New energy power transmission status monitoring method and system based on artificial intelligence algorithm

Abstract

The present invention provides a method and system for monitoring the state of renewable energy power transmission based on an artificial intelligence algorithm, belonging to the field of power system automation technology. The method comprises: collecting power transmission data in real time through multiple sensors deployed in a renewable energy power transmission network; inputting the power transmission data collected by each sensor into a pre-trained artificial intelligence model to perform feature extraction and pattern recognition in sequence to analyze the operating state of the renewable energy power transmission network and obtain analysis results; dynamically adjusting the model parameters of the pre-trained artificial intelligence model based on the analysis results; performing multiple rounds of optimization on the dynamically adjusted artificial intelligence model until the prediction accuracy verification result of the latest artificial intelligence model reaches the optimal value, and using the corresponding latest artificial intelligence model to analyze the operating state of the renewable energy power transmission network. This achieves the purpose of accurately monitoring and predicting the state of the power transmission system.

Description

Technical Field

[0001] The present invention relates to the field of power system automation technology, and specifically to a new energy power transmission status monitoring method based on an artificial intelligence algorithm, a new energy power transmission status monitoring system based on an artificial intelligence algorithm, a machine-readable storage medium and an electronic device. Background Art

[0002] Currently, applying artificial intelligence algorithms to monitoring the status of renewable energy power transmission presents the challenge of ensuring that these algorithms accurately monitor and predict the status of the power transmission system within the complex and ever-changing renewable energy power transmission environment. The intermittent and uncertain nature of renewable energy generation (such as wind and solar power) poses significant challenges to the stable operation of the power system. Furthermore, the power transmission network itself is a highly complex system with numerous nonlinear relationships and interactions within it.

[0003] Therefore, it is very difficult to develop algorithms that can maintain high accuracy and robustness in the complex and changing renewable energy power transmission environment. Therefore, how to ensure that artificial intelligence algorithms can achieve accurate status assessment and fault prediction in the complex and changing renewable energy power transmission environment is an urgent problem that needs to be solved. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a method and system for monitoring the status of new energy power transmission based on an artificial intelligence algorithm, so as to at least solve the above-mentioned problem of how to ensure that the artificial intelligence algorithm can achieve accurate status assessment and fault prediction in a complex and changeable new energy power transmission environment.

[0005] To achieve the above objectives, the present invention provides, in a first aspect, a method for monitoring the transmission status of renewable energy power based on an artificial intelligence algorithm, comprising:

[0006] Real-time power transmission data is collected through multiple sensors pre-deployed in the new energy power transmission network;

[0007] The power transmission data collected by each sensor is input into the pre-trained artificial intelligence model to perform feature extraction and pattern recognition in sequence to analyze the operating status of the new energy power transmission network and obtain analysis results;

[0008] Based on the analysis results, the model parameters of the pre-trained artificial intelligence model are dynamically adjusted;

[0009] The dynamically adjusted artificial intelligence model is optimized in multiple rounds until the verification result of the prediction accuracy of the latest artificial intelligence model reaches the optimal value, and the corresponding latest artificial intelligence model is used to analyze the operating status of the new energy power transmission network.

[0010] Optionally, the deployment rules of the above sensors include:

[0011] Determine a sensor deployment plan based on historical operating data of the renewable energy power transmission network; the sensor deployment plan includes the sensor type, sensor deployment location, and data collection frequency of each deployed sensor;

[0012] The collection rules for power transmission data include:

[0013] Based on the data collection frequency of each sensor, a data collection instruction is sent to each sensor, so that each sensor collects power transmission data according to the corresponding data collection frequency;

[0014] The collected power transmission data is preprocessed; wherein the preprocessing includes at least filtering and denoising.

[0015] Optionally, the power transmission data collected by each sensor is input into a pre-trained artificial intelligence model for feature extraction and pattern recognition, including:

[0016] Adjust the input layer parameters of the pre-trained artificial intelligence model based on the statistical characteristics of power transmission data;

[0017] Extract key features from power transmission data using an AI model with adjusted input layer parameters;

[0018] Classify and identify key features in the extracted power transmission data.

[0019] Optionally, the statistical characteristic information of the power transmission data includes the average value μ and variance σ of the power transmission data. 2 ;

[0020] The above adjustment of the input layer parameters of the pre-trained artificial intelligence model based on the statistical characteristics of the power transmission data includes:

[0021] Calculate the mean μ and variance σ of power transmission data 2 ;

[0022] When the variance of power transmission data σ 2 When the variance is greater than the preset threshold T, the weight parameters of each input layer of the artificial intelligence model are adjusted;

[0023] Recalculate the input feature vector of the artificial intelligence model based on the adjusted weight parameters of each input layer of the artificial intelligence model;

[0024] The recalculated input feature vector of the artificial intelligence model is input into the artificial intelligence model to determine the input layer parameters of the adjusted artificial intelligence model.

[0025] Optionally, the above-mentioned recalculation of the input feature vector of the artificial intelligence model based on the adjusted weight parameters of each input layer of the artificial intelligence model includes:

[0026] Normalize the weight parameters of each input layer of the adjusted artificial intelligence model;

[0027] Calculate the importance coefficient of each feature of the artificial intelligence model based on the normalized weight parameters;

[0028] The features of the artificial intelligence model whose importance coefficient is greater than the preset coefficient threshold η are taken as key features, an input feature vector containing the key features is constructed, and the input feature vector is input into the artificial intelligence model.

[0029] Optionally, the features of the artificial intelligence model whose importance coefficient is greater than a preset coefficient threshold η are taken as key features, and an input feature vector containing the key features is constructed, including:

[0030] Filter out all key features whose importance coefficient is greater than the preset coefficient threshold η;

[0031] The key features are sorted from high to low according to their importance coefficients, and an input feature vector containing the sorted key features is constructed.

[0032] Optionally, the pattern recognition process includes an abnormal pattern recognition process, the analysis result includes a result of the abnormal pattern recognition process, and the result of the abnormal pattern recognition process includes a result of determining the presence of an abnormal pattern and a result of determining the absence of an abnormal pattern;

[0033] Based on the analysis results, the model parameters of the pre-trained AI model are dynamically adjusted, including:

[0034] Determine the direction of adjusting model parameters based on the results of the abnormal pattern recognition process;

[0035] The model parameters are updated according to the adjustment direction, and a dynamically adjusted artificial intelligence model is obtained based on the updated model parameters.

[0036] Optionally, during multiple rounds of optimization of the dynamically adjusted artificial intelligence model, each time the dynamic adjustment of the artificial intelligence model is completed, it is necessary to perform a prediction accuracy verification of the corresponding artificial intelligence model; wherein,

[0037] The rules for validating the prediction accuracy of AI models include:

[0038] The power transmission data collected by each sensor is input into the dynamically adjusted artificial intelligence model to re-perform feature extraction and pattern recognition;

[0039] Based on the results of re-feature extraction and pattern recognition, verify whether the prediction accuracy of the dynamically adjusted artificial intelligence model is higher than the prediction accuracy of the pre-trained artificial intelligence model that has not been dynamically adjusted.

[0040] Optionally, the power transmission data collected by each sensor is input into a pre-trained artificial intelligence model to perform feature extraction and pattern recognition in sequence to analyze the operating status of the new energy power transmission network, including:

[0041] Extracting feature vectors associated with voltage and / or current from power transmission data and inputting the feature vectors into a pre-trained artificial intelligence model for preliminary analysis;

[0042] Based on the preliminary analysis results, determine whether there are any unusual patterns;

[0043] When it is determined that an abnormal pattern exists, the abnormal type is identified.

[0044] Optionally, the above determination of whether an abnormal pattern exists based on the preliminary analysis results may include:

[0045] Perform statistics on abnormal patterns in the preliminary analysis results, and calculate the probability P of the abnormal pattern based on the statistical results;

[0046] The probability P of the abnormal pattern occurring is compared with the preset probability threshold T1. If the probability P of the abnormal pattern occurring is greater than the preset probability threshold T1, it is determined that the abnormal pattern exists; otherwise, it is determined that the abnormal pattern does not exist.

[0047] Optionally, the above-mentioned comparison of the probability P of the abnormal pattern occurring with a preset probability threshold T1, if the probability P of the abnormal pattern occurring is greater than the preset probability threshold T1, then it is determined that the abnormal pattern exists; otherwise, it is determined that the abnormal pattern does not exist, includes:

[0048] Calculate the difference ΔP between the probability P of the abnormal pattern occurring and the preset probability threshold T1;

[0049] It is determined whether the difference ΔP is greater than zero. If the difference ΔP is greater than zero, it is determined that an abnormal pattern exists; otherwise, it is determined that no abnormal pattern exists.

[0050] A second aspect of the present invention provides a new energy power transmission status monitoring system based on an artificial intelligence algorithm, comprising:

[0051] The power transmission data acquisition module is used to collect power transmission data in real time through multiple sensors pre-deployed in the new energy power transmission network;

[0052] The operating status analysis module is used to input the power transmission data collected by each sensor into the pre-trained artificial intelligence model to perform feature extraction and pattern recognition in sequence to analyze the operating status of the new energy power transmission network and obtain analysis results;

[0053] A dynamic adjustment module is used to dynamically adjust the model parameters of the pre-trained artificial intelligence model based on the analysis results;

[0054] The prediction accuracy verification module is used to perform multiple rounds of optimization on the dynamically adjusted artificial intelligence model until the verification result of the prediction accuracy of the latest artificial intelligence model reaches the optimal one, and use the corresponding latest artificial intelligence model to analyze the operating status of the new energy power transmission network.

[0055] In a third aspect of the present invention, a machine-readable storage medium is provided, on which instructions are stored. When the instructions are executed by a processor, the processor is configured to execute the above-mentioned new energy power transmission status monitoring method based on artificial intelligence algorithm.

[0056] In a fourth aspect of the present invention, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for monitoring the new energy power transmission status based on the artificial intelligence algorithm is implemented.

[0057] The above-mentioned technical solution provides a method and system for monitoring the status of renewable energy power transmission based on an artificial intelligence algorithm. Multiple sensors are deployed in the renewable energy power transmission network to collect real-time power transmission data. Based on this data, a pre-trained artificial intelligence model is used to extract features and perform pattern recognition to analyze the operating status of the power transmission system (i.e., the renewable energy power transmission network). Based on the analysis results of the artificial intelligence model, model parameters are dynamically adjusted to adapt to the complex and changing power transmission environment, improving monitoring accuracy. The prediction accuracy of the artificial intelligence model is regularly verified. A feedback mechanism continuously optimizes the artificial intelligence model, ensuring that it accurately monitors and predicts the operating status of the renewable energy power transmission network in a complex and changing renewable energy power transmission environment. This ensures that the artificial intelligence algorithm can accurately monitor and predict the status of the power transmission system in this complex and changing renewable energy power transmission environment.

[0058] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present invention, but do not constitute a limitation of the embodiments of the present invention. In the accompanying drawings:

[0060] Figure 1 This is a flow chart of a method for monitoring the state of new energy power transmission based on an artificial intelligence algorithm, provided in one embodiment of the present invention;

[0061] Figure 2 This is a flow chart of another method for monitoring the state of new energy power transmission based on an artificial intelligence algorithm provided by one embodiment of the present invention;

[0062] Figure 3 This is a flow chart of deploying multiple sensors in a new energy power transmission network to collect power transmission data in real time, provided by an embodiment of the present invention;

[0063] Figure 4 This is a flowchart of performing feature extraction and pattern recognition using a pre-trained artificial intelligence model, provided by one embodiment of the present invention;

[0064] Figure 5 is a flowchart of adjusting the input layer parameters of a model provided by one embodiment of the present invention;

[0065] Figure 6 is a flow chart of recalculating the input feature vector of the model provided by one embodiment of the present invention;

[0066] Figure 7 is a flow chart for determining key features provided by one embodiment of the present invention;

[0067] Figure 8 A flowchart of an embodiment of the present invention for analyzing the operating status of a power transmission system by using a pre-trained artificial intelligence model for feature extraction and pattern recognition;

[0068] Figure 9 is a flow chart for determining whether an abnormal pattern exists, provided by one embodiment of the present invention;

[0069] Figure 10 This is a flow chart based on the comparison between the probability P and the preset probability threshold T1 provided by one embodiment of the present invention;

[0070] Figure 11 This is a flow chart for determining whether the difference ΔP is greater than zero, provided by one embodiment of the present invention;

[0071] Figure 12 is a flow chart of updating model parameters based on the results of an abnormal pattern recognition process provided by one embodiment of the present invention;

[0072] Figure 13 This is a block diagram of a new energy power transmission status monitoring system based on artificial intelligence algorithm provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0073] The following describes the specific embodiments of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention and are not intended to limit the present invention.

[0074] Figure 1 This is a flow chart of a new energy power transmission status monitoring method based on artificial intelligence algorithm provided by one embodiment of the present invention. Figure 1 As shown, an embodiment of the present invention provides a new energy power transmission status monitoring method based on an artificial intelligence algorithm, comprising:

[0075] S110: Collects power transmission data in real time through multiple sensors pre-deployed in the new energy power transmission network;

[0076] S120: Inputting the power transmission data collected by each sensor into a pre-trained artificial intelligence model to perform feature extraction and pattern recognition in sequence to analyze the operating status of the new energy power transmission network and obtain analysis results;

[0077] S130: Dynamically adjust the model parameters of the pre-trained artificial intelligence model based on the analysis results;

[0078] S140: Perform multiple rounds of optimization on the dynamically adjusted artificial intelligence model until the verification result of the prediction accuracy of the latest artificial intelligence model reaches the optimal value, and use the corresponding latest artificial intelligence model to analyze the operating status of the new energy power transmission network.

[0079] Specifically, this method deploys multiple sensors within the renewable energy power transmission network to collect real-time power transmission data. Based on this data, a pre-trained artificial intelligence model is used to extract features and perform pattern recognition to analyze the operating status of the power transmission system (i.e., the renewable energy power transmission network). Based on the AI ​​model's analysis results, model parameters are dynamically adjusted to adapt to the complex and changing power transmission environment, improving monitoring accuracy. The AI ​​model's prediction accuracy is regularly verified. A feedback mechanism continuously optimizes the AI ​​model, ensuring that it accurately monitors and predicts the operating status of the renewable energy power transmission network within this complex and changing renewable energy power transmission environment. This ensures that the AI ​​algorithm can accurately monitor and predict the status of the power transmission system within this complex and changing renewable energy power transmission environment.

[0080] Please refer to Figure 2, Figure 2 This is a flowchart of another method for monitoring the transmission status of new energy power based on an artificial intelligence algorithm provided by one embodiment of the present invention. The actual use process steps of the method for monitoring the transmission status of new energy power based on an artificial intelligence algorithm are as follows:

[0081] S101: Real-time power transmission data is collected by deploying multiple high-precision sensors at key nodes in the renewable energy power transmission network. To ensure comprehensive capture of various status information during power transmission, these sensors are carefully placed at different locations in the network, including but not limited to key links such as power stations, substations, transmission lines, and distribution networks. For example, in a wind farm, in addition to installing sensors near wind turbines to monitor environmental factors such as wind speed, temperature, and humidity, current and voltage sensors are also installed at key points on the transmission lines, and vibration sensors are installed in substations to monitor the health of the equipment. These sensors not only provide real-time data streams, but also transmit the data to the central processing unit via wireless or wired means for further analysis and processing. In this way, the acquired data can be ensured to be both comprehensive and timely, laying a solid foundation for the subsequent application of artificial intelligence algorithms.

[0082] S102: Analyze the operating status of the power transmission system by extracting features and recognizing patterns from the collected power transmission data using pre-trained AI models. This stage begins with selecting an appropriate AI model, such as a convolutional neural network (CNN), a recurrent neural network (RNN), or a long short-term memory (LSTM). These models, trained on extensive historical data, can automatically learn and extract important features of the power transmission process. For example, the LSTM model can capture changing trends in power load from time series data, while the CNN excels at extracting features from image data, such as detecting overheating of power equipment by analyzing infrared thermal images. By analyzing these features, the model can identify normal and abnormal operating modes of the power system, thereby promptly identifying potential problems. Furthermore, expert knowledge can be incorporated to fine-tune the model to better suit the needs of actual application scenarios. This series of operations not only improves monitoring accuracy but also reduces false alarms, ensuring the stable operation of the power system.

[0083] S103: Improve monitoring accuracy by dynamically adjusting model parameters based on the analysis results of the artificial intelligence model to adapt to the complex and changing power transmission environment. Since the new energy power transmission environment is affected by many factors, such as weather changes, grid load fluctuations, etc., it is necessary to continuously adjust the model parameters to cope with these changes. For example, during the high temperature period in summer, the demand for electricity may surge. At this time, the learning rate of the model can be increased to adapt to this change more quickly; while in the low temperature conditions in winter, the learning rate may need to be reduced to avoid overfitting the current low load state. In addition, an online learning mechanism can be introduced to enable the model to continuously learn new data samples during operation, so as to better adapt to new situations that may arise in the future. In this way, high monitoring accuracy can be maintained even in complex environments.

[0084] S104: By regularly verifying the prediction accuracy of the artificial intelligence model and continuously optimizing the model through a feedback mechanism, ensure that it accurately monitors and predicts the system status in the complex and changeable renewable energy power transmission environment. In order to ensure the long-term effectiveness of the model, its performance indicators, such as accuracy and recall rate, need to be regularly evaluated and compared with the actual power transmission situation. For example, a comprehensive performance evaluation can be set up every quarter, and a rapid evaluation can be carried out immediately after each major event, such as a large-scale power outage caused by a natural disaster. If the model is found to perform poorly, measures need to be taken to optimize it, which may include retraining the model, adjusting hyperparameters, or introducing new feature variables. In addition, a closed-loop feedback system can be established to incorporate user feedback information into the model improvement process. This will not only improve the robustness of the model, but also enhance its adaptability in the face of unknown challenges.

[0085] The above steps effectively address the problem of ensuring that AI algorithms accurately monitor and predict system status in a complex and volatile renewable energy power transmission environment. This approach not only improves the safety and reliability of power systems but also promotes the effective use of renewable energy, laying the foundation for a more intelligent and efficient energy system.

[0086] Please refer to Figure 3 , Figure 3 This is a flowchart of deploying multiple sensors in a new energy power transmission network to collect power transmission data in real time, provided by one embodiment of the present invention. The specific steps for deploying multiple sensors in a new energy power transmission network to collect power transmission data in real time are as follows:

[0087] S201: Select appropriate sensor types and determine their deployment locations: Based on the characteristics of the power transmission network and monitoring requirements, select sensor types that can accurately measure key parameters such as current, voltage, and temperature. For example, Hall-effect sensors can be used to measure current, while fiber-optic temperature sensors can be used to monitor cable temperature. Furthermore, optimal sensor deployment locations should be determined based on the power line layout and the importance of key nodes to ensure comprehensive coverage of key power transmission links.

[0088] S202: Install sensors and configure data collection frequency: Sensors are installed at selected locations. The data collection frequency is appropriately set based on the operating characteristics of the power transmission system and the required monitoring accuracy. For example, for high-voltage transmission lines, a higher data collection frequency, such as once per second, can be set to detect abnormalities promptly. For low-voltage distribution networks, a lower data collection frequency, such as once per minute, can be appropriately adjusted based on actual needs.

[0089] S203: Activate the sensors to collect data and transmit the data to the central processing unit via wireless or wired means: After completing sensor installation and configuration, activate the sensors to begin data collection. The collected data can be transmitted to the central processing unit via wireless communication modules such as Wi-Fi, 4G / 5G networks, or wired connections such as optical fiber or Ethernet cables. For example, low-power Bluetooth technology can be used to transmit sensor data to a nearby gateway device, which then uploads the data to a cloud server via a 4G network.

[0090] S204: Preprocessing the collected raw data: To improve the accuracy and efficiency of data analysis, the collected raw data needs to be preprocessed. This includes, but is not limited to, filtering to remove noise interference, data smoothing, and outlier detection. For example, a digital filter such as a Kalman filter can be used to reduce random noise in the signal, ensuring the reliability of subsequent analysis results.

[0091] Please refer to Figure 4 , Figure 4 This is a flowchart of a method for performing feature extraction and pattern recognition using a pre-trained artificial intelligence model, provided by one embodiment of the present invention. The specific steps of the method for monitoring the transmission status of new energy power based on artificial intelligence algorithms are as follows:

[0092] S301: Collecting Power Transmission Data and Inputting it into a Pre-trained AI Model: First, various data is collected from the power transmission system in real time or periodically. This data may include factors such as current, voltage, and power. This raw data is then input into a pre-trained AI model. This AI model can be a deep learning network or other machine learning model designed to process the specific type of data from the power system.

[0093] S302: Adjust model input layer parameters to match the statistical characteristics of power transmission data: Before the AI ​​model receives data, the model's input layer parameters are adjusted based on the statistical characteristics of the power transmission data, such as mean and variance. This step ensures that the AI ​​model can more accurately understand the input data and effectively extract meaningful features. For example, if the voltage in the dataset fluctuates significantly, the model may need to be adjusted to better capture this variation.

[0094] S303: Extracting Key Features from Power Transmission Data Using an AI Model: The tuned AI model processes the input data and extracts key features. These features may include, but are not limited to, anomaly detection metrics and equipment health indicators. This process relies on the model's internal structure and algorithms. For example, a convolutional neural network (CNN) can be used to identify patterns in images, while a recurrent neural network (RNN) is suitable for analyzing time series data.

[0095] S304: Classify and identify the extracted features using pattern recognition techniques: Finally, pattern recognition techniques are used to further analyze and classify the extracted key features. This can involve the application of clustering algorithms, support vector machines (SVMs), decision trees, and other techniques to identify different states or potential problems in the power transmission system. For example, by comparing the features of normal operation with the currently extracted features, a fault or abnormality can be determined.

[0096] Please refer to Figure 5 , Figure 5 This is a flowchart of adjusting the input layer parameters of the model provided by one embodiment of the present invention. The specific steps of adjusting the model input layer parameters in the new energy power transmission status monitoring method based on artificial intelligence algorithm are as follows:

[0097] S401: Determine whether the weight parameters of the model input layer need to be adjusted by calculating the statistical characteristics of the power transmission data. First, collect the power transmission data within a certain time window and calculate the average value μ and variance σ of these data. 2 These statistical characteristics can reflect the central tendency and fluctuation of power transmission data, and thus help determine the stability and reliability of the data. 2 If it is greater than the preset threshold T, it is considered that the current data has large volatility, which may affect the accuracy of the artificial intelligence model, so the weight parameters of the model input layer need to be adjusted.

[0098] S402: Adjust the weight parameters of the model input layer to adapt to changes in the power transmission data. When the variance of the power transmission data exceeds a preset threshold T, the method adjusts the weight parameters of the model input layer according to specific rules or algorithms. For example, an adaptive learning rate method can be used to dynamically adjust the weights, or the weights can be adjusted proportionally based on the variance to ensure that the AI ​​model can better capture the changing trends in the data. The adjusted weight parameters are used in subsequent data processing.

[0099] S403: Recalculate the AI ​​model's input feature vector based on the adjusted weight parameters. After adjusting the weight parameters of the model's input layer, the input feature vector needs to be recalculated. This step ensures that the AI ​​model accurately reflects the latest power transmission data status. Specifically, the adjusted weight parameters are weighted and summed with the new power transmission data to obtain the updated feature vector.

[0100] S404: The calculated feature vector of the AI ​​model is input into the model for further processing. Finally, the new feature vector obtained through the above steps is used as input to continue the model's subsequent processing steps, such as feature extraction, classification, or prediction. This ensures the model's performance and accuracy even when power transmission data fluctuates significantly.

[0101] Through the above steps, the robustness and adaptability of the new energy power transmission status monitoring system based on artificial intelligence algorithms can be effectively improved.

[0102] Please refer to Figure 6 , Figure 6 This is a flowchart of recalculating the input feature vector of the model provided by one embodiment of the present invention. In the new energy power transmission status monitoring method based on the artificial intelligence algorithm, the specific steps of recalculating the input feature vector of the artificial intelligence model according to the adjusted weight parameters are as follows:

[0103] S501: First, the adjusted weight parameters are normalized. This process ensures that all weight parameters are on the same scale, thereby avoiding that certain features are given too much importance due to differences in numerical values. Normalization can be achieved in many ways, such as min-max normalization, which maps each weight parameter to a value between 0 and 1. The formula is: w'_i = frac{w_i-minw}{maxw-minw}, where w_i is the original weight parameter, w'_i is the normalized weight parameter, maxw and minw are the maximum and minimum values ​​of all weight parameters, respectively.

[0104] S502: Then, based on the normalized weight parameters, the importance coefficient of each feature is calculated. This step is completed by multiplying the normalized weight parameters with the corresponding feature values. The result reflects the degree of influence of each feature on the model prediction result. The calculation of the importance coefficient can be expressed as: c_i = w'_i cdot f_i, where c_i represents the importance coefficient of the i-th feature, w'_i is the normalized weight parameter, f_i is the original value of the i-th feature, and cdot is the dot product symbol.

[0105] S503: Then, when the importance coefficient is greater than a preset coefficient threshold η, the feature is considered a key feature. The selection of the preset coefficient threshold η depends on the specific application scenario and requirements, and an appropriate value is generally determined through experimentation. For example, in power transmission status monitoring, if the preset coefficient threshold η is set to 0.8, only features with an importance coefficient greater than 0.8 are considered key features.

[0106] S504: Finally, a new feature vector containing key features is constructed and input into the model. This step involves filtering out those marked as key features from the original feature vector and forming a new feature vector. The new feature vector not only contains the most valuable information for model prediction, but also reduces unnecessary computational burden, improving the efficiency and accuracy of the AI ​​model. For example, if the original feature vector contains 10 features, after the above steps, only 5 features may be retained as key features, which will be used in subsequent model training or prediction processes.

[0107] Please refer to Figure 7 , Figure 7 This is a flowchart for determining key features provided by one embodiment of the present invention. The specific steps for screening and utilizing key features in the new energy power transmission status monitoring method based on artificial intelligence algorithm are as follows:

[0108] S601: Screening Key Features: First, the importance coefficients of all features extracted from the renewable energy power transmission system are calculated. The importance coefficients can be weights or other evaluation metrics derived from the machine learning model training process. If a feature's importance coefficient exceeds a pre-set threshold η, it is considered a key feature. This process ensures that only features with a significant impact on the power transmission status are retained.

[0109] S602: Sort Key Features: Once all key features with importance coefficients greater than η have been identified, they need to be sorted. The sorting can be based on the importance coefficient, with features with higher importance coefficients being ranked higher. This step helps determine which features are more important for subsequent analysis and can optimize the structure of the new feature vector.

[0110] S603: Constructing a new feature vector (i.e., the input feature vector containing the sorted key features): Based on the sorting results, a new feature vector is constructed, containing the key features in order. This new feature vector not only reduces the dimensionality of the original dataset but also ensures that the model focuses on the most relevant signals, thereby improving prediction accuracy and efficiency.

[0111] S604: Inputting the New Feature Vector into the AI ​​Model: Finally, the constructed new feature vector is input into the AI ​​model used to monitor the state of renewable energy power transmission. The AI ​​model uses these key features to perform further data analysis, pattern recognition, or prediction tasks to effectively monitor the power transmission system.

[0112] For example, in a practical application, suppose a random forest model has been trained to evaluate the importance coefficient of each feature. Assuming the threshold η is set to 0.5, the importance coefficients of all features can be calculated, and features with a value greater than 0.5 can be selected as key features. Next, these key features are ranked from high to low according to their importance coefficients, and a new feature vector is constructed. Finally, this new feature vector is input into another predictive model, such as a support vector machine, to effectively monitor the transmission status of renewable energy power.

[0113] Please refer to Figure 8 , Figure 8 This is a flowchart of an embodiment of the present invention that uses a pre-trained artificial intelligence model to perform feature extraction and pattern recognition to analyze the operating status of the power transmission system. The specific steps of the new energy power transmission status monitoring method based on artificial intelligence algorithm are as follows:

[0114] S701: By collecting real-time data from power transmission, including but not limited to key parameters such as voltage and current, this method first preprocesses this raw data, such as filtering and normalization, to ensure data quality and consistency. Subsequently, specific data mining techniques or machine learning algorithms are used to extract feature vectors closely related to voltage and current from this preprocessed data. This process aims to convert complex power transmission data into a form that is easy to analyze and facilitates subsequent processing by artificial intelligence models.

[0115] S702: The extracted feature vector is fed into a pre-trained artificial intelligence model for preliminary analysis. This artificial intelligence model can be a deep neural network (CNN), such as a convolutional neural network (CNN), a recurrent neural network (RNN), a support vector machine (SVM), or other machine learning models suitable for processing time series data. During the training phase, the artificial intelligence model has learned a large number of data samples under normal and abnormal power transmission conditions. Therefore, based on the input feature vector, it can quickly determine whether the current power transmission system is operating normally.

[0116] S703: Based on the preliminary analysis results of the AI ​​model, the system automatically determines whether there are any abnormal patterns. If the model detects an abnormal pattern, meaning the output indicates that the current power transmission status has deviated from the normal range, the system proceeds to the next step. If no abnormality is detected, the system continues to monitor new data and repeats the above process.

[0117] S704: Once an abnormal pattern is confirmed, the specific type of anomaly is further identified. This step may involve more detailed feature analysis or the use of models optimized for anomaly detection to determine the specific cause of the anomaly, such as overload, short circuit, or equipment failure. This approach not only enables timely identification of problems in the power transmission system but also provides targeted solutions, effectively improving the stability and safety of the power transmission system.

[0118] Please refer to Figure 9 , Figure 9 This is a flowchart of determining whether an abnormal pattern exists, provided by one embodiment of the present invention. The specific steps for determining whether an abnormal pattern exists in the new energy power transmission status monitoring method based on an artificial intelligence algorithm are as follows:

[0119] S801: Calculate the probability P of an abnormal pattern by statistically processing the preliminary analysis results. This process typically involves analyzing the data output by the AI ​​model, identifying data points that deviate from normal operating conditions, and estimating the probability P of an abnormal pattern based on the number and distribution of these data points.

[0120] S802: Compare the calculated probability P with a pre-defined threshold T1 (i.e., the preset probability threshold T1). This step quantifies the judgment criteria, comparing the probability of an abnormal pattern with a pre-defined threshold (i.e., the preset probability threshold T1) to determine whether further action is needed. If the probability P is greater than the threshold T1, it is considered that an abnormal pattern exists in the current power transmission state; conversely, if the probability P is less than or equal to the threshold T1, it is considered that no abnormal pattern exists in the current state.

[0121] S803: Make a decision based on the comparison results. If P is greater than T1, an abnormal pattern is determined to exist. In this case, it may be necessary to trigger an alarm mechanism or take other measures to further diagnose the problem and prevent potential failures. If P is less than or equal to T1, it is determined that no abnormal pattern exists and the power transmission status can be considered normal. No special measures are required immediately.

[0122] In this way, the method can effectively use artificial intelligence algorithms to monitor the status of renewable energy power transmission in real time and promptly identify potential problems, thereby improving the stability and security of the power system.

[0123] Please refer to Figure 10 , Figure 10 This is a flowchart based on the comparison between probability P and preset probability threshold T1 provided by one embodiment of the present invention. In the new energy power transmission status monitoring method based on artificial intelligence algorithm, the specific steps of comparing probability P with preset threshold T1 are as follows:

[0124] S901: The degree of abnormality in the current power transmission state is assessed by calculating the difference ΔP between the calculated probability P and the preset threshold T1. This calculation can be performed using a simple subtraction operation: ΔP = P - T1. The goal here is to quantify the difference between the probability P and the threshold T1 to determine whether an abnormality exists beyond the normal range.

[0125] S902: Determine whether an abnormal pattern exists by comparing ΔP with zero. If the calculated ΔP is greater than zero, this indicates that the actual detected probability P is higher than the preset threshold T1, meaning that the current power transmission state may have deviated from the normal operating range, and there is a high probability of an abnormal pattern. At this point, the system triggers an appropriate alarm mechanism or takes further diagnostic measures.

[0126] S903: If the calculated ΔP is less than or equal to zero, the current power transmission status is considered to be within the normal range and no abnormal pattern exists. This means that the actual detected probability P does not exceed the preset threshold T1, and the system can continue monitoring without taking immediate action. In this case, the system may continuously monitor the power transmission status and reassess it periodically to ensure system stability and security.

[0127] Through the above steps, artificial intelligence algorithms can be effectively used to monitor the transmission status of renewable energy power in real time and detect anomalies, thereby improving the reliability and efficiency of the power system.

[0128] Please refer to Figure 11 , Figure 11This is a flowchart of determining whether the difference ΔP is greater than zero, provided by one embodiment of the present invention. The specific steps of determining whether ΔP is greater than zero and executing the abnormal pattern recognition process based on this in the new energy power transmission status monitoring method based on artificial intelligence algorithm are as follows:

[0129] S1001: The system's operating status is determined by monitoring and calculating the difference ΔP between the actual power and the expected power of the renewable energy power transmission system within a specific time interval. If ΔP is greater than zero, this indicates that the actual power exceeds the expected power, which may indicate an abnormality in the system, such as an overload or equipment failure. At this point, the system requires further analysis to determine the specific type of abnormality.

[0130] S1002: If ΔP is indeed greater than zero, the abnormal pattern recognition process is initiated. This process can use pre-trained machine learning models, such as support vector machines (SVMs), neural networks, or other classification algorithms, to analyze the current operating data to identify the specific cause of the abnormal increase in ΔP. For example, by analyzing the correlation between historical and current data, it can be determined whether the abnormality is caused by changes in external environmental factors or internal equipment failure.

[0131] S1003: If ΔP is less than or equal to zero, the system is considered to be in normal working condition and no abnormal pattern identification process needs to be performed. This means that the actual power is consistent with the expected power or is lower than expected, and the system does not need to take additional measures to deal with potential abnormal conditions.

[0132] S1004: Regardless of the value of ΔP, the system automatically updates the model parameters used for prediction and monitoring based on the results of the abnormal pattern recognition process. This update mechanism helps improve the model's ability to identify similar abnormal situations in the future, thereby enhancing the accuracy and reliability of the entire monitoring system.

[0133] Through the above steps, the present invention can effectively monitor the operating status of the new energy power transmission system, and promptly discover and handle abnormal situations to ensure the safety and stability of power transmission.

[0134] Please refer to Figure 12 , Figure 12 This is a flowchart of updating model parameters based on the results of the abnormal pattern recognition process provided by one embodiment of the present invention. The specific steps of updating model parameters in the new energy power transmission status monitoring method based on artificial intelligence algorithm are as follows:

[0135] S1101: Analyze the Results of the Anomaly Pattern Recognition Process - In this step, the system first conducts an in-depth analysis of the results of the anomaly pattern recognition process. This step aims to understand how the current model performs when processing specific types of data and identify situations where the model fails to accurately identify anomalies. By analyzing these results, it is possible to determine the direction of model parameter adjustments, namely, which parameters need to be modified and how to modify them to improve model accuracy.

[0136] S1102: Update model parameters based on the adjustment direction - Once the adjustment direction for model parameters has been determined, the next step is to update the model parameters according to these guidelines. This typically involves adjusting weights, biases, or other hyperparameters in the neural network. For example, if the model is found to be overly sensitive to certain types of anomalies, the weights of the relevant layers may need to be reduced; conversely, if the model is not sensitive enough to certain anomalies, the weights may need to be increased. In this way, the model can be gradually optimized to better suit the actual application scenario.

[0137] S1103: Re-perform feature extraction and pattern recognition based on updated model parameters - After updating the model parameters, the feature extraction and pattern recognition process needs to be re-executed using the new model parameters. This means processing the power transmission system data again to verify whether the adjusted model can more accurately identify abnormal patterns. This process may involve using the same training and test data sets, or introducing new datasets to further evaluate the model's generalization capabilities.

[0138] S1104: Verify the performance of the updated model—The final step is to evaluate the performance of the updated model. This is typically done by comparing the performance of the new and old models on the same test set. If the updated model demonstrates higher precision, recall, or other performance metrics in identifying anomaly patterns, the model is considered to have been effectively improved. If performance has not significantly improved or has even deteriorated, return to S1101, reanalyze the results of the anomaly pattern identification process, and adjust the model parameters based on the new findings.

[0139] In actual operation, when this device is used, multiple sensors are first deployed at key nodes in the renewable energy power transmission network. These sensors collect real-time data on various power transmission processes, such as current, voltage, temperature, and other key indicators. This data is then transmitted to the central processing unit, where a pre-trained artificial intelligence model comes into play. Using deep learning techniques, this model extracts features and recognizes patterns from the collected data, analyzing the current operating status of the power transmission system and identifying potential problems or anomalies. To ensure the model's adaptability to the complex and ever-changing power transmission environment and maintain high monitoring accuracy, the system dynamically adjusts its parameters based on its analysis results. This means that over time and in changing environments, the model can self-optimize to better reflect actual conditions. Furthermore, to further improve the model's accuracy and reliability, the system regularly verifies its predictions and continuously optimizes them through a feedback mechanism. For example, if the model predicts a potential overload risk in a certain area, but no overload actually occurs, this information is fed back to the model for subsequent training and adjustments to reduce errors in future predictions. The entire process is a closed-loop control system, in which sensors, artificial intelligence models, and feedback mechanisms work closely together to ensure effective monitoring of the transmission status of renewable energy power. This AI-based approach not only improves monitoring efficiency but also enhances the system's adaptive and predictive capabilities, which is of great significance for ensuring the stable operation of renewable energy power systems.

[0140] Figure 13 This is a block diagram of a new energy power transmission status monitoring system based on artificial intelligence algorithm provided by one embodiment of the present invention. Figure 13 As shown, an embodiment of the present invention provides a new energy power transmission status monitoring system based on an artificial intelligence algorithm, comprising:

[0141] The power transmission data acquisition module is used to collect power transmission data in real time through multiple sensors pre-deployed in the new energy power transmission network;

[0142] The operating status analysis module is used to input the power transmission data collected by each sensor into the pre-trained artificial intelligence model to perform feature extraction and pattern recognition in sequence to analyze the operating status of the new energy power transmission network and obtain analysis results;

[0143] A dynamic adjustment module is used to dynamically adjust the model parameters of the pre-trained artificial intelligence model based on the analysis results;

[0144] The prediction accuracy verification module is used to perform multiple rounds of optimization on the dynamically adjusted artificial intelligence model until the verification result of the prediction accuracy of the latest artificial intelligence model reaches the optimal one, and use the corresponding latest artificial intelligence model to analyze the operating status of the new energy power transmission network.

[0145] Specifically, the system deploys multiple sensors within the renewable energy power transmission network to collect real-time power transmission data. Based on this data, a pre-trained artificial intelligence model is used to extract features and perform pattern recognition to analyze the operating status of the power transmission system (i.e., the renewable energy power transmission network). Based on the AI ​​model's analysis results, model parameters are dynamically adjusted to adapt to the complex and changing power transmission environment, improving monitoring accuracy. The AI ​​model's predictive accuracy is regularly verified. A feedback mechanism continuously optimizes the AI ​​model, ensuring it accurately monitors and predicts the operating status of the renewable energy power transmission network within this complex and changing renewable energy power transmission environment. This ensures that the AI ​​algorithm can accurately monitor and predict the status of the power transmission system within this complex and changing renewable energy power transmission environment.

[0146] The above describes in detail the optional implementation methods of the embodiments of the present invention in conjunction with the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above implementation methods. Within the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the scope of protection of the embodiments of the present invention.

[0147] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe various possible combinations.

[0148] Those skilled in the art will understand that all or part of the steps in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a program, which is stored in a storage medium and includes a number of instructions for causing a single-chip microcomputer, chip or processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program code.

[0149] In addition, various implementations of the embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the embodiments of the present invention, they should also be regarded as the contents disclosed in the embodiments of the present invention.

Claims

1. A new energy power transmission status monitoring method based on artificial intelligence algorithm, characterized in that: include: Real-time power transmission data is collected through multiple sensors pre-deployed in the new energy power transmission network; The power transmission data collected by each sensor is input into the pre-trained artificial intelligence model to perform feature extraction and pattern recognition in sequence to analyze the operating status of the new energy power transmission network and obtain analysis results; Dynamically adjust the model parameters of the pre-trained artificial intelligence model based on the analysis results; Conduct multiple rounds of optimization on the dynamically adjusted AI model until the prediction accuracy of the latest AI model is verified to be optimal. Use the latest AI model to analyze the operating status of the new energy power transmission network. The power transmission data collected by each sensor is input into the pre-trained artificial intelligence model for feature extraction and pattern recognition, including: Adjust the input layer parameters of the pre-trained artificial intelligence model based on the statistical characteristics of power transmission data; Extract key features from power transmission data using an AI model with adjusted input layer parameters; Classify and identify key features in the extracted power transmission data; The statistical characteristic information of the power transmission data includes the average value μ and variance σ of the power transmission data. 2 ; The step of adjusting the input layer parameters of the pre-trained artificial intelligence model based on the statistical characteristic information of the power transmission data includes: Calculate the mean μ and variance σ of power transmission data 2 ; When the variance of power transmission data σ 2 When the variance is greater than the preset threshold T, the weight parameters of each input layer of the artificial intelligence model are adjusted; Recalculate the input feature vector of the artificial intelligence model based on the adjusted weight parameters of each input layer of the artificial intelligence model; The recalculated input feature vector of the artificial intelligence model is input into the artificial intelligence model to determine the input layer parameters of the adjusted artificial intelligence model.

2. The method for monitoring the transmission status of new energy power based on artificial intelligence algorithm according to claim 1 is characterized in that: The deployment rules of the sensor include: Determining a sensor deployment plan based on historical operating data of the new energy power transmission network; wherein the sensor deployment plan includes the sensor type, sensor deployment location, and data collection frequency of each deployed sensor; The power transmission data collection rules include: Based on the data collection frequency of each sensor, a data collection instruction is sent to each sensor, so that each sensor collects power transmission data according to the corresponding data collection frequency; The collected power transmission data is preprocessed; wherein the preprocessing includes at least filtering and denoising.

3. The method for monitoring the transmission status of renewable energy power based on artificial intelligence algorithm according to claim 1, characterized in that: The step of recalculating the input feature vector of the artificial intelligence model based on the adjusted weight parameters of each input layer of the artificial intelligence model includes: Normalize the weight parameters of each input layer of the adjusted artificial intelligence model; Calculate the importance coefficient of each feature of the artificial intelligence model based on the normalized weight parameters; The features of the artificial intelligence model whose importance coefficient is greater than the preset coefficient threshold η are taken as key features, an input feature vector containing the key features is constructed, and the input feature vector is input into the artificial intelligence model.

4. The method for monitoring the transmission status of renewable energy power based on artificial intelligence algorithm according to claim 3 is characterized in that: The features of the artificial intelligence model whose importance coefficient is greater than the preset coefficient threshold η are taken as key features, and an input feature vector containing the key features is constructed, including: Filter out all key features whose importance coefficient is greater than the preset coefficient threshold η; The key features are sorted from high to low according to their importance coefficients, and an input feature vector containing the sorted key features is constructed.

5. The method for monitoring the transmission status of renewable energy power based on artificial intelligence algorithm according to claim 1, characterized in that: The pattern recognition process includes an abnormal pattern recognition process, the analysis result includes a result of the abnormal pattern recognition process, and the result of the abnormal pattern recognition process includes a result of determining whether an abnormal pattern exists and a result of determining whether an abnormal pattern does not exist; Based on the analysis results, the model parameters of the pre-trained artificial intelligence model are dynamically adjusted, including: Determine the direction of adjusting model parameters based on the results of the abnormal pattern recognition process; The model parameters are updated according to the adjustment direction, and a dynamically adjusted artificial intelligence model is obtained based on the updated model parameters.

6. The method for monitoring the transmission status of renewable energy power based on artificial intelligence algorithm according to claim 1, characterized in that: During the multi-round optimization process of the dynamically adjusted artificial intelligence model, each time the dynamic adjustment of the artificial intelligence model is completed, it is necessary to perform a prediction accuracy verification of the corresponding artificial intelligence model; The rules for validating the prediction accuracy of AI models include: The power transmission data collected by each sensor is input into the dynamically adjusted artificial intelligence model to re-perform feature extraction and pattern recognition; Based on the results of re-feature extraction and pattern recognition, verify whether the prediction accuracy of the dynamically adjusted artificial intelligence model is higher than the prediction accuracy of the pre-trained artificial intelligence model that has not been dynamically adjusted.

7. The method for monitoring the transmission status of renewable energy power based on artificial intelligence algorithm according to claim 1, characterized in that: The power transmission data collected by each sensor is input into the pre-trained artificial intelligence model to perform feature extraction and pattern recognition in sequence to analyze the operating status of the new energy power transmission network, including: Extracting feature vectors associated with voltage and / or current from the power transmission data, and inputting the feature vectors into a pre-trained artificial intelligence model for preliminary analysis; Based on the preliminary analysis results, determine whether there are any unusual patterns; When it is determined that an abnormal pattern exists, the abnormal type is identified.

8. The method for monitoring the transmission status of renewable energy power based on artificial intelligence algorithm according to claim 7, characterized in that: Determining whether there are abnormal patterns based on the preliminary analysis results includes: Perform statistics on abnormal patterns in the preliminary analysis results, and calculate the probability P of the abnormal pattern based on the statistical results; The probability P of the abnormal pattern occurring is compared with the preset probability threshold T1. If the probability P of the abnormal pattern occurring is greater than the preset probability threshold T1, it is determined that the abnormal pattern exists; otherwise, it is determined that the abnormal pattern does not exist.

9. The method for monitoring the transmission status of renewable energy power based on artificial intelligence algorithm according to claim 8, characterized in that: The comparing the probability P of the abnormal pattern to be present with the preset probability threshold T1, if the probability P of the abnormal pattern to be present is greater than the preset probability threshold T1, then it is determined that the abnormal pattern exists; otherwise, it is determined that the abnormal pattern does not exist, including: Calculate the difference ΔP between the probability P of the abnormal pattern occurring and the preset probability threshold T1; It is determined whether the difference ΔP is greater than zero. If the difference ΔP is greater than zero, it is determined that an abnormal pattern exists; otherwise, it is determined that no abnormal pattern exists.

10. A new energy power transmission status monitoring system based on artificial intelligence algorithm, characterized in that: include: The power transmission data acquisition module is used to collect power transmission data in real time through multiple sensors pre-deployed in the new energy power transmission network; The operating status analysis module is used to input the power transmission data collected by each sensor into the pre-trained artificial intelligence model to perform feature extraction and pattern recognition in sequence to analyze the operating status of the new energy power transmission network and obtain analysis results; A dynamic adjustment module, configured to dynamically adjust the model parameters of the pre-trained artificial intelligence model based on the analysis results; The prediction accuracy verification module is used to perform multiple rounds of optimization on the dynamically adjusted artificial intelligence model until the prediction accuracy verification result of the latest artificial intelligence model reaches the optimal value. The corresponding latest artificial intelligence model is used to analyze the operating status of the new energy power transmission network; The power transmission data collected by each sensor is input into the pre-trained artificial intelligence model for feature extraction and pattern recognition, including: Adjust the input layer parameters of the pre-trained artificial intelligence model based on the statistical characteristics of power transmission data; Extract key features from power transmission data using an AI model with adjusted input layer parameters; Classify and identify key features in the extracted power transmission data; The statistical characteristic information of the power transmission data includes the average value μ and variance σ of the power transmission data. 2 ; The step of adjusting the input layer parameters of the pre-trained artificial intelligence model based on the statistical characteristic information of the power transmission data includes: Calculate the mean μ and variance σ of power transmission data 2 ; When the variance of power transmission data σ 2 When the variance is greater than the preset threshold T, the weight parameters of each input layer of the artificial intelligence model are adjusted; Recalculate the input feature vector of the artificial intelligence model based on the adjusted weight parameters of each input layer of the artificial intelligence model; The recalculated input feature vector of the artificial intelligence model is input into the artificial intelligence model to determine the input layer parameters of the adjusted artificial intelligence model.

11. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by the processor, the processor is configured to execute the new energy power transmission status monitoring method based on artificial intelligence algorithm as described in any one of claims 1 to 9.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for monitoring the new energy power transmission status based on the artificial intelligence algorithm as described in any one of claims 1 to 9 is implemented.

Images