Early warning method and device of terminal equipment and computer equipment

Abstract

The invention relates to an early warning method and device for terminal equipment and computer equipment. The early warning method comprises the following steps: acquiring a real-time performance index of a monitoring system; the real-time performance index is obtained by acquiring operation data of the monitoring system through a data acquisition assembly and performing standardization processing on the operation data; the monitoring system comprises terminal equipment, a server and a network node, wherein the server and the network node are connected with the terminal equipment; inputting the real-time performance index into a completely trained early warning model, and carrying out intelligent analysis on the operation state of the terminal equipment to obtain an index prediction value of a subsequent time step; and generating warning information based on the index predicted value, thereby solving the problem that early warning for the terminal equipment lacks deep mining and correlation analysis, and realizing multi-equipment, cross-language and cross-platform data integration, thereby performing deep analysis on the operation state of the terminal equipment, and improving the accuracy of early warning.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an early warning method and device for a terminal device and a computer device. Background Art

[0002] In modern monitoring and security systems, the management and maintenance of terminal devices such as cameras are crucial to ensuring safety. However, with the increase in the number of terminal devices and their application in complex environments, traditional monitoring methods are facing the problem of frequent equipment failures. For example, cameras are easily affected by environmental factors (such as temperature, humidity, dust, etc.), leading to failures. Traditional monitoring methods often rely on regular manual inspections and cannot detect potential problems in a timely manner, resulting in maintenance only after a failure occurs, affecting the continuity and reliability of security monitoring.

[0003] Currently, there are some early warning solutions for cameras to monitor and predict the operating status of cameras. However, data from different devices and environments are often generated based on different programming languages ​​or different technical standards. Existing technologies can only process and analyze data from different devices and services independently, and cannot achieve unified centralized monitoring and management. As a result, the prediction results lack in-depth mining and correlation analysis of the overall system, affecting the early warning effect of terminal equipment failures. Summary of the invention

[0004] In this embodiment, a terminal device early warning method, apparatus and computer equipment are provided to solve the problem that unified centralized monitoring and management cannot be achieved in related technologies.

[0005] In a first aspect, an early warning method for a terminal device is provided in this embodiment, and the method includes: Acquire real-time performance indicators of the monitoring system; the real-time performance indicators are obtained by the data acquisition component by acquiring the operating data of the monitoring system and standardizing the operating data; wherein the monitoring system includes a terminal device and a server and a network node connected to the terminal device; Input the real-time performance index into a well-trained early warning model, perform intelligent analysis on the operating status of the terminal device, and obtain the index prediction value for subsequent time steps; Based on the comparison result of the indicator prediction value and the acquired dynamic alarm threshold, alarm information is generated.

[0006] In some of the embodiments, the data collection component is deployed on the terminal device, the server and the network node.

[0007] In some of the embodiments, before inputting the real-time performance indicator into the well-trained early warning model, the method further includes: Obtaining standardized historical performance indicators of the monitoring system; Based on the data characteristics of the historical performance indicators, the architecture of the time series prediction model is adjusted to obtain an initial early warning model; The initial early warning model is trained based on the historical performance data to obtain the fully trained early warning model.

[0008] In some embodiments, obtaining standardized historical performance indicators of the monitoring system further includes: Based on the data collection component deployed on the monitoring system, collect standardized original indicator data, and store the original indicator data in a database; In response to the model training instruction, the corresponding original indicator data is extracted from the database and preprocessed to obtain standardized historical performance indicators.

[0009] In some of the embodiments, based on the data characteristics of the historical performance indicators, the architecture of the time series prediction model is adjusted to obtain an initial early warning model, including: In the case where the historical performance data is three-dimensional time series data, the architecture of the time series prediction model includes a single-layer long short-term memory network and a dropout layer to obtain an initial first warning model; When the historical performance data is six-dimensional data, the architecture of the time series prediction model includes a two-layer long short-term memory network layer and a time attention mechanism layer, and an initial second warning model is obtained; When the historical performance data is twelve-dimensional data, the architecture of the time series prediction model includes a two-layer long short-term memory network layer and a feature fusion layer to obtain an initial third warning model.

[0010] In some of the embodiments, after obtaining the well-trained early warning model, the method further includes: Obtaining the historical performance indicators updated within a preset period; Based on the incremental learning method, the well-trained early warning model is fine-tuned to obtain an updated well-trained early warning model.

[0011] In some embodiments, the method further comprises: Acquire link tracking information collected by the data collection component; and locate the fault based on the indicator prediction value and the link tracking information.

[0012] In a second aspect, in this embodiment, an early warning device of a terminal device is provided, and the device includes: An indicator acquisition module is used to acquire real-time performance indicators of the monitoring system; the real-time performance indicators are obtained by the data acquisition component by acquiring the operating data of the monitoring system and performing standardization processing on the operating data; wherein the monitoring system includes a terminal device and a server and a network node connected to the terminal device; A model prediction module, used to input the real-time performance index into a well-trained early warning model, perform intelligent analysis on the operating status of the terminal device, and obtain the index prediction value for subsequent time steps; The alarm generation module is used to generate alarm information based on the comparison result of the indicator prediction value and the obtained dynamic alarm threshold.

[0013] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the early warning method of the terminal device described in the first aspect is implemented.

[0014] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the early warning method of the terminal device described in the first aspect is implemented.

[0015] Compared with the related art, the early warning method for terminal equipment provided in this embodiment obtains real-time performance indicators of the monitoring system; the real-time performance indicators are obtained by a data acquisition component through obtaining the operating data of the monitoring system and standardizing the operating data; wherein, the monitoring system includes a terminal device and a server and a network node connected to the terminal device; the real-time performance indicators are input into a well-trained early warning model, the operating status of the terminal device is intelligently analyzed, and the indicator prediction value of the subsequent time step is obtained; alarm information is generated based on the indicator prediction value, which solves the problem of being unable to perform equipment early warning based on unified centralized monitoring and management, and realizes multi-device, cross-language, and cross-platform data integration, thereby performing in-depth analysis of the operating status of the terminal device and improving the accuracy of the early warning.

[0016] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1A hardware structure block diagram of a terminal of the early warning method of a terminal device in an embodiment of the present application; Figure 2 This is a flow chart of the early warning method of the terminal device in the embodiment of the present application; Figure 3 A schematic diagram of the data flow process in an embodiment of the present application; Figure 4 This is a flow chart of an early warning method for a terminal device in a preferred embodiment of the present application; Figure 5 This is a flow chart of model training in a preferred embodiment of the present application; Figure 6 This is a structural block diagram of the early warning device of the terminal device in the embodiment of the present application.

[0018] Figure numerals: 102, processor; 104, memory; 106, transmission device; 108, input and output device; 10, indicator acquisition module; 20, model prediction module; 30, alarm generation module. DETAILED DESCRIPTION

[0019] In order to more clearly understand the purpose, technical solutions and advantages of the present application, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0020] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the general meaning understood by people with general skills in the technical field to which this application belongs. The words "one", "a", "the", "these" and the like in this application do not indicate a quantitative limitation, and they may be singular or plural. The terms "include", "comprise", "have" and any variants thereof involved in this application are intended to cover non-exclusive inclusions; for example, a process, method and system, product or device comprising a series of steps or modules (units) is not limited to the listed steps or modules (units), but may include unlisted steps or modules (units), or may include other steps or modules (units) inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" may mean: A exists alone, A and B exist at the same time, and B exists alone. Generally, the character " / " indicates that the objects associated with each other are in an "or" relationship. The terms "first", "second", "third", etc. in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0021] The method embodiment provided in this embodiment can be executed in a terminal, a computer or a similar computing device. For example, running on a terminal, Figure 1 FIG. 4 is a block diagram of the hardware structure of the terminal of the early warning method of the terminal device of this embodiment. Figure 1 As shown, the terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 and a memory 104 for storing data, wherein the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. The above terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0022] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the early warning method of the terminal device in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0023] The transmission device 106 is used to receive or send data via a network. The above network includes a wireless network provided by the communication provider of the terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0024] In this embodiment, a terminal device early warning method is provided. Figure 2 is a flow chart of the early warning method of the terminal device of this embodiment. Figure 2 As shown, the process includes the following steps: Step S210, obtaining real-time performance indicators of the monitoring system; the real-time performance indicators are obtained by the data acquisition component by obtaining operating data of the monitoring system and standardizing the operating data; wherein the monitoring system includes a terminal device and a server and a network node connected to the terminal device.

[0025] Specifically, in the monitoring system, terminal devices include cameras and various sensors. Real-time collection of monitoring system performance data, such as frame rate, latency, packet loss rate, etc.; link tracking information can also be collected to record the flow of requests between different components, so as to analyze latency, packet loss and other issues and accurately locate faults.

[0026] Step S220, input the real-time performance index into the well-trained early warning model, perform intelligent analysis on the operating status of the terminal device, and obtain the index prediction value for the subsequent time step.

[0027] Specifically, the indicator prediction value of the subsequent time step includes the indicator value of the next time step or the multi-step prediction result. Taking the camera as an example, in one of the specific embodiments, the early warning model is used to predict the video encoding time consumption, and the input indicators are time series features, including: historical single-frame encoding time sequence, GPU / CPU utilization fluctuations, video resolution and bit rate change records (such as 4K→1080P), encoding queue length (number of frames to be processed), hardware acceleration module status (such as whether GPU encoding is enabled), ambient temperature and device power consumption; the output indicator is a single frame delay prediction, including: I frame encoding time (unit: ms) in the next time step (such as 5 seconds later); P frame encoding time (unit: ms) in the next time step; queue backlog prediction: the threshold of the number of frames to be processed in the next 30 seconds (such as from 200 frames to 350 frames). Example of the prediction result data structure, json format: { "timestamp": 1762225733000, "prediction": { "IFrameDelay": 158.3, / / Unit: ms "PFrameDelay": 51.8, / / Unit: ms "queueBacklog": 328 / / The number of frames backlogged in the next 30 seconds }, "action": "reduceResolution" / / Trigger optimization actions (such as reducing resolution) } In another specific embodiment, an early warning model is used to predict video packet loss, and the input indicators are network performance characteristics: historical packet loss rate sequence, network jitter intensity (unit: ms), TCP retransmission times and round-trip delay RTT, bandwidth utilization, transmission path stability (such as distributed node CDN (Content Delivery Network) switching frequency), key frame (I frame) sending interval and size; output indicators include: 1. Packet loss rate prediction: the packet loss percentage of the next time step (such as 5 seconds later) (such as 3.5%→5.2%); 2. Key frame integrity prediction: the next key frame complete arrival rate (such as a drop from 98% to 92%), the number of I frame losses (predicted values ​​within the next 10 seconds). ‌Prediction result data structure example, json format: { "timestamp": 1762225733000, "prediction": { "packetLossRate": 5.2, / / Unit: % "IFrameIntegrity": 0.92, / / Key frame integrity rate (0-1) "lostIFrames": 1 / / Number of I-frame losses in the next 10 seconds }, "action": "enableFEC" / / trigger the error correction mechanism } Step S230, generating alarm information based on the comparison result between the indicator prediction value and the acquired dynamic alarm threshold.

[0028] Specifically, the dynamic threshold will be adjusted dynamically based on the environment, device status, business load, historical patterns and other dimensions to reduce false positives and false negatives. For example, dynamic adjustments based on factors such as time period, environment and device health status.

[0029] In this embodiment, real-time performance indicators of the monitoring system are obtained; the real-time performance indicators are obtained by the data acquisition component through obtaining the operating data of the monitoring system and standardizing the operating data; wherein the monitoring system includes a terminal device and a server and a network node connected to the terminal device; the real-time performance indicators are input into a well-trained early warning model, the operating status of the terminal device is intelligently analyzed, and the indicator prediction values ​​for subsequent time steps are obtained; alarm information is generated based on the indicator prediction values, which solves the problem of lack of deep mining and correlation analysis for early warnings of terminal devices, and realizes multi-device, cross-language, and cross-platform data integration, thereby performing in-depth analysis on the operating status of the terminal device and improving the accuracy of the early warning.

[0030] In some of the embodiments, the data collection component is deployed on a terminal device, a server, and a network node.

[0031] Specifically, the data collection component is responsible for collecting performance data in real time, such as frame rate, latency, packet loss rate, etc.; it can also collect link tracking information, which is used to record the flow of requests between different components in order to analyze latency, packet loss and other issues and accurately locate faults.

[0032] In some of these embodiments, the data collection component includes an OpenTelemetry software development kit and an OpenTelemetry agent to receive, process, and export standardized data.

[0033] Specifically, OpenTelemetry is an open source observability framework that aims to provide standardized tools for collecting, processing, and exporting metrics, logs, and tracing data in distributed systems, providing cross-platform and cross-language solutions. Among them, the agent formats the data into standardized metrics, logs, and link tracing data.

[0034] In this embodiment, OpenTelemetry has the advantages of unified standards, low-intrusive distributed tracing, dynamic sampling, and rich ecology compared to traditional Simple Network Management Protocol (SNMP), Syslog, proprietary Software Development Kit (SDK), or customized log collection solutions.

[0035] In some of the embodiments, see Figure 3 , before feeding the real-time performance indicators into the well-trained early warning model, it also includes: Step S240, obtaining standardized historical performance indicators of the monitoring system.

[0036] Specifically, data from different devices and environments are collected through data acquisition components to ensure data consistency and interoperability.

[0037] Step S250, based on the data characteristics of the historical performance indicators, the architecture of the time series prediction model is adjusted to obtain an initial early warning model.

[0038] Specifically, the time series prediction model can use an ARIMA model (AutoRegressive IntegratedMoving Average), a Transformer model, or a long short-term memory network (LSTM) to capture the temporal characteristics of the data, thereby providing more accurate prediction results. In one embodiment, the time series prediction model includes an input layer, multiple LSTM layers, and an output layer. Furthermore, the number of LSTM layers and the number of units in each layer can be adjusted to adapt to the data characteristics.

[0039] Step S260, training an initial early warning model based on historical performance data to obtain a fully trained early warning model.

[0040] Specifically, use mean square error (MSE) as the loss function and select Adam optimizer for model training. During the training process, set the batch size and training rounds, and monitor the change of loss during the training process. Evaluate the model performance on the validation set, monitor and verify the loss in real time to prevent overfitting. After training, evaluate the performance of the model on the test set, and calculate indicators such as root mean square error (RMSE) and coefficient of determination (R²). According to the model evaluation results, adjust the hyperparameters (such as learning rate, batch size, number of LSTM layers, etc.) and retrain the model.

[0041] In some of the embodiments, after obtaining the well-trained early warning model, the method further includes: Step S270, obtaining the historical performance indicators updated within a preset period.

[0042] Step S280, based on the incremental learning method, fine-tune the well-trained early warning model to obtain an updated well-trained early warning model.

[0043] In this embodiment, a regular update mechanism is set to periodically retrain the model according to the arrival of new data to adapt to data changes. When new data arrives, incremental learning can be used to fine-tune the existing model instead of training from scratch to improve model update efficiency.

[0044] In this embodiment, by training historical data, the time series prediction model can identify normal and abnormal states, thereby achieving real-time monitoring and fault prediction of terminal equipment.

[0045] In some embodiments, obtaining standardized historical performance indicators of the monitoring system further includes: Step S241, based on the data collection component deployed on the monitoring system, collect standardized original indicator data, and store the original indicator data in the database.

[0046] Specifically, use databases such as Prometheus or Elasticsearch to store the collected data, including historical performance indicators and link tracking information, for subsequent analysis and query.

[0047] Step S242, in response to the model training instruction, extract the corresponding original indicator data from the database, and perform preprocessing to obtain standardized historical performance indicators.

[0048] Specifically, the original data is denoised, missing values ​​are filled, and features are extracted to ensure that the data input to the model is accurate and reliable. Further data normalization and standardization can be performed to improve the training effect of the model.

[0049] In some of the embodiments, based on the data characteristics of the historical performance indicators, the architecture of the time series prediction model is adjusted to obtain an initial early warning model, including: In the case where the historical performance data is three-dimensional time series data, the architecture of the time series prediction model includes a single-layer long short-term memory network and a dropout layer to obtain an initial first warning model.

[0050] Specifically, in the scenario of device failure warning, the input features include 3D time series data such as CPU temperature, memory usage, and video encoding time; the model structure includes a single-layer LSTM (32 units) + a dropout layer Dropout (0.3). The single-layer LSTM captures the short-term dependency of device performance degradation, and Dropout suppresses noise interference. The optimization goal can be set to predict the device health index in the next 5 minutes. Through MSE loss optimization, the device health index prediction error can be achieved <5%.

[0051] When the historical performance data is six-dimensional data, the architecture of the time series prediction model includes a two-layer long short-term memory network layer and a temporal attention mechanism layer to obtain an initial second warning model.

[0052] Specifically, in the scenario of network attack detection, the input features include bandwidth fluctuation rate, TCP retransmission times, abnormal connection number and other 6-dimensional data; the model structure includes: bidirectional LSTM (64 units × 2 layers) + time attention mechanism, in which, when detecting a sudden increase in network bandwidth, the data before and after 10 seconds are analyzed to determine whether it is a continuous attack. The optimization goal is set to identify DDoS (Distributed Denial of Service) attack mode, using the weighted FocalLoss loss function (setting parameters α=0.8, γ=2).

[0053] When the historical performance data is twelve-dimensional data, the architecture of the time series prediction model includes a two-layer long short-term memory network layer and a feature fusion layer to obtain an initial third warning model.

[0054] Specifically, in the scenario of video analysis linkage, the input features include 12-dimensional data such as behavior recognition confidence, object movement trajectory, and ambient light intensity; video stream features are added: packet loss rate, number of I frame losses, and key frame transmission delay. For example, when the packet loss rate is >5% and three consecutive I frames are lost, a video quality warning is triggered. The model structure includes LSTM (128) + CNN feature fusion layer. CNN extracts spatial features of the video stream (such as key frame image quality), and LSTM integrates the temporal transmission status; dynamic thresholds (such as packet loss rate >5% for 10 seconds) are used in combination with MSE + Focal Loss optimization.

[0055] In this embodiment, the number of LSTM layers and the number of units in each layer are adjusted to adapt to data characteristics, fully utilize model performance, save computing resources, and improve prediction stability.

[0056] In some of the embodiments, the method further includes: acquiring link tracking information collected by a data collection component; and locating the fault based on the indicator prediction value and the link tracking information.

[0057] Specifically, when a business occurs, the link tracking information is used to identify the serial identifier from terminal device -> service A -> service B -> service C. The indicator (performance) data output by each service and unit node, or the business identifier is relatively independent, and the link data can string these things together.

[0058] In some of the embodiments, the method further includes: based on alarm information with a priority higher than a preset level, triggering a preset processing action, such as restarting a camera or reconnecting to a network, to reduce operation and maintenance pressure.

[0059] In some embodiments, the method further includes: formulating corresponding processing strategies based on the predicted values ​​of the indicators to provide decision support for the operation and maintenance personnel. The alarm information and processing strategies can be sent to the operation and maintenance personnel through various means such as emails and text messages.

[0060] In some of the embodiments, the method further includes: sending real-time performance indicators, link tracking information and indicator prediction values ​​to a control interface for display, so that operation and maintenance personnel can intuitively monitor the system status through a visual interface.

[0061] The present embodiment is described and illustrated below through a preferred embodiment. This preferred embodiment provides an early warning method for a terminal device to monitor and warn a camera device in a monitoring system. Figure 4 , the method comprising: S1. Data collection: Deploy OpenTelemetry SDK / Agent on camera devices, servers, and network nodes to collect performance data (such as frame rate, latency, packet loss rate, etc.) and link tracking information (recording the flow of requests between different components) in real time.

[0062] S2, data storage: Use databases such as Prometheus or Elasticsearch to store collected data, including historical performance indicators and link tracking information, for subsequent analysis and query.

[0063] S3, data preprocessing: denoising, filling missing values ​​and extracting features of raw performance data and link tracking information to ensure that the data input to the AI ​​model is accurate and reliable. This module can also perform data normalization and standardization to improve the training effect of the model.

[0064] S4. Model training and deployment, see Figure 5 : S4.1. Build a time series prediction model: First, define the network architecture and create a time series prediction model that includes an input layer, multiple long short-term memory network layers (LSTM layers), and an output layer. The number of LSTM layers and the number of units in each layer can be adjusted to adapt to the data characteristics. Secondly, select the loss function and optimizer, use the mean square error (MSE) as the loss function, and select the Adam optimizer for model training.

[0065] S4.2, Model training: The training process includes using the training set to train the model, setting the appropriate batch size and training rounds, and monitoring the loss changes during the training process. The validation process includes evaluating the model performance on the validation set, monitoring the validation loss in real time, and preventing overfitting.

[0066] S4.3, Model evaluation and optimization: Evaluate the performance of the model on the test set and calculate indicators such as root mean square error (RMSE) and coefficient of determination (R²). According to the model evaluation results, adjust the hyperparameters (such as learning rate, batch size, number of LSTM layers, etc.) and retrain the model.

[0067] S4.4, Model update: Set up a regular update mechanism to periodically retrain the model based on the arrival of new data to adapt to data changes. When new data arrives, you can use incremental learning to fine-tune the existing model instead of training from scratch.

[0068] S4.5, Prediction and Application: Deploy the trained time series prediction model on the server to input new input data into the trained time series prediction model for fault prediction and output the indicator value of the next time step or the multi-step prediction result.

[0069] S5. Real-time analysis and decision-making: The analysis engine deployed on the server processes the data from the data storage module in real time, runs the trained time series prediction model, and performs fault prediction and anomaly detection. According to the analysis results of the time series prediction model, the corresponding processing strategy is formulated to provide decision support for operation and maintenance personnel.

[0070] S6, Intelligent Alarm and Automatic Response: S6.1, Alarm management system: Set dynamic alarm thresholds based on the results of fault prediction and anomaly detection, and generate alarm notifications. Alarm information can be sent to relevant operation and maintenance personnel through various means such as email and SMS.

[0071] S6.2. Automatic response mechanism: For high-priority alarms, the system can automatically trigger preset processing actions, such as restarting the camera or reconnecting to the network, to reduce operation and maintenance pressure.

[0072] S7, Visualization and Monitoring: S7.1, Monitoring dashboard: Provides a real-time monitoring interface to display camera performance indicators, link tracking information, and AI prediction results. Operation and maintenance personnel can intuitively monitor system status through the visual interface.

[0073] S7.2, Fault tracing tool: Analyze the link and root cause of the fault, generate anomaly scores, and help operation and maintenance personnel quickly locate the problem.

[0074] In this preferred embodiment, OpenTelemetry is used to effectively collect data from different devices and environments to ensure data consistency and interoperability. The LSTM algorithm is used for time series analysis, and the system can capture the timing characteristics of the data, thereby providing more accurate fault predictions. The real-time data processing and intelligent anomaly detection functions of this preferred embodiment enable it to quickly identify potential problems and issue alarms in a timely manner through a dynamic alarm mechanism. This automated response mechanism not only reduces the need for manual intervention, but also significantly shortens the fault response time, thereby improving the availability and operation and maintenance efficiency of the system. In addition, powerful data visualization and monitoring capabilities provide an intuitive interface for operation and maintenance personnel, supporting rapid decision-making and fault location.

[0075] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0076] In this embodiment, an early warning device for a terminal device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. The terms "module", "unit", "subunit", etc. used below can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0077] Figure 6 is a structural block diagram of the early warning device of the terminal device of this embodiment, such as Figure 6 As shown, the device includes: an indicator acquisition module 10, a model prediction module 20 and an alarm generation module 30.

[0078] The indicator acquisition module 10 is used to obtain the real-time performance indicators of the monitoring system; the real-time performance indicators are obtained by the data acquisition component through acquiring the operating data of the monitoring system and standardizing the operating data; wherein the monitoring system includes a terminal device and a server and a network node connected to the terminal device.

[0079] The model prediction module 20 is used to input the real-time performance indicators into the well-trained early warning model, perform intelligent analysis on the operating status of the terminal equipment, and obtain the indicator prediction value for the subsequent time step.

[0080] The alarm generation module 30 is used to generate alarm information based on the comparison result between the indicator prediction value and the acquired dynamic alarm threshold.

[0081] In some of the embodiments, the data collection component is deployed on a terminal device, a server, and a network node.

[0082] In some of these embodiments, the data collection component includes an OpenTelemetry software development kit and an OpenTelemetry agent.

[0083] In some of the embodiments, a model training module is also included, which is used to obtain standardized historical performance indicators of the monitoring system; based on the data characteristics of the historical performance indicators, the architecture of the time series prediction model is adjusted to obtain an initial early warning model; the initial early warning model is trained based on the historical performance data to obtain a fully trained early warning model.

[0084] In some of the embodiments, the model training module is also used to collect standardized original indicator data based on the data collection component deployed on the monitoring system, and store the original indicator data in a database; in response to the model training instructions, extract the corresponding original indicator data from the database, and perform preprocessing to obtain standardized historical performance indicators.

[0085] In some of the embodiments, the model training module is also used for, when the historical performance data is three-dimensional time series data, the architecture of the time series prediction model includes a single-layer long short-term memory network and a discard layer, so as to obtain an initial first warning model; when the historical performance data is six-dimensional data, the architecture of the time series prediction model includes a double-layer long short-term memory network layer and a temporal attention mechanism layer, so as to obtain an initial second warning model; when the historical performance data is twelve-dimensional data, the architecture of the time series prediction model includes a double-layer long short-term memory network layer and a feature fusion layer, so as to obtain an initial third warning model.

[0086] In some of the embodiments, a fault location module is also included, which is used to obtain link tracking information collected by the data collection component; and perform fault location based on the indicator prediction value and the link tracking information.

[0087] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0088] In this embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0089] Optionally, the computer device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0090] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and will not be repeated in this embodiment.

[0091] In addition, in combination with the early warning method for the terminal device provided in the above embodiments, a storage medium can also be provided in this embodiment to implement the early warning method for the terminal device. The storage medium stores a computer program; when the computer program is executed by the processor, any of the early warning methods for the terminal device in the above embodiments is implemented.

[0092] It should be understood that the specific embodiments described herein are only used to explain the application, rather than to limit it. Based on the embodiments provided in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the protection scope of this application.

[0093] Obviously, the drawings are only some examples or embodiments of the present application. For ordinary technicians in the field, the present application can also be applied to other similar situations based on these drawings without creative work. In addition, it is understandable that although the work done in this development process may be complicated and lengthy, for ordinary technicians in the field, certain changes in design, manufacturing or production based on the technical content disclosed in this application are only conventional technical means and should not be regarded as insufficient content disclosed in this application.

[0094] The term "embodiment" in this application refers to a specific feature, structure or characteristic described in conjunction with the embodiment that can be included in at least one embodiment of the present application. The appearance of this phrase in various locations in the specification does not necessarily mean the same embodiment, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. It is clearly or implicitly understood by those of ordinary skill in the art that the embodiments described in this application can be combined with other embodiments without conflict.

[0095] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of patent protection. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the scope of protection of the present application. Therefore, the scope of protection of the present application shall be subject to the attached claims.

Claims

1. An early warning method for a terminal device, characterized in that: The method comprises: Acquire real-time performance indicators of the monitoring system; the real-time performance indicators are obtained by the data acquisition component by acquiring the operating data of the monitoring system and standardizing the operating data; wherein the monitoring system includes a terminal device and a server and a network node connected to the terminal device; Input the real-time performance index into a well-trained early warning model, perform intelligent analysis on the operating status of the terminal device, and obtain the index prediction value for subsequent time steps; Based on the comparison result of the indicator prediction value and the acquired dynamic alarm threshold, alarm information is generated.

2. The early warning method of the terminal device according to claim 1, characterized in that: The data collection component is deployed on the terminal device, the server and the network node.

3. The early warning method of the terminal device according to claim 1, characterized in that: Before inputting the real-time performance indicators into the well-trained early warning model, it also includes: Obtaining standardized historical performance indicators of the monitoring system; Based on the data characteristics of the historical performance indicators, the architecture of the time series prediction model is adjusted to obtain an initial early warning model; The initial early warning model is trained based on the historical performance data to obtain the fully trained early warning model.

4. The early warning method of the terminal device according to claim 3, characterized in that: Obtaining standardized historical performance indicators of the monitoring system, further comprising: Based on the data collection component deployed on the monitoring system, collect standardized original indicator data, and store the original indicator data in a database; In response to the model training instruction, the corresponding original indicator data is extracted from the database and preprocessed to obtain standardized historical performance indicators.

5. The early warning method of terminal equipment according to claim 3, characterized in that: Based on the data characteristics of the historical performance indicators, the architecture of the time series prediction model is adjusted to obtain an initial early warning model, including: In the case where the historical performance data is three-dimensional time series data, the architecture of the time series prediction model includes a single-layer long short-term memory network and a dropout layer to obtain an initial first warning model; When the historical performance data is six-dimensional data, the architecture of the time series prediction model includes a two-layer long short-term memory network layer and a time attention mechanism layer, and an initial second warning model is obtained; When the historical performance data is twelve-dimensional data, the architecture of the time series prediction model includes a two-layer long short-term memory network layer and a feature fusion layer to obtain an initial third warning model.

6. The early warning method of the terminal device according to claim 3, characterized in that: After obtaining the well-trained early warning model, the following steps are also included: Obtaining the historical performance indicators updated within a preset period; Based on the incremental learning method, the well-trained early warning model is fine-tuned to obtain an updated well-trained early warning model.

7. The early warning method of terminal equipment according to claim 1, characterized in that: The method further comprises: Acquire link tracking information collected by the data collection component; and locate the fault based on the indicator prediction value and the link tracking information.

8. An early warning device for a terminal device, characterized in that: The device comprises: An indicator acquisition module is used to acquire real-time performance indicators of the monitoring system; the real-time performance indicators are obtained by the data acquisition component by acquiring the operating data of the monitoring system and performing standardization processing on the operating data; wherein the monitoring system includes a terminal device and a server and a network node connected to the terminal device; A model prediction module, used to input the real-time performance index into a well-trained early warning model, perform intelligent analysis on the operating status of the terminal device, and obtain the index prediction value for subsequent time steps; The alarm generation module is used to generate alarm information based on the comparison result of the indicator prediction value and the obtained dynamic alarm threshold.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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