CN111737303A - Data query method, device, computer equipment and storage medium - Google Patents

Abstract

The invention relates to a block chain technology, and provides a data query method, which comprises the following steps: receiving a data query request; detecting whether a newly added data change record exists in a redis cluster; when the detection result is yes, analyzing the data change record to obtain a changed data processor identifier; detecting whether the changed data processor identification is consistent with the target identification; when the detection result is negative, inquiring the data cached in the redis cluster according to the data inquiry request to obtain target inquiry data; and storing the target query data into a target local storage corresponding to the target identifier. The invention also provides a data query device and related equipment. According to the invention, the resource data are prevented from being frequently acquired from the redis cluster in a mode of combining local storage and the redis cache, and the data processing efficiency is improved.

Description

Data query method, device, computer equipment and storage medium

technical field

The present invention relates to the technical field of data processing, and in particular, to a data query method, device, computer equipment and storage medium.

Background technique

In the insurance claims system, each link of insurance claims may involve the intervention of claims adjusters to make manual claims. Every link in a large insurance claims system needs to query information such as user roles and affiliations, assign tasks, control permissions, and verify data. Therefore, in order to improve the data processing capability of insurance claims, it is necessary to increase the query rate of user information.

In a traditional user information query process, user information is generally stored in a relational database, such as Mysql database and Oracle database. The query for user information generally needs to be implemented based on database query. However, when the amount of user information is large, only relying on the relational database to implement information query will result in a longer response time and more redundant data queried.

Therefore, it is urgent to provide a data query method to adapt to the fast query of large resource data volume.

SUMMARY OF THE INVENTION

In view of the above content, it is necessary to provide a data query method, data query device, computer equipment and computer-readable storage medium, so as to improve the data query rate.

An embodiment of the present invention provides a data query method, which is applied to a target data processor, and the data query method includes:

Receive data query requests;

Detect whether there is a new data change record in the redis cluster;

When the detection result is that there is a newly added data change record in the redis cluster, analyze the data change record to obtain the changed data processor identifier;

Detecting whether the changed data processor identification is consistent with the target identification corresponding to the target data processor;

When the detection result is that the changed data processor identifier is inconsistent with the target identifier, query the data cached in the redis cluster according to the data query request to obtain target query data;

The target query data is stored in the target local storage corresponding to the target identifier.

Further, in the above data query method provided by the embodiment of the present invention, before the receiving a data query request, the method further includes:

Get the current remaining resources;

Detecting whether the current remaining resource amount is greater than a preset resource amount;

When the detection result is that the current remaining resource amount is greater than the preset resource amount, a data query request is received.

Further, in the above data query method provided by the embodiment of the present invention, the detecting whether a new data change record exists in the redis cluster includes:

Detecting whether the redis cluster has data in the preset data format, and when the detection result is that the redis cluster has data in the preset data format, determine that a new data change record exists in the redis cluster; or

Detecting whether a preset tag exists in the redis cluster, and when the detection result is that the preset tag exists in the redis cluster, it is determined that a newly added data change record exists in the redis cluster.

Further, in the above data query method provided by the embodiment of the present invention, after the data query request is received, the method further includes:

Detect whether there is a preset mark in the redis cluster;

When the detection result is that the preset mark exists in the redis cluster, the data change record is added;

When the detection result is that the preset mark does not exist in the redis cluster, it is detected whether there is a newly added data change record in the redis cluster.

Further, in the above data query method provided by the embodiment of the present invention, the adding the data change record includes:

querying a preset database, the preset database is a database in the blockchain, and the preset database stores the latest user information;

acquiring the latest user information corresponding to the preset mark;

storing the latest user information in the target local storage corresponding to the target identifier;

The latest user information is stored in the redis cluster, and a data change record is generated according to the target identifier.

Further, in the above data query method provided by the embodiment of the present invention, after the target query data is stored in the target local storage corresponding to the target identifier, the method further includes:

generating a target data change record according to the target identifier;

Store the target data change record in the redis cluster;

Obtain the number of data change records corresponding to the same updated user information in the redis cluster;

Detecting whether the number of the data change records is consistent with the number of the data processors;

When the detection result shows that the number of the data change records is consistent with the number of the data processors, delete the data change records corresponding to the same updated user information in the redis cluster.

Further, in the above data query method provided by the embodiment of the present invention, the method further includes:

When the detection result is that the changed data processor identifier is consistent with the target identifier, determine the target local storage corresponding to the target identifier;

Query the target local storage according to the data query request to obtain query data.

A second aspect of the embodiments of the present invention further provides a data query device, where the data query device includes:

A request receiving module for receiving data query requests;

The record detection module is used to detect whether there is a new data change record in the redis cluster;

an identification parsing module, configured to parse the data change record to obtain the changed data processor identification when the detection result is that a newly added data change record exists in the redis cluster;

a consistency detection module, for detecting whether the changed data processor identification is consistent with the target identification corresponding to the target data processor;

A data query module, configured to query the data cached in the redis cluster according to the data query request to obtain target query data when the detection result is that the changed data processor identifier is inconsistent with the target identifier;

A data storage module, configured to store the target query data in a target local storage corresponding to the target identifier.

A third aspect of the embodiments of the present invention further provides a computer device, where the computer device includes a processor, and the processor is configured to implement any one of the data query methods described above when executing the computer program stored in the memory.

A fourth aspect of the embodiments of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, any one of the data query methods described above is implemented.

Embodiments of the present invention provide a data query method, a data query device, computer equipment, and a computer-readable storage medium. By combining local storage and redis cluster cache, frequent acquisition of resource data from the redis cluster is avoided, thereby improving the performance of the redis cluster. Data processing efficiency.

Description of drawings

FIG. 1 is a flowchart of a data query method provided by a first embodiment of the present invention.

FIG. 2 is a schematic structural diagram of a computer device according to an embodiment of the present invention.

FIG. 3 is an exemplary functional block diagram of the computer device shown in FIG. 2 .

The following specific embodiments will further illustrate the present invention in conjunction with the above drawings.

Detailed ways

In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments may be combined with each other in the case of no conflict.

In the following description, numerous specific details are set forth in order to facilitate a full understanding of the present invention, and the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terms used herein in the description of the present invention are for the purpose of describing specific embodiments only, and are not intended to limit the present invention.

FIG. 1 is a flowchart of a data query method according to a first embodiment of the present invention. The data query method can be applied to an insurance claim settlement system, which includes an insurance claim settlement execution terminal, a redis cluster, a preset database for maintaining user information, a data processor, and a corresponding local storage. It should be emphasized that, in order to further ensure the privacy and security of user information, the above-mentioned preset database can also be a database in the blockchain. As shown in FIG. 1 , the data query method is applied to a target data processor, and the target data processor refers to a data processor that receives the data query method. The data query method may include the following steps:

S11. Receive a data query request.

In at least one embodiment of the present invention, the insurance claims execution end is used to execute related insurance claims procedures, and the insurance claims procedures may include: reporting, acquiring, checking, adjusting, reviewing, closing, and paying. From the reporting link to the payment link, every link may involve the intervention of claims adjusters to make manual claims. Therefore, each link of the insurance claims process needs to query the user roles, affiliations and other information of the claims adjusters, which are used to assign tasks, control permissions, and verify data.

The data query request is a request output by the insurance claim execution end for requesting to query user hotspot information, and the data query request carries the user hotspot information to be queried. The user hotspot information is information that appears frequently during query, and the user hotspot information includes user ID, user role R, and affiliated institution D, wherein the same user may correspond to different user roles and belong to different institutions, for example, the user U1 can correspond to the role of R1 in the organization D1, and can also correspond to the role of R2 in the organization D2, which is not limited here.

In at least one embodiment of the present invention, the insurance claim execution end establishes a communication connection with a data processor, the data processor is essentially an execution unit for information query and processing, and the data processor is used to receive the insurance claim The data query request output by the execution side. For each said data processor, there is a unique identification IP. For example, the number of the data processors is 4, which are data processors A, B, C, and D, respectively, and the corresponding identifiers are IP_A, IP_B, IP_C, and IP_D, respectively. Wherein, it is determined that the data processor receiving the data query request is the target data processor, and the corresponding identifier thereof is the target identifier.

In an insurance claim settlement system, the number of data processors may be multiple, and the number of data processors is determined according to the load balancing requirement of the actual data query request, and the data processors are all used to respond to the data query request. data query. Specifically, the method further includes: acquiring a first number of data query requests received within a preset time period; determining a second number of the data query requests for branch lines of each data processor within the preset time period; according to The first number and the second number determine the number of data processors. The preset duration is preset, for example, the preset duration may be 24 hours. The first number of data query requests received within a preset time period can be obtained by querying the log.

In at least one embodiment of the present invention, when the number of the data processors is multiple, before the receiving a data query request, the method further includes: acquiring a current remaining resource; detecting the current remaining resource Whether the amount is greater than the preset resource amount; when the detection result is that the current remaining resource amount is greater than the preset resource amount, a data query request is received. Wherein, a plurality of the data processors are correspondingly provided with initial resources for processing data query requests. When receiving a data query request, the data processor allocates relevant resources to the insurance claim settlement execution end for executing the current request. The preset amount of resources is the amount of resources needed to process a data query request, and the preset amount of resources can be obtained according to neural network learning, or obtained according to empirical values, which is not limited herein.

Specifically, when a user submits a data query request on the insurance claim execution end, he needs to input relevant user information (including but not limited to user ID, user role, affiliation, etc.) into the user interface program corresponding to the insurance claim execution end , the user interface program will transmit the relevant user information to the task scheduler of the data processor to implement the submission process of the data query request. Wherein, the user interface program may be a web application program, and the user needs to operate the user interface program through a web browser; the task scheduler is used to analyze, schedule and manage the tasks submitted by the insurance claims execution end.

In at least one embodiment of the present invention, after the data query request is received, the method further includes: detecting whether the redis cluster has a preset mark; when the detection result is that the redis cluster has the preset mark , the data change record is newly added; when the detection result is that the preset mark does not exist in the redis cluster, it is detected whether there is a newly added data change record in the redis cluster. Specifically, the adding the data change record includes: querying a preset database, where the latest user information is stored; acquiring the latest user information corresponding to the preset mark; storing the latest user information The updated user information is stored in the target local storage corresponding to the target identifier; the updated user information is stored in the redis cluster, and a data change record is generated according to the target identifier.

In one embodiment, when the preset database updates user information, an update instruction is output to the redis cluster, and a preset marker is added to the redis cluster, and the preset marker includes user hotspot information. In other embodiments, the method further includes: when the detection result is that a preset mark exists in the redis cluster, verifying the first user hotspot information carried in the data query request and the first user hotspot information represented by the preset mark Whether the hotspot information of the two users is related; when the test result is that the hotspot information of the first user carried in the data query request is related to the hotspot information of the second user indicated by the preset mark, the data change record is added; When the verification result is that the first user's hotspot information carried in the data query request is not associated with the second user's hotspot information indicated by the preset mark, it is detected whether there is a newly added data change record in the redis cluster. Wherein, verifying whether the first user hotspot information carried in the data query request is related to the second user hotspot information indicated by the preset mark, that is, verifying the first user hotspot information and the second user hotspot information Whether the information has the same information item (the information item may include information such as user ID, user role R, and affiliated institution D, etc.), if there is the same information item, the two are associated. It can be understood that, for the situation that the hotspot information of the first user is not associated with the hotspot information of the second user, temporarily stopping adding the data change record can reduce the operations that need to be performed for the current data query to a certain extent. Thereby improving the efficiency of data query.

In one embodiment, when the detection result is that there is no preset mark in the redis cluster, it means that the user hotspot information in the preset database has not been updated, and it can be performed to detect whether there is a new data change in the redis cluster. Recording step; when the detection result is that there is a preset mark in the redis cluster, it means that the user hotspot information in the preset database is updated, and the preset database needs to be queried to obtain the updated user hotspot information.

In one embodiment, a processing mechanism is set, and when the preset database is updated, an update instruction is output to the redis cluster, and a preset mark is added to the redis cluster. For the target data processor that currently receives the data query request, when it is detected that a preset mark exists in the redis cluster, updated user information needs to be obtained from the preset database.

In one embodiment, for each data processor, there is a corresponding local storage, and the local storage is used to store all user information. For example, for the data processors A, B, C and D, the corresponding local storage includes local storage A, B, C and D. The data format stored in the local storage includes a Map format, wherein the key is the user ID, and the value is the user's role, organization and other information.

S12. Detect whether a newly added data change record exists in the redis cluster, and when the detection result is that a newly added data change record exists in the redis cluster, step S13 is performed.

In at least one embodiment of the present invention, the redis cluster is associated with a preset database that maintains user information, the preset database may be a Mysql type database, and the preset database stores all user information, Including: user hotspot information and user non-hotspot information, wherein the user's non-hotspot information includes user phone, email, gender and other information. All user hotspot information in the preset database is cached in the Redis cluster, and the load balancing capability and error recovery capability of the redis cluster are used to realize the scalability and high availability of resource data caching, avoiding frequent changes from the preset database. Obtain data from the database, thereby speeding up data reading.

In at least one embodiment of the present invention, the data processor may output a detection instruction to the Redis cluster, where the detection instruction is used to detect whether a new data change record exists in the redis cluster. The data change record is stored according to a preset data format, and a label may be provided on the data change record for identifying the data change record. The data change record includes: the changed data processor IP, whether it has been changed and the changed user ID. The data change record is used to indicate that the data processor corresponding to the current IP has changed the relevant data of the user ID.

In at least one embodiment of the present invention, the detecting whether a new data change record exists in the redis cluster includes: detecting whether the redis cluster has data in a preset data format, and when the detection result is that the redis cluster has the preset data format When setting the data in the data format, it is determined that a newly added data change record exists in the redis cluster. Alternatively, the step of detecting whether a new data change record exists in the redis cluster further includes: detecting whether a preset label exists in the redis cluster, and when the detection result is that the redis cluster has the preset label, determining the redis cluster A new data change record exists in the cluster.

S13. Parse the data change record to obtain the changed data processor identifier.

In at least one embodiment of the present invention, the data change record includes the changed data processor IP, whether it has been changed, and the changed user hotspot information. The data change record is used to indicate that the data processor corresponding to the current IP has changed the relevant data of the user ID.

In other embodiments, before analyzing the data change record to obtain the changed data processor identifier, the method may further include: analyzing the data change record to obtain the changed user hotspot information; verifying the Whether the first user hotspot information carried in the data query request is associated with the changed user hotspot information; when the verification result is that the first user hotspot information carried in the data query request is associated with the changed user hotspot information , continue to perform step S13; when the verification result is that the first user hotspot information carried in the data query request is not associated with the changed user hotspot information, determine the target local storage corresponding to the target data processor, and The data query request is executed in the target local storage. It can be understood that, in the case where the first user hotspot information is not associated with the changed user hotspot information, the first user hotspot information is not changed, and the first user hotspot is stored in the target local storage. information, directly query the target local storage, and improve the data query efficiency.

S14. Detect whether the changed data processor identifier is consistent with the target identifier corresponding to the target data processor. When the detection result is that the changed data processor identifier is inconsistent with the target identifier, step S15 is performed.

In at least one embodiment of the present invention, detecting whether the IP of the data processor is consistent with the target identifier means detecting whether the local storage of the data processor corresponding to the target identifier is the latest user information. When the detection result is that the changed data processor identifier is inconsistent with the target identifier, it is determined that the user information stored in the local storage of the data processor corresponding to the target identifier needs to be updated, and step S15 is executed. When the detection result is that the changed data processor identifier is consistent with the target identifier, it is determined that the latest user information is stored in the local storage of the data processor corresponding to the target identifier.

Specifically, the method further includes: when the detection result is that the changed data processor identifier is consistent with the target identifier, determining the target local storage corresponding to the target identifier; querying the target according to the data query request Local storage, get query data. It can be understood that, when the detection result is that the changed data processor identifier is consistent with the target identifier, querying the local storage according to the data query request can improve data query efficiency.

S15. Query the data cached in the redis cluster according to the data query request to obtain target query data.

In at least one embodiment of the present invention, when the detection result is that the changed data processor identifier is inconsistent with the target identifier, the user hotspot information in the redis cluster is the latest at this time. Therefore, the target data processor is called to query the data cached in the redis cluster to obtain target query data. The target query data is data corresponding to the data query request.

S16. Store the target query data in a target local storage corresponding to the target identifier.

In at least one embodiment of the present invention, when the detection result is that the changed data processor identifier is inconsistent with the target identifier, the user hotspot information in the preset database is updated at this time, and the changed data in the data change record The local storage corresponding to the data processor IP and the user hotspot information in the redis cluster have been updated, while the user hotspot information in the local storage corresponding to other data processors has not been updated. Therefore, the target query data is stored to in the target local storage corresponding to the target identifier.

In at least one embodiment of the present invention, after the step of storing the target query data in a target local storage corresponding to the target identifier, the method further includes: generating a target data change record according to the target identifier ; Store the target data change record in the redis cluster; obtain the number of data change records corresponding to the same updated user information in the redis cluster; Detect the number of the data change records and the data processor Whether the number of data change records is consistent; when the number of data change records in the detection result is consistent with the number of data processors, delete the data change records corresponding to the same updated user information in the redis cluster.

It can be understood that when the detection result is that the number of data change records is consistent with the number of data processors, deleting the data change records corresponding to the same updated user information in the redis cluster can reduce the number of redis clusters. The storage space for the data change records in the system, so as to avoid wasting storage space.

In at least one embodiment of the present invention, the method may further include: within a preset time interval, detecting whether the number of the data change records is consistent with the number of the data processors; when the detection result is the data The number of change records is inconsistent with the number of the data processors, and the identifier corresponding to the data processor that has not been updated with data is determined; a prompt is output to the data processor corresponding to the identifier to prompt the data processor to perform data processing in time. renew. The preset time interval is preset by the computer device, for example, the preset time interval is 30 days. By outputting a prompt to prompt the data processor to update the data, it can ensure that the information in the local storage corresponding to the multiple data processors is in the latest state, thereby improving the data query efficiency.

The data query method provided by the embodiment of the present invention avoids frequently acquiring resource data from the redis cluster by combining the local storage and the redis cluster cache, thereby improving the data processing efficiency.

The above is a detailed description of the methods provided by the embodiments of the present invention. According to different requirements, the execution order of the blocks in the shown flowcharts can be changed, and some blocks can be omitted. The computer device 1 provided by the embodiment of the present invention is described below.

FIG. 2 is a schematic structural diagram of a computer device according to an embodiment of the present invention. As shown in FIG. 2 , the computer device 1 includes a memory 10 , and the data query apparatus 100 is stored in the memory 10 . The computer device 1 may be an electronic device such as a computer, a tablet computer, a personal digital assistant, etc., which has functions such as data processing, analysis, program execution, and display. The data query device 100 can receive a data query request; detect whether a new data change record exists in the redis cluster; when the detection result is that a new data change record exists in the redis cluster, parse the data change record to obtain Changed data processor identification; Detect whether the changed data processor identification is consistent with the target identification corresponding to the target data processor; when the detection result is that the changed data processor identification is inconsistent with the target identification, Query the data cached in the redis cluster according to the data query request to obtain target query data; store the target query data in the target local storage corresponding to the target identifier. Using the present invention, by combining local storage and redis cluster cache, frequent acquisition of resource data from redis cluster is avoided, thereby improving data processing efficiency.

In this embodiment, the computer device 1 may further include a display screen 20 and a processor 30 . The memory 10 and the display screen 20 may be electrically connected to the processor 30, respectively.

The memory 10 can be different types of storage devices for storing various types of data. For example, it can be the memory or internal memory of the computer device 1, or it can be a memory card that can be externally connected to the computer device 1, such as a flash memory, an SM card (SmartMedia Card, smart media card), an SD card (Secure Digital Card, a secure digital card). )Wait. In addition, the memory 10 may include non-volatile memory such as hard disk, internal memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card (Flash Card), At least one disk storage device, flash memory device, or other non-volatile solid state storage device. The memory 10 is used to store various types of data, for example, various types of application programs (Applications) installed in the computer device 1 , data set and acquired by applying the above-mentioned data query method, and other information.

The display screen 20 is installed in the computer device 1 for displaying information.

The processor 30 is configured to execute the data query method and various types of software installed in the computer device 1 , such as an operating system and application display software. The processor 30 includes, but is not limited to, a processor (Central Processing Unit, CPU), a Micro Controller Unit (Micro Controller Unit, MCU) and other devices for interpreting computer instructions and processing data in computer software.

The data query apparatus 100 may include one or more modules, and the one or more modules are stored in the memory 10 of the computer device 1 and configured to be executed by one or more processors (in this embodiment, a The processor 30) executes to complete the embodiment of the present invention. Referring to FIG. 3 , the data query apparatus 100 may include a request receiving module 101 , a record detection module 102 , an identification parsing module 103 , a consistency detection module 104 , a data query module 105 and a data storage module 106 . A module referred to in this embodiment of the present invention may be a program segment that performs a specific function, and is more suitable for describing the execution process of software in the processor 30 than a program.

It can be understood that, corresponding to the various embodiments of the above data query method, the data query apparatus 100 may include some or all of the functional modules shown in FIG. 3 , and the functions of each module will be described in detail below. It should be noted that, the same nouns, related nouns and their specific explanations in the above embodiments of the data query method can also be applied to the following description of the functions of each module. For the sake of saving space and avoiding repetition, details are not repeated here.

The request receiving module 101 may be configured to receive a data query request.

The record detection module 102 can be used to detect whether a new data change record exists in the redis cluster.

The identifier parsing module 103 may be configured to parse the data change record to obtain the changed data processor identifier when the detection result is that a newly added data change record exists in the redis cluster.

The consistency detection module 104 may be configured to detect whether the changed data processor identification is consistent with the target identification corresponding to the target data processor.

The data query module 105 may be configured to query the data cached in the redis cluster according to the data query request to obtain target query data when the detection result is that the changed data processor identifier is inconsistent with the target identifier.

The data storage module 106 may be configured to store the target query data in the target local storage corresponding to the target identifier.

An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by the processor 30, implements the steps of the data query method in any of the foregoing embodiments.

If the data query apparatus 100/computer equipment integrated modules/units are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the processes in the methods of the above embodiments, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer When the program is executed by the processor 30, the steps of the foregoing method embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, and the like. The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory) )Wait.

The so-called processor 30 may be a central processing unit (Central Processing Unit, CPU), and may also be other general-purpose processors, digital signal processors (Digital Signal Processors, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The processor 30 is the control center of the data query apparatus 100/computer equipment 1, and uses various interfaces and lines to connect the entire system. Various parts of the data query apparatus 100/computer equipment 1.

The memory 10 is used to store the computer programs and/or modules, and the processor 30 executes or executes the computer programs and/or modules stored in the memory 10 and calls the data stored in the memory 10, Various functions of the data query apparatus 100/computer device 1 are realized. The memory 10 may mainly include a stored program area and a stored data area, wherein the stored program area may store an operating system, an application program required for at least one function (such as a sound playback function, an image playback function, etc.); the storage data area may Data and the like created according to the use of the computer device 1 are stored.

In the several specific embodiments provided by the present invention, it should be understood that the disclosed computer devices and methods may be implemented in other manners. For example, the system implementations described above are only illustrative. For example, the division of the modules is only a logical function division, and there may be other division manners in actual implementation.

The blockchain referred to in the present invention is a new application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain, essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information to verify its Validity of information (anti-counterfeiting) and generation of the next block. The blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

For those skilled in the art, it is obvious that the embodiments of the present invention are not limited to the details of the above-mentioned exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or essential features of the embodiments of the present invention example. Accordingly, the embodiments are to be considered in all respects as exemplary and not restrictive, the scope of the embodiments of the present invention being defined by the appended claims rather than the foregoing description, and are therefore intended to fall within the scope of All changes within the meaning and scope of equivalents of the claims are included in the embodiments of the present invention. Any reference signs in the claims shall not be construed as limiting the involved claim. Several units, modules or means recited in the system, device or computer device claims can also be realized by one and the same unit, module or means by means of software or hardware.

The above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention and not limit them. Although the embodiments of the present invention have been described in detail with reference to the above preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the embodiments of the present invention can be Modifications or equivalent replacements of the solutions should not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A data query method is applied to a target data processor, and is characterized by comprising the following steps:

receiving a data query request;

detecting whether a newly added data change record exists in a redis cluster;

when the detection result indicates that the newly added data change record exists in the redis cluster, analyzing the data change record to obtain a changed data processor identifier;

detecting whether the changed data processor identification is consistent with a target identification corresponding to the target data processor;

when the detection result is that the changed data processor identifier is inconsistent with the target identifier, querying the data cached in the redis cluster according to the data query request to obtain target query data;

and storing the target query data into a target local storage corresponding to the target identifier.

2. The data query method of claim 1, wherein prior to the receiving a data query request, the method further comprises:

acquiring the current residual resource amount;

detecting whether the current residual resource amount is larger than a preset resource amount;

and receiving a data query request when the detection result indicates that the current residual resource amount is larger than the preset resource amount.

3. The data query method of claim 1, wherein the detecting whether there is a newly added data change record in the redis cluster comprises:

detecting whether a redis cluster has data in a preset data format, and determining that a newly added data change record exists in the redis cluster when a detection result is that the redis cluster has the data in the preset data format; or

Detecting whether a preset label exists in a redis cluster, and determining that a newly added data change record exists in the redis cluster when the detection result indicates that the preset label exists in the redis cluster.

4. The data query method of claim 1, wherein after the receiving a data query request, the method further comprises:

detecting whether a preset mark exists in the redis cluster;

when the detection result indicates that the preset mark exists in the redis cluster, adding the data change record;

and when the detection result indicates that the preset mark does not exist in the redis cluster, detecting whether a newly added data change record exists in the redis cluster.

5. The data query method of claim 4, wherein the adding the data change record comprises:

inquiring a preset database, wherein the preset database is a database in a block chain, and the latest user information is stored in the preset database;

acquiring the latest user information corresponding to the preset mark;

storing the latest user information into a target local storage corresponding to the target identifier;

and storing the latest user information into the redis cluster, and generating a data change record according to the target identifier.

6. The data query method of claim 1, after storing the target query data in a target local storage corresponding to the target identifier, the method further comprising:

generating a target data change record according to the target identifier;

storing the target data change record in the redis cluster;

acquiring the number of data change records corresponding to the same updated user information in the redis cluster;

detecting whether the number of the data change records is consistent with the number of the data processors;

and deleting the data change records corresponding to the same updated user information in the redis cluster when the number of the data change records is consistent with the number of the data processors in the detection result.

7. The data query method of claim 1, further comprising:

when the detection result is that the changed data processor identifier is consistent with the target identifier, determining a target local storage corresponding to the target identifier;

and querying the target local storage according to the data query request to obtain query data.

8. A data query device for use in a target data processor, the data query device comprising:

the request receiving module is used for receiving a data query request;

the recording detection module is used for detecting whether a newly added data change record exists in the redis cluster;

the identification analysis module is used for analyzing the data change record to obtain a changed data processor identification when the detection result indicates that the newly added data change record exists in the redis cluster;

a consistency detection module for detecting whether the changed data processor identification is consistent with the target identification corresponding to the target data processor;

the data query module is used for querying the data cached in the redis cluster according to the data query request to obtain target query data when the detection result is that the changed data processor identifier is inconsistent with the target identifier;

and the data storage module is used for storing the target query data into a target local storage corresponding to the target identifier.

9. A computer device comprising a processor for implementing a data query method as claimed in any one of claims 1 to 7 when executing a computer program stored in a memory.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements a data query method according to any one of claims 1 to 7.

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