Method and system for non-functional testing of distributed financial systems

Abstract

The present invention relates to the field of distributed system testing technology, and in particular to a method and system based on distributed financial system non-functional testing. The method comprises the following steps: collecting original transaction data of a distributed financial system to obtain an original transaction data set; performing data preprocessing on the original transaction data set to obtain a standardized transaction data set; identifying key nodes of the standardized transaction data set to obtain key node data; and performing transaction process transaction processing testing on the distributed financial system according to the key node data to obtain transaction processing efficiency data. Therefore, the present invention realizes multi-dimensional detection of potential performance bottlenecks of a distributed financial system through Internet of Things technology, data analysis technology, intelligent detection technology and automation technology, and introduces a fuse mechanism to automatically fuse the system for protection; thereby accurately evaluating the coping ability of a distributed financial system under a high-pressure environment and the effectiveness of a fuse strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed system testing, and in particular to a method and system for non-functional testing of a distributed financial system. Background Art

[0002] With the widespread application of distributed architecture in the financial field, the importance of non-functional testing has become increasingly prominent. This architecture has greatly promoted the popularization and accessibility of financial services with its excellent elasticity, transparency, resource sharing and high availability. Financial institutions rely on non-functional testing to ensure the stability and reliability of the system, especially when facing high-concurrency transactions and big data processing. The core goal of non-functional testing is to fully verify the performance, stability, security, maintainability and scalability of the system to ensure the real-time nature of financial transactions and the service reliability of the system. This requires that the test solution can adapt to the testing requirements of key indicators such as data consistency and transaction processing real-time in a distributed environment. At the same time, it is also necessary to pay attention to the system's fault tolerance and recovery capabilities in the face of failures and anomalies. However, the existing technology does not conduct multi-dimensional testing of the system's potential performance bottlenecks, and lacks the ability to automatically protect the system through a fuse mechanism under abnormal circumstances, which makes it difficult to accurately evaluate the response capabilities of distributed financial systems under high-pressure environments and the effectiveness of fuse strategies. Summary of the invention

[0003] Based on this, it is necessary to provide a method and system for non-functional testing of a distributed financial system to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for non-functional testing of a distributed financial system is provided, the method comprising the following steps:

[0005] Step S1: collecting original transaction data of the distributed financial system to obtain an original transaction data set; performing data preprocessing on the original transaction data set to obtain a standardized transaction data set; identifying key nodes of the standardized transaction data set to obtain key node data; performing transaction process transaction processing test on the distributed financial system according to the key node data to obtain transaction processing efficiency data;

[0006] Step S2: Perform distributed stress simulation on the distributed financial system according to the transaction processing efficiency data to obtain stress test data; perform distributed performance monitoring on the distributed financial system based on the stress test data to obtain performance monitoring data; perform performance bottleneck diagnosis on the performance monitoring data to obtain performance bottleneck data;

[0007] Step S3: Use the performance bottleneck data to perform a circuit breaker assessment on the distributed financial system to generate circuit breaker assessment data; perform adaptive circuit breaker adjustment on the distributed financial system according to the circuit breaker assessment data to obtain dynamic circuit breaker strategy data; perform reliability analysis on the distributed financial system according to the dynamic circuit breaker strategy data to obtain reliability data; perform scalability testing on the distributed financial system based on the stress test data and the reliability data to obtain scalability data;

[0008] Step S4: Based on the reliability data and scalability data, potential system problems of the distributed financial system are identified to generate system potential problem data; based on the system potential problem data, the distributed financial system is optimized to obtain potential problem optimization data; based on the potential problem optimization data, automated test scenario simulation is performed on the distributed financial system to obtain an automated test scenario set; regression test verification is performed on the automated test scenario set to obtain a regression test verification report; based on the regression test verification report, a high-voltage environment test is performed on the distributed financial system to obtain a system test evaluation report.

[0009] The present invention collects original transaction data of the distributed financial system, and can obtain all transaction records generated when the system is running; preprocesses the original transaction data set to form an accurate, consistent and standardized transaction data set, which provides a reliable data basis for subsequent analysis; analyzes the standardized transaction data set to identify key nodes in the transaction process, where key nodes are key factors in system performance and stability, and can accurately locate bottlenecks that affect system performance; performs transaction processing efficiency tests on key node data to evaluate the performance of the system when processing transactions. Transaction processing efficiency data is used to perform distributed stress simulation on the system to simulate the performance of the system under high load, especially to simulate the performance of the system under extreme conditions; distributed performance monitoring is performed based on stress test data to collect various performance indicators of the system in actual operation, providing data support for the diagnosis of performance bottlenecks; performance bottleneck diagnosis is performed on performance monitoring data to clearly diagnose the performance bottleneck of the system, thereby identifying the vulnerable points of the system under high load. The system is evaluated for circuit breaking using performance bottleneck data to determine under what circumstances the circuit breaking mechanism needs to be activated to protect the system; adaptive circuit breaking adjustments are made to the distributed financial system based on the circuit breaking evaluation data, which can automatically adjust the circuit breaking threshold to adapt to different system load conditions; then, the system is analyzed for reliability based on the dynamic circuit breaking strategy data to clarify the stability and reliability of the system, ensuring that the system can maintain normal operation in the face of uncertainty and high load; the distributed financial system is tested for scalability based on stress test data and reliability data to verify the performance of the system during expansion and ensure that the system can smoothly expand with the growth of business needs. The distributed financial system is optimized using system potential problem data to solve problems found during the test, thereby improving the stability and performance of the system; automated test scenario simulations are performed based on potential problem optimization data to ensure the effectiveness of optimization measures; regression test verification is performed on the automated test scenario set to ensure that the optimized system can meet functional and performance requirements; finally, the distributed financial system is tested in a high-pressure environment based on the regression test verification report to obtain a system test evaluation report, thereby accurately evaluating the system's ability to cope with high-pressure environments. Therefore, the present invention uses Internet of Things technology, data analysis technology, intelligent detection technology and automation technology to achieve multi-dimensional detection of potential performance bottlenecks of distributed financial systems, and introduces a fuse mechanism to automatically protect the system; thereby achieving accurate evaluation of the response capabilities of distributed financial systems in high-pressure environments and the effectiveness of fuse strategies.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Capture transaction records of the distributed financial system to obtain transaction record data; monitor transaction logs of the distributed financial system to obtain transaction log data; integrate the transaction record data and the transaction log data to obtain an original transaction data set;

[0012] Step S12: performing data preprocessing on the original transaction data set to obtain a standardized transaction data set, wherein the data preprocessing includes data decryption, data cleaning, data denoising and data standardization;

[0013] Step S13: extract multi-dimensional interactive features from the standardized transaction data set to obtain transaction feature enhancement data; perform node status recognition on the transaction feature enhancement data to obtain node status data; perform influence analysis on the node status data to obtain node influence data;

[0014] Step S14: determining key influencing factors of the node influence data to obtain key node data; identifying transaction process characteristics of the key node data to obtain transaction process characteristic data; mapping the key node data to the transaction process according to the transaction process characteristic data to generate transaction process mapping data;

[0015] Step S15: Analyze the transaction path of the distributed financial system based on the transaction process mapping data to obtain transaction execution path data; perform concurrent processing on the transaction execution path data to generate concurrent transaction processing data; calculate the processing delay of the concurrent transaction processing data to obtain concurrent processing delay data; perform transaction processing efficiency statistics on the concurrent transaction processing data according to the concurrent processing delay data to obtain transaction processing efficiency data.

[0016] The present invention can collect a comprehensive set of original transaction data by capturing transaction records and monitoring transaction logs of distributed financial systems; integrate these two types of data to ensure data integrity and accuracy; perform data preprocessing on the original transaction data set, including data decryption, cleaning, denoising and standardization, to obtain a standardized transaction data set, ensure data quality and consistency, and provide an accurate data basis for subsequent feature extraction and analysis; perform multi-dimensional interactive feature extraction on the standardized transaction data set to enhance the data expressiveness of transaction features; node status identification and influence analysis further reveal the importance and influence of nodes in transactions, and provide a basis for identifying key nodes; by determining key influencing factors, identify key nodes that have a significant impact on the transaction process; transaction process feature identification and mapping provide a clear perspective for understanding each link in the transaction process; transaction path analysis based on transaction process mapping data can clarify the execution path of the transaction; concurrent processing execution and processing delay calculation can evaluate the efficiency of transaction processing; transaction processing efficiency statistics provide quantitative indicators for measuring and optimizing the transaction processing performance of distributed financial systems.

[0017] Preferably, step S2 comprises the following steps:

[0018] Step S21: Generate distributed scenario stress parameters for the distributed financial system according to the transaction processing efficiency data to obtain stress test parameters;

[0019] Step S22: Perform distributed transaction request simulation on the stress test parameters to obtain simulated transaction request data; perform node throughput analysis on the simulated transaction request data to generate node throughput data; perform node response time test on the simulated transaction request data according to the node throughput data to generate node response time data;

[0020] Step S23: capturing the data delay between nodes on the simulated transaction request data according to the node response time data to obtain node delay analysis data; performing a node stress response test on the simulated transaction request data according to the node throughput data to obtain node stress response data; integrating the node delay analysis data and the node stress response data to obtain stress test data;

[0021] Step S24: performing distributed link tracing on the stress test data to obtain distributed link tracing data; performing resource scheduling analysis on the distributed financial system based on the distributed link tracing data to obtain system resource scheduling data; performing distributed performance monitoring on the distributed financial system based on the system resource scheduling data to obtain performance monitoring data;

[0022] Step S25: diagnose the performance bottleneck of the distributed financial system according to the performance monitoring data to obtain performance bottleneck data.

[0023] The present invention generates distributed scenario stress parameters for distributed financial systems through transaction processing efficiency data, providing an accurate parameter basis for subsequent stress testing; performs distributed transaction request simulation on stress test parameters to ensure that the test can simulate the transaction requests of real distributed financial systems; node throughput analysis and node response time testing can further reveal the performance of the system under different loads, providing key data for evaluating system performance; integrating node delay analysis data and node stress response data can comprehensively reflect the performance of the system under high load conditions, which helps to identify performance problems encountered by the system in actual operation; performs distributed link tracking on stress test data, and performs resource scheduling analysis and distributed performance monitoring on the distributed financial system based on the distributed link tracking data, clarifies the details of system resource usage and performance, and provides a basis for optimizing system performance and resource allocation; diagnoses performance bottlenecks through performance monitoring data, provides a clear direction for system optimization, and can improve the overall performance and stability of distributed financial systems.

[0024] Preferably, step S25 includes the following steps:

[0025] Step S251: performing time series analysis on the performance monitoring data to obtain time series performance data; performing trend identification on the distributed financial system based on the time series performance data to obtain system performance trend data;

[0026] Step S252: performing abnormal fluctuation detection on the system performance trend data to obtain system abnormal fluctuation data; performing bottleneck point location processing on the system abnormal fluctuation data to obtain system bottleneck point data; performing load analysis on the system bottleneck point data to obtain system load analysis data;

[0027] Step S253: performing resource contention analysis on the system load analysis data to obtain system resource contention data; performing fine-grained analysis on the system resource contention data to obtain fine-grained resource contention data;

[0028] Step S254: performing resource competition pattern matching on the fine-grained resource competition data to obtain resource competition pattern data; automatically classifying the resource competition pattern data to obtain classified competition data;

[0029] Step S255: tracing the root cause of the problem on the classified competition data to obtain root cause tracing data; and performing performance diagnosis on the performance monitoring data according to the root cause tracing data to obtain performance bottleneck data.

[0030] The present invention performs time series analysis on performance monitoring data, which helps to understand the law of system performance changes over time; performs trend identification based on time series performance data, which can reveal the long-term change direction of system performance and provide decision-making basis for system maintenance and upgrade; performs abnormal fluctuation detection on system performance trend data, which can timely discover abnormal changes in system performance, so as to quickly respond and take measures; performs bottleneck point positioning processing on system abnormal fluctuation data, so as to accurately identify the specific links affecting system performance; performs load analysis on system bottleneck point data, so as to reasonably allocate system resources and optimize system operation efficiency; performs resource competition analysis on system load analysis data, so as to clearly identify the impact of resource contention on system performance; performs fine-grained analysis on system resource competition data, so as to accurately understand resource usage; performs resource competition pattern matching on fine-grained resource competition data, so as to discover regular problems in resource usage; performs automatic classification processing on resource competition pattern data, so as to quickly identify and handle resource competition problems; performs problem root cause tracing on classified competition data, so as to clarify the internal causes leading to performance problems, so as to take more effective optimization measures; performs performance diagnosis on performance monitoring data, so as to timely discover and solve bottleneck problems of system performance, so as to improve system performance and stability.

[0031] Preferably, step S3 comprises the following steps:

[0032] Step S31: performing threshold analysis on the performance bottleneck data to obtain threshold setting data; performing risk identification on the threshold setting data to obtain risk identification data; performing fuse condition delineation on the risk identification data to obtain fuse condition data;

[0033] Step S32: Perform trigger mechanism analysis on the circuit breaker condition data to obtain trigger mechanism data; perform event statistics on the trigger mechanism data to obtain event statistical data; perform circuit breaker evaluation on the distributed financial system based on the event statistical data to generate circuit breaker evaluation data;

[0034] Step S33: Adaptively adjust the circuit breaker of the distributed financial system according to the circuit breaker assessment data to obtain dynamic circuit breaker strategy data;

[0035] Step S34: measuring the complexity of the distributed financial system according to the dynamic circuit breaker strategy data to obtain complexity assessment data; performing potential fault path analysis on the complexity assessment data to obtain fault path data;

[0036] Step S35: Delimiting the impact range of the fault path data to obtain impact range data; performing stability testing on the distributed financial system according to the impact range data to obtain stability test results; performing reliability verification on the stability test results to obtain reliability data;

[0037] Step S36: Perform scalability testing on the distributed financial system based on the stress test data and reliability data to obtain scalability data.

[0038] The present invention performs threshold analysis on performance bottleneck data, which helps to identify and prevent potential risks, thereby obtaining risk identification data; further, based on the risk identification data, it is possible to define circuit breaking conditions and form circuit breaking condition data, thereby providing protection for the stability and security of the system; performing trigger mechanism analysis on the circuit breaking condition data can ensure that the system automatically executes circuit breaking when the preset conditions are met to protect the system from further damage; performing event statistics on the trigger mechanism data helps to monitor and evaluate the frequency and impact of circuit breaking events, and performing circuit breaking evaluation on the distributed financial system based on the event statistical data, thereby providing a quantitative evaluation basis for the performance and stability of the system. Adaptive circuit breaker adjustments to distributed financial systems based on circuit breaker assessment data can enable the system to automatically adjust the circuit breaker strategy based on real-time performance and load conditions, thereby improving the flexibility and adaptability of the system while ensuring system stability; potential fault path analysis of complexity assessment data can identify weak links in the system that cause failures; impact scoping of fault path data helps to quickly locate and limit the impact of failures when they occur, thereby ensuring stable operation of the system; stability testing of impact scoping data can verify the reliability and stability of the system, which is crucial to ensuring the long-term stable operation of distributed financial systems; scalability testing based on stress test data and reliability data can evaluate the system's scalability in the face of growing loads or numbers of users.

[0039] Preferably, step S33 includes the following steps:

[0040] Step S331: Perform real-time distributed monitoring of the distributed financial system according to the circuit breaker assessment data to obtain circuit breaker triggering status data;

[0041] Step S332: performing a system throughput anomaly detection on the distributed financial system according to the circuit breaker trigger status data to obtain system throughput anomaly data; performing a transaction delay detection on the distributed financial system according to the system throughput anomaly data to obtain system transaction delay data; performing a transaction service error detection on the distributed financial system according to the system transaction delay data to obtain system transaction service error data;

[0042] Step S333: Integrate the system throughput abnormality data, the system transaction delay data and the system transaction service error data to obtain the system abnormality status data; perform abnormal fault isolation on the distributed financial system according to the system abnormality status data to obtain fault isolation data;

[0043] Step S334: automatically locate the fault point on the fault isolation data to obtain fault point location data; perform impact analysis on the fault point location data to obtain fault impact data; perform system service impact analysis on the fault impact data to obtain system service impact data; perform automatic service switching processing on the distributed financial system according to the system service impact data to obtain automatic service switching data; perform standby resource allocation on the automatic service switching data to obtain standby resource allocation data;

[0044] Step S335: performing a service recovery preview on the backup resource deployment data to obtain service recovery preview data; performing a fault injection test on the distributed financial system according to the service recovery preview data to obtain fault injection test data; performing automatic recovery execution on the fault injection test data to obtain automatic recovery execution data;

[0045] Step S336: Perform system performance evaluation on the distributed financial system according to the automatic recovery execution data to obtain performance evaluation data; extract abnormal performance data from the performance evaluation data to obtain system abnormal performance data; perform system service degradation processing on the system abnormal performance data to obtain service degradation data; perform resource reorganization on the service degradation data to obtain resource reorganization data; perform dynamic circuit breaker adjustment on the distributed financial system according to the resource reorganization data to obtain dynamic circuit breaker policy data.

[0046] The present invention performs real-time distributed monitoring of the distributed financial system, and can timely discover potential unstable factors of the system, so as to quickly respond and take measures to ensure the stability and reliability of the system; through system throughput anomaly detection, transaction delay detection and transaction service error detection, it can comprehensively reflect the performance status of the system; by integrating system throughput anomaly data, system transaction delay data and system transaction service error data, it can isolate abnormal faults in the distributed financial system and clarify the scope of the fault; automatically locate the fault point and analyze the impact degree of the fault isolation data, and evaluate the severity and scope of the fault; through automatic service switching processing and spare resource allocation, it helps to minimize the impact of the fault on the business and ensure the continuity and availability of the system; Using resource allocation data to rehearse service recovery helps verify the effectiveness of the system's recovery strategies and processes; conducting fault injection tests and automatic recovery execution can test the system's self-repair capabilities and ensure that services can be quickly restored when a real fault occurs; conducting system performance evaluations on distributed financial systems to clarify system performance evaluations; extracting abnormal performance data can identify and resolve system performance issues; performing system service degradation processing and resource reorganization can help maintain basic system operations when performance issues occur and reduce the impact on normal system operations; performing dynamic circuit breaker adjustments on distributed financial systems based on resource reorganization data can dynamically adjust circuit breaker strategies based on the actual performance of the system to adapt to system changes and improve system stability.

[0047] Preferably, step S36 includes the following steps:

[0048] Step S361: Perform dynamic load simulation on the stress test data to obtain dynamic load simulation data; perform system load capacity measurement on the distributed financial system according to the dynamic load simulation data to obtain system load capacity data;

[0049] Step S362: Performing system performance curve modeling on the distributed financial system based on the system carrying capacity data and reliability data to obtain a performance curve model;

[0050] Step S363: performing system scalability evaluation on the performance curve model to obtain system scalability evaluation data; determining resource requirements on the system scalability evaluation data to obtain extended resource requirement data;

[0051] Step S364: Adaptively adjust the distributed financial system according to the extended resource demand data to obtain adaptive scaling data; perform distributed performance monitoring on the distributed financial system based on the adaptive scaling data to obtain distributed performance monitoring data; perform scalability testing on the distributed performance monitoring data to obtain scalability data.

[0052] The present invention performs dynamic load simulation on stress test data, and can simulate the dynamic load conditions of the system in real usage scenarios; thereby accurately measuring the carrying capacity of the distributed financial system, and providing key performance indicators for system optimization; based on the system carrying capacity data and reliability data, the distributed financial system is modeled with a system performance curve, and the performance of the system under different loads is clearly described; the system scalability evaluation of the performance curve model is helpful to determine the resource requirements of the system when facing increasing load requirements, so as to obtain extended resource requirement data, which is crucial for subsequent optimization of system expansion and upgrade; the distributed financial system is adaptively scaled according to the extended resource requirement data, so that the system can automatically adjust resources according to real-time requirements, thereby optimizing system performance; the distributed financial system is distributedly monitored based on the adaptive scaling data, and the system performance can be tracked in real time, and the distributed performance monitoring data is tested for scalability, which verifies the expansion capability of the system after resource adjustment, and ensures that the system can adapt to the performance requirements of business growth.

[0053] Preferably, step S4 comprises the following steps:

[0054] Step S41: performing a multi-dimensional system stability analysis on the distributed financial system based on the reliability data to obtain multi-dimensional system stability data; performing system weakness identification on the multi-dimensional system stability data to generate system weakness data;

[0055] Step S42: tracking the system pressure point of the system weakness data based on the scalability data to obtain system pressure point tracking data; and calculating the system redundancy measurement of the distributed financial system based on the system pressure point tracking data to obtain system redundancy measurement data;

[0056] Step S43: performing transaction lock contention detection on the distributed financial system according to the system redundancy measurement data to obtain transaction lock contention data; performing resource contention conflict identification on the transaction lock contention data to obtain resource contention conflict data; performing system configuration defect analysis on the resource contention conflict data to obtain system configuration defect data; performing system potential problem analysis on the distributed financial system based on the transaction lock contention data and the system configuration defect data to generate system potential problem data;

[0057] Step S44: optimizing the distributed financial system according to the system potential problem data to obtain potential problem optimization data; simulating automated test scenarios for the distributed financial system based on the potential problem optimization data to obtain an automated test scenario set;

[0058] Step S45: Prioritize the automated test scenario set to obtain priority test data; extract automated stress test scenarios from the priority test data to generate automated stress test scenarios; perform performance indicator regression verification on the automated stress test scenarios to obtain a regression test verification report; perform high-pressure environment testing on the distributed financial system based on the regression test verification report to obtain a system test evaluation report.

[0059] The present invention performs multi-dimensional system stability analysis on the distributed financial system based on reliability data, so as to comprehensively evaluate the stability of the system and identify the weaknesses in the system, thereby providing a basis for subsequent system optimization and reinforcement; based on scalability data, the system pressure point tracking is performed on the system weakness data, so as to determine the vulnerable links of the system under high load conditions, and through system redundancy measurement and calculation, the buffering capacity and fault tolerance of the system are evaluated; transaction processing lock competition detection is performed on the distributed financial system according to the system redundancy measurement data to obtain transaction lock competition data; resource contention conflict identification and system configuration defect analysis are performed on the transaction lock competition data, so as to identify and understand the conflicts and defects in the system, and to discover potential problems of the system. It is of vital importance; optimizing the distributed financial system according to the system potential problem data can improve the system performance and stability; simulating the automated test scenarios of the distributed financial system based on the potential problem optimization data is used to verify the effectiveness of the system optimization measures; prioritizing the automated test scenario set can determine the order and focus of the system test, ensuring that the key scenarios are verified first; extracting automated stress test scenarios and regressing performance indicators to ensure that the system can meet the performance requirements after optimization; conducting high-pressure environment testing on the distributed financial system according to the regression test verification report provides a comprehensive assessment of the system's performance and stability, ensuring that the system can withstand the expected load in actual operation.

[0060] Preferably, step S44 includes the following steps:

[0061] Step S441: performing distributed lock optimization processing on the distributed financial system according to the system potential problem data to obtain distributed lock optimization data; performing transaction isolation level adjustment on the distributed lock optimization data to obtain isolation level adjustment data;

[0062] Step S442: intelligently optimizing the system configuration parameters of the distributed financial system according to the system potential problem data to obtain configuration parameter optimization data; dynamically allocating system resources on the configuration parameter optimization data to obtain resource dynamic allocation data; and implementing load balancing on the resource dynamic allocation data to obtain load balancing implementation data;

[0063] Step S443: performing system hotspot automatic dispersion on the load balancing implementation data to obtain hotspot automatic dispersion data; performing system redundancy automatic adjustment on the hotspot automatic dispersion data to obtain redundancy automatic adjustment data; performing fault domain isolation optimization on the redundancy automatic adjustment data to obtain fault domain isolation optimization data; performing system fault tolerance adjustment on the distributed financial system according to the fault domain isolation optimization data to obtain system fault tolerance adjustment data;

[0064] Step S444: Integrate the isolation level adjustment data, the load balancing implementation data and the system fault tolerance adjustment data to obtain potential problem optimization data;

[0065] Step S445: Based on the potential problem optimization data, the distributed financial system is adapted to distributed scenarios to obtain distributed scenario adaptation data; the distributed scenario adaptation data is mapped to automated test cases to obtain an automated test case set; the automated test case set is integrated with test scenario simulation to obtain an automated test scenario set.

[0066] The present invention optimizes the distributed locks of the distributed financial system, which can improve the system's ability to handle concurrent transactions and reduce delays caused by lock contention; adjusts the transaction isolation level of the distributed lock optimization data, which can prevent data inconsistency problems in concurrent transactions and improve the overall performance of the system; intelligently tunes the system configuration parameters of the distributed financial system according to the system's potential problem data, which can improve the system's performance and stability; dynamically allocates system resources to the configuration parameter tuning data to ensure efficient use of resources; performs load balancing on the dynamic resource allocation data to disperse the system load and avoid single-point overload; automatically disperses system hot spots on the load balancing implementation data, which helps to balance the system load, thereby improving the system's response speed and processing capability; automatically adjusts the system redundancy of the hot spot automatic dispersion data to improve the system's reliability and reduce single-point failures. The system automatically adjusts the data for fault domain isolation and optimizes the redundant automatic adjustment data, so that the problem can be quickly isolated when a fault occurs, reducing system downtime; the distributed financial system is adjusted for system fault tolerance based on the fault domain isolation optimization data, ensuring that the system can continue to operate even when some components fail, thereby ensuring the high availability of the financial system; by integrating the isolation level adjustment data, load balancing implementation data and system fault tolerance adjustment data, potential problem optimization data is obtained, which can fully understand the optimization of the system; based on the potential problem optimization data, the distributed financial system is adapted for distributed scenarios to ensure that the system can maintain good performance in different operating environments; automated test case mapping and test scenario simulation integration ensure that potential problems in the system are discovered and corrected in a timely manner, thereby reducing the occurrence of problems in the production environment and improving the stability and reliability of the system.

[0067] In this specification, a system based on distributed non-functional testing of a financial system is provided, which is used to execute the above-mentioned method based on distributed non-functional testing of a financial system. The system based on distributed non-functional testing of a financial system includes:

[0068] The transaction process transaction processing test module is used to collect original transaction data of the distributed financial system to obtain the original transaction data set; perform data preprocessing on the original transaction data set to obtain the standardized transaction data set; perform key node identification on the standardized transaction data set to obtain key node data; perform transaction process transaction processing test on the distributed financial system based on the key node data to obtain transaction processing efficiency data;

[0069] The distributed performance monitoring module is used to perform distributed stress simulation on the distributed financial system based on the transaction processing efficiency data to obtain stress test data; perform distributed performance monitoring on the distributed financial system based on the stress test data to obtain performance monitoring data; perform performance bottleneck diagnosis on the performance monitoring data to obtain performance bottleneck data;

[0070] The dynamic circuit breaker strategy adjustment module is used to use the performance bottleneck data to perform circuit breaker evaluation on the distributed financial system and generate circuit breaker evaluation data; perform adaptive circuit breaker adjustment on the distributed financial system according to the circuit breaker evaluation data to obtain dynamic circuit breaker strategy data; perform reliability analysis on the distributed financial system according to the dynamic circuit breaker strategy data to obtain reliability data; perform scalability test on the distributed financial system based on the stress test data and reliability data to obtain scalability data;

[0071] The system potential problem optimization module is used to identify system potential problems of the distributed financial system based on reliability data and scalability data, and generate system potential problem data; optimize the distributed financial system according to the system potential problem data to obtain potential problem optimization data; simulate the distributed financial system in automated test scenarios based on the potential problem optimization data to obtain an automated test scenario set; perform regression testing on the automated test scenario set to obtain a regression testing verification report; perform high-voltage environment testing on the distributed financial system according to the regression testing verification report to obtain a system testing evaluation report.

[0072] The present invention collects original transaction data of the distributed financial system through the transaction process transaction processing test module, and can obtain all transaction records generated when the system is running; pre-processes the original transaction data set to form an accurate, consistent and standardized transaction data set, which provides a reliable data basis for subsequent analysis; analyzes the standardized transaction data set to identify the key nodes in the transaction process, wherein the key nodes are the key factors of system performance and stability, and can accurately locate the bottlenecks that affect system performance; performs transaction processing efficiency testing on key node data to evaluate the performance of the system when processing transactions. The distributed performance monitoring module uses transaction processing efficiency data to perform distributed stress simulation on the system, simulating the performance of the system under high load, especially simulating the performance of the system under extreme conditions; performs distributed performance monitoring based on stress test data, collects various performance indicators of the system in actual operation, and provides data support for the diagnosis of performance bottlenecks; performs performance bottleneck diagnosis on performance monitoring data, clearly diagnoses the performance bottleneck of the system, and thus identifies the vulnerable points of the system under high load. The dynamic circuit breaker strategy adjustment module uses performance bottleneck data to evaluate the system's circuit breaker to determine under what circumstances the circuit breaker mechanism needs to be activated to protect the system; the distributed financial system is adaptively adjusted based on the circuit breaker evaluation data, and the circuit breaker threshold can be automatically adjusted to adapt to different system load conditions; then, the system is reliability analyzed based on the dynamic circuit breaker strategy data to clarify the stability and reliability of the system, ensuring that the system can maintain normal operation in the face of uncertainty and high load; the distributed financial system is scalable based on stress test data and reliability data to verify the performance of the system during expansion and ensure that the system can smoothly expand with the growth of business needs. Through the system potential problem optimization module, the system potential problem data is used to optimize the distributed financial system, solve the problems found during the test, and thus improve the stability and performance of the system; based on the potential problem optimization data, the automated test scenario simulation is performed to ensure the effectiveness of the optimization measures; the automated test scenario set is regression tested to ensure that the optimized system can meet the functional and performance requirements; finally, the distributed financial system is tested in a high-pressure environment based on the regression test verification report to obtain a system test evaluation report, thereby accurately evaluating the system's ability to cope with high-pressure environments. Therefore, the present invention uses Internet of Things technology, data analysis technology, intelligent detection technology and automation technology to achieve multi-dimensional detection of potential performance bottlenecks of distributed financial systems, and introduces a fuse mechanism to automatically protect the system; thereby achieving accurate evaluation of the response capabilities of distributed financial systems in high-pressure environments and the effectiveness of fuse strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1A flowchart of the steps of a method for non-functional testing of a distributed financial system is provided;

[0074] Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0075] Figure 3 for Figure 2 Detailed implementation steps of step S33;

[0076] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0077] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0078] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0079] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0080] To achieve this, please refer to Figures 1 to 3 , a method for non-functional testing of a distributed financial system, the method comprising the following steps:

[0081] Step S1: collecting original transaction data of the distributed financial system to obtain an original transaction data set; performing data preprocessing on the original transaction data set to obtain a standardized transaction data set; identifying key nodes of the standardized transaction data set to obtain key node data; performing transaction process transaction processing test on the distributed financial system according to the key node data to obtain transaction processing efficiency data;

[0082] Step S2: Perform distributed stress simulation on the distributed financial system according to the transaction processing efficiency data to obtain stress test data; perform distributed performance monitoring on the distributed financial system based on the stress test data to obtain performance monitoring data; perform performance bottleneck diagnosis on the performance monitoring data to obtain performance bottleneck data;

[0083] Step S3: Use the performance bottleneck data to perform a circuit breaker assessment on the distributed financial system to generate circuit breaker assessment data; perform adaptive circuit breaker adjustment on the distributed financial system according to the circuit breaker assessment data to obtain dynamic circuit breaker strategy data; perform reliability analysis on the distributed financial system according to the dynamic circuit breaker strategy data to obtain reliability data; perform scalability testing on the distributed financial system based on the stress test data and the reliability data to obtain scalability data;

[0084] Step S4: Based on the reliability data and scalability data, potential system problems of the distributed financial system are identified to generate system potential problem data; based on the system potential problem data, the distributed financial system is optimized to obtain potential problem optimization data; based on the potential problem optimization data, automated test scenario simulation is performed on the distributed financial system to obtain an automated test scenario set; regression test verification is performed on the automated test scenario set to obtain a regression test verification report; based on the regression test verification report, a high-voltage environment test is performed on the distributed financial system to obtain a system test evaluation report.

[0085] The present invention collects original transaction data of the distributed financial system, and can obtain all transaction records generated when the system is running; preprocesses the original transaction data set to form an accurate, consistent and standardized transaction data set, which provides a reliable data basis for subsequent analysis; analyzes the standardized transaction data set to identify key nodes in the transaction process, where key nodes are key factors in system performance and stability, and can accurately locate bottlenecks that affect system performance; performs transaction processing efficiency tests on key node data to evaluate the performance of the system when processing transactions. Transaction processing efficiency data is used to perform distributed stress simulation on the system to simulate the performance of the system under high load, especially to simulate the performance of the system under extreme conditions; distributed performance monitoring is performed based on stress test data to collect various performance indicators of the system in actual operation, providing data support for the diagnosis of performance bottlenecks; performance bottleneck diagnosis is performed on performance monitoring data to clearly diagnose the performance bottleneck of the system, thereby identifying the vulnerable points of the system under high load. The system is evaluated for circuit breaking using performance bottleneck data to determine under what circumstances the circuit breaking mechanism needs to be activated to protect the system; adaptive circuit breaking adjustments are made to the distributed financial system based on the circuit breaking evaluation data, which can automatically adjust the circuit breaking threshold to adapt to different system load conditions; then, the system is analyzed for reliability based on the dynamic circuit breaking strategy data to clarify the stability and reliability of the system, ensuring that the system can maintain normal operation in the face of uncertainty and high load; the distributed financial system is tested for scalability based on stress test data and reliability data to verify the performance of the system during expansion and ensure that the system can smoothly expand with the growth of business needs. The distributed financial system is optimized using system potential problem data to solve problems found during the test, thereby improving the stability and performance of the system; automated test scenario simulations are performed based on potential problem optimization data to ensure the effectiveness of optimization measures; regression test verification is performed on the automated test scenario set to ensure that the optimized system can meet functional and performance requirements; finally, the distributed financial system is tested in a high-pressure environment based on the regression test verification report to obtain a system test evaluation report, thereby accurately evaluating the system's ability to cope with high-pressure environments. Therefore, the present invention uses Internet of Things technology, data analysis technology, intelligent detection technology and automation technology to achieve multi-dimensional detection of potential performance bottlenecks of distributed financial systems, and introduces a fuse mechanism to automatically protect the system; thereby achieving accurate evaluation of the response capabilities of distributed financial systems in high-pressure environments and the effectiveness of fuse strategies.

[0086] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of the method for non-functional testing of a distributed financial system according to the present invention. In this example, the method for non-functional testing of a distributed financial system includes the following steps:

[0087] Step S1: collecting original transaction data of the distributed financial system to obtain an original transaction data set; performing data preprocessing on the original transaction data set to obtain a standardized transaction data set; identifying key nodes of the standardized transaction data set to obtain key node data; performing transaction process transaction processing test on the distributed financial system according to the key node data to obtain transaction processing efficiency data;

[0088] In an embodiment of the present invention, data crawler technology or API interface collection technology is used to collect raw transaction data in a distributed financial system; the raw transaction data set obtained contains noise, missing values ​​and outliers; data cleaning technology, such as SimpleImputer in the Scikit-learn library, is used to process missing values, and a mean, median or mode filling strategy is adopted; for outliers, the Z-score is calculated and a threshold is set to identify and process them; in order to eliminate the influence of different dimensions, data standardization technology, such as MinMaxScaler, is used to perform minimum and maximum normalization; finally, a standardized transaction data set is obtained; a key node identification technology based on the K-shell method is used to identify key nodes in the standardized transaction data set; specifically, the K-shell method deletes nodes with a degree of 1 and their edges until such nodes no longer appear in the standardized transaction data set, and then classifies all deleted nodes into a 1-shell layer and assigns a K-shell value to them, thereby obtaining key node data; a transaction process transaction processing test is performed on the distributed financial system according to the key node data, and a distributed transaction technology, such as Seata, is used to perform a transaction processing test; specifically, by comparing indicators before and after transaction processing, such as response time, throughput, etc., the specific situation of transaction processing efficiency is evaluated.

[0089] Step S2: Perform distributed stress simulation on the distributed financial system according to the transaction processing efficiency data to obtain stress test data; perform distributed performance monitoring on the distributed financial system based on the stress test data to obtain performance monitoring data; perform performance bottleneck diagnosis on the performance monitoring data to obtain performance bottleneck data;

[0090] In an embodiment of the present invention, a stress testing tool, such as Apache JMeter or Gatling, is used to simulate high concurrent requests of a distributed financial system based on transaction processing efficiency data, specifically simulating multi-concurrent request behaviors, thereby generating stress testing data; performance monitoring tools, such as Prometheus and Grafana, are used to collect key performance indicators (KPIs) during system runtime, including CPU usage, memory usage, disk I / O, network traffic, etc., thereby generating performance monitoring data; a performance analysis tool, such as New Relic or Datadog, is used to perform distributed performance monitoring of a distributed financial system; and performance bottlenecks, such as database queries, network delays, or code efficiency issues, are specifically analyzed and diagnosed, thereby obtaining performance bottleneck data.

[0091] Step S3: Use the performance bottleneck data to perform a circuit breaker assessment on the distributed financial system to generate circuit breaker assessment data; perform adaptive circuit breaker adjustment on the distributed financial system according to the circuit breaker assessment data to obtain dynamic circuit breaker strategy data; perform reliability analysis on the distributed financial system according to the dynamic circuit breaker strategy data to obtain reliability data; perform scalability testing on the distributed financial system based on the stress test data and the reliability data to obtain scalability data;

[0092] In the embodiment of the present invention, performance bottleneck data is used to prevent system overload by introducing a circuit breaker mechanism. Specifically, a circuit breaker component, such as Hystrix, Resilience4j or Sentinel, is used to implement the circuit breaker function of the distributed financial system, wherein the circuit breaker has three states: closed, open and half-open, to adapt to different system conditions. Implement an adaptive circuit breaker strategy based on circuit breaker assessment data; specifically, dynamically adjust the threshold and behavior of the circuit breaker through real-time analysis of system performance indicators; for example, use Google SRE's adaptive circuit breaker algorithm to achieve more sophisticated flow control by calculating the probability of discarding requests, thereby obtaining dynamic circuit breaker strategy data; conduct reliability analysis on distributed financial systems based on dynamic circuit breaker strategy data, specifically simulate various failure conditions, and use chaos engineering tools, such as Chaos Monkey, to introduce failures into distributed financial systems to evaluate the system's fault tolerance, thereby testing the system's recovery and stability, and obtaining reliability data; combine stress test data with reliability data to conduct scalability testing; specifically use load testing tools, such as Apache JMeter or Gatling, to simulate high-concurrency requests on distributed financial systems, and further evaluate the system's performance under extreme loads, thereby obtaining scalability data.

[0093] Step S4: Based on the reliability data and scalability data, potential system problems of the distributed financial system are identified to generate system potential problem data; based on the system potential problem data, the distributed financial system is optimized to obtain potential problem optimization data; based on the potential problem optimization data, automated test scenario simulation is performed on the distributed financial system to obtain an automated test scenario set; regression test verification is performed on the automated test scenario set to obtain a regression test verification report; based on the regression test verification report, a high-voltage environment test is performed on the distributed financial system to obtain a system test evaluation report.

[0094] In the embodiment of the present invention, reliability data and scalability data are used to identify potential system problems through automated tools. For example, static code analysis tools (such as SonarQube) are used to identify potential defects in the code, and dynamic analysis tools (such as AppDynamics) are used to monitor the performance of the system during operation, thereby generating system potential problem data; system optimization is performed based on the system potential problem data, including increasing resource configuration and adjusting the system architecture; load balancing technology, such as Nginx or HAProxy, is introduced to optimize resource allocation and improve the processing capacity of the system to obtain potential problem optimization data; based on the potential problem optimization data, automated testing tools, such as JUnit, TestNG or Selenium, are used to simulate different test scenarios and generate automated test scenario sets; the test scenarios can cover various business processes and abnormal situations to ensure the stability and reliability of the system under different conditions; regression testing is performed on the automated test scenario set, specifically using a continuous integration and continuous deployment (CI / CD) process, such as Jenkins or GitLab CI, to automatically perform regression testing and generate a regression test verification report; high-pressure environment testing is performed on the distributed financial system based on the regression test verification report, specifically including using stress testing tools, such as Apache JMeter or LoadRunner simulates high-concurrency requests, evaluates the performance of the system under extreme load, and generates a system test evaluation report.

[0095] Preferably, step S1 comprises the following steps:

[0096] Step S11: Capture transaction records of the distributed financial system to obtain transaction record data; monitor transaction logs of the distributed financial system to obtain transaction log data; integrate the transaction record data and the transaction log data to obtain an original transaction data set;

[0097] Step S12: performing data preprocessing on the original transaction data set to obtain a standardized transaction data set, wherein the data preprocessing includes data decryption, data cleaning, data denoising and data standardization;

[0098] Step S13: extract multi-dimensional interactive features from the standardized transaction data set to obtain transaction feature enhancement data; perform node status recognition on the transaction feature enhancement data to obtain node status data; perform influence analysis on the node status data to obtain node influence data;

[0099] Step S14: determining key influencing factors of the node influence data to obtain key node data; identifying transaction process characteristics of the key node data to obtain transaction process characteristic data; mapping the key node data to the transaction process according to the transaction process characteristic data to generate transaction process mapping data;

[0100] Step S15: Analyze the transaction path of the distributed financial system based on the transaction process mapping data to obtain transaction execution path data; perform concurrent processing on the transaction execution path data to generate concurrent transaction processing data; calculate the processing delay of the concurrent transaction processing data to obtain concurrent processing delay data; perform transaction processing efficiency statistics on the concurrent transaction processing data according to the concurrent processing delay data to obtain transaction processing efficiency data.

[0101] In an embodiment of the present invention, a log capture tool, such as Fluentd or Logstash, is used to capture transaction records in a distributed financial system in real time to obtain transaction record data; a log monitoring tool, such as ELK Stack (Elasticsearch, Logstash, Kibana) or EFK Stack (Elasticsearch, Fluentd, Kibana), is configured to monitor transaction logs of a distributed financial system to obtain transaction log data; a data integration tool, such as Apache Nifi or Informatica, is used to integrate transaction record data and transaction log data to construct an original transaction data set. An encryption and decryption library, such as Java Cryptography Architecture or OpenSSL, is used to decrypt sensitive data on the original transaction data set; a data cleaning tool, such as DataFrame API of Apache Spark, is used to clean the original transaction data set; a data denoising technique, such as Isolation Forest or DBSCAN, is used to denoise the original transaction data set; and a standardization tool, such as StandardScaler of Scikit-learn, is used to standardize the original transaction data set. Use feature extraction libraries, such as Featuretools, to extract multi-dimensional interactive features from standardized transaction data sets; use graph analysis tools, such as Apache Spark GraphX ​​or Neo4j, to identify node states of transaction feature enhanced data to obtain node state data; use statistical analysis tools, such as Python's Statsmodels library, to perform influence analysis on node state data to obtain node influence data; use machine learning algorithms, such as random forests or gradient boosting machines, to perform key influencing factor analysis on node influence data; identify transaction process features of key node data to obtain transaction process feature data; use mapping tools, such as Microsoft Visio or Lucidchart, to map transaction processes for key node data based on transaction process feature data; perform transaction path analysis on distributed financial systems based on transaction process mapping data; use concurrent processing frameworks, such as Java concurrent packages or Node.js's Cluster module, to perform concurrent processing on transaction execution path data; use distributed performance monitoring tools, such as New Relic or Dynatrace, to calculate processing delays on transaction concurrent processing data to obtain concurrent processing delay data; use database performance analysis tools, such as MySQL's Performance Schema or Oracle's AWR, perform transaction processing efficiency statistics on transaction concurrent processing data based on concurrent processing delay data to obtain transaction processing efficiency data.

[0102] Preferably, step S2 comprises the following steps:

[0103] Step S21: Generate distributed scenario stress parameters for the distributed financial system according to the transaction processing efficiency data to obtain stress test parameters;

[0104] Step S22: Perform distributed transaction request simulation on the stress test parameters to obtain simulated transaction request data; perform node throughput analysis on the simulated transaction request data to generate node throughput data; perform node response time test on the simulated transaction request data according to the node throughput data to generate node response time data;

[0105] Step S23: capturing the data delay between nodes on the simulated transaction request data according to the node response time data to obtain node delay analysis data; performing a node stress response test on the simulated transaction request data according to the node throughput data to obtain node stress response data; integrating the node delay analysis data and the node stress response data to obtain stress test data;

[0106] Step S24: performing distributed link tracing on the stress test data to obtain distributed link tracing data; performing resource scheduling analysis on the distributed financial system based on the distributed link tracing data to obtain system resource scheduling data; performing distributed performance monitoring on the distributed financial system based on the system resource scheduling data to obtain performance monitoring data;

[0107] Step S25: diagnose the performance bottleneck of the distributed financial system according to the performance monitoring data to obtain performance bottleneck data.

[0108] In an embodiment of the present invention, distributed scenario stress parameters are generated for a distributed financial system based on transaction processing efficiency data through a distributed performance testing tool, such as Apache JMeter; distributed transaction requests are simulated based on the stress testing parameters using the distributed testing function of JMeter to obtain simulated transaction request data; performance monitoring tools, such as New Relic or Dynatrace, are used to perform node throughput analysis on the simulated transaction request data; node response time is tested on the simulated transaction request data based on the node throughput data, and monitoring tools, such as Grafana and InfluxDB, are used to generate node response time data; network performance testing tools, such as TcpTest, are used to capture data delays between nodes on the simulated transaction request data based on the node response time data to obtain node delay analysis data. Use stress testing tools, such as Apache JMeter, to perform node stress response tests on simulated transaction request data based on node throughput data; use data integration tools, such as Apache Nifi or Informatica, to integrate node delay analysis data and node stress response data to obtain stress testing data; use link tracking tools, such as SkyWalking or Zipkin, to perform distributed link tracking on stress testing data to obtain distributed link tracking data; use resource scheduling analysis tools, such as Kubernetes, to perform resource scheduling analysis on distributed financial systems based on distributed link tracking data; use performance monitoring tools, such as Prometheus and Grafana, to perform distributed performance monitoring on distributed financial systems based on system resource scheduling data; use performance analysis tools, such as Google SRE's adaptive circuit breaker algorithm, to diagnose performance bottlenecks in distributed financial systems based on performance monitoring data to obtain performance bottleneck data.

[0109] Preferably, step S25 includes the following steps:

[0110] Step S251: performing time series analysis on the performance monitoring data to obtain time series performance data; performing trend identification on the distributed financial system based on the time series performance data to obtain system performance trend data;

[0111] Step S252: performing abnormal fluctuation detection on the system performance trend data to obtain system abnormal fluctuation data; performing bottleneck point location processing on the system abnormal fluctuation data to obtain system bottleneck point data; performing load analysis on the system bottleneck point data to obtain system load analysis data;

[0112] Step S253: performing resource contention analysis on the system load analysis data to obtain system resource contention data; performing fine-grained analysis on the system resource contention data to obtain fine-grained resource contention data;

[0113] Step S254: performing resource competition pattern matching on the fine-grained resource competition data to obtain resource competition pattern data; automatically classifying the resource competition pattern data to obtain classified competition data;

[0114] Step S255: tracing the root cause of the problem on the classified competition data to obtain root cause tracing data; and performing performance diagnosis on the performance monitoring data according to the root cause tracing data to obtain performance bottleneck data.

[0115] In an embodiment of the present invention, a performance monitoring tool, such as Prometheus, is used to collect performance indicators of a distributed financial system during operation, such as CPU usage, memory usage, disk I / O, etc., and the collected performance data is stored in a time series database, such as InfluxDB, for time series analysis; time series analysis technology, such as a machine learning algorithm (such as an ARIMA model), is applied to analyze the performance monitoring data to obtain time series performance data; and trend identification of the distributed financial system is performed based on the time series performance data to obtain system performance trend data. Use intelligent detection algorithms, such as Metis, to detect abnormal fluctuations in system performance trend data; specifically, identify performance changes that do not meet expectations; locate system bottlenecks by analyzing abnormal data and combining system logs and call chain tracing (such as Zipkin) to obtain system bottleneck data; perform load analysis on system bottleneck data; use performance analysis tools (such as jVisualVM) to simulate system load conditions and determine system load analysis data; analyze system resource competition data, and use concurrent testing tools (such as JMeter) to simulate multi-user concurrent access to obtain system resource competition data; perform fine-grained analysis on resource competition data, and use Flink's fine-grained resource management technology to obtain fine-grained resource competition data; use pattern recognition algorithms to match resource competition patterns on fine-grained resource competition data to discover potential resource competition problems; automatically classify resource competition pattern data to obtain classified competition data; trace the root cause of the classified competition data, and use data tracing technology, such as Apache Kafka, track data flow and identify the source of contention issues; use database performance diagnostic tools, such as DBdoctor, to perform performance diagnosis on performance monitoring data based on root traceability data to obtain performance bottleneck data.

[0116] As an example of the present invention, refer to Figure 2 As shown, in this example, step S3 includes:

[0117] Step S31: performing threshold analysis on the performance bottleneck data to obtain threshold setting data; performing risk identification on the threshold setting data to obtain risk identification data; performing fuse condition delineation on the risk identification data to obtain fuse condition data;

[0118] Step S32: Perform trigger mechanism analysis on the circuit breaker condition data to obtain trigger mechanism data; perform event statistics on the trigger mechanism data to obtain event statistical data; perform circuit breaker evaluation on the distributed financial system based on the event statistical data to generate circuit breaker evaluation data;

[0119] Step S33: Adaptively adjust the circuit breaker of the distributed financial system according to the circuit breaker assessment data to obtain dynamic circuit breaker strategy data;

[0120] Step S34: measuring the complexity of the distributed financial system according to the dynamic circuit breaker strategy data to obtain complexity assessment data; performing potential fault path analysis on the complexity assessment data to obtain fault path data;

[0121] Step S35: Delimiting the impact range of the fault path data to obtain impact range data; performing stability testing on the distributed financial system according to the impact range data to obtain stability test results; performing reliability verification on the stability test results to obtain reliability data;

[0122] Step S36: Perform scalability testing on the distributed financial system based on the stress test data and reliability data to obtain scalability data.

[0123] In the embodiment of the present invention, a threshold analysis is performed on the performance bottleneck data using dynamic threshold technology. Specifically, a CPU usage threshold is set. For example, when the CPU usage of the distributed financial system reaches 95%, the system will enter a performance bottleneck state. Based on historical data and business needs, a risk threshold is set. For example, when the response time exceeds 200ms, it is considered a risk, and a machine learning algorithm (such as logistic regression, decision tree) is used to identify and classify the data that exceeds the threshold. According to the risk identification results, a fuse condition is set. For example, if the response time exceeds the threshold for 5 consecutive minutes, the fuse is triggered. A fuse algorithm, such as the circuit breaker algorithm of Hystrix, is used to automatically perform a fuse operation on the distributed financial system. The trigger mechanism analysis is performed on the fuse condition data to implement the trigger mechanism. When the fuse condition is met, a fuse event is sent through an event-driven architecture (such as Kafka, RabbitMQ). After the fuse event is triggered, a preset downgrade strategy is executed, such as returning to the default value, error page, or retry logic. Through log analysis tools (such as ELK The system uses the distributed financial system to collect circuit breaker events and conduct statistical analysis; based on the event statistical data, the data analysis model is used to conduct circuit breaker assessment on the distributed financial system and generate circuit breaker assessment data; the circuit breaker threshold and strategy are dynamically adjusted according to the circuit breaker assessment data; adaptive algorithms, such as feedback control-based algorithms, are applied to optimize circuit breaker parameters in real time; and algorithm complexity analysis methods (such as Big O notation) are used to evaluate the complexity of system operations; code complexity is detected through code scanning tools (such as SonarQube) to identify potential performance bottlenecks; transfer path analysis (TPA) technology is applied to determine the fault path in the system by tracing the signal transmission path; fault injection tools (such as ChaosMonkey) are used to simulate faults and verify the fault tolerance of the system; based on the fault path data, system dependency graphs are constructed using graphical tools (such as Gephi) to analyze the fault propagation path; the impact range of the fault path data is delineated to obtain the impact range data; the stability of the distributed financial system is tested through stress testing tools (such as JMeter); the reliability of the system is verified through reliability testing, such as fault injection testing; scalability testing is performed based on stress testing data and reliability data to determine the scalability of the system when the load increases. Specifically, the system's processing capacity is tested by increasing the number of virtual users or the amount of data to obtain scalability data.

[0124] As an example of the present invention, refer to Figure 3 As shown, in this example, step S33 includes:

[0125] Step S331: Perform real-time distributed monitoring of the distributed financial system according to the circuit breaker assessment data to obtain circuit breaker triggering status data;

[0126] Step S332: performing a system throughput anomaly detection on the distributed financial system according to the circuit breaker trigger status data to obtain system throughput anomaly data; performing a transaction delay detection on the distributed financial system according to the system throughput anomaly data to obtain system transaction delay data; performing a transaction service error detection on the distributed financial system according to the system transaction delay data to obtain system transaction service error data;

[0127] Step S333: Integrate the system throughput abnormality data, the system transaction delay data and the system transaction service error data to obtain the system abnormality status data; perform abnormal fault isolation on the distributed financial system according to the system abnormality status data to obtain fault isolation data;

[0128] Step S334: automatically locate the fault point on the fault isolation data to obtain fault point location data; perform impact analysis on the fault point location data to obtain fault impact data; perform system service impact analysis on the fault impact data to obtain system service impact data; perform dynamic service switching processing on the distributed financial system according to the system service impact data to obtain automatic service switching data; perform standby resource allocation on the automatic service switching data to obtain standby resource allocation data;

[0129] Step S335: performing a service recovery preview on the backup resource deployment data to obtain service recovery preview data; performing a fault injection test on the distributed financial system according to the service recovery preview data to obtain fault injection test data; performing automatic recovery execution on the fault injection test data to obtain automatic recovery execution data;

[0130] Step S336: Perform system performance evaluation on the distributed financial system according to the automatic recovery execution data to obtain performance evaluation data; extract abnormal performance data from the performance evaluation data to obtain system abnormal performance data; perform system service degradation processing on the system abnormal performance data to obtain service degradation data; perform resource reorganization on the service degradation data to obtain resource reorganization data; perform dynamic circuit breaker adjustment on the distributed financial system according to the resource reorganization data to obtain dynamic circuit breaker policy data.

[0131] In the embodiment of the present invention, a distributed tracing system, such as Zipkin or Jaeger, is used to track the call chain between microservices and monitor the service status in real time; and combined with the real-time monitoring function provided by Sentinel, key indicators such as the number of service calls and response time are monitored; when the monitoring data reaches the preset fuse threshold, Sentinel will trigger the fuse and record the fuse status data, thereby obtaining the fuse trigger status data; by integrating Prometheus and Grafana, the throughput of the distributed financial system is monitored in real time; specifically, data is collected through Prometheus, and the data is visualized using Grafana. When abnormal throughput data is detected, such as a sudden increase or decrease in the throughput indicator on the Grafana monitoring panel, the system will record the abnormal data to obtain the system throughput abnormal data. Apache Kafka is used as a message queue to monitor the message delay of the transaction process; specifically, the delay alarm is configured through the Broker of Kafka. When the delay of the message in the queue exceeds the preset threshold, the alarm is triggered and the transaction delay data is recorded to obtain the system transaction delay data. ELK Stack (Elasticsearch, Logstash, Kibana) is used to collect and analyze the logs of the transaction service. Specifically, Logstash is responsible for log collection and processing, Elasticsearch is used to store log data, and Kibana is used to visualize log data. By setting error log parsing rules, transaction service errors are detected in real time, and error data is recorded to obtain system transaction service error data. Integrate throughput anomaly data, transaction delay data, and transaction service error data to form system abnormal status data; use distributed coordination services such as Apache ZooKeeper to isolate abnormal faults in distributed financial systems. When the system detects abnormal status data, ZooKeeper will control the start and stop of related services to achieve fault isolation; use artificial intelligence algorithms, such as decision trees or neural networks in machine learning, to automatically locate the fault point of fault isolation data; at the same time, evaluate the impact of the fault point by analyzing the impact of the fault on other parts of the system; use SOFAStack's microservice governance center to perform system service impact analysis based on the fault impact data; automatically perform service switching based on the analysis results, such as switching traffic to a backup service instance; after the service is switched, use Kubernetes to allocate backup resources; dynamically adjust service resource allocation based on actual load conditions through Kubernetes' automatic scaling function.Perform service recovery rehearsal on the backup resource allocation data to obtain service recovery rehearsal data; use Chaos Monkey to perform fault injection testing on the distributed financial system; specifically, once the fault injection test passes, execute automatic recovery operations to obtain automatic recovery execution data; use SOFAStack's distributed transactions to evaluate system performance, and use SOFAStack's dynamic configuration center to perform service degradation processing for abnormal performance data found in the system performance evaluation, and adjust system resource allocation to optimize system performance; use SOFAStack's microservice governance center to adjust dynamic circuit breaker policies based on resource reorganization data to obtain dynamic circuit breaker policy data.

[0132] Preferably, step S36 includes the following steps:

[0133] Step S361: Perform dynamic load simulation on the stress test data to obtain dynamic load simulation data; perform system load capacity measurement on the distributed financial system according to the dynamic load simulation data to obtain system load capacity data;

[0134] Step S362: Performing system performance curve modeling on the distributed financial system based on the system carrying capacity data and reliability data to obtain a performance curve model;

[0135] Step S363: performing system scalability evaluation on the performance curve model to obtain system scalability evaluation data; determining resource requirements on the system scalability evaluation data to obtain extended resource requirement data;

[0136] Step S364: Adaptively adjust the distributed financial system according to the extended resource demand data to obtain adaptive scaling data; perform distributed performance monitoring on the distributed financial system based on the adaptive scaling data to obtain distributed performance monitoring data; perform scalability testing on the distributed performance monitoring data to obtain scalability data.

[0137] In an embodiment of the present invention, a load generation tool such as Apache JMeter or Gatling is used to simulate requests under different load conditions, and a system is stress-tested to generate dynamic load simulation data; the carrying capacity of the system, that is, the performance and stability of the system under different load levels, is determined according to the dynamic load simulation data; a system performance curve model is constructed using the system carrying capacity data and reliability data, and specifically statistical analysis and machine learning techniques are used to construct the performance curve model using the scikit-learn library in Python; the performance curve model is analyzed to evaluate the scalability of the system, that is, the degree of improvement in the performance of the system after adding resources, to obtain system scalability evaluation data; the resources required for system expansion are determined according to the system scalability evaluation data, including hardware resources such as servers and network devices, and software resources such as databases and middleware, to form expansion resource demand data; according to the expansion resource demand data, an automatic expansion service of a cloud service provider such as AWS Auto Scaling is used to perform adaptive expansion adjustment on a distributed financial system to obtain adaptive expansion data. At the same time, deploy distributed performance monitoring tools such as Apache SkyWalking to monitor the system in real time and collect distributed performance monitoring data; use distributed performance monitoring data to perform scalability tests, evaluate the performance of the system under high load, and obtain scalability data; specific testing tools can use JMeter or Gatling to test the system's responsiveness and stability by simulating high-concurrency requests.

[0138] Preferably, step S4 comprises the following steps:

[0139] Step S41: performing a multi-dimensional system stability analysis on the distributed financial system based on the reliability data to obtain multi-dimensional system stability data; performing system weakness identification on the multi-dimensional system stability data to generate system weakness data;

[0140] Step S42: tracking the system pressure point of the system weakness data based on the scalability data to obtain system pressure point tracking data; and calculating the system redundancy measurement of the distributed financial system based on the system pressure point tracking data to obtain system redundancy measurement data;

[0141] Step S43: performing transaction lock contention detection on the distributed financial system according to the system redundancy measurement data to obtain transaction lock contention data; performing resource contention conflict identification on the transaction lock contention data to obtain resource contention conflict data; performing system configuration defect analysis on the resource contention conflict data to obtain system configuration defect data; performing system potential problem analysis on the distributed financial system based on the transaction lock contention data and the system configuration defect data to generate system potential problem data;

[0142] Step S44: optimizing the distributed financial system according to the system potential problem data to obtain potential problem optimization data; simulating automated test scenarios for the distributed financial system based on the potential problem optimization data to obtain an automated test scenario set;

[0143] Step S45: Prioritize the automated test scenario set to obtain priority test data; extract automated stress test scenarios from the priority test data to generate automated stress test scenarios; perform performance indicator regression verification on the automated stress test scenarios to obtain a regression test verification report; perform high-pressure environment testing on the distributed financial system based on the regression test verification report to obtain a system test evaluation report.

[0144] In an embodiment of the present invention, a distributed tracing system such as Zipkin or SkyWalking is used to collect performance indicators of each service node in a distributed financial system, including response time, throughput, and error rate, etc., to obtain multi-dimensional system stability data; by analyzing the multi-dimensional system stability data, the weaknesses of the system are identified and system weakness data is generated; based on the scalability data and the system weakness data, a performance testing tool such as Apache JMeter is used to simulate the system performance under different load conditions, and system pressure point tracking is performed to obtain system pressure point tracking data; the redundancy measurement of the system is calculated according to the system pressure point tracking data, thereby obtaining system redundancy measurement data; a distributed lock solution technology such as Redis or ZooKeeper is used to specifically detect the transaction processing lock competition in the distributed financial system to obtain transaction lock competition data; and the transaction lock competition data is analyzed to identify resource contention conflicts and obtain resource contention conflict data; the resource contention conflict data is further analyzed to identify system configuration defects and obtain system configuration defect data; in combination with the transaction lock competition data and the system configuration defect data, a machine learning algorithm is used to perform pattern recognition and anomaly detection, specifically analyze potential problems of the system, and generate system potential problem data. According to the system potential problem data, apply system tuning technologies such as load balancing, database optimization and code optimization to optimize the distributed financial system and obtain potential problem optimization data; based on the potential problem optimization data, use automated testing frameworks such as Selenium or JUnit to simulate different test scenarios and obtain a set of automated testing scenarios; use test management tools such as Jira or TestRail to prioritize the set of automated testing scenarios and obtain priority test data; extract key test scenarios based on the priority test data and perform automated stress testing to generate automated stress testing scenarios; collect performance indicator data through performance testing tools, perform performance indicator regression verification on automated stress testing scenarios, and obtain a regression test verification report; based on the regression test verification report, use stress testing tools such as Apache JMeter or Gatling to test the distributed financial system in a simulated high-pressure environment to obtain a system test evaluation report.

[0145] Preferably, step S44 includes the following steps:

[0146] Step S441: performing distributed lock optimization processing on the distributed financial system according to the system potential problem data to obtain distributed lock optimization data; performing transaction isolation level adjustment on the distributed lock optimization data to obtain isolation level adjustment data;

[0147] Step S442: intelligently optimizing the system configuration parameters of the distributed financial system according to the system potential problem data to obtain configuration parameter optimization data; dynamically allocating system resources on the configuration parameter optimization data to obtain resource dynamic allocation data; and implementing load balancing on the resource dynamic allocation data to obtain load balancing implementation data;

[0148] Step S443: performing system hotspot automatic dispersion on the load balancing implementation data to obtain hotspot automatic dispersion data; performing system redundancy automatic adjustment on the hotspot automatic dispersion data to obtain redundancy automatic adjustment data; performing fault domain isolation optimization on the redundancy automatic adjustment data to obtain fault domain isolation optimization data; performing system fault tolerance adjustment on the distributed financial system according to the fault domain isolation optimization data to obtain system fault tolerance adjustment data;

[0149] Step S444: Integrate the isolation level adjustment data, the load balancing implementation data and the system fault tolerance adjustment data to obtain potential problem optimization data;

[0150] Step S445: Based on the potential problem optimization data, the distributed financial system is adapted to distributed scenarios to obtain distributed scenario adaptation data; the distributed scenario adaptation data is mapped to automated test cases to obtain an automated test case set; the automated test case set is integrated with test scenario simulation to obtain an automated test scenario set.

[0151] In an embodiment of the present invention, a distributed lock management system, such as Redis or ZooKeeper, is used to optimize the lock mechanism in a distributed financial system; specifically, by adjusting the lock granularity and the lock timeout time, lock competition is reduced, thereby improving the efficiency of transaction processing; at the same time, the transaction isolation level of the distributed lock optimization data is adjusted to obtain isolation level adjustment data; machine learning algorithms and performance testing tools, such as Apache JMeter, are used to intelligently tune system configuration parameters, and by real-time monitoring of system performance indicators, configuration parameters, such as thread pool size, memory allocation, etc., are dynamically adjusted to adapt to changes in system load; based on the configuration parameter tuning data, container orchestration tools such as Kubernetes are used to dynamically allocate system resources; load balancing is implemented in combination with load balancing technology, such as Nginx or HAProxy, to obtain load balancing implementation data to prevent single point overload; hotspot detection and automatic dispersion technology are used to analyze load balancing implementation data to avoid local overheating of system resources; fault domain isolation optimization technology, such as data center level isolation, is combined to ensure that faults are isolated and distributed. It can quickly locate and isolate fault points when they occur; adjust the system fault tolerance of distributed financial systems through redundant mechanisms and failover strategies; integrate isolation level adjustment data, load balancing implementation data and system fault tolerance adjustment data to obtain potential problem optimization data; adapt distributed scenarios to distributed financial systems based on potential problem optimization data; use automated testing frameworks such as Selenium or JUnit to perform automated test case mapping on adapted scenarios; perform test scenario simulation integration on automated test case sets to obtain automated test scenario sets; verify the stability and reliability of the system by simulating various distributed scenarios, such as network partitions, service failures, etc.

[0152] In this specification, a system based on distributed non-functional testing of a financial system is provided, which is used to execute the above-mentioned method based on distributed non-functional testing of a financial system. The system based on distributed non-functional testing of a financial system includes:

[0153] The transaction process transaction processing test module is used to collect original transaction data of the distributed financial system to obtain the original transaction data set; perform data preprocessing on the original transaction data set to obtain the standardized transaction data set; perform key node identification on the standardized transaction data set to obtain key node data; perform transaction process transaction processing test on the distributed financial system based on the key node data to obtain transaction processing efficiency data;

[0154] The distributed performance monitoring module is used to perform distributed stress simulation on the distributed financial system based on the transaction processing efficiency data to obtain stress test data; perform distributed performance monitoring on the distributed financial system based on the stress test data to obtain performance monitoring data; perform performance bottleneck diagnosis on the performance monitoring data to obtain performance bottleneck data;

[0155] The dynamic circuit breaker strategy adjustment module is used to use the performance bottleneck data to perform circuit breaker evaluation on the distributed financial system and generate circuit breaker evaluation data; perform adaptive circuit breaker adjustment on the distributed financial system according to the circuit breaker evaluation data to obtain dynamic circuit breaker strategy data; perform reliability analysis on the distributed financial system according to the dynamic circuit breaker strategy data to obtain reliability data; perform scalability test on the distributed financial system based on the stress test data and reliability data to obtain scalability data;

[0156] The system potential problem optimization module is used to identify system potential problems of the distributed financial system based on reliability data and scalability data, and generate system potential problem data; optimize the distributed financial system according to the system potential problem data to obtain potential problem optimization data; simulate the distributed financial system in automated test scenarios based on the potential problem optimization data to obtain an automated test scenario set; perform regression testing on the automated test scenario set to obtain a regression testing verification report; perform high-voltage environment testing on the distributed financial system according to the regression testing verification report to obtain a system testing evaluation report.

[0157] The present invention collects original transaction data of the distributed financial system through the transaction process transaction processing test module, and can obtain all transaction records generated when the system is running; pre-processes the original transaction data set to form an accurate, consistent and standardized transaction data set, which provides a reliable data basis for subsequent analysis; analyzes the standardized transaction data set to identify the key nodes in the transaction process, wherein the key nodes are the key factors of system performance and stability, and can accurately locate the bottlenecks that affect system performance; performs transaction processing efficiency testing on key node data to evaluate the performance of the system when processing transactions. The distributed performance monitoring module uses transaction processing efficiency data to perform distributed stress simulation on the system, simulating the performance of the system under high load, especially simulating the performance of the system under extreme conditions; performs distributed performance monitoring based on stress test data, collects various performance indicators of the system in actual operation, and provides data support for the diagnosis of performance bottlenecks; performs performance bottleneck diagnosis on performance monitoring data, clearly diagnoses the performance bottleneck of the system, and thus identifies the vulnerable points of the system under high load. The dynamic circuit breaker strategy adjustment module uses performance bottleneck data to evaluate the system's circuit breaker to determine under what circumstances the circuit breaker mechanism needs to be activated to protect the system; the distributed financial system is adaptively adjusted based on the circuit breaker evaluation data, and the circuit breaker threshold can be automatically adjusted to adapt to different system load conditions; then, the system is reliability analyzed based on the dynamic circuit breaker strategy data to clarify the stability and reliability of the system, ensuring that the system can maintain normal operation in the face of uncertainty and high load; the distributed financial system is scalable based on stress test data and reliability data to verify the performance of the system during expansion and ensure that the system can smoothly expand with the growth of business needs. Through the system potential problem optimization module, the system potential problem data is used to optimize the distributed financial system, solve the problems found during the test, and thus improve the stability and performance of the system; based on the potential problem optimization data, the automated test scenario simulation is performed to ensure the effectiveness of the optimization measures; the automated test scenario set is regression tested to ensure that the optimized system can meet the functional and performance requirements; finally, the distributed financial system is tested in a high-pressure environment based on the regression test verification report to obtain a system test evaluation report, thereby accurately evaluating the system's ability to cope with high-pressure environments. Therefore, the present invention uses Internet of Things technology, data analysis technology, intelligent detection technology and automation technology to achieve multi-dimensional detection of potential performance bottlenecks of distributed financial systems, and introduces a fuse mechanism to automatically protect the system; thereby achieving accurate evaluation of the response capabilities of distributed financial systems in high-pressure environments and the effectiveness of fuse strategies.

[0158] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0159] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for non-functional testing of a distributed financial system, characterized in that: The following steps are involved: Step S1: collecting original transaction data from a distributed financial system to obtain an original transaction data set; performing data preprocessing on the original transaction data set to obtain a standardized transaction data set; identifying key nodes on the standardized transaction data set to obtain key node data; Conduct transaction processing tests on the distributed financial system based on key node data to obtain transaction processing efficiency data; Step S2: Perform distributed stress simulation on the distributed financial system according to the transaction processing efficiency data to obtain stress test data; perform distributed performance monitoring on the distributed financial system based on the stress test data to obtain performance monitoring data; Perform performance bottleneck diagnosis on performance monitoring data to obtain performance bottleneck data; Step S3: Use the performance bottleneck data to perform a circuit breaker assessment on the distributed financial system to generate circuit breaker assessment data; perform adaptive circuit breaker adjustment on the distributed financial system according to the circuit breaker assessment data to obtain dynamic circuit breaker strategy data; perform reliability analysis on the distributed financial system according to the dynamic circuit breaker strategy data to obtain reliability data; perform scalability testing on the distributed financial system based on the stress test data and the reliability data to obtain scalability data; Step S4: Identify potential system problems of the distributed financial system based on the reliability data and scalability data, and generate system potential problem data; Optimize the distributed financial system based on the system potential problem data to obtain potential problem optimization data; Based on the potential problem optimization data, the distributed financial system is simulated with automated test scenarios to obtain an automated test scenario set; the automated test scenario set is verified with regression testing to obtain a regression test verification report; According to the regression test verification report, the distributed financial system is tested in a high-pressure environment to obtain a system test evaluation report.

2. The method for non-functional testing of a distributed financial system according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Capture transaction records of the distributed financial system to obtain transaction record data; monitor transaction logs of the distributed financial system to obtain transaction log data; integrate the transaction record data and the transaction log data to obtain an original transaction data set; Step S12: performing data preprocessing on the original transaction data set to obtain a standardized transaction data set, wherein the data preprocessing includes data decryption, data cleaning, data denoising and data standardization; Step S13: extract multi-dimensional interactive features from the standardized transaction data set to obtain transaction feature enhancement data; perform node status recognition on the transaction feature enhancement data to obtain node status data; perform influence analysis on the node status data to obtain node influence data; Step S14: determining key influencing factors of the node influence data to obtain key node data; identifying transaction process characteristics of the key node data to obtain transaction process characteristic data; mapping the key node data to the transaction process according to the transaction process characteristic data to generate transaction process mapping data; Step S15: Analyze the transaction path of the distributed financial system based on the transaction process mapping data to obtain transaction execution path data; perform concurrent processing on the transaction execution path data to generate concurrent transaction processing data; calculate the processing delay of the concurrent transaction processing data to obtain concurrent processing delay data; perform transaction processing efficiency statistics on the concurrent transaction processing data according to the concurrent processing delay data to obtain transaction processing efficiency data.

3. The method for distributed financial system non-functional testing according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Generate distributed scenario stress parameters for the distributed financial system according to the transaction processing efficiency data to obtain stress test parameters; Step S22: Perform distributed transaction request simulation on the stress test parameters to obtain simulated transaction request data; perform node throughput analysis on the simulated transaction request data to generate node throughput data; perform node response time test on the simulated transaction request data according to the node throughput data to generate node response time data; Step S23: capturing the data delay between nodes on the simulated transaction request data according to the node response time data to obtain node delay analysis data; performing a node stress response test on the simulated transaction request data according to the node throughput data to obtain node stress response data; integrating the node delay analysis data and the node stress response data to obtain stress test data; Step S24: performing distributed link tracing on the stress test data to obtain distributed link tracing data; performing resource scheduling analysis on the distributed financial system based on the distributed link tracing data to obtain system resource scheduling data; performing distributed performance monitoring on the distributed financial system based on the system resource scheduling data to obtain performance monitoring data; Step S25: diagnose the performance bottleneck of the distributed financial system according to the performance monitoring data to obtain performance bottleneck data.

4. The method for distributed financial system non-functional testing according to claim 3, characterized in that: Step S25 includes the following steps: Step S251: performing time series analysis on the performance monitoring data to obtain time series performance data; performing trend identification on the distributed financial system based on the time series performance data to obtain system performance trend data; Step S252: performing abnormal fluctuation detection on the system performance trend data to obtain system abnormal fluctuation data; performing bottleneck point location processing on the system abnormal fluctuation data to obtain system bottleneck point data; performing load analysis on the system bottleneck point data to obtain system load analysis data; Step S253: performing resource contention analysis on the system load analysis data to obtain system resource contention data; performing fine-grained analysis on the system resource contention data to obtain fine-grained resource contention data; Step S254: performing resource competition pattern matching on the fine-grained resource competition data to obtain resource competition pattern data; automatically classifying the resource competition pattern data to obtain classified competition data; Step S255: tracing the root cause of the problem on the classified competition data to obtain root cause tracing data; performing performance diagnosis on the performance monitoring data according to the root cause tracing data to obtain performance bottleneck data.

5. The method for distributed financial system non-functional testing according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing threshold analysis on the performance bottleneck data to obtain threshold setting data; performing risk identification on the threshold setting data to obtain risk identification data; performing fuse condition delineation on the risk identification data to obtain fuse condition data; Step S32: Perform trigger mechanism analysis on the circuit breaker condition data to obtain trigger mechanism data; perform event statistics on the trigger mechanism data to obtain event statistical data; perform circuit breaker evaluation on the distributed financial system based on the event statistical data to generate circuit breaker evaluation data; Step S33: Adaptively adjust the circuit breaker of the distributed financial system according to the circuit breaker assessment data to obtain dynamic circuit breaker strategy data; Step S34: measuring the complexity of the distributed financial system according to the dynamic circuit breaker strategy data to obtain complexity assessment data; performing potential fault path analysis on the complexity assessment data to obtain fault path data; Step S35: Delimiting the impact range of the fault path data to obtain impact range data; performing stability testing on the distributed financial system according to the impact range data to obtain stability test results; performing reliability verification on the stability test results to obtain reliability data; Step S36: Perform scalability testing on the distributed financial system based on the stress test data and reliability data to obtain scalability data.

6. The method for distributed financial system non-functional testing according to claim 5, characterized in that: Step S33 includes the following steps: Step S331: Perform real-time distributed monitoring of the distributed financial system according to the circuit breaker assessment data to obtain circuit breaker triggering status data; Step S332: performing a system throughput anomaly detection on the distributed financial system according to the circuit breaker trigger status data to obtain system throughput anomaly data; performing a transaction delay detection on the distributed financial system according to the system throughput anomaly data to obtain system transaction delay data; performing a transaction service error detection on the distributed financial system according to the system transaction delay data to obtain system transaction service error data; Step S333: Integrate the system throughput abnormality data, the system transaction delay data and the system transaction service error data to obtain the system abnormality status data; perform abnormal fault isolation on the distributed financial system according to the system abnormality status data to obtain fault isolation data; Step S334: automatically locate the fault point on the fault isolation data to obtain fault point location data; perform impact analysis on the fault point location data to obtain fault impact data; perform system service impact analysis on the fault impact data to obtain system service impact data; perform dynamic service switching processing on the distributed financial system according to the system service impact data to obtain automatic service switching data; perform standby resource allocation on the automatic service switching data to obtain standby resource allocation data; Step S335: performing a service recovery preview on the backup resource deployment data to obtain service recovery preview data; performing a fault injection test on the distributed financial system according to the service recovery preview data to obtain fault injection test data; performing automatic recovery execution on the fault injection test data to obtain automatic recovery execution data; Step S336: Perform system performance evaluation on the distributed financial system according to the automatic recovery execution data to obtain performance evaluation data; extract abnormal performance data from the performance evaluation data to obtain system abnormal performance data; perform system service degradation processing on the system abnormal performance data to obtain service degradation data; perform resource reorganization on the service degradation data to obtain resource reorganization data; perform dynamic circuit breaker adjustment on the distributed financial system according to the resource reorganization data to obtain dynamic circuit breaker policy data.

7. The method for distributed financial system non-functional testing according to claim 5, characterized in that: Step S36 includes the following steps: Step S361: Perform dynamic load simulation on the stress test data to obtain dynamic load simulation data; perform system load capacity measurement on the distributed financial system according to the dynamic load simulation data to obtain system load capacity data; Step S362: Performing system performance curve modeling on the distributed financial system based on the system carrying capacity data and reliability data to obtain a performance curve model; Step S363: performing system scalability evaluation on the performance curve model to obtain system scalability evaluation data; determining resource requirements on the system scalability evaluation data to obtain extended resource requirement data; Step S364: Adaptively adjust the distributed financial system according to the extended resource demand data to obtain adaptive scaling data; perform distributed performance monitoring on the distributed financial system based on the adaptive scaling data to obtain distributed performance monitoring data; perform scalability testing on the distributed performance monitoring data to obtain scalability data.

8. The method for distributed financial system non-functional testing according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing a multi-dimensional system stability analysis on the distributed financial system based on the reliability data to obtain multi-dimensional system stability data; performing system weakness identification on the multi-dimensional system stability data to generate system weakness data; Step S42: tracking the system pressure point of the system weakness data based on the scalability data to obtain system pressure point tracking data; and calculating the system redundancy measurement of the distributed financial system based on the system pressure point tracking data to obtain system redundancy measurement data; Step S43: performing transaction lock contention detection on the distributed financial system according to the system redundancy measurement data to obtain transaction lock contention data; performing resource contention conflict identification on the transaction lock contention data to obtain resource contention conflict data; performing system configuration defect analysis on the resource contention conflict data to obtain system configuration defect data; performing system potential problem analysis on the distributed financial system based on the transaction lock contention data and the system configuration defect data to generate system potential problem data; Step S44: optimizing the distributed financial system according to the system potential problem data to obtain potential problem optimization data; simulating automated test scenarios for the distributed financial system based on the potential problem optimization data to obtain an automated test scenario set; Step S45: Prioritize the automated test scenario set to obtain priority test data; extract automated stress test scenarios from the priority test data to generate automated stress test scenarios; perform performance indicator regression verification on the automated stress test scenarios to obtain a regression test verification report; perform high-pressure environment testing on the distributed financial system based on the regression test verification report to obtain a system test evaluation report.

9. The method for distributed non-functional testing of financial systems according to claim 8, characterized in that: Step S44 includes the following steps: Step S441: performing distributed lock optimization processing on the distributed financial system according to the system potential problem data to obtain distributed lock optimization data; performing transaction isolation level adjustment on the distributed lock optimization data to obtain isolation level adjustment data; Step S442: intelligently optimizing the system configuration parameters of the distributed financial system according to the system potential problem data to obtain configuration parameter optimization data; dynamically allocating system resources on the configuration parameter optimization data to obtain resource dynamic allocation data; and implementing load balancing on the resource dynamic allocation data to obtain load balancing implementation data; Step S443: performing system hotspot automatic dispersion on the load balancing implementation data to obtain hotspot automatic dispersion data; performing system redundancy automatic adjustment on the hotspot automatic dispersion data to obtain redundancy automatic adjustment data; performing fault domain isolation optimization on the redundancy automatic adjustment data to obtain fault domain isolation optimization data; performing system fault tolerance adjustment on the distributed financial system according to the fault domain isolation optimization data to obtain system fault tolerance adjustment data; Step S444: Integrate the isolation level adjustment data, the load balancing implementation data and the system fault tolerance adjustment data to obtain potential problem optimization data; Step S445: Based on the potential problem optimization data, the distributed financial system is adapted to distributed scenarios to obtain distributed scenario adaptation data; the distributed scenario adaptation data is mapped to automated test cases to obtain an automated test case set; the automated test case set is integrated with test scenario simulation to obtain an automated test scenario set.

10. A system based on distributed non-functional testing of financial systems, characterized in that: A method for performing a distributed financial system non-functional test as claimed in claim 1, wherein the distributed financial system non-functional test system comprises: The transaction process transaction processing test module is used to collect original transaction data of the distributed financial system to obtain the original transaction data set; perform data preprocessing on the original transaction data set to obtain the standardized transaction data set; perform key node identification on the standardized transaction data set to obtain key node data; perform transaction process transaction processing test on the distributed financial system based on the key node data to obtain transaction processing efficiency data; The distributed performance monitoring module is used to perform distributed stress simulation on the distributed financial system based on the transaction processing efficiency data to obtain stress test data; perform distributed performance monitoring on the distributed financial system based on the stress test data to obtain performance monitoring data; perform performance bottleneck diagnosis on the performance monitoring data to obtain performance bottleneck data; The dynamic circuit breaker strategy adjustment module is used to use the performance bottleneck data to perform circuit breaker evaluation on the distributed financial system and generate circuit breaker evaluation data; perform adaptive circuit breaker adjustment on the distributed financial system according to the circuit breaker evaluation data to obtain dynamic circuit breaker strategy data; perform reliability analysis on the distributed financial system according to the dynamic circuit breaker strategy data to obtain reliability data; perform scalability test on the distributed financial system based on the stress test data and reliability data to obtain scalability data; The system potential problem optimization module is used to identify system potential problems of the distributed financial system based on reliability data and scalability data, and generate system potential problem data; optimize the distributed financial system according to the system potential problem data to obtain potential problem optimization data; simulate the distributed financial system in automated test scenarios based on the potential problem optimization data to obtain an automated test scenario set; perform regression testing on the automated test scenario set to obtain a regression testing verification report; perform high-voltage environment testing on the distributed financial system according to the regression testing verification report to obtain a system testing evaluation report.

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