Method for monitoring indexes of security transaction system in real time based on distributed data acquisition

Abstract

The invention relates to a method for monitoring indexes of a security transaction system in real time based on distributed data acquisition, which comprises the following steps of: deploying a plurality of log collectors on each node of the security transaction system, and capturing log data generated by the transaction system in real time, the collected log data are transmitted to a log processing server through a secure network channel; the received log data are cleaned, irrelevant information is removed, and logs in different formats are subjected to standardization processing and then uniformly converted into structured data; obtaining the preprocessed structured data, performing feature extraction on the structured data according to a predefined monitoring index template, and generating a data set containing key features; monitoring indexes are defined according to the business scene, calculation is carried out based on the extracted feature data, and standardized monitoring indexes are generated and stored in a monitoring database persistently; a visual interface is provided through a monitoring system, monitoring indexes are displayed in real time, and alarm pushing is carried out on abnormal indexes.

Description

Technical Field

[0001] This invention relates to the field of computer application technology, and more particularly to the field of securities trading systems, specifically a method for real-time monitoring of securities trading system indicators based on distributed data acquisition. Background Technology

[0002] With the rapid development of the securities trading market, trading volume and frequency have continued to increase, making real-time indicator monitoring of securities trading systems a key technology for ensuring stable market operation. Monitoring securities trading indicators involves the collection, processing, and analysis of massive amounts of data, placing extremely high demands on data transmission reliability, system response speed, and processing performance. This invention aims to address the challenges of current log monitoring systems, such as complex configuration, high resource consumption, and poor performance.

[0003] Currently, most mainstream methods for monitoring log indicators in securities trading systems employ a centralized data acquisition architecture to achieve real-time monitoring and data analysis. However, this approach suffers from drawbacks such as insufficient reliability, high latency, low data filtering accuracy, limited scalability, and a lack of visualization support. Insufficient reliability leads to incomplete collected indicators and the omission of crucial information. High latency negatively impacts monitoring effectiveness and hinders rapid handling of anomalies. Low data filtering accuracy makes monitoring key indicators difficult and may introduce redundant data. Limited scalability makes it difficult to adapt to the increasingly complex business scenarios in the securities industry. The lack of visualization support negatively impacts the monitoring experience and decision-making efficiency of operations and maintenance personnel. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for real-time monitoring of securities trading system indicators based on distributed data acquisition, which has the characteristics of high reliability, low latency, easy scalability, accurate extraction of key indicators, and visualization, while meeting the requirements of high availability.

[0005] To achieve the above objectives, the present invention provides a method for real-time monitoring of securities trading system indicators based on distributed data acquisition as follows:

[0006] The main feature of this method for real-time monitoring of securities trading system indicators based on distributed data acquisition is that the method includes the following steps:

[0007] (1) Deploy multiple log collectors on each node of the securities trading system to capture the log data generated by the trading system in real time, and transmit the collected log data to the log processing server through a secure network channel to ensure the integrity and real-time performance of the data.

[0008] (2) Clean the received log data, remove irrelevant information, and standardize logs of different formats and convert them into structured data.

[0009] (3) Obtain the preprocessed structured data, extract features from the structured data according to the predefined monitoring indicator template, and generate a dataset containing key features for subsequent use;

[0010] (4) Define monitoring metrics according to business scenarios, calculate based on extracted feature data, generate standardized monitoring metrics, and persist them in the monitoring database to provide data support for real-time monitoring and alarms.

[0011] (5) The monitoring system provides a visual interface to display monitoring indicators in real time, push alarms for abnormal indicators, and supports querying and analyzing historical monitoring data to help locate the root cause of the problem.

[0012] Preferably, step (1) specifically comprises:

[0013] The system monitors various types of logs through a distributed log collection module, including component heartbeat information, exception and error information, and key data information, and supports custom extensions. Furthermore, by introducing a caching queue mechanism, the collected logs are sent to the log processing server through different caching queues, thereby improving system response speed and ensuring data consistency.

[0014] Preferably, step (2) specifically includes:

[0015] The log preprocessing module transforms raw, messy, multi-source logs into highly available, structured data assets, including two main functions: log data cleaning and log format standardization. The log data cleaning uses a rule engine to filter redundant information, while the log format standardization uses a regular expression parser and dynamic template matching to achieve data transformation.

[0016] Preferably, step (3) includes the following processing procedures:

[0017] (3.1) Loading predefined templates: The feature extraction module loads a configuration file containing various monitoring indicator templates. The indicator templates define the key information of the feature types, formats and expected value ranges to be extracted.

[0018] (3.2) Data scanning and parsing: The feature extraction module performs a comprehensive scan of the preprocessed structured data and identifies and parses the feature data related to the monitoring indicators one by one according to the definition in the indicator template;

[0019] (3.3) Feature extraction and encapsulation: After successfully identifying the feature data, the feature extraction module extracts features according to the requirements of the indicator template and encapsulates them into a standardized data format.

[0020] Preferably, the required features include:

[0021] Error codes: Records various error codes that occur in the system, facilitating quick location and resolution of problems;

[0022] Abnormal events: Capture and analyze abnormal behaviors or events in the system to provide key clues for early warning and troubleshooting;

[0023] Key operations: Track and record key user actions to ensure system security and compliance;

[0024] Performance metrics: Monitor and analyze various performance metrics of the system in real time to assess the system's operating status and performance bottlenecks;

[0025] Heartbeat detection: Regularly monitor the system's operating status to ensure the activity and availability of system components.

[0026] Preferably, step (4) includes the following processing procedures:

[0027] (4.1) Feature data reception: The key feature data that has been extracted and encapsulated is received from the feature extraction module through the indicator generation module;

[0028] (4.2) Indicator Calculation: Based on the predefined monitoring indicator template, the indicator generation module performs mathematical calculations and logical judgments on the received feature data to obtain the required data indicators;

[0029] (4.3) Standardization processing: The indicator generation module performs standardization processing on the calculated indicator values, including data format conversion and outlier handling, to ensure the consistency and comparability of the monitoring indicators.

[0030] (4.4) Persistent storage: The monitoring metrics, after calculation and standardization, are stored in batches in a distributed database through persistent threads.

[0031] Preferably, step (5) includes the following processing procedures:

[0032] (5.1) Data query and display: Provides a data query interface to interact with the database and support the quick acquisition of the required monitoring indicator data; at the same time, the front-end page supports the display of indicator data in the form of charts, reports or logs;

[0033] (5.2) Alarm triggering condition setting: Based on business needs and monitoring objectives, set reasonable alarm triggering conditions for each monitoring indicator; when the indicator value exceeds the preset threshold, the real-time monitoring and alarm module will trigger the alarm mechanism and send alarm information to the operation and maintenance personnel.

[0034] (5.3) Data export and report generation: Used to export historical monitoring data for further analysis and processing by operation and maintenance personnel, and to automatically generate monitoring reports for reference and use.

[0035] The main feature of this device for real-time monitoring of securities trading system indicators based on distributed data acquisition is that the device includes:

[0036] A processor is configured to execute computer-executable instructions;

[0037] The memory stores one or more computer-executable instructions, which, when executed by the processor, implement the steps of the method for real-time monitoring of securities trading system indicators based on distributed data acquisition described above.

[0038] The processor for real-time monitoring of securities trading system indicators based on distributed data acquisition is characterized in that the processor is configured to execute computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the method for real-time monitoring of securities trading system indicators based on distributed data acquisition described above are implemented.

[0039] The main feature of this computer-readable storage medium is that it stores a computer program thereon, which can be executed by a processor to implement the steps of the method for real-time monitoring of securities trading system indicators based on distributed data acquisition described above.

[0040] This invention employs a method for real-time monitoring of securities trading system indicators based on distributed data acquisition. By automatically extracting monitoring indicators from log data, it significantly improves the flexibility of the monitoring system. Real-time processing throughout the entire process, from log collection to alarm triggering, ensures rapid response and real-time performance. It supports customizable monitoring indicator templates to adapt to the monitoring needs of different systems. By extracting various types of features, it can more comprehensively reflect the system's operational status and promptly identify potential problems. The automated and intelligent monitoring method reduces manual intervention and lowers monitoring costs. The monitoring and early warning mechanism based on real-time log data can quickly generate monitoring indicators, more accurately predict and detect system anomalies, and improve system stability and reliability. In practical applications, this invention, through multi-dimensional dynamic monitoring of core business indicators such as gateways, function numbers, and transaction paths, can promptly identify and warn of potential trading risks, significantly reducing economic losses that may be caused by system anomalies or trading failures. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating the method for real-time monitoring of securities trading system indicators based on distributed data acquisition according to the present invention. Detailed Implementation

[0042] To more clearly describe the technical content of the present invention, the following description is provided in conjunction with specific embodiments.

[0043] Before describing the embodiments of the present invention in detail, it should be noted that, in the following, the terms “comprising,” “including,” or any other variations 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 inherent to such process, method, article, or apparatus.

[0044] This method for real-time monitoring of securities trading system indicators based on distributed data acquisition includes the following processing modules:

[0045] 1. The distributed log collection module deploys log collectors (AGENTs) on various nodes of the trading system to capture log data generated by the system in real time. The collected log data is transmitted to the log processing server via a secure network channel, ensuring data integrity and timeliness. The log collector is mainly implemented by multiple file listening and collection threads. It primarily utilizes the FileAlterationObserver and FileFilterUtils classes from the Apache Commons IO library to monitor file changes in specified directories. By combining different file filters, it can accurately monitor log files that conform to specific naming patterns. For each type of file change, a FileListener is added to collect this change data and send it to the log processing server in real time.

[0046] 2. Log Preprocessing Module: This module is mainly implemented by a log preprocessing thread. First, it receives data pushed by the log collector, reads the raw data through the log reading class, then cleans each log data through the log cleaning class to remove duplicate and irrelevant information, and finally transforms the cleaned log data into structured data through the log transformation class, laying the foundation for subsequent feature extraction and analysis.

[0047] 3. Feature Extraction Module: This module acquires preprocessed structured data and, based on predefined monitoring metric templates, further extracts features from this data to generate a dataset containing key features for use by the metric generation module. These features include, but are not limited to, error codes, abnormal events, key operations, performance metrics, and heartbeat detection.

[0048] 4. Metric Generation Module: Define monitoring metrics based on business scenarios, calculate and generate standardized monitoring metrics based on extracted feature data, and persistently store them in the monitoring database to provide data support for real-time monitoring and alarms.

[0049] 5. Real-time monitoring and alarm module: The monitoring system provides a visual interface to display monitoring indicators in real time, push alarms for abnormal indicators to ensure stable system operation, and supports querying and analyzing historical monitoring data to help locate the root cause of problems.

[0050] The distributed log collection module can monitor various types of logs, such as component heartbeat information, exception error information, and critical data information, and supports custom extensions. A caching queue mechanism is introduced, sending the collected logs to the log processing server via different caching queues to improve system response speed and ensure data consistency.

[0051] The log preprocessing module is responsible for transforming raw, messy, multi-source logs into highly available, structured data assets, encompassing two main functions: log data cleaning and log format standardization. In log data cleaning, a rule engine filters redundant information. In log format standardization, a regular expression parser and dynamic template matching achieve data transformation.

[0052] In a preferred embodiment of the present invention, the specific processing flow of the feature extraction module is as follows:

[0053] Loading Predefined Templates: The feature extraction module loads a configuration file containing templates for various monitoring metrics. The configuration file can be customized by the user as needed. The feature extraction module implements automatic loading of the configuration file, parsing and loading it. After loading, it stores various templates in memory. These templates define in detail the key information such as the type, format, and expected value range of the features to be extracted.

[0054] Data Scanning and Parsing: The module then performs a comprehensive scan of the preprocessed structured data, removing useless information according to the template definitions, and identifying and parsing the feature data related to the monitoring indicators one by one. The specific processing method is as follows:

[0055] 1) Full scan: During the scan, the module will traverse the pre-processed structured data to identify feature data related to the monitoring indicators.

[0056] 2) Useless information removal: Based on the definition in the template, the module will identify and remove useless information that is irrelevant to the monitoring indicators, so as to reduce the complexity of data processing and improve processing efficiency.

[0057] 3) Feature Data Identification and Parsing: During the scanning process, the module will identify feature data related to the monitoring indicators one by one and parse it according to its feature type and format. The parsing process includes numerical conversion, string parsing, timestamp processing, and other operations.

[0058] Feature Extraction and Encapsulation: This module implements a feature extraction encapsulator. After successfully identifying feature data, the module extracts it according to template requirements. The extraction process includes operations such as calculation, transformation, or aggregation of the raw data to generate feature values ​​that meet the monitoring indicator requirements and encapsulates them into a standardized data format. These features include, but are not limited to:

[0059] 1) Error Codes: Records various error codes that occur in the system, making it easier to quickly locate and resolve problems.

[0060] 2) Abnormal events: Capture and analyze abnormal behaviors or events in the system to provide key clues for early warning and troubleshooting.

[0061] 3) Critical Operations: Track and record users' critical operational behaviors to ensure system security and compliance.

[0062] 4) Performance metrics: Monitor and analyze various system performance metrics in real time, such as CPU utilization, memory usage, and response time, to assess system operating status and performance bottlenecks.

[0063] 5) Heartbeat detection: Regularly check the system's operating status to ensure the activity and availability of system components.

[0064] As a preferred embodiment of the present invention, the specific processing flow of the indicator generation module is as follows:

[0065] Feature Data Reception: This module receives extracted and encapsulated key feature data from the feature extraction module. It provides a data receiving interface and, considering the system's high concurrency and asynchronous processing requirements, uses a message queue for data transmission. This module subscribes to and receives messages from the message queue.

[0066] Metric Calculation: Based on predefined monitoring metric templates, the module iterates through each template and calculates the received feature data. Several calculation methods are available. After calculation, the result for each metric is saved to a metric result object.

[0067] For simple numerical statistics (such as summation and average), the numerical fields in the feature data can be used directly for calculation.

[0068] For complex mathematical operations (such as ratio and trend analysis), calculations can be performed by writing custom functions.

[0069] For logical judgments (such as whether a threshold is exceeded), the calculated index value is compared with the threshold to obtain a Boolean value or status indicator.

[0070] Standardization Processing: To ensure the consistency and comparability of monitoring indicators, the module will standardize the calculated indicator values. The specific steps are as follows:

[0071] 1) Data format conversion: Convert indicator values ​​into a unified format, such as retaining two decimal places and converting time to a standard time format.

[0072] 2) Outlier Handling: Check indicator values ​​according to preset rules (such as maximum and minimum value ranges). If a value exceeds the range, it can be set to a default value (such as NULL) or corrected (such as using boundary values). Anomaly logs should also be recorded.

[0073] 3) Encapsulate the processed metric results into instances of a specific class for easy storage later.

[0074] Persistent storage: The calculated and standardized monitoring metrics are stored in batches in a distributed database via persistent threads. The specific implementation method is as follows:

[0075] 1) The module maintains a thread-safe buffer to temporarily store standardized indicator data.

[0076] 2) Start multiple persistent threads to retrieve data from the buffer in batches (every 1000 records, configurable) and write it to the distributed database.

[0077] 3) After a successful write operation, clear the relevant data from memory; if the write operation fails, retry and record the error log.

[0078] As a preferred embodiment of the present invention, the specific processing flow of the real-time monitoring and alarm module is as follows:

[0079] Data Query and Display: Provides a data query interface that interacts with the database to quickly obtain the required monitoring metric data. The front-end page supports displaying metric data in the form of charts, reports, or logs, making it easy for operations and maintenance personnel to intuitively understand the system's operating status.

[0080] Alarm trigger condition settings: Based on business needs and monitoring objectives, set reasonable alarm trigger conditions for each monitoring indicator. When the indicator value exceeds the preset threshold, the module will trigger the alarm mechanism and send alarm information to the operations and maintenance personnel.

[0081] Data export and report generation: It supports exporting historical monitoring data for further analysis and processing by operations and maintenance personnel. It can also automatically generate monitoring reports, including system performance overview, abnormal event statistics, etc., as an important reference for operations and maintenance work, which helps to improve the efficiency and quality of operations and maintenance management.

[0082] In a specific embodiment of the present invention, please refer to the actual processing procedure of this technical solution. Figure 1 As shown:

[0083] 1. Log Collector: The log collector is deployed on various nodes of the distributed system. The collection rule class in the collector configures various log formats and types that need to be collected. The file listening class is used to listen for real-time changes of log files. The file collection class is used to capture log files that meet the rules in real time. The message queue is used to store the captured log data. The file transfer class transmits the log data in the message queue to the log processing cluster.

[0084] 2. Log Processing Cluster: Composed of multiple log processors, employing a distributed architecture, and supporting horizontal scaling to handle massive amounts of log data. The log processors are specifically divided into the following modules:

[0085] 2-1 Preprocessing Module: This module is primarily responsible for the initial processing of the received raw log data. The data cleaning class allows users to preset the raw information to be filtered, such as redundant fields and invalid characters. The data formatting class transforms the initially cleaned data into a structured data format, making subsequent data processing and analysis more efficient and accurate.

[0086] 2-2 Feature Extraction Module: After the preprocessing module completes data cleaning and structure transformation, the feature extraction module begins its work. This module accurately extracts key features from the structured data based on predefined monitoring indicator templates. These feature data form the foundation for subsequent indicator generation and data analysis. Combined with the feature extraction strategy, the module ensures that the extracted feature data meets business requirements and fully reflects the core information of the log data.

[0087] The 2-3 Metric Generation Module is a crucial part of the log processing workflow. It receives feature data from the feature extraction module and performs in-depth calculations and analysis on this data according to preset calculation rules and formulas. This module can generate multiple standardized metrics, such as error rate, response time, and throughput, which are essential for monitoring the system's operational status and performance. After completing the calculations, the module persistently stores the generated metric data in a distributed database for subsequent data analysis and querying.

[0088] 3. Database cluster: Used to store standardized indicator data and interact with the monitoring system to provide comprehensive indicator data to the monitoring system.

[0089] 4. Monitoring System: A visual dashboard is built using a self-developed front-end (ECharts + Vue3). First, the Vue3 project is initialized using VueCLI, then the ECharts library is installed, and various types of charts are added as needed. Various backend monitoring interfaces are defined, and monitoring metrics are displayed in real time by reading data from the monitoring database. Alarms are generated for abnormal metrics by configuring alarm templates and rules. Additionally, historical data query and statistics interfaces have been developed to facilitate troubleshooting and performance analysis.

[0090] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0091] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution device.

[0092] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0093] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0094] In the description of this specification, references to terms such as "an embodiment," "some embodiments," "example," "specific example," or "embodiment," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, 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 any suitable manner in one or more embodiments or examples.

[0095] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

[0096] This invention employs a method for real-time monitoring of securities trading system indicators based on distributed data acquisition. By automatically extracting monitoring indicators from log data, it significantly improves the flexibility of the monitoring system. Real-time processing throughout the entire process, from log collection to alarm triggering, ensures rapid response and real-time performance. It supports customizable monitoring indicator templates to adapt to the monitoring needs of different systems. By extracting various types of features, it can more comprehensively reflect the system's operational status and promptly identify potential problems. The automated and intelligent monitoring method reduces manual intervention and lowers monitoring costs. The monitoring and early warning mechanism based on real-time log data can quickly generate monitoring indicators, more accurately predict and detect system anomalies, and improve system stability and reliability. In practical applications, this invention, through multi-dimensional dynamic monitoring of core business indicators such as gateways, function numbers, and transaction paths, can promptly identify and warn of potential trading risks, significantly reducing economic losses that may be caused by system anomalies or trading failures.

[0097] In this specification, the invention has been described with reference to specific embodiments thereof. However, it will be apparent that various modifications and variations can be made without departing from the spirit and scope of the invention. Therefore, the specification and drawings should be considered illustrative rather than restrictive.

Claims

1. A method for real-time monitoring of indicators in a securities trading system based on distributed data acquisition, characterized in that, The method includes the following steps: (1) Deploy multiple log collectors on each node of the securities trading system to capture the log data generated by the trading system in real time, and transmit the collected log data to the log processing server through a secure network channel to ensure the integrity and real-time performance of the data. (2) Clean the received log data, remove irrelevant information, and standardize logs of different formats and convert them into structured data. (3) Obtain the preprocessed structured data, extract features from the structured data according to the predefined monitoring indicator template, and generate a dataset containing key features for subsequent use; (4) Define monitoring metrics according to business scenarios, calculate based on extracted feature data, generate standardized monitoring metrics, and persist them in the monitoring database to provide data support for real-time monitoring and alarms. (5) The monitoring system provides a visual interface to display monitoring indicators in real time, push alarms for abnormal indicators, and supports querying and analyzing historical monitoring data to help locate the root cause of the problem.

2. The method for real-time monitoring of securities trading system indicators based on distributed data acquisition according to claim 1, characterized in that, The specific steps (1) are as follows: The system monitors various types of logs through a distributed log collection module, including component heartbeat information, exception and error information, and key data information, and supports custom extensions. Furthermore, by introducing a caching queue mechanism, the collected logs are sent to the log processing server through different caching queues, thereby improving system response speed and ensuring data consistency.

3. The method for real-time monitoring of securities trading system indicators based on distributed data acquisition according to claim 1, characterized in that, Step (2) specifically refers to: The log preprocessing module transforms raw, messy, multi-source logs into highly available, structured data assets, including two main functions: log data cleaning and log format standardization. The log data cleaning uses a rule engine to filter redundant information, while the log format standardization uses a regular expression parser and dynamic template matching to achieve data transformation.

4. The method for real-time monitoring of securities trading system indicators based on distributed data acquisition according to claim 3, characterized in that, Step (3) includes the following processing procedures: (3.1) Loading predefined templates: The feature extraction module loads a configuration file containing various monitoring indicator templates. The indicator templates define the key information of the feature types, formats and expected value ranges to be extracted. (3.2) Data scanning and parsing: The feature extraction module performs a comprehensive scan of the preprocessed structured data and identifies and parses the feature data related to the monitoring indicators one by one according to the definition in the indicator template; (3.3) Feature extraction and encapsulation: After successfully identifying the feature data, the feature extraction module extracts features according to the requirements of the indicator template and encapsulates them into a standardized data format.

5. The method for real-time monitoring of securities trading system indicators based on distributed data acquisition according to claim 4, characterized in that, The required characteristics include: Error codes: Records various error codes that occur in the system, facilitating quick location and resolution of problems; Abnormal events: Capture and analyze abnormal behaviors or events in the system to provide key clues for early warning and troubleshooting; Key operations: Track and record key user actions to ensure system security and compliance; Performance metrics: Monitor and analyze various performance metrics of the system in real time to assess the system's operating status and performance bottlenecks; Heartbeat detection: Regularly monitor the system's operating status to ensure the activity and availability of system components.

6. The method for real-time monitoring of securities trading system indicators based on distributed data acquisition according to claim 5, characterized in that, Step (4) includes the following processing procedures: (4.1) Feature data reception: The key feature data that has been extracted and encapsulated is received from the feature extraction module through the indicator generation module; (4.2) Indicator Calculation: Based on the predefined monitoring indicator template, the indicator generation module performs mathematical calculations and logical judgments on the received feature data to obtain the required data indicators; (4.3) Standardization processing: The indicator generation module performs standardization processing on the calculated indicator values, including data format conversion and outlier handling, to ensure the consistency and comparability of the monitoring indicators. (4.4) Persistent storage: The monitoring metrics, after calculation and standardization, are stored in batches in a distributed database through persistent threads.

7. The method for real-time monitoring of securities trading system indicators based on distributed data acquisition according to claim 6, characterized in that, Step (5) includes the following processing procedures: (5.1) Data query and display: Provides a data query interface to interact with the database and support the quick acquisition of the required monitoring indicator data; at the same time, the front-end page supports the display of indicator data in the form of charts, reports or logs; (5.2) Alarm triggering condition setting: Based on business needs and monitoring objectives, set reasonable alarm triggering conditions for each monitoring indicator; when the indicator value exceeds the preset threshold, the real-time monitoring and alarm module will trigger the alarm mechanism and send alarm information to the operation and maintenance personnel. (5.3) Data export and report generation: Used to export historical monitoring data for further analysis and processing by operation and maintenance personnel, and to automatically generate monitoring reports for reference and use.

8. A device for real-time monitoring of securities trading system indicators based on distributed data acquisition, characterized in that, The device includes: A processor is configured to execute computer-executable instructions; The memory stores one or more computer-executable instructions, which, when executed by the processor, implement the steps of the method for real-time monitoring of securities trading system indicators based on distributed data acquisition as described in any one of claims 1 to 7.

9. A processor for real-time monitoring of securities trading system indicators based on distributed data acquisition, characterized in that, The processor is configured to execute computer-executable instructions, which, when executed by the processor, implement the steps of the method for real-time monitoring of securities trading system indicators based on distributed data acquisition as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that can be executed by a processor to implement the steps of the method for real-time monitoring of securities trading system indicators based on distributed data acquisition as described in any one of claims 1 to 7.

Images