Data query method and device, electronic equipment and computer storage medium

Abstract

The invention relates to a data query method and device, electronic equipment and a computer storage medium, and the method comprises the steps: obtaining a data query request for target data, the data query request comprising a query statement of the target data; determining a target query keyword corresponding to the query statement; judging whether the target query keyword is a query keyword in a keyword list or not; and if the target query keyword is the query keyword in the keyword list, obtaining the target data from the cache according to the target query keyword, and if the target query keyword is not in the keyword list, obtaining the target data from the third-party query platform according to the target query keyword. By means of the method, on the basis of the dynamic query mode provided by the scheme, the query strategy can be dynamically adjusted, meanwhile, the data corresponding to the target query keyword does not need to be traversed instead of being directly traversed on a cache or a third-party platform, and therefore the query efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing. Specifically, the present invention relates to a data query method, apparatus, electronic device, and computer storage medium. Background Art

[0002] Since the development of AI to date, general large models have matured, but they only apply to the general public. The construction of private models is one of the most important tasks at present. For the operator industry, the training of private models is inseparable from the interface query capabilities provided by current products. However, the original interface query capabilities cannot meet the efficiency requirements of AI queries. With the rapid development of information technology, big data processing and information query have become the core needs of various industries. However, existing systems often face challenges such as slow query speed, high resource occupancy, and poor user experience when dealing with large-scale data queries. These problems not only affect the overall performance of the system but also limit the effective utilization of information. Therefore, how to design an efficient and stable query interface response mechanism has become an urgent technical problem to be solved. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a data query method, apparatus, electronic device, and computer storage medium, aiming to solve at least one of the above technical problems.

[0004] In a first aspect, the technical solution of the present invention to solve the above technical problem is as follows: A data query method, the method comprising:

[0005] Obtain a data query request for target data, where the data query request includes a query statement for the target data;

[0006] Determine a target query keyword corresponding to the query statement;

[0007] Judge whether the target query keyword is a query keyword in a keyword list;

[0008] If the target query keyword is a query keyword in the keyword list, obtain the target data from a cache according to the target query keyword; if the target query keyword is not a query keyword in the keyword list, obtain the target data from a third-party query platform according to the target query keyword.

[0009] The beneficial effects of the present invention are as follows: Based on a data query request, first determine the target query keyword corresponding to the query statement, and then based on the target query keyword and the keyword list, the target data can be obtained from the cache or a third-party query platform. Based on the dynamic query method provided by this solution, it is not necessary to directly traverse the data on the cache or the third-party platform, but to traverse the data corresponding to the target query keyword, thereby improving the query efficiency. At the same time, the query strategy can also be dynamically adjusted based on the target query keyword to improve the service experience of the query service.

[0010] Based on the above technical solution, the present invention can be further improved as follows.

[0011] Further, the method further includes:

[0012] According to the query statement, determine the service type identifier corresponding to the target data;

[0013] According to the service type identifier, determine the keyword list corresponding to the service type identifier.

[0014] Further, the keyword list is a list formed by query keywords corresponding to frequencies greater than a set frequency in historical query frequencies.

[0015] Further, if the target data is data for multiple services, the method further includes:

[0016] Distribute the data query request to the corresponding query server according to the query server corresponding to each service, so as to complete the query of the target data in parallel through the corresponding query server.

[0017] Further, the obtaining of the data query request for the target data includes:

[0018] Obtain the data query request for the target data through a query interface.

[0019] In a second aspect, the present invention also provides a data query device to solve the above technical problems. The device includes:

[0020] An obtaining module, configured to obtain a data query request for target data, where the data query request includes a query statement for the target data;

[0021] A keyword determination module, configured to determine a target query keyword corresponding to the query statement;

[0022] A judgment module, configured to judge whether the target query keyword is a query keyword in the keyword list;

[0023] A query module, configured to, if the target query keyword is a query keyword in the keyword list, obtain the target data from the cache according to the target query keyword; and if the target query keyword is not a query keyword in the keyword list, obtain the target data from a third-party query platform according to the target query keyword.

[0024] In a third aspect, to solve the above technical problem, the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the data query method of the present application is implemented.

[0025] In a fourth aspect, to solve the above technical problem, the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the data query method of the present application is implemented.

[0026] Additional aspects and advantages of the present application will be given in part in the following description, and these will become apparent from the following description or be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the accompanying drawings required for describing the embodiments of the present invention.

[0028] Figure 1 A flowchart of a data query method provided by an embodiment of the present invention;

[0029] Figure 2 A structural diagram of a data query device provided by an embodiment of the present invention;

[0030] Figure 3 A structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] The following describes the principles and features of the present invention. The examples given are only used to explain the present invention and are not intended to limit the scope of the present invention.

[0032] The following uses specific embodiments to detail the technical solutions of the present invention and how the technical solutions of the present invention solve the above technical problems. These several specific embodiments can be combined with each other. For the same or similar concepts or processes, they may not be repeated in some embodiments. The following will describe the embodiments of the present invention with reference to the accompanying drawings.

[0033] The following technical problems exist in the prior art:

[0034] If an interface depends on certain cached data, and this cached data becomes invalid or is not updated in a timely manner, or caching technology is not used at all, it may cause the interface to slow down.

[0035] If the AI private model calls a third-party service and the service fails or its response speed slows down, it will also affect the performance of the interface.

[0036] The interface code implementation is not optimized enough or there are performance bottlenecks, such as nested loops, repeated calculations, etc. These problems may cause the interface to consume too much time and resources when processing requests.

[0037] If the system architecture of the AI private model is unreasonable, such as using a synchronous blocking call method, etc., it may cause the interface response to be slow.

[0038] The load on the database exceeds its tolerance, resulting in slow query and update operations. When the AI private model processes requests, it may need to access the database frequently to obtain or update data.

[0039] Based on the above technical problems, the solution of this application provides a data query method. The solution provided by the embodiments of the present invention can be applied to any application scenario that needs to query target data. The solution provided by the embodiments of the present invention can be executed by any electronic device. For example, it can be the user's terminal device, including at least one of the following: smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, smart TV, smart vehicle device.

[0040] The embodiments of the present invention provide a possible implementation manner, as Figure 1 shown, a flowchart of a data query method is provided. This solution can be executed by any electronic device. For example, it can be a terminal device, or jointly executed by a terminal device and a server. For ease of description, the method provided by the embodiments of the present invention will be described below by taking the server as the execution subject as an example, as Figure 1 shown in the flowchart, this method may include the following steps:

[0041] S10. Obtain a data query request for target data, where the data query request includes a query statement for the target data;

[0042] S20. Determine a target query keyword corresponding to the query statement;

[0043] S30. Determine whether the target query keyword is a query keyword in a keyword list;

[0044] S40. If the target query keyword is a query keyword in the keyword list, obtain the target data from the cache according to the target query keyword. If the target query keyword is not a query keyword in the keyword list, obtain the target data from a third-party query platform according to the target query keyword.

[0045] Through the method of the present invention, based on a data query request, first determine the target query keyword corresponding to the query statement, and then based on the target query keyword and the keyword list, the target data can be obtained from the cache or a third-party query platform. Based on the dynamic query method provided by this solution, it is not necessary to directly traverse the data in the cache or the third-party platform, but to traverse the data corresponding to the target query keyword, thereby improving the query efficiency. At the same time, the query strategy can also be dynamically adjusted based on the target query keyword to enhance the service experience of the query service.

[0046] The following further illustrates the solution of the present invention in combination with the following specific embodiments. In this embodiment, a data query method provided may include the following steps:

[0047] S10. Obtain a data query request for target data, where the data query request includes a query statement for the target data;

[0048] Among them, the data query request refers to a request to query target data. This request can be a request generated based on a user's trigger operation on the client interface of the terminal device. The specific form of this trigger operation is configured according to needs. For example, it can be a trigger action at a specific operation position on the interface of the application program on the terminal device. In actual use, the trigger operation can be a trigger selection operation for a relevant trigger identifier. Among them, the specific form of the trigger identifier can be configured according to actual needs. For example, it can be a specified virtual button or input box on the client interface. Specifically, for example, it can be a virtual button with "XXX" displayed on the client interface, and the operation of the user clicking this virtual button indicates that the user wants to query the target data corresponding to "XXX".

[0049] Among them, the query statement can be a statement in any expression form of user data.

[0050] Specifically, in the above S10, obtaining a data query request for target data includes:

[0051] Obtain a data query request for target data through a query interface.

[0052] Among them, the query interface refers to a service interface that provides target data query. It can be an AI interface.

[0053] S20. Determine the target query keyword corresponding to the query statement;

[0054] Among them, the target query keyword refers to the keyword that can represent the main meaning of the query statement, which can be a word included in the query statement or a word generated based on the query statement. The target query keyword can be represented in a preset data format, which is convenient for subsequent program processing.

[0055] In the above S20, the implementation method of determining the target query keyword corresponding to the query statement is any one of the following:

[0056] The first one:

[0057] Extract the query keyword in the query statement, and analyze the semantics of the query statement to obtain a semantic analysis result;

[0058] If the query keyword is similar to the semantics expressed by the semantic analysis result, then use this query keyword as the target query keyword corresponding to the query statement;

[0059] If the query keyword is not similar to the semantics expressed by the semantic analysis result, then generate the target query keyword corresponding to the query statement based on the semantic analysis result.

[0060] The second one:

[0061] Analyze the semantics of the query statement to obtain a semantic analysis result;

[0062] Generate the target query keyword corresponding to the query statement based on the semantic analysis result.

[0063] S30. Determine whether the target query keyword is a query keyword in the keyword list;

[0064] Among them, different query keywords are stored in the keyword list, and different query keywords correspond to different data. Therefore, based on the keyword list, the target query keyword corresponding to the query statement can be accurately found, and it is not necessary to traverse all data at once based on the query statement, reducing the data query volume.

[0065] Optionally, several implementation methods of the keyword list:

[0066] The first one can be a list formed by query keywords corresponding to frequencies greater than the set frequency in the historical query frequencies.

[0067] Second, the keyword list can also be constructed from query statements corresponding to different services and corresponding query results (target data). During the historical query process, collect query statements corresponding to different services, and based on the above-mentioned method for determining target query keywords, determine the query keywords corresponding to the query statements for each service, and then generate a keyword list.

[0068] Third, obtain the original data. The original data refers to the data used for querying without any processing, usually text data. Perform preprocessing such as data cleaning and format conversion on the original data to obtain the initial data. Divide the initial data into statements to obtain multiple text segments; according to the semantic features of each text segment, merge text segments with the same semantics (such as the same business type) into a candidate text; according to the method described above, determine the query keywords corresponding to each candidate text, and then form the corresponding keyword list.

[0069] It should be noted that each query keyword in the keyword list is constructed based on the services, requests, etc. covered. In the keyword list, it is basically possible to find the query keywords corresponding to possible data query requests.

[0070] Furthermore, the implementation method for dividing the initial data into statements to obtain multiple text segments is as follows:

[0071] First, segment the initial data by paragraph. For any paragraph obtained after segmentation, segment the paragraph by sentence according to the period or semicolon. Real-time judge the length of the sentence obtained by segmentation, and based on the sentence length and the preset segmentation rules, further segment each sentence to obtain multiple text segments.

[0072] Among them, for each sentence, the specific implementation process of segmenting the sentence into multiple text segments based on the sentence length of the sentence and the preset segmentation rules is as follows:

[0073] For each sentence, if the length of the sentence is greater than the set length, segment the corresponding sentence into a first number of first text segments, and the length of each first text segment is the first length. The total length of all the first text segments is less than the sentence length. Take the text segment other than all the first text segments in the sentence as the second text segment. The sentence length of the second text segment is usually not greater than the first length. Then, the second text segment can be directly used as a new first text segment, or the second text segment can be spliced with the next adjacent sentence of the sentence to obtain a third text segment with the first length, and the third text segment is used as a new first text segment. All the first text segments (including the new first text segment) are used as the multiple text segments corresponding to the sentence.

[0074] For the next adjacent statement of this statement, if a part of the adjacent statement has been occupied by the third text segment of this statement, then the part of the adjacent statement except this part can be used as a new statement and segmented in the above manner.

[0075] Second, segment the initial data by paragraph, determine a fixed segmentation length, and segment any paragraph according to the fixed segmentation length to obtain at least two chunks. It should be further noted that during the segmentation according to the fixed segmentation length, if there are commas or semicolons in the chunks obtained during the segmentation, re-segmentation is required, that is, segment according to the commas or semicolons, and after segmentation, start re-segmenting according to the fixed segmentation length from the first text content after the comma or semicolon. For each result obtained by segmentation, perform slicing processing according to the slicing method in the first method to obtain the text segments corresponding to each statement.

[0076] S40. If the target query keyword is the query keyword in the keyword list, then obtain the target data from the cache according to the target query keyword; if the target query keyword is not the query keyword in the keyword list, then obtain the target data from a third-party query platform according to the target query keyword.

[0077] Among them, this solution introduces a caching mechanism to cache frequently queried data or query results in the cache, so as to reduce the number of direct accesses to the database or third-party platform and reduce query latency.

[0078] Among them, the target data in the solution of this application can be data corresponding to operator products, and the types of services can specifically be services involved in operators.

[0079] Optionally, the method further includes:

[0080] Determine the service type identifier corresponding to the target data according to the query statement;

[0081] Determine the keyword list corresponding to the service type identifier according to the service type identifier.

[0082] Among them, different service types correspond to different service type identifiers, which can also be called indexes. Then, according to the service type corresponding to the query statement, the search range can be further narrowed and the query efficiency can be improved.

[0083] Optionally, if the target data is data for multiple services, the method further includes:

[0084] Distribute the data query request to the corresponding query server according to each service, so as to complete the query of the target data in parallel through the corresponding query server.

[0085] Specifically, through load balancing technology, distribute the data query request to multiple query servers to achieve parallel processing of query tasks, which can improve the concurrent processing ability of the system.

[0086] It can be understood that one query server corresponds to one service type, and each query server can query the data corresponding to the corresponding service type, then the target data is composed of the data queried by multiple query servers.

[0087] Optionally, the method further includes:

[0088] If the target data cannot be obtained from the cache, obtain the target data from a third-party query platform according to the target query keyword.

[0089] In the above manner, when the cached data expires or is not updated in time, or the caching technology is not used at all, the target data can be obtained more quickly and accurately.

[0090] The server involved in the embodiments of the present invention can also be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0091] To better illustrate and understand the principle of the method provided by the present invention, the solution of the present invention will be described below in conjunction with an optional specific embodiment. It should be noted that the specific implementation manners of the steps in this specific embodiment should not be construed as a limitation to the solution of the present invention. Based on the principle of the solution provided by the present invention, other implementation manners that can be thought of by those skilled in the art should also be regarded as within the protection scope of the present invention.

[0092] The method of this solution can be implemented by the following several modules:

[0093] Data preprocessing module: Preprocess the original data, including steps such as data cleaning, format conversion, and index establishment, to improve the efficiency and accuracy of data query.

[0094] Query optimization module: Dynamically adjust the query strategy according to the characteristics of the user query request, such as selecting an appropriate index, optimizing the query statement, avoiding unnecessary full table scans, etc., to further improve the query speed.

[0095] Cache management module: Introduce a caching mechanism to cache frequently queried data or query results in memory to reduce the number of direct accesses to the database and reduce query latency.

[0096] Load balancing module: Through load balancing technology, query requests are distributed to multiple query servers to achieve parallel processing of query tasks and improve the system's concurrent processing capabilities.

[0097] Real-time monitoring and feedback module: monitor the system's operating status and query performance in real time, collect user feedback, and adjust optimization strategies in a timely manner to ensure that the system is always in the best operating state.

[0098] Through the scheme of the present invention, there are the following beneficial effects:

[0099] 1. The method of this solution can significantly improve the speed and stability of the efficient query interface response of the operator's private model docking system in AI, reduce resource consumption, and improve user experience. The specific performance is:

[0100] Shorten query response time and improve user satisfaction;

[0101] Reduce database load and extend system life;

[0102] Optimize resource allocation and improve overall system performance;

[0103] Real-time monitoring and feedback ensure continuous system optimization.

[0104] 2. By adopting the dynamic adjustment scheme in this solution, when the service provided by the third-party platform fails or the response speed slows down, the query strategy can be adjusted in time to ensure query efficiency and accuracy.

[0105] 3. The implementation logic provided by this solution is simple, and the corresponding code reduces loop nesting, repeated calculations, etc., which can further improve query efficiency.

[0106] 4. Use parallel query to improve data query efficiency.

[0107] 5. This application solution uses cache and third-party platforms to reduce the number of visits to the database.

[0108] Based on Figure 1 Based on the same principle as the method shown in , the embodiment of the present invention also provides a data query device 20, such as Figure 2 As shown in , the data query device 20 may include an acquisition module 210, a keyword determination module 220, a judgment module 230 and a query module 240, wherein:

[0109] An acquisition module 210 is used to acquire a data query request for target data, wherein the data query request includes a query statement for the target data;

[0110] A keyword determination module 220, configured to determine a target query keyword corresponding to the query statement;

[0111] A judgment module 230, configured to judge whether the target query keyword is a query keyword in a keyword list;

[0112] A query module 240, configured to, if the target query keyword is a query keyword in the keyword list, obtain the target data from a cache according to the target query keyword, and if the target query keyword is not a query keyword in the keyword list, obtain the target data from a third-party query platform according to the target query keyword.

[0113] Optionally, the device further includes:

[0114] A keyword list determination module, configured to determine a service type identifier corresponding to the target data according to the query statement; and determine a keyword list corresponding to the service type identifier according to the service type identifier.

[0115] Optionally, the keyword list is a list formed by query keywords corresponding to frequencies greater than a set frequency in historical query frequencies.

[0116] Optionally, if the target data is data for multiple services, the device further includes:

[0117] A parallel processing module, configured to distribute the data query request to corresponding query servers according to query servers corresponding to each service, so as to complete the query of the target data in parallel through the corresponding query servers.

[0118] Optionally, when the obtaining module 210 obtains a data query request for target data, it is specifically configured to:

[0119] Obtain a data query request for target data through a query interface.

[0120] The data query device according to an embodiment of the present invention can execute the data query method provided by the embodiment of the present invention, and the implementation principle is similar. The actions performed by each module and unit in the data query device in each embodiment of the present invention correspond to the steps in the data query method in each embodiment of the present invention. For the detailed function descriptions of the modules of the data query device, reference can be specifically made to the descriptions in the corresponding data query methods shown above, and details are not described herein again.

[0121] Wherein, the above data query device may be a computer program (including program code) running in a computer device. For example, the data query device is an application software; the device may be used to execute corresponding steps in the method provided by the embodiment of the present invention.

[0122] In some embodiments, the data query device provided by the embodiment of the present invention can be implemented in a combination of software and hardware. As an example, the data query device provided by the embodiment of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the data query method provided by the embodiment of the present invention. For example, the processor in the form of a hardware decoding processor can adopt one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs) or other electronic components.

[0123] In other embodiments, the data query device provided by the embodiment of the present invention can be implemented in software. Figure 2 A data query device stored in a memory is shown, which may be software in the form of a program and a plug-in, and includes a series of modules, including an acquisition module 210, a keyword determination module 220, a judgment module 230 and a query module 240, for implementing the data query method provided in an embodiment of the present invention.

[0124] The modules involved in the embodiments of the present invention may be implemented by software or hardware, wherein the name of a module does not limit the module itself in some cases.

[0125] Based on the same principle as the method shown in the embodiments of the present invention, an electronic device is also provided in the embodiments of the present invention, which may include but is not limited to: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the method shown in any embodiment of the present invention by calling the computer program.

[0126] In an alternative embodiment, an electronic device is provided, such as Figure 3 As shown, Figure 3The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may also include a transceiver 4004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0127] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0128] The bus 4002 may include a path to transmit information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0129] The memory 4003 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0130] The memory 4003 is used to store the application code (computer program) for executing the solution of the present invention, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the application code stored in the memory 4003 to implement the content shown in the above method embodiment.

[0131] The electronic device may also be a terminal device. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0132] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0133] According to another aspect of the present invention, a computer program product or a computer program is also provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the implementation modes of the above-mentioned various embodiments.

[0134] Computer program code for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0135] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a portion of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0136] The computer-readable storage medium provided by the embodiments of the present invention may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0137] The above computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to execute the method shown in the above embodiments.

[0138] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles applied. Those skilled in the art should understand that the scope of disclosure involved in the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosure concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in the present invention.

Claims

1. A data query method, characterized in that: include: Obtaining a data query request for target data, wherein the data query request includes a query statement for the target data; Determine a target query keyword corresponding to the query statement; Determining whether the target query keyword is a query keyword in the keyword list; If the target query keyword is a query keyword in the keyword list, the target data is obtained from the cache according to the target query keyword. If the target query keyword is not a query keyword in the keyword list, the target data is obtained from a third-party query platform according to the target query keyword.

2. The method according to claim 1, characterized in that: The method further comprises: Determine, according to the query statement, a business type identifier corresponding to the target data; According to the service type identifier, a keyword list corresponding to the service type identifier is determined.

3. The method according to claim 1, characterized in that The keyword list is a list formed by query keywords corresponding to frequencies greater than a set frequency in historical query frequencies.

4. The method according to claim 1, characterized in that: If the target data is data for multiple services, the method further includes: According to the query server corresponding to each business, the data query request is distributed to the corresponding query server, so that the query of the target data is completed in parallel through the corresponding query server.

5. The method according to claim 1, characterized in that The obtaining of a data query request for target data includes: Obtain data query requests for target data through the query interface.

6. A data query device, characterized in that: include: An acquisition module, used to acquire a data query request for target data, wherein the data query request includes a query statement for the target data; A keyword determination module, used to determine the target query keyword corresponding to the query statement; A judgment module, used to judge whether the target query keyword is a query keyword in the keyword list; A query module is used to obtain the target data from the cache according to the target query keyword when the target query keyword is a query keyword in the keyword list, and to obtain the target data from a third-party query platform according to the target query keyword when the target query keyword is not a query keyword in the keyword list.

7. The device according to claim 6, characterized in that The device also includes: The keyword list determination module is used to determine the business type identifier corresponding to the target data according to the query statement; and determine the keyword list corresponding to the business type identifier according to the business type identifier.

8. The device according to claim 6, characterized in that The keyword list is a list formed by query keywords corresponding to frequencies greater than a set frequency in historical query frequencies.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 5 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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