Intelligent inspection technology based on artificial intelligence and electric red-hong Internet-of-Things operation system

Abstract

The invention relates to the technical field of electric power systems, Internet of Things and computing, in particular to an intelligent inspection technology based on artificial intelligence and an electric Hongshu Internet of Things operating system. The invention provides an intelligent inspection technology based on artificial intelligence and an electric red-hong Internet of Things operating system, and intelligent analysis and inspection of power equipment data are realized by constructing an intelligent data processing method flow. The technical scheme has the advantages of high efficiency, accuracy, reliability and the like, and has important significance for improving the management level of power equipment. According to the invention, intelligent processing and routing inspection of the power equipment data can be realized, and the routing inspection efficiency and accuracy are improved. And meanwhile, prediction and early warning of the equipment state are realized by using an artificial intelligence technology, and economic loss and potential safety hazards caused by equipment faults can be effectively avoided. In addition, a data visualization and report generation function provides visual and convenient decision support for management personnel.

Description

Technical Field

[0001] The present invention relates to the technical fields of Internet of Things, artificial intelligence, data processing, and power equipment inspection. Specifically, it is an intelligent inspection technology based on artificial intelligence and the DH IoT operating system, aiming to achieve intelligent processing and inspection of power equipment data by deeply integrating artificial intelligence technology and the DH IoT operating system, and improve the inspection efficiency and accuracy. Background Art

[0002] With the rapid development of Internet of Things technology, the management and monitoring of power equipment have gradually become intelligent. The introduction of the DH IoT operating system provides strong platform support for data collection and processing of power equipment. However, how to quickly and accurately identify the device status in the vast amount of data is still a challenge faced currently. Therefore, the present invention proposes to deeply integrate artificial intelligence technology into the DH IoT operating system to achieve intelligent inspection of power equipment. Summary of the Invention

[0003] The present invention deeply integrates artificial intelligence technology into the DH IoT operating system, and realizes intelligent analysis and inspection of power equipment data by constructing an intelligent data processing method flow. This method flow includes steps such as data collection, preprocessing, feature extraction, model training, intelligent analysis, and early warning, aiming to improve the inspection efficiency and accuracy and reduce the labor cost.

[0004] An intelligent inspection technology based on artificial intelligence and the DH IoT operating system includes:

[0005] 1. Data collection:

[0006] Utilize the distributed soft bus technology of the DH IoT operating system to achieve intelligent interconnection and data communication between power equipment of different brands and types.

[0007] Deploy intelligent sensors and cameras to collect the operation data and environmental information of power equipment in real time, including parameters such as current, voltage, temperature, and humidity.

[0008] 2. Data preprocessing:

[0009] Clean the collected raw data to remove noise and invalid data.

[0010] Perform format conversion and normalization processing on the data to ensure the consistency and comparability of the data.

[0011] 3. Feature extraction:

[0012] Utilize artificial intelligence technology to extract features from the preprocessed data and identify key information.

[0013] Feature extraction methods include, but are not limited to, principal component analysis (PCA), linear discriminant analysis (LDA), etc.

[0014] 4. Model Training

[0015] Build an intelligent analysis model based on deep learning, such as convolutional neural network (CNN), recurrent neural network (RNN), etc.

[0016] Use historical data and label information to train and optimize the model so that it can accurately identify device status and anomalies.

[0017] 5. Intelligent Analysis and Warning:

[0018] Input the real-time collected data into the trained model for intelligent analysis.

[0019] Based on the analysis results, identify device failures and anomalies, and automatically trigger the warning mechanism to send warning messages to the inspection personnel.

[0020] 6. Data Visualization and Report Generation:

[0021] Use the data visualization function of the Dianhong Internet of Things operating system to display the analysis results in the form of charts, curves, etc., facilitating managers to intuitively understand the device status.

[0022] Automatically generate inspection reports, including device status, anomaly information, warning records, etc., providing decision-making support for managers.

[0023] By implementing the present invention, intelligent processing and inspection of power equipment data can be realized, improving the inspection efficiency and accuracy. At the same time, using artificial intelligence technology to realize the prediction and warning of device status can effectively avoid economic losses and safety hazards caused by device failures. In addition, the data visualization and report generation functions provide intuitive and convenient decision-making support for managers. Brief Description of the Drawings

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0025] Figure 1 It is the overall step block diagram provided by the embodiment of the present invention. Detailed Embodiment

[0026] In order to make the technical problems, technical solutions and beneficial effects to be solved by this application more clear and understandable, the following further details this application in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0027] See Figure 1 As shown, a further description is made of the optimal embodiment of the present invention;

[0028] The present invention proposes an intelligent inspection technology based on artificial intelligence and the Dianhong Internet of Things operating system. By constructing an intelligent data processing method process, intelligent analysis and inspection of power equipment data are realized. This technical solution has the advantages of high efficiency, accuracy, reliability, etc., and is of great significance for improving the management level of power equipment.

[0029] The following details the present invention based on embodiments:

[0030] I. Data acquisition stage: Utilize the data acquisition function of the Dianhong Internet of Things operating system to obtain the operation data and environmental information of power equipment in real time.

[0031] In the intelligent inspection technology based on artificial intelligence and the Dianhong Internet of Things operating system, data acquisition is the basis and prerequisite of the entire process. This step mainly uses the distributed soft bus technology of the Dianhong Internet of Things operating system and intelligent sensors and cameras to achieve comprehensive monitoring of power equipment and its operating environment. The specific process can be realized as follows:

[0032] 1. Distributed soft bus technology: The Dianhong Internet of Things operating system adopts advanced distributed soft bus technology. This technology realizes intelligent interconnection between power equipment of different brands and types through a unified communication protocol and data exchange standard. This interconnection not only breaks the information islands between devices but also enables data to be transmitted to the central processing unit in real time and efficiently.

[0033] Example: Suppose a power system contains key equipment such as transformers and switchgear of various brands and models. Through the distributed soft bus technology of the Dianhong Internet of Things operating system, these devices can be seamlessly connected to the system to achieve data sharing and interoperability. When a certain transformer fails, its status information can be immediately captured by the system and transmitted to the central control room.

[0034] 2. Deployment of intelligent sensors and cameras: In order to collect the operation data and environmental information of power equipment in real time, intelligent sensors and cameras need to be deployed at key positions. These sensors and cameras can monitor key parameters such as current, voltage, temperature, and humidity in real time and transmit the data to the system.

[0035] Example: Install temperature sensors and humidity sensors inside the high-voltage switchgear to monitor the temperature and humidity changes inside the switchgear in real time. At the same time, install high-definition cameras outside the switchgear to monitor its appearance and operating status. When the temperature or humidity exceeds the preset threshold, the sensors will immediately send an alarm message to the system; when the camera captures abnormal behavior (such as illegal intrusion), it will also trigger an alarm.

[0036] Functions and beneficial effects:

[0037] 1. Achieve comprehensive monitoring: Through the deployment of distributed soft bus technology and intelligent sensors and cameras, comprehensive monitoring of power equipment and its operating environment can be achieved. This helps to detect equipment failures and abnormal situations in a timely manner and reduce the risk of failures.

[0038] 2. Improve data quality: The accuracy and real-time nature of data collection are the basis for subsequent data processing and analysis. By adopting advanced communication technologies and sensor technologies, it can be ensured that the collected data has high precision and real-time nature.

[0039] 3. Break information silos: Distributed soft bus technology breaks the information silos between power equipment of different brands and types, enabling data sharing and interoperability. This helps to build a more open and collaborative power Internet of Things ecosystem.

[0040] 4. Support intelligent decision-making: The real-time collected data provides strong support for subsequent intelligent analysis and early warning. Through data analysis, equipment failures and abnormal situations can be detected in a timely manner, providing a decision-making basis for management personnel and improving the operation and maintenance efficiency.

[0041] In summary, data collection plays a crucial role in the intelligent inspection technology based on artificial intelligence and the Dianhong Internet of Things operating system. By adopting advanced communication technologies and sensor technologies, comprehensive monitoring of power equipment and its operating environment can be achieved, providing strong support for subsequent data processing and analysis.

[0042] II. Data preprocessing stage: Clean, format convert, and normalize the collected data to ensure data quality.

[0043] In intelligent inspection technology, data preprocessing is an important bridge connecting data collection with subsequent feature extraction, model training, and other links. The main task of this step is to clean, format convert, and normalize the collected raw data to ensure the quality and consistency of the data and provide a reliable basis for subsequent intelligent analysis. The specific process can be achieved as follows:

[0044] 1. Data cleaning:

[0045] Remove noise and invalid data from the original data. Noise data may include outliers caused by sensor errors, data transmission errors, etc.; invalid data may include missing data due to equipment failures or communication interruptions.

[0046] Use methods such as statistical filtering and moving average filtering to remove noise data; for missing data, interpolation methods, regression prediction, etc. are used for filling according to the data characteristics.

[0047] Example: Suppose in the collected temperature data, the value at a certain moment suddenly jumps to an extremely high or low value, which is very likely noise data caused by sensor failures or data transmission errors. At this time, the moving average filtering method can be used to replace the data at this moment with the average value of the data at the previous and next several moments, thereby removing the noise.

[0048] 2. Format conversion:

[0049] Convert the original data into a format suitable for subsequent processing. Since different devices and different sensors may use different data formats and coding methods, format conversion is required to ensure data consistency.

[0050] According to the data format conversion rules, convert the original data into a unified format, such as JSON, XML, or a database table, etc.

[0051] Example: Suppose the data collected by a certain sensor is stored in binary format, while text format data is required for subsequent feature extraction and model training. At this time, a data format conversion program can be written to convert the binary data into text format, such as a CSV file.

[0052] 3. Normalization processing:

[0053] The task description is to convert the original data into numerical values with the same dimension and range to ensure the comparability between data. Normalization processing helps to speed up the model training speed and improve the accuracy and generalization ability of the model.

[0054] Common normalization methods include Min-Max normalization, Z-score normalization, etc. Min-Max normalization linearly transforms the original data into the range of [0,1] or [-1,1]; Z-score normalization is transformed according to the mean and standard deviation of the data, making the data have zero mean and unit variance.

[0055] Example: Suppose the collected current data range is [0A, 100A], and the voltage data range is [0V, 220V]. To perform normalization processing, the Min-Max normalization method can be used to convert the current data and voltage data into numerical values within the range of [0,1] respectively.

[0056] Functions and beneficial effects:

[0057] 1. Improve data quality: By performing data cleaning and format conversion, noise and invalid data in the original data can be removed, ensuring the accuracy and consistency of the data.

[0058] 2. Accelerate the model training speed: Normalization processing converts the original data into numerical values with the same dimension and range, which helps to accelerate the model training speed.

[0059] 3. Improve the model accuracy: Normalization processing enhances the comparability between data, which helps to improve the accuracy and generalization ability of the model.

[0060] 4. Provide a reliable basis for subsequent analysis: The preprocessed data has high quality and consistency, providing a reliable basis for subsequent feature extraction, model training, and other processes.

[0061] In summary, data preprocessing plays a crucial role in intelligent patrol inspection technology. By adopting appropriate data cleaning, format conversion, and normalization processing methods, the quality and consistency of data can be ensured, providing a reliable basis for subsequent intelligent analysis.

[0062] III. Feature extraction stage: Utilize artificial intelligence technology to extract key information from data, providing a basis for intelligent analysis.

[0063] Feature extraction is a key link in intelligent patrol inspection technology. It uses artificial intelligence technology to extract key information useful for equipment status recognition from the preprocessed data. These features can reflect the operating status, abnormal patterns, etc. of the equipment, providing an important basis for subsequent intelligent analysis and early warning.

[0064] In the process of feature extraction, common methods include but are not limited to principal component analysis (PCA), linear discriminant analysis (LDA), independent component analysis (ICA), and deep learning-based methods (such as autoencoders, convolutional layers in convolutional neural networks, etc.). These methods can extract low-dimensional, compact, and informative feature representations from the original data.

[0065] Principal component analysis (PCA): PCA is a linear dimensionality reduction technique that extracts features by calculating the principal components of the data (i.e., the directions with the largest data variance). In power patrol inspection, PCA can be used to extract the principal component features that best reflect the changes in equipment status from multiple sensor data.

[0066] Linear Discriminant Analysis (LDA): LDA is a supervised dimensionality reduction technique that aims to find a linear transformation that makes the projected points of samples of the same class as close as possible, while the projected points of samples of different classes are as far apart as possible. In power line inspections, LDA can be used to extract linear features that can distinguish between normal and abnormal states.

[0067] Independent Component Analysis (ICA): ICA is a statistical and computational technique used to extract statistically independent components from multivariate data. In power line inspections, ICA can be used to separate independent signal sources from complex sensor data, thereby extracting useful features.

[0068] Deep learning-based methods: Deep learning models (such as autoencoders, convolutional neural networks, etc.) can automatically learn and extract hierarchical feature representations from raw data. In power line inspections, these models can be used to extract features useful for device status recognition from data such as images and videos.

[0069] Example: Suppose in a power line inspection, we collect data from multiple sensors such as current, voltage, and temperature of a certain power device. To extract useful features, we can use the PCA method for processing. First, we form a matrix with this data, where each row represents the sensor data at a certain time point, and each column represents the data of a sensor. Then, we calculate the covariance matrix of this matrix and find the eigenvalues and eigenvectors of the covariance matrix. Finally, we select the first few eigenvectors with the largest eigenvalues as the principal components and project the original data onto these principal components to obtain the dimensionality-reduced feature representation. These feature representations can reflect the operating status and abnormal patterns of the device, providing an important basis for subsequent intelligent analysis and early warning.

[0070] Functions and beneficial effects:

[0071] Feature extraction plays an important role in intelligent inspection technology. It can extract key information useful for device status recognition from raw data. Through feature extraction, we can reduce the dimensionality of the data, reduce the computational amount, improve the training speed and recognition accuracy of the model. At the same time, feature extraction can also remove redundant information in the raw data, improving the interpretability and visualization effect of the data. In power line inspections, feature extraction helps to detect abnormal states of devices in a timely manner, prevent potential safety hazards, and improve the inspection efficiency and accuracy.

[0072] In summary, feature extraction is a crucial step in intelligent inspection technology. It uses artificial intelligence technology to extract useful feature representations from preprocessed data, providing an important basis for subsequent intelligent analysis and early warning. By adopting appropriate feature extraction methods, we can improve the recognition accuracy and efficiency of the model, providing more reliable and intelligent support for power inspection.

[0073] IV. Model Training Stage: Build an intelligent analysis model and use historical data for training and optimization.

[0074] In intelligent inspection technology, model training is a key step in building an efficient and accurate analysis model. This step is mainly based on deep learning technology. By constructing a suitable neural network model and using historical data and label information to train and optimize the model, it can accurately identify the status and abnormalities of power equipment. The specific process can be achieved as follows:

[0075] ① Model Construction:

[0076] 1. Select a neural network model: According to the specific requirements of the inspection task and the characteristics of the data, select a suitable neural network model. For example, for the analysis of image data, a convolutional neural network (CNN) can be selected; for the analysis of time series data, a recurrent neural network (RNN) or its variants, such as long short-term memory network (LSTM), gated recurrent unit (GRU), etc., can be selected.

[0077] 2. Design the network structure: Determine the number of layers of the neural network, the number of neurons in each layer, activation functions and other parameters. The selection of these parameters will directly affect the performance of the model. For example, in CNN, the feature extraction ability of the model can be improved by increasing the number of convolutional layers; in RNN, the processing ability of the model for time series data can be enhanced by increasing the number of hidden layers.

[0078] ② Data Preparation:

[0079] 1. Data division: Divide the historical data into a training set, a validation set and a test set. The training set is used to train the model, the validation set is used to adjust the hyperparameters of the model, and the test set is used to evaluate the performance of the model.

[0080] 2. Data augmentation: To improve the generalization ability of the model, the training data can be augmented. For example, in image data, more training samples can be generated through operations such as rotation, scaling, and cropping; in time series data, the diversity of data can be increased by adding noise and adjusting the time step.

[0081] ③ Model Training and Optimization:

[0082] 1. Define the loss function: According to the specific requirements of the task, select an appropriate loss function to measure the difference between the predicted results of the model and the actual labels. For example, in a classification task, the cross-entropy loss function can be selected; in a regression task, the mean squared error loss function can be selected, etc.

[0083] 2. Select an optimization algorithm: According to the complexity of the model and the data scale, select an appropriate optimization algorithm to update the model's parameters. Commonly used optimization algorithms include Stochastic Gradient Descent (SGD), Adam, etc.

[0084] 3. Training process: Input the training data into the neural network model, calculate the predicted results through forward propagation, then use the loss function to calculate the difference between the predicted results and the actual labels, and update the model's parameters through the backpropagation algorithm. Repeat this process until the performance of the model reaches the optimal on the validation set or no longer improves significantly.

[0085] Example: Suppose we are performing intelligent analysis on the temperature data of power equipment to identify whether the equipment is overheated. We can choose to build an LSTM-based neural network model to process time series data. First, we divide the historical temperature data into training set, validation set, and test set. Then, we perform augmentation on the training data, such as adding noise to simulate temperature fluctuations in the actual environment. Next, we define the cross-entropy loss function as the loss function of the model and select the Adam optimization algorithm to update the model's parameters. Finally, we input the training data into the LSTM model for training and optimization. During the training process, we can evaluate the performance of the model by observing the loss function value on the validation set and adjust the hyperparameters of the model to obtain better performance.

[0086] Functions and beneficial effects:

[0087] Model training plays a crucial role in intelligent inspection technology. By building a suitable neural network model and using historical data for training and optimization, we can obtain an intelligent analysis model that can accurately identify the status and anomalies of power equipment. This model can monitor the operating status of the equipment in real time, discover potential safety hazards in a timely manner, and provide timely warning information for inspection personnel. At the same time, through the data visualization and report generation functions, managers can intuitively understand the status information of the equipment and provide support for decision-making. Therefore, model training is one of the key steps to achieve efficient and accurate inspection in intelligent inspection technology.

[0088] V. Intelligent analysis and warning stage: Input the real-time data into the model for analysis, identify the equipment status and anomalies, and trigger the warning mechanism.

[0089] In intelligent inspection technology, intelligent analysis and early warning are the core links, which are directly related to the inspection efficiency and the timeliness of fault handling. This step mainly includes two key sub-steps: real-time data analysis and triggering of the early warning mechanism.

[0090] 1. Real-time data analysis:

[0091] Input data: The operation data of power equipment and environmental information collected in real time, such as current, voltage, temperature, humidity, etc., are input into the trained deep learning model after preprocessing.

[0092] Model analysis: Based on the deep learning model obtained from the aforementioned model training, such as convolutional neural network (CNN) or recurrent neural network (RNN), intelligent analysis is performed according to the input data. These models can identify patterns, anomalies or trends in the data, thereby judging the current state of the equipment.

[0093] Result output: The result of the model analysis is one or more metrics, which reflect the health status of the equipment or potential fault risks.

[0094] 2. Triggering of the early warning mechanism:

[0095] Threshold setting: A series of thresholds are preset in the system, which represent the normal range of the equipment state or the acceptable degree of abnormality.

[0096] Comparison and judgment: Compare the result of the model analysis with the preset threshold. If the analysis result exceeds the threshold range, it indicates that the equipment may have a fault or anomaly.

[0097] Early warning information generation: Once a fault or anomaly is detected, the system immediately generates early warning information, including detailed information such as equipment name, fault type, severity, etc.

[0098] Information sending: The early warning information is sent to the inspection personnel or relevant management personnel in real time through the communication function of the Dianhong Internet of Things operating system, so that they can take measures in time.

[0099] Example: Suppose that during the operation of a transformer in a power substation, its temperature data abnormally rises. The temperature data collected in real time by intelligent sensors is input into the trained CNN model after preprocessing. The model analysis result shows that the temperature of the transformer has exceeded the preset safety threshold. At this time, the system immediately triggers the early warning mechanism, generates an early warning message containing information such as the transformer name, temperature anomaly, and recommended inspection, and sends it to the inspection personnel via text message or email. After receiving the early warning, the inspection personnel quickly go to the site for inspection, find that the transformer cooling system has failed, and repair it in time, thus avoiding a possible major accident.

[0100] Functions and Beneficial Effects:

[0101] Intelligent analysis and early warning play a crucial role in intelligent inspection technology. It can not only monitor the operating status of equipment in real time, detect potential fault risks in a timely manner, but also automatically trigger the early warning mechanism and send the early warning information to relevant personnel in real time, thus greatly improving the inspection efficiency and the timeliness of fault handling. In addition, through intelligent analysis means, it can also reduce the subjectivity and errors of human judgment and improve the accuracy and reliability of inspection results. This is of great significance for ensuring the stable operation of the power system, reducing fault losses, and improving the overall operation efficiency.

[0102] VI. Data Visualization and Report Generation Phase: Display the analysis results in a visual form and automatically generate inspection reports.

[0103] In the final stage of intelligent inspection technology, data visualization and report generation play a crucial role. This step not only helps managers intuitively understand the equipment status, but also provides them with decision-making support. The specific process can be achieved as follows:

[0104] 1. Data Visualization:

[0105] Visualization Tools: Utilize the data visualization tools built into the DH IoT operating system, such as dashboards, line charts, bar charts, pie charts, etc., to display the analysis results in a graphical way.

[0106] Real-time Update: The visualization interface can update data in real time to ensure that managers always see the latest equipment status information.

[0107] Interactive Functions: Provide interactive functions such as data filtering, zooming in, zooming out, dragging, etc., enabling managers to explore data more deeply and discover potential problems or trends.

[0108] Multi-dimensional Display: Display data from multiple dimensions (such as time, equipment type, geographical location, etc.) according to requirements to help managers comprehensively understand the equipment status.

[0109] 2. Report Generation:

[0110] Template Design: Design a standardized inspection report template, including key contents such as equipment basic information, operating status, abnormal information, early warning records, etc.

[0111] Automatic Generation: Automatically generate inspection reports according to the analysis results. The report content is accurate and comprehensive without manual intervention.

[0112] Export Function: Provide multiple export formats (such as PDF, Excel, Word, etc.) for the convenience of managers to use in different scenarios.

[0113] Historical records: Save historical report records to facilitate trend analysis and comparison by management personnel.

[0114] Example: Suppose the management personnel of a power company are using intelligent inspection technology based on artificial intelligence and the Dianhong Internet of Things operating system. On the data visualization interface, he can see a real-time dashboard that displays key parameters such as current, voltage, and temperature of each substation. By clicking on a substation, he can further view the detailed data of that substation, including historical trend charts, abnormal records, etc. In addition, the system automatically generates an inspection report that details the status of each device, abnormal information, and warning records. Based on the report content, management personnel can take timely measures to ensure the stable operation of the power system.

[0115] Functions and beneficial effects:

[0116] Data visualization and report generation play an important role in intelligent inspection technology. Through intuitive graphical displays and standardized report generation, management personnel can more quickly understand the device status, identify potential problems, and make decisions. This not only improves the inspection efficiency but also reduces the risk of human errors. In addition, historical report records also provide a basis for trend analysis and comparison for management personnel, helping them better plan future inspection work. Generally speaking, data visualization and report generation are an indispensable part of intelligent inspection technology, and they provide strong support for the stable operation of the power system.

[0117] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included within the protection scope of the present application.

Claims

1. An intelligent inspection technology based on artificial intelligence and the Dianhong Internet of Things operating system, characterized in that, It includes the following steps: Data collection: Utilize the distributed soft bus technology of the Dianhong Internet of Things operating system to achieve intelligent interconnection and data communication between power equipment, and deploy intelligent sensors and cameras to collect the operation data and environmental information of power equipment in real time; Data preprocessing: Clean, convert the format, and normalize the collected raw data; Feature extraction: Utilize artificial intelligence technology to extract features from the preprocessed data; Model training: Construct an intelligent analysis model based on deep learning, and use historical data and label information to train and optimize the model; Intelligent analysis and early warning: Input the real-time collected data into the trained model for analysis, identify equipment failures and anomalies, and automatically trigger the early warning mechanism; Data visualization and report generation: Utilize the data visualization function of the Dianhong Internet of Things operating system to display the analysis results and automatically generate inspection reports.

2. The intelligent inspection technology according to claim 1, characterized in that, In the data collection step, the distributed soft bus technology is used to achieve intelligent interconnection and data communication between power equipment of different brands and types.

3. The intelligent inspection technology according to claim 1, wherein In the data preprocessing step, noise and invalid data are removed to ensure the consistency and comparability of the data.

4. The intelligent patrol inspection technology according to claim 1, characterized in that, In the feature extraction step, artificial intelligence technology methods including but not limited to principal component analysis (PCA), linear discriminant analysis (LDA), etc. are used for feature extraction.

5. The intelligent inspection technology according to claim 1, wherein In the model training step, the constructed intelligent analysis model includes deep learning models including but not limited to convolutional neural network (CNN), recurrent neural network (RNN), etc.

6. The intelligent inspection technology according to claim 1, characterized in that, In the intelligent analysis and early warning step, equipment failures and anomalies are identified according to the analysis results, and early warning information is automatically sent to the inspection personnel through a preset early warning mechanism.

7. The intelligent inspection technology according to claim 1, wherein In the data visualization and report generation step, the analysis results are intuitively displayed in forms such as charts and curves, and the generated inspection reports include key information such as equipment status, abnormal information, and early warning records.

8. The intelligent inspection technology according to claim 1, wherein It also includes a data security management module for ensuring the security and privacy protection of data during the processes of data collection, processing, analysis, and report generation.

9. The intelligent patrol inspection technology according to claim 1, wherein The intelligent sensors and cameras are deployed at key parts of the power equipment to achieve real-time monitoring of the equipment operation data and environmental information.

10. The intelligent inspection technology according to claim 1, characterized in that, The intelligent analysis model can continuously learn and optimize according to historical data and label information to improve the accuracy of equipment status and anomaly identification.

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