Dial testing engine based on distributed architecture

Abstract

The invention belongs to the technical field of dial testing engines, and particularly relates to a dial testing engine based on a distributed architecture, which comprises the following steps of: improving the accuracy and the real-time performance of data in a dial testing system by adopting a real-time data processing engine; the flexibility and the expandability of data in the dial testing system are improved by adopting a micro-service architecture; an alarm mechanism is perfected; and the data analysis capability is enhanced. The dial test data synchronization engine provided by the invention can realize efficient data synchronization. The engine adopts an incremental data synchronization and event-driven architecture, and only transmits and synchronizes changed data, so that the overhead of transmitting a large amount of redundant data is avoided, and the data synchronization efficiency is remarkably improved. According to the engine, a distributed transaction coordination mechanism is adopted, and it is ensured that data operation across multiple micro-services can be executed in an atomicity mode. Therefore, the consistency of the data among different micro-services is ensured, and the problems of data conflict and inconsistency are avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of dialing test engines, and in particular relates to a dialing test engine based on a distributed architecture. Background Art

[0002] The invention of this dialing test system is to comprehensively cover various dialing test scenarios and solve technical problems such as the inability to guarantee data accuracy and real-time performance, insufficient flexibility and scalability, imperfect alarm mechanism, and weak data analysis capabilities.

[0003] In real dial-up test scenarios, the scenarios are complex and changeable, and the network environment is also uneven, making it difficult to accurately simulate all possible situations, making some special scenarios and edge cases blind spots for monitoring. This may lead to inaccurate and unreal-time data under specific conditions, posing hidden dangers to system operation. Inaccurate data may lead to wrong judgments and decisions, and the lack of real-time performance will delay the discovery and handling of problems, thereby expanding the scope of the problem, bringing a bad experience to users, and even causing economic losses.

[0004] The dial-up test system is unable to cope with the rapidly changing business needs and technical architecture. Fixed configuration and customization methods cannot respond to new requirements quickly, which greatly reduces the adaptability of the dial-up test system and cannot fully meet the needs of business development. Therefore, it lacks flexibility and scalability. This system solves the flexibility and scalability problems of the dial-up test system by adopting microservice architecture, designing a common data model, plug-in architecture, configuration management, data interface standardization, cloud native technology, data layering and abstraction, version control and rollback, etc.

[0005] The imperfection of the alarm mechanism is also troubling. Unreasonable alarm rules will generate too many invalid alarms, causing operation and maintenance personnel to fall into the dilemma of information overload, and it is easy to ignore the real key problems. On the contrary, if the key problems are not reported in time, the best time to deal with them may be missed, and small problems may turn into major failures. This system solves the problem of imperfect alarm mechanism through intelligent alarm rule setting, alarm filtering and suppression, alarm correlation analysis, real-time notification and hierarchical push, alarm confirmation and tracking, visual alarm display, regular optimization and verification, etc.

[0006] The weak data analysis capability makes it impossible to fully utilize a large amount of dial test data. If valuable information and trends cannot be mined, it is difficult to discover potential problems in advance, and it is impossible to provide strong support for system optimization and decision-making. This system uses real-time stream processing technology, distributed computing framework, data warehouse and data mart, algorithm library, data visualization tools, automated data cleaning and preprocessing, deep learning technology, data lineage and metadata management, and enhanced data governance to solve the problem of weak data analysis capabilities of traditional dial test systems. Summary of the invention

[0007] In response to the above technical problems, the present invention provides a dialing test engine based on a distributed architecture, which solves the flexibility and scalability problems of the dialing test system by adopting a microservice architecture, designing a common data model, plug-in architecture, configuration management, data interface standardization, cloud native technology, data layering and abstraction, version control and rollback, etc.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0009] A distributed architecture-based dialing test engine includes the following steps:

[0010] S1. Use real-time data processing engine to improve the accuracy and real-time performance of data in the dial test system;

[0011] S2. Use microservice architecture to improve data flexibility and scalability in the dial-up test system;

[0012] S3. Improve the alarm mechanism;

[0013] S4. Strengthen data analysis capabilities.

[0014] The method of using the real-time data processing engine in S1 to improve the accuracy and real-time performance of data in the dial test system is:

[0015] S1.1. Optimize data collection methods: Use high-precision timestamps and measurement tools to ensure the time accuracy and measurement accuracy of collected data; increase data collection points and frequency to obtain more comprehensive and timely data, but be careful to avoid excessive load on the system;

[0016] S1.2, Data verification and error correction: Establish a data verification mechanism to check the rationality and range of the collected data; use data cleaning technology to remove duplicate, erroneous or abnormal data; use data error correction algorithms to correct and compensate for data that may have deviations;

[0017] S1.3, Real-time data transmission: Utilize efficient network protocols and data transmission technologies, based on TCP reliable transmission or use message queues to ensure real-time data transmission, reduce data transmission delays and losses; optimize bandwidth allocation for data transmission, and ensure that dial test data can be transmitted first;

[0018] S1.4, Edge computing and caching: Perform preliminary data processing and analysis on edge devices close to the data source, and only transmit critical and processed data, reducing the amount of data and improving transmission efficiency; use caching technology to temporarily store data when the network is unstable or data processing is delayed, and transmit and process it when conditions permit;

[0019] S1.5, Real-time data processing engine: Adopt high-performance real-time data processing framework, which can quickly process and analyze large amounts of real-time data streams; optimize data processing algorithms and processes, reduce computing time and resource consumption

[0020] S1.6, Data synchronization and calibration: Ensure the time synchronization between the dialing system and the monitored system to avoid data errors caused by time inconsistency; regularly calibrate the collected data, compare and adjust it with the actual business data or standard test data;

[0021] S1.7, monitor and optimize system performance: continuously monitor the performance of the dial-up system itself, including server resource usage, network bandwidth usage, etc., to promptly identify and resolve performance bottlenecks that may affect data accuracy and real-time performance; dynamically adjust system configuration and resource allocation based on changes in business needs and data volume;

[0022] S1.8. Data quality assessment and feedback: Establish a data quality assessment indicator system, and regularly assess and report on the accuracy and timeliness of data; based on the assessment results, timely adjust data collection, transmission and processing strategies to form a closed loop of continuous improvement of data quality.

[0023] The method of using microservice architecture in S2 to improve data flexibility and scalability in the dial test system is as follows:

[0024] S2.1. Adopt microservice architecture: Split the dial test system into multiple independent microservices, each of which is responsible for a specific function. Use data collection, data analysis, and task management to independently expand and update each service to improve the flexibility and scalability of the system.

[0025] S2.2. Design a universal data model: Create a universal and flexible data model that can adapt to different types of dial test data and new data requirements, using an object-oriented database or a NoSQL database to more easily handle changing data structures;

[0026] S2.3, plug-in architecture: Develop a plug-in framework that allows third-party developers or users to easily write and integrate custom dial-up plug-ins, data analysis modules or data output formats;

[0027] S2.4, Configuration Management: Provides powerful configuration management functions, allowing users to easily adjust the parameters of the dialing task, the frequency of data collection, the rules of data processing, etc. through the configuration file or management interface without modifying the code;

[0028] S2.5, Data interface standardization: define standard data input and output interfaces so that the dial test system can be easily integrated and exchange data with other systems, facilitating the expansion of integration capabilities with other systems in the future;

[0029] S2.6, Cloud Native Technology: Utilize cloud native technology and adopt containerization or container orchestration to achieve rapid deployment, elastic expansion, and efficient use of resources, and dynamically adjust the resources of the dial test system according to the business load;

[0030] S2.7, Data stratification and abstraction: Divide data into different layers, such as raw data layer, processed data layer and result data layer, and perform abstract encapsulation. When new data requirements are added, they can be expanded and processed at the appropriate layer without affecting other parts;

[0031] S2.8, Version control and rollback: Perform version control on the system configuration and data processing logic. When problems arise or new requirements are not met, you can easily roll back to the previous stable version.

[0032] The method for improving the alarm mechanism in S3 is:

[0033] S3.1, Intelligent alarm rule setting: Based on historical data and machine learning algorithms, automatically analyze the normal performance indicator range, dynamically adjust the alarm threshold, and reduce false alarms; introduce multi-dimensional alarm rules, comprehensively consider multiple indicators such as response time, error rate, and resource utilization, rather than relying on a single indicator to trigger an alarm; set the alarm priority and perform graded alarms according to the severity of the problem;

[0034] S3.2, Alarm filtering and suppression: Establish an alarm filtering mechanism to merge or suppress the same alarms that appear repeatedly within a short period of time to avoid alarm storms; exclude alarms generated during known planned maintenance or testing to avoid unnecessary interference;

[0035] S3.3, Alarm correlation analysis: Correlate and analyze related alarms to find possible root causes. If the server's CPU utilization is too high and the memory usage is too high, the alarm may point to the same problem of insufficient system resources. By correlating alarms from different devices or services, the impact scope and possible propagation path of the fault can be quickly located.

[0036] S3.4, Real-time notification and hierarchical push: Support multiple notification methods and ensure that alarm information can be delivered to relevant personnel in real time; push alarms in different levels according to the responsibilities and levels of operation and maintenance personnel to ensure that key alarms can be delivered to personnel with problem-solving capabilities in a timely manner;

[0037] S3.5, Alarm confirmation and tracking: Require the person who receives the alarm to perform confirmation operations in order to track the processing status of the alarm; establish an alarm processing tracking mechanism to record the processing process and results, and provide a basis for subsequent problem review and optimization;

[0038] S3.6, Visual alarm display: Display alarm information through intuitive charts and dashboards, so that operation and maintenance personnel can quickly understand the overall situation and trend of alarms; display the system architecture and device relationship related to alarms in the form of maps and topology diagrams to help quickly locate problem nodes;

[0039] S3.7. Regular optimization and verification: Regularly review and analyze the effectiveness of alarms, adjust and optimize alarm rules and strategies based on actual conditions; conduct simulated fault tests to verify the accuracy and timeliness of the alarm mechanism in different scenarios.

[0040] The method for enhancing data analysis capability in S4 is:

[0041] S4.1. Real-time stream processing technology: Using Apache Kafka as a message queue and combining it with Apache Flink for real-time stream data processing can quickly process and analyze a steady stream of dial test data and detect anomalies and trends in a timely manner;

[0042] S4.2, Distributed computing framework: Use Spark to process large-scale dial test data in parallel to improve analysis efficiency;

[0043] S4.3, Data warehouse and data mart: Build a data warehouse based on relational database and big data storage to integrate and standardize the dial test data for complex query and analysis. At the same time, create data marts according to specific needs to improve the performance of specific analysis scenarios.

[0044] S4.4, Algorithm library: Introduce machine learning libraries such as PyTorch or Scikit-learn, apply classification, regression, and clustering algorithms, and perform pattern recognition and predictive analysis on dial test data;

[0045] S4.5. Data visualization tools: Use PowerBI tools to display analysis results in intuitive charts and dashboards to help users quickly gain insights into the information in the data;

[0046] S4.6. Automated data cleaning and preprocessing: Use Python's Pandas library to automatically clean, denoise, process missing values, and standardize the raw dial test data to improve data quality;

[0047] S4.7. Deep learning techniques: Use convolutional neural networks (CNNs) or recurrent neural networks (RNNs) to detect complex patterns and anomalies, especially when processing time series data.

[0048] S4.8. Data lineage and metadata management: Using the tool Apache Atlas to track the source, transformation, and flow of data, as well as managing data metadata, helps understand the context of the data and improve data credibility, thereby supporting more accurate analysis.

[0049] S4.9. Strengthen data governance: Establish a data quality assessment system, data standards, and data access control strategy to ensure the accuracy, integrity, and security of data, and provide a reliable foundation for data analysis.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. Compared with the traditional method of manually writing code or using middleware, the dial-test data synchronization engine provided by the present invention can achieve efficient data synchronization. The engine adopts incremental data synchronization and event-driven architecture, only transmitting and synchronizing changed data, avoiding the overhead of transmitting a large amount of redundant data, thereby significantly improving the efficiency of data synchronization.

[0052] 2. The engine of the present invention adopts a distributed transaction coordination mechanism to ensure that data operations across multiple microservices can be performed in an atomic manner. This ensures the consistency of data between different microservices and avoids the occurrence of data conflicts and inconsistency problems.

[0053] 3. The engine of the present invention has fault tolerance and fault recovery mechanisms, which can cope with abnormal situations such as network failures and service unavailability. The design of the engine takes into account the reliability and elasticity of the system to ensure the continuity and stability of data synchronization.

[0054] 4. The engine of the present invention provides flexible configuration options, allowing customization and adjustment according to specific business needs. Users can flexibly configure parameters such as data synchronization strategy, event processor and data transmission according to the characteristics and requirements of their own applications to achieve the best data synchronization effect.

[0055] 5. The engine of the present invention provides comprehensive monitoring and logging functions, which can monitor the status, delays and errors of data synchronization in real time. This helps to timely discover and solve problems in data synchronization, and improve the maintainability and troubleshooting efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0057] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0058] Figure 1 It is a flow chart of the steps of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. These descriptions are only to further illustrate the features and advantages of the present invention, rather than to limit the claims of the present invention. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0060] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0061] In this embodiment, if Figure 1 As shown, the following steps are included:

[0062] Step 1: Use a real-time data processing engine to improve the accuracy and real-time performance of data in the dialing system.

[0063] 1. Optimize data collection methods: Use high-precision timestamps and measurement tools to ensure the time accuracy and measurement accuracy of collected data; increase data collection points and frequency to obtain more comprehensive and timely data, but be careful to avoid excessive load on the system.

[0064] 2. Data verification and error correction: Establish a data verification mechanism to conduct rationality checks and range checks on the collected data; use data cleaning technology to remove duplicate, erroneous or abnormal data; use data error correction algorithms to correct and compensate for data that may have deviations.

[0065] 3. Real-time data transmission: Use efficient network protocols and data transmission technologies, such as TCP-based reliable transmission or the use of message queues to ensure real-time data delivery and reduce data transmission delays and losses; optimize bandwidth allocation for data transmission to ensure that dial-up data can be transmitted first.

[0066] 4. Edge computing and caching: Perform preliminary data processing and analysis on edge devices close to the data source, and only transmit critical and processed data to reduce the amount of data and improve transmission efficiency. Use caching technology to temporarily store data when the network is unstable or data processing is delayed, and transmit and process it when conditions permit.

[0067] 5. Real-time data processing engine: It uses high-performance real-time data processing frameworks, such as Apache Flink and Apache Spark Streaming, to quickly process and analyze large amounts of real-time data streams; optimizes data processing algorithms and processes to reduce computing time and resource consumption.

[0068] 6. Data synchronization and calibration: Ensure the time synchronization between the dialing system and the monitored system to avoid data errors caused by time inconsistency; regularly calibrate the collected data, compare and adjust it with the actual business data or standard test data.

[0069] 7. Monitor and optimize system performance: Continuously monitor the performance of the dial-up system itself, including server resource usage, network bandwidth usage, etc., to promptly identify and resolve performance bottlenecks that may affect data accuracy and real-time performance; dynamically adjust system configuration and resource allocation based on changes in business needs and data volume.

[0070] 8. Data quality assessment and feedback: Establish a data quality assessment indicator system, and regularly assess and report on the accuracy and timeliness of data; based on the assessment results, timely adjust data collection, transmission and processing strategies to form a closed loop of continuous improvement of data quality.

[0071] Step 2: Use microservice architecture to improve data flexibility and scalability in the dialing system.

[0072] 1. Adopt microservice architecture: Split the dialing system into multiple independent microservices, each of which is responsible for specific functions, such as data collection, data analysis, task management, etc. This allows each service to be expanded and updated independently, improving the flexibility and scalability of the system.

[0073] 2. Design a universal data model: Create a universal and flexible data model that can adapt to different types of dial test data and new data requirements. Use an object-oriented database or a NoSQL database to more easily handle changing data structures.

[0074] 3. Plug-in architecture: Develop a plug-in framework that allows third-party developers or users to easily write and integrate custom dial-up plug-ins, data analysis modules, or data output formats.

[0075] 4. Configuration management: It provides powerful configuration management functions, allowing users to easily adjust the parameters of the dialing task, the frequency of data collection, the rules of data processing, etc. through the configuration file or management interface without modifying the code.

[0076] 5. Data interface standardization: Define standard data input and output interfaces so that the dialing system can be easily integrated and exchange data with other systems, facilitating future expansion of integration capabilities with other systems.

[0077] 6. Cloud native technology: Using cloud native technologies, such as containerization (Docker) and container orchestration (Kubernetes), rapid deployment, elastic expansion, and efficient use of resources can be achieved, and the resources of the dial-up system can be dynamically adjusted according to the business load.

[0078] 7. Data stratification and abstraction: Divide data into different layers, such as raw data layer, processed data layer and result data layer, and perform abstract encapsulation. In this way, when new data requirements are added, they can be expanded and processed at the appropriate layer without affecting other parts.

[0079] 8. Version control and rollback: Perform version control on the system configuration and data processing logic. When problems arise or new requirements are not met, you can easily roll back to the previous stable version.

[0080] Step 3: Improve the alarm mechanism.

[0081] 1. Intelligent alarm rule setting: Based on historical data and machine learning algorithms, it automatically analyzes the normal performance indicator range, dynamically adjusts the alarm threshold, and reduces false alarms; introduces multi-dimensional alarm rules, comprehensively considers multiple indicators such as response time, error rate, resource utilization, etc., rather than relying solely on a single indicator to trigger an alarm; sets the alarm priority, and performs graded alarms based on the severity of the problem (such as complete service interruption, partial function failure, slight performance degradation, etc.).

[0082] 2. Alarm filtering and suppression: Establish an alarm filtering mechanism to merge or suppress the same alarms that appear repeatedly within a short period of time to avoid alarm storms; exclude alarms generated during known planned maintenance or testing to avoid unnecessary interference.

[0083] 3. Alarm correlation analysis: Correlate and analyze related alarms to find possible root causes. For example, if the server's CPU utilization is too high and the memory usage is too high, it may point to the same problem of system resource shortage. By correlating alarms from different devices or services, the impact range and possible propagation path of the fault can be quickly located.

[0084] 4. Real-time notification and hierarchical push: Support multiple notification methods, such as SMS, email, instant messaging tools, etc., and ensure that alarm information can be delivered to relevant personnel in real time; according to the responsibilities and levels of operation and maintenance personnel, alarms are pushed in a hierarchical manner to ensure that key alarms can be delivered to personnel with problem-solving capabilities in a timely manner.

[0085] 5. Alarm confirmation and tracking: Require the person receiving the alarm to perform confirmation operations in order to track the processing status of the alarm; establish an alarm processing tracking mechanism to record the processing process and results to provide a basis for subsequent problem review and optimization.

[0086] 6. Visual alarm display: Display alarm information through intuitive charts and dashboards, so that operation and maintenance personnel can quickly understand the overall situation and trend of alarms; display the system architecture and equipment relationship related to alarms in the form of maps, topology diagrams, etc., to help quickly locate problem nodes.

[0087] 7. Regular optimization and verification: Regularly review and analyze the effectiveness of alarms, adjust and optimize alarm rules and strategies based on actual conditions; conduct simulated fault tests to verify the accuracy and timeliness of the alarm mechanism in different scenarios.

[0088] Step 4: Strengthen data analysis capabilities.

[0089] 1. Real-time stream processing technology: Using Apache Kafka as a message queue and combining it with Apache Flink for real-time stream data processing can quickly process and analyze a steady stream of dial test data and detect anomalies and trends in a timely manner.

[0090] 2. Distributed computing framework: Use Spark to process large-scale dial test data in parallel to improve analysis efficiency.

[0091] 3. Data warehouse and data mart: Build a data warehouse based on relational database and big data storage to integrate and standardize the dial test data for complex query and analysis. At the same time, you can create a data mart based on specific needs to improve the performance of specific analysis scenarios.

[0092] 4. Algorithm library: Introduce machine learning libraries such as PyTorch or Scikit-learn, apply classification, regression, clustering and other algorithms, and perform pattern recognition and predictive analysis on dial test data.

[0093] 5. Data visualization tools: Use PowerBI tools to display analysis results in intuitive charts and dashboards to help users quickly gain insight into information in the data.

[0094] 6. Automated data cleaning and preprocessing: Use Python's Pandas library to automatically clean, denoise, process missing values, and standardize the raw dial test data to improve data quality.

[0095] 7. Deep learning techniques: Use convolutional neural networks (CNN) or recurrent neural networks (RNN) to detect complex patterns and anomalies, especially when processing time series data.

[0096] 8. Data lineage and metadata management: Using the tool Apache Atlas to track the source, transformation, and flow of data, as well as managing data metadata, helps understand the context of the data and improve data credibility, thereby supporting more accurate analysis.

[0097] 9. Strengthen data governance: Establish a data quality assessment system, data standards, and data access control strategy to ensure the accuracy, integrity, and security of data, and provide a reliable foundation for data analysis.

[0098] Only the preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention, and various changes should be included in the protection scope of the present invention.

Claims

1. A dial test engine based on a distributed architecture, characterized in that: The following steps are involved: S1. Use real-time data processing engine to improve the accuracy and real-time performance of data in the dial test system; S2. Use microservice architecture to improve data flexibility and scalability in the dial-up test system; S3. Improve the alarm mechanism; S4. Strengthen data analysis capabilities.

2. According to claim 1, a distributed architecture-based dialing test engine is characterized in that: The method of using the real-time data processing engine in S1 to improve the accuracy and real-time performance of data in the dial test system is: S1.

1. Optimize data collection methods: Use high-precision timestamps and measurement tools to ensure the time accuracy and measurement accuracy of collected data; increase data collection points and frequency to obtain more comprehensive and timely data, but be careful to avoid excessive load on the system; S1.2, Data verification and error correction: Establish a data verification mechanism to check the rationality and range of the collected data; use data cleaning technology to remove duplicate, erroneous or abnormal data; use data error correction algorithms to correct and compensate for data that may have deviations; S1.3, Real-time data transmission: Utilize efficient network protocols and data transmission technologies, based on TCP reliable transmission or use message queues to ensure real-time data transmission, reduce data transmission delays and losses; optimize bandwidth allocation for data transmission, and ensure that dial test data can be transmitted first; S1.4, Edge computing and caching: Perform preliminary data processing and analysis on edge devices close to the data source, and only transmit critical and processed data, reducing the amount of data and improving transmission efficiency; use caching technology to temporarily store data when the network is unstable or data processing is delayed, and transmit and process it when conditions permit; S1.5, Real-time data processing engine: Adopt high-performance real-time data processing framework, which can quickly process and analyze large amounts of real-time data streams; optimize data processing algorithms and processes, reduce computing time and resource consumption S1.6, Data synchronization and calibration: Ensure the time synchronization between the dialing system and the monitored system to avoid data errors caused by time inconsistency; regularly calibrate the collected data, compare and adjust it with the actual business data or standard test data; S1.7, monitor and optimize system performance: continuously monitor the performance of the dial-up system itself, including server resource usage, network bandwidth usage, etc., to promptly identify and resolve performance bottlenecks that may affect data accuracy and real-time performance; dynamically adjust system configuration and resource allocation based on changes in business needs and data volume; S1.

8. Data quality assessment and feedback: Establish a data quality assessment indicator system, and regularly assess and report on the accuracy and timeliness of data; based on the assessment results, timely adjust data collection, transmission and processing strategies to form a closed loop of continuous improvement of data quality.

3. According to the distributed architecture-based dialing test engine of claim 1, it is characterized in that: The method of using microservice architecture in S2 to improve data flexibility and scalability in the dial test system is as follows: S2.

1. Adopt microservice architecture: Split the dial test system into multiple independent microservices, each of which is responsible for a specific function. Use data collection, data analysis, and task management to independently expand and update each service to improve the flexibility and scalability of the system. S2.

2. Design a universal data model: Create a universal and flexible data model that can adapt to different types of dial test data and new data requirements, using an object-oriented database or a NoSQL database to more easily handle changing data structures; S2.3, plug-in architecture: Develop a plug-in framework that allows third-party developers or users to easily write and integrate custom dialing plug-ins, data analysis modules or data output formats; S2.4, Configuration Management: Provides powerful configuration management functions, allowing users to easily adjust the parameters of the dialing task, the frequency of data collection, the rules of data processing, etc. through the configuration file or management interface without modifying the code; S2.5, Data interface standardization: define standard data input and output interfaces so that the dial test system can be easily integrated and exchange data with other systems, facilitating the expansion of integration capabilities with other systems in the future; S2.6, Cloud Native Technology: Utilize cloud native technology and adopt containerization or container orchestration to achieve rapid deployment, elastic expansion, and efficient use of resources, and dynamically adjust the resources of the dial test system according to the business load; S2.7, Data stratification and abstraction: Divide data into different layers, such as raw data layer, processed data layer and result data layer, and perform abstract encapsulation. When new data requirements are added, they can be expanded and processed at the appropriate layer without affecting other parts; S2.8, Version control and rollback: Perform version control on the system configuration and data processing logic. When problems arise or new requirements are not met, you can easily roll back to the previous stable version.

4. The distributed architecture-based dialing test engine according to claim 1, characterized in that: The method for improving the alarm mechanism in S3 is: S3.1, Intelligent alarm rule setting: Based on historical data and machine learning algorithms, automatically analyze the normal performance indicator range, dynamically adjust the alarm threshold, and reduce false alarms; introduce multi-dimensional alarm rules, comprehensively consider multiple indicators such as response time, error rate, and resource utilization, rather than relying on a single indicator to trigger an alarm; set the alarm priority and perform graded alarms according to the severity of the problem; S3.2, Alarm filtering and suppression: Establish an alarm filtering mechanism to merge or suppress the same alarms that appear repeatedly within a short period of time to avoid alarm storms; exclude alarms generated during known planned maintenance or testing to avoid unnecessary interference; S3.3, Alarm correlation analysis: Correlate and analyze related alarms to find possible root causes. If the server's CPU utilization is too high and the memory usage is too high, the alarm may point to the same problem of insufficient system resources. By correlating alarms from different devices or services, the impact scope and possible propagation path of the fault can be quickly located. S3.4, Real-time notification and hierarchical push: Support multiple notification methods and ensure that alarm information can be delivered to relevant personnel in real time; push alarms in different levels according to the responsibilities and levels of operation and maintenance personnel to ensure that key alarms can be delivered to personnel with problem-solving capabilities in a timely manner; S3.5, Alarm confirmation and tracking: Require the person who receives the alarm to perform confirmation operations in order to track the processing status of the alarm; establish an alarm processing tracking mechanism to record the processing process and results, and provide a basis for subsequent problem review and optimization; S3.6, Visual alarm display: Display alarm information through intuitive charts and dashboards, so that operation and maintenance personnel can quickly understand the overall situation and trend of alarms; display the system architecture and device relationship related to alarms in the form of maps and topology diagrams to help quickly locate problem nodes; S3.

7. Regular optimization and verification: Regularly review and analyze the effectiveness of alarms, adjust and optimize alarm rules and strategies based on actual conditions; conduct simulated fault tests to verify the accuracy and timeliness of the alarm mechanism in different scenarios.

5. The distributed architecture-based dialing test engine according to claim 1, characterized in that: The method for enhancing data analysis capability in S4 is: S4.

1. Real-time stream processing technology: Using Apache Kafka as a message queue and combining it with Apache Flink for real-time stream data processing can quickly process and analyze a steady stream of dial test data and detect anomalies and trends in a timely manner; S4.2, Distributed computing framework: Use Spark to process large-scale dial test data in parallel to improve analysis efficiency; S4.3, Data warehouse and data mart: Build a data warehouse based on relational database and big data storage to integrate and standardize the dial test data for complex query and analysis. At the same time, create data marts according to specific needs to improve the performance of specific analysis scenarios. S4.4, Algorithm library: Introduce machine learning libraries such as PyTorch or Scikit-learn, apply classification, regression, and clustering algorithms, and perform pattern recognition and predictive analysis on dial test data; S4.

5. Data visualization tools: Use PowerBI tools to display analysis results in intuitive charts and dashboards to help users quickly gain insights into the information in the data; S4.

6. Automated data cleaning and preprocessing: Use Python's Pandas library to automatically clean, denoise, process missing values, and standardize the raw dial test data to improve data quality; S4.

7. Deep learning techniques: Use convolutional neural networks (CNNs) or recurrent neural networks (RNNs) to detect complex patterns and anomalies, especially when processing time series data. S4.

8. Data lineage and metadata management: Using the tool Apache Atlas to track the source, transformation, and flow of data, as well as managing data metadata, helps understand the context of the data and improve data credibility, thereby supporting more accurate analysis. S4.

9. Strengthen data governance: Establish a data quality assessment system, data standards, and data access control strategy to ensure the accuracy, integrity, and security of data, and provide a reliable foundation for data analysis.

Images