System and method for grid processing of financial data, apparatus, computing device

Abstract

A grid processing system and method, device and computing equipment for financial data, the method comprising: obtaining a data processing request, the data processing request comprising a request identifier; determining a target storage device from a plurality of storage devices according to the request identifier, wherein the target storage device is a storage device corresponding to the request identifier, and there is a corresponding relationship between the type of the financial data and the storage device; reading a plurality of data groups from the target storage device, wherein the target storage device comprises a plurality of storage units, different storage units in the same storage device correspond to different value ranges of the financial data, and the data groups correspond to the storage units one by one; and processing the financial data in the plurality of data groups to obtain a processing result. Through the scheme of the present application, the processing efficiency and other performances of the financial data can be improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a grid-based processing system, method, apparatus, and computing device for financial data. Background Technology

[0002] In existing technologies, the historical data of multiple financial products is typically processed, and the financial product to be purchased is determined based on the processing results. However, since historical data of financial products is usually massive, the efficiency and performance of processing financial data using existing technologies remain low.

[0003] Therefore, there is an urgent need for a grid-based processing method for financial data to improve the efficiency and performance of financial data processing. Summary of the Invention

[0004] The technical problem solved by this invention is to provide a gridded processing method for financial data to improve the processing efficiency and other performance aspects of financial data.

[0005] To address the aforementioned technical problems, this invention provides a gridded processing method for financial data. The method includes: acquiring a data processing request, the data processing request including a request identifier, the request identifier indicating the type of financial data requested by the user; determining a target storage device from multiple storage devices based on the request identifier, wherein there is a correspondence between the type of financial data and the storage devices, and the target storage device is the storage device corresponding to the request identifier; reading multiple data groups from the target storage device, wherein the target storage device includes multiple storage units, different storage units in the same storage device correspond to different value ranges of financial data, and the data groups correspond one-to-one with the storage units; and processing the financial data in the multiple data groups to obtain a processing result.

[0006] Optionally, the types of financial data correspond one-to-one with the storage devices, or each storage device corresponds to multiple types, and the types corresponding to any two storage devices are different, wherein the multiple types corresponding to each storage device are related.

[0007] Optionally, the types of financial data correspond one-to-one with the storage devices, and there is an association between the types of financial data. Specifically, a copy of the financial data stored in each storage device is stored in a storage device that is associated with that storage device.

[0008] Optionally, the request identifier includes a first identifier and a second identifier, wherein the first identifier is different from the second identifier. The target storage device includes a first storage module and a second storage module. The first storage module includes multiple first storage units, and the second storage module includes multiple second storage units. Different storage units in the same storage module correspond to different ranges of financial data values. Reading multiple data groups from the target storage device includes: reading multiple data groups corresponding to the first identifier from the multiple first storage units in the first storage module; and reading multiple data groups corresponding to the second identifier from the multiple second storage units in the second storage module.

[0009] Optionally, determining the target storage device from multiple storage devices based on the request identifier includes: reading information about a storage network, the storage network including multiple nodes, each node corresponding to a type of financial data, the information about the storage network including storage devices corresponding to each node; searching for a target node corresponding to the request identifier from the multiple nodes, and using the storage device corresponding to the target node as the target storage device.

[0010] Optionally, the information of the storage network also includes connection lines between nodes, which are used to represent the correlation between the types of financial data. The connection lines have weights, and the magnitude of the weights of the connection lines is used to indicate the degree of correlation between the types. Nodes whose weights of the connection lines are greater than a preset threshold correspond to the same storage device.

[0011] Optionally, the data processing request includes multiple request identifiers, and the method further includes: updating the weights of the connection lines between the nodes corresponding to the multiple request identifiers according to the multiple request identifiers.

[0012] This invention also provides a gridded processing device for financial data, comprising: a request acquisition module for acquiring a data processing request, the data processing request including a request identifier, the request identifier indicating the type of financial data requested by the user; a device determination module for determining a target storage device from multiple storage devices based on the request identifier, wherein there is a correspondence between the type of financial data and the storage device, and the target storage device is the storage device corresponding to the request identifier; a reading module for reading multiple data groups from the target storage device, wherein the target storage device includes multiple storage units, the data groups correspond one-to-one with the storage units, and different storage units in the same storage device correspond to different value ranges of financial data; and a processing module for processing the financial data in the multiple data groups to obtain a processing result.

[0013] This invention also provides a storage medium storing a computer program, which, when run by a processor, performs the steps of the above-described method for gridding financial data.

[0014] This invention also provides a computing device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the steps of the above-described gridded processing method for financial data when running the computer program.

[0015] This invention also provides a gridded processing system for financial data. The system includes: a computing platform for executing the above-described gridded processing method for financial data; and multiple storage devices, each storage device corresponding to a type of financial data, each storage device including multiple storage units, and different storage units within the same storage device corresponding to different ranges of financial data values.

[0016] Compared with the prior art, the technical solution of the embodiments of the present invention has the following beneficial effects:

[0017] In the embodiments of this invention, since there is a correspondence between storage devices and types of financial data, and the request identifier is used to indicate the type of financial data requested by the user, the target storage device can be determined from multiple storage devices based on the request identifier. Furthermore, since the target storage device includes multiple storage units, and the value ranges of financial data corresponding to different storage units within the same storage device are different, multiple data groups can be read from the multiple storage units of the target storage device, and the financial data of the multiple data groups can be processed. Compared with the prior art scheme of reading and processing various types of financial data from the same database, the scheme in this embodiment reads different types of financial data from different storage devices and reads financial data with different value ranges from different storage units. That is, it decouples massive amounts of financial data according to the type and value range of the financial data, and reads different types and value ranges of financial data from different storage locations when processing the financial data, which is beneficial to improving the processing efficiency and other performance aspects of financial data.

[0018] Furthermore, in the embodiments of the present invention, each storage device corresponds to multiple types, and the types corresponding to any two storage devices are different, with a correlation between the types corresponding to each storage device. By adopting this approach, financial data with correlation between types are stored on the same storage device, which facilitates the reading and processing of related financial data, thereby further improving the processing efficiency and other performance aspects of financial data.

[0019] Furthermore, in the solution of this embodiment of the invention, the storage network includes multiple nodes. The information of the storage network includes the storage devices corresponding to each node and the connection lines between the nodes. Since there is a one-to-one correspondence between nodes and types of financial data, the connection lines can be used to represent the association between types of financial data. The connection lines have weights, and the magnitude of the weights of the connection lines is used to indicate the degree of association between types. When a data processing request includes multiple request identifiers, the weights of the connection lines between the nodes corresponding to the multiple request identifiers are updated according to the multiple request identifiers. Thus, the degree of association between types of financial data can be dynamically updated according to the user's data processing request. Since nodes with connection line weights greater than a preset threshold correspond to the same storage device, the storage location of the financial data can also be dynamically adjusted, which is beneficial to further improve the processing efficiency and other performance aspects of financial data. Attached Figure Description

[0020] Figure 1 This is a schematic diagram illustrating an application scenario of a gridded processing method for financial data according to an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram illustrating an application scenario of another grid-based processing method for financial data in this embodiment of the invention;

[0022] Figure 3 This is a flowchart illustrating a gridded processing method for financial data according to an embodiment of the present invention;

[0023] Figure 4 This is a schematic diagram of the structure of a gridded processing device for financial data in an embodiment of the present invention. Detailed Implementation

[0024] As described in the background section, there is an urgent need for a gridded processing method for financial data that can improve the processing efficiency and other performance aspects of financial data.

[0025] The inventors of this invention discovered through research that processing financial data first requires retrieving the financial data from the database, and then processing the retrieved financial data. During financial data processing, it is typically necessary to process financial data with different value ranges separately. In existing technologies, financial data is usually stored in a centralized manner, meaning that various types of financial data are usually stored in the same database. As the types and volume of financial data continue to increase, this centralized storage method results in relatively low processing efficiency and cannot meet practical needs.

[0026] To address the aforementioned technical problems, embodiments of the present invention provide a gridded processing method for financial data. In this embodiment, since there is a correspondence between storage devices and types of financial data, and a request identifier indicates the type of financial data requested by the user, a target storage device can be determined from multiple storage devices based on the request identifier. Furthermore, since the target storage device includes multiple storage units, and different storage units within the same storage device correspond to different value ranges of financial data, multiple data groups can be read from the multiple storage units of the target storage device, and the financial data in these multiple data groups can be processed. Compared to existing solutions that read and process various types of financial data from the same database, the solution in this embodiment reads different types of financial data from different storage devices and reads financial data with different value ranges from different storage units. That is, it decouples massive amounts of financial data based on the type and value range of the financial data, and reads different types and value ranges of financial data from different storage locations during processing, which improves the efficiency and performance of financial data processing.

[0027] To make the above-mentioned objectives, features and beneficial effects of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0028] Reference Figure 1 , Figure 1 This is a schematic diagram illustrating an application scenario of a gridded processing method for financial data according to an embodiment of the present invention. The financial data can be various types of data used for financial statistics and analysis, such as parameters of financial products like stocks and funds, or various types of data obtained through financial statistics, such as indicator data of financial products like stocks and funds, or financial data of a company entity, but is not limited to these.

[0029] Specifically, the computing platform 12 can be coupled to the user terminal 11 for data interaction. The computing platform 12 can also be coupled to multiple storage devices 13 for data interaction with each storage device 13 (e.g., storage device 1, storage device 2, ..., storage device n). The computing platform 12 may include at least one server, and the user terminal 11 may be a terminal used by a user, such as a mobile phone, computer, tablet computer, or other suitable terminal device. It should be noted that this embodiment of the invention does not limit the number of user terminals 11.

[0030] Furthermore, the plurality of storage devices 13 can be various existing devices with data storage functions, such as database servers, but are not limited thereto. It should be noted that the number of the plurality of storage devices 13 is n, where n is a positive integer greater than 1, and the embodiments of the present invention do not impose a limit on the number of storage devices 13.

[0031] Furthermore, multiple storage devices 13 can be used to store different types of financial data, wherein there is a correspondence between the types of financial data and the storage devices 13. More specifically, each storage device 13 can correspond to one or more types of financial data. In a non-limiting example, the financial data can be the historical indicator data of financial products such as stocks; that is, the type of financial data is the type of indicator, such as the relative strength index, stochastic oscillator, trend indicator, etc.

[0032] In a specific example, there is a one-to-one correspondence between the types of financial data and the storage devices 13; that is, each storage device 13 corresponds to one type of financial data, and the types of financial data stored on multiple storage devices 13 are different. Furthermore, copies of the financial data stored in each storage device 13 can be stored on other storage devices 13 that are associated with that storage device. The association between the storage devices 13 is determined based on the association between the types of financial data.

[0033] In another specific example, each storage device 13 can correspond to multiple types, and every two storage devices 13 correspond to different types. The multiple types corresponding to storage devices 13 are related. It should be noted that some storage devices 13 may correspond to one type of financial data, while other storage devices 13 may correspond to multiple types of financial data. This embodiment of the invention does not impose any limitations on this.

[0034] Furthermore, the storage device 13, which corresponds to various types of storage devices, may include multiple storage modules (not shown in the figure), and the storage modules may correspond one-to-one with the types of financial data.

[0035] Furthermore, each storage device 13 may include multiple storage units (not shown), and the value ranges of the financial data corresponding to the multiple storage units of the same storage device 13 are different. That is, financial data of the same type can be stored in different storage units according to the value range to which the financial data belongs. More specifically, the storage device 13 may include multiple storage modules, and each storage module may include multiple storage units, with different value ranges of financial data corresponding to different storage units in the same storage module. As a non-limiting example, a storage module may be a disk array, and correspondingly, a storage unit may be a disk in the disk array; or, a storage module may be a disk, and correspondingly, a storage unit may be a partition in the disk.

[0036] Furthermore, the computing platform 12 can obtain multiple pieces of financial data to be stored from the user terminal 11, and store the multiple pieces of financial data into the corresponding storage devices 13 according to their types. More specifically, for each piece of financial data, after determining the storage device 13 corresponding to its type, the platform can determine the corresponding storage unit based on the value range of the financial data, and write the financial data into that storage unit. Thus, a grid-based approach can be used to store financial data in multiple storage devices 13.

[0037] Furthermore, the computing platform 12 can also obtain data processing requests from the user terminal 11. These requests may include a request identifier, which can indicate the type of financial data requested by the user. Even further, the computing platform 12 can read the requested financial data from multiple storage devices 13 based on the request identifier. After reading the financial data, the computing platform 12 can either directly send the read financial data to the user terminal 11, or process the read financial data and then send the processing result to the user terminal 11, etc.

[0038] Reference Figure 2 , Figure 2 This is a schematic diagram illustrating an application scenario of another grid-based processing method for financial data in this embodiment of the invention. The following is... Figure 2 and Figure 1 Explain the differences. For example... Figure 2 As shown, the computing platform 12 may include a first scheduling server 121, a second scheduling server 122, and multiple computing servers 123 (such as computing server 1, computing server 2, ... computing server k).

[0039] Specifically, the first scheduling server 121 can be coupled to multiple user terminals 11 and also to multiple computing servers 123. The first scheduling server 121 can obtain multiple data processing requests from multiple user terminals 11 (such as user terminal 1, user terminal 2, ..., user terminal m), and allocate these requests to the multiple computing servers 123 for processing based on their available computing resources. The number of user terminals 11 is m, where m is a positive integer greater than 1.

[0040] Furthermore, the second scheduling server 122 can be coupled to multiple computing servers 123, or to multiple storage devices 13 (such as storage device 1, storage device 2, ..., storage device n). For each computing server 123, the second scheduling server 122 can obtain the request identifier from the data processing request from the computing server 123, determine the target storage device based on the request identifier, read multiple data groups from multiple storage units of the target storage device, and send the financial data of the read multiple data groups to the computing server 123 for processing to obtain the processing result.

[0041] about Figure 2 For more information on the application scenarios of another grid-based financial data processing method shown above, please refer to the section on... Figure 1 The relevant descriptions will not be repeated here.

[0042] Reference Figure 3 , Figure 3 This is a flowchart illustrating a gridded processing method for financial data according to an embodiment of the present invention. The method can be executed by a computing device, which can be any existing device with data receiving and processing capabilities, such as a server, or, for example, a computer. Figure 3 The computing platform 12 shown is an example, but not limited to it. Through... Figure 3 The gridded processing method for financial data shown can efficiently read and process financial data from multiple storage devices. Figure 3 The grid-based analysis method for financial data shown may include the following steps:

[0043] Step S301: Obtain a data processing request, wherein the data processing request includes a request identifier;

[0044] Step S302: Determine the target storage device from multiple storage devices based on the request identifier;

[0045] Step S303: Read multiple data groups from the target storage device;

[0046] Step S304: Process the financial data in the multiple data groups to obtain the processing result.

[0047] It is understood that, in specific implementations, the method can be implemented using a software program that runs in a processor integrated within the chip or chip module; or, the method can be implemented using hardware or a combination of hardware and software.

[0048] In a specific implementation of step S301, a data processing request can be obtained from an external source, for example, from... Figure 1 or Figure 2The user terminal 11 shown receives a data processing request. This data processing request may include a request identifier, which can be used to indicate the type of financial data requested by the user. Each data processing request may include one request identifier, or it may include multiple request identifiers; this embodiment of the invention does not impose any limitation on this.

[0049] In the specific implementation of step S302, the target storage device can be determined from multiple storage devices based on the request identifier.

[0050] Specifically, there can be a correspondence between storage devices and types of financial data. That is, each storage device can correspond to one or more types of financial data, and the types of financial data corresponded to by each storage device are different. In other words, the correspondence between storage devices and types of financial data can be one-to-one or one-to-many.

[0051] For more information on the correspondence between storage devices and types of financial data, please refer to the above text. Figure 1 The relevant descriptions will not be repeated here.

[0052] Furthermore, since the request identifier can be used to indicate the type of data requested by the user, the storage device corresponding to the target type can be determined from multiple storage devices based on the request identifier, and denoted as the target storage device. Here, the target type is the type of data indicated by the request identifier.

[0053] Specifically, when the data request identifier contains only a single request identifier, the target storage device can be a single storage device. When the data request identifier includes multiple request identifiers, the target storage device can be multiple storage devices or a single storage device.

[0054] In a specific example, information about a storage network can be read. This storage network may include multiple nodes, each corresponding one-to-one with a type of financial data. The information may include the storage devices corresponding to each node; for example, it may include the identifiers of the storage devices for each node, but is not limited to this. It should be noted that the node and the type of financial data are in a one-to-one correspondence; therefore, the storage device corresponding to each node is the storage device corresponding to the data type for that node.

[0055] Furthermore, based on the request identifier, a node corresponding to the type of financial data indicated by the request identifier can be found from multiple nodes in the storage network. This node is designated as the target node, and the storage device corresponding to the target node is designated as the target storage device.

[0056] Furthermore, the information in the storage network may also include connections between nodes, which represent the relationships between different types of financial data. In a specific example, there is a connection between every two nodes, and each connection has a weight. The weight of the connection indicates the degree of association between the types of nodes connected by the connection; the greater the weight of the connection, the greater the degree of association between the types of nodes connected by the connection. Nodes with connection weights greater than a preset threshold can correspond to the same storage device. That is, if the weight of the connection between two nodes is greater than the preset threshold, the financial data corresponding to those two nodes are stored in the same storage device. The preset threshold can be pre-set, and this embodiment of the invention does not limit the value of the preset threshold.

[0057] Furthermore, if the data processing request includes multiple request identifiers, the weights of the connections between the nodes corresponding to those identifiers can be updated based on these identifiers. Specifically, the weights of the connections between any two nodes corresponding to any two request identifiers in the data processing request can be updated. It should be noted that the updated weights are greater than the original weights.

[0058] In a specific example, the data processing request includes a first identifier and a second identifier, where the first identifier differs from the second identifier. The type of financial data indicated by the first identifier is denoted as the first target type, and the type of financial data indicated by the second identifier is denoted as the second target type. Further, the node corresponding to the first target type is denoted as the first target node, and the node corresponding to the second target type is denoted as the second target node. Updating the weight of the connection lines between nodes corresponding to the request identifiers can include updating the weight of the connection lines between the first and second target nodes, and can also include updating the weight of the connection lines between the first target node and the second associated node. The second associated node is any node in the storage network connected to the second target node, excluding the first target node. This scheme improves the correlation between different types and facilitates dynamic adjustment of the storage location of financial data.

[0059] Furthermore, for two nodes connected by a connection line, if the weight before the update is less than or equal to a preset threshold and the weight after the update is greater than the preset threshold, then the financial data stored in the storage device corresponding to either of the two nodes can be migrated to the storage device corresponding to the other node, and the information of the storage network can be modified so that the two nodes correspond to the same storage device.

[0060] It should be noted that the step of updating the weight of the connection line can be performed before step S303, after step S303 and before step S304, or after step S304. This embodiment of the invention does not limit this.

[0061] In the specific implementation of step S303, multiple data groups can be read from the target storage device. Specifically, each storage device may include multiple storage units, and different storage units within the same storage device correspond to different value ranges of financial data. Therefore, there is a one-to-one correspondence between data groups and value ranges of financial data. For the target storage device, multiple data groups can be read from multiple storage units, where financial data read from the same storage unit belongs to the same data group. Thus, financial data of multiple value ranges of the target type can be read.

[0062] In a specific example, the target storage device can be used to store multiple types of financial data; that is, the target storage device corresponds to multiple types of financial data. The target storage device may include multiple storage modules, and the types of financial data stored in every two storage modules are different. Each storage module may include multiple storage units, and financial data of the corresponding type can be read from the multiple storage units of each storage module.

[0063] More specifically, the data processing request may include a first identifier and a second identifier, wherein the type of financial data indicated by the first identifier is denoted as a first target type, and the type of financial data indicated by the second identifier is denoted as a second target type. The target storage device may include a first storage module and a second storage module. The first storage module is used to store financial data of the first target type, and the second storage module is used to store financial data of the second target type. The first storage module may include multiple first storage units, and the second storage module may include multiple second storage units. Different storage units within the same storage module correspond to different value ranges of financial data. Multiple data groups of the first target type can be read from the multiple first storage units in the first storage module, and multiple data groups of the second target type can also be read from the multiple second storage units in the second storage module. That is, the type of financial data in the multiple data groups read from the multiple first storage units is the first target type, and the type of financial data in the multiple data groups read from the multiple second storage units is the second target type.

[0064] In a specific implementation of step S304, financial data from multiple data groups can be processed to obtain processing results. Furthermore, the processing results can be sent to an external user terminal. It should be noted that this embodiment of the invention does not impose any limitations on the processing procedure.

[0065] In a specific example, for each type of financial data, the optimal value range corresponding to that target type can be determined, and the optimal value range corresponding to the target type can be sent to the user terminal as the processing result.

[0066] Specifically, for each data set, the financial data within that set can be processed to determine the intermediate processing result. For multiple data sets of the same type, the intermediate processing results of the multiple data sets can be compared, and the optimal intermediate processing result can be determined from among them. Furthermore, since there is a one-to-one correspondence between data sets and value ranges, the value range corresponding to the optimal intermediate processing result can be taken as the optimal value range for that type.

[0067] As described above, the solution of this embodiment of the invention decouples massive amounts of financial data according to the type and value range of the financial data. When processing the financial data, it reads financial data of different types and value ranges from different storage locations, which helps to improve the processing efficiency and other performance of financial data.

[0068] refer to Figure 4 , Figure 4 This is a gridded processing device for financial data in an embodiment of the present invention. Figure 4 The apparatus shown may include:

[0069] The request acquisition module 41 is used to acquire a data processing request, the data processing request including a request identifier, the request identifier being used to indicate the type of financial data requested by the user;

[0070] Device determination module 42 is used to determine a target storage device from multiple storage devices according to the request identifier, wherein there is a correspondence between the type of financial data and the storage devices, and the target storage device is the storage device corresponding to the request identifier;

[0071] The reading module 43 is used to read multiple data groups from the target storage device, wherein the target storage device includes multiple storage units, the data groups correspond one-to-one with the storage units, and the value range of financial data corresponding to different storage units in the same storage device is different;

[0072] The processing module 44 is used to process the financial data in the multiple data groups to obtain the processing results.

[0073] In specific implementation, the aforementioned gridded processing device for financial data can correspond to a chip with data processing function within the terminal; or to a chip module with data processing function within the terminal; or to the terminal itself.

[0074] about Figure 4 For more information on the working principle, operation, and beneficial effects of the grid-based financial data processing device shown, please refer to the above section on... Figures 1 to 3 The relevant descriptions will not be repeated here.

[0075] This invention also provides a storage medium storing a computer program, which, when executed by a processor, performs the steps of the aforementioned gridded processing method for financial data. The storage medium may include ROM, RAM, a hard disk, or an optical disk, etc. The storage medium may also include non-volatile memory or non-transitory memory, etc.

[0076] This invention also provides a computing device, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it performs the steps of the above-described gridded processing method for financial data. The computing device includes, but is not limited to, terminal devices such as mobile phones, computers, tablets, and servers.

[0077] Reference Figure 1 or Figure 2 This invention also provides a gridded processing system for financial data. The system may include: a computing platform 12, which can be used to execute the above-described gridded processing method for financial data; and multiple storage devices 13, which correspond to the types of financial data. Each storage device includes multiple storage units, and different storage units in the same storage device 13 correspond to different ranges of values ​​for the financial data.

[0078] For more information on the working principles, methods, and benefits of grid-based financial data processing systems, please refer to the above section. Figures 1 to 4 The relevant descriptions will not be repeated here.

[0079] It should be understood that in the embodiments of this application, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0080] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0081] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means.

[0082] In the several embodiments provided in this application, it should be understood that the disclosed methods, apparatus, and systems can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for example, the division of units is merely a logical functional division, and other division methods may exist in actual implementation; for example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0083] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can be physically included separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or in a combination of hardware and software functional units. For example, for various devices or products applied to or integrated into a chip, each module / unit can be implemented using hardware such as circuits, or at least some modules / units can be implemented using software programs running on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware such as circuits; for various devices or products applied to or integrated into a chip module, each module / unit can be implemented using hardware such as circuits, and different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware such as circuits. The components can be implemented using software programs that run on the processor integrated within the chip module. The remaining (if any) modules / units can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into the terminal, each of its components / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or in different components within the terminal. Alternatively, at least some modules / units can be implemented using software programs that run on the processor integrated within the terminal, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits.

[0084] It should be understood that the term "and / or" in this article 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, or B existing alone. Additionally, the character " / " in this article indicates that the preceding and following related objects have an "or" relationship.

[0085] In the embodiments of this application, "multiple" refers to two or more.

[0086] The descriptions of "first," "second," etc., appearing in the embodiments of this application are for illustrative purposes and to distinguish the objects being described. They have no order and do not indicate any special limitation on the number of devices in the embodiments of this application, nor do they constitute any limitation on the embodiments of this application.

[0087] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A grid-based processing method for financial data, characterized in that, The method includes: Obtain a data processing request, the data processing request including a request identifier, the request identifier being used to indicate the type of financial data requested by the user; A target storage device is determined from multiple storage devices based on the request identifier, wherein there is a correspondence between the type of financial data and the storage devices, and the target storage device is the storage device corresponding to the request identifier; Multiple data groups are read from the target storage device, wherein the target storage device includes multiple storage units, and different storage units in the same storage device correspond to different ranges of financial data. Each data group corresponds to a storage unit. The financial data in the multiple data groups are processed to obtain the processing results; Determining the target storage device from multiple storage devices based on the request identifier includes: Read information about the storage network, which includes multiple nodes. Each node corresponds to a type of financial data. The information about the storage network includes the storage devices corresponding to each node and the connection lines between the nodes. The connection lines are used to represent the association between the types of financial data. Each connection line has a weight. The magnitude of the weight of the connection line is used to indicate the degree of association between the types. Nodes whose weight of the connection line is greater than a preset threshold correspond to the same storage device. Find the target node corresponding to the request identifier from the plurality of nodes, and use the storage device corresponding to the target node as the target storage device; The data processing request includes multiple request identifiers, and the method further includes: Update the weights of the connections between the nodes corresponding to the multiple request identifiers; For two nodes connected by a connection line, in response to the weight before the update being less than or equal to a preset threshold and the weight after the update being greater than the preset threshold, the financial data stored in the storage device corresponding to either of the two nodes is migrated to the storage device corresponding to the other node, and the information of the storage network is modified so that the two nodes correspond to the same storage device.

2. The grid-based processing method for financial data according to claim 1, characterized in that, The types of financial data correspond one-to-one with the storage devices. or, Each storage device corresponds to multiple types, and every two storage devices correspond to different types. Among these, the multiple types corresponding to each storage device are related to each other.

3. The grid-based processing method for financial data according to claim 2, characterized in that, The types of financial data correspond one-to-one with the storage devices, and there are correlations between the types of financial data. Each storage device stores a copy of the financial data in another storage device, which is associated with that storage device.

4. The grid-based processing method for financial data according to claim 1, characterized in that, The request identifier includes a first identifier and a second identifier, wherein the first identifier is different from the second identifier. The target storage device includes a first storage module and a second storage module. The first storage module includes multiple first storage units, and the second storage module includes multiple second storage units. Different storage units within the same storage module correspond to different value ranges of financial data. Reading multiple data groups from the target storage device includes: Read multiple data groups corresponding to the first identifier from multiple first storage units in the first storage module; Multiple data groups corresponding to the second identifier are read from multiple second storage units in the second storage module.

5. A grid-based processing device for financial data, characterized in that, The device includes: The request acquisition module is used to acquire data processing requests, the data processing requests including a request identifier, the request identifier being used to indicate the type of financial data requested by the user; The device determination module is used to determine a target storage device from multiple storage devices based on the request identifier, wherein there is a correspondence between the type of financial data and the storage devices, and the target storage device is the storage device corresponding to the request identifier; The reading module is used to read multiple data groups from the target storage device, wherein the target storage device includes multiple storage units, the data groups correspond one-to-one with the storage units, and the value range of financial data corresponding to different storage units in the same storage device is different; The processing module is used to process the financial data in the multiple data groups to obtain the processing results; The data processing request includes multiple request identifiers, and the device determination module performs the following steps: Read information about the storage network, which includes multiple nodes. Each node corresponds to a type of financial data. The information about the storage network includes the storage devices corresponding to each node and the connection lines between the nodes. The connection lines are used to represent the association between the types of financial data. Each connection line has a weight. The magnitude of the weight of the connection line is used to indicate the degree of association between the types. Nodes whose weight of the connection line is greater than a preset threshold correspond to the same storage device. Find the target node corresponding to the request identifier from the plurality of nodes, and use the storage device corresponding to the target node as the target storage device; Update the weights of the connections between the nodes corresponding to the multiple request identifiers; For two nodes connected by a connection line, in response to the weight before the update being less than or equal to a preset threshold and the weight after the update being greater than the preset threshold, the financial data stored in the storage device corresponding to either of the two nodes is migrated to the storage device corresponding to the other node, and the information of the storage network is modified so that the two nodes correspond to the same storage device.

6. A storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it performs the steps of the gridded processing method for financial data as described in any one of claims 1 to 4.

7. A computing device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor runs the computer program, it performs the steps of the gridded processing method for financial data as described in any one of claims 1 to 4.

8. A grid-based processing system for financial data, characterized in that, The system includes: A computing platform, wherein the computing platform is used to execute the gridded processing method for financial data as described in any one of claims 1 to 4; Multiple storage devices are provided, and the storage devices correspond to the types of financial data. Each storage device includes multiple storage units, and the value range of the financial data corresponding to different storage units in the same storage device is different.

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