A data real-time processing method, system, terminal device and storage medium

Abstract

The application relates to the technical field of data processing, and discloses a data real-time processing method, a system, a terminal device and a storage medium. The data real-time processing method comprises the following steps: acquiring calculation data of different calculation tasks from a business database; acquiring a real-time updated data calculation rule, and performing verification processing on the real-time updated data calculation rule; under the condition that the real-time updated data calculation rule passes the verification, processing the calculation data according to the real-time updated data calculation rule to obtain a calculation result; and writing the calculation result into a data lake. The application introduces a dynamic data calculation rule mechanism, improves the flexibility and adaptability of the calculation task, and changes the problem that a traditional data calculation rule is fixed and cannot be dynamically adjusted. The application does not need to modify the fixed data calculation rule code, avoids the restart and redeployment of the calculation task after the code modification, guarantees the continuity and efficiency of task calculation, and can flexibly cope with the change of data calculation demand.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a real-time data processing method, system, terminal device, and storage medium. Background Technology

[0002] With the continuous development of big data and real-time computing technologies, streaming data processing is playing an increasingly important role in financial scenarios. Traditional real-time data computing typically employs a customized data task approach for development and deployment. This involves developers writing computational logic code based on specific business needs and encapsulating this code as an independent task module for deployment and execution. In this traditional model, the computational code for data tasks is fixed and cannot be dynamically adjusted. Modifications require restarting the computational task, redeploying it, and manual intervention in maintenance, impacting computational efficiency. Summary of the Invention

[0003] In view of this, embodiments of this application provide a real-time data processing method, system, terminal device, and storage medium, which can effectively solve problems such as the solidification of data task calculation code, which affects task calculation efficiency.

[0004] In a first aspect, embodiments of this application provide a real-time data processing method, including:

[0005] Retrieve computation data for different computing tasks from the business database;

[0006] Obtain the real-time updated data calculation rules and perform verification processing on the real-time updated data calculation rules;

[0007] Under the condition that the real-time updated data calculation rules pass the verification, the calculation data is processed according to the real-time updated data calculation rules to obtain the calculation result;

[0008] The calculation results are written into the data lake.

[0009] In a first possible embodiment of the first aspect, different computational tasks correspond to different data computation rules, and the method further includes:

[0010] If the real-time updated data calculation rule passes the verification, the tag of the real-time updated data calculation rule is modified so that if the tag change is detected, the data calculation rule of the calculation task is determined to be updated.

[0011] In a second possible embodiment of the first aspect, obtaining computational data for different computational tasks from the business database includes:

[0012] The calculated data is collected in real time from multiple data tables in the business database using a streaming data acquisition operator;

[0013] Each of the computational data is converted into a memory storage format so that the computational data in the memory storage format is temporarily stored in the memory.

[0014] In a third possible embodiment of the first aspect, it further includes:

[0015] Under the condition that the data calculation rules of the computing task are updated, the real-time updated data calculation rules are obtained, and the computing data temporarily stored in the memory is processed according to the unupdated data calculation rules.

[0016] The step of processing the calculation data according to the real-time updated data calculation rules to obtain the calculation result includes:

[0017] After the computational data temporarily stored in the memory is processed according to the unupdated data computation rules, real-time computational data is obtained, and the real-time computational data is processed according to the real-time updated data computation rules.

[0018] In a fourth possible embodiment of the first aspect, the step of obtaining the real-time updated data calculation rules and performing verification processing on the real-time updated data calculation rules includes:

[0019] The data calculation rules are periodically read from the dynamic rule database and the real-time updated data calculation rules are subjected to syntax validation to obtain the syntax validation results.

[0020] Based on the syntax verification results, determine whether the real-time updated data calculation rules have passed the verification.

[0021] In a fifth possible embodiment of the first aspect, it further includes:

[0022] An anomaly alarm will be triggered if the real-time updated data calculation rules fail to pass verification.

[0023] The real-time updated data calculation rules that fail verification are stored in the audit analysis database to record the real-time updated data calculation rules that fail verification.

[0024] In a sixth possible embodiment of the first aspect, determining whether the real-time updated data calculation rule passes the verification based on the syntax verification result includes:

[0025] If the syntax check result is incorrect, it is determined that the real-time updated data calculation rule check has failed.

[0026] If the syntax verification result is correct, the real-time updated data calculation rule is deemed to have passed the verification.

[0027] Secondly, embodiments of this application provide a real-time data processing system, including:

[0028] The data acquisition module is used to obtain computational data from the business database;

[0029] The rule processing module is used to obtain real-time updated data calculation rules and to perform verification processing on the real-time updated data calculation rules;

[0030] The data processing module is used to process the calculation data according to the real-time updated data calculation rules, obtain the calculation results, and write the calculation results into the data lake, provided that the real-time updated data calculation rules have passed the verification.

[0031] The embodiments of this application have the following beneficial effects:

[0032] This embodiment of a real-time data processing method includes: obtaining computational data for different computational tasks from a business database; obtaining real-time updated data computation rules and verifying the real-time updated data computation rules; processing the computational data according to the real-time updated data computation rules, provided that the verification of the real-time updated data computation rules is passed, to obtain computational results; and writing the computational results into a data lake. This application introduces a dynamic computational rule mechanism to improve the flexibility and adaptability of computational tasks, changing the problem of traditional data computation rules being fixed and unable to be dynamically adjusted. By reading real-time updated data computation rules and verifying them, the computational data is processed according to the updated data computation rules. This application does not require modification of the fixed data computation rule code, avoiding task restarts after code modifications, ensuring the continuity and efficiency of task computation, and flexibly responding to changes in data computation requirements. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This paper illustrates a first flowchart of a real-time data processing method according to an embodiment of the present application.

[0035] Figure 2 A second flowchart of the real-time data processing method according to an embodiment of this application is shown;

[0036] Figure 3 A third flowchart of the real-time data processing method according to an embodiment of this application is shown;

[0037] Figure 4 A schematic diagram of a real-time data processing system according to an embodiment of this application is shown.

[0038] Explanation of key component symbols:

[0039] 200 - Real-time data processing system; 210 - Data acquisition module; 220 - Rule processing module; 230 - Data processing module. Detailed Implementation

[0040] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0041] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0042] In the following text, the terms "comprising," "having," and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more combinations thereof. Furthermore, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0043] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0044] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0045] Data synchronization refers to synchronizing data from the business database to the data lake. The data lake provides transactional management, version control capabilities, and data structure change capabilities, supporting real-time data synchronization. Combined with CDC (Change Data Capture) technology, changes to upstream databases or data sources can be captured in real time and seamlessly merged into the data lake through stream processing jobs, ensuring the data lake's real-time performance and consistency. Current industry-standard solutions have shortcomings: they commonly employ offline scheduling methods based on time intervals (such as hourly or daily) for data synchronization. This method relies on scheduled task systems, resulting in significant data latency. In business scenarios with high data timeliness requirements (such as real-time risk control, real-time reporting, and streaming analysis), it cannot meet the business system's needs for real-time data updates and immediate responses. In traditional real-time computing tasks, data calculation rules are usually embedded in the program as fixed code. Once deployed and running, the data calculation rules for the computing task are fixed. If business requirements change, the source code must be manually modified, recompiled, and the task resubmitted. This approach suffers from poor flexibility, high maintenance costs, and the need to restart the task after code modification, which may lead to data flow interruption, state loss, and affect task continuity and data consistency. After the data calculation rules are modified, there is a lack of an effective verification mechanism for the new logic. Errors are usually only discovered during task execution, which poses a high risk of task failure.

[0046] To address the aforementioned issues, this application proposes a real-time data processing method, system, terminal device, and storage medium. This achieves real-time synchronization of computational data, overcoming timeliness bottlenecks and abandoning the traditional offline scheduling model that synchronizes data at fixed time intervals. It enables real-time updates of data computation rules, improving task flexibility and adaptability. Adjustments to dynamic data computation rules do not require redeployment or restarting of the computation task; dynamic data computation rules automatically take effect after verification. The entire process requires no manual intervention, significantly reducing manpower and greatly improving task operation efficiency and overall performance. An online verification mechanism is constructed to verify updated data computation rules in real time, preventing online task failures caused by script issues in the data computation rules from the source, greatly improving task stability.

[0047] The following examples illustrate the real-time data processing method.

[0048] Figure 1 A flowchart of a real-time data processing method according to an embodiment of this application is shown. Exemplarily, the real-time data processing method includes the following steps:

[0049] S110 retrieves computation data for different computation tasks from the business database.

[0050] In one embodiment, this application uses a streaming data acquisition operator to collect computational data from multiple data tables in a business database in real time; the computational data is then converted into a memory storage format to temporarily store the computational data in memory. In this embodiment, computational data refers to data that needs to be processed in different computational tasks. A computational task refers to processing the computational data in real time according to preset computational rules or logical expressions to generate computational results. These computational results may include, but are not limited to, the customer's average balance over the past 30 days, the total daily transaction amount of the account, and the account balance change curve.

[0051] In one implementation, such as Figure 2 As shown, the streaming data acquisition operator is the Flink CDC Source operator. CDC (Change Data Capture) is used to capture changes in data in the source business database, listening to logs in the business database to obtain real-time changing computational data, such as the binlog (binary) log in the business database. The binlog log records all changes on the database and is stored in the database in binary form. The Flink CDCSource operator is a CDC-based Source operator built into Flink (the streaming processing framework) or provided as a plugin. It is used to connect to the business database and read its change logs. The Flink CDC Source operator is used to collect computational data from the business database in real time, convert the computational data into an in-memory storage format, and push it to in-memory storage.

[0052] Understandably, the above method improves data synchronization timeliness from daily or hourly to real-time, effectively addressing data synchronization and analysis needs in scenarios with high data timeliness requirements, significantly reducing data latency, and ensuring data timeliness and freshness. Computational data for different tasks is stored in multiple tables within the business database. In data integration scenarios where data from the business database is synchronized to the data lake, this application implements real-time adaptive processing of computational data, as well as a synchronization link design that synchronizes data by business database and multiple tables within the business database. Compared to the industry-common practice of synchronously writing data from a single data table to the data lake, this improves the efficiency of computational task processing.

[0053] S120: Obtain the real-time updated data calculation rules and perform verification processing on the real-time updated data calculation rules.

[0054] In one embodiment, such as Figure 3 As shown, the real-time data processing method also includes the following steps:

[0055] S121: Read the real-time updated data calculation rules from the dynamic rule database at regular intervals, perform syntax verification on the real-time updated data calculation rules, and obtain the syntax verification results.

[0056] In this embodiment, as Figure 2 As shown, data computation rules refer to script programs that describe how computational data in different computational tasks are processed, transformed, and computed. A dynamic rule database is a data storage system used to store, manage, and update real-time computation rule scripts. This application stores data computation rules in the form of computational scripts in a dynamic rule database. This is suitable for concurrently acquiring updated data computation rules in computational data nodes of different computational tasks. The acquisition of updated data computation rules, the acquisition of computational data, and the computation itself do not affect each other. Data computation rules can be updated at any time and take effect without restarting the computational task, facilitating the management of data computation rules.

[0057] In one embodiment, this application performs syntax validation on the read data calculation rules to ensure that the data calculation rules meet the requirements of logical correctness, semantic correctness, and structural integrity before execution. Syntax validation includes, but is not limited to, calculation logic checking, syntax structure analysis, variable and function definition and reference checking, and type checking. Calculation logic checking verifies whether the calculation logic specified by the data calculation rules is reasonable and correct. Syntax structure analysis verifies whether the script conforms to the syntax rules of the language used, such as statement structure, bracket matching, statement termination characters, and function definition format. Variable and function definition and reference checking confirms whether the variables and functions used in the script are correctly defined, within their scope, and type-matched. Type checking, for statically typed languages ​​or strongly typed scripting languages, verifies whether the types of variables, function return values, and operands of operators are consistent or convertible.

[0058] S122, determine whether the real-time updated data calculation rules have passed the verification based on the syntax verification results.

[0059] In one embodiment, this application determines that the real-time updated data calculation rule verification has failed if the syntax verification result is incorrect; and determines that the real-time updated data calculation rule verification has passed if the syntax verification result is correct. In this embodiment, if at least one syntax verification error exists, the real-time updated data calculation rule verification is determined to have failed.

[0060] In one embodiment, this application modifies the flag of the real-time updated data calculation rule after the real-time updated data calculation rule has passed verification. This allows for the determination that the data calculation rule for the calculation task has been updated upon detecting a change in the flag. The flag of the data calculation rule is a status identifier used to indicate whether the currently used data calculation rule has been updated and passed verification. By modifying the rule flag, this application notifies that the data calculation rule has been updated, automatically triggering rule loading without restarting the calculation task, thus improving the level of automation.

[0061] In another embodiment, this application issues an anomaly alarm when the real-time updated data calculation rules fail verification. The unverified real-time updated data calculation rules are stored in an audit analysis database to record them. In this embodiment, the alarm content includes, but is not limited to, script content, error type (such as unreasonable calculation logic, syntax errors, etc.), error location, submission time, and submitter. This application can issue anomaly alarms via real-time notification, such as SMS or email. Writing the unverified real-time updated data calculation rules into the audit analysis database supports subsequent manual review and correction.

[0062] This application addresses the issue of traditional data calculation rules failing online verification and requiring manual rollback after modification. After obtaining the calculation rules from the dynamic rule database, it first performs syntax validation. If a validation error occurs, an alert is issued. Only scripts that pass validation are used for data processing, thus preventing online task failures due to script issues from the source, reducing manual rollback operations, and greatly improving task stability.

[0063] S130: Under the condition that the real-time updated data calculation rules pass the verification, the calculation data is processed according to the real-time updated data calculation rules to obtain the calculation result.

[0064] In one embodiment, this application, upon determining that the data calculation rules for a computational task have been updated, obtains the real-time updated data calculation rules and processes the computational data temporarily stored in memory according to the unupdated data calculation rules. After processing the computational data temporarily stored in memory according to the unupdated data calculation rules, real-time computational data is obtained, and then processed according to the real-time updated data calculation rules.

[0065] In this embodiment, before switching data calculation rules, all received but not yet processed cached data in memory is processed and output. This ensures the complete execution of the data processing flow under the old data calculation rules before loading the new rules for subsequent data processing. This ensures that all cached data is processed and not discarded due to rule switching, and that cached data is not processed by the new rules, avoiding logical errors. This application processes real-time calculation data according to real-time updated data calculation rules, changing the problem of traditional real-time calculation task code being fixed and unable to be dynamically adjusted. It eliminates the need to modify fixed code, avoiding task restarts after code modifications, ensuring the continuity and efficiency of task calculation, and flexibly responding to changes in data processing needs. It enables dynamic adjustment and automatic operation of calculation tasks, reducing maintenance costs. In the traditional model, modifying calculation rules requires redeploying the calculation task and relies on manual maintenance. In this solution, adjusting dynamic calculation rules does not require redeploying or restarting the calculation task. The dynamic rules of the calculation task automatically take effect after verification. The entire process requires no manual intervention, significantly reducing manpower input and significantly improving task maintenance efficiency and overall performance.

[0066] S140, write the calculation results into the data lake.

[0067] In this embodiment, the data lake is a centralized, large-scale data storage system that provides transactional management, version control, and data structure modification capabilities for the Hudi (Hadoop Upserts Deletes and Incrementals) data lake, supporting real-time data synchronization. This application supports real-time processing of multiple computing tasks and changes to data computation rules for these tasks before writing to the Hudi data lake, allowing different upstream computing tasks to be processed in different modes without restarting or manual intervention, ensuring the stability and timeliness of the real-time synchronization link.

[0068] In one embodiment, such as Figure 2 As shown, before writing the calculation results to the Hudi data lake, this application first pushes the calculation results to a downstream database, and then writes the calculation results from the downstream database to the Hudi data lake in Hudi table format. In this embodiment, the Hudi data lake involves complex operations such as file merging and index updating. High-frequency writing can easily cause excessive system load. This application uses the downstream database as a caching layer to temporarily store the calculation results, and then writes them to the Hudi data lake in batch processing, thereby alleviating the writing pressure on the Hudi data lake and improving the writing performance and stability of the Hudi data lake.

[0069] Figure 4 A schematic diagram of a real-time data processing system 200 according to an embodiment of this application is shown. Exemplarily, the real-time data processing system 200 includes:

[0070] The data acquisition module 210 is used to acquire computational data from the business database.

[0071] The rule processing module 220 is used to obtain the real-time updated data calculation rules and perform verification processing on the real-time updated data calculation rules.

[0072] The data processing module 230 is used to process the calculation data according to the real-time updated data calculation rules, obtain the calculation results, and write the calculation results into the data lake, provided that the real-time updated data calculation rules have passed the verification.

[0073] It is understood that the system in this embodiment corresponds to the real-time data processing method in the above embodiments, and the options in the above embodiments are also applicable to this embodiment, so they will not be described again here.

[0074] This application also provides a terminal device, exemplary of which includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to enable the terminal device to perform the functions of the above-described real-time data processing method or the various modules in the above-described real-time data processing system.

[0075] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Network Processor (NP), Digital Signal Processor (DSP), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0076] Memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), and Electrically Erasable Programmable Read-Only Memory (EEPROM). Memory is used to store computer programs, and the processor can execute these programs upon receiving execution instructions.

[0077] This application also provides a computer-readable storage medium for storing the computer program used in the aforementioned terminal device. For example, the computer-readable storage medium may include, but is not limited to, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0078] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can also be implemented in other ways. The embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0079] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0080] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0081] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A real-time data processing method, characterized in that, include: Retrieve computation data for different computing tasks from the business database; Given that the data calculation rules for the computation task have been updated, the real-time updated data calculation rules are obtained, and the computation data temporarily stored in memory is processed according to the unupdated data calculation rules. The real-time updated data calculation rules are validated. Data computation rules refer to script programs that describe how computational data is processed, transformed, and computed in different computational tasks. Under the condition that the real-time updated data calculation rules pass the verification, the calculation data is processed according to the real-time updated data calculation rules to obtain the calculation result; The step of processing the calculation data according to the real-time updated data calculation rules to obtain the calculation result includes: After the computational data temporarily stored in the memory is processed according to the unupdated data computation rules, real-time computational data is obtained, and the real-time computational data is processed according to the real-time updated data computation rules. The calculation results are written into the data lake; Different computational tasks correspond to different data computation rules, and the method further includes: If the real-time updated data calculation rule passes the verification, the tag of the real-time updated data calculation rule is modified so that if the tag change is detected, the data calculation rule of the calculation task is updated. The data calculation rule is marked with a status identifier, which indicates whether the current data calculation rule has been updated and passed the verification.

2. The real-time data processing method according to claim 1, characterized in that, The step of obtaining computation data for different computation tasks from the business database includes: The calculated data is collected in real time from multiple data tables in the business database using a streaming data acquisition operator; Each of the computational data is converted into a memory storage format so that the computational data in the memory storage format is temporarily stored in the memory.

3. The real-time data processing method according to claim 1, characterized in that, The process of obtaining real-time updated data calculation rules and verifying these rules includes: The data calculation rules are periodically read from the dynamic rule database and the real-time updated data calculation rules are subjected to syntax validation to obtain the syntax validation results. Based on the syntax verification results, determine whether the real-time updated data calculation rules have passed the verification.

4. The real-time data processing method according to claim 1, characterized in that, Also includes: An anomaly alarm will be triggered if the real-time updated data calculation rules fail to pass verification. The real-time updated data calculation rules that fail verification are stored in the audit analysis database to record the real-time updated data calculation rules that fail verification.

5. The real-time data processing method according to claim 3, characterized in that, The step of determining whether the real-time updated data calculation rule passes the validation based on the syntax validation result includes: If the syntax check result is incorrect, it is determined that the real-time updated data calculation rule check has failed. If the syntax verification result is correct, the real-time updated data calculation rule is deemed to have passed the verification.

6. A real-time data processing system, characterized in that, include: The data acquisition module is used to obtain computational data from the business database; The rule processing module is used to obtain real-time updated data calculation rules and to perform verification processing on the real-time updated data calculation rules; Data computation rules refer to script programs that describe how computational data is processed, transformed, and computed in different computational tasks. Different computing tasks correspond to different data computing rules. The rule processing module is also used to modify the marker of the real-time updated data computing rule when the real-time updated data computing rule is verified to pass, so as to determine that the data computing rule of the computing task is updated when the marker is detected to change. The data calculation rule is marked with a status identifier, which indicates whether the current data calculation rule has been updated and passed the verification. The data processing module is used to process the computational data temporarily stored in memory according to the unupdated data computational rules when the data computational rules of the computational task are determined to be updated; and to process the computational data according to the real-time updated data computational rules when the real-time updated data computational rules are verified to obtain the computational results and write the computational results into the data lake. The step of processing the calculation data according to the real-time updated data calculation rules to obtain the calculation result includes: After the computational data temporarily stored in the memory is processed according to the unupdated data computation rules, real-time computational data is obtained, and the real-time computational data is processed according to the real-time updated data computation rules.

7. A terminal device, characterized in that, The terminal device includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the real-time data processing method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed on a processor, implements the real-time data processing method according to any one of claims 1-5.

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