Intelligent fault early warning system of ship environment monitoring equipment

Abstract

The invention discloses an intelligent fault early warning system of ship environment monitoring equipment, which relates to the technical field of data prediction analysis and comprises a data transmission monitoring module, a computing resource monitoring module, a real-time evaluation module, a classification module and a decision optimization module. The CPU occupancy rate and the fluctuation amplitude are collected in real time by combining the computing resource monitoring module, the real-time performance evaluation module comprehensively analyzes delay and the abnormal degree of computing resources and accurately evaluates the real-time performance ability of the system, and the classification module divides early warning into real-time early warning and non-real-time early warning. And the decision optimization module carries out deep analysis and decision optimization on non-real-time early warning, so that the accuracy and response efficiency of fault early warning are effectively improved, efficient and accurate early warning of ship faults in a complex environment is realized, and the stability of the system and the reliability of decision making of operators are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data prediction and analysis, and in particular to an intelligent fault early warning system for ship environment monitoring equipment. Background Art

[0002] Intelligent fault warning of ship environmental monitoring equipment refers to the use of modern intelligent technology to analyze and predict the operating status of ship environmental monitoring equipment in real time, so as to issue early warning signals before the failure occurs. This technology continuously monitors the key performance indicators of the equipment (such as temperature, humidity, gas concentration or vibration) through sensor networks, data acquisition systems and intelligent algorithms (such as machine learning and big data analysis). When the system detects abnormal trends or potential risks that may lead to failures, it can automatically generate early warning notifications to remind relevant personnel to take preventive measures.

[0003] This intelligent fault warning has significant advantages. First, it can help ship operators discover potential problems in advance, reduce unexpected downtime or safety accidents caused by equipment failure, and improve operational efficiency and safety. Second, the early warning system can provide detailed fault cause analysis and optimization suggestions for operation and maintenance personnel, support accurate decision-making, and thus reduce maintenance costs and time investment. This intelligent method not only improves the reliability of monitoring equipment, but also provides technical support for green shipping and efficient management of ships.

[0004] The prior art has the following deficiencies:

[0005] The fault warning system relies on high-frequency data transmission and real-time analysis, but the limited network environment of the ship may cause abnormal operation of the environmental monitoring equipment, resulting in data delay or packet loss. In addition, the edge computing capability is insufficient, the system's analysis speed and efficiency are reduced, and the warning cannot be issued in time at the early stage of the fault, missing the best time for intervention. In addition, operators may make wrong decisions due to delayed warning signals, such as delaying shutdown or taking ineffective intervention measures, further worsening the fault condition. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent fault warning system for ship environment monitoring equipment to solve the shortcomings of the background technology.

[0007] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: an intelligent fault warning system for ship environment monitoring equipment, comprising a data transmission monitoring module, a computing resource monitoring module, a real-time evaluation module, a classification module and a decision optimization module;

[0008] Data transmission monitoring module: records the sending and receiving time of sensor data from the acquisition end to the edge computing unit through timestamp technology, calculates the transmission delay of a single data packet, and performs several measurements under different network conditions to determine the overall interference degree of data packet transmission delay;

[0009] Computing resource monitoring module: When the overall interference level of data packet transmission delay is high, the system monitoring tool is used to collect the CPU occupancy rate of edge computing devices in real time, and the fluctuation range of the CPU occupancy rate collected in real time is analyzed to determine the abnormality level of the CPU occupancy rate of edge computing devices;

[0010] Real-time evaluation module: In the fault warning task, the overall interference degree of data packet transmission delay and the abnormal degree of CPU occupancy of edge computing devices are comprehensively analyzed to evaluate the real-time performance capability of the warning system in the fault warning task;

[0011] Classification module: Based on the evaluation results, the early warning system's real-time performance in the fault warning task is divided into real-time warning and non-real-time warning;

[0012] Decision optimization module: For non-real-time warnings, the abnormal degree of the real-time performance of the warning system in the fault warning task within a fixed time period is deeply analyzed, and decision optimization is performed based on the analysis results to improve the accuracy of fault warnings.

[0013] Preferably, in the data transmission monitoring module, the data delay drift index is generated after analyzing the overall change trend of the data delay, and the method for obtaining the data delay drift index is:

[0014] Continuously measure the transmission delay of multiple data packets; obtain the delay sample value set: {Delay1, Delay2, ..., Delay n}, calculate the mean μ of the delayed samples, the expression is: Calculate the standard deviation σ of the delay: Where Delay i is the transmission delay of the ith data packet, n is the number of samples; the distribution symmetry S of the delay data is calculated, and the expression is: σ 3 Indicates that the deviation value is normalized; select the delay distribution skewness coefficient S of the reference time period baseline , as the reference value for judging the current delay drift, calculate the data delay drift index, the expression is: HK=|SS baseline |; Where HK is the data delay drift index.

[0015] Preferably, in the computing resource monitoring module, the fluctuation range of the CPU occupancy rate collected in real time is analyzed to generate a CPU occupancy rate fluctuation index, and the method for obtaining the CPU occupancy rate fluctuation index is:

[0016] Record CPU usage data in real time to form a time series: {CPU t}, t=1,2,…,N; N is the total number of sampling points. Remove the mean component of the data to eliminate the interference of the DC component on the frequency domain analysis: CPU t ′=CPU t -μe, CPU t ′ is the CPU occupancy rate after processing, μe is the mean value of the CPU occupancy rate data in the time series; t ′} Apply fast Fourier transform to obtain the complex signal in frequency domain: In the formula, F(f k ) is the frequency domain signal at frequency f k The complex value on the frequency domain signal is calculated as the power spectral density P(f k ), the expression is: P(f k )=|F(f k )| 2 ; Set the frequency threshold f threshold , the part higher than the frequency, that is, the high-frequency component, is regarded as the wave component: calculate the total energy E of the high-frequency component high , the expression is: Calculate the total energy E total , the expression is: Calculate the high-frequency energy ratio as the CPU occupancy fluctuation index. The expression is: GH is the CPU usage fluctuation index.

[0017] Preferably, in the real-time evaluation module, the overall interference degree of the data packet transmission delay and the abnormal degree of the CPU occupancy rate of the edge computing device are comprehensively analyzed to evaluate the real-time performance capability of the early warning system in the fault early warning task, specifically:

[0018] The data delay drift index and the CPU occupancy rate fluctuation index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as the input of the machine learning model. The machine learning model uses each group of comprehensive feature vectors to predict the real-time performance capability value label of the early warning system in the fault warning task as the prediction target, and takes minimizing the sum of the prediction errors of the real-time performance capability value labels of all early warning systems in the fault warning task as the training target. The machine learning model is trained until the sum of the prediction errors converges and the model training is stopped. The real-time performance capability value of the early warning system in the fault warning task is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0019] Preferably, in the classification module, according to the evaluation results, the fault real-time nature of the early warning system in the fault warning task is divided into real-time warning and non-real-time warning, specifically:

[0020] The obtained real-time performance capability value of the early warning system in the fault warning task is compared with the reference threshold of the real-time performance capability value set under normal conditions according to historical data. If the real-time performance capability value of the early warning system in the fault warning task is greater than or equal to the reference threshold of the real-time performance capability value, it means that the real-time performance of the early warning system in the fault warning task is high. At this time, a high real-time signal is generated, and the fault real-time performance of the early warning system in the fault warning task is classified as a real-time warning; if the real-time performance capability value of the early warning system in the fault warning task is less than the reference threshold of the real-time performance capability value, it means that the real-time performance of the early warning system in the fault warning task is low. At this time, a low real-time signal is generated, and the fault real-time performance of the early warning system in the fault warning task is classified as a non-real-time warning.

[0021] Preferably, in the decision optimization module, for non-real-time warnings, an in-depth analysis is conducted on the abnormality of the real-time performance capability of the warning system in the fault warning task within a fixed time period, and decision optimization is performed based on the analysis results, specifically:

[0022] For non-real-time warnings, that is, the real-time performance capability value of the warning system generated within a fixed time period in the fault warning task is less than the reference threshold of the real-time performance capability value, the real-time performance capability value of the warning system in the fault warning task that is less than the reference threshold of the real-time performance capability value generated within a subsequent fixed time period is collected, and a corresponding data set is established, the mean and standard deviation of the data set are calculated, and after analyzing it, decision optimization is performed based on the analysis results to improve the accuracy of fault warnings.

[0023] Preferably, if the mean of the real-time performance capability values ​​in the data set is greater than or equal to the reference threshold of the mean of the real-time performance capability values, and the standard deviation of the real-time performance capability values ​​is less than the reference threshold of the standard deviation of the real-time performance capability values, it means that the real-time performance of the early warning system is generally good with small fluctuations. At this time, the system operation should be continuously monitored to ensure that the real-time performance remains stable;

[0024] If the mean of the real-time performance capability value is greater than or equal to the reference threshold of the mean of the real-time performance capability value, and the standard deviation of the real-time performance capability value is greater than or equal to the reference threshold of the standard deviation of the real-time performance capability value, the overall real-time performance of the early warning system is good but the fluctuation is large. At this time, the high fluctuation period or sudden task should be identified to dynamically optimize the system load;

[0025] If the mean of the real-time performance capability value is less than the reference threshold of the mean of the real-time performance capability value, and the standard deviation of the real-time performance capability value is greater than or equal to the reference threshold of the standard deviation of the real-time performance capability value, the real-time performance of the early warning system is insufficient and fluctuates violently. At this time, the system performance bottleneck should be checked and the task scheduling strategy should be optimized;

[0026] If the mean of the real-time performance capability value is less than the reference threshold of the mean of the real-time performance capability value, and the standard deviation of the real-time performance capability value is less than the reference threshold of the standard deviation of the real-time performance capability value, the early warning system lacks real-time performance but performs stably. At this time, comprehensive optimization should be carried out on the factors affecting real-time performance to improve the basic processing capability of the system and restore real-time performance.

[0027] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0028] 1. The present invention can effectively solve the problems of warning delay, data packet loss and decision-making errors caused by network limitations and insufficient edge computing capabilities in the prior art. The data transmission monitoring module records and analyzes the data packet transmission delay, the computing resource monitoring module collects and evaluates the CPU occupancy fluctuation of the edge computing device in real time, and the real-time evaluation module uses a machine learning model to comprehensively analyze the delay drift index and CPU fluctuation index to accurately evaluate the system's real-time performance in fault warning tasks. The classification module divides the real-time warning task into real-time warning and non-real-time warning, and combines the decision optimization module to conduct in-depth analysis and optimization of non-real-time warnings, effectively improving the accuracy and response efficiency of fault warnings.

[0029] 2. Through multi-indicator monitoring and analysis, the present invention enables the system to dynamically evaluate and classify the real-time performance of early warnings, provide flexible response strategies, and avoid misjudgments and system performance degradation caused by delays and fluctuations. Secondly, the decision optimization module performs statistical analysis on the real-time performance capabilities of non-real-time early warnings, and proposes targeted optimization suggestions based on the characteristics of the mean and standard deviation of fluctuations to ensure the stability and reliability of the early warning system. At the same time, the present invention uses machine learning models to improve evaluation accuracy, combined with dynamic adjustment strategies, which greatly improves the real-time and accuracy of fault warning tasks, meeting the needs of efficient fault management under complex ship environmental conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0031] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] For examples, see Figure 1 As shown, the intelligent fault warning system of the ship environment monitoring equipment described in this embodiment includes a data transmission monitoring module, a computing resource monitoring module, a real-time evaluation module, a classification module and a decision optimization module;

[0034] Data transmission monitoring module: records the sending and receiving time of sensor data from the acquisition end to the edge computing unit through timestamp technology, calculates the transmission delay of a single data packet, and performs several measurements under different network conditions to determine the overall interference degree of data packet transmission delay;

[0035] Computing resource monitoring module: When the overall interference level of data packet transmission delay is high, the system monitoring tool is used to collect the CPU occupancy rate of edge computing devices in real time, and the fluctuation range of the CPU occupancy rate collected in real time is analyzed to determine the abnormality level of the CPU occupancy rate of edge computing devices;

[0036] Real-time evaluation module: In the fault warning task, the overall interference degree of data packet transmission delay and the abnormal degree of CPU occupancy of edge computing devices are comprehensively analyzed to evaluate the real-time performance capability of the warning system in the fault warning task;

[0037] Classification module: Based on the evaluation results, the early warning system's real-time performance in the fault warning task is divided into real-time warning and non-real-time warning;

[0038] Decision optimization module: For non-real-time warnings, the abnormal degree of the real-time performance of the warning system in the fault warning task within a fixed time period is deeply analyzed, and decision optimization is performed based on the analysis results to improve the accuracy of fault warnings.

[0039] In the data transmission monitoring module, at the sensor data acquisition end, a high-precision timestamp Tsend (in milliseconds or microseconds) is generated before each data packet is sent. The timestamp can be obtained through the system clock or synchronous clock protocol (such as NTP, Network Time Protocol) to ensure the time consistency between the acquisition end and the receiving end. When the edge computing unit receives the data packet, the arrival timestamp Treceive is recorded immediately. Ensure that the clock of the receiving device is synchronized to eliminate errors introduced by time differences. For each data packet, calculate its transmission delay: Delay = Treceive-Tsend; the delay value directly reflects the transmission time of the data packet in the network.

[0040] After analyzing the overall change trend of data delay, a data delay drift index is generated to determine the overall interference degree of data packet transmission delay. The method for obtaining the data delay drift index is as follows:

[0041] Under different network conditions (such as signal strength changes, bandwidth fluctuations), continuously measure the transmission delay of multiple data packets; obtain the delay sample value set: {Delay1, Delay2, ..., Delay n}, calculate the mean μ of the delayed samples, the expression is: Calculate the standard deviation σ of the delay, reflecting the instability of the delay: Where Delay i is the transmission delay of the ith data packet, n is the number of samples; the distribution symmetry S of the delay data is calculated, and the expression is: σ 3 Indicates that the deviation value is normalized to eliminate the influence of data magnitude on the result; the delay distribution skewness coefficient S of the reference time period is selected baseline , as the reference value for judging the current delay drift, calculate the data delay drift index, the expression is: HK=|SS baseline |; Where HK is the data delay drift index.

[0042] When the data delay drift index is larger, it means that the distribution of data packet transmission delay deviates from the baseline state, and the asymmetry of delay data increases significantly. This usually means that the instability of network performance is aggravated, such as frequent delay peaks, or the transmission time of some data packets deviates greatly from the average. When the drift index is large, the overall interference level is higher, which may lead to a decrease in the transmission reliability of real-time data, thereby affecting the response speed and accuracy of the fault warning system.

[0043] On the contrary, when the data delay drift index is smaller, it means that the distribution of data packet transmission delay is close to the baseline state, the delay fluctuation is small, and the network performance is more stable. This shows that the degree of interference to data transmission is low and the delay variation tends to be predictable. When the drift index is small, the system can maintain a high degree of real-time performance, and the transmission and processing of warning signals are more efficient, providing stable support for intelligent fault warning.

[0044] Computing resource monitoring module: When the overall interference level of data packet transmission delay is high, the system monitoring tool is used to collect the CPU occupancy rate of the edge computing device in real time, and the fluctuation range of the CPU occupancy rate collected in real time is analyzed to determine the abnormality level of the CPU occupancy rate of the edge computing device.

[0045] Use system monitoring tools, including: Common tools: Linux environment: top, htop, mpstat, sar, vmstat and other commands. Special monitoring tools: Prometheus, Zabbix or built-in monitoring API. Programming interface: Get CPU usage through Python's psutil library.

[0046] Set a reasonable collection interval, such as once per second, to ensure sufficient accuracy and real-time performance. Record the CPU usage value of each collection to form time series data: {CPU t1 ,CPU t2 ,...,CPU tn}; Among them, CPU ti The CPU usage rate collected for the ith time (expressed as a percentage). The collected CPU usage rate data is stored in a local log file or a remote database for subsequent analysis and trend monitoring.

[0047] The CPU usage fluctuation index is generated by analyzing the fluctuation range of the CPU usage collected in real time to determine the abnormality of the CPU usage of the edge computing device. The CPU usage fluctuation index is obtained as follows:

[0048] Record CPU usage data in real time to form a time series: {CPU t}, t=1,2,…,N; N is the total number of sampling points. Remove the mean component of the data to eliminate the interference of the DC component on the frequency domain analysis: CPU t ′=CPU t -μe, CPU t ′ is the processed CPU occupancy rate, μe is the mean of the CPU occupancy rate data in the time series. Use Hamming or Hanning window to smooth the data to reduce spectrum leakage.

[0049] For time series {CPUt ′} Apply fast Fourier transform to obtain the complex signal in frequency domain: In the formula, F(f k ) is the frequency domain signal at frequency f k The complex value on the frequency domain signal is calculated as the power spectral density P(f k ), the expression is: P(f k )=|F(f k )| 2 ; Set the frequency threshold f threshold , the part higher than the frequency, that is, the high-frequency component, is regarded as the fluctuation component: f threshold Usually it is selected according to actual needs. For example, 0.1Hz represents the fluctuation within 10 seconds. The total energy E of the high-frequency component is calculated. high , the expression is: Calculate the total energy E total , the expression is: Calculate the high-frequency energy ratio as the CPU occupancy fluctuation index. The expression is: GH is the CPU usage fluctuation index.

[0050] When the CPU usage fluctuation index is larger, it means that the high-frequency fluctuation component accounts for a higher proportion of CPU usage, and the CPU usage abnormality of the edge computing device is more serious. This usually means that there is significant instability in CPU usage, which may be caused by sudden tasks, resource competition, or unbalanced task scheduling. When the fluctuation index is large, the CPU load changes dramatically, which may cause computing tasks to be delayed or fail, affecting the real-time performance and reliability of the system.

[0051] On the contrary, when the CPU occupancy fluctuation index is smaller, it means that the high-frequency fluctuation of CPU usage is less, the CPU occupancy of edge computing devices is relatively stable, and the degree of abnormality is low. This shows that the system's task execution distribution is more balanced, the load is relatively stable, and it is not easy to cause performance bottlenecks or resource exhaustion due to short-term fluctuations. When the fluctuation index is small, the device can complete real-time computing tasks more efficiently, ensuring the stability and accuracy of the fault warning system.

[0052] Real-time evaluation module: In the fault warning task, the overall interference degree of data packet transmission delay and the abnormal degree of CPU occupancy of edge computing devices are comprehensively analyzed to evaluate the real-time performance capability of the warning system in the fault warning task.

[0053] The data delay drift index and the CPU occupancy rate fluctuation index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as the input of the machine learning model. The machine learning model uses each group of comprehensive feature vectors to predict the real-time performance capability value label of the early warning system in the fault warning task as the prediction target, and takes minimizing the sum of the prediction errors of the real-time performance capability value labels of all early warning systems in the fault warning task as the training target. The machine learning model is trained until the sum of the prediction errors converges and the model training is stopped. The real-time performance capability value of the early warning system in the fault warning task is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0054] The method for obtaining the real-time performance capability value of the early warning system in the fault warning task is: from the comprehensive feature vector training data of the trained machine learning model, obtain the corresponding function expression: LR=F(HK, GH); where F is the output function of the model, HK is the data delay drift index, GH is the CPU occupancy fluctuation index, and LR is the real-time performance capability value of the early warning system in the fault warning task.

[0055] Classification module: According to the evaluation results, the early warning system's fault real-time warning in the fault warning task is divided into real-time warning and non-real-time warning.

[0056] The obtained real-time performance capability value of the early warning system in the fault warning task is compared with the reference threshold of the real-time performance capability value set under normal conditions according to historical data. If the real-time performance capability value of the early warning system in the fault warning task is greater than or equal to the reference threshold of the real-time performance capability value, it means that the real-time performance of the early warning system in the fault warning task is high. At this time, a high real-time signal is generated, and the fault real-time performance of the early warning system in the fault warning task is classified as a real-time warning; if the real-time performance capability value of the early warning system in the fault warning task is less than the reference threshold of the real-time performance capability value, it means that the real-time performance of the early warning system in the fault warning task is low. At this time, a low real-time signal is generated, and the fault real-time performance of the early warning system in the fault warning task is classified as a non-real-time warning.

[0057] For real-time warning, it means that the system can respond and handle fault warning tasks in a timely manner in the current state, and no additional optimization measures are needed. In this case, the system should be ensured to maintain a high real-time operation state, continuously monitor the data delay drift index and CPU occupancy fluctuation index to prevent performance degradation, and record the key parameters of high real-time operation as a reference for subsequent performance evaluation and optimization.

[0058] Decision optimization module: For non-real-time warnings, the abnormal degree of the real-time performance of the warning system in the fault warning task within a fixed time period is deeply analyzed, and decision optimization is performed based on the analysis results to improve the accuracy of fault warnings.

[0059] For non-real-time warnings, that is, the real-time performance capability value of the warning system generated within a fixed time period in the fault warning task is less than the reference threshold of the real-time performance capability value, the real-time performance capability value of the warning system in the fault warning task that is less than the reference threshold of the real-time performance capability value generated within a subsequent fixed time period is collected, and a corresponding data set is established, the mean and standard deviation of the data set are calculated, and after analyzing it, decision optimization is performed based on the analysis results to improve the accuracy of fault warnings.

[0060] If the mean of the real-time performance capability value in the data set is greater than or equal to the reference threshold of the mean of the real-time performance capability value, and the standard deviation of the real-time performance capability value is less than the reference threshold of the standard deviation of the real-time performance capability value, it means that the real-time performance of the early warning system is generally good with small fluctuations, and the system is stable in the non-real-time early warning state. At this time, the system operation should be continuously monitored, and the scheduling of non-critical tasks should be appropriately optimized to ensure that the real-time performance remains stable.

[0061] If the mean of the real-time performance capability value is greater than or equal to the reference threshold of the mean of the real-time performance capability value, and the standard deviation of the real-time performance capability value is greater than or equal to the reference threshold of the standard deviation of the real-time performance capability value, the overall real-time performance of the early warning system is good but the fluctuation is large, and there is a risk of short-term performance degradation. At this time, high-fluctuation periods or sudden tasks should be identified, and the system load should be dynamically optimized, such as adjusting the priority of key tasks or balancing the allocation of computing resources.

[0062] If the mean of the real-time performance capability value is less than the reference threshold of the mean of the real-time performance capability value, and the standard deviation of the real-time performance capability value is greater than or equal to the reference threshold of the standard deviation of the real-time performance capability value, the early warning system is insufficient in real time and fluctuates violently, and the performance capability is unstable. At this time, the system performance bottleneck (such as network delay and computing load) should be checked, the task scheduling strategy should be optimized, and spare computing resources or hardware performance should be introduced if necessary.

[0063] If the mean of the real-time performance capability value is less than the reference threshold of the mean of the real-time performance capability value, and the standard deviation of the real-time performance capability value is less than the reference threshold of the standard deviation of the real-time performance capability value, the early warning system is not real-time enough but the performance is relatively stable, indicating that the system performance is limited and there is a large room for optimization. At this time, the main factors affecting real-time performance (such as delay and algorithm efficiency) should be comprehensively optimized to improve the basic processing capability of the system and restore real-time performance.

[0064] In this embodiment, a data transmission monitoring module records the sending and receiving time of sensor data through timestamp technology, calculates transmission delay and determines the degree of network interference; a computing resource monitoring module monitors the CPU occupancy rate of edge computing devices in real time and analyzes its fluctuation range when the transmission delay interference level is high; a real-time evaluation module evaluates the real-time performance capability of the system based on the data transmission delay and the abnormal degree of CPU occupancy; a classification module divides fault warnings into real-time warnings and non-real-time warnings according to the real-time evaluation results; and a decision optimization module conducts in-depth analysis of the abnormal performance of non-real-time warnings, and optimizes system operation according to the results to improve the accuracy of warnings. These modules jointly ensure the real-time and reliability of the warning system.

[0065] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0066] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). 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 contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0067] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. Intelligent fault warning system for ship environment monitoring equipment, characterized by: It includes data transmission monitoring module, computing resource monitoring module, real-time evaluation module, classification module and decision optimization module; Data transmission monitoring module: records the sending and receiving time of sensor data from the acquisition end to the edge computing unit through timestamp technology, calculates the transmission delay of a single data packet, and performs several measurements under different network conditions to determine the overall interference degree of data packet transmission delay; Computing resource monitoring module: When the overall interference level of data packet transmission delay is high, the system monitoring tool is used to collect the CPU occupancy rate of edge computing devices in real time, and the fluctuation range of the CPU occupancy rate collected in real time is analyzed to determine the abnormality level of the CPU occupancy rate of edge computing devices; Real-time evaluation module: In the fault warning task, the overall interference degree of data packet transmission delay and the abnormal degree of CPU occupancy of edge computing devices are comprehensively analyzed to evaluate the real-time performance capability of the warning system in the fault warning task; Classification module: Based on the evaluation results, the early warning system's real-time performance in the fault warning task is divided into real-time warning and non-real-time warning; Decision optimization module: For non-real-time warnings, the abnormal degree of the real-time performance of the warning system in the fault warning task within a fixed time period is deeply analyzed, and decision optimization is performed based on the analysis results to improve the accuracy of fault warnings.

2. The intelligent fault early warning system for ship environment monitoring equipment according to claim 1 is characterized in that: In the data transmission monitoring module, the data delay drift index is generated after analyzing the overall change trend of the data delay. The method for obtaining the data delay drift index is as follows: Continuously measure the transmission delay of multiple data packets; obtain the delay sample value set: {Delay1, Delay2, ..., Delay n }, calculate the mean μ of the delayed samples, the expression is: Calculate the standard deviation σ of the delay: Where Delay i is the transmission delay of the ith data packet, n is the number of samples; the distribution symmetry S of the delay data is calculated, and the expression is: σ 3 Indicates normalizing the deviation value; Select the delay distribution skewness coefficient S of the reference time period baseline , as the reference value for judging the current delay drift, calculate the data delay drift index, the expression is: HK=|SS baseline |; Where HK is the data delay drift index.

3. The intelligent fault early warning system for ship environment monitoring equipment according to claim 2 is characterized in that: In the computing resource monitoring module, the fluctuation range of the CPU usage collected in real time is analyzed to generate a CPU usage fluctuation index. The CPU usage fluctuation index is obtained as follows: Record CPU usage data in real time to form a time series: {CPU t }, t=1,2,…,N; N is the total number of sampling points. Remove the mean component of the data to eliminate the interference of the DC component on the frequency domain analysis: CPU t ′=CPU t -μe, CPU t ′ is the CPU occupancy rate after processing, μe is the mean value of the CPU occupancy rate data in the time series; t ′} Apply fast Fourier transform to obtain the complex signal in frequency domain: k=0,1,…,N-1;where F(f k ) is the frequency domain signal at frequency f k The complex value on the frequency domain signal is calculated as the power spectral density P(f k ), the expression is: P(f k )=|F(f k )| 2 ; Set the frequency threshold f threshold , the part higher than the frequency, that is, the high-frequency component, is regarded as the wave component: calculate the total energy E of the high-frequency component high , the expression is: Calculate the total energy E total , the expression is: Calculate the high-frequency energy ratio as the CPU occupancy fluctuation index. The expression is: GH is the CPU usage fluctuation index.

4. The intelligent fault warning system for ship environment monitoring equipment according to claim 3 is characterized by: In the real-time evaluation module, the overall interference degree of data packet transmission delay and the abnormal degree of CPU occupancy of edge computing devices are comprehensively analyzed to evaluate the real-time performance of the early warning system in the fault early warning task, specifically: The data delay drift index and the CPU occupancy rate fluctuation index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as the input of the machine learning model. The machine learning model uses each group of comprehensive feature vectors to predict the real-time performance capability value label of the early warning system in the fault warning task as the prediction target, and takes minimizing the sum of the prediction errors of the real-time performance capability value labels of all early warning systems in the fault warning task as the training target. The machine learning model is trained until the sum of the prediction errors converges and the model training is stopped. The real-time performance capability value of the early warning system in the fault warning task is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

5. The intelligent fault warning system for ship environment monitoring equipment according to claim 4 is characterized in that: In the classification module, according to the evaluation results, the fault real-time nature of the early warning system in the fault warning task is divided into real-time warning and non-real-time warning, specifically: The obtained real-time performance capability value of the early warning system in the fault early warning task is compared with the reference threshold of the real-time performance capability value set under the normal state according to the historical data. If the real-time performance capability value of the early warning system in the fault early warning task is greater than or equal to the reference threshold of the real-time performance capability value, it means that the real-time performance of the early warning system in the fault early warning task is high. At this time, a high real-time signal is generated, and the fault real-time performance of the early warning system in the fault early warning task is classified as a real-time warning; If the real-time performance capability value of the early warning system in the fault warning task is less than the reference threshold of the real-time performance capability value, it means that the real-time performance of the early warning system in the fault warning task is low. At this time, a low real-time signal is generated, and the fault real-time performance of the early warning system in the fault warning task is classified as a non-real-time warning.

6. The intelligent fault warning system for ship environment monitoring equipment according to claim 1 is characterized in that: In the decision optimization module, for non-real-time warnings, the abnormal degree of the real-time performance of the warning system in the fault warning task within a fixed time period is deeply analyzed, and decision optimization is performed based on the analysis results, specifically: For non-real-time warnings, that is, the real-time performance capability value of the warning system generated within a fixed time period in the fault warning task is less than the reference threshold of the real-time performance capability value, the real-time performance capability value of the warning system in the fault warning task that is less than the reference threshold of the real-time performance capability value generated within a subsequent fixed time period is collected, and a corresponding data set is established, the mean and standard deviation of the data set are calculated, and after analyzing it, decision optimization is performed based on the analysis results to improve the accuracy of fault warnings.

7. The intelligent fault warning system for ship environment monitoring equipment according to claim 6 is characterized by: If the mean of the real-time performance capability values ​​in the data set is greater than or equal to the reference threshold of the mean of the real-time performance capability values, and the standard deviation of the real-time performance capability values ​​is less than the reference threshold of the standard deviation of the real-time performance capability values, it means that the real-time performance of the early warning system is generally good with small fluctuations. At this time, the system operation should be continuously monitored to ensure that the real-time performance remains stable; If the mean of the real-time performance capability value is greater than or equal to the reference threshold of the mean of the real-time performance capability value, and the standard deviation of the real-time performance capability value is greater than or equal to the reference threshold of the standard deviation of the real-time performance capability value, the overall real-time performance of the early warning system is good but the fluctuation is large. At this time, the high fluctuation period or sudden task should be identified to dynamically optimize the system load; If the mean of the real-time performance capability value is less than the reference threshold of the mean of the real-time performance capability value, and the standard deviation of the real-time performance capability value is greater than or equal to the reference threshold of the standard deviation of the real-time performance capability value, the real-time performance of the early warning system is insufficient and fluctuates violently. At this time, the system performance bottleneck should be checked and the task scheduling strategy should be optimized; If the mean of the real-time performance capability value is less than the reference threshold of the mean of the real-time performance capability value, and the standard deviation of the real-time performance capability value is less than the reference threshold of the standard deviation of the real-time performance capability value, the early warning system lacks real-time performance but performs stably. At this time, comprehensive optimization should be carried out on the factors affecting real-time performance to improve the basic processing capability of the system and restore real-time performance.

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