Remote diagnosis system and method for complex system in Internet of Things environment

Abstract

The invention relates to the technical field of Internet of Things, and discloses a remote diagnosis system and method for a complex system in an Internet of Things environment, and the system comprises a data collection module which is used for collecting the operation state information of the complex system; the data transmission module is used for transmitting the running state information to the cloud processing center; the cloud processing center is used for performing fault analysis according to the received operation state information based on a machine learning algorithm, generating a fault diagnosis result and a corresponding maintenance suggestion and transmitting the fault diagnosis result and the corresponding maintenance suggestion to the user terminal; and the user terminal is used for receiving the fault diagnosis result and the maintenance suggestion, remotely accessing the system state report of the complex system and remotely controlling the complex system. According to the invention, efficient remote monitoring, accurate fault diagnosis and real-time maintenance guidance of the complex system in the Internet of Things environment can be realized, the maintenance cost is reduced while the maintenance efficiency is improved, and thus the maintainability and safety of the complex system are enhanced.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) technology, and in particular to a remote diagnostic system and method for complex systems in an IoT environment. Background Technology

[0002] With the development of IoT technology, more and more devices are being connected to the internet, enabling remote monitoring, diagnosis, and maintenance. Complex systems, as an important navigation technology, are widely used in aviation, aerospace, shipbuilding, automotive, and other fields.

[0003] However, the maintenance of complex systems still relies primarily on on-site operations, which presents several problems: First, professionals must spend a significant amount of time traveling to the site, leading to slow repair responses; second, the risks and difficulties of on-site maintenance increase dramatically in hazardous or special environments. Furthermore, traditional maintenance methods are inefficient in data collection and analysis, making it difficult to achieve real-time, accurate monitoring and fault diagnosis of complex systems.

[0004] The preceding description is intended to provide general background information and does not necessarily constitute prior art. Summary of the Invention

[0005] Based on this, it is necessary to address at least one of the problems mentioned above. In this invention, a remote diagnostic system and method for complex systems in an Internet of Things (IoT) environment are provided. By constructing a remote diagnostic system that includes data acquisition, transmission, cloud analysis and processing, and user terminals, machine learning algorithms are used to intelligently analyze the operating status of complex systems, thereby achieving efficient remote monitoring, accurate fault diagnosis, and real-time maintenance guidance for complex systems in an IoT environment.

[0006] Firstly, this application provides a remote diagnostic system for complex systems in an Internet of Things (IoT) environment, comprising:

[0007] The data acquisition module is used to collect operational status information of complex systems;

[0008] A data transmission module, connected to the data acquisition module, is used to transmit the operating status information to the cloud processing center;

[0009] The cloud processing center is used to perform fault analysis based on the received operating status information using machine learning algorithms, generate fault diagnosis results and corresponding maintenance suggestions, and transmit them to the user terminal.

[0010] The user terminal is connected to the cloud processing center and is used to receive the fault diagnosis results and the maintenance suggestions, and to remotely access the system status report of the complex system and to remotely control the complex system.

[0011] Furthermore, in some embodiments of this application, the data acquisition module includes:

[0012] The sensor unit includes multiple sensors installed at target monitoring points in the complex system, used to monitor the operating status parameters of the target equipment in real time and send them to the data acquisition unit.

[0013] The data acquisition unit, connected to the sensor unit, is used to receive the operating status parameters collected by the sensor unit, perform preprocessing, data conversion and encapsulation, and generate operating status information in a unified format.

[0014] Furthermore, in some embodiments of this application, the data transmission module includes:

[0015] A communication protocol adaptation unit is used to select the network layer protocol in the data transmission process according to the application scenario.

[0016] The wireless communication unit is used to select one or more communication technologies in the data transmission process according to the application scenario, so as to transmit the operation status information to the cloud processing center using the communication technology.

[0017] Furthermore, in some embodiments of this application, the cloud processing center includes:

[0018] Distributed computing units are used to preprocess, extract features, and train models from received data using the Hadoop and / or Spark frameworks.

[0019] The machine learning engine unit integrates multiple machine learning algorithms for fault mode identification and fault prediction of the complex system based on the machine learning algorithms.

[0020] The data storage unit includes a NoSQL database and a relational database, used to store raw sensor data and processed structured data, respectively.

[0021] Furthermore, in some embodiments of this application, the data storage unit includes:

[0022] The data synchronization subunit is used to synchronize raw data from a NoSQL database to a relational database using ETL tools or the Apache Kafka streaming framework.

[0023] The data backup and recovery subunit is used to perform full and incremental backups of NoSQL and relational databases on a regular basis, as well as data recovery based on timestamps;

[0024] The data archiving subunit is used to migrate structured data that exceeds a preset time threshold to a distributed file system or object storage.

[0025] Furthermore, in some embodiments of this application, the cloud processing center further includes:

[0026] The alarm triggering unit is used to trigger graded alarms based on the monitored threshold exceeding results, fault mode matching results, or abnormal data trends.

[0027] A visualization unit is used to display the equipment operating status and fault prediction results of the complex system using line graphs, heat maps, and / or dashboards.

[0028] Furthermore, in some embodiments of this application, the user terminal includes:

[0029] The real-time alarm push unit is used to receive and display alarm information via a message queue or stream processing framework.

[0030] The remote collaboration interface unit provides an interface that supports video calls, file sharing, and online expert guidance.

[0031] The dynamic fault guide unit is used to generate customized maintenance guides based on the equipment status.

[0032] Furthermore, in some embodiments of this application, the system further includes a security control module, which is integrated into the data transmission and processing link and is used to control the secure transmission and secure storage of data.

[0033] Furthermore, in some embodiments of this application, the security control module includes:

[0034] A transmission encryption unit is used to encrypt the data transmission link using the SSL / TLS protocol;

[0035] A data integrity verification unit is used to verify data integrity through digital signatures or hash algorithms.

[0036] Access control unit, used to restrict user terminal access to the cloud processing center based on role permissions.

[0037] Secondly, this application also provides a remote diagnostic method for complex systems in an Internet of Things (IoT) environment, executed in the remote diagnostic system for complex systems in an IoT environment as described above, including the following steps:

[0038] Collect operational status information of complex systems;

[0039] The operational status information is transmitted to the cloud processing center;

[0040] Based on machine learning algorithms, fault analysis is performed on the received operating status information to generate fault diagnosis results and corresponding maintenance suggestions, which are then transmitted to the user terminal.

[0041] Receive the fault diagnosis results and the maintenance suggestions, remotely access the system status report of the complex system, and perform remote control operations on the complex system.

[0042] As described above, this application provides a remote diagnostic system and method for complex systems in an Internet of Things (IoT) environment. The remote diagnostic system includes: a data acquisition module for collecting operational status information of the complex system; a data transmission module connected to the data acquisition module for transmitting the operational status information to a cloud processing center; a cloud processing center for performing fault analysis based on the received operational status information using machine learning algorithms, generating fault diagnosis results and corresponding maintenance suggestions, and transmitting them to a user terminal; and a user terminal connected to the cloud processing center for receiving fault diagnosis results and maintenance suggestions, remotely accessing the system status report of the complex system, and remotely controlling the complex system. This application proposes a remote diagnostic system and method for complex systems in an IoT environment. The data acquisition module monitors the status of the complex system in real time, and the data transmission module securely and efficiently transmits the data to the cloud processing center. The cloud processing center uses machine learning algorithms to deeply analyze the data, achieving fault diagnosis and prediction, and generating maintenance suggestions. The user terminal receives the diagnostic results and suggestions and supports remote access and control. As can be seen, this application constructs a remote diagnostic system that includes data acquisition, transmission, cloud analysis and processing, and user terminals. It utilizes machine learning algorithms to intelligently analyze the operating status of complex systems, thereby achieving efficient remote monitoring, accurate fault diagnosis, and real-time maintenance guidance for complex systems in the Internet of Things environment. This improves maintenance efficiency while reducing maintenance costs, thus enhancing the maintainability and security of complex systems. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] in:

[0045] Figure 1 This is a schematic diagram of the structure of a remote diagnostic system for complex systems in an Internet of Things environment provided in an embodiment of this application;

[0046] Figure 2 This is another structural schematic diagram of the remote diagnostic system for complex systems in the Internet of Things environment provided in the embodiments of this application;

[0047] Figure 3This is a flowchart illustrating the remote diagnostic system method for complex systems in an Internet of Things environment provided in the embodiments of this application;

[0048] Figure 4 This is a schematic diagram of the structure of the computer device provided in the embodiments of this application. Detailed Implementation

[0049] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of systems and methods consistent with those detailed in the appended claims or with some aspects of this application.

[0050] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover descriptions such as non-exclusive inclusion, so that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0051] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0052] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0053] With the development of IoT technology, more and more devices are being connected to the internet, enabling remote monitoring, diagnosis, and maintenance. Complex systems, as an important navigation technology, are widely used in aviation, aerospace, shipbuilding, automotive, and other fields. However, due to the complexity and specialization of complex systems, their fault diagnosis and repair usually require on-site operation by professional personnel, which is not only time-consuming and labor-intensive but also difficult to implement in some special environments (such as extreme weather or dangerous areas). Therefore, developing a system that can achieve remote diagnosis and repair of complex systems through IoT technology has become particularly important.

[0054] To address the aforementioned technical issues, this application provides a remote diagnostic system and method for complex systems in an Internet of Things (IoT) environment. By constructing a remote diagnostic system that includes data acquisition, transmission, cloud analysis and processing, and user terminals, machine learning algorithms are used to intelligently analyze the operating status of complex systems, thereby achieving efficient remote monitoring, accurate fault diagnosis, and real-time maintenance guidance for complex systems in an IoT environment.

[0055] Please see Figure 1 , Figure 1 This is a schematic diagram of the structure of a remote diagnostic system for a complex system in an Internet of Things (IoT) environment, as provided in an embodiment of this application. Specifically, the remote diagnostic system may include a data acquisition module 10, a data transmission module 20, a cloud processing center 30, and a user terminal 40, as follows:

[0056] Data acquisition module 10 is used to collect operating status information of complex systems;

[0057] Specifically, the data acquisition module 10 includes a sensor unit and a data acquisition unit. The sensor unit is installed at key monitoring points in complex systems, such as temperature sensors monitoring engine hotspots, pressure sensors monitoring hydraulic systems, and vibration sensors monitoring rotating machinery bearings, collecting operational status parameters in real time. The data acquisition unit connects to the sensors, receives the collected parameters, performs preprocessing (such as filtering and noise reduction, signal amplification), data conversion (analog-to-digital conversion), and encapsulation to generate unified format operational status information, such as JSON, XML, or CSV. The sensor unit can integrate multiple types of sensors, such as temperature, pressure, vibration, flow, and position sensors, to comprehensively monitor the operational status of complex systems. The data acquisition unit can have edge computing capabilities; after preliminary data analysis, it only uploads abnormal or critical data, reducing transmission volume.

[0058] The data transmission module 20 is connected to the data acquisition module 10 and is used to transmit the operating status information to the cloud processing center 30.

[0059] Specifically, for the data transmission module 20, the data transmission module includes a communication protocol adaptation unit and a wireless communication unit. The communication protocol adaptation unit selects an appropriate network layer protocol based on the application scenario; for example, IPv6 is suitable for large-scale IoT device connections. The wireless communication unit selects one or more communication technologies as needed; for example, 5G can be selected in industrial IoT scenarios to ensure high bandwidth and low latency transmission. The communication protocol adaptation unit can support dynamic protocol switching, automatically adjusting according to network conditions and data priorities. The wireless communication unit can adopt multi-network convergence technology, combining 5G, NB-IoT, etc., to achieve complementary advantages.

[0060] The cloud processing center 30 is used to perform fault analysis based on the received operating status information using machine learning algorithms, generate fault diagnosis results and corresponding maintenance suggestions, and transmit them to the user terminal 40.

[0061] Specifically, the cloud processing center 30 includes a distributed computing unit, a machine learning engine unit, and a data storage unit. The distributed computing unit uses the Hadoop and Spark frameworks for data preprocessing, feature extraction, and model training. The machine learning engine unit integrates multiple algorithms, such as random forests and deep learning, to achieve fault mode recognition and prediction. The data storage unit includes NoSQL and relational databases, storing raw sensor data and processed structured data respectively. The distributed computing unit can be extended to real-time stream processing, using frameworks such as Apache Flink for real-time data analysis and processing. The data storage unit adopts a data lake architecture, uniformly storing various types of data and supporting deep analysis and mining.

[0062] User terminal 40 is connected to cloud processing center 30 to receive fault diagnosis results and maintenance suggestions, and to remotely access system status reports of complex systems, as well as to remotely control complex systems.

[0063] Specifically, user terminal 40 is connected to cloud processing center 30, enabling it to receive fault diagnosis results and maintenance suggestions, remotely access system status reports, and remotely control complex systems. User terminal 40 can integrate augmented reality (AR) technology to overlay virtual fault information and maintenance steps, assisting on-site maintenance personnel. It can also feature an intelligent voice assistant, allowing users to query equipment status, receive alarm notifications, and request maintenance advice via voice.

[0064] The remote diagnostic system provided in this embodiment monitors complex systems in real time and collects operational status information via a data acquisition module, which then efficiently transmits this information to a cloud processing center via a data transmission module. The cloud processing center uses machine learning algorithms to deeply analyze the data, accurately diagnose and predict faults, and generate maintenance suggestions. The user terminal allows users to conveniently receive diagnostic results and suggestions, supporting remote monitoring and operation. This system enables real-time monitoring, accurate fault diagnosis, and remote maintenance guidance for complex systems, improving maintenance efficiency and system reliability, reducing maintenance costs, and ensuring stable operation of systems in hazardous or difficult-to-reach environments.

[0065] Furthermore, in some embodiments, the data acquisition module 10 may specifically include:

[0066] Sensor unit 11 includes multiple sensors installed at target monitoring points in a complex system, used to monitor the operating status parameters of the target equipment in real time and send them to the data acquisition unit;

[0067] Specifically, sensor unit 11 consists of multiple sensors installed at key monitoring points in the complex system to monitor the operating status parameters of the target equipment in real time. These sensors include, but are not limited to, temperature sensors, pressure sensors, vibration sensors, flow sensors, and position sensors.

[0068] The data acquisition unit 12 is connected to the sensor unit 11 and is used to receive the operating status parameters collected by the sensor unit, perform preprocessing, data conversion and encapsulation, and generate operating status information in a unified format.

[0069] Specifically, the data acquisition unit 12, connected to the sensor unit, is responsible for receiving the operating status parameters acquired by the sensors. It typically includes a signal conditioning circuit, an analog-to-digital converter (ADC), and a data processing unit. The signal conditioning circuit amplifies and filters the analog signals output from the sensors; the ADC converts the processed analog signals into digital signals; and the data processing unit preprocesses, converts, and encapsulates the digital signals to generate operating status information in a unified format.

[0070] The sensor unit in this embodiment monitors key components in real time using a variety of high-sensitivity sensors, and utilizes intelligent sensor technology and wireless sensor networks to improve data reliability and system flexibility. The data acquisition unit not only performs signal conditioning and analog-to-digital conversion, but also optimizes data processing and transmission efficiency through edge computing and multi-format encapsulation, enabling the system to more accurately and in real time grasp the operating status of complex systems, providing high-quality data support for subsequent fault diagnosis and analysis, and improving the performance and reliability of the remote diagnostic system.

[0071] In specific embodiments, the data acquisition module is installed in a complex system and mainly consists of high-performance sensors (such as temperature sensors, pressure sensors, vibration sensors, etc.) and embedded data acquisition units. Sensors with high sensitivity, wide measurement range, and low power consumption are selected to ensure accurate data acquisition. The high-performance sensors are connected to the embedded data acquisition unit via specific interfaces; typically, the sensor's output signal is transmitted to the data acquisition unit. The data acquisition unit has built-in signal conditioning circuitry that processes the analog or digital signals from the sensors and converts them into digital signals for subsequent processing and analysis. Each sensor has a one-to-one connection with the data acquisition unit, ensuring accurate signal acquisition and processing.

[0072] Sensors are installed in critical parts of complex systems to monitor the equipment's operating status in real time and transmit the data to a data acquisition unit via a serial interface (such as RS485). The data acquisition unit is responsible for converting the sensor data into digital signals and encapsulating them according to a predetermined protocol.

[0073] 1. Key parts:

[0074] Temperature sensor: Installed in hot areas of the equipment, such as near the engine, motor or electronic components, to monitor temperature changes.

[0075] Pressure sensors: installed in hydraulic systems, pneumatic systems, or pipelines to monitor pressure changes.

[0076] Vibration sensors: installed on the bearings or housings of rotating machinery (such as motors, pumps, and fans) to monitor vibration frequency and amplitude.

[0077] Flow sensor: Installed in fluid pipelines to monitor the flow rate of liquids or gases.

[0078] Position sensor: Installed on moving parts to monitor changes in position or displacement.

[0079] 2. Operating Status:

[0080] Temperature: Monitors whether the equipment is overheating or has an abnormal temperature.

[0081] Pressure: Monitor whether the system pressure is within a safe range.

[0082] Vibration: Monitor whether the equipment is experiencing abnormal vibration or mechanical failure.

[0083] Flow rate: Monitors whether the fluid flow is normal.

[0084] Location: Monitors whether the position of moving parts is accurate.

[0085] 3. Protocol Usage and Types

[0086] (1) Determine the data format:

[0087] Unified standard format: Common data formats (such as JSON, XML, CSV, etc.) are usually chosen to facilitate cross-platform and cross-system data exchange.

[0088] ①JSON format: lightweight, easy to read and parse, and supports nested data structures.

[0089] ②XML format: Supports complex data structures and is suitable for scenarios that require strict data validation.

[0090] ③CSV format: Simple, easy to store and process, suitable for large-scale data.

[0091] (2) Select a communication protocol:

[0092] The choice of communication protocol depends on the application scenario, data transmission requirements, and device capabilities. The following are common communication protocols and their applicable scenarios:

[0093] ①Application layer protocol:

[0094] MQTT (Message Queuing Telemetry Transport): Lightweight, low-bandwidth, suitable for low-power devices. Applicable to communication between IoT devices and cloud platforms (such as sensor data transmission). Data format typically uses JSON or binary format.

[0095] HTTP / HTTPS: Widely supported and easy to implement, but with higher overhead. Suitable for communication between devices and servers (such as API calls). Data format typically uses JSON or XML.

[0096] CoAP (Constrained Application Protocol): Designed for low-power devices, it supports UDP transmission. Suitable for resource-constrained IoT devices (such as smart home devices). Data format typically uses JSON or binary format.

[0097] ② Transport Layer Protocol

[0098] TCP: Reliable and connection-oriented, suitable for scenarios requiring high reliability. Applicable to Industrial IoT and remote control.

[0099] UDP: Connectionless, low latency, suitable for real-time data transmission. Applicable to video streaming and real-time monitoring.

[0100] ③ Network layer protocols

[0101] IPv6: Supports large-scale device connectivity, suitable for IoT scenarios. Applicable to smart cities and large-scale sensor networks.

[0102] ④ Data Link Layer Protocol

[0103] LoRaWAN: Long-range, low-power, suitable for wide area network coverage. Applicable to agricultural IoT and smart cities.

[0104] Zigbee: Low power consumption, short range, suitable for local area network communication. Applicable to smart homes and industrial sensor networks.

[0105] 3. Steps to determine data format and protocol

[0106] ① Requirements Analysis: Determine the real-time, reliability, and bandwidth requirements for data transmission. Assess the computing power and power consumption limitations of the equipment.

[0107] ② Select data format: Choose JSON, XML or CSV format according to the complexity of the data and the parsing requirements.

[0108] ③ Select a communication protocol: Choose protocols such as MQTT, HTTP, or CoAP according to the application scenario.

[0109] ④ Testing and Verification: Test the compatibility, performance, and reliability of data formats and protocols in a real-world environment.

[0110] The collected data is encoded in a standardized format (such as JSON) to facilitate subsequent data parsing and processing.

[0111] Data parsing includes:

[0112] Format parsing: Parses data in formats such as JSON and XML into structured data (such as dictionaries and lists) that can be read by the program.

[0113] Field extraction: Extract key fields (such as temperature, pressure, vibration value, etc.) from the parsed data.

[0114] Data validation: Check the integrity and validity of the data, and exclude outliers or missing values.

[0115] Data processing includes:

[0116] Data cleaning: removing noisy, duplicate, or invalid data.

[0117] Data conversion: Converting raw data into the required units or formats (e.g., converting temperature from Celsius to Fahrenheit).

[0118] Data aggregation: summarizing time series data (such as calculating the average, maximum, minimum, etc.).

[0119] Data analysis: using statistical methods or machine learning algorithms to analyze data trends, anomalies, or patterns.

[0120] Data storage: The processed data is stored in a database or file for easy retrieval and use later.

[0121] Data visualization: Displaying data trends and status through charts (such as line charts, bar charts, and heatmaps).

[0122] Report generation: Generate equipment operation status reports based on the analysis results for decision-making purposes.

[0123] Furthermore, in some embodiments, the data transmission module 20 may specifically include:

[0124] The communication protocol adaptation unit 21 is used to select the network layer protocol in the data transmission process according to the application scenario.

[0125] Specifically, the communication protocol adaptation unit 21 is responsible for selecting the appropriate network layer protocol for data transmission based on different application scenarios. For example, in an industrial IoT environment, IPv6 can be selected to support large-scale device connections and efficient address management; in smart city applications requiring low power consumption and wide coverage, LoRaWAN or NBIoT protocols can be selected. The communication protocol adaptation unit can further support dynamic protocol switching, adjusting the protocol used in real time according to network conditions and data transmission requirements. For example, when the network is congested, it automatically switches to a more reliable protocol, or selects the optimal communication protocol based on signal strength in different areas. In addition, a protocol conversion gateway can be integrated to achieve seamless integration between different protocols, ensuring data transmission compatibility and flexibility.

[0126] The wireless communication unit 22 is used to select one or more communication technologies in the data transmission process according to the application scenario, so as to transmit the operation status information to the cloud processing center using communication technologies;

[0127] Specifically, the wireless communication unit 22 is responsible for selecting one or more communication technologies based on the application scenario to transmit operational status information to the cloud processing center. Common communication technologies include 5G, LoRa, NBIoT, and Zigbee. For example, in industrial control scenarios requiring high bandwidth and low latency, 5G communication technology can be selected; in agricultural IoT, LoRa technology can be selected to achieve long-distance, low-power data transmission. The wireless communication unit can support multi-mode communication modules, that is, simultaneously support multiple communication technologies (such as 5G, NBIoT, LoRa, etc.) and intelligently switch according to actual needs. For example, 5G networks are prioritized in urban environments, while switching to LoRa or satellite communication in remote areas. In addition, communication signal enhancement technologies, such as relay stations and signal amplifiers, can be integrated to improve signal coverage and transmission quality.

[0128] This embodiment achieves protocol optimization and flexible selection of communication technologies during data transmission through the collaborative work of the communication protocol adaptation unit and the wireless communication unit. The communication protocol adaptation unit can dynamically select and switch network layer protocols according to the application scenario, ensuring the efficiency and stability of data transmission. The wireless communication unit supports multiple communication technologies and can intelligently switch according to actual needs, improving the flexibility and coverage of data transmission. These technical measures collectively ensure the reliable transmission of complex system operating status information, providing a solid data foundation for subsequent cloud processing and fault diagnosis, and enhancing the performance and applicability of the entire remote diagnostic system.

[0129] In a specific embodiment, the data transmission module sends the encapsulated data packets to the cloud server via a wireless communication module (such as an LTE-M / NBIoT module). The encapsulation stage is located in the embedded data acquisition unit. Specifically, after the data acquisition unit completes data parsing, processing, and formatting, the encapsulation stage encapsulates the data according to a predetermined protocol (such as Modbus, MQTT, etc.) and then sends it to the cloud server via the wireless communication module (such as an LTE-M / NBIoT module).

[0130] Furthermore, in some embodiments, the cloud processing center 30 may specifically include:

[0131] Distributed computing unit 31 is used to preprocess, extract features and train models on received data using Hadoop and / or Spark frameworks;

[0132] Specifically, for distributed computing unit 31, the Hadoop and / or Spark frameworks are used to preprocess the received data, extract features, and train models. The Hadoop framework is mainly used for batch processing and storage of large-scale data, achieving highly reliable data storage and parallel computing through the HDFS distributed file system. The Spark framework excels at in-memory computing and real-time data processing, enabling rapid iterative computation and complex data transformation operations.

[0133] The machine learning engine unit 32 integrates a variety of machine learning algorithms for fault mode identification and fault prediction of complex systems based on machine learning algorithms.

[0134] Specifically, the machine learning engine unit 32 integrates various machine learning algorithms, such as random forest, support vector machine (SVM), and deep learning models (such as convolutional neural network (CNN) and recurrent neural network (RNN), to perform fault mode identification and fault prediction for complex systems based on machine learning algorithms. For example, by training a random forest model to identify the differences between the normal operating state and abnormal state of the equipment, it can provide early warnings of potential faults.

[0135] Data storage unit 33 includes a NoSQL database and a relational database, used to store raw sensor data and processed structured data respectively;

[0136] Specifically, data storage unit 33 includes a NoSQL database and a relational database, used to store raw sensor data and processed structured data, respectively. NoSQL databases (such as MongoDB and Cassandra) are suitable for storing unstructured or semi-structured data, such as raw sensor data, and offer high scalability and flexibility. Relational databases (such as MySQL and PostgreSQL) are suitable for storing structured data, such as processed equipment operating status and fault records, facilitating complex queries and analysis.

[0137] In this embodiment, the distributed computing unit utilizes frameworks such as Hadoop and Spark to efficiently process large-scale data, the machine learning engine unit integrates multiple algorithms to achieve accurate fault diagnosis and prediction, and the data storage unit uses NoSQL and relational databases to ensure high availability and consistency of data. Together, they improve the system's data processing capabilities, fault diagnosis accuracy, and storage reliability, providing strong technical support for remote diagnosis of complex systems in the Internet of Things environment.

[0138] Furthermore, in some embodiments, the data storage unit 33 may specifically include:

[0139] The data synchronization subunit is used to synchronize raw data from a NoSQL database to a relational database using ETL tools or the Apache Kafka streaming framework.

[0140] Specifically, for the data synchronization subunit, raw data is synchronized from NoSQL databases to relational databases using ETL tools or the Apache Kafka stream processing framework. ETL tools are responsible for data extraction, transformation, and loading, ensuring data consistency and integrity across different storage systems. For example, ETL tools can periodically clean and transform raw sensor data from NoSQL databases before loading it into a relational database for structured storage. Apache Kafka, on the other hand, is suitable for real-time data synchronization, efficiently transmitting data through a message queue mechanism.

[0141] The data backup and recovery subunit is used to perform full and incremental backups of NoSQL and relational databases on a regular basis, as well as data recovery based on timestamps;

[0142] Specifically, for the data backup and recovery subunit, data security and availability are ensured by regularly performing full and incremental backups of NoSQL and relational databases, as well as data recovery based on timestamps. Full backups are performed regularly (e.g., weekly) to back up the entire database, while incremental backups record all data changes between two full backups. When data recovery is needed, the data can be quickly located and restored to the specified point in time based on the timestamp.

[0143] The data archiving subunit is used to migrate structured data that exceeds a preset time threshold to a distributed file system or object storage.

[0144] Specifically, the data archiving subunit is used to migrate structured data that has exceeded a preset time threshold to a distributed file system (such as HDFS) or object storage (such as Amazon S3). By setting a reasonable data lifecycle strategy, historical data can be archived to low-cost storage media, freeing up storage space in the production database. For example, historical data of equipment operation exceeding one year can be archived to object storage to reduce storage costs.

[0145] In this embodiment, the data synchronization subunit ensures real-time and batch data synchronization, providing high-quality data support; the data backup and recovery subunit enhances data disaster recovery capabilities and recovery efficiency through full and incremental backup mechanisms; and the data archiving subunit optimizes storage costs and improves the utilization rate of storage resources through intelligent archiving strategies, ensuring the system's data reliability, security, and economy, and providing a solid storage foundation for the remote diagnosis and maintenance of complex systems.

[0146] Furthermore, in some embodiments, the cloud processing center 30 may specifically include:

[0147] The alarm triggering unit 34 is used to trigger a graded alarm based on the monitored threshold exceeding result, fault mode matching result, or abnormal data trend.

[0148] Specifically, the alarm triggering unit 34 is used to trigger tiered alarms based on monitored threshold exceedances, fault mode matching results, or abnormal data trends. For example, an alarm is triggered when the equipment temperature exceeds a preset threshold (e.g., 80℃); or when the monitoring data trend shows that the equipment status is rapidly deteriorating, a corresponding alarm is triggered. The tiered alarm mechanism can be divided into three levels: warning, severe, and emergency, based on the severity of the fault. The alarm triggering unit can further integrate intelligent early warning algorithms, using machine learning models to evaluate the equipment's operating status in real time, predict potential faults, and trigger early warnings. In addition, a dynamic threshold adjustment mechanism can be introduced to automatically adjust the alarm threshold based on the equipment's historical operating data and current operating status, improving the accuracy and timeliness of alarms.

[0149] Visualization unit 35 is used to display the equipment operating status and fault prediction results of complex systems using line graphs, heat maps and / or dashboards;

[0150] Specifically, visualization unit 35 is used to display the equipment operating status and fault prediction results of complex systems through line graphs, heat maps, and / or dashboards. For example, line graphs can show the temperature change trend of equipment over time, heat maps can show the distribution of operating status of different components of the equipment, and dashboards can provide real-time monitoring and alarm display of key indicators. The visualization unit can further support 3D visualization and virtual reality (VR) / augmented reality (AR) technologies to provide immersive equipment operating status displays. For example, through VR technology, maintenance personnel can remotely view the 3D model inside the equipment to intuitively understand the operating status and fault location of the equipment. In addition, interactive visualization analysis tools can be integrated, allowing users to customize chart types, time ranges, and data dimensions for in-depth data analysis.

[0151] The alarm triggering unit provided in this embodiment uses an intelligent early warning and hierarchical alarm mechanism to promptly detect and notify users of potential faults, ensuring rapid response. The visualization unit utilizes various charts and advanced VR / AR technology to provide an intuitive display of equipment operating status, helping users quickly understand and analyze complex systems. This improves the system's monitorability, operability, and intelligence, providing users with personalized and efficient remote diagnostic and maintenance services, further enhancing the user experience and system usability.

[0152] In specific implementations, the cloud processing center uses distributed computing architectures, such as Hadoop and Spark frameworks, to support large-scale data processing needs. Distributed computing architectures (such as Hadoop and Spark) are primarily used to support large-scale data processing needs, especially in data analysis and fault prediction on cloud service platforms, including:

[0153] 1. Data Preprocessing

[0154] Data cleaning: Distributed architecture can efficiently handle noise and missing values ​​in massive amounts of data, ensuring data quality.

[0155] Feature extraction: Quickly extracting features (such as mean, variance, maximum, minimum, etc.) from large-scale data using distributed computing frameworks (such as Spark).

[0156] Data normalization: Distributed normalization processing of large-scale data to prevent certain features from dominating the model due to excessively large numerical ranges.

[0157] 2. Model Training

[0158] Dataset partitioning: Distributed architectures can efficiently partition large-scale datasets into training, validation, and test sets.

[0159] Feature selection: Implement feature selection methods such as correlation analysis and principal component analysis (PCA) through distributed computing frameworks (such as Spark MLlib).

[0160] Model selection: Supports distributed training of various machine learning algorithms (such as random forest, support vector machine, deep learning models, etc.).

[0161] 3. Model Training Process

[0162] Random Forest:

[0163] Decision tree construction: Distributed architecture can build multiple decision trees in parallel, significantly improving training efficiency.

[0164] Voting Mechanism: The distributed computing framework supports an efficient voting mechanism to quickly generate the final prediction results.

[0165] Hyperparameter tuning: Optimize hyperparameters through distributed grid search or random search.

[0166] Deep learning:

[0167] Model design: Distributed architecture supports the training of large-scale neural networks (such as TensorFlow on Spark).

[0168] Forward and Backward Propagation: Distributed computing frameworks can accelerate the forward and backward propagation processes.

[0169] Hyperparameter tuning: Adjusting hyperparameters such as learning rate and batch size through distributed optimization algorithms.

[0170] 4. Pattern Recognition

[0171] Feature space partitioning: Distributed architecture can efficiently partition large-scale feature spaces.

[0172] Clustering analysis: Supports distributed clustering algorithms (such as Kmeans and DBSCAN) for clustering analysis of large-scale data.

[0173] Anomaly detection: Quickly identify anomalous data points using distributed algorithms (such as OneClass SVM and Isolation Forest).

[0174] 5. Fault prediction

[0175] Time series analysis: Distributed architecture supports the analysis of large-scale time series data (such as ARIMA, LSTM).

[0176] State transition matrix: The state transition matrix is ​​constructed through distributed computing to analyze the changes in system state.

[0177] Threshold setting: The distributed architecture can quickly calculate the threshold of key indicators and trigger alerts.

[0178] 6. Model Evaluation and Optimization

[0179] Performance evaluation: The distributed computing framework supports efficient computation of performance metrics such as accuracy, recall, and F1 score.

[0180] Cross-validation: The generalization ability of the model is evaluated through distributed K-fold cross-validation.

[0181] Continuous optimization: The distributed architecture supports periodic retraining of the model and introduction of new data to continuously optimize model performance.

[0182] 7. Results Presentation and Feedback

[0183] Visualization: Distributed architectures can quickly generate visualizations of large-scale data (such as charts and dashboards).

[0184] Alarm mechanism: Real-time monitoring of data and triggering of alarms through a distributed computing framework.

[0185] Feedback loop: Distributed architecture supports efficient collection of user feedback and improvement of models.

[0186] Use a NoSQL database (such as MongoDB) to store the raw data, and a relational database (such as MySQL) to store the processed data.

[0187] Machine learning algorithms (such as random forests and deep learning) are used to perform pattern recognition and fault prediction on the collected data. By training the model, the differences between normal and abnormal equipment states can be identified, and potential faults can be predicted in advance.

[0188] Pattern recognition and fault identification processes:

[0189] 1. Data Preprocessing

[0190] Data cleaning: removing noisy data, missing values, and outliers.

[0191] Feature extraction: Extracting useful features from raw data.

[0192] Data normalization: Normalize data to the same scale to prevent certain features from dominating the model due to excessively large numerical ranges.

[0193] 2. Dataset partitioning

[0194] The training set is used to train the model; the validation set is used for parameter tuning and model selection; and the test set is used to evaluate model performance.

[0195] 3. Feature Selection

[0196] Correlation analysis: Select features that are highly correlated with the target variable using methods such as Pearson correlation coefficient.

[0197] Principal Component Analysis (PCA): Dimensionality reduction reduces the number of features and lowers model complexity.

[0198] 4. Model Selection

[0199] Random Forest:

[0200] Decision tree construction: Randomly extract samples and features from the training set to construct multiple decision trees.

[0201] Voting mechanism: Each tree performs classification or regression prediction on the samples, and the final result is determined by a vote of all the trees' prediction results.

[0202] Hyperparameter tuning: Adjust hyperparameters such as the number of trees and maximum depth through grid search or random search.

[0203] Deep learning:

[0204] Model design: Design neural network structures (such as convolutional layers, pooling layers, and fully connected layers) according to task requirements.

[0205] Forward propagation: Input data is propagated forward through the network to calculate the output.

[0206] Loss function: Define a loss function (such as mean squared error, cross-entropy, etc.) to measure the difference between the model's predicted value and the true value.

[0207] Backpropagation: Update network weights using optimization algorithms such as gradient descent to minimize the loss function.

[0208] Hyperparameter tuning: Adjusting the learning rate, batch size, regularization parameters, etc.

[0209] 5. Pattern Recognition

[0210] Feature space partitioning: The feature space is divided into multiple regions, each corresponding to a certain operating state (normal or abnormal).

[0211] Cluster analysis: Using clustering algorithms such as K-means and DBSCAN, similar data points are clustered together to identify different operating patterns.

[0212] Anomaly detection: Using algorithms such as One-Class SVM and Isolation Forest, abnormal data points that deviate from the normal pattern are identified.

[0213] 6. Fault Prediction

[0214] Time series analysis: Using time series models such as ARIMA and LSTM, predicting the state of the system over a future period.

[0215] State transition matrix: Construct the state transition matrix to analyze the probability of the system transitioning from a normal state to a fault state.

[0216] Threshold setting: Set thresholds for key indicators, and trigger an alert when the indicator exceeds the threshold.

[0217] 7. Model Evaluation and Optimization

[0218] Performance evaluation: The model performance is evaluated using metrics such as accuracy, recall, F1 score, and AUC.

[0219] Cross-validation: K-fold cross-validation is used to ensure the model's generalization ability.

[0220] Continuous optimization: Regularly retrain the model, introduce new data, and continuously optimize model performance.

[0221] 8. Results Display and Feedback

[0222] Visualization: The system status and fault prediction results are displayed intuitively through charts, dashboards and other forms.

[0223] Alarm mechanism: When an anomaly is detected or a potential fault is predicted, relevant personnel will be notified promptly via email, SMS, or other means.

[0224] Feedback loop: Collect user feedback to continuously improve models and algorithms, thereby enhancing the reliability and accuracy of the system.

[0225] Fault mode identification: Establish a fault mode database based on historical data. When new data matches a known fault mode, an alarm mechanism is triggered.

[0226] Fault mode identification triggering methods and alarm mechanisms:

[0227] 1. Fault Mode Identification

[0228] Historical data collection: Collect historical data on the normal operation and fault conditions of the equipment.

[0229] Failure mode modeling: Using machine learning algorithms (such as cluster analysis and classification models) to model historical data and identify different failure modes.

[0230] Pattern matching: Matching new data with known fault patterns to identify potential faults.

[0231] 2. Alarm Triggering Conditions

[0232] Threshold trigger: Set a threshold for key indicators, and trigger an alarm when the data exceeds the threshold.

[0233] Pattern matching trigger: An alarm is triggered when new data matches a known fault pattern.

[0234] Trend Trigger: Triggers an alarm when the data trend is abnormal.

[0235] 3. Alarm Mechanism

[0236] Real-time alerts: Alerts are triggered immediately upon detection of anomalies or faults. Data is monitored in real time via message queues (such as Kafka) or stream processing frameworks (such as Flink). Rule engines (such as Drools) or machine learning models are used to determine in real time whether to trigger an alert.

[0237] Tiered Alarms: Alarms are triggered based on the severity of the fault. Different alarm rules are defined for each level (e.g., "Warning," "Critical," "Urgent"). The appropriate alarm level is triggered based on the fault mode matching result.

[0238] Delayed Alarm: An alarm is triggered after an anomaly has persisted for a certain period, avoiding false alarms. A time window is set to track the duration of abnormal data. An alarm is triggered when the duration of the anomaly exceeds a threshold.

[0239] 4. Alarm Notification Methods

[0240] Email notification: Alarm information will be sent to the relevant personnel's email addresses.

[0241] SMS notification: The alarm information will be sent to the mobile phones of the relevant personnel.

[0242] Mobile app notifications: Send alarm information via mobile apps (such as WeChat Work and DingTalk).

[0243] Dashboard display: Alarm information is displayed in real time on the monitoring dashboard.

[0244] 5. Alarm Handling and Feedback

[0245] Alarm Logs: Alarm information is recorded in the database for later analysis and retrieval. Information includes device ID, fault type, timestamp, and processing status.

[0246] Alarm Confirmation: Relevant personnel confirm the handling status after receiving the alarm. Mark the alarm as "Handled" in the mobile application or dashboard.

[0247] Alarm Analysis: Regularly analyze alarm data to optimize alarm rules and models. Statistically analyze alarm frequency and rate, and adjust thresholds or model parameters accordingly.

[0248] In a specific embodiment, the data analysis and fault prediction process in the cloud processing center is as follows:

[0249] 1. Data Preprocessing

[0250] Data cleaning: Remove noise and missing values ​​to ensure data quality.

[0251] Feature extraction: Extracting useful features from raw data, such as the mean, variance, maximum, and minimum values ​​of sensor readings.

[0252] Data normalization: Normalize data to the same scale to prevent certain features from dominating the model due to excessively large numerical ranges.

[0253] 2. Model Training

[0254] Dataset partitioning: The dataset is divided into a training set, a validation set, and a test set. The training set is used to train the model, the validation set is used for parameter tuning, and the test set is used for final model performance evaluation.

[0255] Feature selection: Select the most relevant features through methods such as correlation analysis and principal component analysis (PCA) to reduce model complexity and the risk of overfitting.

[0256] Model selection: Choose appropriate machine learning algorithms, such as random forest, support vector machine (SVM), deep learning models (such as convolutional neural network CNN, recurrent neural network RNN), etc.

[0257] 3. Model Training Process

[0258] Random Forest:

[0259] Decision tree construction: Randomly extract samples and features from the training set to construct multiple decision trees.

[0260] Voting mechanism: Each tree performs classification or regression prediction on the samples, and the final result is determined by a vote of all the trees' prediction results.

[0261] Hyperparameter tuning: Adjust hyperparameters such as the number of trees and maximum depth through methods such as grid search or random search to optimize model performance.

[0262] Deep learning:

[0263] Model design: Design neural network structures based on task requirements, such as convolutional layers, pooling layers, and fully connected layers.

[0264] Forward propagation: Input data is propagated forward through the network to calculate the output.

[0265] Loss function: Define a loss function (such as mean squared error, cross-entropy, etc.) to measure the difference between the model's predicted value and the true value.

[0266] Backpropagation: Update network weights using optimization algorithms such as gradient descent to minimize the loss function.

[0267] Hyperparameter tuning: Adjusting the learning rate, batch size, regularization parameters, etc., to optimize model performance.

[0268] 4. Pattern Recognition

[0269] Feature space partitioning: The feature space is divided into multiple regions, each corresponding to a certain operating state (normal or abnormal).

[0270] Cluster analysis: Using clustering algorithms such as K-means and DBSCAN, similar data points are clustered together to identify different operating patterns.

[0271] Anomaly detection: Using algorithms such as One-Class SVM and Isolation Forest, abnormal data points that deviate from the normal pattern are identified.

[0272] 5. Fault prediction

[0273] Time series analysis: Using time series models such as ARIMA and LSTM, predicting the state of the system over a future period.

[0274] State transition matrix: Construct the state transition matrix to analyze the probability of the system transitioning from a normal state to a fault state.

[0275] Threshold setting: Set thresholds for key indicators, and trigger an alert when the indicator exceeds the threshold.

[0276] 6. Model Evaluation and Optimization

[0277] Performance evaluation: The model performance is evaluated using metrics such as accuracy, recall, F1 score, and AUC.

[0278] Cross-validation: Methods such as K-fold cross-validation are used to ensure the model's generalization ability.

[0279] Continuous optimization: Regularly retrain the model, introduce new data, and continuously optimize model performance.

[0280] 7. Results Presentation and Feedback

[0281] Visualization: The system status and fault prediction results are displayed intuitively through charts, dashboards and other forms.

[0282] Alarm mechanism: When an anomaly is detected or a potential fault is predicted, relevant personnel will be notified promptly via email, SMS, or other means.

[0283] Feedback loop: Collect user feedback to continuously improve models and algorithms, thereby enhancing the reliability and accuracy of the system.

[0284] Through the detailed identification process described above, the cloud processing center can effectively perform pattern recognition and fault prediction, providing early warnings of potential faults, thereby improving the system's maintenance efficiency and reliability.

[0285] Furthermore, in some embodiments, the user terminal 40 may specifically include:

[0286] Real-time alarm push unit 41 is used to receive and display alarm information through a message queue or stream processing framework;

[0287] Specifically, the real-time alarm push unit 41 receives and displays alarm information via message queues (such as Kafka, RabbitMQ) or stream processing frameworks (such as Apache Flink). When the alarm triggering unit detects an anomaly or fault, the alarm information is sent to the user terminal in real time via the message queue. For example, when the device temperature exceeds a threshold, the alarm information is immediately sent to the user's mobile device APP via the message queue. The real-time alarm push unit can further integrate intelligent push algorithms to prioritize and push high-priority alarm information based on user preferences and historical response behavior, and supports multiple push methods (such as email, SMS, and mobile application notifications). In addition, a geolocation awareness function can be introduced to ensure that alarm information is pushed to the maintenance personnel closest to the faulty device.

[0288] Remote collaboration interface unit 42 is used to provide an interface that supports video calls, file sharing and online expert guidance.

[0289] Specifically, the remote collaboration interface unit 42 supports video calls, file sharing, and online expert guidance. For example, on-site maintenance personnel can conduct video calls with remote experts via mobile devices to view equipment malfunctions in real time; remote experts can provide maintenance guidance and suggestions by sharing files, and can also overlay maintenance steps onto the equipment using AR technology. The remote collaboration interface unit can further integrate intelligent translation functions to support multilingual communication and ensure efficient collaboration among multinational teams. In addition, virtual reality (VR) technology can be introduced to provide remote experts with an immersive on-site experience, further improving the accuracy and efficiency of guidance.

[0290] Dynamic fault guidance unit 43 is used to generate customized maintenance guidelines based on equipment status;

[0291] Specifically, the dynamic fault guidance unit 43 is used to generate customized maintenance guidelines based on equipment status. For example, when an abnormality is detected in a component of the equipment, the system will generate detailed maintenance steps and precautions based on the equipment's operating status and historical fault data, and provide them to maintenance personnel through the user terminal. The dynamic fault guidance unit can be further integrated with an intelligent recommendation system to recommend the most suitable maintenance plan and spare parts based on the specific fault mode and maintenance history of the equipment. In addition, it can support a user feedback mechanism to continuously optimize the content and structure of the maintenance guidelines, improving their practicality and ease of use.

[0292] The dynamic fault guidance unit provided in this embodiment helps maintenance personnel quickly locate and resolve problems by offering customized maintenance guidance, thereby improving maintenance efficiency and accuracy. The introduction of an intelligent recommendation system and user feedback mechanism further enhances the personalization and practicality of the guidance, ensuring that maintenance personnel receive the most effective support and reducing maintenance time and costs.

[0293] In a specific embodiment, the user terminal is designed with an application (APP) suitable for Android / iOS platforms. Users can use this APP to view device status and receive fault alarm information. The APP integrates a push notification service, instantly sending alarm notifications to the user when the system detects an anomaly in the device. It also provides online customer service, allowing users to directly communicate with the technical support team through the APP and obtain remote repair guidance. The user interface is simple and easy to use, displaying the device's main parameters and providing troubleshooting steps.

[0294] Furthermore, in some embodiments, the remote diagnostic system of this embodiment also includes a security control module 50, which is integrated into the data transmission and processing link and is used to control the secure transmission and secure storage of data.

[0295] Specifically, the security control module 50 can be further integrated with a zero-trust security architecture to continuously verify and monitor each access request, ensuring that only users and devices with strict authentication can access system resources. Furthermore, blockchain technology can be introduced for data integrity verification, leveraging the immutability of blockchain to further enhance data security and trustworthiness.

[0296] Furthermore, in some embodiments, the security control module 50 may specifically include:

[0297] The transmission encryption unit 51 is used to encrypt the data transmission link using the SSL / TLS protocol;

[0298] Specifically, the transmission encryption unit 51 employs the SSL / TLS protocol to encrypt the data transmission link, ensuring the security and confidentiality of data during transmission. The SSL / TLS protocol uses SSL certificates to authenticate the server and client, and uses encryption algorithms (such as AES and RSA) to encrypt the data. For example, during the transmission of data from the sensor to the cloud processing center, the transmission encryption unit ensures that the data is not stolen or tampered with. The transmission encryption unit can further employ quantum encryption technology, utilizing the non-cloning property of quantum states to provide higher encryption security. Furthermore, it can support Chinese national cryptographic algorithms (such as SM2, SM3, and SM4) to meet the requirements of specific industries for domestically developed cryptographic algorithms.

[0299] Data integrity verification unit 52 is used to verify data integrity through digital signatures or hash algorithms;

[0300] Specifically, the data integrity verification unit 52 is used to verify data integrity through digital signatures or hash algorithms, ensuring that the received data is consistent with the sent data. For example, the SHA-256 algorithm is used to generate a hash value for the data. The sender sends the hash value along with the data, and the receiver recalculates the hash value and compares it to verify whether the data has been tampered with. The data integrity verification unit can further employ blockchain technology to record the hash value of the data on the blockchain, utilizing the immutability of the blockchain to ensure data integrity and trustworthiness. In addition, multi-factor authentication mechanisms can be supported, combining digital signatures and timestamps to further enhance data integrity and non-repudiation.

[0301] Access control unit 53 is used to restrict user terminals' access to the cloud processing center based on role permissions;

[0302] Specifically, the access control unit 53 is used to restrict user terminal access to the cloud processing center based on role-based permissions, ensuring that only authorized users can access sensitive data and functions. For example, system administrators can assign different permissions to different roles (such as maintenance personnel, administrators, and ordinary users) to restrict their access to device data, diagnostic results, and control functions. The access control unit can further adopt a zero-trust security architecture to continuously verify and monitor each access request, ensuring that only users and devices with strict authentication can access system resources. In addition, biometric technologies (such as fingerprint and facial recognition) can be integrated for identity authentication, improving the security and convenience of authentication.

[0303] The transmission encryption unit provided in this embodiment ensures the security and confidentiality of data during transmission, the data integrity verification unit prevents data tampering, and the access control unit ensures that only authorized users can access sensitive information through permission management. The introduction of quantum encryption, blockchain technology, zero-trust architecture, and biometric technology further enhances the security and reliability of the system, ensuring the security of data and the system, and providing a solid security guarantee for remote diagnosis of complex systems in the Internet of Things environment.

[0304] In a specific embodiment, the implementation process of a remote diagnostic system for complex systems in an Internet of Things (IoT) environment is as follows:

[0305] 1. Sensor installation and data acquisition:

[0306] Multiple sensors, including accelerometers, gyroscopes, magnetometers, barometers, temperature sensors, and humidity sensors, are installed on key components of complex systems.

[0307] The data acquisition module periodically reads data from these sensors, as well as other system operating parameters, such as power status, software version, hardware status, communication status, memory usage, and CPU load.

[0308] 2. Data transmission:

[0309] The collected data is transmitted to the cloud server via a 5G network. The high speed and low latency of the 5G network ensure the real-time performance and reliability of data transmission.

[0310] Encryption protocols are used during data transmission to ensure data security.

[0311] 3. Data Analysis:

[0312] The data analysis engine on the cloud server uses deep learning algorithms to process the transmitted data. Specific steps include:

[0313] Data preprocessing: Cleaning the data, removing noise and outliers, extracting useful features, and normalizing the data.

[0314] Feature extraction: Extract key features from raw data, such as the mean, variance, maximum, and minimum values ​​of sensor readings.

[0315] Model training: The model is trained using algorithms such as random forest, support vector machine (SVM), convolutional neural network (CNN), and recurrent neural network (RNN) to identify the differences between normal and abnormal states of the equipment.

[0316] Pattern recognition: Different operating patterns and potential abnormal states are identified through methods such as cluster analysis and time series analysis.

[0317] Fault prediction: Using time series models such as ARIMA and LSTM, predict the system state over a future period, set thresholds, and trigger an early warning when key indicators exceed the thresholds.

[0318] 4. Fault Diagnosis and Alarms:

[0319] When the system detects an anomaly in a complex system, it sends an alert to the user's mobile app to notify technical personnel.

[0320] The app provides a basic troubleshooting guide to help technicians quickly locate problems.

[0321] For more complex faults, the system can also provide video guidance and help on-site staff solve problems more quickly through remote video calls, file sharing, and other functions.

[0322] As can be seen, the remote diagnostic system for complex systems in an IoT environment provided in this application, compared with existing technologies, not only improves the accuracy of fault diagnosis in complex systems but also significantly shortens maintenance response time and reduces production losses caused by equipment failure. For complex systems located in dangerous or difficult-to-reach locations, remote diagnostics and maintenance greatly enhance operational safety, possessing significant economic and social value. Furthermore, this system supports remote collaboration, facilitating communication between maintenance personnel and remote experts, effectively improving maintenance efficiency and reducing maintenance costs for complex systems, while also enhancing safety when working in dangerous or difficult-to-reach environments.

[0323] Accordingly, this embodiment also provides a remote diagnostic method for complex systems in an Internet of Things (IoT) environment. Please refer to [link to relevant documentation]. Figure 3 , Figure 3 This is a flowchart illustrating the remote diagnostic method for complex systems in an IoT environment provided in this embodiment. The remote diagnostic method is executed within the remote diagnostic system for complex systems in an IoT environment as described above, and the specific process is as follows:

[0324] S1. Collect operational status information of complex systems;

[0325] Specifically, in step S1, sensors (such as temperature, pressure, and vibration sensors) installed in key parts of the complex system are used to collect the operating status parameters of the equipment in real time. The data acquisition module is responsible for converting the sensor data into digital signals and encapsulating them according to a predetermined protocol (such as JSON, XML, or CSV).

[0326] S2. Transmit the operating status information to the cloud processing center;

[0327] Specifically, for step S2, the data transmission module selects appropriate communication protocols (such as MQTT, HTTP, CoAP) and communication technologies (such as 5G, LoRa, NBIoT) to efficiently transmit the operating status information to the cloud processing center.

[0328] S3. Based on machine learning algorithms, perform fault analysis on the received operating status information, generate fault diagnosis results and corresponding maintenance suggestions, and transmit them to the user terminal;

[0329] Specifically, in step S3, the cloud processing center uses machine learning algorithms (such as random forests and deep learning models) to analyze the collected data, identify fault modes, and predict potential faults. Based on the analysis results, a detailed fault diagnosis report and maintenance recommendations are generated.

[0330] S4. Receive fault diagnosis results and maintenance suggestions, remotely access system status reports of complex systems, and perform remote control operations on complex systems;

[0331] Specifically, in step S4, the cloud processing center uses machine learning algorithms (such as random forests and deep learning models) to analyze the collected data, identify fault modes, and predict potential faults. Based on the analysis results, a detailed fault diagnosis report and maintenance recommendations are generated.

[0332] Suppose that during a routine check, the system detects abnormal fluctuations in the gyroscope readings of a complex device. The specific process is as follows:

[0333] Data Acquisition: Sensors mounted on the gyroscope detect changes in angular velocity and send the data to the data acquisition module. The data acquisition module periodically reads this data and transmits it to the cloud server via a 5G network.

[0334] Data Analysis: After receiving the data, the data analysis engine on the cloud server preprocesses it and extracts key features. Deep learning models (such as LSTM) are then used to analyze the data and identify abnormal fluctuations in gyroscope readings. Pattern recognition algorithms are then used to confirm that these abnormal fluctuations belong to a potential fault mode.

[0335] Fault Diagnosis and Alarms: The system sends an alarm via the user's mobile app to notify technicians of equipment malfunctions. The app provides a preliminary troubleshooting guide, suggesting technicians check the gyroscope's calibration status and power supply. Technicians perform an initial check according to the guide. If the problem persists, they can request remote expert support via the app. Experts guide technicians through further diagnosis and repair via video call to ensure the equipment is restored to normal operation as quickly as possible.

[0336] The above is the flowchart of the remote diagnostic method for complex systems in the Internet of Things environment of this application.

[0337] As described above, this application provides a remote diagnostic method for complex systems in an Internet of Things (IoT) environment. By constructing a remote diagnostic method that includes data acquisition, transmission, cloud analysis and processing, and user terminals, and utilizing machine learning algorithms to intelligently analyze the operating status of complex systems, this method achieves efficient remote monitoring, accurate fault diagnosis, and real-time maintenance guidance for complex systems in an IoT environment. This improves maintenance efficiency while reducing maintenance costs, thereby enhancing the maintainability and security of complex systems.

[0338] It should be noted that, in the embodiments of this application, if the above-mentioned experimental methods are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0339] Accordingly, this application also discloses a storage medium storing a computer program that can be loaded by a processor and executed as described above for the remote diagnostic method of a complex system in an Internet of Things environment.

[0340] This application also discloses a computer device, such as... Figure 4 As shown, the system includes a processor 100, at least one communication bus 200, a user interface 300, at least one external communication interface 400, and a memory 500. The processor 100 provides computing and control capabilities, the communication bus 200 is configured to enable communication between these components, the user interface 300 may include a display screen, the external communication interface 400 may include standard wired and wireless interfaces, and the memory 500 stores remote diagnostic methods for complex systems in an IoT environment. The processor 100 is also used to execute the steps of the remote diagnostic methods for complex systems in an IoT environment stored in the memory 500.

[0341] Optionally, in some embodiments, the processor performs the following steps when executing a computer program:

[0342] Collect operational status information of complex systems; transmit operational status information to a cloud processing center; perform fault analysis based on the received operational status information using machine learning algorithms, generate fault diagnosis results and corresponding maintenance suggestions, and transmit them to user terminals; receive fault diagnosis results and maintenance suggestions, remotely access system status reports of complex systems, and perform remote control operations on complex systems.

[0343] It should be understood that the phrases "one embodiment" or "some embodiments" throughout the specification mean that a specific feature, structure, or characteristic related to an embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in one embodiment" appearing throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0344] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0345] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0346] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0347] In addition, all functional units in the embodiments of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the above-mentioned integrated units can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0348] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0349] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a device to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0350] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A remote diagnostic system for complex systems in an Internet of Things (IoT) environment, characterized in that, include: The data acquisition module is used to collect operational status information of complex systems; A data transmission module, connected to the data acquisition module, is used to transmit the operating status information to the cloud processing center; The cloud processing center is used to perform fault analysis based on the received operating status information using machine learning algorithms, generate fault diagnosis results and corresponding maintenance suggestions, and transmit them to the user terminal. The user terminal is connected to the cloud processing center and is used to receive the fault diagnosis results and the maintenance suggestions, and to remotely access the system status report of the complex system and to remotely control the complex system.

2. The remote diagnostic system for complex systems in an Internet of Things environment according to claim 1, characterized in that, The data acquisition module includes: The sensor unit includes multiple sensors installed at target monitoring points in the complex system, used to monitor the operating status parameters of the target equipment in real time and send them to the data acquisition unit. The data acquisition unit, connected to the sensor unit, is used to receive the operating status parameters collected by the sensor unit, perform preprocessing, data conversion and encapsulation, and generate operating status information in a unified format.

3. The remote diagnostic system for complex systems in an Internet of Things environment according to claim 1, characterized in that, The data transmission module includes: A communication protocol adaptation unit is used to select the network layer protocol in the data transmission process according to the application scenario. The wireless communication unit is used to select one or more communication technologies in the data transmission process according to the application scenario, so as to transmit the operation status information to the cloud processing center using the communication technology.

4. The remote diagnostic system for complex systems in an Internet of Things environment according to claim 1, characterized in that, The cloud processing center includes: Distributed computing units are used to preprocess, extract features, and train models from received data using the Hadoop and / or Spark frameworks. The machine learning engine unit integrates multiple machine learning algorithms for fault mode identification and fault prediction of the complex system based on the machine learning algorithms. The data storage unit includes a NoSQL database and a relational database, used to store raw sensor data and processed structured data, respectively.

5. The remote diagnostic system for complex systems in an Internet of Things environment according to claim 4, characterized in that, The data storage unit includes: The data synchronization subunit is used to synchronize raw data from a NoSQL database to a relational database using ETL tools or the Apache Kafka streaming framework. The data backup and recovery subunit is used to perform full and incremental backups of NoSQL and relational databases on a regular basis, as well as data recovery based on timestamps; The data archiving subunit is used to migrate structured data that exceeds a preset time threshold to a distributed file system or object storage.

6. The remote diagnostic system for complex systems in an Internet of Things environment according to claim 4, characterized in that, The cloud processing center also includes: The alarm triggering unit is used to trigger graded alarms based on the monitored threshold exceeding results, fault mode matching results, or abnormal data trends. A visualization unit is used to display the equipment operating status and fault prediction results of the complex system using line graphs, heat maps, and / or dashboards.

7. The remote diagnostic system for complex systems in an Internet of Things environment according to claim 1, characterized in that, The user terminal includes: The real-time alarm push unit is used to receive and display alarm information via a message queue or stream processing framework. The remote collaboration interface unit provides an interface that supports video calls, file sharing, and online expert guidance. The dynamic fault guide unit is used to generate customized maintenance guides based on the equipment status.

8. The remote diagnostic system for complex systems in an Internet of Things environment according to claim 1, characterized in that, The system also includes a security control module, which is integrated into the data transmission and processing link and is used to control the secure transmission and secure storage of data.

9. The remote diagnostic system for complex systems in an Internet of Things environment according to claim 8, characterized in that, The security control module includes: A transmission encryption unit is used to encrypt the data transmission link using the SSL / TLS protocol; A data integrity verification unit is used to verify data integrity through digital signatures or hash algorithms. Access control unit, used to restrict user terminal access to the cloud processing center based on role permissions.

10. A remote diagnostic method for complex systems in an Internet of Things (IoT) environment, executed in the remote diagnostic system for complex systems in an IoT environment as described in any one of claims 1-9, characterized in that, The steps include: Collect operational status information of complex systems; The operational status information is transmitted to the cloud processing center; Based on machine learning algorithms, fault analysis is performed on the received operating status information to generate fault diagnosis results and corresponding maintenance suggestions, which are then transmitted to the user terminal. Receive the fault diagnosis results and the maintenance suggestions, remotely access the system status report of the complex system, and perform remote control operations on the complex system.

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