Information processing method and device, equipment and storage medium

Abstract

This application discloses an information processing method, apparatus, device, and storage medium. The method includes displaying a data monitoring configuration interface for monitoring configuration items in each of N data processing processes; generating a transaction monitoring task flow based on the configuration data of the target monitoring configuration items; determining the transaction time-series indicator data of the target monitoring configuration item in each data processing process within a preset business scenario according to the transaction monitoring task flow; and displaying alarm information to indicate anomalies in the transaction time-series indicator data. This allows business monitoring configuration personnel to generate the transaction monitoring task flow with simple data configuration input, reducing their reliance on specialized expertise. Furthermore, the calculation tools for executing the transaction monitoring task flow and alarms do not rely on customized development, compressing data monitoring deployment time and improving the efficiency of transaction quality monitoring deployment.

Description

Technical Field

[0001] This application belongs to the field of information processing technology, and in particular relates to an information processing method, apparatus, device and storage medium. Background Technology

[0002] With the rapid development of technology, the business scale of various trading service platforms has surged and the number of users has grown exponentially, making it increasingly difficult for trading service platforms to deploy transaction quality monitoring.

[0003] In related technologies, to enable trading service platforms to effectively monitor transaction quality, deep learning networks can be used to predict transaction data, and experts can be relied upon to model and test the transaction data. However, this approach relies heavily on human expertise, and data modeling requires large-scale data samples for model debugging and algorithm training. The overall cycle from design to deployment of the computing tools used in this data modeling process is long, hindering the rapid application of computing tools and affecting the efficiency of transaction quality monitoring deployment. Summary of the Invention

[0004] This application provides an information processing method, apparatus, device, and storage medium that can solve the problems in the prior art where the monitoring of transaction quality in transaction service platforms relies heavily on human expertise and has low deployment efficiency.

[0005] In a first aspect, embodiments of this application provide an information processing method, which may include:

[0006] The data monitoring configuration interface is displayed. The data monitoring configuration interface includes monitoring configuration items for each of the N data processing processes, where N is a positive integer.

[0007] Upon receiving configuration input for a target monitoring configuration item in the monitoring configuration items, a transaction monitoring task flow is generated based on the configuration data of the target monitoring configuration item corresponding to the configuration input. The transaction monitoring task flow includes tasks corresponding to each data processing process.

[0008] According to the transaction monitoring task flow, determine the transaction time-series indicator data of the target monitoring configuration item in the preset business scenario for each data processing process;

[0009] The alarm information is displayed to indicate that the target time-series indicator data that meets the preset monitoring conditions has an anomaly in the preset business scenario.

[0010] Secondly, embodiments of this application provide an information processing apparatus, which may include:

[0011] The display module is used to display the data monitoring configuration interface. The data monitoring configuration interface includes monitoring configuration items for each of the N data processing processes, where N is a positive integer.

[0012] The generation module is used to generate a transaction monitoring task flow based on the configuration data of the target monitoring configuration item corresponding to the configuration input when receiving configuration input for the target monitoring configuration item in the monitoring configuration item. The transaction monitoring task flow includes tasks corresponding to each data processing process.

[0013] The determination module is used to determine the transaction time-series indicator data of the target monitoring configuration item in the preset business scenario according to the transaction monitoring task flow;

[0014] The display module is also used to display alarm information, which is used to indicate that the target time series indicator data that meets the preset monitoring conditions in the transaction time series indicator data has anomalies in the preset business scenario.

[0015] Thirdly, embodiments of this application provide a computing device, which includes: a processor and a memory storing computer program instructions;

[0016] When the processor executes computer program instructions, it implements the information processing method as described in the first aspect.

[0017] Fourthly, embodiments of this application provide a computer storage medium storing computer program instructions, which, when executed by a processor, implement the information processing method as described in the first aspect.

[0018] Fifthly, embodiments of this application provide a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the information processing method as shown in the first aspect.

[0019] In a sixth aspect, embodiments of this application provide a computer program product stored in a storage medium, which is executed by at least one processor to implement the information processing method as described in the first aspect.

[0020] The information processing method, apparatus, device, and storage medium of this application embodiment display a data monitoring configuration interface. The data monitoring configuration interface includes monitoring configuration items in each of N data processing processes. Thus, upon receiving configuration input for a target monitoring configuration item in the monitoring configuration items, a transaction monitoring task flow is generated based on the configuration data of the target monitoring configuration item corresponding to the configuration input. The transaction monitoring task flow includes tasks corresponding to each data processing process. According to the transaction monitoring task flow, the transaction time-series indicator data of the target monitoring configuration item in each data processing process in a preset business scenario is determined. Then, alarm information is displayed to indicate that the target time-series indicator data that meets the preset monitoring conditions in the transaction time-series indicator data has an anomaly in the preset business scenario. Thus, through a visual data monitoring configuration interface, business monitoring personnel can generate and execute transaction monitoring task flows simply by inputting data configuration information. This process determines the target monitoring configuration items for each data processing step within the preset business scenario's transaction time-series indicator data. This reduces the reliance on specialized expertise for business monitoring personnel, and the computational tools executing the transaction monitoring task flows do not depend on customized development, possessing business universality. Furthermore, it reduces the overall time from design to deployment of computational tools during data modeling. Additionally, the streamlined execution process of the transaction monitoring task flows supports data monitoring. When target time-series indicator data that meets preset monitoring conditions shows anomalies within the preset business scenario, alarm information can be displayed. This compresses data monitoring deployment time, eliminates significant model debugging and algorithm training processes, and improves the efficiency of transaction quality monitoring deployment. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the structure of an information processing system provided in an embodiment of this application;

[0023] Figure 2 A flowchart illustrating an information processing method provided in an embodiment of this application;

[0024] Figure 3 One of the interface diagrams for an information processing method provided in an embodiment of this application;

[0025] Figure 4 A second schematic diagram of an interface for an information processing method provided in an embodiment of this application;

[0026] Figure 5 The third schematic diagram of an interface for an information processing method provided in this application embodiment;

[0027] Figure 6 A schematic diagram of the execution code for an information processing method provided in an embodiment of this application;

[0028] Figure 7 This is one of the flowcharts illustrating an information processing method provided in an embodiment of this application;

[0029] Figure 8 A second schematic flowchart illustrating an information processing method provided in an embodiment of this application;

[0030] Figure 9 A schematic diagram of transaction time-series indicator data for an information processing method provided in an embodiment of this application;

[0031] Figure 10 This is a schematic diagram of the structure of an information processing apparatus provided in one embodiment of this application;

[0032] Figure 11 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. Detailed Implementation

[0033] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0034] It should be noted that, in this document, relational terms such as "first" and "second" are used merely 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..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0035] In related technologies, deep learning networks such as Convolutional Neural Networks (CNNs) can be used to predict transaction data, relying on experts in deep learning to model and analyze the data. However, using deep learning networks for transaction data modeling and analysis places high technical demands on implementers, such as business monitoring and configuration personnel, requiring machine learning experts to perform targeted modeling and debugging, resulting in a high degree of professional dependence. Furthermore, data modeling relying on deep learning networks requires large-scale data samples for model debugging and algorithm training, leading to a long overall cycle from tool design to deployment, impacting the efficiency of transaction quality monitoring deployment. Additionally, during transaction quality monitoring, to facilitate user access to transaction status, transaction code retrieval tools can be used to quickly display transaction indicators. Since this method directly references the source data retrieval format, it places additional pressure on the storage containers of the transaction service platform, posing a risk of intrusion to payment transaction systems within the platform, and further increasing the overall stability risk of the transaction service platform.

[0036] To address the aforementioned issues, this application provides a convenient, timely, and accurate information processing method for monitoring transaction quality. It simplifies the transaction monitoring process, including data collection, calculation of transaction time-series indicators, and real-time alerts. This reduces the need for data development and algorithm expert involvement. A universal data monitoring configuration interface allows business monitoring personnel to generate transaction monitoring task flows simply by inputting data configuration information, thus reducing their reliance on specialized expertise. Furthermore, the calculation tool is deployed without requiring customized development, possessing business versatility. The streamlined task configuration process supports agile transaction time-series indicator monitoring, compressing deployment time for monitoring transaction quality and eliminating significant model debugging and algorithm training processes. This provides a quantifiable and intelligent monitoring and assurance system for the transaction service platform, enabling timely problem detection and ensuring the stability of transaction services.

[0037] Based on this, embodiments of this application provide an information processing method, apparatus, computer device, and storage medium. The following will describe in conjunction with the appendix... Figures 1 to 10 This application describes in detail the information processing system, method, apparatus, server, and storage medium of the embodiments thereof. It should be noted that these embodiments are not intended to limit the scope of this application.

[0038] First, the information processing system of the information processing method provided in the embodiments of this application will be described.

[0039] like Figure 1As shown, the information processing system 10 includes a distributed transaction system 101, a transaction monitoring task flow generation engine 102, a data aggregation engine 103, a streaming computing engine 104, and an alarm engine 105. In one example, the information processing system 10 can be deployed in a transaction service platform to detect transaction data, thereby promptly identifying problems and ensuring the stability of the transaction service.

[0040] The following sections will provide a detailed description of each part of its information processing system 10.

[0041] The distributed transaction system 101 includes a transaction system and a distributed transaction database. The transaction system provides services for user order generation and payment transaction processing, feeding back transaction data such as order information and transaction details to users, resource trading institutions (such as financial institutions or third-party trading institutions), and merchants. It also synchronously records this data into a database, such as a transactional distributed transaction database, for persistent storage, employing an N*M distributed storage architecture. Here, N refers to the number of data shards, determined by business scale and preset business scenarios, and M refers to the number of database backups, determined by the database (such as a master-slave transactional database or a disaster recovery transactional database) and read-write separation strategy architecture. In one example, this distributed transaction database can provide the data aggregation engine 102 with transaction data and database log files corresponding to the target monitoring configuration items. The transaction data can include order information and transaction details already persistently stored in the database, as well as incremental transaction data, i.e., order information and transaction details added, updated, or deleted in real time.

[0042] The transaction monitoring task flow generation engine 102 provides a general data monitoring configuration interface and preset task generation code. The preset task generation code generates a task flow to be executed based on any combination of detection configuration items in the data monitoring configuration interface. In one example, the data monitoring configuration interface may include monitoring configuration items in each of N data processing processes, where N is a positive integer. Based on this, upon receiving configuration input for a target monitoring configuration item, a transaction monitoring task flow is generated according to the configuration data of the target monitoring configuration item corresponding to the configuration input. The transaction monitoring task flow includes tasks corresponding to each data processing process.

[0043] The data collection engine 103 is used to receive the transaction monitoring task flow sent by the transaction monitoring task flow generation engine 102, and according to the transaction monitoring task flow, to obtain incremental transaction data corresponding to the target monitoring configuration item from the distributed transaction system 101. The incremental transaction data includes transaction data flow and order data flow, and pushes the incremental transaction data to the streaming computing engine 104 for the calculation of transaction time series indicator data. In this way, the impact of the monitoring operation on the original online transaction system is isolated.

[0044] It should be noted that the data aggregation engine 103 supports data source aggregation and processing for various types of databases. During the data acquisition and processing, it incorporates multiple parsing, transformation, and calculation methods to ensure the quality of transaction data from the data source and to perform normalization checks. These databases include, but are not limited to, at least one of the following: relational databases (MySQL), Oracle databases, SQL Server databases, DB2 databases, etc.

[0045] In one example, the data aggregation engine 103 may include a data collector and a distributed message pipeline. The data collector includes interceptors and partitioners; the distributed message pipeline includes message partitions.

[0046] Specifically, the data collector can be used to collect incremental business transaction data by reading the database log files generated by the distributed transaction system 101, thus isolating the performance impact on the transaction system itself. Further, the interceptor is used to parse the database operation statements (such as Insert, Update, and Delete, excluding Select) in the log files, and send each data addition, update, and deletion, along with an operation behavior tag in key-value format, to the streaming computing engine 104 to obtain incremental transaction data. Additionally, the partitioner is used to distribute tagged data based on the business primary key using a hash function, ensuring that data with the same business primary key enters the same distributed message pipeline in time order. Each distributed message pipeline can correspond to a transmission link to ensure the time order consistency of downstream data processing and avoid data out-of-order issues. Each distributed message pipeline can send tagged data to the streaming computing engine 104.

[0047] Thus, through multiple distributed message pipelines, incremental transaction data can be aggregated in a parallel, multi-tasking manner, achieving millisecond-level latency, supporting high availability, supporting breakpoint resumption, and ensuring zero data loss. Combined with the transaction monitoring task flow generation engine 102, which provides a visual interactive interface such as a data monitoring configuration interface, the system monitors the distributed transaction database connection information, data table dictionary, and the running status of data transmission tasks, enabling users to quickly create transaction monitoring task flows. Furthermore, the data aggregation engine 103 reads data from the distributed transaction system 101, avoiding intrusive access to the distributed transaction database, reducing the performance pressure on the distributed transaction database while ensuring data timeliness.

[0048] The streaming computing engine 104 reads transaction and order information from the distributed message pipeline. Based on the dimension labels in the preset dimension table cache data, it combines the two streams to form a wide transaction table. It supports scrolling or sliding window segmentation of the wide transaction table stream according to time series, and calculates transaction time-series indicator data by overlaying statistical operators and stores it in the indicator database. Here, the indicator database can be set in the distributed transaction system 101 or distributed in the streaming computing engine 104 to facilitate monitoring by the alarm engine 105.

[0049] This allows users to select built-in operators or add custom operators, enabling instant referencing of custom operators in the streaming computation code editor. This eliminates the need to write complex logic code within tasks, significantly improving the ease of data computation from the source data table. Furthermore, it allows users to set delay thresholds based on their actual business needs, balancing monitoring accuracy and latency.

[0050] Alarm Engine 105 is used to alert users that target time-series indicator data meeting preset monitoring conditions exists in a preset business scenario. In one example, the transaction time-series indicator data is time-series data. Taking transaction volume as an example, cumulative values, average values, and growth rates can be calculated using a scrolling or sliding window and compared with preset thresholds to determine if target time-series indicator data meeting preset monitoring conditions exists. In another example, the presence of target time-series indicator data meeting preset monitoring conditions can be determined through expert-based rule setting, anomaly detection algorithms, and time-series model decomposition algorithms.

[0051] It should be noted that the information processing method provided in this application embodiment can be applied to scenarios with high concurrency, multiple data source business monitoring needs, or business real-time visualization needs.

[0052] Therefore, based on the aforementioned information processing system and application scenarios, this application provides an information processing method, apparatus, device, and storage medium. The following will be discussed in conjunction with the appendix... Figures 2 to 9 This application describes in detail the information processing methods, apparatus, computer equipment, and storage media of the embodiments thereof. It should be noted that these embodiments are not intended to limit the scope of this application.

[0053] First, combined Figure 2 The information processing method provided in the embodiments of this application will be described in detail.

[0054] Figure 2 This is a flowchart of an information processing method provided in an embodiment of this application.

[0055] like Figure 2As shown, this information processing method can be applied to, for example... Figure 1 In the information processing system shown, the information processing method may specifically include the following steps:

[0056] Step 210: Display the data monitoring configuration interface, which includes monitoring configuration items for each of the N data processing processes, where N is a positive integer. Step 220: Upon receiving configuration input for a target monitoring configuration item, generate a transaction monitoring task flow based on the configuration data of the target monitoring configuration item corresponding to the configuration input. The transaction monitoring task flow includes tasks corresponding to each data processing process. Step 230: According to the transaction monitoring task flow, determine the transaction time-series indicator data for the target monitoring configuration item in each data processing process within a preset business scenario. Step 240: Display alarm information, which indicates that the target time-series indicator data that meets the preset monitoring conditions has an anomaly in the preset business scenario.

[0057] Therefore, through a visual data monitoring configuration interface, business monitoring configuration personnel can generate and execute transaction monitoring task flows simply by inputting data configuration information. This process determines the target monitoring configuration items for each data processing step within the preset business scenario's transaction time-series indicator data. This reduces the reliance on specialized expertise for business monitoring configuration personnel, and the computational tools executing the transaction monitoring task flows do not depend on customized development, possessing business universality. Furthermore, it reduces the overall time from design to deployment of computational tools during data modeling. In addition, the streamlined execution process of the transaction monitoring task flows supports data monitoring. When target time-series indicator data that meets preset monitoring conditions shows anomalies within the preset business scenario, alarm information can be displayed. This compresses data monitoring deployment time, eliminates significant model debugging and algorithm training processes, and improves the efficiency of transaction quality monitoring deployment.

[0058] The above steps are explained in detail below:

[0059] First, regarding step 210, in one or more possible embodiments, the N data processing processes may include a data source connection configuration process, a message cache connection configuration process, a data source (transaction details information and order details information) and preset dimension table cache data configuration process, and a transaction monitoring task flow configuration process.

[0060] Specifically, such as Figure 3As shown, the data detection configuration interface corresponds to the data source connection configuration process. The detection configuration items in this interface can include basic information about the data source (name, tags, description, etc.), database information corresponding to the data source (such as database type, database port, database address, etc.), and connection information (such as hash identifier, data read date selection, etc.). Figure 4 As shown, the data detection configuration interface can be the interface corresponding to the message cache connection configuration process. The detection configuration items in this interface can include basic information about the cache connection (name, tag, description, etc.) and connection information (such as the connection data source, the connection port for calling the message middleware, the message body, the maximum number of records to read, etc.). Additionally, the data detection configuration interface can also be the interface corresponding to the data source (transaction details and order details) and preset dimension table cache data configuration process. After receiving the user's configuration selection of specific transaction details and order details, it can jump to display the data detection configuration interface corresponding to the transaction monitoring task flow configuration process. This data detection configuration interface corresponding to the transaction monitoring task flow configuration process can include monitoring configuration items (such as execution count, execution time, etc.). Then, as... Figure 5 As shown, the data monitoring configuration interface can be a settings interface for alarm rules, i.e., preset monitoring conditions. At this time, the monitoring configuration items can include indicator settings (such as rule name, monitoring indicator, aggregation method), rule settings (such as rule type, judgment rule, monitoring period and alarm level), and notification settings (such as alarm message template, alarm notification, etc.). Among them, the rule type can be one of the four methods in step 240, which will not be repeated here. For details, please refer to step 240.

[0061] Next, regarding step 220, in one or more possible embodiments, based on the user's configuration input for the target monitoring configuration item in step 210 above, a transaction monitoring task flow is generated according to the configuration data of the target monitoring configuration item corresponding to the configuration input. For example, by establishing a streaming data job, further data development of the accessed streaming data is achieved. Visual indicator calculation development is implemented using data model-driven low-code programming. The job editor supports selecting data source tables and data result tables. After selecting a data table, such as selecting the "Transaction Table,"... Figure 6 As shown, structured code for data sources and data storage targets will be automatically generated.

[0062] In this way, it supports the configuration of streaming computing resources on the front-end page. By adjusting fixed running parameters, the maximum utilization of task running resources can be achieved, thus isolating complex back-end operations.

[0063] Furthermore, regarding step 230, in one or more possible embodiments, the transaction monitoring task flow includes a task of acquiring incremental transaction data corresponding to the target monitoring configuration item and a task of determining transaction time-series indicator data. Based on this, step 230 may specifically include:

[0064] Step 2301: Obtain incremental transaction data corresponding to the target monitoring configuration item from the distributed transaction database. The incremental transaction data includes transaction data streams and order data streams.

[0065] Step 2302: Determine transaction time-series indicator data based on transaction data stream, order data stream, and preset dimension table cache data.

[0066] Specifically, the distributed transaction database includes database log files, and based on this, step 2301 above may specifically include:

[0067] Step 23011: Using the database operation statements in the database log file, add operation behavior tags to the newly added, updated, and deleted data corresponding to the target monitoring configuration items in the distributed transaction database to obtain tag data;

[0068] Step 23012: Using a hash algorithm, tag data with the same business primary key are sent to the same distributed message transmission link.

[0069] Step 23013: Collect and process the tag data in multiple distributed message transmission links to obtain incremental transaction data.

[0070] For example, such as Figure 7 As shown, the data aggregation engine aggregates incremental business data by reading database log files generated by the distributed transaction system, isolating the performance impact on the transaction system itself. By parsing the database operation statements in the log files, each data addition, update, and deletion is tagged with a key-value pair and sent to the partitioner. Simultaneously, the data aggregation engine distributes data using a hash function based on the business primary key, sending tagged data with the same business primary key to the same distributed message transmission link. This ensures that data with the same business primary key enters the same distributed message pipeline in time order, guaranteeing consistency in data consumption downstream and preventing data out-of-order issues. In this way, by aggregating incremental transaction data through parallel multi-task processing, millisecond-level latency is achieved, supporting high availability, breakpoint resumption, and ensuring zero data loss.

[0071] It should be noted that the monitoring configuration items corresponding to the task of acquiring incremental transaction data corresponding to the target monitoring configuration items can include database connection information, data table dictionary, data transmission tasks, etc. The monitoring configuration items corresponding to the task of determining transaction time-series indicator data can include real-time monitoring data read volume, data write volume, data latency, etc. In this way, through a graphical data access configuration, support for data aggregation from databases used for transaction operations is achieved, while the parallel multi-task data aggregation strategy ensures data timeliness during peak business periods.

[0072] Furthermore, step 2302 above may specifically include:

[0073] Step 23021: According to the time series, the transaction data stream and order data stream are segmented separately by a sliding window to obtain the transaction details and order details within each sliding window. The transaction details are the information in the transaction data stream, and the order details are the information in the order data stream.

[0074] Step 23022: Based on the dimension labels in the preset dimension table cache data, perform data matching processing on the transaction details and order details in each sliding window to obtain the transaction wide table;

[0075] Step 23023: Sort and aggregate the transaction wide tables corresponding to multiple sliding windows to obtain transaction time series indicator data.

[0076] For example, such as Figure 8 As shown, according to the time series, the transaction data stream and order data stream are segmented using a sliding window such as window1 to obtain transaction details (5132) and order details (5132) within window1. Based on the dimension labels in the preset dimension table cache data, such as transaction type, business type, and resource transaction institution, the transaction details (5132) and order details (5132) within window1 are processed to obtain a transaction wide table. Since the transaction data stream and order data stream change continuously over time, the transaction details and order details obtained from the sliding window segmentation also change accordingly. Therefore, the transaction wide table will also change accordingly. Based on this, the transaction wide table will also appear in the form of a data stream (i.e., Figure 8 (Data flow of the wide table in the middle). Based on this, according to the time series, the transaction wide tables corresponding to multiple sliding windows, such as window1 and window2, can be combined, sorted, and aggregated to obtain transaction time series indicator data. The content of the transaction time series indicator data can be found in [reference]. Figure 9As shown, the derived indicators are indicators that have undergone secondary calculation. Then, the transaction time-series indicator data can be stored in a time-series data storage cluster for monitoring by the alarm engine. Furthermore, it is worth noting that different transaction time-series indicator data can be generated based on different preset business scenarios in this embodiment.

[0077] Therefore, by using a streaming computing engine to process transaction data in real time, it supports data segmentation by time window and the mixing and calculation of multiple data streams, providing more dimensions for monitoring indicators and expanding the perspective of business monitoring.

[0078] It should be noted that the transaction data stream in this application embodiment includes information about resource trading institutions related to the transaction, and the order data stream includes information about user terminals and merchant terminals related to the transaction;

[0079] The transaction data stream includes at least one of the following: the name of the resource trading institution, the payment account linked to the resource trading institution, and the name of the card issuer of the payment account;

[0080] The order data stream includes at least one of the following: the name of the product traded between the user and the merchant, the user's payment method, and the time and location of the transaction.

[0081] In another or more possible embodiments, prior to step 23021 above, the information processing method may further include:

[0082] Step 23024: Obtain the storage time of each piece of information in the transaction wide table corresponding to each sliding window in the first preset time period of the time series, and the processing time for performing data matching processing on the transaction details and order details in each sliding window;

[0083] Step 23025: Display the delay time configuration interface. The delay time configuration interface includes prompt information, storage time, processing time and preset business scenario. The prompt information is used to prompt you to set the target delay time under the preset business scenario based on the storage time and processing time.

[0084] Step 23026: Upon receiving input to set the target delay time, determine the size of the sliding window within the second preset time period in the time series based on the target delay time.

[0085] For example, this application embodiment can also provide a water level function, which is used to represent the difference between the storage time of transaction details information and order details information and the processing time for performing data matching processing on transaction details information and order details information. A low-latency water level ensures the timeliness of alarm generation, but delayed out-of-order data may cause the calculated transaction time-series index data to be lower than the actual value; conversely, a conservative water level increases the waiting time for calculating transaction time-series index data, making alarm notifications relatively delayed. Therefore, in order to support users in setting a delayed water level according to actual business needs, and to allow users to establish a balance between monitoring accuracy and latency, this application embodiment can obtain the storage time of each piece of information in the transaction wide table corresponding to each sliding window within a first preset time period in the time series, and the processing time for performing data matching processing on transaction details information and order details information within each sliding window. Based on this storage time and processing time, the embodiment provides prompts to the user, allowing the user to choose according to actual business needs, i.e., providing open water level configuration with high flexibility.

[0086] In this way, it supports the configuration of streaming computing runtime resources on the front-end page. By adjusting fixed runtime parameters, the utilization of task runtime resources can be maximized, isolating complex back-end operations. It supports the selection of built-in operators or the addition of custom operators, enabling instant referencing of custom operators in the streaming computing code editing area. This eliminates the need to write code for complex logic in the task, facilitates the convenience of data calculation on the source data table, and improves the efficiency of transaction quality monitoring and deployment.

[0087] Then, regarding step 240, the transaction time-series indicator data in this embodiment is typical time-series data. Taking the transaction volume indicator as an example, cumulative values, average values, growth rates, and other indicator values ​​can be calculated based on a scrolling or sliding window. Alarm notifications can be configured through two methods: setting rules based on expert experience and using relevant algorithms. This embodiment provides the following four methods to detect anomalies in real-time transaction time-series indicator data. Based on this, this embodiment provides the following four methods to monitor whether transaction time-series indicator data meets preset monitoring conditions, as detailed below.

[0088] In one or more possible embodiments, prior to step 240, the information processing method may further include:

[0089] Rules are set based on expert experience to monitor transaction time-series indicator data;

[0090] If there are target time series indicator data that match the rules set by expert experience in the trading time series indicator data, it is determined that the trading time series indicator data meets the preset monitoring conditions.

[0091] For example, based on expert experience, fixed thresholds or averages and medians of transaction time-series indicators in different preset business scenarios can be set for the data. In this way, if it is detected that the real-time calculated transaction time-series indicator data does not match the fixed threshold or the average or median of the specific time window, it is determined that the transaction time-series indicator data meets the preset monitoring conditions, that is, the target transaction time-series indicator data exists. At this time, an alarm message can be displayed to prompt the user that the transaction time-series indicator data is abnormal.

[0092] In another or more possible embodiments, prior to step 240, the information processing method may further include:

[0093] By using an anomaly detection algorithm, the transaction wide table within each sliding window of the transaction time series indicator data is calculated to obtain the statistical parameter values ​​corresponding to the anomaly detection parameters.

[0094] By pre-setting normalized standard values ​​for periodic sensitive data and data-sensitive data, the statistical parameter values ​​are corrected to obtain adjusted statistical parameter values.

[0095] If the adjusted statistical parameter values ​​are outside the preset threshold range, the transaction time series indicator data is determined to meet the preset monitoring conditions.

[0096] For example, the anomaly detection algorithm in this application embodiment mines regularities in time series data to detect values ​​or sequences that deviate from normal patterns. This is predicated on the detection object being regular time series data. Based on expert experience, transaction-related indicator data often exhibits clear regularities; for example, public transportation services show obvious morning and evening peak characteristics. Therefore, statistical anomaly detection algorithms can be applied, assuming that transactions conform to cyclical characteristics, to determine whether the current moment is abnormal. This can be done by comparing historical data from multiple days, calculating the mean and standard deviation of the same time over the past few days, and using the k-sigma method to determine the current anomaly. However, traditional detection methods are sensitive to time deviations in the data. For example, if transaction volume peaked at 8:30 a few days ago, but the peak at the current detection time is delayed, this can easily lead to missed or false alarms. Based on this, this application embodiment proposes a frequency domain-based anomaly detection algorithm, introducing "cycle sensitivity: p" and "numerical sensitivity: q" as weighting parameters to solve the problem of missed and false alarms caused by sudden increases or decreases in cyclical indicator data.

[0097] Taking trading volume as an example, assuming this indicator follows a Gaussian distribution, add...

[0098] Adjust the statistical parameter value (variance parameter)

[0099] in, The normalized standard values ​​represent the periodic sensitivity and numerical sensitivity, d represents the time value within the d-time window, d0 represents the center value of the time window, and σ represents the time value within the d-time window. d Half-window constant, p is the periodicity, y is the sequence value within the time window, y0 represents the value at the center time point of the time window, σ y The standard deviation of the window values ​​is represented by p, and the numerical sensitivity by q. The weights of data within the window are controlled by p and q. The smaller p is, the greater the weight of time points closer to the center of the window; the smaller q is, the greater the weight of data points whose values ​​are closer to the detected point.

[0100] Based on this, when the adjusted statistical parameter values ​​are calculated in real time and are not within the preset threshold range, the transaction time series indicator data is determined to meet the preset monitoring conditions, and an alarm message is displayed.

[0101] In yet another or more possible embodiments, prior to step 240, the information processing method may further include:

[0102] Using a time series model decomposition algorithm, a dynamic baseline corresponding to the first preset time period is calculated based on the transaction time series indicator data within the first preset time period. The time series model decomposition algorithm includes a linear trend function, a Logistic function, a Fourier series fitting function, a preset stress test baseline and preset residuals for special times, including public holidays and marketing days.

[0103] If the transaction time series indicator data within the second preset time period is not within the monitoring range corresponding to the dynamic baseline, it is determined that the transaction time series indicator data meets the preset monitoring conditions.

[0104] For example, baseline-type algorithms predict the baseline for a future period based on curves fitted from historical data. Traditional time series forecasting methods are all autoregressive models, such as moving average models, which use weighted averages as future observations. These algorithms have poor interpretability and are difficult to debug. In this application, embodiments introduce a time series model decomposition algorithm based on transaction data (such as order information and transaction information), as shown in formula (2).

[0105] Prophet model y(t)=g(t)+s(t)+h(t)+∈ t (2)

[0106] Here, g(t) represents the predicted trend, which can be expressed as a piecewise linear function g(t) = kt + m, where k represents the coefficient of change and m is a constant. g(t) = (k + a(t)) τ δ)t+(m+a(t) τ γ), or logistic function Fitting. Here, C(t) represents the maximum asymptotic growth of the curve, k represents the growth rate, a(t) represents the number of times the abrupt change point changes before time t, δ represents the fitness, m represents the offset, γ represents the piecewise linear function, and τ represents the length of the historical period. s(t) represents the Fourier series used for periodic prediction. Fitting. Where P represents the time series period, N represents the number of such periods to be used in the model (for transactional businesses, weekday and weekend periods are generally considered); h(t) represents specific holidays, marketing days, marketing activities, and marketing data can be given based on the stress testing baseline, etc.; ∈ t : Residual.

[0107] In one or more other possible embodiments, prior to step 240, the information processing method may further include:

[0108] Based on the transaction time series indicator data within the third preset time period, calculate the year-on-year / month-on-month data and obtain the period difference value;

[0109] If the real-time calculated transaction time-series indicator data is not within the preset threshold range of the same / month-on-month data, it is determined that the transaction time-series indicator data meets the preset monitoring conditions.

[0110] Therefore, it supports the deployment of various monitoring strategies, including fixed thresholds, year-on-year and month-on-month comparisons, anomaly detection, and dynamic baselines. Based on business and indicator characteristics and the monitoring focus, the monitoring strategy can be dynamically adjusted to improve the effectiveness of alarms.

[0111] Based on the same inventive concept, this application also provides an information processing device. (Specifically combined with...) Figure 10 Please provide a detailed explanation.

[0112] Figure 10 This is a schematic diagram of the structure of an information processing device provided in one embodiment of this application.

[0113] like Figure 10 As shown, the information processing device 100 may specifically include:

[0114] Display module 1001 is used to display the data monitoring configuration interface. The data monitoring configuration interface includes monitoring configuration items in each of the N data processing processes, where N is a positive integer.

[0115] The generation module 1002 is used to generate a transaction monitoring task flow based on the configuration data of the target monitoring configuration item corresponding to the configuration input when receiving configuration input for the target monitoring configuration item in the monitoring configuration item. The transaction monitoring task flow includes tasks corresponding to each data processing process.

[0116] The determination module 1003 is used to determine the transaction time sequence indicator data of the target monitoring configuration item in the preset business scenario according to the transaction monitoring task flow;

[0117] The display module 1001 is also used to display alarm information, which is used to indicate that the target time series indicator data that meets the preset monitoring conditions in the transaction time series indicator data has an anomaly in the preset business scenario.

[0118] The information processing device 100 in the embodiments of this application will be described in detail below.

[0119] In one or more optional embodiments, the information processing device 100 in this application embodiment may further include a first acquisition module: wherein,

[0120] The first acquisition module is used to acquire incremental transaction data corresponding to the target monitoring configuration item from the distributed transaction database when the transaction monitoring task flow includes the task of acquiring incremental transaction data corresponding to the target monitoring configuration item and the task of determining transaction time series indicator data. The incremental transaction data includes transaction data stream and order data stream.

[0121] The determination module 1003 can also be used to determine transaction time series indicator data based on transaction data stream, order data stream and preset dimension table cache data.

[0122] In another or more alternative embodiments, the information processing device 100 in this application embodiment may further include an adding module, a dividing module, and a first processing module: wherein,

[0123] The module is used to add operation behavior tags to the newly added, updated, and deleted data in the distributed transaction database that corresponds to the target monitoring configuration item, using the database operation statements in the database log file, when the distributed transaction database includes database log files, and obtain tag data.

[0124] The partitioning module is used to partition tag data with the same business primary key into the same distributed message transmission link using a hash algorithm.

[0125] The first processing module is used to aggregate and process tag data from multiple distributed message transmission links to obtain incremental transaction data.

[0126] In another or more alternative embodiments, the information processing device 100 in this application embodiment may further include a segmentation module, a second processing module, and an aggregation module: wherein,

[0127] The segmentation module is used to segment the transaction data stream and order data stream according to the time series using a sliding window, and obtain the transaction details and order details within each sliding window. The transaction details are the information in the transaction data stream, and the order details are the information in the order data stream.

[0128] The second processing module is used to perform data matching processing on the transaction details and order details in each sliding window based on the dimension labels in the preset dimension table cache data, so as to obtain the transaction wide table;

[0129] The collection module is used to sort and aggregate the transaction wide tables corresponding to multiple sliding windows to obtain transaction time series indicator data.

[0130] In another or more alternative embodiments, the transaction data stream in this application embodiment includes information about resource trading institutions related to the transaction, and the order data stream includes information about user terminals and merchant terminals related to the transaction;

[0131] The transaction data stream includes at least one of the following: the name of the resource trading institution, the payment account linked to the resource trading institution, and the name of the card issuer of the payment account;

[0132] The order data stream includes at least one of the following: the name of the product traded between the user and the merchant, the user's payment method, and the time and location of the transaction.

[0133] In another or more alternative embodiments, the information processing device 100 in this application embodiment may further include a second acquisition module: wherein,

[0134] The second acquisition module is used to acquire the storage time of each piece of information in the transaction wide table corresponding to each sliding window in the first preset time period in the time series, and the processing time for performing data matching processing on the transaction details and order details in each sliding window;

[0135] The display module 1001 is also used to display a delay time configuration interface, which includes prompt information, storage time, processing time and preset business scenarios. The prompt information is used to prompt the user to set the target delay time under the preset business scenario based on the storage time and processing time.

[0136] The determining module 1003 is further configured to, upon receiving a setting input for the target delay time, determine the size of the sliding window within the second preset time period in the time series based on the target delay time.

[0137] In another or more alternative embodiments, the information processing device 100 in this application embodiment may further include a monitoring module: wherein,

[0138] The monitoring module is used to monitor transaction time-series indicator data by setting rules based on expert experience;

[0139] The determination module 1003 is also used to determine whether the transaction time series indicator data meets the preset monitoring conditions when there is target time series indicator data that matches the rules set by expert experience.

[0140] In another or more alternative embodiments, the information processing device 100 in this application embodiment may further include a first calculation module and a correction module: wherein,

[0141] The first calculation module is used to calculate the transaction wide table in each sliding window of the transaction time series indicator data through an anomaly detection algorithm, and obtain the statistical parameter values ​​corresponding to the anomaly detection parameters.

[0142] The correction module is used to correct the statistical parameter values ​​by using preset periodic sensitive data and normalized standard values ​​of data sensitive data, so as to obtain adjusted statistical parameter values.

[0143] The determination module 1003 is also used to determine whether the transaction time series indicator data meets the preset monitoring conditions when the adjusted statistical parameter value is not within the preset threshold range.

[0144] In another or more alternative embodiments, the information processing device 100 in this application embodiment may further include a second computing module: wherein,

[0145] The second calculation module is used to calculate the dynamic baseline corresponding to the first preset time period based on the transaction time series indicator data within the first preset time period using a time series model decomposition algorithm. The time series model decomposition algorithm includes a linear trend function, a Logistic function, a Fourier series fitting function, a preset stress test baseline and a preset residual for special times, and special times include public holidays and marketing days.

[0146] The determination module 1003 is also used to determine that the transaction time series indicator data meets the preset monitoring conditions when the transaction time series indicator data is not within the monitoring range corresponding to the dynamic baseline during the second preset time period.

[0147] Therefore, the data monitoring configuration interface includes monitoring configuration items for each of the N data processing processes. Upon receiving configuration input for a target monitoring configuration item, a transaction monitoring task flow is generated based on the configuration data of the target monitoring configuration item corresponding to the configuration input. This task flow includes tasks corresponding to each data processing process, and, according to the task flow, determines the transaction time-series indicator data for the target monitoring configuration item in a preset business scenario for each data processing process. Then, alarm information is displayed, indicating that the target time-series indicator data, which meets the preset monitoring conditions, is abnormal in the preset business scenario. Thus, through a visual data monitoring configuration interface, business monitoring configuration personnel can generate and execute the transaction monitoring task flow with simple data configuration input, determining the transaction time-series indicator data for the target monitoring configuration item in the preset business scenario for each data processing process. This reduces the professional dependence of business monitoring configuration personnel, and the calculation tool executing the transaction monitoring task flow does not rely on customized development, possessing business universality. Furthermore, it reduces the overall time from design to deployment of the calculation tool during data modeling. Furthermore, the streamlined transaction monitoring task flow supports data monitoring. When a target time-series indicator that meets the preset monitoring conditions is abnormal in a preset business scenario, an alarm message can be displayed. This reduces the deployment time for data monitoring, saves a lot of model debugging and algorithm training processes, and improves the efficiency of transaction quality monitoring deployment.

[0148] Based on the same inventive concept, this application also provides a computer device. (Specifically combined with...) Figure 11 Please provide a detailed explanation.

[0149] Figure 11 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application.

[0150] like Figure 11 As shown, the computer device may include devices corresponding to each module of the information processing system in this embodiment (such as a distributed transaction system, a data aggregation engine, a streaming computing engine, and an alarm engine). The computer device may include a processor 1101 and a memory 1102 storing computer program instructions.

[0151] Specifically, the processor 1101 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0152] Memory 1102 may include a large-capacity storage for data or instructions. For example, and not limitingly, memory 1102 may include a hard disk drive (HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 1102 may include removable or non-removable (or fixed) media. Where appropriate, memory 1102 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 1102 is non-volatile solid-state memory. In a particular embodiment, memory 1102 includes solid-state storage (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0153] The processor 1101 implements any of the information processing methods described in the above embodiments by reading and executing computer program instructions stored in the memory 1102.

[0154] In one example, the computer device may also include a communication interface 1103 and a bus 110. Wherein, as... Figure 11 As shown, the processor 1101, memory 1102, and communication interface 1103 are connected through bus 110 and communicate with each other.

[0155] The communication interface 1103 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0156] Bus 110 includes hardware, software, or both, that couples components of a flow control device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 110 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0157] The data processing device can execute the information processing method described in the embodiments of this application, thereby achieving the combination Figures 1 to 10 The described information processing methods and apparatus.

[0158] Furthermore, in conjunction with the information processing methods in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the information processing methods in the above embodiments.

[0159] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0160] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0161] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0162] The above are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. An information processing method, comprising: The data monitoring configuration interface is displayed, which includes monitoring configuration items for each of the N data processing processes, where N is a positive integer. Upon receiving configuration input for a target monitoring configuration item in the monitoring configuration items, a transaction monitoring task flow is generated based on the configuration data of the target monitoring configuration item corresponding to the configuration input. The transaction monitoring task flow includes tasks corresponding to each data processing process, and includes tasks for acquiring incremental transaction data corresponding to the target monitoring configuration item and determining transaction time series indicator data. According to the transaction monitoring task flow, determine the transaction time sequence indicator data of the target monitoring configuration item of each data processing process in the preset business scenario; Displaying alarm information, the alarm information being used to indicate that the target time-series indicator data in the transaction time-series indicator data that meets the preset monitoring conditions is abnormal in the preset business scenario; wherein, determining the transaction time-series indicator data of the target monitoring configuration item in the preset business scenario according to the transaction monitoring task flow includes: Incremental transaction data corresponding to the target monitoring configuration item is obtained from the distributed transaction database. The incremental transaction data includes transaction data streams and order data streams. According to the time series, the transaction data streams and order data streams are segmented using sliding windows to obtain transaction details and order details within each sliding window. The transaction details are information from the transaction data streams, and the order details are information from the order data streams. Based on the dimension labels in the preset dimension table cache data, the transaction details and order details within each sliding window are processed to obtain a transaction wide table. The transaction wide tables corresponding to multiple sliding windows are sorted and aggregated to obtain the transaction time series indicator data.

2. The method of claim 1, wherein, The distributed transaction database includes database log files; obtaining incremental transaction data corresponding to the target monitoring configuration item from the distributed transaction database includes: By using the database operation statements in the database log file, operation behavior tags are added to the newly added, updated, and deleted data in the distributed transaction database that correspond to the target monitoring configuration item, thus obtaining tag data; Using a hash algorithm, tag data with the same business primary key are grouped into the same distributed message transmission link. The incremental transaction data is obtained by aggregating and processing the tag data from multiple distributed message transmission links.

3. The method according to claim 1, characterized in that, The transaction data stream includes information about resource trading institutions related to the transaction, and the order data stream includes information about user terminals and merchant terminals related to the transaction; The transaction data stream includes at least one of the following: the name of the resource trading institution, the payment account bound to the resource trading institution, and the name of the card issuer of the payment account; The order data stream includes at least one of the following: the name of the product traded between the user and the merchant, the payment method of the user, and the time and location of the transaction.

4. The method of claim 1, wherein, Before segmenting the transaction data stream and the order data stream according to the time series using a sliding window to obtain the transaction details and order details within each sliding window, the method further includes: The storage time of each piece of information in the transaction wide table corresponding to each sliding window within the first preset time period in the time series is obtained, and the processing time for performing the data matching process on the transaction details and order details within each sliding window is executed. The display delay time configuration interface includes a prompt message, the storage time, the processing time, and the preset business scenario. The prompt message is used to prompt you to set the target delay time under the preset business scenario based on the storage time and the processing time. Upon receiving input to set the target delay time, the size of the sliding window within the second preset time period in the time series is determined based on the target delay time.

5. The method of claim 1, wherein, Before displaying the alarm information, the method further includes: Rules are set based on expert experience to monitor the transaction time-series indicator data; If there is target time-series indicator data in the transaction time-series indicator data that matches the expert experience setting rules, it is determined that the transaction time-series indicator data meets the preset monitoring conditions.

6. The method of claim 1, wherein, Before displaying the alarm information, the method further includes: An anomaly detection algorithm is used to calculate the transaction wide table within each sliding window of the transaction time series indicator data to obtain the statistical parameter value corresponding to the anomaly detection parameter. The statistical parameter values ​​are corrected by using the normalized standard values ​​of preset periodic sensitive data and preset numerical sensitive data to obtain the adjusted statistical parameter values. If the adjusted statistical parameter value is not within the preset threshold range, the transaction time series indicator data is determined to meet the preset monitoring conditions.

7. The method of claim 1, wherein, Before displaying the alarm information, the method further includes: Using a time series model decomposition algorithm, a dynamic baseline corresponding to the first preset time period is calculated based on the transaction time series indicator data within the first preset time period. The time series model decomposition algorithm includes a linear trend function, a Logistic function, a Fourier series fitting function, a preset stress test baseline for special times, and a preset residual. The special times include public holidays and marketing days. If the transaction time-series indicator data within the second preset time period is not within the monitoring range corresponding to the dynamic baseline, it is determined that the transaction time-series indicator data meets the preset monitoring conditions.

8. An information processing apparatus, comprising: The display module is used to display the data monitoring configuration interface, which includes monitoring configuration items for each of the N data processing processes, where N is a positive integer. The generation module is used to generate a transaction monitoring task flow based on the configuration data of the target monitoring configuration item corresponding to the configuration input when receiving configuration input for the target monitoring configuration item in the monitoring configuration items. The transaction monitoring task flow includes tasks corresponding to each data processing process. The transaction monitoring task flow includes a task to obtain incremental transaction data corresponding to the target monitoring configuration item and a task to determine transaction time series indicator data. The determination module is used to determine the transaction time-series indicator data of the target monitoring configuration item in the preset business scenario according to the transaction monitoring task flow; The display module is also used to display alarm information, which is used to indicate that the target time series indicator data that meets the preset monitoring conditions in the transaction time series indicator data is abnormal in the preset business scenario; Specifically, the determining module is used to: obtain incremental transaction data corresponding to the target monitoring configuration item from a distributed transaction database, wherein the incremental transaction data includes transaction data streams and order data streams; segment the transaction data streams and order data streams according to the time series using sliding windows to obtain transaction details and order details within each sliding window, wherein the transaction details are information from the transaction data streams and the order details are information from the order data streams; perform data matching processing on the transaction details and order details within each sliding window based on the dimension labels in the preset dimension table cache data to obtain a transaction wide table; and sort and aggregate the transaction wide tables corresponding to multiple sliding windows to obtain the transaction time series indicator data.

9. A computing device, the device comprising: Processor and memory storing computer program instructions; When the processor executes the computer program instructions, it implements the information processing method as described in any one of claims 1-7.

10. A storage medium storing computer program instructions, which, when executed by a processor, implement the information processing method as described in any one of claims 1-7.

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