Business data monitoring method based on Flink engine and related device

Abstract

The invention discloses a service data monitoring method based on an Flink engine and a related device, and the method comprises the steps: obtaining service data flows from different service systems, and carrying out the real-time association processing of the service data flows based on a preset association rule through employing the Flink engine, generating a summarized data stream containing business data contents from a plurality of business systems; performing consistency analysis on the summarized data flow according to a preset monitoring dimension to generate a data consistency statistical result; according to the embodiment of the invention, the method achieves the real-time collection and consistency monitoring of the data of the multi-service system, improves the real-time performance and accuracy of the monitoring of the cross-system service data, and reduces the manual monitoring cost.

Description

Technical Field

[0001] This invention pertains to business data processing technology, and particularly relates to a business data monitoring method and related apparatus based on the Flink engine. Background Technology

[0002] As businesses expand and information system architectures evolve, multiple interconnected business systems typically run simultaneously within an enterprise, such as order systems, product systems, payment systems, and equipment systems. These systems are often built and maintained by different teams and have independent data collection, processing, and storage mechanisms. However, they also have close data relationships within the overall business process, and the business data they generate should logically maintain consistency.

[0003] Currently, data consistency issues between multiple business systems are typically addressed using offline statistical analysis or post-event verification. For example, data is periodically extracted from each business system and analyzed according to a preset cycle to identify discrepancies in business data between different systems. While these techniques can meet the needs of low-frequency, post-event analysis to some extent, their analysis cycles are usually measured in days or longer, making it difficult to promptly reflect anomalies during business operations.

[0004] As business requirements become increasingly demanding in terms of real-time performance, inconsistencies in business data may arise within a short period due to system failures, interface anomalies, data delays, duplicate writes, or data loss. Failure to detect and address these anomalies within a short timeframe can escalate business risks and even adversely affect business decisions and system stability. However, current monitoring methods for cross-business system data primarily focus on outcome analysis rather than process monitoring, making it difficult to achieve minute-level data consistency detection and anomaly alerts.

[0005] Furthermore, due to differences in data structures, field meanings, and data generation rhythms across different business systems, existing technologies lack a unified data processing mechanism for centralized management and continuous analysis of data from multiple business systems. In practical applications, it is often necessary to design monitoring logic and statistical tasks separately for different business scenarios. This not only results in high development and maintenance costs but also leads to fragmented monitoring rules, making it difficult to flexibly configure monitoring dimensions and anomaly detection conditions, thus limiting the system's versatility and scalability. Summary of the Invention

[0006] Based on this, the present invention aims to propose a business data monitoring method and related apparatus based on the Flink engine, which integrates business data from different business systems using the Flink engine to improve the real-time performance, flexibility and reliability of data monitoring.

[0007] In a first aspect, the present invention provides a business data monitoring method based on the Flink engine, comprising: Acquire business data streams from different business systems; The Flink engine is used to perform association processing on business data streams from different business systems based on preset association rules, generating a summary data stream. Perform consistency analysis on the aggregated data stream according to preset monitoring dimensions and generate data consistency statistics; Based on the data consistency statistics, monitoring operations are performed, including triggering alarms and not triggering alarms.

[0008] Furthermore, before performing consistency analysis on the aggregated data stream according to preset monitoring dimensions, the following steps are included: Write the aggregated data stream to a data storage system or message middleware.

[0009] Furthermore, the preset association rules include at least one association field, which is used to identify the correspondence between business data from different business systems.

[0010] Furthermore, the Flink engine is used to perform association processing on the business data streams of different business systems based on preset association rules, generating a summary data stream including: The Flink engine is used to group business data streams from different business systems based on related fields, so that data with the same related fields enters the same processing instance. Maintain corresponding intermediate state data for associated fields; A summary data stream is generated based on the intermediate state data. Each data entry in the summary data stream includes business data content from at least two business systems.

[0011] Furthermore, using the Flink engine to perform association processing on business data streams from different business systems based on preset association rules includes: Use the Flink engine to set the data arrival time tolerance range for each business system; For business data streams that have a time difference in arrival time and come from different business systems, the business data streams are correlated based on the tolerance range of data arrival time.

[0012] Furthermore, consistency analysis is performed on the aggregated data stream according to preset monitoring dimensions, generating data consistency statistics including: A single data entry in the aggregated data stream includes business data content from at least two business systems. For a single data entry in the aggregated data stream, the business data content from different business systems in the single data entry is compared to see if they are consistent under the preset monitoring dimensions. If they are consistent, they are recorded as normal data; otherwise, they are recorded as abnormal data. The normal and abnormal data in the data stream are statistically summarized separately to generate data consistency statistics.

[0013] Furthermore, monitoring operations are performed based on the data consistency statistics. These monitoring operations include triggering alarms and not triggering alarms. Compare the abnormal data statistics results in the data consistency statistics results with the preset threshold. When the abnormal data statistics results reach the preset threshold, an alarm is triggered.

[0014] Secondly, the present invention provides a business data monitoring device based on the Flink engine, comprising: The business data acquisition module is used to acquire business data streams from different business systems; The business data aggregation module is used to use the Flink engine to perform association processing on business data streams from different business systems based on preset association rules, and generate an aggregated data stream. The data consistency analysis module is used to perform consistency analysis on the aggregated data stream according to preset monitoring dimensions and generate data consistency statistics. The data monitoring operation module is used to perform monitoring operations based on the data consistency statistics results. The monitoring operations include triggering alarms and not triggering alarms.

[0015] Thirdly, the present invention provides an electronic device including a memory storing computer-executable instructions and a processor, wherein when the computer-executable instructions are executed by the processor, the device performs the various steps of the business data monitoring method based on the Flink engine provided in the first aspect.

[0016] Fourthly, the present invention provides a readable storage medium storing a computer-executable program, which, when executed, can implement the various steps of the business data monitoring method based on the Flink engine provided in the first aspect.

[0017] The present invention has the following beneficial effects: This invention proposes a business data monitoring method and related apparatus based on the Flink engine. By utilizing the Flink engine to perform real-time correlation processing and aggregation of business data streams from different business systems, it achieves centralized management of cross-system business data. Combined with preset monitoring dimensions, it performs consistency analysis and anomaly statistics, thereby enabling timely and automatic detection of business data anomalies. A further embodiment triggers alarms through configurable anomaly thresholds, making the monitoring process highly flexible and scalable, thus significantly improving the real-time performance, accuracy, and reliability of business data monitoring, reducing the cost of manual intervention, and enhancing the overall data governance capabilities in a multi-business system environment. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 The flowchart illustrates the implementation of the business data monitoring method based on the Flink engine provided in this embodiment of the invention. Figure 2 This is a schematic diagram of the business data monitoring device based on the Flink engine provided in an embodiment of the present invention; Figure 3 This is an electronic device architecture diagram provided for an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Flink is a high-performance, scalable distributed stream processing framework capable of low-latency, continuous computation on large-scale data streams, supporting features such as state management, event-driven processing, and windowed computation. Flink can simultaneously process business data streams from multiple data sources, achieving efficient cross-stream association and aggregation by partitioning data by key and maintaining intermediate states. In the following embodiments of this invention, the Flink engine is used to perform real-time aggregation, analysis, and monitoring of data from different business systems, ensuring consistency of business data across multiple systems and timely detection of anomalies, thereby providing a foundation for subsequent data statistics and alerts.

[0022] See Figure 1 One embodiment of the present invention proposes a business data monitoring method based on the Flink engine, comprising the following steps: Step S110. Obtain business data streams from different business systems.

[0023] The business data streams obtained in this step come from different business systems, which may include order systems, product systems, payment systems, device management systems, user behavior systems, etc. Each system generates its own business data stream independently.

[0024] In a further embodiment, business data streams can be accessed through standard interfaces, message queues (such as Kafka, RocketMQ) or database change logs (CDC). Each piece of business data includes at least one unique identifier (such as business ID, user ID or device ID) for cross-business system association, as well as the core fields of the business data.

[0025] In a further embodiment, the data access process may include data format normalization and preliminary cleaning, such as standardizing timestamp formats, mapping field names, converting data types, handling null values, and deduplicating duplicate data. These preprocessing steps ensure that data from different business systems have a consistent structure and comparability, laying the foundation for subsequent correlation processing.

[0026] In some embodiments, the data access module can also set priority or delay processing strategies for the business data streams of different systems to ensure that high-priority business data arrives at the processing instance in a timely manner, or to perform buffering and tolerance processing in the event of data delay, so as to ensure the stability of associated processing.

[0027] Step S120. Use the Flink engine to perform association processing on the business data streams of different business systems based on preset association rules to generate a summary data stream.

[0028] In this step, the data streams from different business systems are correlated in real time using the Flink engine to generate a summary data stream. Specifically, the correlation processing is performed using preset correlation rules, each of which includes at least one correlation field to identify the correspondence between data from different business systems.

[0029] The generated summary data stream contains individual data entries from at least two business systems, which can be integrated into a unified data structure through aggregation, merging, or concatenation for multi-dimensional analysis in subsequent steps. This summary data stream can be output to a data storage system or message middleware in real time, providing a data source for further monitoring and analysis. In a further embodiment, the Flink engine groups the business data stream by related fields, such as using KeyBy or equivalent partitioning operations, ensuring that data with the same related identifier is routed to the same processing instance. Simultaneously, during the association processing, corresponding intermediate state data is maintained for each related field. This intermediate state may include the arrived data set, aggregation results, and event processing identifiers, used to track the arrival status of data from different systems and the completion status of association processes.

[0030] In a further embodiment, considering that data from different systems may have arrival time differences, the Flink engine can set a tolerance range or time window for data arrival time based on the business scenario, allowing data arriving late within a certain range to participate in the same association process. For data exceeding the tolerance range, temporary processing records can be generated or delayed processing can be performed to ensure the integrity of the association process and data consistency.

[0031] For example, taking the data association processing of business data flows from the order system and the product system as an example, we can further illustrate how to use the Flink engine to associate and summarize data.

[0032] Assume that each business data item generated by the order system contains fields such as orderId (order ID), orderStatus (order status), and userId (user ID), while each business data item generated by the product system contains fields such as orderId (order ID), productId (product ID), and productPrice (product price). orderId is a common associated field between the two business systems.

[0033] Business data streams from the order system and product system are fed into the Flink engine. The Flink engine performs grouping (KeyBy) on the two data streams by orderId, ensuring that order data and product data with the same orderId are routed to the same processing instance. Each processing instance maintains corresponding intermediate state data in memory to record arriving order and product information.

[0034] For data with the same orderId, when both order and product data arrive, the Flink engine merges them to generate a summary record, including the order amount, product ID, product amount, and user information. This summary record can be further appended with fields such as timestamps and business source identifiers for multi-dimensional monitoring and consistency analysis in subsequent steps.

[0035] In some embodiments, to handle the time difference between the arrival of order and product data, the Flink engine sets a time tolerance range for each orderId, for example, allowing order data and corresponding product data to arrive within a certain time window. If a piece of data has not arrived within the tolerance range, the processing instance can temporarily store the arrived data in an intermediate state and complete the association to generate summary data after subsequent data arrives, thereby ensuring data integrity and consistency.

[0036] In a further embodiment, the generated summary data stream can be output to a data storage system or message middleware in real time, thereby enabling low-latency, cross-system automatic association and unified management of business data between the order system and the product system, ensuring the accuracy and real-time performance of subsequent monitoring and alarm operations.

[0037] Step S130. Perform consistency analysis on the aggregated data stream according to the preset monitoring dimensions and generate data consistency statistics.

[0038] This step compares business data from different business systems under the same monitoring dimension to determine whether there are differences or anomalies between the data from different business systems. Consistency conditions can include numerical equality, differences within an allowable range, consistent status, or fulfillment of preset logical relationships. When the comparison result meets the consistency conditions, the corresponding summary data is marked as normal data; when the comparison result does not meet the consistency conditions, the corresponding summary data is marked as abnormal data.

[0039] Preset monitoring dimensions can include business dimensions (such as order dimensions, user dimensions, and device dimensions) or indicator dimensions (such as amount, quantity, and status identifier). For each summary data point, corresponding business data content from different business systems is extracted under the preset monitoring dimensions as input data for consistency judgment.

[0040] In further embodiments, the statistical process can employ various methods, such as counting, summing, averaging, and maximum / minimum difference, to support flexible analysis of abnormal data. In some embodiments, the statistical results can be categorized according to different monitoring dimensions to reflect the distribution of abnormal data in different business scenarios, and can generate real-time or periodic reports for subsequent alarm triggering.

[0041] In a further embodiment, in the event of data delay or partial data loss, temporary statistical results can be generated based on the data that has arrived, and the statistical results can be updated after subsequent data arrives, thereby ensuring the continuity and real-time nature of the analysis.

[0042] Step S140. Perform monitoring operations based on the data consistency statistics results. Monitoring operations include triggering alarms and not triggering alarms.

[0043] This step performs monitoring operations based on data consistency statistics to enable timely detection and response to abnormal events. Monitoring operations include determining whether an alarm has been triggered.

[0044] Specifically, the anomaly statistics are compared with user-preset thresholds. When the statistics reach or exceed the threshold, an alarm is triggered; otherwise, no alarm is triggered. Alarm actions can take various forms, such as sending message notifications, logging, calling external interfaces, or triggering automated processing flows.

[0045] In a further embodiment, alarm policies can be differentiated by dimension, business type, or anomaly level, supporting multi-level alarms and custom alarm rules. For example, different levels of alarm notifications can be triggered based on the severity of the anomaly, or different thresholds can be set for different monitoring dimensions according to business needs.

[0046] In some embodiments, monitoring operations may also include archiving or recording abnormal data for subsequent analysis and system optimization. Meanwhile, to ensure monitoring scalability, the monitoring module can dynamically update thresholds and alarm policies to adapt to business changes and system expansion.

[0047] The disclosed method can be implemented using various types of devices. Therefore, the present invention also discloses an apparatus corresponding to the above method, and specific embodiments are given below for detailed description.

[0048] like Figure 2 As shown, one embodiment of the present invention provides a business data monitoring device based on the Flink engine, comprising: Business data acquisition module 202 is used to acquire business data streams from different business systems; The business data aggregation module 204 is used to use the Flink engine to perform association processing on the business data streams of different business systems based on preset association rules, and generate an aggregated data stream. The data consistency analysis module 206 is used to perform consistency analysis on the aggregated data stream according to preset monitoring dimensions and generate data consistency statistics. The data monitoring operation module 208 is used to perform monitoring operations based on the data consistency statistics results. The monitoring operations include triggering alarms and not triggering alarms.

[0049] The device provided in this application embodiment has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0050] The methods and related apparatuses mentioned in the above embodiments are described with reference to the method flowcharts and / or structural diagrams provided in the embodiments of this application. Specifically, each block of the method flowchart and / or structural diagram, as well as combinations of blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the process. Figure 1 A schematic diagram of one or more processes and / or structures. Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 A schematic diagram of one or more processes and / or structures. Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 A process or multiple processes and / or structures illustrate the steps of the functions specified in one or more boxes.

[0051] The following embodiments illustrate the application of this method to a computer device. It is understood that the computer device can be any device with computing and processing capabilities, including but not limited to servers or personal laptops. In one embodiment, the computer device can be an application server, which can be a server used to run the application under test.

[0052] See Figure 3This document illustrates a hardware block diagram of an electronic device intended to represent various forms of digital computers, such as laptops, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present application described and / or claimed herein.

[0053] like Figure 3 As shown, the electronic device includes: at least one processor 1, at least one communication interface 2, at least one memory 3, and at least one communication bus 4; In this embodiment of the application, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and processor 1, communication interface 2, and memory 3 communicate with each other through communication bus 4; Processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. Memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device; The memory stores a program, which the processor can call. The program is used to implement the various processing flows of the aforementioned business data monitoring solution based on the Flink engine.

[0054] This invention also provides a readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements various processing flows of the business data monitoring scheme based on the Flink engine provided in any possible implementation of the above embodiments and / or in combination with the embodiments.

[0055] The invention has been described in particular detail above with respect to possible scenarios, and those skilled in the art will recognize that the invention can be practiced through other embodiments. Specific naming of components, capitalization of terms, attributes, data structures, or any other programming or structural aspects are not mandatory or important, and the mechanisms or features of implementing the invention may have different names, forms, or procedures. The system can be implemented through a combination of hardware and software (as described), entirely through hardware elements, or entirely through software elements. The specific division of functions among the various system components described herein is merely exemplary and not mandatory; rather, the functions performed by a single system component can be performed by multiple components, or the functions performed by multiple components can be performed by a single component.

[0056] Those skilled in the art should understand that the various steps of the disclosed methods can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using device-executable program code, which can then be stored in a storage device for execution by the computing device. Alternatively, they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Therefore, the embodiments disclosed in this invention are not limited to any specific hardware and software combination.

[0057] The programs (also referred to as programs, software, software applications, or code) executable by these computing devices include machine instructions of a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term “machine-readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0058] Certain aspects of this invention include the process steps and instructions described herein in algorithmic form. It should be noted that the process steps and instructions of this invention can be implemented in software, firmware, and / or hardware, and when implemented in software, they can be downloaded, stored on various operating systems and operated from said platforms.

[0059] Those skilled in the art will understand that the structures shown in the figures are merely block diagrams of some structures related to the present application and do not constitute a limitation on the terminal device to which the present application is applied. Specific terminal devices may include more or fewer components than those shown in the figures, or combine certain components, or have different component arrangements.

[0060] In the description of this specification, the use of terms such as "one embodiment," "some embodiments," "example," "specific example," or "possible design," etc., refers to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0061] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0062] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A business data monitoring method based on the Flink engine, characterized in that, include: Acquire business data streams from different business systems; The Flink engine is used to perform association processing on business data streams from different business systems based on preset association rules, generating a summary data stream. Perform consistency analysis on the aggregated data stream according to preset monitoring dimensions and generate data consistency statistics; Based on the data consistency statistics, monitoring operations are performed, including triggering alarms and not triggering alarms.

2. The method according to claim 1, characterized in that, Before performing consistency analysis on the aggregated data stream according to preset monitoring dimensions, the following steps are included: Write the aggregated data stream to a data storage system or message middleware.

3. The method according to claim 1, characterized in that, The preset association rule includes at least one association field, which is used to identify the correspondence between business data from different business systems.

4. The method according to claim 3, characterized in that, The process of using the Flink engine to correlate business data streams from different business systems based on preset association rules to generate a summary data stream includes: The Flink engine is used to group business data streams from different business systems based on related fields, so that data with the same related fields enters the same processing instance. Maintain corresponding intermediate state data for associated fields; A summary data stream is generated based on the intermediate state data, and each data item in the summary data stream includes business data content from at least two business systems.

5. The method according to claim 1 or 4, characterized in that, The process of using the Flink engine to correlate business data streams from different business systems based on preset association rules includes: Use the Flink engine to set the data arrival time tolerance range for each business system; For business data streams that have a time difference in arrival time and come from different business systems, the business data streams are correlated based on the tolerance range of data arrival time.

6. The method according to claim 1, characterized in that, The process of performing consistency analysis on the aggregated data stream according to preset monitoring dimensions and generating data consistency statistics includes: A single data entry in the aggregated data stream includes business data content from at least two business systems. For a single data entry in the aggregated data stream, the business data content from different business systems in the single data entry is compared to see if they are consistent under the preset monitoring dimensions. If they are consistent, they are recorded as normal data; otherwise, they are recorded as abnormal data. The normal and abnormal data in the data stream are statistically summarized separately to generate data consistency statistics.

7. The method according to claim 1, characterized in that, The monitoring operation performed based on the data consistency statistics includes triggering alarms and not triggering alarms. Compare the abnormal data statistics results in the data consistency statistics results with the preset threshold. When the abnormal data statistics results reach the preset threshold, an alarm is triggered.

8. A business data monitoring device based on the Flink engine, characterized in that, include: The business data acquisition module is used to acquire business data streams from different business systems; The business data aggregation module is used to use the Flink engine to perform association processing on business data streams from different business systems based on preset association rules, and generate an aggregated data stream. The data consistency analysis module is used to perform consistency analysis on the aggregated data stream according to preset monitoring dimensions and generate data consistency statistics. The data monitoring operation module is used to perform monitoring operations based on the data consistency statistics results. The monitoring operations include triggering alarms and not triggering alarms.

9. An electronic device, characterized in that, It includes a memory storing computer-executable instructions and a processor, which, when executed by the processor, causes the device to perform the business data monitoring method based on the Flink engine as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that, It stores a computer-executable program that, when executed, can implement the business data monitoring method based on the Flink engine as described in any one of claims 1 to 7.

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