Reverse-retrieval relational data storage system, data processing method and device

Abstract

This specification relates to the technical field of relational data storage systems, and provides a relational data storage system, data processing method, and apparatus with reverse retrieval capability. The method includes: receiving a retrieval request; determining a main business data table, a retrieval meta-model, and an extended meta-model corresponding to the retrieval request; extracting retrieval metadata from the retrieval request based on the retrieval meta-model; generating summary data from the retrieval metadata according to a preset summarization algorithm; retrieving extended data matching the summary data from the extended data table based on the extended meta-model; and retrieving main business data matching the extended data from the main business data table. The embodiments in this specification can achieve reverse retrieval of main data from extended data while maintaining system performance.

Description

Technical Field

[0001] This specification relates to the field of relational data storage system technology, and in particular to a relational data storage system, data processing method and apparatus with reverse retrieval capability. Background Technology

[0002] As business grows, the computer processing systems supporting those operations become increasingly complex. To cope with the explosive growth of new data models across multiple business lines, systems typically employ dynamic storage expansion based on relational data storage. However, due to the inconsistent number and types of retrieval fields in the expanded data models, reverse retrieval requires creating too many indexes. Yet, an excessive number of indexed columns and indexes in relational data storage can lead to performance issues. Therefore, how to achieve reverse retrieval of master data from expanded data while maintaining system performance has become a pressing technical problem to be solved. Summary of the Invention

[0003] The purpose of the embodiments in this specification is to provide a relational data storage system, data processing method and apparatus with reverse retrieval capability, so as to realize the reverse retrieval of master data from extended data while taking into account system performance.

[0004] To achieve the above objectives, in one aspect, embodiments of this specification provide a method for reverse retrieval of main business data from extended data, including:

[0005] Receive search request;

[0006] Determine the main business data table, retrieval meta-model, and extended meta-model corresponding to the retrieval request;

[0007] Based on the retrieval meta-model, retrieval metadata is extracted from the retrieval request;

[0008] The retrieved metadata is used to generate summary data according to a preset summarization algorithm;

[0009] Based on the extended meta-model, retrieve extended data that matches the summary data from the extended data table;

[0010] Retrieve the main business data that matches the extended data from the main business data table.

[0011] In the method for reverse retrieval of main business data from extended data in the embodiments of this specification, determining the main business data table, retrieval metamodel, and extended metamodel corresponding to the retrieval request includes:

[0012] Based on the business type in the search request, the corresponding main business data table is matched from the main business data table set, the corresponding search metamodel is matched from the search metamodel table, and the corresponding extended metamodel is matched from the extended metamodel table.

[0013] In the method for reverse retrieval of main business data from extended data in the embodiments of this specification, before retrieving main business data matching the extended data from the main business data table, the method further includes:

[0014] Compare the corresponding elements of the extended data with the retrieved metadata;

[0015] When the extended data and the corresponding element of the retrieved metadata are the same, the retrieval of the main business data matching the extended data is triggered from the main business data table.

[0016] On the other hand, embodiments of this specification also provide a data storage method, including:

[0017] Receive storage request;

[0018] Determine the main business data table, retrieval metamodel, and extended metamodel corresponding to the storage request;

[0019] Based on the extended meta-model and the retrieval meta-model, the storage request is split into main business data, extended data, and retrieval metadata;

[0020] The retrieved metadata is used to generate summary data according to a preset summarization algorithm;

[0021] Write the main business data into the main business data table, and write the extended data and the summary data into the extended data table.

[0022] In the data storage method of this specification's embodiments, determining the main business data table, retrieval metamodel, and extended metamodel corresponding to the storage request includes:

[0023] Based on the business type corresponding to the storage request, the corresponding main business data table is matched from the main business data table set, the corresponding retrieval metamodel is matched from the retrieval metamodel table, and the corresponding extended metamodel is matched from the extended metamodel table.

[0024] In the data storage method of this specification embodiment, the storage request is split into main business data, extended data, and retrieval metadata according to the extended meta-model and the retrieval meta-model, including:

[0025] Extended data is extracted from the storage request based on the extended metamodel;

[0026] Retrieval metadata is extracted from the storage request based on the retrieval metamodel; and...

[0027] Extract the specified fields from the storage request and use them as the main business data.

[0028] On the other hand, embodiments of this specification also provide a data retrieval device, including:

[0029] The first receiving module is used to receive retrieval requests;

[0030] The first determining module is used to determine the main business data table, retrieval meta-model, and extended meta-model corresponding to the retrieval request;

[0031] The first splitting module is used to split retrieval metadata from the retrieval request according to the retrieval meta-model;

[0032] The first generation module is used to generate summary data from the retrieved metadata according to a preset summary algorithm;

[0033] The first retrieval module is used to retrieve extended data that matches the summary data from the extended data table according to the extended meta-model;

[0034] The second retrieval module is used to retrieve main business data that matches the extended data from the main business data table.

[0035] On the other hand, embodiments of this specification also provide a data storage device, including:

[0036] The second receiving module is used to receive storage requests;

[0037] The second determining module is used to determine the main business data table, retrieval meta-model, and extended meta-model corresponding to the storage request;

[0038] The second splitting module is used to split the storage request into main business data, extended data, and retrieval metadata according to the extended meta-model and the retrieval meta-model.

[0039] The second generation module is used to generate summary data from the retrieved metadata according to a preset summary algorithm;

[0040] The data writing module is used to write the main business data into the main business data table, and to write the extended data and the summary data into the extended data table.

[0041] On the other hand, embodiments of this specification also provide a relational data storage system with reverse retrieval capability, the relational data storage system comprising: the data retrieval device described above, and the data storage device described above.

[0042] On the other hand, embodiments of this specification also provide a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the computer program, when run by the processor, executes instructions for the above-described method.

[0043] On the other hand, embodiments of this specification also provide a computer storage medium storing a computer program thereon, which, when run by the processor of a computer device, executes instructions for the above-described method.

[0044] On the other hand, embodiments of this specification also provide a computer program product, which includes a computer program that, when run by the processor of a computer device, executes instructions for the above-described method.

[0045] As can be seen from the technical solutions provided in the embodiments of this specification above, after determining the main business data table, retrieval meta-model, and extended meta-model corresponding to the retrieval request, the embodiments of this specification can extract retrieval metadata from the retrieval request according to the retrieval meta-model; generate summary data from the retrieval metadata according to a preset summarization algorithm; retrieve extended data matching the summary data from the extended data table according to the extended meta-model; and finally retrieve the main business data matching the extended data from the main business data table. This achieves reverse retrieval of main business data using extended data, thereby improving retrieval efficiency. Furthermore, since a fixed number of summary data sets can be generated from any number and type of retrieval metadata according to the summarization algorithm, the system performance degradation problem that may be caused by a large number or variety of retrieval metadata is avoided or reduced. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0047] Figure 1 This specification illustrates application scenarios of a relational data storage system with reverse retrieval capability in some embodiments.

[0048] Figure 2 This specification shows a block diagram of a relational data storage system capable of reverse retrieval in some embodiments;

[0049] Figure 3 A flowchart illustrating a method for reverse retrieval of main business data from extended data in some embodiments of this specification is shown;

[0050] Figure 4 Flowcharts of data storage methods in some embodiments of this specification are shown;

[0051] Figure 5 This specification shows a structural block diagram of a data retrieval device in some embodiments;

[0052] Figure 6 This specification shows structural block diagrams of data storage devices in some embodiments;

[0053] Figure 7 A structural block diagram of a computer device in some embodiments of this specification is shown.

[0054] [Explanation of Labels in the Attached Image]

[0055] 10. Client-side application;

[0056] 20. A relational data storage system with reverse retrieval capability;

[0057] 21. Data storage device;

[0058] 22. Data retrieval device;

[0059] 221. First receiving module;

[0060] 222. First Determined Module;

[0061] 223. First splitting module;

[0062] 224. First generation module;

[0063] 225. First search module;

[0064] 226. Second search module;

[0065] 211. Second receiving module;

[0066] 212. Second Determination Module;

[0067] 213. Second splitting module;

[0068] 214. Second generation module;

[0069] 215. Data writing module;

[0070] 702. Computer equipment;

[0071] 704, Processor;

[0072] 706. Memory;

[0073] 708. Drive mechanism;

[0074] 710. Input / output interfaces;

[0075] 712. Input devices;

[0076] 714. Output devices;

[0077] 716. Presentation equipment;

[0078] 718. Graphical User Interface;

[0079] 720. Network interface;

[0080] 722. Communication link;

[0081] 724. Communication bus. Detailed Implementation

[0082] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0083] The embodiments described in this specification relate to a relational data storage system technology with reverse retrieval capability, enabling the function of reverse retrieval of master data from extended data while maintaining system performance. This technology can be applied to scenarios such as online transaction processing (e.g., financial transaction systems, retail systems, ticketing sales systems, etc.) and online analytical processing (e.g., reporting systems, CRM systems, financial risk prediction and early warning systems, anti-money laundering systems, data marts).

[0084] Figure 1 The diagram illustrates application scenarios of a relational data storage system 20 with reverse retrieval capability in some embodiments of this specification. The relational data storage system 20 with reverse retrieval capability can receive retrieval requests and / or storage requests from client 10, generate corresponding retrieval results and / or storage results based on the retrieval requests and / or storage requests, and return them. Combined with... Figure 2 As shown, the relational data storage system 20 with reverse retrieval capability may include a data storage device 21 and a data retrieval device 22. Wherein:

[0085] The data storage device 21 can be used to receive storage requests; determine the main business data table, retrieval meta-model, and extended meta-model corresponding to the storage request; split the storage request into main business data, extended data, and retrieval metadata according to the extended meta-model and the retrieval meta-model; generate summary data from the retrieval metadata according to a preset summary algorithm; write the main business data into the main business data table, and write the extended data and the summary data into the extended data table.

[0086] The data retrieval device 22 can be used to receive retrieval requests; determine the main business data table, retrieval meta-model, and extended meta-model corresponding to the retrieval request; extract retrieval metadata from the retrieval request according to the retrieval meta-model; generate summary data from the retrieval metadata according to a preset summarization algorithm; retrieve extended data matching the summary data from the extended data table according to the extended meta-model; and retrieve main business data matching the extended data from the main business data table. This enables reverse retrieval of main business data from extended data. Furthermore, since a fixed number of summary data entries can be generated from any number and type of retrieval metadata according to the summarization algorithm, the system performance degradation problem that may be caused by a large number or variety of retrieval metadata entries is avoided or reduced.

[0087] In some embodiments, the relational data storage system 20 with reverse retrieval capability can be an electronic device with computing and network interaction functions; it can also be software running on the electronic device that provides business logic for data processing and network interaction. The client 10 can be a self-service terminal device, a mobile terminal (i.e., a smartphone), a monitor, a desktop computer, a tablet computer, a laptop computer, a digital assistant, or a smart wearable device, etc. Smart wearable devices can include smart bracelets, smartwatches, smart glasses, or smart helmets, etc. Of course, the client is not limited to the aforementioned physical electronic devices; it can also be software running on the aforementioned electronic devices.

[0088] This specification provides an embodiment of a method for reverse retrieval of main business data from extended data, which can be applied to the aforementioned relational data storage system with reverse retrieval capability. (Refer to...) Figure 3 As shown, in some embodiments, the method for retrieving main business data from extended data may include the following steps:

[0089] Step 301: Receive search request.

[0090] Step 302: Determine the main business data table, retrieval meta-model, and extended meta-model corresponding to the retrieval request.

[0091] In the embodiments of this specification, a set of master business data tables can be defined. Each master business data table in the set can correspond to a specific business type. For example, one master business data table might correspond to payment services, while another might correspond to coupon services. Each master business data table stores business fields that do not change across different scenarios. For example, taking the payment scenario as an example, when a user makes a payment using the same account, offline card payment and online payment belong to different scenarios. However, regardless of whether it's offline card payment or online payment, fields such as the payment account and payer remain fixed; therefore, they can serve as master business data.

[0092] In the embodiments of this specification, an extended business table can be defined to store fields with unique meanings in the business scenario. For example, the transaction reference number is a field specific to card payment, and the authorization code is a field specific to online payment.

[0093] An extended metamodel refers to a metamodel of extended data, used to define the structure and semantics of extended data. Similarly, a retrieval metamodel refers to a metamodel of retrieval metadata, used to define the structure and semantics of retrieval metadata. Retrieval metadata can also be referred to as retrieval criteria.

[0094] In some embodiments, based on the business type in the search request, the system can match the corresponding main business data table from the main business data table set, match the corresponding search metamodel from the search metamodel table, and match the corresponding extended metamodel from the extended metamodel table. For example, in an exemplary embodiment, taking payment business as a search condition, the system can match the main business data table corresponding to the payment business from the main business data table set, match the search metamodel corresponding to the payment business from the search metamodel table (each search metamodel can correspond to multiple records in the search metamodel table), and match the extended metamodel corresponding to the payment business from the extended metamodel table (each extended metamodel corresponds to one record in the extended metamodel table).

[0095] Table 1

[0096] M001 The province where the merchant is located The city where the merchant is located Issuing bank Receiving bank Payment IP M002 Coupon amount Coupon Name Discount coupon amount Discount Coupon Name Total discount amount … … … … … …

[0097] For example, in an exemplary embodiment, taking the extended meta-model table shown in Table 1 as an example, metaId (metadata identifier) ​​is the identifier of the extended meta-model (e.g., M001, M002, etc.), and each row in Table 1 corresponds to an extended meta-model.

[0098] Table 2

[0099]

[0100]

[0101] For example, in an exemplary embodiment, taking the retrieval metamodel table shown in Table 2 as an example, ID (identifier) ​​is the row identifier. In Table 2, multiple rows with the same metaId value constitute a retrieval metamodel. For example, when metaId is M001, the three rows with IDs ID001, ID002, and ID003 constitute a retrieval metamodel. By matching the metaId and column of Table 2 with Table 1, the specific meaning of the retrieval metamodel can be reconstructed. The meaning of the above retrieval metamodel is: the merchant's province, the merchant's city, and the receiving bank.

[0102] Step 303: Extract retrieval metadata from the retrieval request based on the retrieval meta-model.

[0103] In some embodiments, a search request mainly includes search conditions. For example, in an exemplary embodiment, taking Table 2 above as an example, when the search conditions are ID001 to ID003, the metaId can be determined according to the business scenario of the search request; the corresponding extended metamodel can be found and identified based on the metaId; the columns to be searched can be identified based on the metaId; the search columns can be found from the extended metamodel to identify which fields need to be searched; and these fields are then identified from the search request. That is, for the business scenario where metaId is M001, columns 1, 2, and 4 (the merchant's province, the merchant's city, and the receiving bank) in the extended data table need to be used as the search metamodel, and the search data in the search request is identified based on the search metamodel.

[0104] Step 304: Generate summary data from the retrieved metadata according to the preset summary algorithm.

[0105] The preset digest algorithm can be customized as needed. For example, in some embodiments, the digest algorithm may include, but is not limited to, MD5, SHA256, etc.

[0106] Step 305: Retrieve extended data that matches the summary data from the extended data table according to the extended meta-model.

[0107] For example, in an exemplary embodiment, taking the extended meta-model table shown in Table 1 as an example, "Merchant's province, merchant's city, and receiving bank" in Table 1 are extended data corresponding to the exemplary summary data in step 304.

[0108] Step 306: Retrieve the main business data that matches the extended data from the main business data table.

[0109] In the embodiments of this specification, the primary key of the main business data table corresponding to the extended data can be used to retrieve the main business data table corresponding to the retrieval request, thereby obtaining the retrieval results, which can then be returned to the client. For example, taking the extended meta-model table shown in Table 1 as an example, the retrieval data can be determined based on M001, thereby generating a retrieval summary indexCode. Based on the indexCode, an extended data record is retrieved in the extended data table. Based on the masterId of this record, the main business data can be retrieved in the corresponding main business data table. In this way, the main business data can be retrieved by using extended data to retrieve it in reverse, which is beneficial to improving retrieval efficiency. Moreover, since a fixed number of summary data can be generated from any number and type of retrieval metadata according to the summary algorithm, the system performance degradation problem that may be caused by a large number or many types of retrieval metadata is avoided or reduced.

[0110] In other embodiments, before retrieving the main business data matching the extended data from the main business data table, the process may further include:

[0111] The extended data is compared with the corresponding elements of the retrieval metadata. If the corresponding elements of the extended data and the retrieval metadata are all the same, the retrieval of the main business data matching the extended data from the main business data table is triggered; otherwise, the process ends. The digest algorithm can map data of arbitrary length to a fixed-length string, and when the inputs are different, the output fixed-length strings are highly likely to be different. However, it is impossible to completely eliminate the possibility of different inputs yielding the same output. Therefore, to improve the accuracy of the retrieval results, the corresponding elements of the extended data and the retrieval metadata can be compared, and the comparison result can be used to determine whether it is necessary to proceed to the next step (i.e., retrieving the main business data matching the extended data from the main business data table).

[0112] This specification also provides a data storage method, which can be applied to the aforementioned relational data storage system with reverse retrieval capability. This data storage method is the foundation and prerequisite for realizing the method of reverse retrieval of main business data from extended data. (Reference) Figure 4 As shown, in some embodiments, the data storage method may include the following steps:

[0113] Step 401: Receive storage request.

[0114] Step 402: Determine the main business data table, retrieval metamodel, and extended metamodel corresponding to the storage request.

[0115] In this embodiment of the specification, the corresponding main business data table can be matched from the main business data table set, the corresponding retrieval metamodel can be matched from the retrieval metamodel table, and the corresponding extended metamodel can be matched from the extended metamodel table, according to the business type corresponding to the storage request.

[0116] Step 403: Based on the extended meta-model and the retrieval meta-model, split the storage request into main business data, extended data, and retrieval metadata.

[0117] In the embodiments of this specification, splitting the storage request into main business data, extended data, and retrieval metadata according to the extended metamodel and the retrieval metamodel may include:

[0118] Extended data is extracted from the storage request based on the extended metamodel;

[0119] Retrieval metadata is extracted from the storage request based on the retrieval metamodel; and...

[0120] Extract the specified fields from the storage request and use them as the main business data.

[0121] For example, in an exemplary embodiment, if a storage request contains the contents shown in Table 3 below:

[0122] Table 3

[0123]

[0124]

[0125] (1) If the extended metamodel includes: "Merchant location, payment issuing bank, payment IP", then the following extended data can be extracted from the content of the storage request shown in Table 3:

[0126] {M001, XX Province, YY City, RR Bank, SS Bank, 29.1.xx.5, b1192d3}

[0127] (2) If the retrieval metamodel includes "merchant location, payment card issuer", the following retrieval metadata can be extracted from the content of the storage request shown in Table 3:

[0128] {M001, XX Province, YY City, RR Bank}

[0129] (3) If the payment account and payer fields are specified as the main business data, then the following main business data will be extracted from the content of the storage request shown in Table 3:

[0130] {10bcde,Liu xx}

[0131] Step 404: Generate summary data from the retrieved metadata according to the preset summary algorithm.

[0132] Step 405: Write the main business data into the main business data table, and write the extended data and the summary data into the extended data table.

[0133] Unlike traditional data storage methods, the data storage method in this embodiment, based on determining the main business data table, retrieval meta-model, and extended meta-model corresponding to the storage request, can split the storage request into main business data, extended data, and retrieval metadata according to the retrieval meta-model and extended meta-model, and generate summary data from the retrieval metadata according to a preset summary algorithm; then, the main business data is written into the main business data table, and the extended data and the summary data are written into the extended data table; thereby providing a foundation for subsequent reverse retrieval of main business data based on extended data, and also improving data insertion (storage) efficiency.

[0134] Although the process described above includes multiple operations that occur in a specific order, it should be clearly understood that these processes may include more or fewer operations, which may be executed sequentially or in parallel (e.g., using parallel processors or a multithreaded environment).

[0135] With the above Figure 3 Corresponding to the extended data reverse retrieval method for main business data shown, this specification also provides a data retrieval device in its embodiments, see reference. Figure 5 As shown, in some embodiments, the data retrieval device may include:

[0136] The first receiving module 221 is used to receive retrieval requests;

[0137] The first determining module 222 is used to determine the main business data table, retrieval meta-model and extended meta-model corresponding to the retrieval request;

[0138] The first splitting module 223 is used to split retrieval metadata from the retrieval request according to the retrieval meta-model;

[0139] The first generation module 224 is used to generate summary data from the retrieved metadata according to a preset summary algorithm;

[0140] The first retrieval module 225 is used to retrieve extended data that matches the summary data from the extended data table according to the extended meta-model;

[0141] The second retrieval module 226 is used to retrieve main business data that matches the extended data from the main business data table.

[0142] With the above Figure 4 Corresponding to the data storage method shown, this specification also provides a data storage device in its embodiments, see reference. Figure 6 As shown, in some embodiments, the data storage device may include:

[0143] The second receiving module 211 is used to receive storage requests;

[0144] The second determining module 212 is used to determine the main business data table, retrieval meta-model and extended meta-model corresponding to the storage request;

[0145] The second splitting module 213 is used to split the storage request into main business data, extended data and retrieval metadata according to the extended meta-model and the retrieval meta-model.

[0146] The second generation module 214 is used to generate summary data from the retrieved metadata according to a preset summary algorithm;

[0147] The data writing module 215 is used to write the main business data into the main business data table, and to write the extended data and the summary data into the extended data table.

[0148] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0149] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this specification are all information and data authorized and agreed upon by the user and fully authorized by all parties.

[0150] Embodiments of this specification also provide a computer device. For example... Figure 7 As shown, in some embodiments of this specification, the computer device 702 may include one or more processors 704, such as one or more central processing units (CPUs) or graphics processing units (GPUs), each of which may implement one or more hardware threads. The computer device 702 may also include any memory 706 for storing information of any kind, such as code, settings, data, etc. In one specific embodiment, a computer program is stored on the memory 706 and can run on the processor 704. When the computer program is run by the processor 704, it can execute instructions described in any of the above embodiments. Without limitation, for example, the memory 706 may include any type of RAM, any type of ROM, flash memory, hard disk, optical disk, etc. More generally, any memory can use any technology to store information. Further, any memory can provide volatile or non-volatile retention of information. Further, any memory may represent a fixed or removable component of the computer device 702. In one case, when the processor 704 executes associated instructions stored in any memory or combination of memories, the computer device 702 can perform any operation of the associated instructions. The computer device 702 also includes one or more drive mechanisms 708 for interacting with any memory, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.

[0151] Computer device 702 may also include an input / output interface 710 (I / O) for receiving various inputs (via input device 712) and providing various outputs (via output device 714). A specific output mechanism may include a presentation device 716 and an associated graphical user interface 718 (GUI). In other embodiments, the input / output interface 710 (I / O), input device 712, and output device 714 may be omitted, and the device may function solely as a computer device within a network. Computer device 702 may also include one or more network interfaces 720 for exchanging data with other devices via one or more communication links 722. One or more communication buses 724 couple the components described above together.

[0152] Communication link 722 can be implemented in any way, such as via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, or any combination thereof. Communication link 722 may include any combination of hardwired links, wireless links, routers, gateway functions, name-reverse-retrieval relational data storage systems, etc., governed by any protocol or combination of protocols.

[0153] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), computer-readable storage media, and computer program products according to some embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processor to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processor, create a mechanism for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0154] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processor to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0155] These computer program instructions may also be loaded onto a computer or other programmable data processor, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0156] In a typical configuration, a computer device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0157] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0158] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by computer equipment. As defined in this specification, computer-readable media does not include transient media, such as modulated data signals and carrier waves.

[0159] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of computer program products implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0160] The embodiments described in this specification can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. The embodiments of this specification can also be practiced in distributed computing environments where tasks are performed by remote processors connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0161] It should also be understood that, in the embodiments of this specification, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0162] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0163] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the embodiments of this specification. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0164] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for reverse retrieval of main business data from extended data, characterized in that, include: Receive search request; Determine the main business data table, retrieval meta-model, and extended meta-model corresponding to the retrieval request; The retrieval metamodel is metadata used to define the structure and semantics of retrieval metadata, and the extended metamodel is metadata used to define the structure and semantics of extended data; Based on the retrieval meta-model, retrieval metadata is extracted from the retrieval request; The retrieved metadata is used to generate summary data according to a preset summarization algorithm; Based on the extended meta-model, extended data matching the summary data is retrieved from the extended data table; the extended data table is a data table that stores the extended data and its corresponding summary data, and the extended data is field data with special meaning in the business scenario; Retrieve the main business data that matches the extended data from the main business data table.

2. The method for reverse retrieval of main business data from extended data as described in claim 1, characterized in that, Determining the main business data table, retrieval metamodel, and extended metamodel corresponding to the retrieval request includes: Based on the business type in the search request, the corresponding main business data table is matched from the main business data table set, the corresponding search metamodel is matched from the search metamodel table, and the corresponding extended metamodel is matched from the extended metamodel table.

3. The method for reverse retrieval of main business data from extended data as described in claim 1, characterized in that, Before retrieving the main business data that matches the extended data from the main business data table, the process also includes: Compare the corresponding elements of the extended data with the retrieved metadata; When the extended data and the corresponding element of the retrieved metadata are the same, the retrieval of the main business data matching the extended data is triggered from the main business data table.

4. A data storage method, characterized in that, include: Receive storage request; Determine the main business data table, retrieval metamodel, and extended metamodel corresponding to the storage request; The retrieval metamodel is metadata used to define the structure and semantics of retrieval metadata, and the extended metamodel is metadata used to define the structure and semantics of extended data; Based on the extended meta-model and the retrieval meta-model, the storage request is split into main business data, extended data, and retrieval metadata; the extended data consists of field data with unique meanings in the business scenario. The retrieved metadata is used to generate summary data according to a preset summarization algorithm; Write the main business data into the main business data table, and write the extended data and the summary data into the extended data table; The extended data table is a data table that stores the extended data and its corresponding summary data.

5. The data storage method as described in claim 4, characterized in that, Determining the main business data table, retrieval metamodel, and extended metamodel corresponding to the storage request includes: Based on the business type corresponding to the storage request, the corresponding main business data table is matched from the main business data table set, the corresponding retrieval metamodel is matched from the retrieval metamodel table, and the corresponding extended metamodel is matched from the extended metamodel table.

6. The data storage method as described in claim 4, characterized in that, Based on the extended metamodel and the retrieval metamodel, the storage request is split into main business data, extended data, and retrieval metadata, including: Extended data is extracted from the storage request based on the extended metamodel; Retrieval metadata is extracted from the storage request based on the retrieval metamodel; and... Extract the specified fields from the storage request and use them as the main business data.

7. A data retrieval device, characterized in that, include: The first receiving module is used to receive retrieval requests; The first determining module is used to determine the main business data table, retrieval meta-model, and extended meta-model corresponding to the retrieval request; The retrieval metamodel is metadata used to define the structure and semantics of retrieval metadata, and the extended metamodel is metadata used to define the structure and semantics of extended data; The first splitting module is used to split retrieval metadata from the retrieval request according to the retrieval meta-model; The first generation module is used to generate summary data from the retrieved metadata according to a preset summary algorithm; The first retrieval module is used to retrieve extended data that matches the summary data from the extended data table according to the extended meta-model; the extended data table is a data table that stores the extended data and its corresponding summary data, and the extended data is field data with special meaning in the business scenario; The second retrieval module is used to retrieve main business data that matches the extended data from the main business data table.

8. A data storage device, characterized in that, include: The second receiving module is used to receive storage requests; The second determining module is used to determine the main business data table, retrieval meta-model, and extended meta-model corresponding to the storage request; The retrieval metamodel is metadata used to define the structure and semantics of retrieval metadata, and the extended metamodel is metadata used to define the structure and semantics of extended data; The second splitting module is used to split the storage request into main business data, extended data, and retrieval metadata according to the extended meta-model and the retrieval meta-model. The extended data consists of field data that has unique meaning in the business scenario; The second generation module is used to generate summary data from the retrieved metadata according to a preset summary algorithm; The data writing module is used to write the main business data into the main business data table, and to write the extended data and the summary data into the extended data table; The extended data table is a data table that stores the extended data and its corresponding summary data.

9. A relational data storage system with reverse retrieval capability, characterized in that, The relational data storage system includes: the data retrieval device of claim 7, and the data storage device of claim 8.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, When the computer program is run by the processor, it executes the instructions of the method according to any one of claims 1-6.

11. A computer storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor of the computer device, it executes the instructions of the method according to any one of claims 1-6.

12. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, performs instructions according to any one of claims 1-6.

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