Fatigue and distraction driving monitoring device based on physiological features and monitoring method of fatigue and distraction driving monitoring device

Abstract

The invention discloses a fatigue and distraction driving monitoring device based on physiological features and a monitoring method thereof, particularly relates to the technical field of target extraction, can realize accurate reconstruction of a system state by collecting and utilizing system event data, overcomes limitation and defects of a traditional method, and improves efficiency and accuracy of state reconstruction. The event data is converted into the graphic structure, and the visual and efficient representation method is provided and used for displaying the causal relationship and sequence of the events. According to the technology, large-scale event data can be quickly processed, and the integrity and accuracy of the graph are ensured. Event data are collected and updated in real time, and the system state can be monitored and updated in real time. According to the method, the change of the system state can be quickly responded, and real-time state information is provided, so that the maintainability and the reliability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of target extraction, and more particularly to a fatigue and distracted driving monitoring device based on physiological characteristics and a monitoring method thereof. Background Art

[0002] In today's internet age, with the continuous expansion of business scale and user growth, many systems and applications have accumulated vast amounts of operational data. This data contains a variety of system operational events, such as operations, exceptions, and failures. This makes system states increasingly difficult to understand and track. These events often have complex causal relationships, which are crucial for understanding and reconstructing system states. Traditional state reconstruction methods typically rely on large amounts of log data and periodic snapshots, but this data can be incomplete or tampered with, resulting in inaccurate state reconstruction.

[0003] In order to solve the above-mentioned defects, a technical solution is now provided. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a fatigue and distracted driving monitoring device and a monitoring method thereof based on physiological characteristics to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A fatigue and distracted driving monitoring device and a monitoring method based on physiological characteristics include the following steps:

[0007] Step S1 collects various event data generated during system operation and converts this data into a visual event graph. By collecting and utilizing system event data, the efficiency and accuracy of state reconstruction are improved. Converting event data into a graph structure provides an intuitive and efficient way to represent the causal relationships and sequence of events. This allows for rapid processing of large amounts of event data while ensuring the integrity and accuracy of the graph.

[0008] In step S2, the collected event data is normalized, a state analysis model is established, and the first comprehensive performance index of the system state is generated; key events are automatically identified by analyzing information such as the attributes, impact range, and frequency of occurrence of the event. This method can quickly locate events that have a significant impact on the system state, improving the efficiency and accuracy of state reconstruction. When verifying the accuracy and reliability of the state reconstruction method based on event tracing, a method combining simulation testing and actual scenario testing is used to verify the accuracy and reliability of the state reconstruction method based on event tracing. The performance and effectiveness of the method can be evaluated by comparing the reconstructed state with the actual state.

[0009] Step S3, for the acquired event graph data, first analyze it and automatically identify the key events therein; substitute these key events into the state analysis model established in advance, calculate the second comprehensive performance index through the model, and finally compare the first comprehensive performance index with the second comprehensive performance index to derive the degree of influence of the key events on the system state; analyze the acquired event graph data and automatically identify the key events therein. This helps to extract events that have a greater impact on the system state from a large amount of data, thereby improving efficiency. Compare the first comprehensive performance index with the second comprehensive performance index to derive the degree of influence of the key events on the system state. By comparing the comprehensive performance indexes at different time points, you can more intuitively understand the impact of key events on the system state.

[0010] Step S4 reconstructs and processes the system state by analyzing the impact of key events on the system state and the causal relationships between key events and other events. Analyzing key events helps identify the most important issues and events in the system, allowing the system to focus on resolving the most pressing problems and improving the efficiency and priority of problem resolution. By analyzing key events and their causal relationships, the system state can be reconstructed and processed in a targeted manner, effective measures can be taken to resolve issues, and system performance can be improved. Analyzing key events and their causal relationships helps predict potential system issues and implement preventative maintenance measures, thereby reducing the occurrence and impact of system failures.

[0011] Step S5 monitors changes in the system status in real time, and uses distributed and parallel processing technologies to process large-scale event data; through distributed and parallel processing technologies, large-scale event data processing and analysis are achieved. This method can improve the efficiency of processing large-scale data. Distributed processing refers to distributing the collection, processing and analysis tasks of event data to multiple nodes for processing. This method utilizes technical tools such as distributed databases, message queues and parallel computing frameworks to improve the processing power and efficiency of large-scale systems. Specifically, distributed databases are used to store and manage large amounts of event data, message queues are used to achieve asynchronous transmission and decoupling of data, and parallel computing frameworks are used to process data in parallel on multiple nodes to accelerate the processing process. This distributed processing method can effectively cope with the growth of system scale and the increase in data volume, and improve the scalability and performance of the system.

[0012] In step S6, after the system reconstruction is complete, a combination of simulation testing and real-world scenario testing is used to verify the accuracy of the event-based state reconstruction method. By analyzing the impact of key events on the system state, the events with the greatest impact on system performance, stability, and other aspects can be accurately identified. By analyzing key events and their causal relationships, system administrators can promptly identify potential issues and take appropriate measures, thereby improving system efficiency and reliability and reducing the likelihood of system failures.

[0013] Preferably, when collecting system event data, data filling and repair technology is used to predict and supplement missing or damaged data using known events and status information. The repaired event data, known events, system status information, and original event data are marked as Q1, K1, K2, and K3, respectively.

[0014] The expression for predicting and supplementing the repaired event data is Q1=K3+f(K1+K2), where f() is used to predict and generate missing or damaged event data based on the known event K1 and the system status information K2.

[0015] Preferably, when converting to an event graph, the data is first normalized and then converted into a time series graph. Next, the constructed event graph is analyzed in real time using a state analysis model to identify key events and analyze the causal relationships and impact relationships between events. This event data includes, but is not limited to, error logs, user operation records, system anomalies or errors, system crashes, service interruptions, data loss, malicious attacks, and security vulnerability exposures.

[0016] Preferably, key events include system anomalies or errors, system crashes, service interruptions, data loss, malicious attacks, and security vulnerability exposures.

[0017] Preferably, a machine learning algorithm is used to train the state analysis model so that the state analysis model automatically learns and identifies the causal relationship between events. The training logic is:

[0018] Collect various event data generated in the system, including error logs and user operation records. Based on the nature of the task and the characteristics of the data, select an appropriate machine learning algorithm to build a state analysis model. Use the preprocessed and feature-engineered data to train the selected machine learning model. Use a validation set or cross-validation method to evaluate the performance of the trained model. Based on the evaluation results, tune the model.

[0019] Preferably, the state analysis model uses known event data to identify and predict the causal relationship of events, thereby deriving the impact of key events on the system state and evaluating changes in the system state. The evaluation logic is:

[0020] The state analysis model uses known event data to identify and predict the causal relationship of events, thereby deriving the impact of key events on the system state and evaluating changes in the system state. Its evaluation logic is as follows:

[0021] Use the trained state analysis model to analyze known event data, identify and predict the causal relationship between events, and determine the impact of key events on the system state based on the identified event causal relationship; determine the impact of key events on the system state based on the identified event causal relationship.

[0022] Preferably, a third comprehensive index is generated in the reconstructed state and compared with the first comprehensive index. If the third comprehensive index is lower than the first comprehensive index, it means that the system state has not been fully restored. The first comprehensive index is 90, and the third comprehensive index is not less than 1.

[0023] Preferably, when processing large-scale event data, the data is stored in the distributed storage system Amazon S3, and the parallel computing framework Apache Flink is used to process the data in parallel.

[0024] Technical effects and advantages of the present invention:

[0025] By collecting and utilizing system event data, the present invention enables accurate reconstruction of system status, overcoming the limitations and shortcomings of traditional methods and improving the efficiency and accuracy of state reconstruction. Converting event data into a graphical structure provides an intuitive and efficient representation method for displaying the causal relationships and sequence of events. This technology can rapidly process large amounts of event data while ensuring the integrity and accuracy of the graph. Real-time collection and updating of event data enables real-time monitoring and updating of system status. This method can rapidly respond to changes in system status and provide real-time status information, thereby improving the maintainability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0027] Figure 1 This is a structural diagram of Example 1 of the present invention. DETAILED DESCRIPTION

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0029] Example 1

[0030] Figure 1 The present invention provides a fatigue and distracted driving monitoring device and a monitoring method based on physiological characteristics, which include the following steps:

[0031] Step S1 collects various event data generated during system operation and converts this data into a visual event graph. By collecting and utilizing system event data, the efficiency and accuracy of state reconstruction are improved. Converting event data into a graph structure provides an intuitive and efficient way to represent the causal relationships and sequence of events. This allows for rapid processing of large amounts of event data while ensuring the integrity and accuracy of the graph.

[0032] In step S2, the collected event data is normalized, a state analysis model is established, and the first comprehensive performance index of the system state is generated; key events are automatically identified by analyzing information such as the attributes, impact range, and frequency of occurrence of the event. This method can quickly locate events that have a significant impact on the system state, improving the efficiency and accuracy of state reconstruction. When verifying the accuracy and reliability of the state reconstruction method based on event tracing, a method combining simulation testing and actual scenario testing is used to verify the accuracy and reliability of the state reconstruction method based on event tracing. The performance and effectiveness of the method can be evaluated by comparing the reconstructed state with the actual state.

[0033] Step S3, for the acquired event graph data, first analyze it and automatically identify the key events therein; substitute these key events into the state analysis model established in advance, calculate the second comprehensive performance index through the model, and finally compare the first comprehensive performance index with the second comprehensive performance index to derive the degree of influence of the key events on the system state; analyze the acquired event graph data and automatically identify the key events therein. This helps to extract events that have a greater impact on the system state from a large amount of data, thereby improving efficiency. Compare the first comprehensive performance index with the second comprehensive performance index to derive the degree of influence of the key events on the system state. By comparing the comprehensive performance indexes at different time points, you can more intuitively understand the impact of key events on the system state.

[0034] Step S4 reconstructs and processes the system state by analyzing the impact of key events on the system state and the causal relationships between key events and other events. Analyzing key events helps identify the most important issues and events in the system, allowing the system to focus on resolving the most pressing problems and improving the efficiency and priority of problem resolution. By analyzing key events and their causal relationships, the system state can be reconstructed and processed in a targeted manner, effective measures can be taken to resolve issues, and system performance can be improved. Analyzing key events and their causal relationships helps predict potential system issues and implement preventative maintenance measures, thereby reducing the occurrence and impact of system failures.

[0035] Step S5 monitors changes in the system status in real time, and uses distributed and parallel processing technologies to process large-scale event data; through distributed and parallel processing technologies, large-scale event data processing and analysis are achieved. This method can improve the efficiency of processing large-scale data. Distributed processing refers to distributing the collection, processing and analysis tasks of event data to multiple nodes for processing. This method utilizes technical tools such as distributed databases, message queues and parallel computing frameworks to improve the processing power and efficiency of large-scale systems. Specifically, distributed databases are used to store and manage large amounts of event data, message queues are used to achieve asynchronous transmission and decoupling of data, and parallel computing frameworks are used to process data in parallel on multiple nodes to accelerate the processing process. This distributed processing method can effectively cope with the growth of system scale and the increase in data volume, and improve the scalability and performance of the system.

[0036] In step S6, after the system reconstruction is complete, a combination of simulation testing and real-world scenario testing is used to verify the accuracy of the event-based state reconstruction method. By analyzing the impact of key events on the system state, the events with the greatest impact on system performance, stability, and other aspects can be accurately identified. By analyzing key events and their causal relationships, system administrators can promptly identify potential issues and take appropriate measures, thereby improving system efficiency and reliability and reducing the likelihood of system failures.

[0037] In this embodiment, when collecting system event data, data filling and repair technology is used to predict and supplement missing or damaged data using known events and status information. The repaired event data, known events, system status information, and original event data are marked as Q1, K1, K2, and K3 respectively.

[0038] The expression for predicting and supplementing repaired event data is Q1 = K3 + f(K1 + K2), where f() is used to predict and generate missing or corrupted event data based on known events K1 and system status information K2. Using data filling and repair techniques ensures the integrity and accuracy of event data. This means that when processing system event data, the model can identify missing or corrupted data and use available information to predict and fill this data. This approach avoids incomplete or inaccurate information caused by missing or corrupted data, ensuring data quality and reliability. By predicting and supplementing missing data, the model can fully utilize existing information in the system and use this information to generate more event data, thereby increasing data utilization and value. Furthermore, repairing missing or corrupted data helps reduce the risk of data loss, protects important event data in the system, and prevents missing or incomplete information caused by data loss. These measures collectively promote system stability and reliability, ensuring the integrity and availability of system event data.

[0039] In this embodiment, when converting the data into an event graph, the data is first normalized and then converted into a time series graph. Next, the constructed event graph is analyzed in real time using a state analysis model to identify key events and analyze the causal relationships and impact relationships between events. This event data includes, but is not limited to, error logs, user operation records, system anomalies or errors, system crashes, service interruptions, data loss, malicious attacks, and security vulnerability exposures. By identifying key events and analyzing the causal relationships and impact relationships between events, the model can more quickly identify problems and potential risks in the system. This allows the model to take early action to prevent further deterioration. Real-time analysis of the event graph helps the model gain a deeper understanding of the system's operation. Furthermore, the correlations and impacts between various events can be understood, leading to a better understanding of the system's overall operational status. Real-time analysis of the event graph helps the model promptly detect and respond to security issues such as malicious attacks and security vulnerabilities, thereby improving the security and stability of the system.

[0040] In this embodiment, key events include system anomalies or errors, system crashes, service interruptions, data loss, malicious attacks, and security vulnerability exposure.

[0041] In this embodiment, a machine learning algorithm is used to train the state analysis model so that the state analysis model automatically learns and identifies the causal relationship between events. The training logic is as follows:

[0042] Collect all types of event data generated in the system, including error logs and user operation records. Based on the nature of the task and the characteristics of the data, select an appropriate machine learning algorithm to build a state analysis model. Use preprocessed and feature-engineered data to train the selected machine learning model. Evaluate the performance of the trained model using a validation set or cross-validation, and fine-tune the model based on the evaluation results. Training the state analysis model with a machine learning algorithm enables the model to automatically learn and identify causal relationships between events without the need for manual rule encoding or specification of causal relationships. Actively collect all types of event data generated in the system, including error logs and user operation records. This data provides a rich source of information for the model, helping to build a more comprehensive and accurate state analysis model. When selecting a machine learning algorithm to build the state analysis model, the model will be selected based on the nature of the task and the characteristics of the data to ensure that the model is best suited to different types of data and problems, and to improve its performance and effectiveness. Furthermore, the model will be fine-tuned based on the evaluation results, continuously improving its performance and accuracy, allowing the state analysis model to better adapt to system changes and needs.

[0043] In this embodiment, the state analysis model uses known event data to identify and predict the causal relationship of events, thereby deriving the impact of key events on the system state and evaluating changes in the system state. The evaluation logic is as follows:

[0044] Use the trained state analysis model to analyze known event data, identify and predict causal relationships between events, and determine the impact of key events on the system state based on the identified event causal relationships. Label the state analysis model A, the identified event causal relationships R, the impact levels C, and the known event dataset D.

[0045] The degree of impact C of a critical event on the system state is determined as: C = (f(D)). This allows the state analysis model to be used to analyze known event data to determine the degree of impact of a critical event on the system state. By analyzing the impact of critical events on the system state, changes in the system state can be assessed in real time. This enables system managers to promptly identify anomalies in the system and take timely action to address them, thereby ensuring stable system operation. Based on the trained state analysis model, the system can automatically analyze event data without manual intervention, saving human resource costs and improving the speed and accuracy of decision-making. By determining the degree of impact of critical events on the system state, changes in the system state can be quantitatively assessed.

[0046] In this embodiment, a third comprehensive index is generated in the reconstructed state and compared with the first comprehensive index. If the third comprehensive index is lower than the first comprehensive index, it indicates that the system state has not been fully recovered, wherein the first comprehensive index is 90 and the third comprehensive index is not less than 1. The first comprehensive index and the third comprehensive index provide a quantitative assessment of the system state. By comparing the two, the degree of recovery of the system state can be quantitatively assessed. Setting thresholds for the first comprehensive index and the third comprehensive index helps to standardize the process of system state assessment. When the third comprehensive index does not meet expectations, the corresponding management process can be triggered, such as notifying relevant personnel to handle the problem or initiating a fault repair process, thereby improving management efficiency and the standardization of the work process.

[0047] In this embodiment, when processing large-scale event data, the data is stored in the distributed storage system Amazon S3, and the parallel computing framework Apache Flink is used to process the data in parallel. Using a distributed storage system (such as Amazon S3) can store large-scale event data and has high scalability. Using a parallel computing framework (such as Apache Flink) to process data in parallel can improve processing speed and efficiency. Flink has good parallel processing capabilities, can effectively process large-scale data, and can achieve high-performance data processing in a distributed environment. S3 supports the storage of large amounts of data and can dynamically expand to meet the growing data demand. Distributed storage systems and parallel computing frameworks generally have good fault tolerance and can maintain the stability and reliability of the system in situations such as node failures or network interruptions. This ensures that the system can continue to operate normally in the face of unexpected situations and does not lose data or processing results.

[0048] Explanation of relevant terms

[0049] Event sourcing: Event sourcing is a design pattern for recording all activities or events of a system so that the state and history of the system can be accurately reconstructed.

[0050] State reconstruction: In event tracing, state reconstruction refers to reconstructing the state of the system at a specific point in time by analyzing stored event data.

[0051] Key events: Key events are events that have a significant impact on the system state in the event graph.

[0052] Event Graph: An event graph is a graphical structure that represents events and their relationships.

[0053] Expected results: During the state reconstruction process, the expected results refer to the state changes that each key event should cause based on the business logic and rules of the event.

[0054] Verification results: Verification results refer to evaluating the accuracy and reliability of the state reconstruction method by comparing the reconstructed system state with the actual state.

[0055] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0056] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0057] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0058] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A device and method for monitoring fatigue and distracted driving based on physiological characteristics, characterized by: The steps include: Step S1, collecting various event data generated during system operation and converting these data into a visual event graph; Step S2, normalizing the collected event data, establishing a state analysis model, and generating a first comprehensive performance index of the system state; Step S3: First, analyze the acquired event graph data to automatically identify key events; substitute these key events into a pre-established state analysis model, calculate a second comprehensive performance index through the model, and finally compare the first comprehensive performance index with the second comprehensive performance index to determine the degree of impact of the key events on the system state; Step S4, by analyzing the impact of key events on the system state and analyzing the causal relationship between key events and other events, the system state is reconstructed and processed; Step S5: monitor the changes in system status in real time and use distributed and parallel processing technologies to process large-scale event data; Step S6: After the system reconstruction is completed, a combination of simulation testing and actual scenario testing is used to verify the accuracy of the state reconstruction method based on event tracing.

2. The device and method for monitoring fatigue and distracted driving based on physiological characteristics according to claim 1, characterized in that: When collecting system event data, data filling and repair technology is used to predict and supplement missing or damaged data using known events and status information. The repaired event data, known events, system status information, and original event data are marked as Q1, K1, K2, and K3 respectively. The expression for predicting and supplementing the repaired event data is Q1=K3+f(K1+K2), where f() is used to predict and generate missing or damaged event data based on the known event K1 and the system status information K2.

3. The device and method for monitoring fatigue and distracted driving based on physiological characteristics according to claim 1, characterized in that: When converting to an event graph, the data is first normalized and then converted into a time series graph. Next, the constructed event graph is analyzed in real time using a state analysis model to identify key events and analyze the causal relationships and impact relationships between events. This event data includes, but is not limited to, error logs, user operation records, system anomalies or errors, system crashes, service interruptions, data loss, malicious attacks, and security vulnerability exposures.

4. The device and method for monitoring fatigue and distracted driving based on physiological characteristics according to claim 3, characterized in that: Critical events include system anomalies or errors, system crashes, service interruptions, data loss, malicious attacks, and security vulnerability exposure.

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