Data storage method based on multi-type risk control data and medium

Abstract

The invention discloses a data storage method based on multi-type risk control data and a medium. The method comprises the following steps: acquiring to-be-stored risk control data in real time, and acquiring current risk control data description information corresponding to the to-be-stored risk control data; analyzing the current risk control data description information through a preset risk control data type analysis method to obtain a current risk control data type analysis result; and inputting the current risk control data type analysis result into a pre-trained data storage strategy matching model to obtain a current data storage matching result, and storing the risk control data according to the current data storage matching result. The problems of low data security and low access efficiency caused by storage of different types of databases through a single relational database in the prior art are solved, the security, efficiency and accuracy of risk control data storage are improved, and the availability of the risk control data is enhanced.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to a data storage method and medium based on multi-type risk control data. Background Technology

[0002] In the field of financial risk control, this typically refers to software systems that provide risk analysis and identification services for various types of business data in the financial industry. They usually accept requests from financial business systems and, based on these requests, query the necessary internal and external data according to pre-defined risk control rules. They then use this data to calculate risk levels or risk indices, returning the results to the business systems for their use. Therefore, the technology for accurate and efficient data storage and access is crucial.

[0003] In the process of developing this invention, the inventors discovered the following shortcomings in the existing technology: Currently, in existing financial risk control systems, the storage media used to store different types of data are generally quite limited. A common approach is to store various types of data in the same relational database. Under this approach, the overall availability of the system is low. Once the database experiences access failure or requires maintenance, the system cannot provide any external services, resulting in low data security and inefficient data access. Summary of the Invention

[0004] This invention provides a data storage method and medium based on multiple types of risk control data, so as to improve the security, efficiency and accuracy of risk control data storage.

[0005] According to one aspect of the present invention, a data storage method based on multi-type risk control data is provided, comprising:

[0006] Real-time acquisition of risk control data to be stored, and acquisition of current risk control data description information corresponding to the risk control data to be stored;

[0007] Among them, different types of risk control data correspond to different types of risk control data description information;

[0008] The current risk control data description information is parsed using a pre-set risk control data type parsing method to obtain the current risk control data type parsing result;

[0009] The current risk control data type parsing result is input into the pre-trained data storage strategy matching model to obtain the current data storage matching result, and the risk control data is stored according to the current data storage matching result;

[0010] The current data storage matching result includes at least one type of database for data storage.

[0011] According to another aspect of the present invention, a data storage device based on multi-type risk control data is provided, comprising:

[0012] The current risk control data description information acquisition module is used to acquire the risk control data to be stored in real time, and to acquire the current risk control data description information corresponding to the risk control data to be stored.

[0013] Among them, different types of risk control data correspond to different types of risk control data description information;

[0014] The current risk control data type parsing result determination module is used to parse the current risk control data description information using a pre-set risk control data type parsing method to obtain the current risk control data type parsing result;

[0015] The risk control data storage module is used to input the parsing result of the current risk control data type into a pre-trained data storage strategy matching model to obtain the current data storage matching result, and to store the risk control data according to the current data storage matching result;

[0016] The current data storage matching result includes at least one type of database for data storage.

[0017] According to another aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the data storage method based on multi-type risk control data as described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the data storage method based on multi-type risk control data as described in any embodiment of the present invention.

[0019] The technical solution of this invention acquires risk control data to be stored in real time and obtains current risk control data description information corresponding to the risk control data to be stored; it then parses the current risk control data description information using a pre-set risk control data type parsing method to obtain a current risk control data type parsing result; the current risk control data type parsing result is input into a pre-trained data storage strategy matching model to obtain a current data storage matching result, and the risk control data is stored according to the current data storage matching result. This solves the problems of low data security and low access efficiency caused by storing different types of databases using a single relational database in the prior art, improves the security, efficiency, and accuracy of data storage, and enhances the usability of risk control data.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a data storage method based on multi-type risk control data provided in Embodiment 1 of the present invention;

[0023] Figure 2 This is a detailed flowchart of a data storage method based on multi-type risk control data according to Embodiment 2 of the present invention;

[0024] Figure 3 This is a schematic diagram of a data storage device based on multi-type risk control data according to Embodiment 3 of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of an electronic device provided according to Embodiment 4 of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "target," "current," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] It is worth noting that the information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse; if the user chooses to refuse, the process will proceed to the expert decision-making process.

[0029] Example 1

[0030] Figure 1 The flowchart of a data storage method based on multiple types of risk control data is provided in Embodiment 1 of the present invention. This embodiment is applicable to the storage of different types of risk control data. The method can be executed by a data storage device based on multiple types of risk control data, which can be implemented in hardware and / or software.

[0031] Correspondingly, such as Figure 1 As shown, the method includes:

[0032] S110. Obtain the risk control data to be stored in real time, and obtain the current risk control data description information corresponding to the risk control data to be stored.

[0033] Different types of risk control data correspond to different types of risk control data description information.

[0034] In this embodiment, after obtaining the risk control data to be stored, it is necessary to obtain the data description information corresponding to the risk control data to be stored. Specifically, the current risk control data description information includes the current data importance, the current data volume, the current data description characteristics, and the current data access information; wherein, the current data description characteristics include data generated by the current user editing, data generated by the current interface, and data generated at the current time; the current data access information includes the current data access method and the current data access frequency.

[0035] Among these, the importance of current data can represent the degree of importance of risk control data. Data generated by current user editing can be data generated after the user edits the data. Data generated by current interface can be data generated by different interfaces. Data generated at current time can be time-related, resulting in time-series data. The current data access method can be read or write; the current data access frequency can include frequently accessed data (hot data) and infrequently accessed data (non-hot data).

[0036] S120. The current risk control data description information is parsed using a pre-set risk control data type parsing method to obtain the current risk control data type parsing result.

[0037] Optionally, the step of parsing the current risk control data description information using a pre-set risk control data type parsing method to obtain the current risk control data type parsing result includes: using a pre-set data preprocessing method to perform different types of standardization processing on the current data importance, current data volume, current data description characteristics, and current data access information in the current risk control data description information to obtain current standard data importance, current standard data volume, current standard data description characteristics, and current standard data access information; parsing the current standard data importance, current standard data volume, current standard data description characteristics, and current standard data access information using the risk control data type parsing method to obtain the current risk control data type parsing result; wherein, the current risk control data type parsing result includes a first type of risk control data type, a second type of risk control data type, and a third type of risk control data type.

[0038] Specifically, it also includes: The first type of risk control data type is a risk control rule definition data type, wherein the data properties of the risk control rule definition data type are a first-level read frequency and a first-level read efficiency, and a second-level full data volume and a first-level hot data volume; the second type of risk control data type is institutional user description data, wherein the data properties of the institutional user description data are a first-level read frequency and a first-level read efficiency, and a first-level full data volume and a second-level hot data volume; the third type of risk control data type is risk control rule calculation record data, wherein the data properties of the risk control rule calculation record data are a first-level write frequency and a first-level write efficiency, and a first-level full data volume and a second-level hot data volume; and the risk control rule calculation record data has time characteristics; wherein the number of second-level full data volumes is less than the number of first-level full data volumes; and the number of second-level hot data volumes is less than the number of first-level hot data volumes.

[0039] In this embodiment, the current risk control data description information from different aspects is processed according to different standardization methods in the data preprocessing approach to obtain the current standard data importance, current standard data volume, current standard data description characteristics, and current standard data access information. Specifically, the current standard data description characteristics include current standard user-generated data, current standard interface-generated data, and current standard time-generated data; the current standard data access information includes current standard data access method and current standard data access frequency.

[0040] Furthermore, the risk control data type parsing method can be used to parse it and obtain the current risk control data type parsing result, which corresponds to one of the first type of risk control data type, the second type of risk control data type, or the third type of risk control data type.

[0041] Specifically, the first type of risk control data type is the risk control rule definition data type. The data properties of this type of data are: first-level read frequency and first-level read efficiency, and second-level total data volume and first-level hot data volume. In detail, the risk control rule definition data type is characterized by extremely high importance, small volume, and read-heavy, write-light characteristics. This data type has high security requirements and is the system's core data asset. During system operation, this data type has a high read frequency (corresponding to the first-level read frequency), therefore requiring high read efficiency (corresponding to the first-level read efficiency). The total data volume of this risk control rule definition data type is relatively small (corresponding to the second-level total data volume), and it is primarily hot data (corresponding to the first-level hot data volume).

[0042] Correspondingly, the second type of risk control data is institutional user description data. This institutional user description data is characterized by a first-level read frequency and a first-level read efficiency, and a first-level total data volume and a second-level hot data volume. Institutional user description data can include financial institution user description data and third-party institutional user description data. Financial institution user description data has high security requirements and is the system's core data asset. During system operation, financial institution user data and third-party institutional user data have a high read frequency, thus requiring high read efficiency. Furthermore, financial institution user description data and third-party institutional user description data have a large total data volume (corresponding to the first-level total data volume), while hot data accounts for a smaller proportion (corresponding to the second-level hot data volume).

[0043] Similarly, the third type of risk control data type is risk control rule calculation record data. The data properties of this risk control rule calculation record data include a first-level write frequency and a first-level write efficiency, with a first-level total data volume and a second-level hot data volume. Furthermore, this risk control rule calculation record data has time characteristics. The risk control rule calculation record data has a high write frequency, therefore requiring high write efficiency (i.e., a first-level write frequency corresponds to a high write frequency; a first-level write efficiency corresponds to a high write efficiency). The risk control rule calculation record data has a large total data volume, with a small proportion of hot data; and it also has time characteristics, being closely related to time. This allows for obtaining accurate current risk control data type parsing results through the risk control data type parsing method, enabling better storage operations for risk control data.

[0044] S130. Input the current risk control data type parsing result into the pre-trained data storage strategy matching model to obtain the current data storage matching result, and store the risk control data according to the current data storage matching result.

[0045] The current data storage matching result includes at least one type of database for data storage.

[0046] Specifically, the data storage strategy matching model can be a model that matches the parsing results of different current risk control data types with different data storage strategy results. The current data storage matching results include data storage in at least one type of database, which can include one or more of the following: in-memory key-value database, relational database, or in-memory queue database.

[0047] Optionally, the step of inputting the current risk control data type parsing result into a pre-trained data storage strategy matching model to obtain the current data storage matching result includes: if the current risk control data type parsing result is a first type of risk control data type, then matching it with the data storage strategy matching model to obtain a current first data storage matching result; if the current risk control data type parsing result is a second type of risk control data type, then matching it with the data storage strategy matching model to obtain a current second data storage matching result; if the current risk control data type parsing result is a third type of risk control data type, then matching it with the data storage strategy matching model to obtain a current third data storage matching result; wherein, the current first data storage matching result is the result of inputting the current risk control data type parsing result into a pre-trained data storage strategy matching model to obtain a current first data storage matching result. A predetermined number of risk control data to be stored are stored in an in-memory key-value database, and a preset risk control rule calculation application is instructed to execute data query commands. A predetermined number of risk control data to be stored are also stored in a relational database, and a preset degradation strategy is executed by instructing a preset risk control rule calculation application. The current second data storage matching result involves storing frequently accessed hot data from the risk control data to be stored in the in-memory key-value database, and storing the risk control data to be stored in the relational database, and executing the preset degradation strategy and data query commands by instructing a preset risk control data service application. The current third data storage matching result involves sequentially storing the risk control data to be stored in an in-memory queue database, and instructing a preset operational data statistics application to execute data statistical analysis commands.

[0048] Relational databases, on the other hand, organize data using a relational model. They employ two-dimensional tables composed of rows and columns as the basic unit, with each row representing a single data entry and each column representing a field. Relational databases offer high flexibility, adapting to the data storage needs of most business scenarios. However, due to their high requirements for atomicity, consistency, isolation, and durability of data operations, their overall read / write performance is not outstanding, and in some high-concurrency scenarios, they can even become a bottleneck. Regarding high availability, common relational databases typically employ a master-slave cluster architecture. If the master node fails, the system can automatically replace it with a backup node, but this process often takes time, during which the database may be in a read-only or even completely unavailable state. In-memory key-value databases, on the other hand, are non-relational databases that store data in a hash table-like key-value pair format. Generally, key-value databases use memory as their primary physical storage medium and disk as a secondary physical storage medium. Therefore, their data read / write performance is significantly higher than that of relational databases. However, because memory capacity is often more limited than disk capacity, their data storage capacity is usually much lower than that of relational databases. In terms of high availability, common in-memory key-value databases typically employ a multi-node cluster architecture, capable of tolerating a certain number of node failures without affecting data access. Furthermore, node failures and recovery generally do not significantly impact data access. In-memory queue databases, which organize data using a time-first-in, first-out (FIFO) queue, typically use memory as their primary physical storage medium and disk as secondary. Due to the limitations of memory capacity in their data organization and storage capacity, in-memory queue databases generally only support reading and writing data sequentially within the queue, and their use cases are relatively limited. In terms of high availability, common in-memory queue databases generally employ a multi-node cluster architecture, and their fault tolerance is similar to that of in-memory key-value databases.

[0049] In this embodiment, the first type of risk control data type can be matched with the corresponding storage strategy result, which is the current first data storage matching result. Specifically, a preset number of risk control data to be stored are stored in an in-memory key-value database, and a preset risk control rule calculation application is instructed to execute a data query instruction. A preset number of risk control data to be stored are also stored in a relational database, and a preset downgrade strategy is executed by instructing a preset risk control rule calculation application.

[0050] The risk control rule calculation application is responsible for providing the core system function, namely risk control rule calculation. This application can use parameters provided by the requester, combined with relevant risk control rule definition data, financial institution user description data, and third-party institution user description data, to calculate whether the request meets risk control requirements. Specifically, this application is responsible for maintaining the various data required for risk control rule calculation, primarily including financial institution user description data and third-party institution user description data. It periodically synchronizes the latest data from various internal and external data sources and updates it to the system. Simultaneously, it provides real-time data query functionality to the risk control rule calculation application, returning the corresponding dataset results based on the parameters provided in the request.

[0051] Specifically, for the first type of risk control data type, namely the risk control rule definition data type, due to its extremely high importance, small volume, and read-heavy, write-light characteristics, a predetermined amount of risk control data to be stored can be entirely stored in an in-memory key-value database for use by the risk control rule calculation application upon receiving a data query command. Simultaneously, this data can also be entirely stored in a relational database, and a degradation strategy can be implemented through the risk control rule calculation application. That is, during data access, if the in-memory key-value database fails and becomes unavailable, access can be degraded to the relational database.

[0052] Furthermore, the current second data storage matching result is to store the frequently accessed hot data in the risk control data to be stored in an in-memory key-value database, and to store the risk control data to be stored in a relational database, and to execute the preset degradation strategy and data query instructions by instructing the preset risk control data service application.

[0053] Specifically, for institutional user descriptive data, which is characterized by high importance, large volume, and frequent reads with few writes, the frequency of application access to different data is calculated based on risk control rules. Some frequently accessed hot data is stored in an in-memory key-value database. Simultaneously, all risk control data awaiting storage is stored in a relational database, and the risk control data service application implements query and degradation strategies. Furthermore, when the in-memory key-value database fails to find the corresponding data or is unavailable, access is degraded to the relational database.

[0054] Additionally, the current third data storage matching result is to sequentially store the risk control data to be stored in a memory queue-type database, and instruct a preset operational data statistics application to execute data statistics analysis instructions.

[0055] The operational data statistics application can be used to collect various behavioral data generated by the risk control rule calculation application during its operation, including intermediate calculation results, accessed internal and external interfaces (request parameters and results), and final calculation results. By associating these behavioral data with upstream business data, it provides basic business statistical analysis functions.

[0056] Specifically, risk control rule calculation records are characterized by low importance, large volume, and frequent writes and infrequent reads. Given their generation follows a strict temporal order, the risk control data to be stored is sequentially stored in an in-memory queue database. New data can also be continuously written to this database. Simultaneously, operational data statistics applications continuously retrieve data from the in-memory queue database in strict temporal order, further process and refine it into various statistical data according to business needs, and store these statistical data in a relational database for subsequent querying or analysis. Within the in-memory queue database, outdated and processed data is automatically evicted and deleted.

[0057] Furthermore, this system demonstrates a significant performance improvement compared to similar systems. This is largely due to the fact that a substantial portion of the data is read from the in-memory key-value database, resulting in a reduced average latency for core service requests and improved access efficiency. The in-memory queue database effectively stabilizes traffic, limiting the negative impact of writing non-critical data during peak traffic periods on overall system performance to an acceptable level. This avoids placing excessive pressure on the relational database, preventing system performance fluctuations.

[0058] The technical solution of this invention acquires risk control data to be stored in real time and obtains current risk control data description information corresponding to the risk control data to be stored; it then parses the current risk control data description information using a pre-set risk control data type parsing method to obtain a current risk control data type parsing result; the current risk control data type parsing result is input into a pre-trained data storage strategy matching model to obtain a current data storage matching result, and the risk control data is stored according to the current data storage matching result. This solves the problems of low data security and low access efficiency caused by storing different types of databases using a single relational database in the prior art, improves the security, efficiency, and accuracy of data storage, and enhances the usability of risk control data.

[0059] Example 2

[0060] Figure 2This is a detailed flowchart of a data storage method based on multiple types of risk control data according to Embodiment 2 of the present invention. This embodiment is an optimization based on the above embodiments. In this embodiment, after storing the risk control data according to the current data storage matching result, it further includes determining the result of the current data access request based on the received current data access request.

[0061] S210. Obtain the risk control data to be stored in real time, and obtain the current risk control data description information corresponding to the risk control data to be stored.

[0062] Different types of risk control data correspond to different types of risk control data description information.

[0063] S220. The current risk control data description information is parsed using a pre-set risk control data type parsing method to obtain the current risk control data type parsing result.

[0064] S230. Input the current risk control data type parsing result into the pre-trained data storage strategy matching model to obtain the current data storage matching result, and store the risk control data according to the current data storage matching result.

[0065] The current data storage matching result includes at least one type of database for data storage.

[0066] S240. Receive the current data access request in real time, parse the current data access request, and obtain the parsing result of the current data access request.

[0067] The parsing result of the current data access request includes the current risk control data type.

[0068] In this embodiment, after the risk control data storage is completed, it is also necessary to receive current data access requests in real time to perform access operations on the corresponding type of data.

[0069] S250. Using a preset data acquisition method, based on the current risk control data type, data is acquired in the data storage system based on multi-type risk control data to obtain the current data access request result.

[0070] Optionally, the step of acquiring data in the data storage system based on multi-type risk control data according to the current risk control data type using a preset data acquisition method to obtain the current data access request result includes: if the current risk control data type is a first type of risk control data type, combining the corresponding current first data storage matching result, and acquiring data in a memory key-value database according to the data acquisition method, determining whether the data acquisition was successful; if not, executing a preset degradation strategy through the risk control rule calculation application, acquiring data in the relational database, and obtaining the current data access request result; if the current risk control data type is a second type of risk control data type, combining the corresponding current second data storage matching result, and acquiring data in a memory key-value database according to the data acquisition method, determining whether the data acquisition was successful; if not, executing a preset degradation strategy through the risk control data service application, acquiring data in the relational database, and obtaining the current data access request result; if the current risk control data type is a third type of risk control data type, combining the corresponding current third data storage matching result, and acquiring data in a memory queue database according to the data acquisition method, and obtaining the current data access request result.

[0071] Specifically, for the current risk control data type being the first type, the matching result of the corresponding first-type data storage can be used, and data can be retrieved in the in-memory key-value database according to the data retrieval method. The success of data retrieval is then determined; if successful, feedback is directly sent to the user. Otherwise, a preset downgrade strategy needs to be executed through the risk control rule calculation application, switching to data retrieval in a relational database to obtain the current data access request result. The same processing operation applies to the second type of risk control data type, and will not be elaborated here.

[0072] In this embodiment, by using different types of risk control data with different types of storage media (i.e., different types of databases), availability can be improved, enabling the system to cope with a certain range of storage media failures without affecting core functions. For example, when an in-memory key-value database fails and becomes unavailable, a relational database can serve as a substitute, providing the complete data required by the system. Conversely, when a relational database fails and becomes unavailable, an in-memory key-value database can provide the necessary data required by the system. Although the timeliness of the service is somewhat affected, the core functions can still operate normally. In addition, in-memory queue databases can play a decoupling role in this situation, ensuring that data recording is not interrupted for a period of time. This allows for the rapid replenishment of missing statistical data after the relational database fails and recovers, ensuring data integrity and continuity.

[0073] S260. Feedback the current data access request result to the user.

[0074] The technical solution of this invention involves: acquiring risk control data to be stored in real time and obtaining current risk control data description information corresponding to the risk control data to be stored; parsing the current risk control data description information using a pre-set risk control data type parsing method to obtain a current risk control data type parsing result; inputting the current risk control data type parsing result into a pre-trained data storage strategy matching model to obtain a current data storage matching result, and storing the risk control data according to the current data storage matching result; receiving current data access requests in real time and parsing the current data access requests to obtain a current data access request parsing result; acquiring data in the data storage system based on multi-type risk control data using a preset data acquisition method according to the current risk control data type to obtain a current data access request result; and feeding back the current data access request result to the user. This allows for different data access methods for different types of stored risk control data, improving the timeliness and accuracy of risk control data access, enhancing the availability of risk control data, effectively responding to risk control data access requests, and improving the user experience.

[0075] Example 3

[0076] Figure 3 This is a schematic diagram of a data storage device based on multi-type risk control data provided in Embodiment 3 of the present invention. The data storage device based on multi-type risk control data provided in this embodiment can be implemented by software and / or hardware, and can be configured in a terminal device or server to implement a data storage method based on multi-type risk control data in this embodiment of the present invention. Figure 3 As shown, the device includes: a current risk control data description information acquisition module 310, a current risk control data type parsing result determination module 320, and a risk control data storage module 330.

[0077] The current risk control data description information acquisition module 310 is used to acquire the risk control data to be stored in real time and acquire the current risk control data description information corresponding to the risk control data to be stored.

[0078] Among them, different types of risk control data correspond to different types of risk control data description information;

[0079] The current risk control data type parsing result determination module 320 is used to parse the current risk control data description information using a pre-set risk control data type parsing method to obtain the current risk control data type parsing result;

[0080] The risk control data storage module 330 is used to input the current risk control data type parsing result into a pre-trained data storage strategy matching model to obtain the current data storage matching result, and to store the risk control data according to the current data storage matching result.

[0081] The current data storage matching result includes at least one type of database for data storage.

[0082] The technical solution of this invention acquires risk control data to be stored in real time and obtains current risk control data description information corresponding to the risk control data to be stored; it then parses the current risk control data description information using a pre-set risk control data type parsing method to obtain a current risk control data type parsing result; the current risk control data type parsing result is input into a pre-trained data storage strategy matching model to obtain a current data storage matching result, and the risk control data is stored according to the current data storage matching result. This solves the problems of low data security and low access efficiency caused by storing different types of databases using a single relational database in the prior art, improves the security, efficiency, and accuracy of data storage, and enhances the usability of risk control data.

[0083] Based on the above embodiments, the current risk control data description information includes the current data importance, the current data volume, the current data description characteristics, and the current data access information; wherein, the current data description characteristics include data generated by the current user editing, data generated by the current interface, and data generated at the current time; the current data access information includes the current data access method and the current data access frequency.

[0084] Based on the above embodiments, the current risk control data type parsing result determination module 320 can be specifically used to: perform different types of standardization processing on the current data importance, current data volume, current data description characteristics, and current data access information in the current risk control data description information through a pre-set data preprocessing method to obtain current standard data importance, current standard data volume, current standard data description characteristics, and current standard data access information; parse the current standard data importance, current standard data volume, current standard data description characteristics, and current standard data access information through the risk control data type parsing method to obtain the current risk control data type parsing result; wherein, the current risk control data type parsing result includes a first type of risk control data type, a second type of risk control data type, and a third type of risk control data type.

[0085] Based on the above embodiments, the data further includes: the first type of risk control data type is a risk control rule definition data type, wherein the data properties of the risk control rule definition data type are a first-level read frequency and a first-level read efficiency, and a second-level full data volume and a first-level hot data volume; the second type of risk control data type is institutional user description data, wherein the data properties of the institutional user description data are a first-level read frequency and a first-level read efficiency, and a first-level full data volume and a second-level hot data volume; the third type of risk control data type is risk control rule calculation record data, wherein the data properties of the risk control rule calculation record data are a first-level write frequency and a first-level write efficiency, and a first-level full data volume and a second-level hot data volume; and the risk control rule calculation record data has time characteristics; wherein the number of second-level full data volumes is less than the number of first-level full data volumes; and the number of second-level hot data volumes is less than the number of first-level hot data volumes.

[0086] Based on the above embodiments, the risk control data storage module 330 can be specifically used to: if the current risk control data type parsing result is a first type of risk control data type, then match it with the data storage strategy matching model to obtain a current first data storage matching result; if the current risk control data type parsing result is a second type of risk control data type, then match it with the data storage strategy matching model to obtain a current second data storage matching result; if the current risk control data type parsing result is a third type of risk control data type, then match it with the data storage strategy matching model to obtain a current third data storage matching result; wherein, the current first data storage matching result is a preset number of risk control data types to be stored. The data is stored in a memory key-value database, and a preset risk control rule calculation application is instructed to execute data query commands. A preset number of risk control data to be stored are stored in a relational database, and a preset degradation strategy is executed by instructing the preset risk control rule calculation application. The current second data storage matching result involves storing frequently accessed hot data from the risk control data to be stored in the memory key-value database, and storing the risk control data to be stored in the relational database, executing the preset degradation strategy and data query commands by instructing the preset risk control data service application. The current third data storage matching result involves sequentially storing the risk control data to be stored in a memory queue database, and instructing a preset operational data statistics application to execute data statistical analysis commands.

[0087] Based on the above embodiments, the system further includes a current data access request result determination module, which may specifically include: a current data access request parsing result acquisition unit, which may be specifically used to: after storing risk control data according to the current data storage matching result, receive the current data access request in real time, parse the current data access request, and obtain the current data access request parsing result; wherein the current data access request parsing result includes the current risk control data type; a current data access request result determination unit, which may be specifically used to: acquire data in the data storage system based on multi-type risk control data according to the current risk control data type using a preset data acquisition method, and obtain the current data access request result; and a current data access request result feedback unit, which may be specifically used to: feed back the current data access request result to the user.

[0088] Based on the above embodiments, the current data access request result determination unit can be specifically used for: if the current risk control data type is a first type of risk control data type, combining the corresponding current first data storage matching result, and performing data acquisition in the memory key-value database according to the data acquisition method, determining whether the data acquisition was successful; if not, executing a preset degradation strategy through the risk control rule calculation application, performing data acquisition in the relational database, and obtaining the current data access request result; if the current risk control data type is a second type of risk control data type, combining the corresponding current second data storage matching result, and performing data acquisition in the memory key-value database according to the data acquisition method, determining whether the data acquisition was successful; if not, executing a preset degradation strategy through the risk control data service application, performing data acquisition in the relational database, and obtaining the current data access request result; if the current risk control data type is a third type of risk control data type, combining the corresponding current third data storage matching result, and performing data acquisition in the memory queue database according to the data acquisition method, and obtaining the current data access request result.

[0089] The data storage device based on multi-type risk control data provided in the embodiments of the present invention can execute the data storage method based on multi-type risk control data provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0090] Example 4

[0091] Figure 4 A schematic diagram of an electronic device 10, which can be used to implement Embodiment 4 of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0092] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0093] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0094] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as data storage methods based on multi-type risk control data.

[0095] In some embodiments, the data storage method based on multi-type risk control data can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the data storage method based on multi-type risk control data described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the data storage method based on multi-type risk control data by any other suitable means (e.g., by means of firmware).

[0096] The method includes: acquiring risk control data to be stored in real time, and acquiring current risk control data description information corresponding to the risk control data to be stored; wherein, different types of risk control data correspond to risk control data description information of different natures; parsing the current risk control data description information using a pre-set risk control data type parsing method to obtain a current risk control data type parsing result; inputting the current risk control data type parsing result into a pre-trained data storage strategy matching model to obtain a current data storage matching result, and storing the risk control data according to the current data storage matching result; wherein, the current data storage matching result includes at least one type of database for data storage.

[0097] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0098] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0099] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0100] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0101] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0102] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0103] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0104] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

[0105] Example 5

[0106] Embodiment 5 of the present invention also provides a computer-readable storage medium, wherein the computer-readable instructions, when executed by a computer processor, are used to execute a data storage method based on multiple types of risk control data. The method includes: acquiring risk control data to be stored in real time, and acquiring current risk control data description information corresponding to the risk control data to be stored; wherein different types of risk control data correspond to risk control data description information of different natures; parsing the current risk control data description information using a pre-set risk control data type parsing method to obtain a current risk control data type parsing result; inputting the current risk control data type parsing result into a pre-trained data storage strategy matching model to obtain a current data storage matching result, and storing the risk control data according to the current data storage matching result; wherein the current data storage matching result includes at least one type of database for data storage.

[0107] Of course, the computer-readable storage medium provided in the embodiments of the present invention has computer-executable instructions that are not limited to the method operations described above, but can also perform related operations in data storage based on multi-type risk control data provided in any embodiment of the present invention.

[0108] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0109] It is worth noting that in the above embodiments of data storage based on multiple types of risk control data, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0110] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A data storage method based on multi-type risk control data, characterized by, The method comprises the following steps: obtaining risk control data to be stored in real time, and obtaining current risk control data description information corresponding to the risk control data to be stored; wherein different types of risk control data correspond to different properties of risk control data description information; parsing the current risk control data description information through a pre-set risk control data type parsing method to obtain a current risk control data type parsing result; inputting the current risk control data type parsing result into a pre-trained data storage strategy matching model to obtain a current data storage matching result, and storing the risk control data according to the current data storage matching result; wherein the current data storage matching result includes at least one type of database for data storage.

2. The method of claim 1, wherein, The current risk control data description information includes current data importance, current data size, current data description characteristics, and current data access information; wherein the current data description characteristics include current user editing generated data, current interface generated data, and current time generated data; the current data access information includes current data access mode and current data access frequency.

3. The method of claim 2, wherein, The current risk control data description information is parsed through a pre-set risk control data type parsing method to obtain a current risk control data type parsing result, which comprises: standardizing the current data importance, current data size, current data description characteristics, and current data access information in the current risk control data description information through a pre-set data preprocessing method to obtain current standard data importance, current standard data size, current standard data description characteristics, and current standard data access information; the current standard data importance, current standard data size, current standard data description characteristics, and current standard data access information are parsed through the risk control data type parsing method to obtain the current risk control data type parsing result; wherein the current risk control data type parsing result includes a first type of risk control data type, a second type of risk control data type, and a third type of risk control data type.

4. The method of claim 3, wherein, Further comprising: wherein the first type of risk control data type is a risk control rule definition data type, and the data property of the risk control rule definition data type is a first level read frequency and a first level read efficiency, and a second level full data size and a first level hot data size; the second type of risk control data type is an institution user description data, and the data property of the institution user description data is a first level read frequency and a first level read efficiency, and a first level full data size and a second level hot data size; the third type of risk control data type is a risk control rule calculation record data, and the data property of the risk control rule calculation record data is a first level write frequency and a first level write efficiency, and a first level full data size and a second level hot data size; and the risk control rule calculation record data has a time characteristic; The number of the second-level full-amount data magnitudes is less than the number of the first-level full-amount data magnitudes; and the number of the second-level hotspot data magnitudes is less than the number of the first-level hotspot data magnitudes.

5. The method of claim 4, wherein, The inputting of the current risk control data type analysis result into the pre-trained data storage strategy matching model to obtain a current data storage matching result comprises: If the current risk control data type analysis result is a first type risk control data type, a current first data storage matching result is obtained by matching with the data storage strategy matching model; If the current risk control data type analysis result is a second type risk control data type, a current second data storage matching result is obtained by matching with the data storage strategy matching model; If the current risk control data type analysis result is a third type risk control data type, a current third data storage matching result is obtained by matching with the data storage strategy matching model; The current first data storage matching result is to store a preset number of to-be-stored risk control data in a memory key-value type database, execute a data query instruction by indicating a preset risk control rule calculation application, and store the preset number of to-be-stored risk control data in a relational database, execute a preset degradation strategy by indicating the preset risk control rule calculation application. The current second data storage matching result is to store hotspot data with high access frequency in to-be-stored risk control data in a memory key-value type database, and store the to-be-stored risk control data in a relational database, execute a preset degradation strategy and a data query instruction by indicating a preset risk control data service application. The current third data storage matching result is to store to-be-stored risk control data in a memory queue type database in sequence, and execute a data statistical analysis instruction by indicating a preset operation data statistical application.

6. The method of claim 5, wherein, After the storage of the risk control data according to the current data storage matching result, the method further comprises: receiving a current data access request in real time, and analyzing the current data access request to obtain a current data access request analysis result; The current data access request analysis result comprises a current risk control data type. According to the current risk control data type, data is acquired in the data storage system based on multiple types of risk control data by using a preset data acquisition method, to obtain a current data access request result; The current data access request result is fed back to a user.

7. The method of claim 6, wherein, According to the current risk control data type, data is acquired in the data storage system based on multiple types of risk control data by using a preset data acquisition method, to obtain a current data access request result, comprises: If the current risk control data type is a first type of risk control data type, the corresponding current first data storage matching result is combined, and data is acquired in the in-memory key-value database according to the data acquisition method, it is judged whether the data is successfully acquired, if not, a preset degradation strategy is executed through the risk control rule calculation application, data is acquired in the relational database, and a current data access request result is obtained; If the current risk control data type is a second type of risk control data type, the corresponding current second data storage matching result is combined, and data is acquired in the in-memory key-value database according to the data acquisition method, it is judged whether the data is successfully acquired, if not, a preset degradation strategy is executed through the risk control data service application, data is acquired in the relational database, and a current data access request result is obtained; If the current risk control data type is a third type of risk control data type, the corresponding current third data storage matching result is combined, and data is acquired in the in-memory queue type database, and a current data access request result is obtained.

8. A data storage device based on multi-type risk control data, characterized by, It includes: A current risk control data description information acquisition module is used for acquiring risk control data to be stored in real time, and acquiring current risk control data description information corresponding to the risk control data to be stored; Different types of risk control data correspond to different properties of risk control data description information; A current risk control data type analysis result determination module is used for analyzing the current risk control data description information by a pre-set risk control data type analysis method, and obtaining a current risk control data type analysis result; A risk control data storage module is used for inputting the current risk control data type analysis result into a pre-trained data storage strategy matching model, obtaining a current data storage matching result, and storing risk control data according to the current data storage matching result; The current data storage matching result includes at least one type of database for data storage.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the data storage method based on multiple types of risk control data in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are used to make the processor execute to realize the data storage method based on multiple types of risk control data in any one of claims 1-7.

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