Remote monitoring and diagnosis method for generator set based on cloud computing

Abstract

The invention discloses a generator set remote monitoring and diagnosis method based on cloud computing, and the method effectively improves the data processing capability, enhances the accuracy of fault early warning and diagnosis, improves the real-time performance and reliability of remote monitoring through the technologies of edge computing preprocessing, adaptive data transmission, cloud platform intelligent analysis, grading early warning and the like. And adaptive optimization and intelligent feedback of the system are realized, so that the problems of data processing limitation, inaccurate fault early warning, poor remote monitoring real-time performance, insufficient intelligence and the like of a traditional monitoring system are solved, and more efficient, accurate, stable and intelligent monitoring and fault diagnosis of the generator set are realized.

Description

Technical Field

[0001] The present application relates to the technical field of remote monitoring and diagnosis of generator sets, and in particular to a remote monitoring and diagnosis method of generator sets based on cloud computing. Background Art

[0002] As key power equipment, generator sets are widely used in power, industrial and commercial facilities. Their operating status directly affects the stability and safety of power supply. With the development of science and technology, the demand for real-time monitoring and fault diagnosis during the operation of generator sets is gradually increasing. Traditional generator set monitoring systems usually rely on local monitoring devices and data processing equipment, mainly through regular inspections and local sensor data monitoring to determine the operating status of the equipment. However, traditional monitoring methods generally have the following technical problems:

[0003] Traditional monitoring systems often use local processing equipment for data storage and analysis, which faces the problem of limited data processing capabilities. When the number of sensors in the generator sets increases and the amount of real-time data gradually increases, local processing equipment often finds it difficult to cope with the needs of high-frequency data collection and analysis, resulting in data processing lags and affected diagnostic accuracy.

[0004] Existing generator monitoring systems mostly judge abnormalities by setting thresholds and generate warning information based on monitoring data of a single parameter. This method has shortcomings in the diagnosis of sudden and complex faults, and often cannot predict and accurately locate faults in advance. In actual applications, equipment failures are random and diverse. Traditional threshold judgment methods are prone to missed reports or false reports when facing various types of faults, affecting the stable operation of the equipment.

[0005] Traditional monitoring systems mostly rely on local networks or private communication methods to achieve data transmission, which makes it difficult to maintain the stability of data transmission over long distances. Especially for generators in remote power stations or remote areas, due to network conditions, traditional systems are difficult to meet the real-time data requirements, resulting in the inability to remotely monitor the operating status of the generator set in real time and handle faults.

[0006] In traditional monitoring methods, the system generally only stores or periodically analyzes the collected data, lacking intelligent learning and optimization capabilities. Most monitoring systems cannot self-adjust or optimize based on feedback from historical data, resulting in relatively fixed diagnostic models and fault identification methods for the system, which cannot be adaptively optimized with the accumulated data in actual applications.

[0007] In summary, the traditional generator set monitoring system has obvious deficiencies in data processing capabilities, real-time fault diagnosis, reliability of remote monitoring, and system intelligence. With the increasing demand for generator set operation, how to achieve more efficient, intelligent, and real-time remote monitoring and fault diagnosis has become an urgent problem to be solved in the industry. Summary of the invention

[0008] The present application provides a method for remote monitoring and diagnosis of generator sets based on cloud computing, aiming to solve the obvious deficiencies in traditional generator set monitoring systems in terms of data processing capability, real-time fault diagnosis, reliability of remote monitoring, and system intelligence.

[0009] A method for remote monitoring and diagnosis of a generator set based on cloud computing, the method comprising:

[0010] The key operating parameters of the generator set are collected in real time through the intelligent data acquisition module, including temperature, pressure, vibration and sound spectrum information;

[0011] The collected operating parameters are preprocessed by edge computing devices, and the preprocessing includes data filtering, noise reduction and signal enhancement to reduce noise interference and improve the stability of signal transmission;

[0012] Using the adaptive data transmission module, the pre-processed data is transmitted to the cloud computing platform through a dynamic bandwidth management mechanism. When the network bandwidth is insufficient, key data is transmitted first.

[0013] In the cloud computing platform, the received data is analyzed and fault diagnosis is performed through the distributed data processing framework and adaptive machine learning diagnostic model to generate a diagnostic report on the operating status of the generator set;

[0014] Based on the diagnostic report, a three-layer fault prediction mechanism is used to perform short-term warning, mid-term trend analysis, and long-term risk prediction, providing real-time warning of potential faults of the generator set;

[0015] When the adaptive machine learning diagnostic model detects an anomaly, it further confirms the fault type and location based on spectrum analysis and envelope analysis technology, and generates corresponding warning information based on the fault type;

[0016] The warning information is sent to the user through the multi-level alarm system of the user terminal, including the fault cause analysis, recommended maintenance plan and risk level classification;

[0017] Receive user feedback on warning information and upload the feedback information to the cloud computing platform. Adjust the weight parameters of the diagnosis model based on the self-optimization mechanism of user feedback data, thereby achieving adaptive optimization of the diagnosis model.

[0018] In the above scheme, optionally, the preprocessing step of the edge computing device includes filtering and compressing abnormal signals before data transmission to reduce redundant data and improve transmission efficiency.

[0019] In the above solution, optionally, the data acquisition module includes a multimodal sensor, and the multimodal sensor is based on the layout of different key components of the generator set to achieve fusion of different types of data to ensure the comprehensiveness and accuracy of the detection data.

[0020] In the above solution, optionally, the adaptive data transmission module includes a data loss tolerance and error tolerance mechanism, and the mechanism uses a check code technology to ensure the accuracy and stability of data during transmission.

[0021] In the above scheme, optionally, the machine learning diagnostic model includes a dynamic learning algorithm based on historical data and real-time data streams, and the algorithm has an adaptive feature weight adjustment function to automatically optimize diagnostic rules according to fault historical characteristics and real-time data, thereby improving the accuracy and real-time performance of fault diagnosis.

[0022] In the above solution, optionally, the three-layer fault prediction mechanism includes:

[0023] Short-term warning: rapid identification of current anomalies based on real-time data;

[0024] Mid-term trend analysis: predict future trends through comprehensive analysis of historical data and real-time data;

[0025] Long-term risk prediction: Predict potential equipment failure risks through cluster analysis and pattern recognition technology.

[0026] In the above scheme, optionally, the spectrum analysis and envelope analysis are used to detect weak signal changes in vibration data, and to perform early identification of the wear status and fault type of key components of the generator set by extracting specific fault spectrum features.

[0027] In the above scheme, optionally, the multi-level alarm system classifies the early warning information and sends warnings to users according to different fault levels and risk levels, ensuring that users can quickly identify and respond to high-risk faults.

[0028] In the above scheme, optionally, the user feedback is received and analyzed by the cloud computing platform through a deep learning optimization mechanism, and based on a self-optimization mechanism of historical feedback data, the feature weights and priority parameters in the diagnosis model are adjusted in real time, thereby improving the diagnostic accuracy and adaptability of the system.

[0029] In the above scheme, optionally, the user terminal provides an adaptive and configurable data visualization interface, allowing the user to view the real-time operating status, historical data records, diagnostic records and forecast reports of the generator set so as to take maintenance measures in a timely manner.

[0030] Compared with the prior art, this application has at least the following beneficial effects:

[0031] Based on further analysis and research on the existing technical problems, this application recognizes that the traditional generator set monitoring system has obvious deficiencies in data processing capabilities, real-time fault diagnosis, reliability of remote monitoring and system intelligence. Through edge computing preprocessing, adaptive data transmission, cloud platform intelligent analysis and graded warning technologies, the data processing capability is effectively improved, the accuracy of fault warning and diagnosis is enhanced, the real-time and reliability of remote monitoring are improved, and the adaptive optimization and intelligent feedback of the system are realized, thereby overcoming the problems of traditional monitoring systems in data processing limitations, inaccurate fault warnings, poor real-time remote monitoring and insufficient intelligence, and realizing more efficient, accurate, stable and intelligent monitoring and fault diagnosis of generator sets. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A flowchart of a method for remote monitoring and diagnosis of a generator set based on cloud computing is provided in accordance with an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0034] In one embodiment, Figure 1 As shown, a remote monitoring and diagnosis method for a generator set based on cloud computing is provided, comprising the following steps:

[0035] The following steps are involved:

[0036] The key operating parameters of the generator set are collected in real time through the intelligent data acquisition module, including temperature, pressure, vibration and sound spectrum information;

[0037] The collected operating parameters are preprocessed by edge computing devices, and the preprocessing includes data filtering, noise reduction and signal enhancement to reduce noise interference and improve the stability of signal transmission;

[0038] Using the adaptive data transmission module, the pre-processed data is transmitted to the cloud computing platform through a dynamic bandwidth management mechanism. When the network bandwidth is insufficient, key data is transmitted first.

[0039] In the cloud computing platform, the received data is analyzed and fault diagnosis is performed through the distributed data processing framework and adaptive machine learning diagnostic model to generate a diagnostic report on the operating status of the generator set;

[0040] Based on the diagnostic report, a three-layer fault prediction mechanism is used to perform short-term warning, mid-term trend analysis, and long-term risk prediction, providing real-time warning of potential faults of the generator set;

[0041] When the adaptive machine learning diagnostic model detects an anomaly, it further confirms the fault type and location based on spectrum analysis and envelope analysis technology, and generates corresponding warning information based on the fault type;

[0042] The warning information is sent to the user through the multi-level alarm system of the user terminal, including the fault cause analysis, recommended maintenance plan and risk level classification;

[0043] Receive user feedback on warning information and upload the feedback information to the cloud computing platform. Adjust the weight parameters of the diagnosis model based on the self-optimization mechanism of user feedback data, thereby achieving adaptive optimization of the diagnosis model.

[0044] This embodiment provides a remote monitoring and diagnosis method for generator sets based on cloud computing. Through multi-level technologies such as data acquisition, edge computing preprocessing, adaptive data transmission, cloud platform intelligent analysis and graded warning, it overcomes the problems of data processing limitations, inaccurate fault warning, poor real-time remote monitoring, and insufficient intelligence of traditional generator set monitoring systems.

[0045] Preprocessing data through edge computing devices reduces redundant information in data transmission, making high-frequency data collection and transmission more efficient. At the same time, the cloud computing platform combined with the distributed data processing framework can process a large amount of real-time data in parallel, ensuring the efficiency and real-time nature of monitoring and diagnosis, overcoming the problem of insufficient processing capacity of traditional local devices.

[0046] This method uses multimodal sensors to collect various data such as temperature, pressure, vibration and sound spectrum, combined with the cloud-based adaptive machine learning diagnostic model, to determine the status of the generator set based on multi-parameter comprehensive analysis. By dynamically adjusting the feature weights of the diagnostic model, this method can more accurately identify equipment anomalies and issue early warnings, avoiding the false alarms or missed alarms of traditional threshold judgment methods, and significantly improving the accuracy of fault prediction.

[0047] The adaptive data transmission module supports dynamic bandwidth management and data loss tolerance, enabling the system to prioritize the transmission of key data when network conditions fluctuate, ensuring the stability of remote monitoring and the real-time nature of data transmission. This enables remote real-time monitoring and timely fault handling of generator sets even in remote areas, overcoming the transmission limitations of traditional local area networks.

[0048] Through the deep learning optimization mechanism of user feedback and historical data, this method can continuously update and adjust the parameters of the diagnostic model according to the fault occurrence and user operation feedback. Through the adaptive optimization mechanism, the system can continuously improve its diagnostic accuracy and response capabilities, enhance the fault identification and early warning effects of the equipment, and make up for the shortcomings of traditional systems in intelligent learning and optimization capabilities.

[0049] The remote monitoring and diagnosis method provided in this embodiment solves the shortcomings of traditional monitoring systems in data processing, fault warning, real-time remote monitoring and intelligent feedback, and realizes more efficient, accurate, stable and intelligent monitoring and fault diagnosis of generator sets.

[0050] In this embodiment, the preprocessing step of the edge computing device includes filtering and compressing abnormal signals before data transmission to reduce redundant data and improve transmission efficiency.

[0051] This embodiment significantly improves the efficiency and real-time performance of data transmission of generator sets by introducing filtering and compression of abnormal signals in the preprocessing step of edge computing devices, and solves the limitation of traditional generator set monitoring systems in data processing. Specifically, the edge computing device filters and compresses abnormal signals before data transmission, which helps to effectively reduce the amount of redundant data during data collection and transmission, thereby saving transmission bandwidth and improving the response speed of the real-time monitoring system.

[0052] In traditional generator monitoring systems, the collected raw data is uploaded directly to the central processing equipment without being processed. This mode often leads to excessive data volume. Especially when there are many sensors and the data update frequency is high, the central processing equipment is often difficult to respond in real time, which can easily cause data transmission congestion and delay. By performing preliminary data preprocessing on the edge device, abnormal signals that do not meet the set standards are filtered out, which greatly reduces the amount of data transmitted and effectively alleviates the transmission pressure. At the same time, the edge computing device also compresses the necessary data, so that key data can still be transmitted under limited bandwidth conditions, thereby improving the effectiveness of data transmission.

[0053] In addition, edge computing can filter and compress abnormal signals to provide high-quality data for cloud data processing and analysis, avoid the impact of noise data or invalid data on diagnostic results, and improve the accuracy of cloud diagnostic models. Edge computing devices can filter out invalid data according to the set algorithms and rules, and complete compression processing before transmission. This method can not only reduce the delay caused by data transmission, but also improve data utilization and diagnostic accuracy, ensuring that key abnormal information can be quickly uploaded to the cloud for accurate diagnosis and early warning.

[0054] This embodiment significantly improves the data transmission efficiency through data filtering and compression of edge computing devices, solves the data processing lag problem in traditional generator set monitoring systems, and provides technical support for achieving real-time and accurate remote monitoring.

[0055] In this embodiment, the data acquisition module includes a multimodal sensor, and the multimodal sensor is based on the layout of different key components of the generator set to achieve the fusion of different types of data and ensure the comprehensiveness and accuracy of the detection data.

[0056] This embodiment enhances the fault detection accuracy and data comprehensiveness of the generator set monitoring system by introducing the layout and data fusion of multimodal sensors, overcoming the limitations of traditional systems that rely on a single or a small number of parameters. Multimodal sensors are arranged on different key components of the generator set and can collect different types of operating data, such as temperature, pressure, vibration, sound spectrum and other multidimensional data. This multimodal data collection method can fully capture the performance of the generator set under different operating conditions, thereby improving the accuracy of fault identification.

[0057] In traditional monitoring systems, data is often collected through a single or a small number of sensors. However, different faults have different performance characteristics in various parameters. It is difficult to fully reflect the operating status of the generator set with a single data, which may lead to limitations in monitoring effects. For example, temperature data can reflect the thermal state of the generator set, while vibration data is closely related to mechanical failures, and sound data helps to identify small changes in mechanical structures. Through the multi-point layout of multimodal sensors, more dimensional data can be obtained to reflect the various operating states of the generator set.

[0058] In addition, the different data types collected by multimodal sensors are fused during the edge device and cloud analysis process, allowing the system to make a comprehensive judgment on the fault. Data fusion technology integrates and analyzes data from different sensors, allowing the monitoring system to more accurately judge the type and extent of the fault and reduce the errors that may be caused by single data. This multi-dimensional monitoring method can identify abnormal changes earlier and more accurately, allowing the system to provide stable detection results when monitoring complex operating conditions.

[0059] This embodiment significantly improves the fault detection accuracy of the monitoring system through multi-modal sensor layout and data fusion technology, comprehensively solves the technical problems of single data and limited detection results of traditional monitoring systems, and provides more reliable technical guarantee for the operating stability and safety of the generator set.

[0060] In this embodiment, the adaptive data transmission module includes a data loss tolerance and error tolerance mechanism, which uses a check code technology to ensure the accuracy and stability of data during transmission.

[0061] The introduction of the adaptive data transmission module in this embodiment, including the design of the data loss tolerance and fault tolerance mechanism, effectively improves the real-time performance and data transmission reliability of the generator set monitoring system during remote transmission. Traditional monitoring systems rely on local networks or private communication methods to achieve data transmission. Due to the fluctuation of network conditions, it is often difficult to maintain the stability of data transmission over long distances. The adaptive data transmission module can be dynamically adjusted according to the network bandwidth conditions, and adopt the data loss tolerance and fault tolerance mechanism when necessary to ensure efficient and stable data transmission under complex network conditions.

[0062] In remote areas, generator sets often face the problem of unstable network. Data transmission may experience packet loss, delay or transmission interruption, which makes the monitoring system unable to obtain the operating data of the generator set in real time, thus affecting the real-time judgment of the status of the generator set. The adaptive data transmission module can give priority to the transmission of high-quality data when the network is in good condition, and compress and reduce the transmission speed when the network is unstable, so as to ensure that the core data can be uploaded to the cloud first. In addition, the data loss and error tolerance mechanism adopts the checksum technology, which can recover and correct the data at the receiving end even if the data packet is lost during the transmission process, so as to ensure the integrity and accuracy of the data. Through this design, the system can maintain the stability of data transmission under network fluctuations, avoiding the loss of monitoring data and response delay caused by poor network conditions. In addition, the data loss and error tolerance mechanism also improves the reliability and robustness of the system, enabling the monitoring system to adapt to a variety of network environments more flexibly, and providing technical support for the remote monitoring of generator sets in remote or harsh environments.

[0063] This embodiment improves the stability and real-time performance of the data transmission process through adaptive data transmission and loss tolerance and fault tolerance mechanisms, effectively solves the monitoring blind spot problem caused by network problems in the traditional monitoring system during remote transmission, and provides reliable technical support for the application of remote monitoring systems under complex network conditions.

[0064] In this embodiment, the machine learning diagnostic model includes a dynamic learning algorithm based on historical data and real-time data streams. The algorithm has an adaptive feature weight adjustment function to automatically optimize diagnostic rules based on fault historical characteristics and real-time data, thereby improving the accuracy and real-time performance of fault diagnosis.

[0065] This embodiment achieves adaptive optimization and continuous improvement of fault diagnosis by introducing a dynamic learning algorithm based on historical data and real-time data streams into the machine learning diagnostic model, significantly improving the fault identification capability and accuracy of the monitoring system under complex working conditions. Traditional fault diagnosis systems often rely on fixed diagnostic rules and models, and are unable to self-optimize and adjust according to changes in actual working conditions, resulting in insufficient diagnostic accuracy when fault types vary. The dynamic learning algorithm combines historical data and real-time data streams, and adaptively adjusts feature weights by continuously learning accumulated fault features, thereby optimizing the recognition accuracy of the fault diagnosis model.

[0066] This adaptive optimization mechanism can update the diagnostic model in real time during system operation. For example, when the system detects a new fault type, it can automatically record its characteristics and data patterns into historical data, and automatically adjust the weights of relevant features through continuous analysis of real-time data streams, so that the model can gradually adapt to the identification of new faults. Unlike traditional fixed rule diagnostic models, this dynamic learning-based model can adjust its diagnostic strategies and parameters at any time, thereby maintaining efficient fault identification capabilities in complex and changing operating environments, and significantly improving adaptability.

[0067] In addition, the diagnostic model based on dynamic learning can automatically filter the impact of data noise and outliers, avoiding the misjudgment and false alarm of traditional models when facing high-frequency data. In the data stream, some instantaneous fluctuations may not represent faults, but temporary changes caused by external factors. The dynamic learning algorithm can identify such noise characteristics and adaptively adjust the threshold by learning the characteristics of historical data, reducing the false alarm rate, and thus providing more accurate diagnostic results.

[0068] Through the application of dynamic learning algorithms, this embodiment enables the machine learning diagnostic model to have the function of adaptive feature weight adjustment, which solves the problem that traditional monitoring systems lack self-optimization capabilities in fault diagnosis, realizes intelligent and dynamic fault identification, and provides strong technical support for efficient and accurate diagnosis of generator sets.

[0069] In this embodiment, the three-layer fault prediction mechanism includes:

[0070] Short-term warning: rapid identification of current anomalies based on real-time data;

[0071] Mid-term trend analysis: predict future trends through comprehensive analysis of historical data and real-time data;

[0072] Long-term risk prediction: Predict potential equipment failure risks through cluster analysis and pattern recognition technology.

[0073] The three-layer fault prediction mechanism proposed in this embodiment uses a hierarchical and multi-period prediction method to enable the system to provide fault warnings within different time ranges, solving the problem of low prediction accuracy under the traditional single time scale, comprehensively enhancing the system's fault prediction capabilities, and providing important guarantees for the continuous and stable operation of the generator set.

[0074] In this embodiment, the spectrum analysis and envelope analysis are used to detect weak signal changes in vibration data, and to perform early identification of the wear status and fault type of key components of the generator set by extracting specific fault spectrum features.

[0075] This embodiment significantly enhances the system's fault identification capability through the combination of spectrum analysis and envelope analysis, especially in early wear and weak fault identification. This method effectively makes up for the shortcomings of traditional vibration monitoring methods in sensitivity and accuracy, provides more accurate and timely technical support for fault detection of generator sets, and helps to ensure the long-term stable operation of equipment.

[0076] In this embodiment, the multi-level alarm system classifies the early warning information and sends warnings to users according to different fault levels and risk levels, ensuring that users can quickly identify and respond to high-risk faults.

[0077] This embodiment uses the design of a multi-level alarm system to enable the generator set monitoring system to achieve a hierarchical response to fault risks, effectively avoiding the shortcomings of a single alarm mechanism in traditional systems. By classifying and grading early warning information, it ensures that users can respond to faults of different levels efficiently and accurately, providing technical support for the stable operation and efficient management of generator sets.

[0078] In this embodiment, the user feedback is received and analyzed by the cloud computing platform through a deep learning optimization mechanism, and based on a self-optimization mechanism of historical feedback data, the feature weights and priority parameters in the diagnosis model are adjusted in real time, thereby improving the diagnostic accuracy and adaptability of the system.

[0079] This embodiment greatly improves the intelligence and adaptability of the diagnostic model through a deep learning optimization mechanism based on user feedback, enabling the system to have the ability of self-learning and self-optimization during continuous operation, solving the shortcomings of traditional static models and providing support for continuous improvement and accuracy enhancement of fault diagnosis.

[0080] In this embodiment, the user terminal provides an adaptive and configurable data visualization interface, allowing the user to view the real-time operating status, historical data records, diagnostic records and forecast reports of the generator set so as to take maintenance measures in a timely manner.

[0081] The adaptive and configurable data visualization interface of the user terminal of this embodiment greatly improves the user experience, makes the system data display more flexible and intuitive, and provides more efficient and personalized technical support for the operation monitoring of the generator set.

[0082] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A remote monitoring and diagnosis method for a generator set based on cloud computing, characterized in that: The following steps are involved: The key operating parameters of the generator set are collected in real time through the intelligent data acquisition module, including temperature, pressure, vibration and sound spectrum information; The collected operating parameters are preprocessed by edge computing devices, and the preprocessing includes data filtering, noise reduction and signal enhancement to reduce noise interference and improve the stability of signal transmission; Using the adaptive data transmission module, the pre-processed data is transmitted to the cloud computing platform through a dynamic bandwidth management mechanism. When the network bandwidth is insufficient, key data is transmitted first. In the cloud computing platform, the received data is analyzed and fault diagnosis is performed through the distributed data processing framework and adaptive machine learning diagnostic model to generate a diagnostic report on the operating status of the generator set; Based on the diagnostic report, a three-layer fault prediction mechanism is used to perform short-term warning, mid-term trend analysis, and long-term risk prediction, providing real-time warning of potential faults of the generator set; When the adaptive machine learning diagnostic model detects an anomaly, it further confirms the fault type and location based on spectrum analysis and envelope analysis technology, and generates corresponding warning information based on the fault type; The warning information is sent to the user through the multi-level alarm system of the user terminal, including the fault cause analysis, recommended maintenance plan and risk level classification; Receive user feedback on warning information and upload the feedback information to the cloud computing platform. Adjust the weight parameters of the diagnosis model based on the self-optimization mechanism of user feedback data, thereby achieving adaptive optimization of the diagnosis model.

2. The method according to claim 1, characterized in that The preprocessing step of the edge computing device includes filtering and compressing abnormal signals before data transmission to reduce redundant data and improve transmission efficiency.

3. The method according to claim 1, characterized in that: The data acquisition module includes a multimodal sensor, which is based on the layout of different key components of the generator set to achieve the fusion of different types of data and ensure the comprehensiveness and accuracy of the detection data.

4. The method according to claim 1, characterized in that: The adaptive data transmission module includes a data loss tolerance and error tolerance mechanism, which uses a check code technology to ensure the accuracy and stability of data during transmission.

5. The method according to claim 1, characterized in that The machine learning diagnostic model includes a dynamic learning algorithm based on historical data and real-time data streams. The algorithm has an adaptive feature weight adjustment function to automatically optimize diagnostic rules based on fault historical features and real-time data, thereby improving the accuracy and real-time performance of fault diagnosis.

6. The method according to claim 1, characterized in that The three-layer fault prediction mechanism includes: Short-term warning: rapid identification of current anomalies based on real-time data; Mid-term trend analysis: predict future trends through comprehensive analysis of historical data and real-time data; Long-term risk prediction: Predict potential equipment failure risks through cluster analysis and pattern recognition technology.

7. The method according to claim 1, characterized in that The spectrum analysis and envelope analysis are used to detect weak signal changes in vibration data, and to perform early identification of the wear status and fault type of key components of the generator set by extracting specific fault spectrum features.

8. The method according to claim 1, characterized in that The multi-level alarm system classifies early warning information and sends warnings to users according to different fault levels and risk levels, ensuring that users can quickly identify and respond to high-risk faults.

9. The method according to claim 1, characterized in that: The user feedback is received and analyzed by the cloud computing platform through a deep learning optimization mechanism, and based on a self-optimization mechanism of historical feedback data, the feature weights and priority parameters in the diagnosis model are adjusted in real time, thereby improving the diagnostic accuracy and adaptability of the system.

10. The method according to claim 1, characterized in that The user terminal provides an adaptive and configurable data visualization interface, enabling users to view the real-time operating status, historical data records, diagnostic records and prediction reports of the generator set so as to take maintenance measures in a timely manner.

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