined, and in the case that the similarity between the word and the keyword is greater than the similarity threshold value, the word and the keyword are determined to be matched. For example, in this embodiment, the euclidean distance between word vectors corresponding to two words may be calculated as the similarity between the two words.

According to the embodiment of the disclosure, the retrieval request is sent to the cloud end by the client. And analyzing the retrieval request by the cloud, determining the keywords and sending the keywords to the client. And then, the client retrieves the keywords in the local database according to the keywords determined by the cloud. The problem that massive application data are difficult to retrieve is at least partially solved by utilizing the analysis processing capacity of the cloud. In addition, the retrieval is carried out locally at the client, so that the computing resources consumed by the cloud are saved.

The method has the beneficial effects that the problem of searching massive application data can be solved by utilizing the analysis processing capacity of the cloud. In addition, the local retrieval saves the computing resources consumed by the cloud retrieval.

Fig. 3 schematically shows a flow chart of a method of retrieving data according to another embodiment of the present disclosure.

As shown in fig. 3, the method 300 includes operations S310 to S3120.

In operation S310, the client uploads application data of the application program to the cloud.

According to the embodiment of the disclosure, after the application program in the client generates the application data, the client can send the application data to the cloud end to be stored by the cloud end.

In operation S320, the cloud acquires application data from the client.

According to the embodiment of the disclosure, after the cloud acquires the application data from the client, the application data can be stored in the cloud.

In operation S330, the cloud performs content analysis processing on the application data to obtain feature data.

According to embodiments of the present disclosure, a content analysis process may be used to analyze attributes and content of application data and generate feature data for representing the attributes and content. It is understood that the data amount of the characteristic data is smaller than the data amount of the application data.

For example, in the present embodiment, the feature data may include attribute information and content features of the application data. The attribute information of the application data may include, for example, a file name, an upper directory, and the like. For example, in this embodiment, the attribute information may be directly read from the application data, and the content characteristics may be determined according to the content of the application data.

According to embodiments of the present disclosure, a machine learning model may be trained in advance for extracting features of the content of application data. Based on this, the application data can be input into a machine learning model trained in advance, and the content of the application data is analyzed by using the machine learning model, so that the attribute information and the content characteristics of the application data are obtained as the characteristic data.

In operation S340, the cloud sends the feature data to the client.

In operation S350, the client acquires feature data from the cloud.

In operation S360, the feature data is stored to the local database.

According to the embodiment of the disclosure, after the feature data from the cloud is acquired, the reverse index information can be determined according to the feature data. For words not included in the word dictionary in the feature data, a corresponding index item may be added to the word dictionary, and a corresponding inverted item may be added to the inverted list. For a word in the feature data that is already contained in the word dictionary, the feature data may be added to the inverted item in the inverted list corresponding to the word.

In operation S370, the client sends the acquired search request for the application data to the cloud in response to the acquired search request for the application data.

In operation S380, the cloud acquires a retrieval request from the client.

In operation S390, the cloud determines at least one keyword according to the retrieval request.

In operation S3100, the cloud sends at least one keyword to the client.

In operation S3110, the client receives at least one keyword from the cloud.

In operation S3120, data in the local database is retrieved according to the at least one keyword, and a retrieval result is obtained.

According to an embodiment of the present disclosure, for example, reference may be made to operations S380 to S3120 above, which are not described herein again.

According to the embodiment of the disclosure, the client sends the application data to the cloud, the cloud analyzes and processes the content of the application data while storing the application data to obtain the characteristic data, and the characteristic data is sent back to the client and stored in the local database by the client. Since the data volume of the feature data is smaller than that of the application data, the space occupation of the local database can be reduced.

The method of retrieving data is further described with reference to FIG. 4 in conjunction with specific embodiments. Those skilled in the art will appreciate that the following example embodiments are only for the understanding of the present disclosure, and the present disclosure is not limited thereto.

Fig. 4 schematically shows a schematic diagram of a method of retrieving data according to an embodiment of the present disclosure.

In fig. 4, a user 410, a client 420, and a cloud 430 are shown, where the client 420 includes a local database 421 and the cloud 430 includes a cloud database 431. In response to user 410 launching an application, client 420 requests a data synchronization service through a background process, which is used to perform data upload operations 43. The client 420 may upload the application data to be uploaded to the cloud 430 through the data upload operation 43. The cloud 430 then stores the application data received from the client 420 in the cloud database 431, performs content analysis 44 on the application data to obtain feature data, and then sends the feature data to the client 420 through the data synchronization operation 45, and the feature data is stored in the local database 421 by the client 420. Wherein the data synchronization operation 45 may be performed by a background process.

When the user 410 initiates a real-time search operation, a search character is entered via the client 420. The client 420 generates a search request (query)41 according to the search character, and sends the query 41 to the cloud 430. The cloud 430 performs query analysis processing 46 on the received query 41 to understand the search intention of the user, and generates one or more keywords to be sent to the client 420. The client 420 performs the recall and inverted index operation 42 on the data in the local database 421 according to the keyword, and obtains a retrieval result. Client 420 then presents the results of the search to user 410.

According to the embodiment of the disclosure, the analysis processing capacity of the cloud is utilized in the retrieval process, so that the retrieval is more intelligent. In addition, the client executes the reverse index, so that the problem of searching massive application data is at least partially solved, and resources consumed by the cloud for establishing the reverse index are saved.

Fig. 5 schematically shows a block diagram of an apparatus for retrieving data according to an embodiment of the present disclosure.

As shown in fig. 5, the apparatus 500 for retrieving data includes a first sending module 510, a receiving module 520, and a retrieving module 530. The apparatus 500 may be applied to the client shown above.

The first sending module 510 may be configured to send the obtained search request to the cloud, so that the cloud determines at least one keyword according to the search request.

The receiving module 520 may be configured to receive at least one keyword from a cloud.

The retrieving module 530 may be configured to retrieve data in the local database according to the at least one keyword to obtain a retrieval result.

Fig. 6 schematically shows a block diagram of an apparatus for sending retrieved data to a client according to another embodiment of the present disclosure.

As shown in fig. 6, the apparatus 600 for sending the retrieved data to the client includes an obtaining module 610, a determining module 620 and a second sending module 630. The apparatus 600 may be applied to the cloud end shown above.

The obtaining module 610 may be configured to obtain a retrieval request from a client.

The determining module 620 may be configured to determine at least one keyword according to the retrieval request.

The second sending module 630 may be configured to send the at least one keyword to the client.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

FIG. 7 illustrates a schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 7, the device 700 comprises a computing unit 701, which may perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM)702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other by a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Various components in the device 700 are connected to the I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, or the like; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, optical disk, or the like; and a communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

Computing unit 701 may be a variety of general purpose and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The computing unit 701 executes the respective methods and processes described above, such as a method of retrieving data. For example, in some embodiments, the method of retrieving data may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of a computer program may be loaded onto and/or installed onto device 700 via ROM 702 and/or communications unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of the method of retrieving data described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured by any other suitable means (e.g., by means of firmware) to perform the method of retrieving data.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method of retrieving data, comprising:

sending an acquired retrieval request aiming at application data to a cloud end so that the cloud end determines at least one keyword according to the retrieval request;

receiving the at least one keyword from the cloud; and

and retrieving data in the local database according to the at least one keyword to obtain a retrieval result.

2. The method of claim 1, wherein the local database comprises a plurality of feature data; the retrieving data in the local database according to the at least one keyword includes:

and inquiring feature data containing the keywords in a local database according to the inverted index information as the retrieval result aiming at each keyword in the at least one keyword, wherein the feature data correspond to the application data.

3. The method of claim 1, further comprising:

uploading application data of an application program to a cloud end so that the cloud end determines characteristic data corresponding to the application data;

acquiring characteristic data from the cloud; and

and storing the characteristic data to a local database, and determining reverse index information according to the characteristic data.

4. The method according to any of claims 1-3, wherein the method is applied to a client.

5. A method of sending retrieved data to a client, comprising:

acquiring a retrieval request from a client;

determining at least one keyword according to the retrieval request; and

and sending the at least one keyword as retrieval data to the client.

6. The method of claim 5, wherein said determining at least one keyword from said search request comprises:

performing word segmentation processing on the retrieval request to obtain at least one original word;

performing error correction processing on the at least one original word to obtain at least one original word after error correction processing; and

and performing expansion processing on the at least one original word after the error correction processing to obtain the at least one keyword.

7. The method of claim 5, further comprising:

acquiring application data from a client;

performing content analysis processing on the application data to obtain characteristic data; and

and sending the characteristic data to a client.

8. The method of claim 7, wherein the feature data includes attribute information and content features of application data; the content analysis processing is performed on the application data to obtain feature data, and the method comprises the following steps:

identifying attribute information of the application data; and

and analyzing the content of the application data by utilizing a machine learning model to obtain the content characteristics of the application data as the characteristic data.

9. The method according to any one of claims 5-8, wherein the method is applied in the cloud.

10. An apparatus for retrieving data, comprising:

the first sending module is used for sending the acquired retrieval request to a cloud end so that the cloud end can determine at least one keyword according to the retrieval request;

a receiving module, configured to receive the at least one keyword from the cloud; and

and the retrieval module is used for retrieving the data in the local database according to the at least one keyword to obtain a retrieval result.

11. The apparatus of claim 10, wherein the apparatus is applied to a client.

12. An apparatus for sending retrieved data to a client, comprising:

the acquisition module is used for acquiring a retrieval request from a client;

the determining module is used for determining at least one keyword according to the retrieval request; and

and the second sending module is used for sending the at least one keyword as retrieval data to the client.

13. The apparatus of claim 12, wherein the apparatus is applied in the cloud.

14. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.

15. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-9.

16. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-9.