Business anomaly detection system based on pattern matching

Abstract

The invention provides a service exception detection system based on mode matching. The service exception detection system comprises a data acquisition module, a data preprocessing module, a mode definition module, a mode matching engine, an exception detection and alarm module, a report generation module and a subsequent processing interface, according to the method, mode matching and feature extraction are carried out on the data by utilizing the mode matching engine, so that the system can identify abnormal behaviors more accurately, and the situations of misinformation and missing report are reduced; data are collected and processed in real time through the data collection module, abnormal events are found rapidly, corresponding measures are taken for prevention and control, and safe and stable operation of a service system is guaranteed; by optimizing an algorithm and a data processing flow, the calculation cost is reduced, and the efficiency and the performance of the system are improved; and a friendly user interface can be provided by utilizing the report generation module and the subsequent processing interface, and an anomaly detection result and a report are displayed through data visualization, so that a user can intuitively know the operation state and the anomaly condition of the service system.

Description

Technical Field

[0001] The present invention relates to a business anomaly detection system, in particular to a business anomaly detection system based on pattern matching, and belongs to the technical field of business monitoring. Background Art

[0002] Data collection: responsible for obtaining data information from various data sources (such as logs, databases, systems). These data may include transaction records, behavior records, system status information, etc.

[0003] Anomaly detection and alarm notification: refers to the alarm information sent to the terminal device when the collected data is analyzed and the analysis meets the preset conditions, including the alarm type, alarm time and alarm indicator. In addition, a report can be generated to further investigate and analyze the root cause of the anomaly.

[0004] Business anomaly detection mainly uses data analysis technology to identify behaviors that do not conform to normal patterns in business processes. This technology can help companies promptly discover and respond to potential risks, fraud, system errors or other abnormal situations, thereby protecting the company's asset security and improving operational efficiency.

[0005] In the current information age, the business processes of enterprises are becoming more and more complex, and the business links usually composed of multiple systems are also becoming more and more complex. In order to ensure the stability and reliability of the business, it is necessary to detect the business links so that problems can be discovered as early as possible to avoid or reduce losses.

[0006] At present, the commonly used anomaly detection systems are mainly business anomaly systems based on big data analysis. The system perspective is to analyze massive business data in real time and detect abnormal behaviors through data mining and pattern recognition. They usually have the characteristics of high performance, high throughput and low latency, but still have the following problems:

[0007] 1. Since it usually relies on a large amount of data for anomaly detection, when the data source is limited or the data quality is not high, it will affect the accuracy and reliability of anomaly detection;

[0008] 2. Processing large-scale data requires powerful computing power and storage resources, which leads to high system costs and large resource consumption, especially when the amount of data grows rapidly;

[0009] 3. Since the system usually needs to use complex algorithms and models to process data, the system complexity and maintenance cost are high, and it is easy to affect the stability and performance of the system;

[0010] 4. For scenarios that require rapid response, systems based on big data analysis cannot meet real-time requirements;

[0011] 5. As the amount of data and business needs grow, systems based on big data analysis need to be continuously optimized and adjusted; this leads to higher expansion and maintenance costs, and affects the reliability and stability of the system.

[0012] Therefore, a business anomaly detection system based on pattern matching is proposed. Summary of the invention

[0013] In view of this, the present invention provides a business anomaly detection system based on pattern matching, which aims to realize automatic identification and reporting of abnormal behaviors or data in various business processes, so as to solve or alleviate the technical problems existing in the prior art, and at least provide a beneficial choice.

[0014] The technical solution of the embodiment of the present invention is implemented as follows: a business anomaly detection system based on pattern matching, comprising a data acquisition module, a data preprocessing module, a pattern definition module, a pattern matching engine, an anomaly detection and alarm module, a report generation module and a subsequent processing interface, wherein the data acquisition module is connected to the data preprocessing module, the data preprocessing module is connected to the pattern definition module, the pattern definition module is connected to the pattern matching engine, the pattern matching engine is connected to the anomaly detection and alarm module, the anomaly detection and alarm module is connected to the report generation module, and the report generation module is connected to the subsequent processing interface;

[0015] Wherein, the pattern definition module is used to define rules and patterns for identifying anomalies;

[0016] The pattern matching engine is used to analyze data in real time or periodically and match it according to predefined patterns to mark potential anomalies;

[0017] The anomaly detection and alarm module is used to analyze and verify potential anomalies marked by the pattern matching engine.

[0018] Further preferably, the data collection module is used to collect business-related data from a data source;

[0019] Among them, data sources include log files, databases, and business systems;

[0020] Among them, relevant data include transaction records, user behavior logs and system status information.

[0021] Further preferably, the data preprocessing module is used to process the raw data collected by the data collection module;

[0022] Among them, the processing methods include data cleaning, formatting and standardization.

[0023] Further preferably, the system uses data mining and analysis technology to extract key information from business data to provide support for pattern matching.

[0024] Further preferably, the pattern defined by the pattern definition module is based on historical data analysis, business logic or industry standards.

[0025] Further preferably, the system adopts a pattern recognition algorithm;

[0026] Among them, the pattern recognition algorithm is a combination of machine learning and statistical analysis algorithms, which is used to identify and define business abnormal patterns;

[0027] Among them, the identification target includes simple threshold judgment and complex behavior pattern.

[0028] Further preferably, the alarm method of the abnormality detection and alarm module is to send alarm information via email, text message or system notification.

[0029] Further preferably, the report generation module is used to generate a detailed report for the detected anomaly;

[0030] The report includes the time, location, scope of impact and cause analysis of the abnormality.

[0031] Further preferably, the subsequent processing interface is used to provide an interface or tool for the system for the business team to perform further analysis and processing based on the report;

[0032] Among them, the interface is used to export data;

[0033] Among them, the tools are deep analysis tools or integration tools with other systems.

[0034] Further preferably, the system also adopts real-time data processing technology to provide the system with the ability to process high-speed inflow data and support real-time data analysis and pattern matching.

[0035] The embodiment of the present invention has the following advantages due to the adoption of the above technical solution:

[0036] Improve anomaly detection accuracy: The present invention uses a pattern matching engine to perform pattern matching and feature extraction on data, so that the system can more accurately identify abnormal behavior and reduce false positives and false negatives.

[0037] Real-time monitoring and rapid response: The present invention collects and processes data in real time through the data acquisition module, quickly discovers abnormal events, and takes corresponding measures for prevention and control to ensure the safe and stable operation of the business system.

[0038] Reduce computing costs: The present invention reduces computing costs and improves system efficiency and performance by optimizing algorithms and data processing flows.

[0039] Visualization and reporting: The present invention utilizes a report generation module and a subsequent processing interface to provide a user-friendly interface, and displays anomaly detection results and reports through data visualization, so that users can intuitively understand the operating status and abnormal conditions of the business system.

[0040] Flexibility and scalability: The system of the present invention adopts a modular design and can be flexibly expanded according to business needs to adapt to the anomaly detection needs in different scenarios.

[0041] Continuous optimization and updating: Through the rule and model update module, the system of the present invention can continuously optimize and update the rules and models of anomaly detection, thereby improving the accuracy and real-time performance of anomaly detection.

[0042] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 It is a schematic block diagram of the system structure of the present invention;

[0045] Figure 2 The figure is a flowchart of abnormal data acquisition of the present invention. DETAILED DESCRIPTION

[0046] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0047] It should be noted that the terms "first", "second", "symmetrical", "array", etc. are only used to distinguish between description and position description purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the definition of "first", "symmetrical", etc. can explicitly or implicitly include one or more of these features; similarly, when the number of certain features is not limited in the form of words such as "two" or "three", it should be noted that this feature also explicitly or implicitly includes one or more feature quantities.

[0048] Related terms:

[0049] Log Files are files that record events and activities in a system. They are usually used to record operations, errors, warnings, and other important information when a program is running. Log files are essential for monitoring system status, tracking errors, and performing system maintenance. In large systems, log files are usually rolled in real time to ensure that the system can continue to record events without affecting performance due to excessive file size. When complying with data protection regulations, log files also need to be properly managed to ensure the security of personal privacy and sensitive information;

[0050] Databases are systems that store and organize data. They are used to store structured data so that it can be quickly retrieved, updated, and managed by applications. Modern database systems can be relational, such as MySQL, Oracle, and SQL Server, or non-relational, such as MongoDB, Red is, and Cassandra. Databases play a central role in ensuring the accuracy, consistency, and availability of data;

[0051] Business Systems: Business systems are software applications that support an organization's daily operations and business processes. These systems can be enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, supply chain management (SCM) systems, etc.

[0052] Transaction Records: Transaction records refer to data generated in electronic transactions or other types of business activities to prove the occurrence of a transaction or activity. These records usually contain key information such as the time, date, parties involved, transaction amount, and transaction type of the transaction;

[0053] User Behavior Logs: User behavior logs are data that records the user's interactive behavior when using an application or website. These logs can help developers understand user usage habits, preferences, and potential problems, thereby optimizing product design and services;

[0054] System Status Information System status information refers to data that records system operation status, resource usage, performance indicators, etc. This information is crucial for system administrators, as it can help administrators discover system problems in a timely manner, predict system capacity requirements, and ensure stable system operation.

[0055] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0056] Embodiment 1

[0057] like Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a business anomaly detection system based on pattern matching, including a data acquisition module, a data preprocessing module, a pattern definition module, a pattern matching engine, an anomaly detection and alarm module, a report generation module and a subsequent processing interface, the data acquisition module is connected to the data preprocessing module, the data preprocessing module is connected to the pattern definition module, the pattern definition module is connected to the pattern matching engine, the pattern matching engine is connected to the anomaly detection and alarm module, the anomaly detection and alarm module is connected to the report generation module, and the report generation module is connected to the subsequent processing interface;

[0058] Among them, the pattern definition module is used to define the rules and patterns for identifying anomalies;

[0059] Among them, the pattern matching engine is used to analyze data in real time or periodically, and match it according to predefined patterns to mark potential anomalies;

[0060] Among them, the anomaly detection and alarm module is used to analyze and verify the potential anomalies marked by the pattern matching engine.

[0061] In one embodiment, the data collection module is used to collect business-related data from data sources;

[0062] Among them, data sources include log files, databases, and business systems;

[0063] Among them, relevant data includes transaction records, user behavior logs and system status information;

[0064] By utilizing the data acquisition module, the system can collect and process data in real time so as to quickly discover abnormal events later.

[0065] In one embodiment, the data preprocessing module is used to process the raw data collected by the data collection module;

[0066] Among them, the processing methods include data cleaning, formatting and standardization;

[0067] Data cleaning is a key step in the data preprocessing process. Its purpose is to identify and correct errors, inconsistencies, and noise in the data set, thereby improving the quality of the data. Data cleaning is essential to ensure the accuracy and reliability of data analysis. The following are commonly used data cleaning methods:

[0068] Identify and handle missing values: Analyze missing values ​​in the data and decide whether to fill missing values, delete records with missing values, or use a specific value (such as mean, median, mode) to fill missing values.

[0069] Deduplication: Detect and remove duplicate records in a dataset to avoid biasing analysis results.

[0070] Correcting erroneous values: Identifying and correcting errors in data, such as correcting typos, handling anomalous data, etc.

[0071] Handling outliers: Detecting outliers in the data and deciding whether to keep, correct, or delete them. Outliers may be caused by measurement errors or other abnormal conditions.

[0072] Data type conversion: Convert data to the correct data type, such as converting text to numeric type, or unifying the date and time format.

[0073] Consistency of data units and ranges: Ensure that the units and ranges in your dataset are consistent, such as converting all prices to the same currency unit.

[0074] Remove irrelevant data: Remove columns of data that are irrelevant or no longer needed to simplify the dataset and reduce the complexity of analysis.

[0075] Feature extraction and transformation: extracting useful features from raw data, such as converting categorical data into numerical data through encoding;

[0076] Data formatting is a key step in data preprocessing. Its purpose is to ensure the consistency and readability of data for further data analysis and processing. The following are common methods for data formatting:

[0077] Unify data types: Unify data columns into the correct data type, such as converting text to numeric type and formatting dates and times into a unified format.

[0078] Adjust data length: Adjust the length of data, such as truncating or padding overlong text data to ensure that all data has the same length.

[0079] Data alignment: Ensure that data is aligned consistently across columns, such as ensuring that timestamps are aligned when processing time series data.

[0080] Remove leading and trailing blanks: Remove leading and trailing blank characters in data to ensure data accuracy and consistency.

[0081] Convert data units: unify the units in the data, such as converting all prices to the same currency unit.

[0082] Data Splitting and Merging: Split data into different columns or merge multiple columns as needed.

[0083] Marking special data:

[0084] Mark special data or outliers for special treatment in subsequent analysis;

[0085] Data standardization is an important part of achieving data unification, standardization and efficient use. It occupies a core position in the current digital economy and intelligent development process. Data standardization refers to the processing of data sorting, classification, coding, format conversion, etc. according to certain regulations to achieve the purpose of unifying data format, improving data quality, and promoting data sharing and exchange. This process is crucial to ensure the accuracy, consistency and interoperability of data.

[0086] In one embodiment, the system uses data mining and analysis techniques to extract key information from business data to provide support for pattern matching;

[0087] By adopting advanced data mining technology, key information can be extracted from a large amount of business data to provide support for pattern matching;

[0088] Data mining and analysis technology is a technology that uses intelligent methods to discover patterns, trends and associations from large amounts of data and extract useful information. These technologies are widely used in various fields, including business intelligence, financial market analysis, healthcare, network security, recommendation systems, etc. The following are some of the key components of data mining and analysis technology.

[0089] In one embodiment, the pattern defined by the pattern definition module is based on historical data analysis;

[0090] Define business anomaly patterns based on historical data analysis;

[0091] Historical data analysis refers to the collection, processing, and analysis of past data to extract valuable information and insights. This information can help companies better understand market trends, customer behavior, business operation efficiency, etc. By analyzing historical data, companies can make more informed decisions, optimize business processes, predict future trends, and develop corresponding strategies.

[0092] In one embodiment, the system employs a pattern recognition algorithm;

[0093] Among them, the pattern recognition algorithm is a combination of machine learning and statistical analysis algorithms, which is used to identify and define business abnormal patterns;

[0094] Among them, identification targets include simple threshold judgments and complex behavior patterns;

[0095] By combining machine learning, statistical analysis and other methods, various business abnormal patterns can be effectively identified and defined, including simple threshold judgment and complex behavior pattern recognition;

[0096] Pattern recognition algorithms are an important branch of artificial intelligence and machine learning, which involves extracting useful information from data and converting it into a certain pattern or structure. These patterns can be used to understand the basic characteristics of the data, make predictions, or make decisions. Pattern recognition algorithms are widely used in many fields such as image recognition, speech recognition, natural language processing, bioinformatics, financial market analysis, etc.

[0097] In one embodiment, the alarm mode of the abnormality detection and alarm module is to send the alarm information via email;

[0098] By choosing to send the alarm information in the form of email, you can attach files when sending the alarm information. The information is detailed and users can view it at any time.

[0099] In one embodiment, the report generation module is used to generate a detailed report for the detected anomaly;

[0100] The report includes the time, location, impact scope and cause analysis of the abnormality;

[0101] By generating a detailed report for each detected anomaly, the subsequent business team can conduct analysis based on the report.

[0102] In one embodiment, the subsequent processing interface is used to provide an interface for the system for the business team to perform further analysis and processing based on the report;

[0103] Among them, the interface is used to export data;

[0104] By utilizing the subsequent processing interface to expand the system's functionality and by expanding the user interface, the data can be visualized so that anomaly detection results and reports can be displayed through data visualization, allowing users to intuitively understand the operating status and abnormal situations of the business system.

[0105] In one embodiment, the system also uses real-time data processing technology to provide the system with the ability to process high-speed inflow data and support real-time data analysis and pattern matching;

[0106] By utilizing real-time data processing technology, the system is equipped with the ability to process high-speed inflow data and supports real-time data analysis and pattern matching to ensure that the system responds immediately to anomalies;

[0107] Real-time data processing technology refers to the technology of collecting, storing, analyzing and applying data in the shortest possible time after it is generated. Real-time data processing technology enables the system to respond quickly to data and supports rapid decision-making, which is crucial to improving the efficiency and effectiveness of business processes.

[0108] Real-time data processing can also support personalized services. For example, the recommendation system can quickly adjust the recommended content based on the user's real-time behavior. In the field of security monitoring, real-time data processing technology can quickly analyze the monitoring images and detect abnormal behavior in a timely manner.

[0109] Embodiment 2

[0110] Taking the pattern of defining anomalies according to business logic as an example, an embodiment of the present invention further provides a business anomaly detection system based on pattern matching, including a data acquisition module, a data preprocessing module, a pattern definition module, a pattern matching engine, an anomaly detection and alarm module, a report generation module and a subsequent processing interface, the data acquisition module is connected to the data preprocessing module, the data preprocessing module is connected to the pattern definition module, the pattern definition module is connected to the pattern matching engine, the pattern matching engine is connected to the anomaly detection and alarm module, the anomaly detection and alarm module is connected to the report generation module, and the report generation module is connected to the subsequent processing interface;

[0111] Among them, the pattern definition module is used to define the rules and patterns for identifying anomalies;

[0112] Among them, the pattern matching engine is used to analyze data in real time or periodically, match it according to predefined patterns, and mark potential anomalies;

[0113] Among them, the anomaly detection and alarm module is used to analyze and verify the potential anomalies marked by the pattern matching engine.

[0114] In one embodiment, the data collection module is used to collect business-related data from data sources;

[0115] Among them, data sources include log files, databases, and business systems;

[0116] Among them, relevant data includes transaction records, user behavior logs and system status information;

[0117] By utilizing the data acquisition module, the system can collect and process data in real time so as to quickly discover abnormal events later.

[0118] In one embodiment, the data preprocessing module is used to process the raw data collected by the data collection module;

[0119] Among them, the processing methods include data cleaning, formatting and standardization;

[0120] Data cleaning is a key step in the data preprocessing process, which aims to identify and correct errors, inconsistencies and noise in the data set, thereby improving the quality of the data. Data cleaning is essential to ensure the accuracy and reliability of data analysis.

[0121] Data formatting is a key step in data preprocessing, which aims to ensure the consistency and readability of data for further data analysis and processing.

[0122] Data standardization is an important part of achieving data unification, standardization and efficient use. It occupies a core position in the current digital economy and intelligent development process. Data standardization refers to the processing of data sorting, classification, coding, format conversion, etc. according to certain regulations to achieve the purpose of unifying data format, improving data quality, and promoting data sharing and exchange. This process is crucial to ensure the accuracy, consistency and interoperability of data.

[0123] In one embodiment, the system uses data mining and analysis techniques to extract key information from business data to provide support for pattern matching;

[0124] By adopting advanced data mining technology, key information can be extracted from a large amount of business data to provide support for pattern matching;

[0125] Data mining and analysis technology is a technology that uses intelligent methods to discover patterns, trends and associations from large amounts of data and extract useful information. These technologies are widely used in various fields, including business intelligence, financial market analysis, healthcare, network security, recommendation systems, etc. The following are some of the key components of data mining and analysis technology.

[0126] In one embodiment, the mode defined by the mode definition module is based on business logic;

[0127] Define business exception patterns based on business logic analysis;

[0128] Business logic refers to the business rules and processes within an enterprise that define how to perform specific business tasks and operations. Business logic is often closely related to the core competitiveness and value creation activities of an enterprise. When designing and implementing an information system, it is crucial to ensure the correct implementation of business logic, as this directly affects the effectiveness of the system and the user experience.

[0129] In one embodiment, the system employs a pattern recognition algorithm;

[0130] Among them, the pattern recognition algorithm is a combination of machine learning and statistical analysis algorithms, which is used to identify and define business abnormal patterns;

[0131] Among them, identification targets include simple threshold judgments and complex behavior patterns;

[0132] By combining machine learning, statistical analysis and other methods, various business abnormal patterns can be effectively identified and defined, including simple threshold judgment and complex behavior pattern recognition;

[0133] Pattern recognition algorithms are an important branch of artificial intelligence and machine learning, which involves extracting useful information from data and converting it into a certain pattern or structure. These patterns can be used to understand the basic characteristics of the data, make predictions, or make decisions. Pattern recognition algorithms are widely used in many fields such as image recognition, speech recognition, natural language processing, bioinformatics, financial market analysis, etc.

[0134] In one embodiment, the alarm mode of the abnormal detection and alarm module is to send alarm information via SMS notification;

[0135] By selecting to send the alarm information in the form of SMS, the alarm information can be sent to the user in time, and the user will receive the information almost immediately.

[0136] In one embodiment, the report generation module is used to generate a detailed report for the detected anomaly;

[0137] The report includes the time, location, impact scope and cause analysis of the abnormality;

[0138] By generating a detailed report for each detected anomaly, the subsequent business team can conduct analysis based on the report.

[0139] In one embodiment, the subsequent processing interface is used to provide the system with tools for the business team to perform further analysis and processing based on the report;

[0140] Among them, the tool is a deep analysis tool;

[0141] By utilizing the subsequent processing interface to expand the functionality of the system;

[0142] Deep analysis tools refer to a class of advanced analysis tools used to deeply explore data and discover the inherent relationships and patterns in the data. These tools usually use complex algorithms and mathematical models to process large amounts of data to provide deep insights and understanding.

[0143] In one embodiment, the system also uses real-time data processing technology to provide the system with the ability to process high-speed inflow data and support real-time data analysis and pattern matching;

[0144] By utilizing real-time data processing technology, the system is equipped with the ability to process high-speed inflow data and supports real-time data analysis and pattern matching to ensure that the system responds immediately to anomalies;

[0145] Real-time data processing technology refers to the technology of collecting, storing, analyzing and applying data in the shortest possible time after it is generated. Real-time data processing technology enables the system to respond quickly to data and supports rapid decision-making, which is crucial to improving the efficiency and effectiveness of business processes.

[0146] Real-time data processing can also support personalized services. For example, the recommendation system can quickly adjust the recommended content based on the user's real-time behavior. In the field of security monitoring, real-time data processing technology can quickly analyze the monitoring images and detect abnormal behavior in a timely manner.

[0147] Embodiment 3

[0148] Taking the pattern of defining anomalies according to industry standards as an example, an embodiment of the present invention further provides a business anomaly detection system based on pattern matching, including a data acquisition module, a data preprocessing module, a pattern definition module, a pattern matching engine, an anomaly detection and alarm module, a report generation module and a subsequent processing interface, the data acquisition module is connected to the data preprocessing module, the data preprocessing module is connected to the pattern definition module, the pattern definition module is connected to the pattern matching engine, the pattern matching engine is connected to the anomaly detection and alarm module, the anomaly detection and alarm module is connected to the report generation module, and the report generation module is connected to the subsequent processing interface;

[0149] Among them, the pattern definition module is used to define the rules and patterns for identifying anomalies;

[0150] Among them, the pattern matching engine is used to analyze data in real time or periodically, and match it according to predefined patterns to mark potential anomalies;

[0151] Among them, the anomaly detection and alarm module is used to analyze and verify the potential anomalies marked by the pattern matching engine.

[0152] In one embodiment, the data collection module is used to collect business-related data from data sources;

[0153] Among them, data sources include log files, databases, and business systems;

[0154] Among them, relevant data includes transaction records, user behavior logs and system status information;

[0155] By utilizing the data acquisition module, the system can collect and process data in real time so as to quickly discover abnormal events later.

[0156] In one embodiment, the data preprocessing module is used to process the raw data collected by the data collection module;

[0157] Among them, the processing methods include data cleaning, formatting and standardization;

[0158] Data cleaning is a key step in the data preprocessing process, which aims to identify and correct errors, inconsistencies and noise in the data set, thereby improving the quality of the data. Data cleaning is essential to ensure the accuracy and reliability of data analysis.

[0159] Data formatting is a key step in data preprocessing, which aims to ensure the consistency and readability of data for further data analysis and processing.

[0160] Data standardization is an important part of achieving data unification, standardization and efficient use. It occupies a core position in the current digital economy and intelligent development process. Data standardization refers to the processing of data sorting, classification, coding, format conversion, etc. according to certain regulations to achieve the purpose of unifying data format, improving data quality, and promoting data sharing and exchange. This process is crucial to ensure the accuracy, consistency and interoperability of data.

[0161] In one embodiment, the system uses data mining and analysis techniques to extract key information from business data to provide support for pattern matching;

[0162] By adopting advanced data mining technology, key information can be extracted from a large amount of business data to provide support for pattern matching;

[0163] Data mining and analysis technology is a technology that uses intelligent methods to discover patterns, trends and associations from large amounts of data and extract useful information. These technologies are widely used in various fields, including business intelligence, financial market analysis, healthcare, network security, recommendation systems, etc. The following are some of the key components of data mining and analysis technology.

[0164] In one embodiment, the patterns defined by the pattern definition module are based on industry standards;

[0165] Define business anomaly patterns through analysis based on industry standards;

[0166] Industry standards are developed by professional organizations, associations, or government agencies in a specific industry, and they specify best practices for products, services, and operations. Industry standards are designed to ensure the quality, security, and interoperability of products and services. Compliance with industry standards is critical for businesses because it helps increase market acceptance of their products, build consumer trust, and reduce operational risks.

[0167] In one embodiment, the system employs a pattern recognition algorithm;

[0168] Among them, the pattern recognition algorithm is a combination of machine learning and statistical analysis algorithms, which is used to identify and define business abnormal patterns;

[0169] Among them, identification targets include simple threshold judgments and complex behavior patterns;

[0170] By combining machine learning, statistical analysis and other methods, various business abnormal patterns can be effectively identified and defined, including simple threshold judgment and complex behavior pattern recognition;

[0171] Pattern recognition algorithms are an important branch of artificial intelligence and machine learning, which involves extracting useful information from data and converting it into a certain pattern or structure. These patterns can be used to understand the basic characteristics of the data, make predictions, or make decisions. Pattern recognition algorithms are widely used in many fields such as image recognition, speech recognition, natural language processing, bioinformatics, financial market analysis, etc.

[0172] In one embodiment, the alarm mode of the abnormal detection and alarm module is to send alarm information through system notification;

[0173] By selecting the system notification form to send the alarm information, it can be directly displayed on the user interface, which is more intuitive and easy to understand.

[0174] In one embodiment, the report generation module is used to generate a detailed report for the detected anomaly;

[0175] The report includes the time, location, impact scope and cause analysis of the abnormality;

[0176] By generating a detailed report for each detected anomaly, the subsequent business team can conduct analysis based on the report.

[0177] In one embodiment, the subsequent processing interface is used to provide the system with tools for the business team to perform further analysis and processing based on the report;

[0178] Among them, tools are integration tools of other systems;

[0179] By utilizing the subsequent processing interface to expand the functionality of the system;

[0180] Integration tools for other systems are software or hardware solutions used to connect different systems, applications, or services so that they can exchange data and functions with each other. These tools are essential for automating workflows, data synchronization, and application integration within or across enterprises.

[0181] In one embodiment, the system also uses real-time data processing technology to provide the system with the ability to process high-speed inflow data and support real-time data analysis and pattern matching;

[0182] By utilizing real-time data processing technology, the system is equipped with the ability to process high-speed inflow data and supports real-time data analysis and pattern matching to ensure that the system responds immediately to anomalies;

[0183] Real-time data processing technology refers to the technology of collecting, storing, analyzing and applying data in the shortest possible time after it is generated. Real-time data processing technology enables the system to respond quickly to data and supports rapid decision-making, which is crucial to improving the efficiency and effectiveness of business processes.

[0184] Real-time data processing can also support personalized services. For example, the recommendation system can quickly adjust the recommended content based on the user's real-time behavior. In the field of security monitoring, real-time data processing technology can quickly analyze the monitoring images and detect abnormal behavior in a timely manner.

[0185] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of various changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A business anomaly detection system based on pattern matching, comprising a data acquisition module, a data preprocessing module, a pattern definition module, a pattern matching engine, an anomaly detection and alarm module, a report generation module and a subsequent processing interface, characterized in that: The data acquisition module is connected to the data preprocessing module, the data preprocessing module is connected to the pattern definition module, the pattern definition module is connected to the pattern matching engine, the pattern matching engine is connected to the anomaly detection and alarm module, the anomaly detection and alarm module is connected to the report generation module, and the report generation module is connected to the subsequent processing interface; Wherein, the pattern definition module is used to define rules and patterns for identifying anomalies; The pattern matching engine is used to analyze data in real time or periodically and match it according to predefined patterns to mark potential anomalies; The anomaly detection and alarm module is used to analyze and verify potential anomalies marked by the pattern matching engine.

2. The business anomaly detection system based on pattern matching according to claim 1, characterized in that: The data collection module is used to collect business-related data from data sources; Among them, data sources include log files, databases, and business systems; Among them, relevant data include transaction records, user behavior logs and system status information.

3. The business anomaly detection system based on pattern matching according to claim 2 is characterized in that: The data preprocessing module is used to process the raw data collected by the data collection module; Among them, the processing methods include data cleaning, formatting and standardization.

4. The business anomaly detection system based on pattern matching according to claim 3 is characterized in that: The system uses data mining and analysis technology to extract key information from business data and provide support for pattern matching.

5. The business anomaly detection system based on pattern matching according to claim 1, characterized in that: The patterns defined by the pattern definition module are based on historical data analysis, business logic or industry standards.

6. The business anomaly detection system based on pattern matching according to claim 4 is characterized in that: The system uses a pattern recognition algorithm; Among them, the pattern recognition algorithm is a combination of machine learning and statistical analysis algorithms, which is used to identify and define abnormal business patterns; Among them, identification targets include simple threshold judgments and complex behavior patterns.

7. The business anomaly detection system based on pattern matching according to claim 1, characterized in that: The alarm mode of the abnormal detection and alarm module is to send alarm information via email, text message or system notification.

8. The business anomaly detection system based on pattern matching according to claim 7 is characterized in that: The report generation module is used to generate a detailed report for the detected anomaly; The report includes the time, location, scope of impact and cause analysis of the abnormality.

9. The business anomaly detection system based on pattern matching according to claim 8, characterized in that: The subsequent processing interface is used to provide an interface or tool for the system so that the business team can conduct further analysis and processing based on the report; Among them, the interface is used to export data; Among them, the tools are deep analysis tools or integration tools with other systems.

10. The business anomaly detection system based on pattern matching according to claim 6, characterized in that: The system also uses real-time data processing technology to provide the system with the ability to process high-speed inflow data and support real-time data analysis and pattern matching.

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