Real-time big data stream processing and anomaly detection system

Abstract

The invention relates to the technical field of data detection, and discloses a real-time big data stream processing and anomaly detection system, which comprises a data acquisition module for collecting big data streams from a plurality of sources in real time, and the data acquisition module is equipped with an intelligent adapter for automatically identifying and adapting to data sources of different formats and protocols; the high-performance data preprocessing module is integrated with dynamic data cleaning, analysis and standardization functions and is used for processing the collected big data flow; the distributed stream computing platform is used for executing complex data analysis and computing tasks by real-time data streams; the anomaly detection module integrates a machine learning algorithm and a statistical analysis algorithm, and the algorithm is an unsupervised anomaly detection algorithm; the system can efficiently process real-time big data streams and accurately detect anomalies, and has intelligent resource management and strong safety protection capabilities. An advanced unsupervised anomaly detection algorithm is adopted, and a visual monitoring and alarm mechanism is provided.

Description

Technical Field

[0001] The present invention relates to the field of data detection technology, and in particular to a real-time large data stream processing and anomaly detection system. Background Art

[0002] In today's era of accelerated digital transformation, enterprises and organizations are facing an unprecedented flood of data, especially the explosive growth of real-time data, which places higher demands on the speed, efficiency and intelligence of data processing. Traditional data processing technologies are often unable to cope with large-scale, highly concurrent real-time data streams, especially in anomaly detection, which makes it difficult to achieve real-time, accurate analysis and response. Existing big data processing systems often have several key problems:

[0003] Diversified data sources and complex protocols: Data sources are extensive, covering multiple channels such as social media, sensor networks, and transaction systems. Each data source may use different transmission protocols and data formats, which poses a challenge to data collection and integration.

[0004] Data quality issues: Real-time data streams are often mixed with noise, missing values, and inconsistencies. Unprocessed low-quality data directly involved in analysis will lead to biased analysis results and affect the accuracy of decision-making.

[0005] Inflexible allocation of computing resources: When faced with a sudden increase in data volume, traditional data processing systems often encounter processing bottlenecks due to fixed resource allocation and are unable to quickly adapt to changes in data traffic, affecting system stability and response speed.

[0006] Anomaly detection efficiency and accuracy: Traditional anomaly detection methods are mostly based on supervised learning or fixed statistical models, which are difficult to adapt to dynamically changing anomaly patterns in large data streams, and have high false positive and false negative rates.

[0007] Lagging monitoring and alarm mechanisms: Existing systems often lack real-time, intuitive monitoring interfaces and are unable to provide real-time feedback on system status and data anomalies, which affects the timely discovery and handling of problems and increases the risk of business interruption.

[0008] Security and privacy issues: As the amount of data grows, the security of data during transmission, storage, and processing, as well as the protection of personal privacy, are becoming increasingly prominent. Traditional security measures may not be sufficient to cope with complex network threats.

[0009] Therefore, a real-time big data stream processing and anomaly detection system is proposed. Summary of the invention

[0010] The purpose of the present invention is to provide a real-time large data stream processing and anomaly detection system to solve the problems raised in the above background technology.

[0011] To achieve the above object, the present invention provides the following technical solutions:

[0012] A real-time big data stream processing and anomaly detection system, comprising:

[0013] A data collection module for collecting large data streams from multiple sources in real time, and the data collection module is equipped with an intelligent adapter for automatically identifying and adapting to data sources of different formats and protocols;

[0014] At least one high-performance data pre-processing module, integrated with dynamic data cleaning, parsing and standardization functions for processing the collected big data streams;

[0015] A distributed stream computing platform for performing complex data analysis and computing tasks on real-time data streams;

[0016] The anomaly detection module integrates machine learning and statistical analysis algorithms. The algorithm uses an unsupervised anomaly detection algorithm. The algorithms include the following:

[0017] Build a tree by randomly selecting features and split points, and calculate the average path length of data points;

[0018] For each data point x i , whose average path length in the forest is h(x i ) is used to calculate the anomaly score, the anomaly score s(x i ) can be obtained by normalizing the average path length of all trees and the height of the tree, and the formula is:

[0019]

[0020] Among them, E(h(x i ) is x i The average value of the path length in the forest, c(Ψ) is the expected maximum path length of the trees in the forest, which is usually close to log(n), where n is the number of data points;

[0021] Visual monitoring interface, used to provide intuitive data flow processing status monitoring and abnormal alarm;

[0022] Elastic scaling mechanism to automatically adjust system resource allocation for real-time data traffic;

[0023] The security and privacy protection module implements data encryption transmission, access control, and data desensitization processing.

[0024] Preferably, the data acquisition module further includes a data quality assessment subsystem, which can perform preliminary quality scoring before the data flows into the system and determine the priority and processing path of the data based on the scoring results.

[0025] Preferably, the distributed stream computing platform adopts containerized deployment and supports microservice architecture.

[0026] Preferably, the distributed stream computing platform is based on an advanced stream processing framework to achieve real-time analysis and processing of high-throughput data and support complex event processing and window computing.

[0027] Preferably, the visual monitoring interface provides an interactive analysis tool for users to perform real-time query, filtering and correlation analysis on data streams.

[0028] Preferably, the elastic scaling mechanism is combined with an intelligent prediction algorithm to schedule resources in advance based on historical data traffic trends and real-time analysis results.

[0029] Preferably, the security and privacy protection module implements a fine-grained data access control policy to ensure that only authorized users or services can access specific data sets.

[0030] Preferably, the elastic expansion mechanism includes horizontal expansion of data processing nodes and upward expansion of single-node processing capabilities.

[0031] Preferably, the security and privacy protection module integrates encryption technology and access control mechanism to ensure the security of data and protect user privacy during data collection, transmission and processing.

[0032] Preferably, the system poster has built-in multi-level data backup and failover strategies to ensure the continuity and integrity of data processing.

[0033] The present invention has at least the following beneficial effects:

[0034] By adopting microservice architecture and advanced stream processing technology, this solution can process a large number of concurrent data streams, significantly improving the real-time and throughput of data processing. This architecture is highly flexible and easy to expand, and can effectively cope with the explosive growth of data volume, ensuring business continuity and response speed. The integrated unsupervised machine learning algorithms, especially the Isolation Forest model, can automatically learn the normal behavior patterns of data streams, dynamically adjust the anomaly detection threshold, reduce false positives and false negatives, greatly enhance the accuracy and timeliness of anomaly detection, provide instant feedback for business operations, and effectively prevent potential risks. The system's elastic scaling mechanism can automatically adjust computing resources, storage resources, and network bandwidth according to real-time data traffic, realize dynamic optimization of resource configuration, ensure processing efficiency, maintain stable operation of the system, effectively avoid resource waste and overload problems, and reduce operating costs. The interactive monitoring interface provided not only intuitively displays the data processing status, resource usage, and anomaly detection results, but also allows users to customize alarm thresholds and rules, greatly improving the operability and management efficiency of the system, enabling operation and maintenance personnel to quickly identify problems and take action.

[0035] Ensure that the system can efficiently process real-time big data streams, accurately detect anomalies, and have intelligent resource management and strong security protection capabilities. It can automatically adapt to various data sources, optimize data quality in real time, dynamically adjust resource allocation, use advanced unsupervised anomaly detection algorithms, provide intuitive monitoring and alarm mechanisms, and implement strict data protection measures to meet the high requirements of modern data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0037] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0038] 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.

[0039] Example:

[0040] Specific implementations can be customized and developed based on the above technical solutions in combination with specific application scenarios and requirements. For example, in a financial transaction scenario, the system can process transaction flows in real time, quickly identify abnormal transaction behaviors through anomaly detection algorithms, and take timely measures to protect financial security.

[0041] See also Figure 1 The present invention provides a technical solution: a real-time big data stream processing and anomaly detection system, comprising:

[0042] A data collection module for collecting large data streams from multiple sources in real time, and the data collection module is equipped with an intelligent adapter for automatically identifying and adapting to data sources of different formats and protocols;

[0043] At least one high-performance data preprocessing module, which integrates dynamic data cleaning, parsing and standardization functions to process the collected big data streams, ensure data quality, and support online configuration and algorithm self-optimization;

[0044] The data acquisition module is a highly flexible and intelligent system that automatically identifies the protocols and formats of different data sources through intelligent adapter technology, including but not limited to HTTP, MQTT, Kafka protocols, etc. The equipped adapter is based on a plug-in architecture and is easy to expand to support new data sources. Before data access, the data quality assessment subsystem performs a preliminary score on the data, prioritizes the data based on the score results, and assigns it to the most suitable processing path to optimize subsequent processing efficiency. The high-performance data preprocessing module integrates dynamic data cleaning, parsing and standardization functions, and uses rule engines and machine learning models to adjust the processing logic online to adapt to changes in data flows. Users can configure cleaning rules and parsing templates through a graphical interface, and the system automatically optimizes the algorithm based on the configuration to improve data quality. This module supports a real-time feedback mechanism, and continuously optimizes the algorithm model based on the processing result feedback to ensure the accuracy and efficiency of data processing.

[0045] A distributed stream computing platform for performing complex data analysis and computing tasks on real-time data streams;

[0046] The distributed stream computing platform uses containerization technology and container orchestration tools such as Kubernetes to implement the deployment of microservice architecture, ensuring high availability and fast fault recovery. It uses advanced stream processing frameworks such as Apache Flink and Spark Streaming to support real-time analysis and processing of high-throughput data, and implement complex event processing and time window computing to meet diverse business needs.

[0047] Among them, containerization is to package the application and its dependencies into a lightweight, portable container, so that the application runs consistently in different environments. This approach simplifies deployment and management and improves resource utilization. Kubernetes (K8s for short): It is an open source container orchestration platform for automating the deployment, expansion and management of containerized applications. In the stream computing platform, Kubernetes is responsible for dynamically scheduling containerized microservices to ensure high availability (ensuring that services are always online through replica sets) and optimal allocation of resources. It can also achieve rapid fault detection and recovery. Once a computing node has a problem, Kubernetes will automatically migrate the service to other healthy nodes. Microservice architecture: Split large and complex applications into a group of small, independent services, each of which is responsible for a specific function and communicates through APIs. In the stream computing platform, this means that independent services can be designed for different data processing tasks (such as data ingestion, processing logic, storage, etc.), which is easy to expand and maintain, and also promotes the diversity of the technology stack, because each service can choose the most suitable technology according to its needs.

[0048] Therefore, by adopting containerization technology, Kubernetes-based orchestration, microservice architecture, and advanced stream processing frameworks such as Apache Flink and Spark Streaming, the distributed stream computing platform can flexibly respond to various business scenarios, from real-time processing of large-scale data to complex data analysis, and can provide efficient, scalable and reliable solutions to meet the diverse needs of different industries and application scenarios.

[0049] The anomaly detection module integrates machine learning and statistical analysis algorithms. The algorithm uses an unsupervised anomaly detection algorithm. The algorithms include the following:

[0050] By randomly selecting features and split points to build a tree, the average path length of the data points is calculated (the shorter the path length, the more abnormal it is);

[0051] For each data point x i , whose average path length in the forest is h(x i ) is used to calculate the anomaly score, the anomaly score s(x i ) can be obtained by normalizing the average path length of all trees and the height of the tree, and the formula is:

[0052]

[0053] Among them, E(h(x i ) is x i The average value of the path lengths in the forest, c(Ψ), is the expected maximum path length among the trees in the forest and is usually close to log(n), where n is the number of data points.

[0054] The unsupervised anomaly detection algorithm integrated in the anomaly detection module is based on the Isolation Forest method. It is constructed through a random forest and calculates the average path length of the data point as the anomaly score. In specific implementation, the system dynamically calculates the average path length of each data point in the forest and standardizes the anomaly score through a formula to identify abnormal data. The algorithm automatically adjusts to adapt to changes in the data flow and improves detection accuracy.

[0055] Visual monitoring interface, used to provide intuitive data flow processing status monitoring and abnormal alarm, supporting custom threshold settings and alarm rule configuration;

[0056] The visual monitoring interface is developed using the Web technology stack and integrates open source tools such as Grafana and Kibana to display data stream processing status, resource usage, and anomaly detection results in real time. Users can customize alarm thresholds and rules through the interface to achieve flexible monitoring configuration. In addition, the system provides interactive analysis tools that support instant query, data filtering, and correlation analysis to assist decision makers in quickly responding to business changes.

[0057] Elastic scaling mechanism, which is used to automatically adjust system resource allocation according to real-time data traffic to ensure processing efficiency and stability, including dynamic adjustment of computing resources, storage resources and network bandwidth;

[0058] The elastic scaling mechanism combines historical data analysis with intelligent prediction algorithms to adjust resources in advance according to data traffic trends. When a data traffic peak is predicted, the system automatically adds computing nodes or improves the processing capacity of a single node to ensure efficient and stable data processing. This mechanism is integrated into the cloud platform and uses the elastic resource pool of cloud services to achieve on-demand allocation and release of resources.

[0059] The elastic scaling mechanism combines historical data analysis with an intelligent prediction algorithm, and the algorithm simplifies the prediction model as follows:

[0060] If a linear regression model is used for prediction, its general form can be expressed as:

[0061] y=β 0+ β1x 1+ β2x 2+····+ β n x n+ ∈

[0062] In this formula:

[0063] y represents the target variable to be predicted, such as predicted future data traffic or resource demand.

[0064] x 1， x 2，···， xn Represents the characteristic variables that affect the prediction results, which can be historical time series data, timestamps, seasonal factors, etc.

[0065] β 0， β 1，···， β n are model parameters obtained by fitting the training data.

[0066] ∈ is the error term, reflecting unpredictable random disturbances.

[0067] For more complex prediction algorithms, such as using LSTM (Long Short-Term Memory Network) for time series prediction, the construction of the model involves the design of the neural network structure, including the input layer, hidden layer (including LSTM units) and output layer, as well as a large number of weights and bias parameters. Its "formula" is more reflected in the network structure and the back-propagation algorithm during training.

[0068] The core of the intelligent prediction algorithm in the elastic scaling mechanism is to use historical data to train models to predict future data traffic or resource requirements, and then dynamically adjust resources based on the prediction results. In actual applications, the appropriate prediction model will be selected based on data characteristics and business needs.

[0069] The security and privacy protection module implements data encryption transmission, access control, and data desensitization to ensure information security and compliance during data processing.

[0070] The security and privacy protection module implements fine-grained data access control to ensure strict management of data access rights. Data is encrypted using SSL / TLS during transmission, static encryption technology is used when stored, and dynamic data desensitization is used when processed, such as random replacement or masking of sensitive fields, to ensure security and compliance throughout the data processing process.

[0071] In some embodiments, the data acquisition module further includes a data quality assessment subsystem that can perform preliminary quality scoring before the data flows into the system and determine the priority and processing path of the data based on the scoring results.

[0072] The data acquisition module further includes a data quality assessment subsystem, which can perform preliminary quality scoring before the data flows into the system and determine the priority and processing path of the data based on the scoring results.

[0073] The distributed stream computing platform is based on an advanced stream processing framework to achieve real-time analysis and processing of high-throughput data, and supports complex event processing and window computing.

[0074] Furthermore, the distributed stream computing platform is built on top of advanced stream processing technologies, such as Apache Kafka, Flink, Spark Streaming, or Apache Storm. These frameworks are good at processing high-throughput data streams and can perform continuous and real-time processing while data is generated, which is suitable for large-scale, high-concurrency real-time data processing scenarios. Support for complex event processing includes the ability to identify patterns, associations, and sequences in data streams, and process complex event logic, such as sequence pattern matching, anomaly detection, and multi-condition triggering. This is crucial for discovering hidden associations and predicting future trends or abnormal behaviors.

[0075] The visual monitoring interface provides interactive analysis tools for users to conduct real-time query, filtering and correlation analysis on data streams to assist in decision making.

[0076] The elastic scaling mechanism is combined with an intelligent prediction algorithm to schedule resources in advance to cope with expected data traffic peaks based on historical data traffic trends and real-time analysis results.

[0077] The security and privacy protection module implements fine-grained data access control strategies to ensure that only authorized users or services can access specific data sets. At the same time, it uses dynamic data desensitization technology to perform on-demand desensitization processing on sensitive information.

[0078] The elastic expansion mechanism is designed to automatically adjust resource allocation according to data flow and processing requirements, including horizontal expansion of data processing nodes and upward expansion of single-node processing capabilities, to ensure that efficient and stable operation can be maintained under the impact of large amounts of data.

[0079] The security and privacy protection module integrates encryption technology and access control mechanism to ensure the security of data and user privacy protection during data collection, transmission and processing, in compliance with industry standards and regulatory requirements.

[0080] The system poster has built-in multi-level data backup and failover strategies to ensure that services can be quickly restored in the event of a single point failure, thereby ensuring the continuity and integrity of data processing.

[0081] The system has designed a multi-level data backup and failover strategy, including but not limited to master-slave replication, distributed storage redundancy, cross-regional disaster recovery, etc. When a single node or data center fails, the system automatically switches to the backup node or region to ensure the continuity of data processing tasks and the integrity of data.

[0082] The workflow and principle of the present invention: The core workflow of the present application revolves around efficient data collection, intelligent preprocessing, real-time analysis and calculation, anomaly detection, dynamic resource management and security assurance, ensuring the efficiency, stability and security of data processing.

[0083] First, the system uses a highly intelligent data collection module and intelligent adapter technology to automatically identify and adapt to the protocols and formats of various data sources, such as HTTP, MQTT, Kafka, etc., to achieve real-time data collection. Before entering the system, the data is scored by the data quality assessment subsystem, prioritized according to the score, and assigned to the most appropriate processing path to ensure processing efficiency and quality. Then the high-performance data preprocessing module integrates dynamic data cleaning, parsing and standardization functions, and uses the rule engine and machine learning model to automatically adjust the processing logic. The real-time feedback mechanism ensures that the algorithm model is continuously optimized according to the processing results, maintaining efficient and accurate data processing. Using orchestration tools such as containerization technology and Kubernetes, the system deploys a distributed stream computing platform with a microservice architecture, supports frameworks such as Apache Flink and SparkStreaming, realizes real-time processing of high-throughput data and complex event analysis, and ensures high availability and rapid fault recovery of the system.

[0084] Secondly, the unsupervised Isolation Forest algorithm is used to calculate the average path length of data points by constructing a random forest, which is used as the basis for anomaly scoring. The system automatically adjusts the algorithm to adapt to changes in data flow and improves the accuracy of anomaly detection. A web interface is provided, and Grafana and Kibana tools are integrated to display the system status in real time. Customized alarm settings and interactive analysis are supported to assist decision makers in responding quickly. Combining historical data with intelligent prediction algorithms (such as linear regression, LSTM, etc.), computing resources, storage, and network bandwidth are dynamically adjusted according to data traffic trends, and resources are prepared in advance for data traffic peaks to ensure stable and efficient operation of the system. Finally, fine-grained data access control, data encryption transmission, static and dynamic data encryption (including data desensitization) are implemented to ensure security and compliance during data processing.

[0085] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0086] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A real-time big data stream processing and anomaly detection system, characterized in that: include: A data collection module for collecting large data streams from multiple sources in real time, and the data collection module is equipped with an intelligent adapter for automatically identifying and adapting to data sources of different formats and protocols; At least one high-performance data pre-processing module, integrated with dynamic data cleaning, parsing and standardization functions for processing the collected big data streams; A distributed stream computing platform for performing complex data analysis and computing tasks on real-time data streams; The anomaly detection module integrates machine learning and statistical analysis algorithms. The algorithm uses an unsupervised anomaly detection algorithm. The algorithms include the following: By randomly selecting features and split points to build a tree, the average path length of the data points is calculated; For each data point x i , whose average path length in the forest is h(x i ) is used to calculate the anomaly score, the anomaly score s(x i ) can be obtained by normalizing the average path length of all trees and the height of the tree, and the formula is: Among them, E(h(x i ) is x i The average value of the path length in the forest, c(Ψ) is the expected maximum path length of the trees in the forest, which is usually close to log(n), where n is the number of data points; Visual monitoring interface, used to provide intuitive data flow processing status monitoring and abnormal alarm; Elastic scaling mechanism to automatically adjust system resource allocation for real-time data traffic; The security and privacy protection module implements data encryption transmission, access control, and data desensitization processing.

2. A real-time big data stream processing and anomaly detection system according to claim 1, characterized in that: The data acquisition module further includes a data quality assessment subsystem, which can perform preliminary quality scoring before the data flows into the system and determine the priority and processing path of the data based on the scoring results.

3. A real-time big data stream processing and anomaly detection system according to claim 1, characterized in that: The distributed stream computing platform adopts containerized deployment and supports microservice architecture.

4. The real-time big data stream processing and anomaly detection system according to claim 1, characterized in that: The distributed stream computing platform is based on an advanced stream processing framework to achieve real-time analysis and processing of high-throughput data, and supports complex event processing and window computing.

5. The real-time big data stream processing and anomaly detection system according to claim 1, characterized in that: The visual monitoring interface provides interactive analysis tools for users to perform real-time query, filtering and correlation analysis on data streams.

6. A real-time big data stream processing and anomaly detection system according to claim 1, characterized in that: The elastic scaling mechanism is combined with an intelligent prediction algorithm to schedule resources in advance based on historical data traffic trends and real-time analysis results.

7. The real-time big data stream processing and anomaly detection system according to claim 1, characterized in that: The security and privacy protection module implements fine-grained data access control policies to ensure that only authorized users or services can access specific data sets.

8. The real-time big data stream processing and anomaly detection system according to claim 1, characterized in that: The elastic expansion mechanism includes horizontal expansion of data processing nodes and upward expansion of single-node processing capabilities.

9. The real-time big data stream processing and anomaly detection system according to claim 1, characterized in that: The security and privacy protection module integrates encryption technology and access control mechanism to ensure the security of data and protect user privacy during data collection, transmission and processing.

10. A real-time big data stream processing and anomaly detection system according to claim 1, characterized in that: The system poster has built-in multi-level data backup and failover strategies to ensure the continuity and integrity of data processing.

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