Information processing method and device based on financial system, electronic equipment and medium

Abstract

The embodiment of the invention provides an information processing method and device based on a financial system, electronic equipment and a medium, and belongs to the technical field of financial science and technology. The method comprises the steps of performing statistics on historical business information of the financial system, and performing database division and table division operation on a database of the financial system according to the historical business information to obtain a plurality of sub-databases; receiving a data calling request sent by an application end of the financial system through the message queue middleware; performing data analysis on the data calling request, and determining multiple pieces of calling data; performing logic analysis on the calling data, determining non-associated service logic and associated service logic, and creating a thread pool; querying a non-associated query result in the sub-database through the thread pool and the cache middleware, and querying an associated query result in the sub-database through the cache middleware; and generating a target query result according to the non-associated query result and the associated query result. According to the embodiment of the invention, the data processing capability and stability of the financial system can be improved.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and in particular to an information processing method, device, electronic device and medium based on a financial system. Background Art

[0002] Financial systems often encounter performance bottlenecks when facing high business traffic, resulting in longer system response times, affecting user experience and business continuity. Traditional order-issuing systems are prone to congestion when there is a sudden increase in requests or sustained high concurrency, affecting system stability and availability. This solution, which relies on manual intervention to increase hardware resources, is not only inefficient, but also difficult to cope with sudden traffic, resulting in inevitable business impacts.

[0003] The challenges faced by financial systems when supporting major marketing activities, such as Taobao's Double Eleven and JD.com's 618, are particularly significant. The traffic peaks brought by these activities place extremely high demands on the stability and processing capabilities of the system. The number of requests processed per second by traditional order-issuing systems is generally between 200 and 300. If there is a sudden increase in requests or continuous high concurrent requests, the system will be blocked, eventually causing the entire system to become unavailable. The only way is to find relevant server operations and maintenance to perform a full-link restart operation to restore the system. Therefore, it is particularly important to improve the stability and processing capabilities of financial systems in the context of high business traffic. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose an information processing method, device, electronic device and medium based on a financial system, which can improve the data processing capability of the financial system and improve the stability of the financial system.

[0005] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes an information processing method based on a financial system, wherein the financial system is configured with a message queue middleware and a cache middleware; the method comprises: Collecting historical business information of the financial system, and performing database and table sharding operations on the database of the financial system according to the historical business information to obtain multiple shard databases; Receiving a data call request sent by the application end of the financial system through the message queue middleware; Performing data analysis on the data call request to determine a plurality of call data corresponding to the data call request; Performing logic analysis on the call data, determining non-associated business logic and associated business logic, and creating a thread pool corresponding to the number of the non-associated business logic; Query the sub-library database for non-associated query results corresponding to the data of the non-associated business logic through the thread pool and the cache middleware, and query the sub-library database for associated query results corresponding to the data of the associated business logic through the cache middleware; A target query result is generated according to the non-associated query result and the associated query result.

[0006] In some embodiments, the database of the financial system is divided into sub-libraries and sub-tables according to the historical business information to obtain multiple sub-library databases, including: Determine the business volume of the historical business information, and increase the business volume by a preset multiple to obtain a target business volume; Performing semantic analysis on the historical business information to obtain multiple business fields; Based on the preset hash sharding rule, the business field and the target business volume, the database of the financial system is sharded to obtain multiple sharded databases.

[0007] In some embodiments, the database of the financial system is partitioned based on the preset hash partitioning rule, the business field, and the target business volume to obtain multiple partitioned databases, including: Determine the business field as a sharding key, and perform a hash function calculation on the sharding key to obtain a hash value of the sharding key; Allocate data for the target business volume according to the sharding key to determine the number of target databases; Performing a database sub-database operation on the financial system according to the number of target databases to obtain multiple sub-databases; For each of the sub-databases, creating a data table corresponding to the sub-database; Performing a modulo operation on the sub-database according to the hash value to determine a storage relationship between the shard key and the sub-database; According to the storage relationship, the data corresponding to the business field is stored in the sub-database to obtain a plurality of sub-databases.

[0008] In some embodiments, storing the business fields in the sub-database according to the corresponding relationship to obtain multiple sub-databases includes: Migrating the data corresponding to the business field to the sub-database according to the corresponding relationship to obtain a database to be verified; For each of the databases to be verified, obtaining a data table to be verified of the database to be verified, and obtaining the data capacity of the data table to be verified; Performing a performance test on the database to be verified by using a preset database monitoring tool to obtain a first performance indicator parameter; When the first performance indicator parameter does not meet the preset parameter requirement, adjusting the data capacity of the to-be-verified data table to update the to-be-verified database to obtain a target database; Performing a performance test on the target database by using the database monitoring tool to obtain a second performance indicator parameter; When the second performance indicator parameter meets the preset parameter requirement, the target database is determined as a sub-database.

[0009] In some embodiments, querying the sub-library database for a non-associated query result corresponding to the data of the non-associated business logic through the thread pool and the cache middleware includes: Classifying the data corresponding to all the non-associated business logics according to the historical business information to obtain first basic data and first non-basic data, wherein the modification frequency of the first basic data is lower than the modification frequency of the first non-basic data; storing the first basic data in the cache of the financial system through the cache middleware; For each of the first non-basic data, determining a first query count of the first non-basic data; Determine the first non-basic data whose first query number is a preset number as a first data set, and determine the first non-basic data whose first query number is greater than the preset number as a second data set; storing the first data set in the cache of the financial system for a first preset duration through the cache middleware; Querying the sub-database database for a first query result corresponding to the data of the second data set through the thread pool; After the first preset time period, querying the sub-database database through the thread pool for a second query result corresponding to the data of the first data set; Querying the sub-library database for a third query result corresponding to the first basic data through the thread pool; A non-associated query result corresponding to the data of the non-associated business logic is obtained according to the first query result, the second query result and the third query result.

[0010] In some embodiments, querying the associated query result corresponding to the data associated with the business logic in the sub-library database through the cache middleware includes: Classifying the data corresponding to all the associated business logics according to the historical business information to obtain second basic data and second non-basic data, wherein the modification frequency of the second basic data is lower than the modification frequency of the second non-basic data; storing the second basic data in the cache of the financial system through the cache middleware; For each of the second non-basic data, determining a second query count of the second non-basic data; determining the second non-basic data whose second query number is a preset number as a third data set, and determining the second non-basic data whose second query number is greater than the preset number as a fourth data set; storing the third data set in the cache of the financial system for a second preset duration through the cache middleware; Querying the sub-database for a fourth query result corresponding to the data of the fourth data set; After the second preset time, querying the sub-database for a fifth query result corresponding to the data of the third data set; Querying the sub-database for a sixth query result corresponding to the second basic data; An associated query result corresponding to the data of the associated business logic is obtained according to the fourth query result, the fifth query result and the sixth query result.

[0011] In some embodiments, after a performance test is performed on the database to be verified by a preset database monitoring tool to obtain a first performance indicator parameter, the method further includes: When the first performance indicator parameter meets the preset parameter requirement, the database to be verified is determined as a sub-database.

[0012] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application proposes an information processing device based on a financial system, wherein the financial system is configured with a message queue middleware and a cache middleware; the device comprises: A database and table sharding module is used to collect statistics on the historical business information of the financial system, and to perform database and table sharding operations on the database of the financial system according to the historical business information to obtain multiple database sharding databases; A request receiving module, used for receiving a data call request sent by the application end of the financial system through the message queue middleware; A data analysis module, used to analyze the data call request and determine a plurality of call data corresponding to the data call request; A logic analysis module, used to perform logic analysis on the call data, determine non-associated business logic and associated business logic, and create a thread pool corresponding to the number of the non-associated business logic; A result query module is used to query the non-associated query results corresponding to the data of the non-associated business logic in the sub-library database through the thread pool and the cache middleware, and query the associated query results corresponding to the data of the associated business logic in the sub-library database through the cache middleware; A target generation module is used to generate a target query result according to the non-associated query result and the associated query result.

[0013] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, an electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the information processing method based on the financial system as described in the first aspect when executing the computer program.

[0014] To achieve the above objectives, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the information processing method based on the financial system as described in the first aspect.

[0015] The information processing method, device, electronic device and storage medium based on the financial system proposed in the present application firstly collect historical business information of the financial system, so as to determine the historical business volume of the financial system through the historical business information, and perform database and table operations on the database of the financial system according to the historical business information to obtain multiple database sub-databases, and solve the problem of high concurrent access pressure of a single database by horizontally splitting the database into multiple databases, reduce the load of a single database, and improve the read and write performance, and then receive the data call request sent by the application end of the financial system through the message queue middleware, realize the direct interaction between the application end and the message queue middleware, without waiting for the database response, and further improve the response speed to the data call request, and then perform data parsing on the data call request, analyze the data required to call the data call request, determine multiple call data corresponding to the data call request, that is, the data that needs to be found in the database, and then perform logical analysis on the call data to realize the response to the data call request. The analysis of the structure, relationship and flow of data can further determine the relevance of the called data, determine the non-associated business logic and the associated business logic, and create a thread pool corresponding to the number of non-associated business logic, so as to facilitate the subsequent parallel processing of non-associated business logic through the thread pool. The non-associated query results corresponding to the data of the non-associated business logic are queried in the sub-library database through the thread pool and the cache middleware, so that the non-associated business logic can be processed in parallel through the thread pool, the processing efficiency of the data corresponding to the non-associated business logic is improved, the query time is shortened, and the processing time is improved. The associated query results corresponding to the data of the associated business logic are queried in the sub-library database through the cache middleware, so as to realize the rapid search of the data of the associated business logic and reduce the average response time of the system within a certain period of time. Finally, the target query results are generated according to the non-associated query results and the associated query results, so as to realize real-time search of data under the premise of ensuring the stability of the financial system. The embodiment of the present application solves the problem of high concurrent access pressure of a single database by dividing the database of the financial system into multiple sub-databases, reduces the load of a single database, performs logical analysis on the call data, queries the data corresponding to the non-associated business logic through the thread pool and the cache middleware, realizes parallel processing of the non-associated business logic, and queries the data corresponding to the associated business logic through the cache middleware. The cache middleware can reduce the dependence on database queries, thereby improving the parallel processing capability of the financial system as a whole, reducing the average response time of the system within a certain period of time, and further improving the stability of the financial system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of an information processing method based on a financial system provided in an embodiment of the present application; Figure 2A flow chart of a method for performing database and table sharding operations on a financial system database according to historical business information provided in an embodiment of the present application; Figure 3 yes Figure 2 Flow chart of step S203 in FIG. Figure 4 yes Figure 3 Flow chart of step S306 in FIG. Figure 5 A flowchart of a method for querying a non-associated query result corresponding to data of non-associated business logic in a sub-library database through a thread pool and a cache middleware provided in an embodiment of the present application; Figure 6 A flowchart of a method for querying an associated query result corresponding to data associated with a business logic in a sub-database through a cache middleware provided in an embodiment of the present application; Figure 7 is a flow chart of an information processing method based on a financial system provided by another embodiment of the present application; Figure 8 is a schematic diagram of the structure of an information processing device based on a financial system provided in an embodiment of the present application; Fig. 9 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0018] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0020] First, some nouns involved in this application are analyzed: Natural Language Processing (NLP): NLP uses computers to process, understand, and apply human languages ​​(such as Chinese, English, etc.). NLP is a branch of artificial intelligence and an interdisciplinary subject between computer science and linguistics. It is often referred to as computational linguistics. Natural language processing includes grammatical analysis, semantic analysis, and text understanding. Natural language processing is commonly used in technical fields such as machine translation, handwritten and printed character recognition, speech recognition and text-to-speech conversion, information intent recognition, information extraction and filtering, text classification and clustering, public opinion analysis, and opinion mining. It involves data mining related to language processing, machine learning, knowledge acquisition, knowledge engineering, artificial intelligence research, and linguistic research related to language computing.

[0021] Term Frequency-Inverse Document Frequency (TF-IDF): TF-IDF is a commonly used weighting technique for information retrieval and text mining. TF (term frequency) indicates the frequency of a word in a document, while the inverse document frequency is the inverse document frequency of a word in a corpus. The TF-IDF value increases in direct proportion to the frequency of a word in a document, and decreases in inverse proportion to the frequency of a word in a corpus. It is used to evaluate the importance of a word to a document set or one of the documents in a corpus.

[0022] Message Digest Algorithm 5 (MD5): MD5 is a widely used cryptographic hash function that can generate a 128-bit (16-byte) hash value, usually represented by a 32-bit hexadecimal string. MD5 is used to ensure the integrity and consistency of information transmission.

[0023] Secure Hash Algorithm 1 (SHA-1): SHA-1 is a cryptographic hash function that takes an input (such as a message) and produces a 160-bit hash value, which is usually represented by a 40-bit hexadecimal string. SHA-1 is widely used in digital signatures, passports, smart cards, and software license protection.

[0024] SHOW PROCESSLIST: SHOW PROCESSLIST is a statement in the MySQL database that is used to display all threads currently running in the system. It lists detailed information about the threads, such as thread ID, user, client address, statement being executed, execution time, etc., which helps database administrators monitor and diagnose database performance issues.

[0025] Performance Schema: Performance Schema is a system library in MySQL database, which is used to monitor and analyze the performance of database server. It provides a series of tables and views to collect and report the performance data of MySQL server during operation, such as threads, locks, I / O and other information, to help database administrators optimize database performance.

[0026] Input / Output (IO): IO refers to the input and output operations of data within a computer system or between external devices. It involves all aspects of the data processing system, including user interfaces, storage devices, network communications, etc. IO performance is one of the important indicators for measuring computer system performance.

[0027] Central Processing Unit (CPU): The CPU is the core component of computer hardware, responsible for executing instructions in the program and processing data. It is responsible for interpreting computer instructions, processing data, and is responsible for the computer's main calculations and logical operations. The performance of the CPU directly affects the operating speed and efficiency of the entire computer system.

[0028] The information processing method and device based on the financial system, the electronic device and the storage medium provided in the embodiments of the present application can improve the data processing capability of the financial system and further improve the stability of the financial system.

[0029] The following embodiments are used to illustrate the present invention in detail. First, the information processing method based on the financial system in the embodiments of the present application is described.

[0030] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0031] The basic technologies of artificial intelligence generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. Artificial intelligence software technologies mainly include computer vision technology, robotics technology, biometrics technology, module management technology of online meeting systems, natural language processing technology, and machine learning / deep learning.

[0032] The information processing method based on the financial system provided in the embodiment of the present application relates to the field of financial technology. The information processing method based on the financial system provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or it can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the information processing method based on the financial system, etc., but is not limited to the above forms.

[0033] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0034] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with the relevant laws, regulations, and standards of the relevant countries and regions. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0035] Financial systems often encounter performance bottlenecks when facing high business traffic, resulting in longer system response times, affecting user experience and business continuity. Traditional order-issuing systems are prone to congestion when there is a sudden increase in requests or sustained high concurrency, affecting system stability and availability. This solution, which relies on manual intervention to increase hardware resources, is not only inefficient, but also difficult to cope with sudden traffic, resulting in inevitable business impacts.

[0036] The challenges faced by financial systems when supporting major marketing activities, such as Taobao's Double Eleven and JD.com's 618, are particularly significant. The traffic peaks brought by these activities place extremely high demands on the stability and processing capabilities of the system. The number of requests processed per second by traditional order-issuing systems is generally between 200 and 300. If there is a sudden increase in requests or continuous high concurrent requests, the system will be blocked, eventually causing the entire system to become unavailable. The only way is to find relevant server operations and maintenance to perform a full-link restart operation to restore the system. Therefore, it is particularly important to improve the stability and processing capabilities of financial systems in the context of high business traffic.

[0037] In order to solve the above problems, this embodiment provides an information processing method, device, electronic device and storage medium based on the financial system. First, the historical business information of the financial system is counted, so that the historical business volume of the financial system can be determined through the historical business information, and the database of the financial system is divided into libraries and tables according to the historical business information to obtain multiple sub-library databases. The problem of high concurrent access pressure of a single database is solved by horizontally splitting the database into multiple libraries, reducing the load of a single database, and improving the read and write performance. Then, the data call request sent by the application end of the financial system is received through the message queue middleware, and direct interaction between the application end and the message queue middleware is realized without waiting for the database response, further improving the response speed to the data call request. After that, the data call request is parsed, the data required to be called by the data call request is analyzed, and multiple call data corresponding to the data call request, that is, the data that needs to be found in the database, are determined, and then the call data is logically analyzed. , in order to realize the analysis of the structure, relationship and flow mode of the data, further determine the relevance of the calling data, determine the non-associated business logic and the associated business logic, and create a thread pool corresponding to the number of non-associated business logic, so as to facilitate the subsequent parallel processing of non-associated business logic through the thread pool, and query the non-associated query results corresponding to the data of the non-associated business logic in the sub-library database through the thread pool and the cache middleware, so that the non-associated business logic can be processed in parallel through the thread pool, the processing efficiency of the data corresponding to the non-associated business logic can be improved, the query time can be shortened, and the processing time efficiency can be improved. The associated query results corresponding to the data of the associated business logic can be queried in the sub-library database through the cache middleware, so as to realize the rapid search of the data of the associated business logic and reduce the average response time of the system within a certain period of time. Finally, the target query results are generated according to the non-associated query results and the associated query results, so as to realize the real-time search of the data under the premise of ensuring the stability of the financial system. The embodiment of the present application solves the problem of high concurrent access pressure of a single database by dividing the database of the financial system into multiple sub-databases, reduces the load of a single database, performs logical analysis on the call data, queries the data corresponding to the non-associated business logic through the thread pool and the cache middleware, realizes parallel processing of the non-associated business logic, and queries the data corresponding to the associated business logic through the cache middleware. The cache middleware can reduce the dependence on database queries, thereby improving the parallel processing capability of the financial system as a whole, reducing the average response time of the system within a certain period of time, and further improving the stability of the financial system.

[0038] The following is a detailed description with reference to the accompanying drawings.

[0039] Figure 1 is an optional flowchart of the information processing method based on the financial system provided in the embodiment of the present application. Figure 1The method may include but is not limited to steps S101 to S106.

[0040] It should be noted that the financial system is configured with message queue middleware and cache middleware.

[0041] Step S101, collect historical business information of the financial system, and perform database and table sharding operations on the database of the financial system according to the historical business information to obtain multiple shard databases.

[0042] In step S101 of some embodiments, historical business information of the financial system is counted, wherein the embodiments of the present application can count historical business information of the financial system within a preset time period, for example, historical business information within a week, historical business information within a month, etc., and can count historical business information of the financial system in a specific time period, for example, historical business information in October last year, historical business information in August last year, etc., and the database of the financial system is divided into databases and tables according to the historical business information to obtain multiple databases. Database division can solve the problem of high concurrent access pressure of a single database, and database division can solve the problem of large table query performance. At the same time, the IO, CPU and disk read and write IO bottlenecks of the system itself can be solved.

[0043] Step S102: receiving a data call request sent by an application end of the financial system through a message queue middleware.

[0044] In step S102 of some embodiments, the embodiments of the present application directly receive the data call request sent by the application end of the financial system through the message queue middleware, that is, the application end directly interacts with the message queue middleware, is decoupled from the database, and is asynchronously consumed and stored by the message queue middleware without waiting for the database response.

[0045] Step S103, performing data analysis on the data calling request to determine a plurality of calling data corresponding to the data calling request.

[0046] In step S103 of some embodiments, data parsing is performed on the data call request. Specifically, the content of the request is analyzed, including the type of request, for example, GET, POST request, etc., the target data resource, and any provided parameters or query options. The extracted data is then parsed from its original format into a structured format that can be further processed, and multiple call data corresponding to the data call request are determined. It is ensured that the data format extracted from multiple data sources is consistent, which facilitates further analysis and processing, and reduces human errors through automated parsing.

[0047] Step S104, performing logic analysis on the call data, determining the non-associated business logic and the associated business logic, and creating a thread pool corresponding to the number of the non-associated business logic.

[0048] In step S104 of some embodiments, a logical analysis is performed on the call data. Specifically, useful features are extracted from the data. These features may include call frequency, call source, data size, etc., and then predefined logical rules are used to determine which data are independent and which data are related, thereby determining related associated business logic and independent non-associated business logic, partitioning associated data and non-associated data, and creating a thread pool corresponding to the number of non-associated business logics, so as to facilitate the subsequent parallel processing of non-associated business logics through the thread pool and improve processing efficiency.

[0049] It can be understood that, taking an interface with 3 sections of business logic or rules as an example, the embodiment of the present application creates three thread pools, which are then divided into 3 thread pools to execute the business logic simultaneously, waiting for the last one to be executed successfully and returning uniformly. If the original interface returns 100ms, it will be within 30ms after the modification.

[0050] Step S105, querying the sub-library database for non-associated query results corresponding to the data of non-associated business logic through the thread pool and cache middleware, and querying the sub-library database for associated query results corresponding to the data of associated business logic through the cache middleware.

[0051] In step S105 of some embodiments, non-associated query results corresponding to data of non-associated business logic are queried in the sub-library database through the thread pool and the cache middleware, so as to achieve accurate search of data of non-associated business logic, and improve the parallel processing capability of the entire financial system through the thread pool, thereby reducing the average response time of the system within a certain period of time, and query the associated query results corresponding to data of associated business logic in the sub-library database through the cache middleware, thereby achieving accurate search of data of associated business logic, thereby reducing the average response time of the system within a certain period of time.

[0052] Step S106: Generate a target query result according to the non-association query result and the association query result.

[0053] In step S106 of some embodiments, a target query result is generated based on the non-association query result and the association query result, thereby enabling real-time search of data while ensuring the stability of the financial system.

[0054] See also Figure 2 , Figure 2 A flow chart of a method for partitioning a database of a financial system according to historical business information provided in an embodiment of the present application, the method including but not limited to steps S201 to S203.

[0055] Step S201, determining the business volume of historical business information, and increasing the business volume by a preset multiple to obtain a target business volume.

[0056] In step S201 of some embodiments, in the process of sub-library and table division of the database of the financial system according to historical business information, the embodiments of the present application first determine the business volume of the historical business information, and expand the business volume by a preset multiple to obtain the target business volume, thereby achieving capacity expansion of the business volume, so as to facilitate the subsequent timely response to requests when the business volume is large, and achieve high concurrency response to requests.

[0057] It can be understood that the preset multiples in the embodiments of the present application can be set according to the needs of the user. For example, the preset multiples can be set to ten times, five times, twenty times, etc. The embodiments of the present application do not impose specific limitations.

[0058] Step S202: perform semantic analysis on the historical business information to obtain multiple business fields.

[0059] In step S202 of some embodiments, semantic analysis is performed on the historical business information. Specifically, data cleaning operations, stop word removal operations, Chinese word segmentation operations, etc. are first performed on the historical business information to prepare a data set suitable for analysis. The text is then converted into numerical features that can be quantified through methods such as bag-of-words models and TF-IDF. The vocabulary is subjected to polysemy analysis based on the numerical features to ensure that the specific meaning of the words in the context is correctly understood. Thereafter, the similarity between the words is evaluated to identify similar or related business concepts. Finally, the historical business information is divided according to the business concepts to obtain multiple field information, and each field information is annotated to obtain multiple business fields, thereby realizing the recognition of business field information and logical relationships, being able to quickly process a large amount of historical business information, and facilitating subsequent database partitioning operations.

[0060] Step S203, based on the preset hash sharding rules, business fields and target business volume, the database of the financial system is sharded to obtain multiple sharded databases.

[0061] In step S203 of some embodiments, the database of the financial system is sharded based on preset hash sharding rules, business fields, and target business volume, thereby enabling the database to be divided while ensuring response time, reducing the load on a single database, and improving read and write performance.

[0062] See also Figure 3 In some embodiments, step S203 may also include but is not limited to steps S301 to S306.

[0063] Step S301, determine the business field as the sharding key, and perform hash function calculation on the sharding key to obtain the hash value of the sharding key.

[0064] Step S302, distribute data to the target business volume according to the sharding key to determine the number of target databases.

[0065] Step S303, performing a database sub-operation on the database of the financial system according to the number of target databases to obtain multiple sub-databases.

[0066] Step S304: for each sub-database, create a data table corresponding to the sub-database.

[0067] Step S305, performing a modulo operation on the sub-database according to the hash value to determine the storage relationship between the shard key and the sub-database.

[0068] Step S306, storing the data corresponding to the business fields into the sub-database according to the storage relationship, and obtaining a plurality of sub-databases.

[0069] In steps S301 to S306 of some embodiments, in the process of performing a database sharding operation on the database of the financial system based on a preset hash sharding rule, a business field, and a target business volume, the embodiment of the present application first determines the business field as a sharding key, and performs a hash function calculation on the sharding key to obtain a hash value of the sharding key. Specifically, the embodiment of the present application can calculate the hash function of the sharding key by means of MD5, SHA-1, etc., and then distribute data to the target business volume according to the sharding key, calculate how many databases are required to distribute the current target business volume, determine the number of target databases, and then perform a database sharding operation on the database of the financial system according to the number of target databases to obtain multiple sub-databases, thereby realizing the division of the sub-databases. For each sub-database, a data table corresponding to the sub-database is created so that each database has the same data table structure. Afterwards, a modulus operation is performed on the sub-database according to the hash value to determine in which database the data should be stored, and to determine the storage relationship between the sharding key and the sub-database. For example, if there are 4 databases, the result of the hash value modulo 4 will determine in which database the data is stored. Afterwards, the data corresponding to the business fields are stored in the sub-database according to the storage relationship. Specifically, according to the hash sharding rules, the existing data is migrated to the new database and data table to obtain multiple sub-databases. By dispersing the amount of data, the load on a single database is reduced and the read and write performance is improved. Moreover, the data is distributed in multiple databases. Even if a database is unavailable, other databases can still provide services.

[0070] It should be noted that in the process of performing modulo operation on the sub-database according to the hash value to determine the storage relationship between the shard key and the sub-database, the hash value of the shard key is divided by the number of target databases, and the remainder is taken out and the data is saved in the database corresponding to the remainder. For example, the remainder 3 is stored in the third database.

[0071] See also Figure 4 In some embodiments, step S306 may also include but is not limited to steps S401 to S406.

[0072] Step S401: Migrate data corresponding to the business fields to the sub-database according to the corresponding relationship to obtain a database to be verified.

[0073] Step S402: for each database to be verified, obtain a data table to be verified of the database to be verified, and obtain the data capacity of the data table to be verified.

[0074] Step S403: Perform a performance test on the database to be verified by using a preset database monitoring tool to obtain a first performance indicator parameter.

[0075] Step S404: when the first performance indicator parameter does not meet the preset parameter requirement, the data capacity of the to-be-verified data table is adjusted to update the to-be-verified database to obtain the target database.

[0076] Step S405: Perform a performance test on the target database using a database monitoring tool to obtain a second performance indicator parameter.

[0077] Step S406: When the second performance indicator parameter meets the preset parameter requirement, the target database is determined as a sub-database.

[0078] In steps S401 to S406 of some embodiments, in the process of storing the data corresponding to the business field in the sub-database according to the storage relationship, the embodiment of the present application first migrates the data corresponding to the business field to the sub-database according to the corresponding relationship, wherein the embodiment of the present application can migrate the data by full migration or incremental migration, etc., to obtain the database to be verified, for each database to be verified, obtain the data table to be verified of the database to be verified, and obtain the data capacity of the data table to be verified, and then perform a performance test on the database to be verified through a preset database monitoring tool, that is, monitor the performance indicators of the current database to be verified to obtain a first performance indicator parameter, wherein the first performance indicator parameter of the embodiment of the present application is used to characterize the response of the database to be verified to the data, so as to reflect the current operating status of the database to be verified, which is convenient for the subsequent database to be verified. Real-time monitoring of the operation status, and then comparing the first performance indicator parameter with the preset parameter requirements. When the first performance indicator parameter does not meet the preset parameter requirements, it means that the response time of the database to be verified is long under the current capacity, or the CPU usage is too high. It is necessary to readjust the data capacity of the data table to be verified, and update the database to be verified according to the adjusted data capacity to obtain the target database. Then, the target database is performance tested through the database monitoring tool to check the size and complexity of the data table to obtain the second performance indicator parameter, thereby reflecting the current operating status of the target database after adjusting the data capacity. When the second performance indicator parameter meets the preset parameter requirements, it means that the response time and read-write performance of the target database meet the requirements. The target database can be directly determined as a sub-database database, thereby solving the bottleneck problem of a single database and improving the query speed and transaction processing capacity of the database. By monitoring key indicators, the embodiment of the present application allows administrators and developers to optimize query statements, find out the reasons for inefficiency, and improve the overall performance of the database.

[0079] It should be noted that the database monitoring tool in the embodiment of the present application may be a tool such as SHOW PROCESSLIST or Performance Schema. The first performance indicator parameter in the embodiment of the present application includes but is not limited to response time, CPU usage, etc., and the preset parameter requirement refers to the corresponding setting of the first performance indicator parameter. Specifically, when the first performance indicator parameter includes response time and CPU usage, the preset parameter requirement includes preset response time and preset usage, and the embodiment of the present application does not make specific restrictions.

[0080] Specifically, the first performance indicator parameter of the embodiment of the present application includes read and write performance. For example, the data capacity of the data table to be verified is 3 billion data. The database to be verified is monitored by a database monitoring tool to obtain a first performance indicator parameter. The first performance indicator parameter is compared with the preset parameter requirements to determine that the read and write performance of the database to be verified is reduced by 70%. At this time, it is necessary to adjust the data capacity of the data table to be verified. For example, the data capacity of the data table to be verified is adjusted to 1 billion data. The adjusted database is then monitored by a database monitoring tool to obtain a second performance indicator parameter. The second performance indicator parameter is compared with the preset parameter requirements. If the second performance indicator parameter meets the preset parameter requirements, it means that the read and write performance in this case is optimal, that is, the target database is determined as a sub-database database.

[0081] In some embodiments, when the second performance indicator parameter does not meet the preset parameter requirements, the data capacity of the data table to be verified continues to be adjusted to update the target database, and then the target database is performance tested through a database monitoring tool to obtain a third performance indicator parameter, that is, steps S404 to S405 are repeated until the performance indicator parameters meet the preset parameter requirements.

[0082] See also Figure 5 , Figure 5 A flowchart of a method for querying non-associated query results corresponding to data of non-associated business logic in a sub-library database through a thread pool and a cache middleware is provided in an embodiment of the present application. The method includes but is not limited to steps S501 to S509.

[0083] Step S501 , classifying data corresponding to all non-associated business logics according to historical business information to obtain first basic data and first non-basic data.

[0084] It should be noted that the modification frequency of the first basic data is lower than the modification frequency of the first non-basic data.

[0085] Step S502: storing the first basic data in the cache of the financial system through the cache middleware.

[0086] Step S503: for each first non-basic data, determine the first query count of the first non-basic data.

[0087] Step S504: determine the first non-basic data whose first query times are a preset number as a first data set, and determine the first non-basic data whose first query times are greater than the preset number as a second data set.

[0088] Step S505: storing the first data set in the cache of the financial system for a first preset duration through the cache middleware.

[0089] Step S506: query the sub-library database for a first query result corresponding to the data of the second data set through the thread pool.

[0090] Step S507: after a first preset time period, query the sub-database through the thread pool for a second query result corresponding to the data of the first data set.

[0091] Step S508: query the sub-library database for a third query result corresponding to the first basic data through the thread pool.

[0092] Step S509: Obtain a non-associated query result corresponding to the data of the non-associated business logic according to the first query result, the second query result and the third query result.

[0093] In steps S501 to S509 of some embodiments, in the process of querying the non-associated query results corresponding to the data of the non-associated business logic in the sub-library database through the thread pool and the cache middleware, the data corresponding to all the non-associated business logics are first classified according to the historical business information. Specifically, the modification frequency of the data corresponding to the non-associated business logic in the historical business information is counted, and the data with a lower modification frequency is used as the first basic data, and the data with a higher modification frequency is used as the first non-basic data. The first basic data is stored in the cache of the financial system through the cache middleware to reduce the time of data access. For each first non-basic data, the first query number of the first non-basic data is determined to facilitate the subsequent determination of whether the database needs to be queried according to the query number of the first non-basic data, the first non-basic data with a first query number of a preset number is determined as the first data set, and the first non-basic data with a first query number greater than the preset number is determined as the second data set, and then After that, the first data set is stored in the cache of the financial system for a first preset time through the cache middleware, so there is no need to query the database in real time, which improves the interface performance. Then, the first query result corresponding to the data of the second data set is queried in the sub-library database through the thread pool, so as to realize the real-time query of the second data set, and the second data set is queried in parallel through the thread pool to improve the processing time efficiency. After the first preset time, the second query result corresponding to the data of the first data set is queried in the sub-library database through the thread pool, so as to avoid frequent database interactions and thus degrading the performance of the financial system. Then, the third query result corresponding to the first basic data is queried in the sub-library database through the thread pool. Finally, the non-associated query result corresponding to the data of the non-associated business logic is obtained according to the first query result, the second query result and the third query result, so as to realize the accurate search of the data of the non-associated business logic, and improve the parallel processing capability of the entire financial system through the thread pool, so as to reduce the average response time of the system within a certain period of time.

[0094] It is worth noting that since the first basic data will not be modified frequently, the embodiment of the present application can directly store the first basic data in the cache. Because the data in the cache is located in the memory of the computing device, the access speed is much faster than obtaining data from the disk or the network. For applications that obtain data through the network, the cache can reduce network latency.

[0095] It can be understood that the preset number of times in the embodiment of the present application can be set according to the needs of the user, for example, set to once, twice or three times, and the first preset duration in the embodiment of the present application can be set according to the business status, for example, set to fifteen minutes, ten minutes, five minutes, etc. Specifically, the preset number of times in the embodiment of the present application is once.

[0096] See also Figure 6 , Figure 6 A flowchart of a method for querying associated query results corresponding to data associated with business logic in a sub-library database through a cache middleware provided in an embodiment of the present application, the method includes but is not limited to steps S601 to S609.

[0097] Step S601: classify the data corresponding to all associated business logics according to the historical business information to obtain second basic data and second non-basic data.

[0098] It should be noted that the modification frequency of the second basic data is lower than the modification frequency of the second non-basic data.

[0099] Step S602: storing the second basic data in the cache of the financial system through the cache middleware.

[0100] Step S603: for each second non-basic data, determine the second query times of the second non-basic data.

[0101] Step S604: determine the second non-basic data whose second query times are the preset times as the third data set, and determine the second non-basic data whose second query times are greater than the preset times as the fourth data set.

[0102] Step S605: The third data set is stored in the cache of the financial system for a second preset time period through the cache middleware.

[0103] Step S606: query the sub-database for a fourth query result corresponding to the data of the fourth data set.

[0104] Step S607: after the second preset time period, query the sub-database for a fifth query result corresponding to the data of the third data set.

[0105] Step S608: query the sub-library database for a sixth query result corresponding to the second basic data.

[0106] Step S609: Obtain associated query results corresponding to the data associated with the business logic according to the fourth query result, the fifth query result, and the sixth query result.

[0107] In steps S601 to S609 of some embodiments, in the process of querying the associated query results corresponding to the data of the associated business logic in the sub-library database through the cache middleware, the embodiment of the present application first classifies the data corresponding to all the associated business logics according to the historical business information. Specifically, the modification frequency of the data corresponding to the associated business logic in the historical business information is counted, and the data with a lower modification frequency is used as the second basic data, and the data with a higher modification frequency is used as the second non-basic data. The second basic data is stored in the cache of the financial system through the cache middleware. The second basic data is stored in the cache of the financial system through the cache middleware, thereby reducing the time for data access. For each second non-basic data, the second query number of the second non-basic data is determined, so as to facilitate the subsequent determination of whether a query is needed based on the query number of the second non-basic data. The database determines the second non-basic data whose second query times are the preset times as the third data set, and determines the second non-basic data whose second query times are greater than the preset times as the fourth data set. After that, the third data set is stored in the cache of the financial system for the second preset time through the cache middleware, so that there is no need to query the database in real time, thereby improving the interface performance. After that, the fourth query result corresponding to the data of the fourth data set is directly queried in the sub-library database to realize real-time query of the fourth data set. After the second preset time, the fifth query result corresponding to the data of the third data set is directly queried in the sub-library database. Finally, the associated query result corresponding to the data of the associated business logic is obtained according to the fourth query result, the fifth query result and the sixth query result, so as to realize accurate search of the data of the associated business logic and reduce the average response time of the system within a certain period of time.

[0108] It is worth noting that since the second basic data will not be modified frequently, the embodiment of the present application can directly store the second basic data in the cache. Because the data in the cache is located in the memory of the computing device, the access speed is much faster than obtaining data from the disk or the network. For applications that obtain data through the network, the cache can reduce network latency.

[0109] It can be understood that the first preset duration in the embodiment of the present application can be set according to the business status, for example, to fifteen minutes, ten minutes, five minutes, etc., and the embodiment of the present application does not make specific limitations.

[0110] See also Figure 7 , Figure 7 is a flowchart of an information processing method based on a financial system provided by another embodiment of the present application. Figure 7The method may include but is not limited to step S701.

[0111] Step S701: When the first performance indicator parameter meets the preset parameter requirement, the database to be verified is determined as a sub-database.

[0112] In step S701 of some embodiments, when the first performance indicator parameter meets the preset parameter requirement, it means that the response time and read and write performance of the database to be verified meet the requirements. The database to be verified can be directly determined as a sub-database, thereby solving the bottleneck problem of a single database and improving the query speed and transaction processing capability of the database.

[0113] See also Figure 8 The embodiment of the present application also provides an information processing device based on a financial system, wherein the financial system is configured with a message queue middleware and a cache middleware; the device comprises: The database and table sharding module 801 is used to collect historical business information of the financial system and perform database and table sharding operations on the database of the financial system according to the historical business information to obtain multiple database sharding databases; The request receiving module 802 is used to receive the data call request sent by the application end of the financial system through the message queue middleware; The data analysis module 803 is used to analyze the data call request and determine a plurality of call data corresponding to the data call request; The logic analysis module 804 is used to perform logic analysis on the call data, determine the non-associated business logic and the associated business logic, and create a thread pool corresponding to the number of non-associated business logic; The result query module 805 is used to query the non-associated query results corresponding to the data of the non-associated business logic in the sub-library database through the thread pool and the cache middleware, and query the associated query results corresponding to the data of the associated business logic in the sub-library database through the cache middleware; The target generation module 806 is used to generate a target query result according to the non-association query result and the association query result.

[0114] The specific implementation of the information processing device based on the financial system is basically the same as the specific implementation of the information processing method based on the financial system described above, and will not be described in detail here.

[0115] The embodiment of the present application also provides an electronic device, which includes: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and when the program is executed by the processor, the above-mentioned information processing method based on the financial system is realized. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0116] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes: The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application; The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the information processing method based on the financial system in the embodiment of this application; Input / output interface 903, used to implement information input and output; Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.); A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904); The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0117] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned information processing method based on the financial system.

[0118] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0119] The information processing method, device, electronic device and storage medium based on the financial system provided by the embodiments of the present application firstly count the historical business information of the financial system, so as to determine the historical business volume of the financial system through the historical business information, and perform database and table operations on the database of the financial system according to the historical business information to obtain multiple database sub-libraries, and solve the problem of high concurrent access pressure of a single database by horizontally splitting the database into multiple databases, reduce the load of a single database, and improve the read and write performance, and then receive the data call request sent by the application end of the financial system through the message queue middleware, realize the direct interaction between the application end and the message queue middleware, without waiting for the database response, and further improve the response speed to the data call request, and then perform data parsing on the data call request, analyze the data required to be called by the data call request, determine multiple call data corresponding to the data call request, that is, the data that needs to be found in the database, and then perform logical analysis on the call data to implement The analysis of the structure, relationship and flow mode of the data can further determine the relevance of the called data, determine the non-associated business logic and the associated business logic, and create a thread pool corresponding to the number of non-associated business logic, so as to facilitate the subsequent parallel processing of non-associated business logic through the thread pool, and query the non-associated query results corresponding to the data of the non-associated business logic in the sub-library database through the thread pool and the cache middleware, so that the non-associated business logic can be processed in parallel through the thread pool, the processing efficiency of the data corresponding to the non-associated business logic can be improved, the query time can be shortened, and the processing time efficiency can be improved. The associated query results corresponding to the data of the associated business logic can be queried in the sub-library database through the cache middleware, so as to realize the rapid search of the data of the associated business logic and reduce the average response time of the system within a certain period of time. Finally, the target query results are generated according to the non-associated query results and the associated query results, so as to realize the real-time search of the data under the premise of ensuring the stability of the financial system. The embodiment of the present application solves the problem of high concurrent access pressure of a single database by dividing the database of the financial system into multiple sub-databases, reduces the load of a single database, performs logical analysis on the call data, queries the data corresponding to the non-associated business logic through the thread pool and the cache middleware, realizes parallel processing of the non-associated business logic, and queries the data corresponding to the associated business logic through the cache middleware. The cache middleware can reduce the dependence on database queries, thereby improving the parallel processing capability of the financial system as a whole, reducing the average response time of the system within a certain period of time, and further improving the stability of the financial system.

[0120] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0121] It can be understood by those skilled in the art that Figure 1-9 The technical solutions shown in the figure do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown in the figure, or a combination of certain steps, or different steps.

[0122] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0123] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0124] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0125] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0126] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0127] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0128] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0129] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0130] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. An information processing method based on a financial system, characterized in that: The financial system is configured with a message queue middleware and a cache middleware; the method comprises: Collecting historical business information of the financial system, and performing database and table sharding operations on the database of the financial system according to the historical business information to obtain multiple shard databases; Receiving a data call request sent by the application end of the financial system through the message queue middleware; Performing data analysis on the data call request to determine a plurality of call data corresponding to the data call request; Performing logic analysis on the call data, determining non-associated business logic and associated business logic, and creating a thread pool corresponding to the number of the non-associated business logic; Query the sub-library database for non-associated query results corresponding to the data of the non-associated business logic through the thread pool and the cache middleware, and query the sub-library database for associated query results corresponding to the data of the associated business logic through the cache middleware; A target query result is generated according to the non-associated query result and the associated query result.

2. The information processing method based on the financial system according to claim 1, characterized in that: The database of the financial system is divided into sub-libraries and sub-tables according to the historical business information to obtain multiple sub-library databases, including: Determine the business volume of the historical business information, and increase the business volume by a preset multiple to obtain a target business volume; Performing semantic analysis on the historical business information to obtain multiple business fields; Based on the preset hash sharding rule, the business field and the target business volume, the database of the financial system is sharded to obtain multiple sharded databases.

3. The information processing method based on the financial system according to claim 2, characterized in that: The database of the financial system is subjected to a database sharding operation based on the preset hash sharding rule, the business field, and the target business volume to obtain a plurality of sharding databases, including: Determine the business field as a sharding key, and perform a hash function calculation on the sharding key to obtain a hash value of the sharding key; Allocate data for the target business volume according to the sharding key to determine the number of target databases; Performing a database sub-database operation on the financial system according to the number of target databases to obtain multiple sub-databases; For each of the sub-databases, creating a data table corresponding to the sub-database; Performing a modulo operation on the sub-database according to the hash value to determine a storage relationship between the shard key and the sub-database; According to the storage relationship, the data corresponding to the business field is stored in the sub-database to obtain a plurality of sub-databases.

4. The information processing method based on the financial system according to claim 3, characterized in that: The business fields are stored in the sub-database according to the corresponding relationship to obtain multiple sub-databases, including: Migrating the data corresponding to the business field to the sub-database according to the corresponding relationship to obtain a database to be verified; For each of the databases to be verified, obtaining a data table to be verified of the database to be verified, and obtaining the data capacity of the data table to be verified; Performing a performance test on the database to be verified by using a preset database monitoring tool to obtain a first performance indicator parameter; When the first performance indicator parameter does not meet the preset parameter requirement, adjusting the data capacity of the to-be-verified data table to update the to-be-verified database to obtain a target database; Performing a performance test on the target database by using the database monitoring tool to obtain a second performance indicator parameter; When the second performance indicator parameter meets the preset parameter requirement, the target database is determined as a sub-database.

5. The information processing method based on the financial system according to claim 1, characterized in that: The querying of the non-associated query result corresponding to the data of the non-associated business logic in the sub-library database through the thread pool and the cache middleware includes: Classifying the data corresponding to all the non-associated business logics according to the historical business information to obtain first basic data and first non-basic data, wherein the modification frequency of the first basic data is lower than the modification frequency of the first non-basic data; storing the first basic data in the cache of the financial system through the cache middleware; For each of the first non-basic data, determining a first query count of the first non-basic data; Determine the first non-basic data whose first query number is a preset number as a first data set, and determine the first non-basic data whose first query number is greater than the preset number as a second data set; storing the first data set in the cache of the financial system for a first preset duration through the cache middleware; Querying the sub-database database for a first query result corresponding to the data of the second data set through the thread pool; After the first preset time period, querying the sub-database database through the thread pool for a second query result corresponding to the data of the first data set; Querying the sub-library database for a third query result corresponding to the first basic data through the thread pool; A non-associated query result corresponding to the data of the non-associated business logic is obtained according to the first query result, the second query result and the third query result.

6. The information processing method based on the financial system according to claim 1, characterized in that: The querying of the associated query result corresponding to the data associated with the business logic in the sub-database through the cache middleware includes: Classifying the data corresponding to all the associated business logics according to the historical business information to obtain second basic data and second non-basic data, wherein the modification frequency of the second basic data is lower than the modification frequency of the second non-basic data; storing the second basic data in the cache of the financial system through the cache middleware; For each of the second non-basic data, determining a second query count of the second non-basic data; determining the second non-basic data whose second query number is a preset number as a third data set, and determining the second non-basic data whose second query number is greater than the preset number as a fourth data set; storing the third data set in the cache of the financial system for a second preset duration through the cache middleware; Querying the sub-database for a fourth query result corresponding to the data of the fourth data set; After the second preset time, querying the sub-database for a fifth query result corresponding to the data of the third data set; Querying the sub-database for a sixth query result corresponding to the second basic data; An associated query result corresponding to the data of the associated business logic is obtained according to the fourth query result, the fifth query result and the sixth query result.

7. The information processing method based on the financial system according to claim 4, characterized in that: After performing a performance test on the database to be verified by using a preset database monitoring tool to obtain a first performance indicator parameter, the method further includes: When the first performance indicator parameter meets the preset parameter requirement, the database to be verified is determined as a sub-database.

8. An information processing device based on a financial system, characterized in that: The financial system is configured with a message queue middleware and a cache middleware; the device comprises: A database and table sharding module is used to collect statistics on the historical business information of the financial system, and to perform database and table sharding operations on the database of the financial system according to the historical business information to obtain multiple database sharding databases; A request receiving module, used for receiving a data call request sent by the application end of the financial system through the message queue middleware; A data analysis module, used to analyze the data call request and determine a plurality of call data corresponding to the data call request; A logic analysis module, used to perform logic analysis on the call data, determine non-associated business logic and associated business logic, and create a thread pool corresponding to the number of the non-associated business logic; A result query module is used to query the non-associated query results corresponding to the data of the non-associated business logic in the sub-library database through the thread pool and the cache middleware, and query the associated query results corresponding to the data of the associated business logic in the sub-library database through the cache middleware; The target generation module is used to generate a target query result according to the non-associated query result and the associated query result.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the information processing method based on the financial system as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the information processing method based on a financial system according to any one of claims 1 to 7 is implemented.

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