Power transformation infrastructure sensing and monitoring system and method based on UWB and millimeter wave radar

Abstract

The invention relates to the technical field of sensing systems, in particular to a power transformation infrastructure sensing and monitoring system and method based on UWB and millimeter wave radar, and the system comprises a data collection module, a data transmission module, a data processing module, an intelligent analysis module, a visual report module, a safety management module, an environment monitoring module, and a structure health monitoring module. According to the invention, based on UWB and radar technologies, artificial intelligence and big data analysis technologies are utilized at the same time, deep analysis and mining are carried out on the data, through establishing an anomaly detection model, an abnormal condition is found in time and an early warning is given out, and based on a data analysis result, the system can be used for carrying out early warning on the abnormal condition. Intelligent decision support is provided, automatic control over substation equipment is achieved, corresponding operation is automatically executed according to monitoring data and a preset strategy, the performance and effect of a sensing system are continuously optimized through continuous feedback and adjustment, and the operation efficiency and reliability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of perception systems, and in particular to a substation infrastructure perception monitoring system and method based on UWB and millimeter wave radar. Background Art

[0002] Perception system refers to the collection of various data from the external environment through sensors and related equipment, and the processing and analysis of these data to achieve perception and monitoring of the environment. Perception system usually includes sensors, data acquisition equipment, data transmission network and data processing and analysis system. Its main function is to obtain environmental information, such as temperature, humidity, light, pressure, vibration, etc. Early perception systems mainly relied on simple mechanical sensors and manual data recording, with low accuracy and low data collection efficiency. With the development of electronic technology, electronic sensors have replaced mechanical sensors, improving the accuracy and reliability of data collection. The introduction of microprocessors and digital signal processing technology has made data collection and processing more efficient, and can monitor and analyze data in real time.

[0003] However, in general, although the perception system has developed rapidly, the existing perception system still has the following problems when it comes to substation infrastructure. Traditional equipment perception relies on mechanical sensors, which leads to low accuracy and cannot provide high-precision data. The data collection and transmission speed is slow, and real-time monitoring and analysis cannot be achieved. Data between different devices cannot be interoperable, forming data islands, which are difficult to analyze comprehensively. Data processing and analysis rely on manual labor, which is inefficient and prone to errors. Most importantly, traditional equipment perception systems lack intelligent analysis and prediction capabilities and cannot make automatic decisions and controls.

[0004] In summary, it is necessary to propose a substation infrastructure perception monitoring system and method based on UWB and millimeter wave radar to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a substation infrastructure perception monitoring system and method based on UWB and millimeter wave radar to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The substation infrastructure perception monitoring system and method based on UWB and millimeter wave radar includes data acquisition module, data transmission module, data processing module, intelligent analysis module, visual reporting module, safety management module, environmental monitoring module, and structural health monitoring module;

[0008] The data acquisition module is used to provide data acquisition inside and outside the substation, the data transmission module is used for wireless and wired transmission, the data processing module is used for real-time data processing and historical data storage and processing, the intelligent analysis module is used for in-depth analysis and intelligent decision support, the visual report module is used for data visualization and report generation, the security management module is used for data encryption and identity authentication, the environmental monitoring module is used for meteorology, environmental pollution monitoring, electromagnetic field monitoring and interference source positioning, and the structural health monitoring module is used for vibration detection, strain monitoring, drone image acquisition, image processing, remote diagnosis and automatic maintenance.

[0009] Preferably, the data acquisition module further includes a UWB sensor unit and a radar sensor unit;

[0010] The UWB sensor unit is used to accurately locate and track the location of objects and personnel inside the substation;

[0011] The radar sensor unit is used to detect and monitor environmental changes around the substation, including object movement and environmental parameters.

[0012] Preferably, the data transmission module further includes a wireless transmission unit and a wired transmission unit;

[0013] The wireless transmission unit is used to transmit the collected data to the central processing system in real time using a wireless network;

[0014] The wired transmission unit transmits data via optical fiber or Ethernet to ensure data transmission stability under certain circumstances.

[0015] Preferably, the data processing module further comprises a real-time data processing unit and a historical data storage processing unit;

[0016] The real-time data processing unit is used to analyze sensor data in real time and generate monitoring reports and alarm information;

[0017] The historical data storage and processing unit is used to store historical data and perform long-term trend analysis and fault prediction.

[0018] Preferably, the intelligent analysis module also includes a machine learning unit and an artificial intelligence unit;

[0019] The machine learning unit applies a machine learning algorithm to perform in-depth analysis on the data for predicting potential failures and optimizing maintenance strategies;

[0020] The artificial intelligence unit is used to use AI technology to achieve automated decision support and anomaly detection.

[0021] Preferably, the visual reporting module further comprises a visualization unit and a report generating unit;

[0022] The visualization unit is used to convert data into charts and graphs, and provide an intuitive user interface for operators to use;

[0023] The report generation unit is used to automatically generate regular and temporary reports to record system operating conditions and monitoring results.

[0024] Preferably, the security management module further includes an identity authentication unit and a data encryption unit;

[0025] The identity verification unit is used to ensure that only authorized personnel can access the system and protect system data security;

[0026] The data encryption unit is used to encrypt the transmitted and stored data to prevent data leakage and tampering.

[0027] Preferably, the environmental monitoring module further includes a meteorological sensing unit, an environmental pollution monitoring unit, an electromagnetic field monitoring unit and an interference source positioning unit;

[0028] The meteorological sensing unit is used to monitor meteorological conditions around the substation, including temperature, humidity and wind speed;

[0029] The environmental pollution monitoring unit is used to detect pollutants and harmful gases in the air to ensure the environmental safety of the substation;

[0030] The electromagnetic field monitoring unit is used to monitor the electromagnetic environment around the substation to ensure that the equipment is not interfered with;

[0031] The interference source locating unit is used to locate and identify the electromagnetic interference source and take measures to reduce the interference impact.

[0032] Preferably, the structural health monitoring module further comprises a vibration sensing unit, a strain sensing unit, a drone control unit, an image processing unit, a remote diagnosis unit and an automatic maintenance unit;

[0033] The vibration sensing unit is used to monitor the vibration of the substation structure and prevent damage to the structure caused by earthquakes and other external forces;

[0034] The strain sensing unit is used to monitor structural stress and deformation and detect structural problems in a timely manner;

[0035] The drone control unit is used to control the drone to perform inspection tasks and collect data from a high-altitude perspective;

[0036] The image processing unit is used to process images and videos collected by the drone and detect equipment anomalies and environmental changes;

[0037] The remote diagnosis unit allows an expert to remotely access the system via a network for diagnosis and troubleshooting;

[0038] The automatic maintenance unit is used to automatically generate a maintenance plan based on monitoring data to reduce manual intervention.

[0039] Based on the above system, the present invention also proposes a substation infrastructure intelligent perception monitoring method based on UWB and radar, comprising the following steps:

[0040] S1. Data collection: Use Decawave DW1000 UWB sensors to accurately locate and track the positions of objects and personnel inside the substation, and use FMCW radar to detect and monitor environmental changes around the substation to capture object movement and environmental parameters;

[0041] S2. Data transmission: Use LoRa or 5G wireless network to transmit the collected data to the central processing system in real time, and transmit data through optical fiber or Ethernet to ensure the stability and efficiency of data transmission;

[0042] S3. Data processing: Analyze sensor data in real time, generate monitoring reports and alarm information using Apache Kafka and Apache Flink, use Hadoop HDFS or Amazon S3 to store historical data, and perform long-term trend analysis and fault prediction;

[0043] S4. Intelligent analysis: Apply TensorFlow to conduct in-depth analysis of data, predict potential failures and optimize maintenance strategies, and use OpenAI GPT to achieve automated decision support and anomaly detection;

[0044] S5. Data visualization and report generation: Use Grafana or Tableau to convert data into charts and graphs, provide an intuitive user interface for operators, and use JasperReports or Crystal Reports to automatically generate regular and ad hoc reports to record system operation status and monitoring results;

[0045] S6. Security management: Implement OAuth and LDAP authentication mechanisms to ensure that only authorized personnel can access the system, protect system data security, encrypt transmitted and stored data, and use AES-256 or RSA algorithms to prevent data leakage and tampering;

[0046] S7. Environmental monitoring: Use meteorological sensors to monitor the meteorological conditions around the substation, including temperature, humidity and wind speed. Use environmental pollution monitoring equipment to detect pollutants and harmful gases in the air to ensure the safety of the substation environment. Use electromagnetic field monitoring equipment to monitor the electromagnetic environment around the substation to ensure that the equipment is not interfered with. Use time domain reflectometers to locate and identify electromagnetic interference sources and take measures to reduce the impact of interference.

[0047] S8. Structural health monitoring: Use vibration sensors and strain sensors to monitor the vibration and stress of the substation structure to prevent damage to the structure caused by earthquakes and other external forces. Control drones to perform inspection tasks, collect data from high-altitude perspectives, use Pix4D or DroneDeploy for image processing, perform diagnosis and troubleshooting through TeamViewer, automatically generate maintenance plans based on monitoring data, and use IBM Maximo or SAP PM to reduce manual intervention.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention uses a variety of sensors to comprehensively collect environmental data and equipment status information of the substation, utilizes a high-speed data transmission network to transmit the collected data to the data center in real time, realizes real-time updating and monitoring of the data, adopts big data storage technology to safely store massive data in the cloud or local server, ensures the security and integrity of the data, utilizes artificial intelligence and big data analysis technology to deeply analyze and mine the data, extracts valuable information and rules, solves the problem of insufficient information mining in traditional systems, establishes an anomaly detection model, monitors data changes in real time, promptly discovers abnormal situations and issues early warnings, solves the problem of delayed response, provides intelligent decision support based on data analysis results, assists managers in making scientific decisions, reduces the risk of manual judgment errors, realizes automatic control of substation equipment, automatically executes corresponding operations according to monitoring data and preset strategies, reduces the complexity of manual operations, and continuously optimizes the performance and effect of the perception system through continuous feedback and adjustment, thereby improving the operating efficiency and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a topological diagram of the substation infrastructure sensing monitoring system and method based on UWB and millimeter wave radar of the present invention;

[0050] Figure 2 This is a flow chart of the intelligent sensing monitoring method for substation infrastructure based on UWB and radar of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Example 1

[0053] See also Figure 1 , a substation infrastructure perception monitoring system and method based on UWB and millimeter wave radar, including a data acquisition module, a data transmission module, a data processing module, an intelligent analysis module, a visual reporting module, a safety management module, an environmental monitoring module, and a structural health monitoring module;

[0054] Among them, it is also necessary to explain that the system provides data collection inside and outside the substation through the data acquisition module, wireless and wired transmission through the data transmission module, real-time data processing and historical data storage processing through the data processing module, in-depth analysis and intelligent decision support through the intelligent analysis module, data visualization and report generation through the visual report module, data encryption and identity authentication through the security management module, meteorological, environmental pollution monitoring, electromagnetic field monitoring and interference source positioning through the environmental monitoring module, vibration detection, strain monitoring, drone image acquisition, image processing, remote diagnosis and automatic maintenance through the structural health monitoring module.

[0055] In this embodiment, it should also be noted that the data acquisition module of the system also includes a UWB sensor unit and a radar sensor unit. The UWB sensor unit is used to accurately locate and track the positions of objects and personnel inside the substation, and the radar sensor unit is used to detect and monitor environmental changes around the substation, including object movement and environmental parameters.

[0056] In this embodiment, it should also be noted that the data transmission module of the system also includes a wireless transmission unit and a wired transmission unit. The wireless transmission unit is used to transmit the collected data to the central processing system in real time using a wireless network, and the wired transmission unit transmits data via optical fiber or Ethernet to ensure the stability of data transmission under certain environments.

[0057] In this embodiment, it should also be noted that the data processing module of the system also includes a real-time data processing unit and a historical data storage processing unit. The real-time data processing unit is used to analyze sensor data in real time, generate monitoring reports and alarm information, and the historical data storage processing unit is used to store historical data and perform long-term trend analysis and fault prediction.

[0058] In this embodiment, it should also be noted that the intelligent analysis module of the system also includes a machine learning unit and an artificial intelligence unit. The machine learning unit applies a machine learning algorithm to perform in-depth analysis of the data for predicting potential failures and optimizing maintenance strategies. The artificial intelligence unit is used to use AI technology to achieve automated decision support and anomaly detection.

[0059] In this embodiment, it should also be noted that the visual reporting module of the system also includes a visualization unit and a report generation unit. The visualization unit is used to convert data into charts and graphs and provide an intuitive user interface for operators to use. The report generation unit is used to automatically generate regular and temporary reports to record the system operating status and monitoring results.

[0060] In this embodiment, it should also be noted that the system security management module also includes an identity authentication unit and a data encryption unit. The identity authentication unit is used to ensure that only authorized personnel can access the system and protect the system data security. The data encryption unit is used to encrypt the transmitted and stored data to prevent data leakage and tampering.

[0061] In this embodiment, it should also be noted that the environmental monitoring module of the system also includes a meteorological sensing unit, an environmental pollution monitoring unit, an electromagnetic field monitoring unit and an interference source positioning unit. The meteorological sensing unit is used to monitor the meteorological conditions around the substation, including temperature, humidity and wind speed. The environmental pollution monitoring unit is used to detect pollutants and harmful gases in the air to ensure the environmental safety of the substation. The electromagnetic field monitoring unit is used to monitor the electromagnetic environment around the substation to ensure that the equipment is not interfered with. The interference source positioning unit is used to locate and identify the source of electromagnetic interference and take measures to reduce the impact of interference.

[0062] In this embodiment, it should also be noted that the structural health monitoring module of the system also includes a vibration sensing unit, a strain sensing unit, a drone control unit, an image processing unit, a remote diagnosis unit and an automatic maintenance unit. The vibration sensing unit is used to monitor the vibration of the substation structure to prevent damage to the structure by earthquakes and other external forces. The strain sensing unit is used to monitor the structural stress and deformation and promptly discover structural problems. The drone control unit is used to control the drone to perform inspection tasks and collect data from a high-altitude perspective. The image processing unit is used to process images and videos collected by the drone to discover equipment anomalies and environmental changes. The remote diagnosis unit allows experts to remotely access the system via the network for diagnosis and troubleshooting. The automatic maintenance unit is used to automatically generate maintenance plans based on monitoring data to reduce manual intervention.

[0063] In this embodiment, it is also necessary to explain the following steps and integration methods of each module of the substation infrastructure perception monitoring system and method based on UWB and millimeter wave radar of the present invention:

[0064] The implementation steps of the data acquisition module are:

[0065] Equipment installation and calibration

[0066] Install the UWB sensor network, deploy sensors at key locations inside the substation, use Decawave's DWM1001 module for position calibration to ensure high-precision positioning, configure sensor nodes, and use DecaWave RTLSManager software to set up and manage the network;

[0067] Data collection and transmission

[0068] Write sensor data acquisition scripts, use Python and Decawave API to read data, transmit positioning data to the central processing system in real time, use MQTT protocol and Eclipse Mosquitto server to implement data caching and retransmission mechanism, use Redis for data caching to ensure transmission reliability;

[0069] Data processing and application

[0070] Data preprocessing, using the Pandas library for data cleaning and format conversion, real-time positioning and trajectory tracking, using the SciPy library for trajectory analysis and position estimation, integrating positioning data into the monitoring system, and visualizing it through Grafana;

[0071] Equipment installation and configuration

[0072] Deploy millimeter-wave radar sensors, select suitable installation locations to cover the area around the substation, use Texas Instruments' IWR6843 sensor module for configuration to ensure the accuracy of environmental monitoring, and use TI's mmWave Studio software for radar parameter setting and debugging;

[0073] Data collection and processing

[0074] Write data acquisition programs, use MATLAB and TI's mmWave SDK to read data, process data in real time, use Python and SciPy libraries for signal processing and target detection, transmit data to the central system, and use Kafka to achieve high-throughput data stream processing;

[0075] Environmental change monitoring

[0076] Real-time monitoring of the substation environment, using SVM for object classification, generating environmental change reports, using Jupyter Notebook for data analysis and report generation, fusing sensor data, and using OpenCV for multi-sensor data fusion to improve monitoring accuracy;

[0077] The implementation steps of the data transmission module are:

[0078] Network configuration and optimization

[0079] Deploy Wi-Fi 6 wireless network, use Cisco's Catalyst 9100 series access points, configure wireless network parameters, use Cisco DNA Center for network optimization and management, perform network coverage testing, and use Ekahau SiteSurvey software for signal coverage analysis;

[0080] Data transmission implementation

[0081] Write data transmission scripts, use Python and MQTT protocol for data publishing and subscription, implement transmission encryption, use TLS protocol and OpenSSL library to ensure data security, configure load balancing, and use HAProxy for data traffic management and distribution;

[0082] Real-time transmission monitoring

[0083] Monitor data transmission status in real time, use Prometheus to collect and monitor indicators, set up alarm mechanisms, use Grafana to configure alarm rules, detect transmission anomalies in a timely manner, generate transmission performance reports regularly, and use JupyterNotebook for data analysis and report generation;

[0084] Fiber optic network deployment

[0085] Laying fiber optic lines, using Corning fiber optic cables and connectors, configuring fiber optic network equipment, using Cisco's Nexus 9000 series switches, performing fiber optic network testing, and using Fluke Networks' fiber optic testers for link verification;

[0086] Ethernet transmission configuration

[0087] Deploy Ethernet transmission equipment, use Netgear's ProSAFE switches, configure Ethernet network parameters, use Netgear Insight for network management and configuration, perform network performance testing, and use iPerf tools for bandwidth and latency testing;

[0088] Data transfer management

[0089] Write data transmission scripts, use Python and TCP / IP protocol for data transmission, implement redundant backup of data transmission, use RSYNC tool for data synchronization, monitor data transmission status in real time, and use Nagios for network monitoring and alarm;

[0090] The implementation steps of the data processing module are:

[0091] Real-time data stream processing

[0092] Deploy Apache Spark Streaming for data stream processing, use YARN for resource management, write Spark Streaming job code, use PySpark API to implement data processing logic, achieve high availability of data streams, and use Apache Kafka for reliable transmission of data streams;

[0093] Data analysis and report generation

[0094] Analyze sensor data in real time, use Pandas and NumPy libraries for data processing and analysis, generate monitoring reports, use Jupyter Notebook for data visualization and report generation, set up alarm mechanisms, use Prometheus for monitoring and Grafana for alarm configuration;

[0095] System Integration and Optimization

[0096] Integrate data processing system and monitoring system, use RESTful API for data interaction, optimize data processing performance, use Apache Spark tuning parameters to improve processing efficiency, achieve high availability of the system, and use Zookeeper for cluster management and coordination;

[0097] Data storage architecture

[0098] Deploy Hadoop clusters, use HDFS for distributed data storage, configure Hive data warehouses, organize and query data, clean and compress data regularly, and use Apache Oozie for task scheduling and management;

[0099] Data import and management

[0100] Use Apache Flume for data import, transfer data from sensors to HDFS, use Apache Sqoop to import relational database data into the Hadoop ecosystem, and configure HBase for historical data management and query;

[0101] Big Data Analysis

[0102] Use Apache Pig for data preprocessing and ETL operations, use Apache Mahout for large-scale data machine learning and data mining, perform data analysis and report generation, and use Jupyter Notebook and Pandas libraries for data analysis and visualization;

[0103] The implementation steps of the intelligent analysis module are:

[0104] Data preprocessing

[0105] Data cleaning and formatting, data preprocessing using Pandas, feature extraction and selection, feature engineering using Scikit-learn, data standardization, and data standardization using StandardScaler;

[0106] Model training and validation

[0107] Select appropriate machine learning algorithms, use random forest and SVM models in Scikit-learn to train models, use training set data to fit models, validate and evaluate models, and use cross-validation methods and metrics modules in Scikit-learn for evaluation;

[0108] Model deployment and optimization

[0109] Deploy trained models, use Flask or Django frameworks for API deployment, perform model optimization, use GridSearchCV for parameter tuning, implement online learning, and use Scikit-multiflow for incremental learning and real-time model updates;

[0110] Data preparation and annotation

[0111] Collect and organize data sets, use Pandas for data organization, data annotation, use Labelbox for data annotation and management, data enhancement, and use OpenCV and Albumentations for image data enhancement;

[0112] AI model development

[0113] Choose a deep learning framework, use TensorFlow or PyTorch for model development, design and train AI models, use Keras API for model building and training, model evaluation and tuning, and use TensorBoard for training monitoring and tuning;

[0114] Automated decision support

[0115] Deploy AI models, use TensorFlow Serving for model deployment, implement automated decision-making, use Python to write decision logic and interfaces, monitor and adjust in real time, and use Prometheus and Grafana for model performance monitoring and adjustment;

[0116] The implementation steps of the visual reporting module are:

[0117] Data preparation and processing

[0118] Data organization, using Pandas for data cleaning and formatting, data processing, using NumPy for data processing and calculation, data storage, using SQLAlchemy to store processed data into the database;

[0119] Graphics and interface design

[0120] Design charts, create various data visualization charts using Matplotlib and Seaborn libraries, create interactive interfaces, use Plotly Dash or Bokeh to implement interactive data display, integrate data charts into user interfaces, and develop visualization front-end pages using Flask or Django frameworks;

[0121] Visual system deployment

[0122] Deploy visualization system, use Nginx and Gunicorn to configure Web application servers, configure data update mechanism, use Celery for scheduled task scheduling and data update, implement user authority management, and use Flask-Security or Django Auth for user authentication and authority control;

[0123] Data aggregation and analysis

[0124] Data aggregation, using Pandas to aggregate and count various types of data, data analysis, using SciPy and StatsModels for statistical analysis and model prediction, data report generation, using Jinja2 template engine for report template design and data filling;

[0125] Automatic report generation

[0126] Write automation scripts, use Python and ReportLab library to generate PDF reports, set up regular generation tasks, use Apache Airflow for regular report generation scheduling, configure report distribution mechanism, use SMTP library for email sending or FTP for file transfer;

[0127] Report storage and access

[0128] Report storage, using MongoDB or MySQL database to store generated report files; report retrieval, using Elasticsearch for full-text retrieval and fast query; user access, using Flask or Django to develop report management and access interfaces;

[0129] The implementation steps of the security management module are:

[0130] Authentication system configuration

[0131] Configure authentication servers, use Keycloak for identity and access management, configure single sign-on (SSO), integrate using OAuth2.0 and OpenID Connect protocols, implement multi-factor authentication, and use Google Authenticator for two-factor authentication;

[0132] User management and permission control

[0133] User management: use Django Admin or Flask-Admin for user management and permission allocation; permission control: use Django's permission framework or Flask-Security for permission control and access restrictions; logging: use Python's logging module to record user operation logs and store them in Elasticsearch;

[0134] System security monitoring

[0135] Monitor user logins and operations in real time, use ELK for log monitoring and visualization, configure security alarms, use Prometheus and Alertmanager for security event monitoring and alarms, conduct regular security audits, and use Nessus for system security scanning and vulnerability detection;

[0136] Data transmission encryption

[0137] Use TLS / SSL protocol to encrypt data transmission, configure OpenSSL for certificate management and encryption settings, configure HTTPS service, use Nginx or Apache to configure HTTPS service and deploy certificates to implement data transmission encryption, and use Python's cryptography library for data encryption and decryption;

[0138] Data storage encryption

[0139] Database encryption, use TDE to encrypt MySQL or PostgreSQL databases, file system encryption, use Linux's eCryptfs for file system encryption, cloud storage encryption, use AWS KMS or Azure Key Vault for cloud storage data encryption;

[0140] Key Management

[0141] Deploy a key management system, use HashiCorp Vault for key management and storage, configure automatic key rotation, use Vault's automatic key rotation function to achieve regular updates, implement key access control, and use RBAC for key permission management;

[0142] The implementation steps of the environmental monitoring module are:

[0143] Sensor Installation and Calibration

[0144] Install weather sensors, use Davis Vantage Pro2 weather station equipment, perform sensor calibration, use standard calibration equipment to calibrate and debug sensors, configure sensor networks, and use Zigbee protocol to build networks and configure communications for sensor nodes;

[0145] Data collection and processing

[0146] Data acquisition, using Python and PySerial library to read weather sensor data, data processing, using Pandas for data cleaning and preprocessing, real-time data transmission, using MQTT protocol to transmit data to the central processing system;

[0147] Meteorological data monitoring

[0148] Monitor weather conditions in real time, use Grafana for data visualization and real-time monitoring, generate weather reports, use Jupyter Notebook for data analysis and report generation, set alarm thresholds, and use Prometheus for threshold setting and alarm configuration;

[0149] Sensor Installation and Configuration

[0150] Install air quality sensors, use Aeroqual's air quality monitoring equipment, perform sensor configuration, use Aeroqual's AQ software for equipment configuration and calibration, establish a sensor network, and use the LoRaWAN protocol for remote data transmission and management;

[0151] Data collection and processing

[0152] Data collection, using Python and pyserial library to read air quality sensor data, data processing, using Pandas to clean and format data, data transmission, using HTTP protocol to send data to the central processing system;

[0153] Pollution Monitoring and Reporting

[0154] Monitor air quality in real time, use Grafana for data visualization and monitoring, generate pollution reports, use Jupyter Notebook for data analysis and report generation, set up alarm mechanisms, and use Prometheus for alarm configuration and alarm notification;

[0155] Sensor Installation and Calibration

[0156] Install electromagnetic field sensors, use Narda's ELT-400 electromagnetic field meter to calibrate sensors, use standard electromagnetic field calibration equipment for debugging, configure sensor networks, and use Zigbee protocol for node networking and communication;

[0157] Data collection and processing

[0158] Data acquisition, using Python and PySerial library to read electromagnetic field sensor data, data processing, using Pandas for data preprocessing and analysis, data transmission, using MQTT protocol to transmit data to the central system in real time;

[0159] Electromagnetic field monitoring

[0160] Real-time monitoring of electromagnetic environment, use Grafana for data display and monitoring, generate monitoring reports, use Jupyter Notebook for data analysis and report generation, set alarm thresholds, use Prometheus for alarm settings and notifications, interference source location unit

[0161] Interference source detection

[0162] Deploy interference source detection equipment, use Tektronix's interference source location system, configure detection parameters, use Tektronix SignalVu-PC software to configure equipment, perform interference source scanning, and use signal analyzers for real-time scanning and detection;

[0163] Data collection and processing

[0164] Data acquisition, using Python and Tektronix API to read data, data processing, using SciPy for signal processing and interference source analysis, data transmission, using HTTP protocol to send data to the central processing system;

[0165] Interference source location

[0166] Locate the interference source, use KNN for location estimation, display the interference source location in real time, use OpenLayers for map display and positioning, generate positioning reports, and use Jupyter Notebook for data analysis and report generation;

[0167] The implementation steps of the structural health monitoring module are:

[0168] Sensor Installation and Configuration

[0169] Install vibration sensors, use PCB Piezotronics accelerometers, configure sensor parameters, use LabVIEW for sensor configuration and debugging, build a sensor network, and use Zigbee protocol for wireless data transmission;

[0170] Data collection and processing

[0171] Data acquisition, using LabVIEW for data reading and acquisition, data processing, using MATLAB for signal processing and vibration analysis, data transmission, using MQTT protocol to transmit processed data to the central processing system, vibration monitoring and analysis

[0172] Monitor structural vibration in real time, use Grafana for data visualization and real-time monitoring, perform vibration analysis, use Jupyter Notebook combined with SciPy and NumPy for data analysis, generate vibration analysis reports, automatically generate regular analysis reports, and provide structural health status assessment;

[0173] Sensor Installation and Calibration

[0174] Install strain sensors, use Vishay strain gauge sensors, perform sensor calibration, use standard calibration equipment to adjust the accuracy of sensors, configure sensor networks, and use the LoRaWAN protocol to achieve remote data transmission;

[0175] Data collection and processing

[0176] Data acquisition, using Python and LabVIEW for real-time reading of strain data, data processing, using Pandas for data cleaning and preprocessing, data transmission, using HTTP protocol to transmit data to the central processing system;

[0177] Strain Monitoring and Analysis

[0178] Monitor structural strain in real time, use Grafana for data display and real-time monitoring, perform strain analysis, use Jupyter Notebook for detailed analysis of strain data, generate strain analysis reports, regularly generate structural health reports, and provide maintenance recommendations;

[0179] Sensor Installation and Configuration

[0180] Install temperature and humidity sensors, use Honeywell temperature and humidity sensor devices, configure sensor parameters, use the software provided by the sensor manufacturer to configure the device, establish a sensor network, and use Zigbee or LoRaWAN protocols for wireless data transmission;

[0181] Data collection and processing

[0182] Data collection, using Python and Serial library to read temperature and humidity data, data processing, using Pandas to clean and format data, data transmission, using MQTT protocol to transmit data to the central data processing system;

[0183] Environmental monitoring and analysis

[0184] Monitor ambient temperature and humidity in real time, use Grafana for real-time data display, perform environmental data analysis, use Jupyter Notebook for statistical analysis of temperature and humidity data, generate environmental monitoring reports, and provide reports and trend analysis of environmental condition changes;

[0185] Data fusion and processing

[0186] Data fusion, integrating vibration, strain, temperature and humidity sensor data, data cleaning and processing, using Pandas and NumPy for data preprocessing and cleaning, data storage, using MySQL or MongoDB database to store processed data;

[0187] Health assessment model construction

[0188] Build evaluation models, use random forest or SVM for model training, model validation and optimization, use cross-validation and hyperparameter tuning to improve model performance, model deployment, and use Flask or Django to deploy the evaluation model as a web service;

[0189] Evaluation results presentation and reporting

[0190] Display evaluation results in real time, use Grafana or Plotly Dash for data visualization, generate health assessment reports, generate structural health status reports regularly, provide detailed analysis, early warning and alarm, set alarm thresholds, use Prometheus for real-time warning and notification, system integration and maintenance

[0191] System Integration

[0192] Integrate various functional modules to ensure smooth data transmission and processing, conduct system testing, use unit testing and integration testing to ensure system stability, optimize system performance, perform performance tuning and bottleneck analysis, and improve system efficiency;

[0193] User interface development

[0194] Develop user interfaces, use React or Vue.js to build front-end pages, implement data interaction, use AJAX or GraphQL to implement front-end and back-end data interaction, optimize user experience, perform UI / UX design, improve user experience, and maintain the system

[0195] System Monitoring

[0196] Monitor system operation status in real time, use Prometheus and Grafana for system monitoring, log management, use ELK stack for log collection, storage and analysis, troubleshoot, establish rapid response mechanism, and handle system failures and exceptions in a timely manner;

[0197] Regular maintenance and updates

[0198] Regularly perform system maintenance, check and update system software and hardware, data backup and recovery, use database backup and recovery mechanisms to protect data security, security audits and optimization, and perform regular security audits to ensure system security and compliance.

[0199] Example 2

[0200] See also Figure 2 Based on the above-mentioned substation infrastructure perception monitoring system and method based on UWB and millimeter wave radar, in the actual application scenario of substation infrastructure intelligent perception monitoring, the substation infrastructure intelligent perception monitoring method specifically includes the following steps:

[0201] S1. Data collection

[0202] Precise positioning and tracking

[0203] Installation of Decawave DW1000 UWB sensors: Decawave DW1000 UWB sensors were installed at various key locations within the substation;

[0204] Configure sensor parameters: Use the API and software tools provided by Decawave to configure the sensor’s frequency, power, and communication protocol;

[0205] Real-time positioning: The position of objects and personnel is calculated in real time through the TWR algorithm. The formula is: d = \frac{(T_{round1}-T_{round2})-(T_{reply1}-T_{reply2})}{2};

[0206] Where (d) is the time difference, (T_{round1}) is the total time of the first round, (T_{round2}) is the total time of the second round, (T_{reply1}) is the reply time of the first round, and (T_{reply2}) is the reply time of the second round;

[0207] Location data integration: Use Python combined with NumPy to process the location data of multiple sensors and integrate them into a unified location information database;

[0208] Location Monitoring: Use Grafana to monitor and visualize location data in real time;

[0209] Environmental change detection

[0210] Install FMCW radar: Install FMCW radar sensors around the substation;

[0211] Radar parameter configuration: configure radar parameters through MATLAB, including frequency modulation range and sampling rate;

[0212] Data collection: Use the FMCW principle to collect environmental data and calculate the target distance. The formula is: d = \frac{c\cdot\Delta f}{2B};

[0213] Where (d) is the target distance, (c) is the speed of light (constant), (\Delta f) is the frequency difference, and (B) is the bandwidth;

[0214] Data processing: Use MATLAB for signal processing, including FFT transformation and noise filtering;

[0215] Environmental monitoring: The processed data is transmitted to Grafana for real-time monitoring;

[0216] S2. Data transmission

[0217] LoRa wireless network transmission

[0218] LoRa gateway installation: deploy LoRa gateway devices in substations;

[0219] Node configuration: configure the LoRa parameters of each sensor node, including frequency and rate;

[0220] Data transmission: Use LoRaWAN protocol to transmit data from sensor nodes to the central gateway;

[0221] Data reception and analysis: Use Python and MQTT protocol to parse the received data in the central gateway;

[0222] Data relay: forward the parsed data to the central processing system via MQTT;

[0223] 5G wireless network transmission

[0224] 5G base station deployment: Deploy 5G base station equipment in substations;

[0225] Device registration: register and configure sensor devices through the operator platform;

[0226] Real-time data transmission: Using the high-speed transmission capacity of the 5G network, data is transmitted to the central processing system in real time;

[0227] Network monitoring: Use Zabbix to monitor the transmission performance of 5G networks;

[0228] Data reception and storage: Apache Kafka is used in the central processing system to receive data streams and store them in HDFS;

[0229] S3. Data processing

[0230] Real-time data analysis

[0231] Kafka data stream processing: Use Apache Kafka to receive and manage data streams;

[0232] Flink real-time computing: Use Apache Flink for data stream processing, real-time computing monitoring reports and alarm information;

[0233] Alarm generation: define alarm rules and use the Flink CEP module for anomaly detection;

[0234] Alarm notification: send alarm information to the predetermined Email or SMS gateway through the Kafka message queue;

[0235] Data storage: Store the processed data in Hadoop HDFS or Amazon S3;

[0236] Long-term trend analysis

[0237] Historical data storage: Store historical data in Hadoop HDFS or Amazon S3;

[0238] Data cleaning: Use Python and Pandas to clean and preprocess historical data;

[0239] Trend analysis: Use NumPy and SciPy for time series analysis. The formula is:

[0240] \text{Trend}=\frac{\sum_{i=1}^n(y_i-\bar{y})(t_i-\bar{t})}{\sum_{i=1}^n(t_i-\bar{t})^2};

[0241] Where (\text{trend}) is the trend, (y_i) is the (i)th observation, (\bar{y}) is the average of the observations, (t_i) is the (i)th time point, and (\bar{t}) is the average of the time points;

[0242] Fault prediction: Apply the ARIMA model for fault prediction, configure model parameters and perform training;

[0243] Result storage and display: Store the analysis results in the database and display them through Grafana;

[0244] S4. Intelligent Analysis

[0245] In-depth analysis

[0246] Data preprocessing: Use Pandas to clean and format data;

[0247] Model training: Use TensorFlow to build a neural network model and perform deep learning analysis on training data;

[0248] Model evaluation: The cross-validation method is used to evaluate the model performance. The formula is:

[0249] \text{Accuracy} = \frac{\text{Number of correct predictions}}{\text{Total number of predictions}};

[0250] Where (\text{accuracy}) is the accuracy of the prediction, (\text{number of correct predictions}) is the number of correct predictions, and (\text{total number of predictions}) is the total number of predictions;

[0251] Model optimization: adjust hyperparameters and optimize the model through Grid Search;

[0252] Fault prediction and optimization: Use trained models to predict faults and optimize maintenance strategies;

[0253] Automated decision support

[0254] Anomaly detection model: Use OpenAI GPT model to build anomaly detection and decision support system;

[0255] Training data preparation: Collect and prepare the data sets required for anomaly detection;

[0256] Model training: Use TensorFlow to train the GPT model and optimize model parameters;

[0257] Model deployment: Use Flask to deploy the model as a Web service and provide an API interface;

[0258] Automated decision-making: Based on the detected abnormalities, decision suggestions are automatically generated and returned through the API interface;

[0259] S5. Data visualization and report generation

[0260] Data Visualization

[0261] Data collection: Collect and integrate data from various sensors and processing systems;

[0262] Grafana configuration: configure data sources in Grafana and create real-time monitoring dashboards;

[0263] Chart generation: Use Grafana's chart and graph components to create visualization interfaces;

[0264] User interface design: design intuitive operation interface according to user needs;

[0265] Real-time monitoring: Provide real-time monitoring and data display functions through Grafana;

[0266] Report Generation

[0267] Report template creation: design report templates using JasperReports;

[0268] Data filling: fill the monitoring data into the report template;

[0269] Report generation: Automatically generate regular and ad hoc reports;

[0270] Report distribution: distribute the generated report to relevant personnel via email or other means;

[0271] Report Archiving: All generated reports are archived in a central storage system;

[0272] S6. Security Management

[0273] Authentication

[0274] OAuth configuration: Configure the OAuth authentication mechanism to ensure secure login for system users;

[0275] LDAP integration: Integrate LDAP to achieve centralized user management and access control;

[0276] Permission allocation: assign access rights based on user roles;

[0277] Multi-factor authentication: Enable multi-factor authentication to enhance system security;

[0278] Logging: record all user operation logs and review them regularly;

[0279] Data encryption

[0280] Transmission encryption: Use TLS protocol to encrypt data transmission;

[0281] Storage encryption: AES-256 algorithm is used to encrypt storage data;

[0282] Key management: Manage encryption keys using AWS KMS or a similar key management service.

[0283] Encryption verification: Regularly verify the integrity and security of encrypted data;

[0284] Anti-tampering mechanism: Use HMAC-SHA256 algorithm to prevent data tampering;

[0285] S7. Environmental monitoring

[0286] Weather monitoring

[0287] Sensor installation: Install meteorological sensors to monitor temperature, humidity and wind speed;

[0288] Data collection: Use Python and the Serial library to read meteorological data in real time;

[0289] Data processing: Use Pandas to clean and format data;

[0290] Data storage: Store the processed data in InfluxDB;

[0291] Environmental monitoring: Real-time display of meteorological data through Grafana;

[0292] Environmental pollution monitoring

[0293] Sensor installation: Install air pollution monitoring equipment to detect pollutants and harmful gases;

[0294] Data acquisition: Use Modbus protocol to read sensor data;

[0295] Data processing: Use Python and Pandas library to clean and initially process data and detect outliers and missing values ​​in the data;

[0296] Data analysis: The SciPy library is used for data analysis and trend prediction. The formula is:

[0297] \text{Concentration prediction} = \text{Current concentration} + (\text{Rate of change} \times \Delta t);

[0298] Where (\text{concentration prediction}) is the future concentration, (\text{current concentration}) is the current concentration, (\text{rate of change}) is the concentration change rate, and (\Delta t) is the time difference;

[0299] Results display: The pollutant concentration trend and current pollution situation are displayed on the monitoring dashboard through Grafana;

[0300] Electromagnetic environment monitoring

[0301] Sensor installation: Install electromagnetic field monitoring equipment around the substation;

[0302] Data acquisition: Use time domain reflectometry to measure the location and strength of electromagnetic interference sources;

[0303] Data processing: Use MATLAB to perform signal processing, eliminate noise and calculate interference source characteristics;

[0304] Interference source identification: Identify the electromagnetic interference source through data analysis, using the formula: \text{Interference source location} = \frac{c\cdot\Delta t}{2};

[0305] Where (\text{interference source position}) is the position of the interference source, (c) is the speed of light (constant), and (\Delta t) is the time difference;

[0306] Interference suppression: formulate and implement targeted interference suppression measures and record the interference treatment effects;

[0307] S8. Structural health monitoring

[0308] Structural vibration monitoring

[0309] Sensor deployment: Install vibration sensors at key structural parts of the substation;

[0310] Data acquisition: Use NI LabVIEW to collect vibration data in real time;

[0311] Signal processing: Apply FFT algorithm to analyze the vibration signal spectrum;

[0312] Anomaly detection: Detect abnormal vibrations using Python combined with the spectrum analysis function of the SciPy library;

[0313] Alarm notification: configure alarm rules and send abnormal alarm notifications via SMS or Email system;

[0314] Structural strain monitoring

[0315] Strain sensor installation: Install strain sensors at key locations of the substation structure;

[0316] Data acquisition: Collect strain data through strain gauges and data acquisition systems;

[0317] Data processing: Use MATLAB to filter and process the strain data to remove noise;

[0318] Stress analysis: Apply the stress-strain relationship formula to perform structural stress analysis:

[0319] \sigma = E\cdot\varepsilon;

[0320] Where (\sigma) is stress, (E) is Young's modulus, and (\varepsilon) is strain;

[0321] Result evaluation: Evaluate the structural health status, generate structural health reports, and store them in a central database;

[0322] Drone inspection

[0323] UAV configuration: configure the flight parameters and path planning of the UAV;

[0324] Task execution: Use Pix4D or DroneDeploy to control the drone to perform inspection tasks;

[0325] Data collection: Collect high-altitude perspective images and video data;

[0326] Image processing: Use Pix4D or DroneDeploy for image stitching and 3D model reconstruction;

[0327] Diagnosis and maintenance planning: remotely diagnose problems through TeamViewer, generate maintenance plans based on monitoring data, and use IBM Maximo or SAP PM systems to reduce manual intervention;

[0328] In summary, combined with steps S1 to S8, the summary and feedback are as follows:

[0329] Establishing system feedback mechanism

[0330] Data integration: Integrate all monitoring and analysis results into a central database;

[0331] User feedback collection: Use online feedback systems to collect feedback from operators and maintenance personnel;

[0332] System optimization: Optimize the performance of each subsystem based on feedback results and system operation data;

[0333] Report generation and distribution: Use JasperReports to automatically generate comprehensive reports and distribute them to relevant personnel;

[0334] Regular review: Regularly review the overall system operation status to ensure continuous improvement of monitoring and maintenance work.

[0335] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A substation infrastructure sensing monitoring system and method based on UWB and millimeter wave radar, characterized in that: It includes data acquisition module, data transmission module, data processing module, intelligent analysis module, visual report module, safety management module, environmental monitoring module and structural health monitoring module; The data acquisition module is used to provide data acquisition inside and outside the substation, the data transmission module is used for wireless and wired transmission, the data processing module is used for real-time data processing and historical data storage and processing, the intelligent analysis module is used for in-depth analysis and intelligent decision support, the visual report module is used for data visualization and report generation, the security management module is used for data encryption and identity authentication, the environmental monitoring module is used for meteorology, environmental pollution monitoring, electromagnetic field monitoring and interference source positioning, and the structural health monitoring module is used for vibration detection, strain monitoring, drone image acquisition, image processing, remote diagnosis and automatic maintenance.

2. The substation infrastructure sensing monitoring system and method based on UWB and millimeter wave radar according to claim 1 is characterized by: The data acquisition module also includes a UWB sensor unit and a radar sensor unit; The UWB sensor unit is used to accurately locate and track the location of objects and personnel inside the substation; The radar sensor unit is used to detect and monitor environmental changes around the substation, including object movement and environmental parameters.

3. The substation infrastructure sensing monitoring system and method based on UWB and millimeter wave radar according to claim 2 is characterized by: The data transmission module also includes a wireless transmission unit and a wired transmission unit; The wireless transmission unit is used to transmit the collected data to the central processing system in real time using a wireless network; The wired transmission unit transmits data via optical fiber or Ethernet to ensure data transmission stability under certain circumstances.

4. The substation infrastructure sensing monitoring system and method based on UWB and millimeter wave radar according to claim 3 is characterized by: The data processing module also includes a real-time data processing unit and a historical data storage processing unit; The real-time data processing unit is used to analyze sensor data in real time and generate monitoring reports and alarm information; The historical data storage and processing unit is used to store historical data and perform long-term trend analysis and fault prediction.

5. The substation infrastructure sensing monitoring system and method based on UWB and millimeter wave radar according to claim 4 is characterized in that: The intelligent analysis module also includes a machine learning unit and an artificial intelligence unit; The machine learning unit applies a machine learning algorithm to perform in-depth analysis on the data for predicting potential failures and optimizing maintenance strategies; The artificial intelligence unit is used to use AI technology to achieve automated decision support and anomaly detection.

6. The substation infrastructure sensing monitoring system and method based on UWB and millimeter wave radar according to claim 5 is characterized by: The visual reporting module also includes a visualization unit and a report generating unit; The visualization unit is used to convert data into charts and graphs, and provide an intuitive user interface for operators to use; The report generation unit is used to automatically generate regular and temporary reports to record system operating conditions and monitoring results.

7. The substation infrastructure sensing monitoring system and method based on UWB and millimeter wave radar according to claim 6 is characterized in that: The security management module also includes an identity authentication unit and a data encryption unit; The identity verification unit is used to ensure that only authorized personnel can access the system and protect system data security; The data encryption unit is used to encrypt the transmitted and stored data to prevent data leakage and tampering.

8. The substation infrastructure sensing monitoring system and method based on UWB and millimeter wave radar according to claim 7 is characterized in that: The environmental monitoring module also includes a meteorological sensing unit, an environmental pollution monitoring unit, an electromagnetic field monitoring unit and an interference source positioning unit; The meteorological sensing unit is used to monitor meteorological conditions around the substation, including temperature, humidity and wind speed; The environmental pollution monitoring unit is used to detect pollutants and harmful gases in the air to ensure the environmental safety of the substation; The electromagnetic field monitoring unit is used to monitor the electromagnetic environment around the substation to ensure that the equipment is not interfered with; The interference source locating unit is used to locate and identify the electromagnetic interference source and take measures to reduce the interference impact.

9. The substation infrastructure sensing monitoring system and method based on UWB and millimeter wave radar according to claim 8 is characterized in that: The structural health monitoring module also includes a vibration sensing unit, a strain sensing unit, a drone control unit, an image processing unit, a remote diagnosis unit and an automatic maintenance unit; The vibration sensing unit is used to monitor the vibration of the substation structure and prevent damage to the structure caused by earthquakes and other external forces; The strain sensing unit is used to monitor structural stress and deformation and detect structural problems in a timely manner; The drone control unit is used to control the drone to perform inspection tasks and collect data from a high-altitude perspective; The image processing unit is used to process images and videos collected by the drone and detect equipment anomalies and environmental changes; The remote diagnosis unit allows an expert to remotely access the system via a network for diagnosis and troubleshooting; The automatic maintenance unit is used to automatically generate a maintenance plan based on monitoring data to reduce manual intervention.

10. Intelligent sensing and monitoring method for substation infrastructure based on UWB and radar, according to the substation infrastructure sensing and monitoring system and method based on UWB and millimeter wave radar according to any one of claims 1-9, characterized in that: The following steps are involved: S1. Data collection: Use Decawave DW1000 UWB sensors to accurately locate and track the positions of objects and personnel inside the substation, and use FMCW radar to detect and monitor environmental changes around the substation to capture object movement and environmental parameters; S2. Data transmission: Use LoRa or 5G wireless network to transmit the collected data to the central processing system in real time, and transmit data through optical fiber or Ethernet to ensure the stability and efficiency of data transmission; S3. Data processing: Analyze sensor data in real time, generate monitoring reports and alarm information using Apache Kafka and Apache Flink, use Hadoop HDFS or Amazon S3 to store historical data, and perform long-term trend analysis and fault prediction; S4. Intelligent analysis: Apply TensorFlow to conduct in-depth analysis of data, predict potential failures and optimize maintenance strategies, and use OpenAI GPT to achieve automated decision support and anomaly detection; S5. Data visualization and report generation: Use Grafana or Tableau to convert data into charts and graphs, provide an intuitive user interface for operators, and use JasperReports or Crystal Reports to automatically generate regular and ad hoc reports to record system operation status and monitoring results; S6. Security management: Implement OAuth and LDAP authentication mechanisms to ensure that only authorized personnel can access the system, protect system data security, encrypt transmitted and stored data, and use AES-256 or RSA algorithms to prevent data leakage and tampering; S7. Environmental monitoring: Use meteorological sensors to monitor the meteorological conditions around the substation, including temperature, humidity and wind speed. Use environmental pollution monitoring equipment to detect pollutants and harmful gases in the air to ensure the safety of the substation environment. Use electromagnetic field monitoring equipment to monitor the electromagnetic environment around the substation to ensure that the equipment is not interfered with. Use time domain reflectometers to locate and identify electromagnetic interference sources and take measures to reduce the impact of interference. S8. Structural health monitoring: Use vibration sensors and strain sensors to monitor the vibration and stress of the substation structure to prevent damage to the structure caused by earthquakes and other external forces. Control drones to perform inspection tasks, collect data from high-altitude perspectives, use Pix4D or DroneDeploy for image processing, perform diagnosis and troubleshooting through TeamViewer, automatically generate maintenance plans based on monitoring data, and use IBM Maximo or SAP PM to reduce manual intervention.

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