A log analysis method, system, smart cockpit, and electronic device.

Abstract

This invention discloses a log analysis method, system, smart cockpit, and electronic device. The method includes the following steps: deploying a log analysis system in an in-vehicle terminal, the log analysis system being configured to interact with the cloud for data exchange; the log analysis system receiving a log analysis model from the cloud, acquiring and filtering log data generated by the in-vehicle terminal, generating corresponding analysis reports from the processed log data and packaging them, and uploading the packaged log data to the cloud; the cloud receiving the packaged log data, analyzing, processing, and storing it. This invention can solve the problems of consuming large amounts of storage space due to uploading large amounts of logs, and the inability to process log data in real time. By preprocessing and filtering logs, it effectively reduces storage costs while ensuring data integrity and traceability.

Description

Technical Field

[0001] This invention relates to an analysis method, system, smart cockpit, and electronic device, and more particularly to a log analysis method, system, smart cockpit, and electronic device. Background Technology

[0002] Current log analysis methods involve uploading client-generated logs to the cloud, then analyzing the uploaded log files to extract anomaly information. However, in-vehicle systems contain numerous clients, generating a large volume of logs during operation. Currently, these logs are not proactively uploaded to the cloud for analysis. Uploading these files would consume significant bandwidth and storage resources, and because real-time uploads are not possible, log files may have already been cleaned up by the time they are needed, preventing log retrieval and problem analysis. Existing technologies lack a corresponding solution and fail to meet requirements, urgently needing improvement. Summary of the Invention

[0003] The purpose of this invention is to provide a log analysis method, system, smart cockpit, and electronic device. The first technical problem to be solved is to propose a log data interaction method that coordinates the vehicle terminal and the cloud. The second technical problem to be solved is to analyze and process log data in real time to avoid consuming bandwidth and storage resources, thereby overcoming the shortcomings of the existing technology.

[0004] This invention provides the following solution:

[0005] A log analysis method, applied to log data interaction between in-vehicle terminals and the cloud, includes:

[0006] A log analysis system is deployed in the vehicle terminal. The log analysis system is configured to interact with the cloud for data exchange. The log analysis system receives a log analysis model from the cloud and the log analysis model is used to filter the log data generated by the vehicle terminal.

[0007] The log analysis system receives a log analysis model from the cloud. The log analysis model includes a preset rule set for filtering log data. The log analysis system monitors the log data generated by the vehicle terminal in real time and performs preliminary filtering of the log data according to the preset rule set.

[0008] The log analysis system performs feature extraction on the filtered log data to identify key information and potential abnormal patterns. Based on machine learning algorithms, it further analyzes the feature extraction results to identify and classify abnormal behaviors in the log data.

[0009] The log analysis system acquires and filters the log data generated by the vehicle terminal, generates corresponding analysis reports from the processed log data, packages them, and uploads the packaged log data to the cloud through the vehicle terminal system.

[0010] The cloud receives the packaged log data, analyzes, processes, and stores it. Furthermore, the deployment of a log analysis system in the vehicle terminal, configured to interact with the cloud for data exchange, further includes:

[0011] The log analysis system periodically caches the log analysis model from the cloud to the vehicle terminal, analyzes and processes the log data based on the cached log analysis model, and reports the analysis and processing results to the cloud according to the log problem handling strategy defined in the log analysis model.

[0012] Furthermore, the log analysis model is used to filter log data generated by the vehicle terminal, and further includes:

[0013] The log analysis model establishes analysis processes, calculation methods, evaluation criteria, and result processing strategies for log issues. Based on these strategies, it generates corresponding log features and performs classification and / or combination processing based on these log features.

[0014] Furthermore, the log analysis system acquires and filters log data generated by the vehicle terminal, generates corresponding analysis reports from the processed log data, packages them, and uploads the packaged log data to the cloud through the vehicle terminal system, further including:

[0015] The log analysis system executes log acquisition, analysis, and processing strategies on the vehicle terminal, intelligently adjusts based on real-time log generation, and generates corresponding analysis strategies to optimize log resources.

[0016] Furthermore, the log analysis system executes a strategy for log acquisition, analysis, and processing on the vehicle terminal, specifically: the log analysis system performs log preprocessing on the log data, which includes data cleaning, formatting, and normalization.

[0017] Furthermore, the cloud receives the packaged log data, analyzes, processes, and stores it, further including:

[0018] The cloud-based system monitors the status of the vehicle terminal's log analysis system in real time. After receiving the packaged log data uploaded by the vehicle terminal, it performs real-time monitoring and anomaly detection on the log data to identify and respond to corresponding abnormal issues in the vehicle system.

[0019] A log analysis system for enabling collaborative log data interaction between an in-vehicle terminal and the cloud, characterized in that it includes:

[0020] The log analysis system deployment module deploys a log analysis system in the vehicle terminal. The log analysis system is configured to interact with the cloud for data exchange. The log analysis system receives a log analysis model from the cloud, and the log analysis model is used to filter the log data generated by the vehicle terminal.

[0021] The log analysis model receiving module receives a log analysis model from the cloud. The log analysis model includes a preset rule set for filtering log data. The log analysis system monitors the log data generated by the vehicle terminal in real time and performs preliminary filtering of the log data according to the preset rule set.

[0022] The log data feature extraction and analysis module performs feature extraction on the filtered log data, identifies key information and potential abnormal patterns, and further analyzes the feature extraction results based on machine learning algorithms to identify and classify abnormal behaviors in the log data.

[0023] The log data filtering and analysis report generation module is used in the log analysis system to obtain and filter log data generated by the vehicle terminal, generate corresponding analysis reports from the filtered log data, package them, and upload the packaged log data to the cloud through the vehicle terminal system.

[0024] A log data cloud processing module receives packaged log data in the cloud, analyzes, processes, and stores it. A smart cockpit includes a log analysis system that executes the aforementioned log analysis method.

[0025] An electronic device includes: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method.

[0026] A computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of the method.

[0027] Compared with the prior art, the present invention has the following advantages:

[0028] This invention solves the problems of excessive storage space consumption caused by uploading large amounts of logs and the inability to process log data in real time. In traditional log uploading methods, the sheer volume of logs generated by the vehicle system requires significant storage space to store these log files. However, by employing the log analysis method of this invention, preprocessing and filtering of logs effectively reduces storage costs while ensuring data integrity and traceability.

[0029] This invention also solves the problem of not being able to upload all log files generated by the vehicle system due to excessive log output. In practical applications, due to network bandwidth limitations or other reasons, it may not be possible to upload all generated log files to the server for analysis in a timely manner. However, the lack of corresponding log files for analysis when problems occur makes troubleshooting and optimization difficult. By using the log analysis method of this invention, reasonable rules and strategies can be set on the vehicle system, and different types and levels of logs can be filtered and prioritized according to indicators such as importance and key information, ensuring that key information is reported and saved in a timely manner.

[0030] This invention also features the ability to proactively report and identify potential problems that can be optimized, thereby improving program optimization efficiency. By monitoring and analyzing data such as the operating status and performance indicators of the vehicle system, and combining this with preset rules and algorithm models, an alarm mechanism can be automatically triggered when potential problems or areas for improvement are detected. The relevant information is then proactively reported to the management platform or developers. This not only accelerates troubleshooting and repair but also provides strong support and reference for program optimization.

[0031] In summary, this invention not only solves the problems of excessive storage space consumption and high cost in traditional methods, and the difficulty in troubleshooting caused by the inability to upload all generated massive log files, but also has many benefits such as proactively reporting potential problems and promoting program optimization and efficiency improvement. It has broad application prospects in the automotive industry, smart cockpits, and intelligent vehicles. Attached Figure Description

[0032] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0033] Figure 1 This is a flowchart of the log analysis method.

[0034] Figure 2 This is the architecture diagram of the log analysis system.

[0035] Figure 3 This is an implementation method of the present invention in a specific application scenario.

[0036] Figure 4 This is a schematic diagram of the electronic device. Detailed Implementation

[0037] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] like Figure 1 The log analysis method shown applies log data interaction between the vehicle terminal and the cloud, including:

[0039] Step S1: Deploy a log analysis system in the vehicle terminal. The log analysis system is configured to interact with the cloud for data exchange. The log analysis system receives a log analysis model from the cloud. The log analysis model is used to filter the log data generated by the vehicle terminal.

[0040] Step S2: The log analysis system receives a log analysis model from the cloud. The log analysis model includes a preset rule set for filtering log data. The log analysis system monitors the log data generated by the vehicle terminal in real time and performs preliminary filtering of the log data according to the preset rule set.

[0041] Step S3: The log analysis system performs feature extraction on the filtered log data, identifies key information and potential abnormal patterns, and further analyzes the feature extraction results based on machine learning algorithms to identify and classify abnormal behaviors in the log data.

[0042] Step S4: The log analysis system acquires and filters the log data generated by the vehicle terminal, generates corresponding analysis reports from the processed log data, packages them, and uploads the packaged log data to the cloud through the vehicle terminal system.

[0043] Step S5: The cloud receives the packaged log data, analyzes, processes, and stores it.

[0044] Preferably, in step S1, deploying a log analysis system in the terminal system, wherein the log analysis system is configured to interact with the cloud for data exchange, further includes:

[0045] The log analysis system periodically caches the log analysis model from the cloud to the vehicle terminal, analyzes and processes the log data based on the cached log analysis model, and reports the analysis and processing results to the cloud according to the log problem handling strategy defined in the log analysis model.

[0046] For example, step S1 describes a log analysis method based on collaboration between the vehicle terminal and the cloud. Step S1 can be further refined into the following steps:

[0047] Step S11, Deployment of vehicle terminal log analysis system: Deploy a log analysis system in the vehicle terminal, which is configured to interact with the cloud securely and reliably.

[0048] Step S12, Receive cloud log analysis model: The log analysis system can receive a log analysis model sent from the cloud. The log analysis model is pre-trained or configured according to specific analysis requirements and is used to effectively filter and analyze the log data generated by the vehicle terminal.

[0049] Step S13, Log data acquisition and filtering: The log analysis system acquires log data generated by the vehicle terminal in real time or periodically, and uses the received log analysis model to filter the log data. The purpose of filtering is to remove redundant information and retain key data related to specific events, problems or analysis needs.

[0050] Step S14, Generating and Packaging Analysis Reports: The filtered log data is processed to generate corresponding analysis reports. These reports may include statistical data, event descriptions, problem diagnosis suggestions, etc. The analysis reports will be packaged into a format suitable for transmission.

[0051] Step S15: Upload the packaged log data: The packaged log data and analysis report are uploaded to the cloud via the vehicle terminal's communication system. The upload process may involve security measures such as data encryption, compression, and transmission protocol selection to ensure data security and integrity.

[0052] Step S16, Cloud Reception, Analysis, and Storage: The cloud server receives packaged data from the vehicle terminal, performs decryption, decompression, and verification operations, and then conducts in-depth analysis, processing, and storage of the log data and analysis reports. The cloud may have more powerful computing capabilities and storage resources, enabling efficient processing of large amounts of data and providing advanced functions such as data visualization and report generation.

[0053] Steps S11 to S16 enable real-time monitoring and analysis of vehicle terminal log data, helping vehicle manufacturers, operators, or service providers to promptly identify and resolve potential problems, improve the reliability and safety of vehicle operation, and provide strong support for vehicle management, maintenance, and optimization through cloud-based data analysis and processing.

[0054] Preferably, in step S2, the log analysis model includes a preset rule set for filtering log data, further including:

[0055] The log analysis model formulates analysis processes, calculation methods, evaluation criteria, and result processing strategies for the log problems to be solved. Based on these strategies, it generates corresponding log features and performs classification and / or combination processing based on these features. In step S2, the log analysis model can handle one type of problem or a class of problems. When handling multiple types of problems, multiple models can be combined to generate a single model that can be deployed to the vehicle terminal.

[0056] Preferably, in step S3, the log analysis system performs feature extraction on the filtered log data, identifies key information and potential abnormal patterns, and further analyzes the feature extraction results based on machine learning algorithms to identify and classify abnormal behaviors in the log data, further including:

[0057] The log analysis system executes log acquisition, analysis, and processing strategies on the vehicle terminal, intelligently adjusts based on real-time log generation, and generates corresponding analysis strategies to optimize log resources.

[0058] The role of a log analysis system is to process and analyze log data. By performing feature extraction on the filtered log data, it identifies key information and potential abnormal patterns, and further analyzes the feature extraction results based on machine learning algorithms in order to accurately identify and classify abnormal behaviors in the log data.

[0059] The role of a log analysis system is to process and analyze log data. By performing feature extraction on the filtered log data, it identifies key information and potential abnormal patterns, and further analyzes the feature extraction results based on machine learning algorithms in order to accurately identify and classify abnormal behaviors in the log data.

[0060] Specifically, the system first filters out the portions of raw log data that need to be analyzed from a large amount of data. Then, for these filtered log data, the system performs feature extraction. Feature extraction involves extracting meaningful metrics or attribute values ​​from the raw data that can represent a category or attribute. For example, in the field of connected vehicles, source IP addresses, destination IP addresses, and port numbers can be extracted as features from the network traffic logs of in-vehicle terminals.

[0061] Next, after feature extraction, the system uses machine learning algorithms to further analyze these features. Machine learning algorithms can train models based on the differences between known normal behavior samples and known abnormal behavior samples, and then apply these models to unknown samples to determine whether they constitute abnormal behavior. For example, in fields such as connected vehicles and intelligent vehicles, machine learning algorithms can be used to detect anomalies in smart cockpits and vehicle driving states.

[0062] For example, the log analysis system executes a strategy for log acquisition, analysis, and processing on the vehicle terminal, specifically: the log analysis system performs log preprocessing on the log data, which includes data cleaning, formatting, and normalization.

[0063] For example, the machine learning algorithm in this embodiment can be applied to feature extraction and further analysis. By comparing the differences between known normal behavior samples and known abnormal behavior samples, the machine learning algorithm can train a model and apply the model to unknown samples to determine whether they belong to abnormal behavior. This embodiment can extract various features from a large amount of log data, such as timestamps, IP addresses, request types, etc., and then use machine learning algorithms to analyze these features and train a classification model based on the differences between known normal logs and abnormal logs. Finally, when new log data arrives, it can be input into the model for judgment. If it is determined to be abnormal behavior, corresponding processing is performed, ultimately achieving more accurate identification of abnormal behavior and realizing automated processing and judgment.

[0064] For example, log data can be classified and regressed using decision tree supervised learning algorithms to identify abnormal behavior patterns. The specific calculation process includes:

[0065] Data preprocessing: Cleaning log data and handling missing and outlier values.

[0066] Feature extraction: Extract useful features from logs, such as the number of failed login attempts, request frequency, IP address, etc.

[0067] Training the model: The decision tree model is trained using the extracted features. The decision tree constructs its tree structure by recursively selecting the optimal features.

[0068] Model evaluation: Use a test set to evaluate the model's accuracy and generalization ability.

[0069] For example, a support vector machine (SVM) classification algorithm can be used to distinguish different categories of log data based on the optimal boundary between them. The calculation process of the SVM classification algorithm includes:

[0070] Data preprocessing: Standardize or normalize log data to improve model performance.

[0071] Feature extraction: Select features that are related to abnormal behavior.

[0072] Kernel function selection: Choose an appropriate kernel function, such as a linear kernel, a polynomial kernel, or a radial basis function kernel.

[0073] Model training: Train the SVM model using the extracted features and kernel functions.

[0074] Model evaluation: Evaluate the model's performance on the test set.

[0075] As a further improvement to step S3, the strategy for acquiring, analyzing, and processing logs generated by the vehicle terminal in real time includes:

[0076] Step S31: Preprocess the log data, including data cleaning, formatting, and normalization;

[0077] Step S32: Apply the received log analysis model to filter the preprocessed log data;

[0078] Step S33: Generate the corresponding analysis report based on the filtered log data;

[0079] The generated analysis report is packaged, and the packaged log data and analysis report are uploaded to the cloud through the vehicle terminal system;

[0080] Steps S31 to S33 package the generated analysis report and upload the packaged log data and analysis report to the cloud through the vehicle terminal system. The log analysis system intelligently adjusts the log acquisition, analysis and processing strategies according to the real-time log generation situation to optimize the processing efficiency of log resources.

[0081] Steps S31 to S33 can filter log data using a preset log level classification strategy. This strategy includes classifying log data into different levels such as errors, warnings, information, and debugging logs to prioritize critical logs. Alternatively:

[0082] The filtered log data undergoes statistical analysis, event identification, and problem diagnosis. The results are presented in visual charts and reports to facilitate quick user understanding of the analysis findings.

[0083] After receiving the packaged log data and analysis report, the cloud performs further analysis, processing and storage, including but not limited to advanced processing such as data mining, correlation analysis and trend prediction of the log data, and feeds the processing results back to the vehicle terminal to realize real-time monitoring and fault warning of the vehicle system.

[0084] As a further improvement to steps S31 to S33, the log analysis system can be equipped with self-learning capabilities based on historical log data and analysis results fed back from the cloud, thereby improving the accuracy and efficiency of log analysis. For example:

[0085] Step S34, Data Preprocessing: This step involves receiving log data and performing data cleaning, deduplication, and format conversion operations.

[0086] Step S35, Feature Extraction: This step extracts key features from the preprocessed log data. The key features include, but are not limited to, timestamps, user identifiers, operation types, frequency patterns, and anomaly indicators.

[0087] Step S36, Model Training: This is used to train the learning model based on historical log data and analysis results from cloud feedback.

[0088] In step S36, at least two machine learning algorithms are trained, including a decision tree algorithm and a support vector machine algorithm, wherein:

[0089] The decision tree algorithm constructs a decision tree by recursively selecting the optimal feature, and is used to classify normal and abnormal behavior in log data;

[0090] The Support Vector Machine (SVM) algorithm selects the optimal kernel function and parameters to find the optimal boundary between data points, which is used to distinguish different categories in log data.

[0091] Step S37, Model Evaluation: This step evaluates the performance of the trained machine learning model on the test set. The evaluation includes, but is not limited to, accuracy and recall, and generates evaluation results.

[0092] Step S38, Adaptive Adjustment: This step is used to dynamically adjust the model parameters and feature weights based on the model evaluation results obtained in step S37 and the feedback from real-time log data, in order to improve the accuracy and efficiency of log analysis.

[0093] Step S39, the results output module, is used to display the analysis results to the user in a visual manner and provide a detailed report of abnormal behavior.

[0094] By dividing the log data into training and test sets through steps S31 to S39, and selecting the optimal model parameters through techniques such as cross-validation, the overall classification performance of log data analysis is improved by combining the prediction results of various machine learning algorithms.

[0095] Preferably, in step S4, the cloud receives the packaged log data, analyzes, processes, and stores it, further including:

[0096] The cloud-based system monitors the status of the vehicle terminal's log analysis system in real time. After receiving the packaged log data uploaded by the vehicle terminal, it performs real-time monitoring and anomaly detection on the log data to identify and respond to corresponding abnormal issues in the vehicle system.

[0097] For the purpose of simplicity, the method steps disclosed in the above embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0098] Any flowchart or other description of a process or method can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process. Furthermore, the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed and implemented not in the order shown or discussed, including substantially simultaneously or in reverse order according to the functions involved, or by executing computer instructions and implementing corresponding functions according to program structures such as loops, branches, etc., as will naturally be understood by those skilled in the art when practicing embodiments of the invention.

[0099] like Figure 2 The log analysis system shown is used to enable collaborative log data interaction between the vehicle terminal and the cloud, including:

[0100] The log analysis system deployment module deploys a log analysis system in the vehicle terminal. The log analysis system is configured to interact with the cloud for data exchange. The log analysis system receives a log analysis model from the cloud, and the log analysis model is used to filter the log data generated by the vehicle terminal.

[0101] The log analysis model receiving module receives a log analysis model from the cloud. The log analysis model includes a preset rule set for filtering log data. The log analysis system monitors the log data generated by the vehicle terminal in real time and performs preliminary filtering of the log data according to the preset rule set.

[0102] The log data feature extraction and analysis module performs feature extraction on the filtered log data, identifies key information and potential abnormal patterns, and further analyzes the feature extraction results based on machine learning algorithms to identify and classify abnormal behaviors in the log data.

[0103] The log data filtering and analysis report generation module is used in the log analysis system to obtain and filter log data generated by the vehicle terminal, generate corresponding analysis reports from the filtered log data, package them, and upload the packaged log data to the cloud through the vehicle terminal system.

[0104] The log data cloud processing module receives packaged log data in the cloud and performs analysis, processing, and storage.

[0105] The implementation methods of the system described above are merely illustrative. For example, the various functional modules, units, or subsystems within the system may or may not be physically separate, or they may or may not be physical units; that is, they may be located in the same place or distributed across multiple different systems and their subsystems or modules. Those skilled in the art can select some or all of the functional modules, units, or subsystems to achieve the objectives of the embodiments of the present invention according to actual needs. Those skilled in the art can understand and implement the above-described situations without any creative effort.

[0106] like Figure 3 The embodiments of the present invention shown are implemented in specific application scenarios. These embodiments can be briefly described as follows:

[0107] Step 200: From extracting features from the logs to be analyzed to generating log analysis, providing a model;

[0108] Step 201: From generating log analysis, provide model-to-model distribution services;

[0109] Step 203: Data interaction between the cloud log analysis model and the model distribution service;

[0110] Step 204: From obtaining the cloud log analysis model to starting the service;

[0111] Step 205: From starting the service to analyzing logs using the model;

[0112] Step 206: From analyzing logs using the model to generating analysis results;

[0113] Step 207: From generating analysis results to reporting analysis results;

[0114] Step 208: From reporting the analysis results to the log analysis results service, finally view the analysis results.

[0115] This embodiment addresses the problem of wasting bandwidth and storage resources by uploading all terminal logs to the cloud before subsequent analysis, and the potential issue of logs becoming unavailable due to deletion. This embodiment allows for the extraction and analysis of log data using a model. The model is then used on the terminal to extract necessary logs (unnecessary logs are not extracted) or generate corresponding reports. The logs or reports are then packaged and proactively uploaded to the cloud for further use.

[0116] Compared with traditional log uploading methods, the log data processing process in this embodiment reduces the number of logs uploaded, consumes less bandwidth and storage, and can upload data processed on the terminal to the cloud, allowing for timely monitoring of the terminal status or detection of anomalies, thus improving system stability. The log data analysis method disclosed in this embodiment is described in detail below:

[0117] The process involves identifying the problem to be solved, developing an analysis workflow, calculation methods, evaluation criteria, and result processing strategies. Information such as log characteristics used in the analysis is then incorporated. Finally, a model is built in the cloud, capable of handling one type or class of problems. When dealing with multiple types of problems, multiple models can be combined to generate a single model that can be deployed to the terminal.

[0118] A log analysis system needs to be developed in advance for the terminal system. The log analysis system can periodically cache the model in the cloud to the vehicle terminal. The terminal can analyze and process the problem to be analyzed based on the model cached from the cloud, and report the analysis results to the cloud according to the result processing strategy defined in the model for further analysis.

[0119] It is worth noting that the invented log analysis system requires prior loading and parsing of the model, and corresponding analysis based on the model. As business develops and the types of sub-models increase, the invented log analysis system can be upgraded and iterated to achieve more functions.

[0120] Based on the specific scenario examples provided in this embodiment, the effects achieved by the embodiments of the present invention can be verified. The embodiments of the present invention are not limited to processing the example scenario, but can handle more scenarios, such as anomaly analysis and reporting, key log extraction and reporting, etc.

[0121] In in-vehicle multimedia terminal systems, it is necessary to perform performance analysis on the song playback function implemented through a smart assistant. This analysis focuses on response speed. First, the response speed is evaluated by the time taken from when the smart assistant generates a search command (hereinafter referred to as NLU) to when the song search ends. A time exceeding one second is considered poor response speed. Furthermore, this process involves network requests and NLU protocol transmission, which can be further divided into several segments (A->B->C, used to indicate the time taken at each end). Abnormal time taken at any end will also be judged as poor performance.

[0122] Then, based on the definition of the above evaluation method, corresponding log features are identified from the logs, from which necessary information can be extracted, such as the start time of sending NLU to the media. Other node time log features are found, and a time extraction strategy for that node is formulated. After obtaining the time of each node, an algorithm for calculating the time consumption between nodes and an evaluation strategy are designed. Finally, a result with a score is generated for the media search record's time consumption. For example, in this process, records with AC consumption exceeding 1 second or B->C (search time) exceeding 7 seconds are considered to have long consumption and need to be reported to the logs for analysis and optimization. The algorithm assigns a performance score to the record based on the length of time consumed (set to 100 points, below 60 points is considered poor and needs optimization). All analysis results are generated into a report, which is subsequently uploaded to the cloud along with the logs.

[0123] Next, based on the time taken to complete the search record in the previous step, the search records below a certain threshold (<=60) are extracted from the log file. For example, if the search record occurs at 13:30 30 seconds, the log for that search record (AC) time period is extracted and stored in the file. This process will extract all abnormal log records to the file in the same way.

[0124] Then, the above analysis process, methods, and strategies are built into a model in the cloud. After the terminal obtains the model, it analyzes the logs generated in the system and uploads the report and results to the cloud for further performance monitoring and anomaly analysis, which facilitates the optimization of terminal search performance.

[0125] This process only extracts, packages, and uploads logs deemed abnormal, while excluding normal search logs, thus reducing the amount of logs uploaded from the terminal to the cloud. Additionally, some abnormally time-consuming records are already scored on the terminal. When engineers see this report on the cloud, they can analyze and optimize the poorly performing records, report performance issues in advance, and improve the efficiency of search optimization.

[0126] As can be seen from the implementation of this invention in specific application scenarios, this invention defines the problem and formulates a corresponding analysis process to determine the problem to be analyzed. For example, for the song playback function in an in-vehicle multimedia terminal system, performance indicators are defined, and the response time is used for evaluation. A time measurement method and evaluation standard are designed, key features are extracted from log data, and an algorithm is designed based on the extracted features to calculate the time between each node. Optimization is performed based on different time consumptions, and the analysis results are processed to generate a report containing scores. The report and logs are uploaded to the cloud, and a log analysis model built in the cloud is used to handle specific or multiple types of problems, providing corresponding analysis processes, methods, and strategies. The log analysis model is loaded on the in-vehicle terminal, the log data is parsed and analyzed, and the log analysis model is periodically updated and iterated. The real-time log data is analyzed and processed based on the updated log analysis model. The cloud receives the analysis results reported by the in-vehicle terminal, performs performance monitoring, and optimizes based on the analysis results, thereby improving system performance.

[0127] The log analysis method provided in this embodiment is a closed-loop system, from problem definition to model building, then to terminal analysis and cloud monitoring, and finally optimization. This system can not only be applied to in-vehicle multimedia terminals, but can also be extended to other scenarios that require performance monitoring and anomaly detection.

[0128] like Figure 4 As shown, in addition to providing a log analysis method and system, this invention also provides corresponding acknowledgments, electronic devices, and storage media:

[0129] A smart cockpit, the smart cockpit including a log analysis system, performs the log analysis method.

[0130] An electronic device includes: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method.

[0131] A computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of the method.

[0132] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Figure 4As shown, device 800 includes a processor 801, a memory 802, a communication interface 803, and a bus 804. The processor 801, memory 802, and communication interface 803 communicate via bus 804, or via other means such as wireless transmission. The memory 802 stores instructions, and the processor 801 executes the instructions stored in the memory 802. The memory 802 stores program code 8021, and the processor 801 can call the program code 8021 stored in the memory 802 to execute the steps of the log analysis method.

[0133] It should be understood that in the embodiments of this application, processor 801 may be a CPU, or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors may be microprocessors or any conventional processors, etc.

[0134] The memory 802 may include read-only memory (ROM) and random access memory (RAM), and provides instructions and data to the processor 801. The memory 802 may also include non-volatile random access memory. The memory 802 may be volatile memory or non-volatile memory, or may include both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0135] In addition to the data bus, bus 804 may also include a power bus, a control bus, and a status signal bus. However, for clarity, all buses are labeled as bus 804 in the diagram.

[0136] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. 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. 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 (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive (SSD).

[0137] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0138] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, any of the embodiments claimed in the claims can be used in any combination of embodiments of the invention.

[0139] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0140] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0141] All features disclosed in this specification, or steps in all disclosed methods or processes, may be combined in any way, except for mutually exclusive features and / or steps. Any feature disclosed in this specification, unless specifically stated otherwise, may be replaced by other equivalent or similar features. That is, unless specifically stated otherwise, each feature is merely one example of a series of equivalent or similar features. Throughout this specification, the same reference numerals indicate the same elements.

[0142] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the corresponding claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the corresponding claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A log analysis method applied to log data interaction between an in-vehicle terminal and the cloud, characterized in that, include: A log analysis system is deployed in an in-vehicle terminal. The log analysis system is configured to interact with the cloud for data exchange. The log analysis system receives a log analysis model from the cloud. The log analysis model is used to filter log data generated by the in-vehicle terminal. The log analysis model formulates analysis processes, calculation methods, evaluation criteria, and result processing strategies for log issues. Based on the strategies, it generates corresponding log features and performs classification and / or combination processing based on the log features. The log analysis system receives a log analysis model from the cloud. The log analysis model includes a preset rule set for filtering log data. The log analysis system monitors the log data generated by the vehicle terminal in real time and performs preliminary filtering of the log data according to the preset rule set. The log analysis system performs feature extraction on the filtered log data to identify key information and potential abnormal patterns. Based on machine learning algorithms, it further analyzes the feature extraction results to identify and classify abnormal behaviors in the log data. The log analysis system acquires and filters the log data generated by the vehicle terminal, generates corresponding analysis reports from the processed log data, packages them, and uploads the packaged log data to the cloud through the vehicle terminal system. The cloud receives the packaged log data, analyzes, processes, and stores it.

2. The log analysis method according to claim 1, characterized in that, The deployment of a log analysis system in the vehicle terminal, wherein the log analysis system is configured to interact with the cloud for data exchange, further includes: The log analysis system periodically caches the log analysis model from the cloud to the vehicle terminal, analyzes and processes the log data based on the cached log analysis model, and reports the analysis and processing results to the cloud according to the log problem handling strategy defined in the log analysis model.

3. The log analysis method according to claim 1, characterized in that, The log analysis system acquires and filters log data generated by the vehicle terminal, generates corresponding analysis reports from the processed log data, packages them, and uploads the packaged log data to the cloud via the vehicle terminal system. Further, it includes: The log analysis system executes log acquisition, analysis, and processing strategies on the vehicle terminal, intelligently adjusts based on real-time log generation, and generates corresponding analysis strategies to optimize log resources.

4. The log analysis method according to claim 3, characterized in that, The log analysis system executes a strategy for log acquisition, analysis, and processing on the vehicle terminal. Specifically, the log analysis system performs log preprocessing on the log data, which includes data cleaning, formatting, and normalization.

5. The log analysis method according to claim 1, characterized in that, The cloud platform receives the packaged log data, analyzes, processes, and stores it, further including: The cloud-based system monitors the status of the vehicle terminal's log analysis system in real time. After receiving the packaged log data uploaded by the vehicle terminal, it performs real-time monitoring and anomaly detection on the log data to identify and respond to corresponding abnormal issues in the vehicle system.

6. A log analysis system for enabling collaborative log data interaction between an in-vehicle terminal and the cloud, characterized in that, The log analysis system is used to execute the log analysis method as described in any one of claims 1-5; The log analysis system includes: The log analysis system deployment module deploys a log analysis system in the vehicle terminal. The log analysis system is configured to interact with the cloud for data exchange. The log analysis system receives a log analysis model from the cloud, and the log analysis model is used to filter the log data generated by the vehicle terminal. The log analysis model receiving module receives a log analysis model from the cloud. The log analysis model includes a preset rule set for filtering log data. The log analysis system monitors the log data generated by the vehicle terminal in real time and performs preliminary filtering of the log data according to the preset rule set. The log data feature extraction and analysis module performs feature extraction on the filtered log data, identifies key information and potential abnormal patterns, and further analyzes the feature extraction results based on machine learning algorithms to identify and classify abnormal behaviors in the log data. The log data filtering and analysis report generation module is used in the log analysis system to obtain and filter log data generated by the vehicle terminal, generate corresponding analysis reports from the filtered log data, package them, and upload the packaged log data to the cloud through the vehicle terminal system. The log data cloud processing module receives packaged log data in the cloud and performs analysis, processing, and storage.

7. An intelligent cockpit, characterized in that, The intelligent cockpit includes the log analysis system of claim 6, which executes the log analysis method of any one of claims 1 to 5.

8. An electronic device, characterized in that, include: The system includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; the memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the method according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, It stores a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of the method according to any one of claims 1 to 5.

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