System business process analysis method

Abstract

The invention relates to the technical field of information, and discloses a system business process analysis method, which comprises the following steps that: an interface module receives a user instruction and forwards the user instruction to a corresponding processing module according to the type, a data acquisition module is started to collect business process interaction data and transmit the business process interaction data to a filtering module, and the filtering module screens the data according to a configuration strategy; effective information is reserved, the analysis module formats effective data and extracts key fields to generate a time sequence diagram and a detailed analysis result, and the interface module returns the analysis result to an upper-layer user. Through the technical means of centralized log processing, context information adding, log header structuring and the like, the problems that in an existing log analysis method, log files are dispersed, and context information is lacked are solved, and the problem that network packet capture analysis cannot deeply analyze an internal module interaction process is solved. Compared with a traditional method, the method has remarkable advantages in accuracy, real-time performance and processing efficiency.

Description

Technical Field

[0001] The invention belongs to the field of information technology, and in particular relates to a system business process analysis method. Background Art

[0002] Currently, the business process analysis of most systems is based on system log analysis methods and network packet capture analysis methods.

[0003] The analysis method based on system logs is one of the most common system maintenance and management methods. It mainly screens and analyzes the log files output by the operating system, network equipment, and various application modules, and finally obtains information such as the system operation status and detailed business process description.

[0004] The analysis method based on network packet capture mainly uses some commonly known network packet capture tools, such as tcpdump, tshark, wireshark, etc., to capture network data packets on network nodes involved in business processes; then use specific tools to parse and analyze the contents of network data packets to understand network performance indicators such as network data transmission volume, network delay, throughput, packet loss rate, response speed, and related business detailed process descriptions.

[0005] Currently, the two analysis methods based on system log analysis and network packet capture have certain processing capabilities for system maintenance management, anomaly detection, system hidden vulnerability discovery, and comprehensive analysis of business processes. However, the biggest problems of these two analysis methods are:

[0006] 1. The system fault analysis, problem location, and business process analysis processes are relatively complex and the analysis efficiency is low.

[0007] 2. The original data source based on the network packet capture analysis method is limited. This method is limited to the analysis of network interface device data and is powerless to analyze the interaction process between internal modules of the system.

[0008] 3. Currently, the known analysis tools based on network packet capture are all based on stand-alone versions, which can only run analysis on a single server and cannot provide full-business, full-process centralized analysis services for system management, engineering maintenance, system testing and other related personnel.

[0009] The main reasons leading to the above problems are as follows:

[0010] 1. Since the system is generally composed of multiple business processing modules, different business modules often run as independent processes, resulting in a large number of log files with various types and inconsistent data formats, which seriously affects the analysis efficiency.

[0011] 2. The log files generated by different modules and components of the system are stored in different directories and files. At the same time, most log files only record the events and operations that occur in the system, lack sufficient contextual information, and the correlation between log files is very weak.

[0012] 3. In a concurrent environment, the logging operations of multiple modules are also performed in parallel. Due to reasons such as operating system process scheduling and switching, the logs generated by different business modules often appear in reverse order, increasing the difficulty of business process analysis.

[0013] In view of this, in order to improve the efficiency of system business process analysis, quickly and accurately locate fault problems, improve system maintainability, and overcome the problem of limited data source in network packet capture analysis method, a system business process analysis method is proposed. Summary of the invention

[0014] The present invention aims to solve the technical problems in the above-mentioned prior art that system log analysis is complex and inefficient, inconsistent log file formats and lack of context information make analysis difficult, network packet capture analysis is limited to network interface data and cannot go deep into the system's internal module interaction process, and existing tools cannot provide centralized analysis services for the entire business and the entire process, affecting system maintenance and testing efficiency.

[0015] To achieve the above object, the present invention provides the following technical solutions:

[0016] A system business process analysis method comprises the following steps:

[0017] S1. Receiving service request instructions: The process analysis service calling interface module receives the request instructions from the upper-level user. The process analysis service interface module forwards the instructions to the filtering policy configuration module or the service start and stop control module for processing according to the type of the instructions received;

[0018] S2. Process interaction data collection: After receiving the service start command, the service start and stop control module starts the business process interaction data collection module and submits the collected process interaction data to the process interaction data filtering module for processing;

[0019] S3, process interaction data filtering: After receiving the original collected data, the process interaction data filtering module filters the business process data according to the filtering configuration strategy, and at the same time, sends the valid data to the process analysis and process output module for further processing;

[0020] S4, process analysis and output: After receiving valid data, the data analysis and processing module formats the data, extracts key fields and parameters from the data, and constructs a business process sequence diagram and a detailed analysis result file;

[0021] S5. Request instruction response: After receiving the analysis results from process analysis and output, the process analysis service calling interface module returns the results to the upper-level user.

[0022] Preferably, in step S1, the process analysis service calling interface module receives a service request instruction sent by an upper-level user through an API, and the upper-level user's request instruction includes service start and stop and filtering policy configuration.

[0023] Preferably, the collected process interaction data includes API call records, database operation logs, and message queue communication content.

[0024] Preferably, in step S3, the filtering configuration policy is stored in a database, a configuration file or a remote configuration server;

[0025] The filtering conditions of the filtering configuration policy include protocol type, port, module name, message name, device ID, and user ID.

[0026] Preferably, in step S3, the filtering of the business process data is to filter the data irrelevant to the business process and invalid data.

[0027] Preferably, in step S4, the data analysis and processing module formats the filtered valid data and converts it into JSON or XML format.

[0028] Compared with the analysis method based on network packet capture and system log, the technical effects and advantages of the present invention are:

[0029] The system business process analysis method can analyze the external communication of the system and the interaction process between different modules within the system in real time, which means that it can monitor and process data instantly instead of relying solely on historical data. In contrast, analysis based on system logs is usually post hoc and cannot provide real-time feedback and adjustments. Real-time data flow analysis can quickly capture the current state and changes of the system, which is crucial for systems that require real-time performance.

[0030] The system business process analysis method integrates multiple data sources and multiple data types, and the present invention can provide more comprehensive and accurate data analysis results. System logs usually only record events and operations that occur in the system, lacking sufficient context information, while the present invention improves the accuracy of analysis by adding context information. The use of intelligent analysis technology, including data association and intelligent inference, reduces the possibility of misjudgment and false alarm, and improves the accuracy of analysis results.

[0031] The business process analysis method of the system is not limited to the network data interface. It can simultaneously receive real-time data streams from different data sources, including file systems, external devices, sensor data, etc., which provides a broader data basis for analysis. The data source of the analysis method based on network packet capture is limited, while the present invention can cover the process data analysis within the system, which means that it can analyze other system internal interactions outside the network data interface, providing a deeper insight into the system behavior.

[0032] In summary, the present invention provides obvious advantages in terms of real-time performance, accuracy and data source, making system business process analysis more efficient and accurate, and adaptable to a wider range of scenario requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a business process analysis structure diagram of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] The analysis method based on system logs is a post-analysis method based on historical data. The post-analysis method mainly relies on historical data for judgment and decision-making, and cannot provide the current situation and changes in real time. The analysis method lacks real-time feedback and adjustment. For some systems with high real-time performance requirements, the post-analysis method based on system logs is not applicable. In addition, the system log data only records the events and operations that occurred in the system, lacking sufficient contextual information. When processing log event analysis, the analysis method based on system logs often results in misjudgments and false alarms.

[0036] The analysis method based on network packet capture has improved the real-time requirements, but the data source received by this method is limited to the network data interface, lacks the ability to analyze process data within the system, and has limited usage scenarios. In addition, the analysis method based on network packet capture often requires manual intervention during the analysis process, which takes a lot of time and resources to screen and process huge network data, and the analysis efficiency is not high.

[0037] Compared with the analysis method based on network packet capture and system log, a system business process analysis method is proposed which has advantages in real-time, accuracy and data source.

[0038] The analysis system of the business process analysis method of the system is mainly composed of processing modules such as business process interaction data acquisition module, process interaction data filtering and processing module, process analysis and output module, filtering strategy configuration module, service start and stop control module and process analysis service call interface module;

[0039] The business process interaction data collection module is responsible for collecting relevant process interaction data in real time from the file subsystem, network subsystem, module communication queue and other subsystems.

[0040] The process data filtering processing module filters the process data according to the filtering strategy set by the user (including filtering conditions such as protocol type, port, module name, message name, device identification, user identification, etc.).

[0041] The process analysis and output module is the core module of the entire solution. It is responsible for the detailed analysis and processing of the filtered process interaction data. It obtains the business process timing diagram and message interaction process detailed analysis file required by the user based on the content analysis of the interaction data.

[0042] The following is combined with Figure 1 Provide further details of this application;

[0043] The present application embodiment discloses a system business process analysis method, comprising the following steps:

[0044] S1. Receive service request instructions: The process analysis service call interface module receives request instructions from upper-level users. The request instructions are divided into two categories: service start and stop and filtering policy configuration. The process analysis service interface module forwards the instructions to the filtering policy configuration module or the service start and stop control module for processing according to the type of instructions received.

[0045] S2. Process interaction data collection: After receiving the service start command, the service start and stop control module starts the business process interaction data collection module and submits the collected process interaction data to the process interaction data filtering module for processing;

[0046] S3, Process interaction data filtering: After receiving the original collected data, the process interaction data filtering module filters the data irrelevant to the business process and invalid data according to the filtering configuration strategy, and sends the valid data to the process analysis and process output module for further processing;

[0047] S4, process analysis and output: After receiving valid data, the data analysis and processing module formats the data, extracts key fields and parameters from the data, and constructs a business process sequence diagram and a detailed analysis result file;

[0048] S5. Request instruction response: After receiving the analysis results from process analysis and output, the process analysis service calling interface module returns the results to the upper-level user.

[0049] The present invention adopts a series of technical means to solve the problems of log file dispersion and lack of context information in existing log analysis methods:

[0050] 1. Centralized log processing: By designing a log processing method and device, the present invention can centrally process log information dispersedly stored in different directories and files, thereby unifying the management and analysis process of log data.

[0051] 2. Adding context information: In the process of logging, the present invention proposes a method for obtaining context information of business requests. Specifically, it extracts context information from business requests and obtains target parameters from them. These target parameters can indicate the target object situation and business-related situation when executing the business request, thereby enriching the context information of the log.

[0052] 3. Structured log header: The method in the present invention adds the extracted target parameters to the log header, so that the log header contains not only basic information such as timestamp and log level, but also business-related context information. Such a structured log header is then added to the log body, forming a log file with a clearer structure and more comprehensive information.

[0053] Through the above technical means, the present invention not only solves the problem of log file dispersion, but also greatly improves the availability of logs, so that log data can provide more complete and relevant information, which is convenient for subsequent analysis and maintenance. Such a design makes system business process analysis more efficient and accurate, reduces misjudgments and false alarms, and thus improves the maintainability of the entire system.

[0054] The system business process analysis method is a real-time data flow analysis method based on multiple inputs, multiple data, and full processes. When implementing real-time data flow analysis, the present invention ensures the comprehensiveness and timeliness of data by integrating real-time data flows from different data sources (such as file systems, network systems, external devices, sensor data, real-time events, etc.). Before analysis, the received data is formatted, key fields and parameters are extracted, and converted into structured data. This step reduces redundant information and improves the efficiency of data processing. By constructing a business process timing diagram, the present invention can monitor and analyze the interaction between different modules in real time, thereby ensuring the accuracy of data processing, and helps to quickly locate problems. Intelligent analysis technology, including machine learning and data mining algorithms, is used to comprehensively analyze data sources, and the relationship between different data is associated, realizing automated analysis and intelligent inference. In a concurrent environment, parallel processing technology is used to analyze multiple data streams, reducing processing delays and improving the speed of data processing. The present invention provides a real-time feedback mechanism that can make immediate adjustments to problems that arise during the analysis process, thereby maintaining the accuracy and efficiency of the analysis. Through these technical means, the present invention can quickly and effectively process and analyze real-time data streams while ensuring data accuracy, providing strong support for system business process analysis.

[0055] The business process analysis method of this system is a method that comprehensively utilizes multiple data sources and multiple data types and performs real-time analysis in the entire data processing process. Compared with the existing technology, it has the following advantages:

[0056] 1. Multiple input data sources: Receive real-time data streams from different data sources at the same time, including but not limited to file systems, network systems, external devices, sensor data, real-time events and other inputs.

[0057] 2. Multi-data full-process analysis: Stream data of different types and formats collected from different data sources and different internal modules in real time are uniformly converted into structured data for easy processing.

[0058] 3. Automated analysis process: This method uses intelligent analysis technology to conduct a comprehensive analysis of data sources and data types, and through the association of the relationship between different data, it realizes automated analysis and intelligent inference of data, reduces manual intervention, and improves analysis efficiency and accuracy.

[0059] This method is a method that comprehensively utilizes multiple data sources and multiple data types to conduct real-time analysis of the external communication of the system and the interaction process between different modules within the system. On the one hand, this method solves the problems of low accuracy, low real-time performance, and processing delays in the analysis method based on system logs. On the other hand, it solves the problem of interaction process analysis between internal modules of the system that cannot be solved by the analysis method of network packet capture.

[0060] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A system business process analysis method, characterized in that: The steps include: S1. Receiving service request instructions: The process analysis service calling interface module receives the request instructions from the upper-level user. The process analysis service interface module forwards the instructions to the filtering policy configuration module or the service start and stop control module for processing according to the type of the instructions received; S2. Process interaction data collection: After receiving the service start command, the service start and stop control module starts the business process interaction data collection module and submits the collected process interaction data to the process interaction data filtering module for processing; S3, process interaction data filtering: After receiving the original collected data, the process interaction data filtering module filters the business process data according to the filtering configuration strategy, and at the same time, sends the valid data to the process analysis and process output module for further processing; S4, process analysis and output: After receiving valid data, the data analysis and processing module formats the data, extracts key fields and parameters from the data, and constructs a business process sequence diagram and a detailed analysis result file; S5. Request instruction response: After receiving the analysis results from process analysis and output, the process analysis service calling interface module returns the results to the upper-level user.

2. A system business process analysis method according to claim 1, characterized in that: In step S1, the process analysis service calling interface module receives service request instructions sent by the upper-level user through the API. The upper-level user's request instructions include service start and stop and filtering policy configuration.

3. A system business process analysis method according to claim 1, characterized in that: The collected process interaction data includes API call records, database operation logs, and message queue communication content.

4. A system business process analysis method according to claim 1, characterized in that: In step S3, the filtering configuration policy is stored in a database, a configuration file or a remote configuration server; The filtering conditions of the filtering configuration policy include protocol type, port, module name, message name, device ID, and user ID.

5. A system business process analysis method according to claim 1, characterized in that: In step S3, the filtering of the business process data is to filter out data irrelevant to the business process and invalid data.

6. A system business process analysis method according to claim 1, characterized in that: In step S4, the data analysis and processing module formats the filtered valid data and converts it into JSON or XML format.

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