Transformer substation external damage prevention intelligent monitoring method and system based on cloud computing and artificial intelligence

Abstract

The invention discloses a transformer substation external damage prevention intelligent monitoring method and system based on cloud computing and artificial intelligence, and the method comprises the steps: data collection: collecting environment data and equipment state data of a transformer substation in real time through high-definition cameras and various sensors disposed around the transformer substation; data transmission: transmitting the collected data to a cloud computing platform for storage and processing; intelligent analysis: performing real-time analysis on the data transmitted to the cloud computing platform by using big data analysis and an artificial intelligence algorithm, and identifying potential external damage behaviors; performing early warning and response, triggering an early warning mechanism when an abnormal behavior is detected, and notifying operation and maintenance personnel; and data visualization and report generation: generating an operation and maintenance report, an alarm report and a risk analysis report for decision reference of operation and maintenance personnel and a management layer. Through the real-time monitoring and intelligent identification technology, the system can accurately capture abnormal behaviors inside and outside the transformer substation, effectively avoid external damage events, and significantly reduce safety risks.

Description

Technical Field

[0001] The present invention belongs to the technical field of security monitoring of power systems, and relates to a smart monitoring method and system for preventing external damage to substations based on cloud computing and artificial intelligence. Background Art

[0002] At present, with the rapid development of the power system, substations, as crucial nodes in the power system, play a decisive role in ensuring the stability and reliability of the entire power grid. However, current substations are facing increasingly severe risks of external damage, which come from a wide range of sources, covering aspects such as human sabotage, theft, and natural disasters.

[0003] For a long time, the security measures adopted by substations mainly rely on manual inspections and conventional monitoring systems. However, traditional security measures have exposed many drawbacks in practical applications: Low efficiency of manual inspections: The manual inspection method is difficult to achieve all-weather and full-range monitoring coverage. Especially during nighttime or in adverse weather conditions, the inspection work not only becomes much more difficult, but also the safety risks faced by inspection personnel are significantly increased. In such cases, problems such as missed reports and false alarms are likely to occur, making it impossible to timely discover and handle some potential safety hazards.

[0004] Insufficient intelligence of the monitoring system: Although existing video monitoring systems can provide real-time monitoring images, their functions are relatively single, lacking the ability to conduct in-depth intelligent analysis and accurate identification of the image content. The system is unable to actively identify abnormal behaviors in the monitoring images and is difficult to issue early warning signals in a timely manner, thus unable to effectively prevent potential external damage behaviors.

[0005] Slow emergency response speed: Traditional security measures highly rely on manual responses. When abnormal situations occur, it is necessary for humans to discover, judge, and handle them, resulting in the inability to effectively respond to abnormal situations in the first place. The delayed emergency response mechanism greatly increases the likelihood of accidents and poses a serious potential threat to the safe operation of substations.

[0006] In summary, the existing security measures for substations are no longer able to meet the actual needs of substation safe operation, and there is an urgent need for a more efficient and intelligent security monitoring system to ensure the safe and stable operation of substations. Summary of the Invention

[0007] The purpose of the present invention is to solve the problem that the security measures adopted by substations in the prior art mainly rely on manual inspections and conventional monitoring systems and are difficult to meet the actual needs of substation safe operation, and to provide a smart monitoring method and system for preventing external damage to substations based on cloud computing and artificial intelligence.

[0008] To achieve the above object, the present invention is implemented by the following technical solutions: A substation anti-external damage intelligent monitoring method based on cloud computing and artificial intelligence, comprising the following steps: Data collection, through high-definition cameras and various sensors deployed around the substation, real-time collection of the substation's environmental data and equipment status data; Data transmission, transmitting the collected data to the cloud computing platform for storage and processing; Intelligent analysis, using big data analysis and artificial intelligence algorithms to perform real-time analysis on the data transmitted to the cloud computing platform to identify potential external damage behaviors; Early warning and response, triggering an early warning mechanism when abnormal behaviors are detected and notifying the operation and maintenance personnel; Data visualization and report generation, generating operation and maintenance reports, alarm reports and risk analysis reports for the reference of operation and maintenance personnel and management for decision-making.

[0009] The data collection is specifically as follows: High-definition cameras are deployed around the transformers, breaker operating areas, near the busbars, and at the entrances and exits of the equipment control rooms in the substation, covering the operation conditions of the equipment in the substation and the personnel activity ranges; according to the collection requirements, various types of sensors are installed in the substation, including temperature sensors, oil level sensors, and gas sensors for detecting the components of dissolved gases in transformer oil around the transformers; current sensors, voltage sensors, and contact temperature sensors are installed near the breakers; voltage transformers and current transformers are installed in the busbar areas; humidity sensors and smoke sensors are installed in the equipment control rooms and near key equipment; Transmitting the data collected by the high-definition cameras and sensors to the cloud computing platform for storage and processing.

[0010] The intelligent analysis, using big data analysis and artificial intelligence algorithms to perform real-time analysis on the data transmitted to the cloud computing platform to identify potential external damage behaviors, is specifically as follows: Data preprocessing, cleaning the collected data, removing noise and duplicate data, and unifying the timestamp format and data units; Data analysis, performing real-time analysis on the collected data, calculating key indicators; using the Isolation Forest or LSTM model to detect abnormal patterns in the data for anomaly detection; training a model based on historical data to predict the future state of the equipment; using time series analysis methods to analyze data trends; Model training, dividing the data into a 70% training set, 20% validation set, and 10% test set; selecting the corresponding model according to the task requirements; iteratively optimizing the model based on the training set, adjusting hyperparameters, and evaluating the generalization ability of the model through cross-validation; using the validation set to evaluate the model performance; deploying the trained model to the production environment to support real-time data analysis.

[0011] The specific data preprocessing is as follows: Data cleaning: Handling missing values, using interpolation method to complete missing data; for time series data, preferentially using linear interpolation of the previous and next moments to retain trend information; eliminating or reconstructing features with a missing ratio exceeding 30%; Outlier handling, identifying data points beyond the normal range based on statistical methods; using machine learning methods to detect outliers in non-linear distributions; correcting or eliminating the identified outliers and recording the processing log; Noise filtering, applying filtering algorithms to smooth random noise in time series data; using wavelet transform to decompose signals, removing high-frequency noise components and retaining key features; for image or video data, using Gaussian filtering or median filtering to eliminate pixel-level noise; Data normalization: Normalization, scaling data to a unified range [0, 1]; Standardization, converting data to a distribution with a mean of 0 and a variance of 1; Data integration: Integrating multi-source data, fusing heterogeneous data from different devices or systems, unifying the data format, and adding unique identifiers; Time synchronization processing, aligning timestamps of different data sources, using interpolation or resampling methods to solve the problem of inconsistent sampling frequencies; Using time window aggregation to reduce time granularity differences; Data dimensionality reduction: Projecting high-dimensional data into a low-dimensional space through linear transformation, retaining the main variance information; Maximizing the inter-class distance and minimizing the intra-class distance while reducing dimensionality.

[0012] The specific data analysis is as follows: Constructing a comprehensive scoring model based on multi-source data to quantify the device health index; Dynamically calculating the device load rate and comparing it with a preset threshold to determine whether there is an overload risk; Using the Isolation Forest model for unsupervised anomaly detection to identify data sparse regions; Using the LSTM model to analyze time series data to capture long-term dependencies; Performing a sliding window analysis on real-time data to extract features; Combining threshold rules and model outputs to determine whether it is an abnormal event; If an anomaly is detected, generating an alarm message and associating the device location, timestamp, and anomaly type; Collecting the device's historical operation data and environmental data, annotating fault events as prediction targets; Using the XGBoost model for regression or classification tasks to predict the remaining life or failure probability of the device; Using the LSTM model for time series prediction to analyze the device performance degradation trend; Inputting real-time data into the trained model, outputting the prediction result, and generating preventive maintenance suggestions based on the prediction result.

[0013] The specific model training is as follows: Dataset division: Divide the original dataset according to the ratio of 70% training set, 20% validation set, and 10% test set; Model selection: Select a model according to the data scale, feature dimension, task complexity, and real-time requirements; Model training: Use the training set for iterative training and record the decline process of the loss function; Model evaluation: Quantify the model performance through the validation set; Model optimization: Further improve the model performance through hyperparameter tuning and feature engineering; Model deployment: Integrate the trained model into the production environment to support real-time data analysis.

[0014] The specific early warning and response are as follows: Trigger differential response measures according to the threat level, including email, SMS notification, and automatic power-off control; Generate risk heat maps and trend analysis reports through Grafana or Tableau; Support users to customize alarm thresholds and monitoring dimensions to achieve dynamic policy configuration.

[0015] The specific data visualization and report generation are as follows: Operation and maintenance report generation, including equipment status: Summarize the operation parameters of key equipment in the substation, mark abnormal values and trend changes; Environmental data monitoring: Display environmental parameters such as temperature, humidity, wind speed, and rainfall, and analyze their potential impacts on equipment operation; Maintenance record integration: Associate historical maintenance work orders, count equipment failure rates, repair durations, and spare part consumption, and predict future maintenance requirements; Alarm report generation, including trigger mechanism: Generate alarm events based on the abnormal detection results of intelligent analysis; Support hierarchical alarms and associate different response strategies according to the threat level; Report content: Timestamp, location, abnormal type; Evidence chain: Associate captured pictures by high-definition cameras, sensor data curves, and analysis results; Risk analysis report generation, including historical risk backtracking: Statistically analyze the frequency, losses, and disposal efficiency of similar abnormal events in the past year; Trend prediction: Predict the equipment failure probability and external damage risk level in the next 30 days based on the LSTM model; Vulnerability assessment: Identify the weak links of the substation and generate a list of improvement priorities.

[0016] A substation anti-external damage intelligent monitoring system based on cloud computing and artificial intelligence includes the following modules: Data acquisition module: Real-time collect the environmental data and equipment status data of the substation through high-definition cameras and various sensors deployed around the substation; Data transmission module: Transmit the collected data to the cloud computing platform through an encrypted transmission protocol to ensure data security; Intelligent Analysis Module: Utilize big data analysis technology to mine a large amount of historical data and establish a baseline model for the operating status of equipment; Based on artificial intelligence algorithms, perform anomaly detection on real-time data to identify potential external sabotage behaviors; Early Warning and Response Module: When an abnormal behavior is detected, trigger a hierarchical early warning mechanism, and associate different response strategies according to the threat level; Notify the operation and maintenance personnel and generate a work order record; Support linkage with the existing SCADA system in the substation to achieve remote device control; Data Visualization and Report Generation Module: The operation and maintenance report summarizes the equipment status, environmental data, and historical maintenance records, generating an equipment health scorecard and a load heat map; The alarm report records the details of abnormal events, the evidence chain, and disposal suggestions; The risk analysis report, based on historical data and prediction models, evaluates the future risk level and generates a list of priorities for vulnerability improvement.

[0017] The system is deployed in a hybrid cloud architecture, including: Edge Computing Layer: Lightweight nodes deployed at the substation site, responsible for real-time data processing and local alarms; Cloud Computing Layer: Integrate a big data platform and AI model training services, supporting massive data storage and global analysis; User Terminal Layer: Provide Web and mobile applications, supporting remote monitoring and decision-making by operation and maintenance personnel.

[0018] Compared with the prior art, the present invention has the following beneficial effects: In the intelligent monitoring method for preventing external damage to substations based on cloud computing and artificial intelligence of the present invention, through real-time monitoring and intelligent identification technology, the system can accurately capture abnormal behaviors inside and outside the substation (such as illegal intrusion, equipment damage, etc.). Experimental data shows that the accuracy rate of abnormal behavior identification reaches over 95%, and the false alarm rate is lower than 5%, effectively avoiding the occurrence of external damage events and significantly reducing security risks.

[0019] Greatly improve the emergency response speed. Based on the automated early warning mechanism, the system can immediately trigger an alarm when an abnormal situation occurs and notify relevant personnel through multiple channels such as text messages and APP push. Experimental comparison shows that the average response time of the system is shortened from 30 minutes of traditional manual patrols to within 5 minutes, greatly improving the emergency handling efficiency and reducing accident losses.

[0020] Significantly reduce the operation and maintenance costs. Through intelligent monitoring and management means, the system replaces some manual patrol work, reducing the manpower input and patrol frequency. Experimental data shows that the operation and maintenance costs are reduced by more than 30%, and at the same time, the service life of the equipment is extended, further saving the long-term operation costs.

[0021] Comprehensively enhance management convenience. Relying on the mobile APP and cloud computing platform, operation and maintenance personnel can access the real-time data, historical records, and alarm information of the substation anytime and anywhere, realizing remote monitoring and collaborative management. The experimental results show that the management efficiency is increased by 40%, the decision-making response speed is accelerated, and the operation and maintenance work is more efficient and flexible. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0023] Figure 1 It is a flowchart of the substation anti-external damage intelligent monitoring method based on cloud computing and artificial intelligence of the present invention; Figure 2 It is a system architecture diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0025] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the present invention to be protected, but only represents the selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0026] It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0027] The present invention will be further described in detail below with reference to the drawings: See Figure 1 , which is the substation anti-external damage intelligent monitoring method based on cloud computing and artificial intelligence in the present invention, specifically including the following steps: Data acquisition, through the high-definition cameras and various sensors deployed around the substation, real-time collect the environmental data and equipment status data of the substation; specifically: High-definition cameras are deployed around the transformers, breaker operating areas, near busbars, and at the entrances and exits of equipment control rooms in the substation, covering the operating conditions of the equipment and the range of personnel activities within the substation; according to the acquisition requirements, various types of sensors are installed in the substation, including temperature sensors, oil level sensors, and gas sensors for detecting the components of dissolved gases in transformer oil around the transformer; current sensors, voltage sensors, and contact temperature sensors are installed near the breaker; voltage transformers and current transformers are installed in the busbar area; humidity sensors and smoke sensors are installed near the equipment control room and key equipment; the data collected by the high-definition cameras and sensors is transmitted to the cloud computing platform for storage and processing.

[0028] Data transmission: Transmit the collected data to the cloud computing platform for storage and processing.

[0029] Intelligent analysis: Use big data analysis and artificial intelligence algorithms to perform real-time analysis on the data transmitted to the cloud computing platform to identify potential external sabotage behaviors; Data preprocessing: Clean the collected data, remove noise and duplicate data, and unify the timestamp format and data units. Specifically: Data cleaning: Missing value processing, use interpolation method to fill in the missing data; for time series data, give priority to using linear interpolation of the previous and subsequent moments to retain trend information; eliminate or reconstruct features with a missing ratio exceeding 30%; Outlier processing, identify data points outside the normal range based on statistical methods; use machine learning methods to detect outliers in non-linear distributions; correct or eliminate the identified outliers and record the processing log; Noise filtering, apply filtering algorithms to smooth the random noise in time series data; use wavelet transform to decompose the signal, remove high-frequency noise components, and retain key features; for image or video data, use Gaussian filtering or median filtering to eliminate pixel-level noise; Data normalization: Normalization, scale the data to a unified range [0,1]; Standardization, convert the data to a distribution with a mean of 0 and a variance of 1; Data integration: Multi-source data integration, fuse heterogeneous data from different devices or systems, unify the data format, and add unique identifiers; Time synchronization processing, align the timestamps of different data sources, and use interpolation or resampling methods to solve the problem of inconsistent sampling frequencies; Use time window aggregation to reduce the time granularity difference; Data dimensionality reduction: Project high-dimensional data into a low-dimensional space through linear transformation, retaining the main variance information; Maximize the distance between classes and minimize the distance within classes while reducing the dimensionality.

[0030] Data analysis: Perform real-time analysis on the collected data, calculate key indicators; use the Isolation Forest or LSTM model to detect abnormal patterns in the data for anomaly detection; train a model based on historical data to predict the future state of the device; use time series analysis methods to analyze data trends. Specifically: Build a comprehensive scoring model based on multi-source data to quantify the device health index; dynamically calculate the device load rate and compare it with a preset threshold to determine whether there is an overload risk; Adopt the Isolation Forest model for unsupervised anomaly detection to identify data sparse regions; use the LSTM model to analyze time series data and capture long-term dependencies; Perform a sliding window analysis on the real-time data to extract features; combine threshold rules and model outputs to determine whether it is an abnormal event; If an anomaly is detected, generate an alarm message and associate the device location, timestamp, and anomaly type; Collect the device's historical operation data and environmental data, and label the fault events as the prediction target; Adopt the XGBoost model for regression or classification tasks to predict the remaining life or failure probability of the device; use the LSTM model for time series prediction to analyze the device performance degradation trend; Input the real-time data into the trained model, output the prediction results, and generate preventive maintenance suggestions based on the prediction results.

[0031] Model training: Divide the data into a 70% training set, 20% validation set, and 10% test set; select the corresponding model according to the task requirements; iteratively optimize the model based on the training set, adjust the hyperparameters, and evaluate the model's generalization ability through cross-validation; use the validation set to evaluate the model performance; deploy the trained model to the production environment to support real-time data analysis. Specifically: Dataset division: Divide the original dataset into a 70% training set, 20% validation set, and 10% test set; Model selection: Select a model according to the data scale, feature dimension, task complexity, and real-time requirements; Model training: Use the training set for iterative training and record the decline process of the loss function; Model evaluation: Quantify the model performance through the validation set; Model optimization: Further improve the model performance through hyperparameter tuning and feature engineering; Model deployment: Integrate the trained model into the production environment to support real-time data analysis.

[0032] Early warning and response: When abnormal behavior is detected, trigger the early warning mechanism and notify the operation and maintenance personnel. Specifically: Trigger differential response measures according to the threat level, including email, SMS notification, and automatic power-off control; Generate risk heat maps and trend analysis reports through Grafana or Tableau; Support users to customize alarm thresholds and monitoring dimensions to achieve dynamic policy configuration.

[0033] Data visualization and report generation: Generate operation and maintenance reports, alarm reports, and risk analysis reports for operation and maintenance personnel and management to make decisions. Specifically: Generation of operation and maintenance reports, including equipment status: Summarize the operation parameters of key equipment in the substation, mark abnormal values and trend changes; Environmental data monitoring: Display environmental parameters such as temperature, humidity, wind speed, and rainfall, and analyze their potential impact on equipment operation; Integration of maintenance records: Associate historical maintenance work orders, count equipment failure rates, repair durations, and spare part consumption, and predict future maintenance needs; Generation of alarm reports, including trigger mechanism: Generate alarm events based on the abnormal detection results of intelligent analysis; Support hierarchical alarms, and associate different response strategies according to the threat level; Report content: Timestamp, location, abnormal type; Evidence chain: Associate captured pictures by high-definition cameras, sensor data curves, and analysis results; Generation of risk analysis reports, including historical risk backtracking: Statistically analyze the frequency, losses, and disposal efficiency of similar abnormal events in the past year; Trend prediction: Predict the equipment failure probability and external damage risk level in the next 30 days based on the LSTM model; Vulnerability assessment: Identify the weak links of the substation and generate a list of improvement priorities.

[0034] An embodiment of the present invention is a substation anti-external damage intelligent monitoring system based on cloud computing and artificial intelligence, including the following modules: Data acquisition module: Real-time collect the environmental data and equipment status data of the substation through high-definition cameras and various sensors deployed around the substation; Data transmission module: Transmit the collected data to the cloud computing platform through an encrypted transmission protocol to ensure data security; Intelligent analysis module: Use big data analysis technology to mine massive historical data and establish a baseline model for equipment operation status; Based on artificial intelligence algorithms, perform abnormal detection on real-time data to identify potential external damage behaviors; Early warning and response module: When abnormal behavior is detected, trigger a hierarchical early warning mechanism, and associate different response strategies according to the threat level; Notify the operation and maintenance personnel and generate work order records; Support linkage with the existing SCADA system of the substation to achieve remote device control; Data Visualization and Report Generation Module: The operation and maintenance report summarizes the device status, environmental data, and historical maintenance records, generating a device health scorecard and a load heat map; the alarm report records the details of abnormal events, the evidence chain, and disposal suggestions; the risk analysis report evaluates the future risk level based on historical data and prediction models, generating a list of priorities for vulnerability improvement.

[0035] The intelligent substation anti-external damage monitoring system based on cloud computing and artificial intelligence is deployed in a hybrid cloud architecture, including: Edge Computing Layer: Lightweight nodes deployed at the substation site, responsible for real-time data processing and local alarms; Cloud Computing Layer: Integrates a big data platform and AI model training services, supporting massive data storage and global analysis; User Terminal Layer: Provides Web and mobile applications, supporting remote monitoring and decision-making by operation and maintenance personnel.

[0036] See Figure 2, which is the application system architecture diagram of the present invention, is composed of a line intelligent terminal, a line merging unit, a main equipment merging unit, a main equipment intelligent terminal, a process layer communication network, a protection device, a measurement and control device, and a station control layer communication network. Among them, the line merging unit (Line Merging Unit, LMU) is a key device for collecting and transmitting current and voltage data of transmission lines. Its core functions include: accurately collecting analog data of current and voltage of transmission lines through current transformers (CTs) and potential transformers (PTs), and merging multiple analog signals into digital signals; in the data processing link, the LMU filters, calibrates, and synchronizes the collected data to ensure the accuracy and real-time nature of the data; the processed digital signals are transmitted to protection devices, measurement and control devices, etc. through the process layer communication network (such as GOOSE, SV protocols) at high speed and reliably to meet the requirements of power system protection and control for real-time data; in addition, the LMU also supports the synchronization function with a clock synchronization system (such as IRIG-B or SNTP) to ensure the consistency of data in the time dimension, thus providing a reliable guarantee for the safe and stable operation of the power system. The main equipment merging unit (Main Equipment Merging Unit, MEMU) is a device for collecting and transmitting current and voltage data designed specifically for main equipment such as transformers and circuit breakers. Its core functions cover: accurately collecting analog data of current and voltage of main equipment through current transformers (CTs) and potential transformers (PTs), and converting multiple analog signals into digital signals; in the data processing stage, the MEMU performs filtering, calibration, and synchronization operations on the collected data to ensure the accuracy and real-time nature of the data; the processed digital signals are transmitted in real time at high speed and reliably through the process layer communication network (such as GOOSE, SV protocols) to accurately connect to protection devices, measurement and control devices, etc. to meet the requirements of power system protection and control for real-time data; in addition, the MEMU also has the synchronization ability with a clock synchronization system (such as IRIG-B or SNTP) to ensure the consistency of data in the time dimension and provide solid technical support for the safe and stable operation of the main equipment of the power system.

[0037] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A smart monitoring method for preventing substation damage based on cloud computing and artificial intelligence, characterized in that: The following steps are involved: Data collection: HD cameras and various sensors deployed around the substation collect environmental data and equipment status data of the substation in real time; Data transmission: transferring the collected data to the cloud computing platform for storage and processing; Intelligent analysis, using big data analysis and artificial intelligence algorithms to analyze data transmitted to the cloud computing platform in real time to identify potential external sabotage behaviors; Early warning and response: When abnormal behavior is detected, the early warning mechanism is triggered and the operation and maintenance personnel are notified; Data visualization and report generation: generate operation and maintenance reports, alarm reports and risk analysis reports for reference by operation and maintenance personnel and management for decision-making.

2. The intelligent monitoring method for preventing external damage to a substation based on cloud computing and artificial intelligence as claimed in claim 1 is characterized in that: The data collection specifically includes: High-definition cameras are deployed around transformers, circuit breaker operation areas, near busbars, and at the entrances and exits of equipment control rooms in substations, covering the operation of equipment in substations and the range of personnel activities. According to data collection requirements, various types of sensors are installed in substations, including temperature sensors, oil level sensors, and gas sensors for detecting dissolved gas components in transformer oil around transformers; current sensors, voltage sensors, and contact temperature sensors are installed near circuit breakers; voltage transformers and current transformers are installed in busbar areas; humidity sensors and smoke sensors are installed in equipment control rooms and near key equipment. The data collected by high-definition cameras and sensors are transmitted to the cloud computing platform for storage and processing.

3. The intelligent monitoring method for preventing external damage to a substation based on cloud computing and artificial intelligence as claimed in claim 1 is characterized in that: The intelligent analysis uses big data analysis and artificial intelligence algorithms to conduct real-time analysis of data transmitted to the cloud computing platform to identify potential external destructive behaviors, specifically: Data preprocessing: cleaning the collected data, removing noise and duplicate data, and unifying the timestamp format and data units; Data analysis: real-time analysis of collected data to calculate key indicators; use isolation forest or LSTM models to detect abnormal patterns in data for anomaly detection; train models based on historical data to predict the future status of equipment; use time series analysis methods to analyze data trends; For model training, the data is divided into 70% training set, 20% validation set, and 10% test set ratios; the corresponding model is selected according to the task requirements; the model is iteratively optimized based on the training set, hyperparameters are adjusted, and the model generalization ability is evaluated through cross-validation; the model performance is evaluated using the validation set; the trained model is deployed to the production environment to support real-time data analysis.

4. The intelligent monitoring method for preventing external damage to a substation based on cloud computing and artificial intelligence as claimed in claim 3 is characterized in that: The data preprocessing is specifically as follows: Data cleaning: missing value processing, using interpolation to complete missing data; for time series data, linear interpolation of previous and next moments is preferred to retain trend information; features with a missing ratio of more than 30% are removed or reconstructed; Outlier processing: identifying data points outside the normal range based on statistical methods; using machine learning methods to detect outliers in nonlinear distributions; correcting or eliminating identified outliers and recording processing logs; Noise filtering, applying filtering algorithms to smooth random noise in time series data; using wavelet transform to decompose signals, remove high-frequency noise components, and retain key features; for image or video data, using Gaussian filtering or median filtering to eliminate pixel-level noise; Data normalization: Normalization, scaling the data to a uniform range [0,1]; Standardization, converting the data into a distribution with a mean of 0 and a variance of 1; Data integration: Multi-source data integration, fusing heterogeneous data from different devices or systems, unifying the data format, and adding unique identifiers; time synchronization processing, aligning the timestamps of different data sources, using interpolation or resampling methods to solve the problem of inconsistent sampling frequencies; using time window aggregation to reduce time granularity differences; Data dimensionality reduction: Project high-dimensional data into low-dimensional space through linear transformation, retaining the main variance information; while reducing the dimensionality, maximize the distance between classes and minimize the distance within classes.

5. The intelligent monitoring method for preventing external damage to a substation based on cloud computing and artificial intelligence as claimed in claim 3 is characterized in that: The data analysis specifically includes: Build a comprehensive scoring model based on multi-source data to quantify the equipment health index; dynamically calculate the equipment load rate and compare it with the preset threshold to determine whether there is an overload risk; Use the isolation forest model for unsupervised anomaly detection to identify data sparse areas; Use LSTM model to analyze time series data and capture long-term dependencies; Perform sliding window analysis on real-time data to extract features; Combine threshold rules and model output to determine whether it is an abnormal event; If an anomaly is detected, an alarm message is generated and associated with the device location, timestamp, and anomaly type; Collect historical equipment operation data and environmental data, and mark fault events as prediction targets; Use the XGBoost model for regression or classification tasks to predict the remaining life or failure probability of equipment; use the LSTM model for time series prediction to analyze the performance attenuation trend of equipment; Input real-time data to the trained model, output prediction results, and generate preventive maintenance recommendations based on the prediction results.

6. The intelligent monitoring method for preventing external damage to a substation based on cloud computing and artificial intelligence as claimed in claim 3 is characterized in that: The model training is specifically as follows: Dataset division: the original dataset is divided into 70% training set, 20% validation set, and 10% test set; Model selection: select a model based on data size, feature dimension, task complexity, and real-time requirements; Model training, use the training set for iterative training and record the decline process of the loss function; Model evaluation, quantifying model performance through a validation set; Model optimization, further improving model performance through hyperparameter tuning and feature engineering; Model deployment integrates the trained model into the production environment to support real-time data analysis.

7. The intelligent monitoring method for preventing external damage to a substation based on cloud computing and artificial intelligence as claimed in claim 1 is characterized in that: The warning and response are specifically as follows: triggering differentiated response measures according to the threat level, including email, SMS notification and automatic power-off control; generating risk heat maps and trend analysis reports through Grafana or Tableau; supporting users to customize alarm thresholds and monitoring dimensions to achieve dynamic policy configuration.

8. The intelligent monitoring method for preventing external damage to a substation based on cloud computing and artificial intelligence as claimed in claim 1 is characterized in that: The data visualization and report generation are specifically as follows: Generation of operation and maintenance reports, including equipment status: summarizing the operating parameters of key equipment in the substation, marking abnormal values ​​and trend changes; environmental data monitoring: displaying environmental parameters such as temperature, humidity, wind speed, and rainfall, and analyzing their potential impact on equipment operation; maintenance record integration: linking historical maintenance work orders, counting equipment failure rates, maintenance time, and spare parts consumption, and predicting future maintenance needs; Alarm report generation, including triggering mechanism: generating alarm events based on abnormal detection results of intelligent analysis; Supports graded alarms and associates different response strategies according to threat levels; Report content: timestamp, location, anomaly type; Evidence chain: linking high-definition camera captured images, sensor data curves and analysis results; Generate risk analysis reports, including historical risk review: statistics on the frequency, losses and handling efficiency of similar abnormal events in the past year; trend Prediction: Predict the probability of equipment failure and external damage risk level in the next 30 days based on the LSTM model; Vulnerability assessment: Identify weak links in substations and generate a priority list for improvement.

9. The substation anti-extrinsic damage intelligent monitoring system based on cloud computing and artificial intelligence is characterized by: Includes the following modules: Data collection module: Through high-definition cameras and various sensors deployed around the substation, the environmental data and equipment status data of the substation are collected in real time; Data transmission module: transmits the collected data to the cloud computing platform through an encrypted transmission protocol to ensure data security; Intelligent analysis module: Use big data analysis technology to mine massive historical data and establish a baseline model for equipment operation status; perform anomaly detection on real-time data based on artificial intelligence algorithms to identify potential external destructive behaviors; Early warning and response module: When abnormal behavior is detected, a graded early warning mechanism is triggered, and different response strategies are associated according to the threat level; Notify the operation and maintenance personnel and generate work order records; support linkage with the existing SCADA system of the substation to achieve remote equipment control; Data visualization and report generation module: The operation and maintenance report summarizes the equipment status, environmental data and historical maintenance records, and generates an equipment health scorecard and load heat map; the alarm report records the details of abnormal events, the chain of evidence and disposal suggestions; the risk analysis report is based on historical data and predictive models to evaluate future risk levels and generate a vulnerability improvement priority list.

10. The substation anti-extrinsic damage intelligent monitoring system based on cloud computing and artificial intelligence as claimed in claim 9, characterized in that: The system is deployed in a hybrid cloud architecture, including: Edge computing layer: lightweight nodes deployed at the substation site, responsible for real-time data processing and local alarms; Cloud computing layer: Integrates big data platform and AI model training services to support massive data storage and global analysis; User terminal layer: provides web and mobile applications to support remote monitoring and decision-making by operation and maintenance personnel.

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