Equipment remote state intelligent early warning monitoring and multi-source data fusion processing system

Abstract

The invention provides an equipment remote state intelligent early warning monitoring and multi-source data fusion processing system, which is characterized in that real-time state data of equipment is acquired through an Internet of Things technology, and the Internet of Things technology comprises a sensor technology, an embedded system and a communication protocol; performing storage, calculation and analysis on the obtained real-time state data by utilizing a big data processing technology, wherein the big data processing technology comprises distributed storage, parallel calculation and a data warehouse; according to the analysis result, anomaly detection, prediction and early warning are conducted on the equipment state through the machine learning technology, and the machine learning technology comprises an anomaly detection algorithm, a prediction model and feature engineering; the data security of the system is guaranteed through a security technology, and the security technology comprises data encryption and an identity verification and authorization mechanism.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an intelligent early warning monitoring and multi-source data fusion processing system for remote device status. Background Art

[0002] Equipment fault early warning is a key challenge in the industrial field. Traditional methods often rely on manual inspections and experience-based judgments, making it difficult to detect potential problems in a timely manner, resulting in unexpected equipment downtime, production interruptions, and increased maintenance costs. With the expansion of industrial scale and the increase in complexity, it is difficult to comprehensively monitor the operating status of a large number of devices solely by manual means. How to achieve real-time and accurate monitoring of equipment status in a large-scale and complex environment and accurately predict potential faults? This involves several technical problems: First, how to efficiently collect and transmit the massive operating data of scattered devices? Second, how to effectively clean and integrate heterogeneous data sources? Third, how to identify key abnormal patterns from the complex data? Finally, how to accurately predict equipment faults under dynamically changing working conditions? These problems are interrelated and jointly constitute a complex technical challenge. If these bottlenecks can be broken through, it will greatly improve the intelligent level of equipment management, reduce the risk of faults, and optimize production efficiency. However, achieving this goal requires interdisciplinary technical integration, including the collaborative application of advanced technologies such as the Internet of Things, big data, and artificial intelligence. Summary of the Invention

[0003] The present invention provides an intelligent early warning monitoring and multi-source data fusion processing system for remote device status, mainly including: Obtaining the real-time status data of the device through Internet of Things technology, which includes sensor technology, embedded systems, and communication protocols; using big data processing technology to store, calculate, and analyze the obtained real-time status data, which includes distributed storage, parallel computing, and data warehouses; according to the analysis results, using machine learning technology to perform anomaly detection, prediction, and early warning on the device status, which includes anomaly detection algorithms, prediction models, and feature engineering; ensuring the data security of the system through security technology, which includes data encryption and authentication and authorization mechanisms.

[0004] Further, the sensor technology in the Internet of Things technology includes: using sensors such as water immersion detection, lightning strike counting, lightning protection status, and vibration sensors to collect the operating status data of the monitoring device in real time.

[0005] Further, the embedded system in the Internet of Things technology includes: embedding a microcomputer system in the monitoring device, and the microcomputer system is responsible for the preliminary processing and transmission of data, realizing the intelligence and networking of the device.

[0006] Further, the communication protocols in the Internet of Things technology include: adopting lightweight communication protocols such as MQTT to achieve real-time and reliable communication between devices and the backend server.

[0007] Further, the distributed storage in the big data processing technology includes: using distributed file systems such as Hadoop HDFS to achieve efficient storage and expansion of monitoring data.

[0008] Further, the parallel computing in the big data processing technology includes: adopting big data processing frameworks such as Spark to achieve rapid processing and analysis of data, improving the timeliness and accuracy of early warnings.

[0009] Further, the anomaly detection in the machine learning technology includes: applying anomaly detection algorithms based on statistics and machine learning, and the anomaly detection algorithms of machine learning include Isolation Forest and Autoencoder to automatically identify anomaly patterns in monitoring data.

[0010] Further, the prediction models in the machine learning technology include: constructing prediction models based on time series analysis, regression analysis, etc., and the time series analysis includes ARIMA and LSTM to achieve prediction and early warning of the status of monitoring devices.

[0011] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: The present invention discloses a device fault early warning method based on Internet of Things and big data technologies. This method uses Internet of Things devices to collect the operation and environmental parameters of monitoring devices in real time, and transmits the data to the processing platform using the MQTT protocol. On the platform, HDFS is used to store the data, and the Spark framework is used for data cleaning and analysis. Based on the processed data, the present invention constructs a machine learning model integrating anomaly detection and prediction algorithms to identify anomaly patterns and predict potential faults. When a fault risk is detected, the system automatically sends a warning message containing detailed information. In addition, the present invention can also generate an operation status report including the prediction of device health and remaining life, and visually display the device status and warning information through visualization technology, providing decision-making support for management personnel. This method realizes real-time monitoring of device status, early warning of faults, and comprehensive improvement of operation efficiency, significantly enhancing the intelligent level of device management. Description of the Drawings

[0012] Figure 1 It is a flowchart of a device remote status intelligent early warning monitoring and multi-source data fusion processing system of the present invention.

[0013] Figure 2 It is a schematic diagram of a device remote status intelligent early warning monitoring and multi-source data fusion processing system of the present invention.

[0014] Figure 3 This is another schematic diagram of an intelligent early warning monitoring and multi-source data fusion processing system for the remote status of equipment according to the present invention.

[0015] Figure 4 This is another schematic diagram of an intelligent early warning monitoring and multi-source data fusion processing system for the remote status of equipment according to the present invention.

[0016] Figure 5 This is another schematic diagram of an intelligent early warning monitoring and multi-source data fusion processing system for the remote status of equipment according to the present invention.

[0017] Figure 6 This is another schematic diagram of an intelligent early warning monitoring and multi-source data fusion processing system for the remote status of equipment according to the present invention.

[0018] Figure 7 This is another schematic diagram of an intelligent early warning monitoring and multi-source data fusion processing system for the remote status of equipment according to the present invention.

[0019] Figure 8 This is another schematic diagram of an intelligent early warning monitoring and multi-source data fusion processing system for the remote status of equipment according to the present invention.

[0020] Figure 9 This is another schematic diagram of an intelligent early warning monitoring and multi-source data fusion processing system for the remote status of equipment according to the present invention. Detailed implementation manners

[0021] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments in this specification, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of this specification.

[0022] Such as Figures 1-9 , a specific intelligent early warning monitoring and multi-source data fusion processing system for the remote status of equipment in this embodiment may specifically include: S101. Obtain status data from at least one monitoring device, where the status data includes the operating parameters and environmental parameters of the monitoring device.

[0023] Connect at least one monitoring device through Internet of Things technology to establish a data transmission channel. Collect operating parameters and environmental parameters from the monitoring device to generate raw status data. Perform format conversion on the raw status data to unify the data structure and units. Adopt a data cleaning algorithm to remove noise data and outliers from the status data. Classify the cleaned status data into operating parameters and environmental parameters according to the preset data mapping rules. Use a standardization processing method to normalize the classified operating parameters and environmental parameters. Store the normalized operating parameters and environmental parameters in a time series database. Extract the operating parameters and environmental parameters within a specified time window from the time series database to generate a status data set. Transmit the status data set to a fault warning model as the model input data.

[0024] Specifically, connect at least one monitoring device through Internet of Things technology to establish a data transmission channel. For example, use the MQTT protocol to transmit data at a frequency of 10 times per second. Collect operating parameters and environmental parameters from the monitoring device to generate raw status data, specifically including parameters such as temperature, humidity, voltage, and current, with a sampling frequency of 1 time per second. Perform format conversion on the raw status data to unify the data structure and units. For example, convert the temperature from Fahrenheit to Celsius and the voltage from volts to millivolts. Adopt a data cleaning algorithm to remove noise data and outliers from the status data. For example, use a median filter algorithm to process temperature data with a window size of 5. Classify the cleaned status data into operating parameters and environmental parameters according to the preset data mapping rules. For example, classify temperature and humidity as environmental parameters, and voltage and current as operating parameters. Use a standardization processing method to normalize the classified operating parameters and environmental parameters. For example, adopt the Z-score standardization method to convert the data into a distribution with a mean of 0 and a variance of 1. Store the normalized operating parameters and environmental parameters in a time series database. For example, use the InfluxDB database with a data storage interval of 1 second. Extract the operating parameters and environmental parameters within a specified time window from the time series database to generate a status data set. For example, extract the data of the last 10 minutes to form a data set containing 600 records. Transmit the status data set to a fault warning model as the model input data. For example, use an LSTM neural network model for fault prediction, with the input data dimension of 4×600 and the output being the fault probability.

[0025] S103. Adopt Internet of Things communication technology to transmit the status data to a data processing platform in real time. The Internet of Things communication technology includes the MQTT communication protocol.

[0026] Adopt sensor technology to collect the operation status data of monitoring devices, including water immersion detection, lightning strike counting, lightning protection status, and vibration sensor data. Convert the operation status data collected by sensors into a structured data format to form a unified data packet. Use the AES encryption algorithm to encrypt the structured data packet to generate an encrypted data packet. Transmit the encrypted data packet to the Internet of Things communication network through the MQTT communication protocol. In the Internet of Things communication network, use the RSA encryption algorithm to perform secondary encryption on the data transmitted by the MQTT communication protocol to ensure data security. Transmit the doubly encrypted data to the data processing platform, and the platform receives and stores the encrypted data. Use the RSA decryption algorithm to decrypt the encrypted data stored by the platform to obtain the structured data packet. Use the AES decryption algorithm to decrypt the structured data packet to restore it to the original operation status data. Input the original operation status data into the real-time monitoring module to generate and display the real-time status information of the device.

[0027] Specifically, adopt sensor technology to collect the operation status data of monitoring devices. For example, when the water immersion detection sensor detects that the water level exceeds 50 mm, it generates an alarm signal; when the lightning strike counting sensor records that the number of lightning strikes reaches 10 times, it triggers a warning; when the lightning protection status sensor detects that the grounding resistance value exceeds 4 ohms, it sends an abnormal signal; when the vibration sensor monitors that the vibration amplitude exceeds 0.5 g, it generates vibration data. Convert the operation status data collected by sensors into a structured data packet in JSON format, including a timestamp, device ID, data type, and specific value. Use the AES-256 encryption algorithm to encrypt the JSON data packet to generate an encrypted data packet with a length of 128 bytes. Transmit the encrypted data packet to the Internet of Things communication network at a frequency of once per second through the MQTT communication protocol, and the MQTT topic is set to " / device / status". In the Internet of Things communication network, use the RSA-2048 encryption algorithm to perform secondary encryption on the data transmitted by MQTT to generate encrypted data with a length of 256 bytes. Transmit the doubly encrypted data to the data processing platform, and the platform uses the Kafka message queue to receive and store the encrypted data. Use the RSA-2048 decryption algorithm to decrypt the encrypted data stored by the platform to obtain the structured data packet in JSON format. Use the AES-256 decryption algorithm to decrypt the JSON data packet to restore it to the original operation status data, including the water level, the number of lightning strikes, the grounding resistance value, and the vibration amplitude. Input the original operation status data into the real-time monitoring module, and generate real-time status information of the device based on preset thresholds, such as "abnormal water level", "lightning strike warning", "abnormal grounding resistance", or "excessive vibration", and display it on the monitoring interface.

[0028] S105. In the data processing platform, big data processing technology is used to process the status data. The big data processing technology includes storing the status data in the distributed file system HDFS and using the Spark big data processing framework to clean, transform, and aggregate the status data.

[0029] Collect status data from monitoring devices. The data includes device operation parameters and environmental indicators. Preprocess the collected status data to remove noise and outliers, obtaining the cleaned data. Store the cleaned data in the distributed file system HDFS, and partition and store the data according to timestamps and device identifiers. Read the status data in HDFS through the Spark big data processing framework, and load the data into memory for processing. Perform transformation operations on the status data to convert the original data into a structured data format, obtaining a standardized data table. Based on the standardized data table, perform data aggregation operations to calculate key indicators of the device operation status, obtaining an aggregation result. Input the aggregation result into a fault warning model. The model evaluates the device operation status based on machine learning algorithms to obtain a fault prediction result. If the fault prediction result exceeds a preset threshold, generate a fault warning message, which includes the device identifier, warning level, and processing suggestions. Encrypt the fault warning message, using the AES and RSA algorithms to encrypt the message, obtaining the encrypted warning message.

[0030] Specifically, status data is collected from monitoring devices. The data includes device operation parameters such as temperature, pressure, vibration frequency, and environmental indicators such as humidity and air quality index. The sampling frequency is once per second. The collected status data is preprocessed. The median filter algorithm is used to remove noise, and the outlier detection threshold is set to ±3 times the standard deviation of the mean to obtain the cleaned data. The cleaned data is stored in the distributed file system HDFS. The data is partitioned and stored according to the timestamp and device identifier, and the size of each partition is 128MB. The status data in HDFS is read through the Spark big data processing framework, and the data is loaded into memory for processing. The RDD data structure is used for distributed computing. Transformation operations are performed on the status data. Spark SQL is used to convert the original data into a structured data format to obtain a standardized data table containing fields such as device ID, timestamp, temperature, and pressure. Based on the standardized data table, data aggregation operations are performed to calculate key indicators of the device operation status such as average temperature and maximum pressure to obtain the aggregation result. The aggregation result is input into the fault warning model. The model uses the random forest algorithm to evaluate the device operation status. The fault threshold temperature is set to 80°C and the pressure is set to 10MPa to obtain the fault prediction result. If the fault prediction result exceeds the preset threshold, a fault warning message is generated. The message includes the device identifier, warning level, and handling suggestions. The warning level is divided into three levels: low, medium, and high. The fault warning message is encrypted. The AES algorithm is used to perform symmetric encryption on the message, and the key length is 256 bits. The RSA algorithm is used to perform asymmetric encryption on the key to obtain the encrypted warning message.

[0031] S107. Based on the processed status data, a device fault warning model is constructed using machine learning techniques. The machine learning techniques include an anomaly detection algorithm and a prediction algorithm. The anomaly detection algorithm is used to identify abnormal patterns in the device status data, and the prediction algorithm is used to predict the potential fault trend of the device.

[0032] Obtain the device operation status data through monitoring devices, including various operation parameters and indicators of the device. Preprocess the collected device status data, remove noise data, fill in missing values, and standardize the data format. Use the Isolation Forest algorithm to perform anomaly detection on the preprocessed device status data and identify the abnormal patterns in the data. According to the anomaly detection results, mark the abnormal points in the device status data and generate an abnormal data set. Use the Autoencoder algorithm to extract features from the abnormal data set and obtain the deep-level features of the abnormal data. Based on the extracted abnormal features, use a time series prediction algorithm to perform trend analysis on the device status data and predict the potential fault trend of the device. According to the predicted fault trend, construct a fault warning model, which includes the probability of fault occurrence and the possible time range. Combine the fault warning model with the real-time monitoring system to analyze the device status data in real time and determine whether there is a fault risk for the device. If the device status data triggers the fault warning model, generate a fault warning message and send it to the monitoring system to remind relevant personnel to handle it.

[0033] Specifically, obtain the device operation status data through monitoring devices, including operation parameters and indicators such as temperature, vibration frequency, and current, with a sampling frequency of once per second. Preprocess the collected device status data, use median filtering to remove noise data, use linear interpolation to fill in missing values, and standardize the data format into a time series format. Use the Isolation Forest algorithm to perform anomaly detection on the preprocessed device status data, set the anomaly score threshold to 0.6, and identify the abnormal patterns in the data, such as a sudden increase in temperature or abnormal fluctuations in vibration frequency. According to the anomaly detection results, mark the abnormal points in the device status data and generate an abnormal data set containing abnormal timestamps and abnormal types. Use the Autoencoder algorithm to extract features from the abnormal data set, set the dimension of the encoding layer to 64 and the dimension of the decoding layer to 128, and obtain the deep-level features of the abnormal data, such as the feature vectors of the fault patterns. Based on the extracted abnormal features, use the ARIMA time series prediction algorithm to perform trend analysis on the device status data, set p = 2, d = 1, q = 1, and predict the potential fault trend of the device, such as the probability of a fault occurring within the next 24 hours. According to the predicted fault trend, construct a fault warning model, which includes the probability of fault occurrence and the possible time range, such as the fault probability is greater than 0.8 and the time range is within 12 hours. Combine the fault warning model with the real-time monitoring system to analyze the device status data in real time and determine whether there is a fault risk for the device, such as whether the current status data triggers the warning conditions. If the device status data triggers the fault warning model, generate a fault warning message and send it to the monitoring system to remind relevant personnel to handle it. The warning message includes the fault type, occurrence time, and handling suggestions.

[0034] S109. If the device fault warning model determines that the device has a fault risk, a fault warning message is sent to a preset receiving end. The fault warning message includes the location information of the faulty device, the fault type, and the time of fault occurrence.

[0035] Obtain the status data of the monitoring device, and clean and standardize the data. Use machine learning algorithms to extract features from the preprocessed data, and construct a feature vector of the device operating state. Input the feature vector into the fault warning model to calculate the device fault risk probability value. If the fault risk probability value exceeds the preset threshold, it is determined that the device has a fault risk. Extract the unique identifier of the faulty device and associate it with the device location information database. Determine the fault type according to the historical operation data and current state of the device. Obtain the current time of the system and record the time information of the fault occurrence. Package the location information, fault type, and time of fault occurrence of the faulty device into a fault warning message. Send the fault warning message to a preset receiving end through a message queue or API interface.

[0036] Specifically, obtain the status data of the monitoring device, and clean and standardize the data. For example, remove outliers through a denoising algorithm and use the Z-Score standardization method to convert the data into a distribution with a mean of 0 and a standard deviation of 1. Use machine learning algorithms to extract features from the preprocessed data and construct a feature vector of the device operating state. For example, use the principal component analysis (PCA) method to reduce the dimensionality of multi-dimensional data to 10 main features. Input the feature vector into the fault warning model to calculate the device fault risk probability value. For example, predict the fault probability through a random forest algorithm, and the output value ranges from 0 to 1. If the fault risk probability value exceeds the preset threshold of 0.8, it is determined that the device has a fault risk. Extract the unique identifier of the faulty device and associate it with the device location information database. For example, obtain the geographical coordinate information of the device from the device management database through an SQL query. Determine the fault type according to the historical operation data and current state of the device. For example, classify the fault into "mechanical wear", "circuit fault", or "sensor failure" through cluster analysis. Obtain the current time of the system and record the time information of the fault occurrence. For example, record it as "2023-10-15 14:30:45" using a timestamp. Package the location information, fault type, and time of fault occurrence of the faulty device into a fault warning message. For example, generate a JSON format data packet. Send the fault warning message to a preset receiving end through a message queue or API interface. For example, use the RabbitMQ message queue to push the information to the operation and maintenance management platform.

[0037] S1011. The data processing platform is also used to analyze the historical state data of the device, and form a device operation status report according to the analysis results. The device operation status report includes content such as the health assessment of the device and the prediction of the remaining life.

[0038] Obtain the historical status data of the device, including running time, fault records, and sensor data. Use time series analysis methods to perform trend analysis on the device historical status data and identify the changing patterns of the device running status. Based on the trend analysis results, construct an ARIMA model to predict the future running status of the device. If the device historical data contains fault information, use regression analysis methods to establish an association model between faults and running parameters. Through the LSTM model, perform deep learning on the device historical status data to capture the long-term dependencies of the device running status. Integrate the prediction results of the ARIMA model, regression analysis model, and LSTM model to generate device health assessment indicators. According to the device health assessment indicators, combine the device running time and fault records to calculate the predicted remaining life of the device. Integrate the device health assessment and the predicted remaining life value to form a device running status report. According to the device running status report, update the device status database and store the device health assessment and remaining life prediction information.

[0039] Specifically, obtain the historical status data of the device, including running time, fault records, and sensor data. For example, extract the temperature, vibration, pressure and other sensor data of the device in the past year and the fault shutdown records from the database. Use time series analysis methods to perform trend analysis on the historical status data of the device, and identify the change rules of the device running status. For example, use the moving average method to calculate the fluctuation trend of the device temperature in the past three months. According to the trend analysis results, construct an ARIMA model to predict the future running status of the device. For example, predict the temperature change trend of the device in the next week through the ARIMA(1,1,1) model. If the device historical data contains fault information, use regression analysis methods to establish an association model between faults and operating parameters. For example, analyze the relationship between the vibration frequency of the device and the number of faults through multiple linear regression. Through the LSTM model, perform deep learning on the historical status data of the device to capture the long-term dependence relationship of the device running status. For example, use the LSTM network to train the device pressure data and predict the pressure change in the next month. Integrate the prediction results of the ARIMA model, regression analysis model, and LSTM model to generate device health assessment indicators. For example, weight-average the prediction results of the three models to obtain the comprehensive health score of the device. According to the device health assessment indicators, combine the device running time and fault records to calculate the predicted remaining life of the device. For example, estimate the remaining service life of the device when the health score is lower than 80% through the Weibull distribution model. Integrate the device health assessment and the predicted remaining life value to form a device running status report. For example, generate a PDF document containing the device health score, predicted remaining life value, and maintenance suggestions. According to the device running status report, update the device status database and store the device health assessment and predicted remaining life information. For example, write the device health score and predicted remaining life value into the corresponding fields of the database.

[0040] S1013. Adopt visualization technology to intuitively display the real-time running status and warning information of the device in the form of charts, curves, etc., and provide auxiliary decision-making support for device management personnel.

[0041] Collect the real-time status data of monitoring devices through Internet of Things technology, including key parameters such as temperature, pressure, and rotational speed. Upload the collected real-time status data to a remote monitoring platform and establish a device operation status database. Preprocess the uploaded real-time status data to remove noise data and ensure the accuracy and integrity of the data. Analyze the current operation status of the device based on the preprocessed data to determine whether there are any abnormalities or potential faults. If the device operation status is abnormal, generate warning information, including the type of fault, severity, and possible scope of influence. Use visualization technology to convert the real-time operation status data of the device into forms such as charts and curves, and intuitively display them on the monitoring interface. Combine the generated warning information with the visualization chart, dynamically mark abnormal data points, and form a comprehensive display page. Extract key operation indicators and warning information from the comprehensive display page to generate a device operation status report. Push the device operation status report to device management personnel to assist them in decision-making support.

[0042] Specifically, collect the real-time status data of monitoring devices through Internet of Things technology. For example, the temperature value is 85 degrees Celsius, the pressure value is 0.8 megapascals, and the rotational speed value is 1200 revolutions per minute. Upload the collected real-time status data to a remote monitoring platform and establish a device operation status database with a storage frequency of once per second. Preprocess the uploaded real-time status data and use the Kalman filter algorithm to remove noise data to ensure the accuracy and integrity of the data. Analyze the current operation status of the device based on the preprocessed data and use the support vector machine algorithm to determine whether there are any abnormalities or potential faults. For example, identify that the temperature value exceeding 90 degrees Celsius is abnormal. If the device operation status is abnormal, generate warning information, including the type of fault being overheating, the severity being high, and the possible scope of influence being device shutdown. Use visualization technology to convert the real-time operation status data of the device into forms such as charts and curves. For example, a line chart showing the change of temperature over time, and intuitively display it on the monitoring interface. Combine the generated warning information with the visualization chart, dynamically mark abnormal data points. For example, mark a red warning point on the temperature curve, and form a comprehensive display page. Extract key operation indicators and warning information from the comprehensive display page to generate a device operation status report, including the average temperature, peak value, and number of warnings. Push the device operation status report to device management personnel to assist them in decision-making support. For example, send the report in real time via email or a mobile application.

[0043] The above-disclosed are only the preferred embodiments of the present invention. Of course, the scope of rights of the present invention cannot be limited thereby. Those of ordinary skill in the art can understand the entire or partial processes of implementing the above embodiments, and the equivalent changes made according to the claims of the present invention still fall within the scope covered by the invention.

Claims

1. An intelligent early warning monitoring system for remote device status and multi-source data fusion processing system, characterized in that Including: Obtaining real-time status data of devices through Internet of Things technology, where the Internet of Things technology includes sensor technology, embedded systems, and communication protocols; Using big data processing technology to store, calculate, and analyze the obtained real-time status data, where the big data processing technology includes distributed storage, parallel computing, and data warehouses; According to the analysis results, adopting machine learning technology to perform anomaly detection, prediction, and early warning on device status, where the machine learning technology includes anomaly detection algorithms, prediction models, and feature engineering; Ensuring the data security of the system through security technology, where the security technology includes data encryption and authentication and authorization mechanisms.

2. The system according to claim 1, wherein The sensor technology in the Internet of Things technology includes: Using sensors such as water immersion detection, lightning strike counting, lightning protection status, and vibration sensors to collect the operation status data of monitoring devices in real time.

3. The system according to claim 1, wherein The embedded system in the Internet of Things technology includes: Embedding a microcomputer system in the monitoring device, where the microcomputer system is responsible for the preliminary processing and transmission of data, realizing the intelligence and networking of the device.

4. The system according to claim 1, wherein The communication protocol in the Internet of Things technology includes: Adopting lightweight communication protocols such as MQTT to achieve real-time and reliable communication between the device and the backend server.

5. The system according to claim 1, wherein The distributed storage in the big data processing technology includes: Using distributed file systems such as Hadoop HDFS to achieve efficient storage and expansion of monitoring data.

6. The system according to claim 1, wherein The parallel computing in the big data processing technology includes: Adopting big data processing frameworks such as Spark to achieve rapid data processing and analysis, improving the timeliness and accuracy of early warning.

7. The system according to claim 1, wherein The anomaly detection in the machine learning technology includes: Applying anomaly detection algorithms based on statistics and machine learning, where the anomaly detection algorithms of machine learning include Isolation Forest and Autoencoder, to automatically identify abnormal patterns in monitoring data.

8. The system according to claim 1, wherein The prediction models in the machine learning technology include: Constructing prediction models based on time series analysis, regression analysis, etc., where the time series analysis includes ARIMA and LSTM, to achieve prediction and early warning of the status of monitoring devices.

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