Electric power equipment intelligent early warning and maintenance management system based on AI Agent

Abstract

The invention discloses a power equipment intelligent early warning and maintenance management system based on an AI Agent, and relates to the technical field of power equipment maintenance management, and the system comprises a data preprocessing and weight correction module, a feature extraction and denoising module, a dynamic prediction and fault identification module, a time sequence analysis and intelligent early warning module, and a fault simulation and maintenance optimization module. And the data preprocessing and weight correction module is used for preprocessing the operation data of the power equipment and carrying out weighted analysis on the data of the multi-source sensor by adopting a self-adaptive weight distribution algorithm. According to the method, the hidden deviation problem of the sensor is solved through the multi-source data correction and dynamic weight distribution technology, and key features are accurately extracted and noise is eliminated in combination with a deep learning feature enhancement and denoising method. And a dynamic prediction model and an intelligent early warning generation method are utilized to realize accurate fault prediction and graded early warning, a maintenance strategy is optimized, and the reliability and safety of a power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment maintenance management, and in particular to an AI Agent-based intelligent early warning and maintenance management system for power equipment. Background Art

[0002] The AI ​​Agent-based intelligent early warning and maintenance management of power equipment aims to use artificial intelligence technology to monitor, analyze and model the operation data of power equipment in real time, identify potential faults and their development trends, provide intelligent early warnings, and formulate optimized maintenance plans based on equipment status data. This system improves the accuracy of fault diagnosis and the efficiency of maintenance management by autonomously learning the historical data and operation mode of the equipment, thereby reducing equipment downtime, reducing maintenance costs, and improving the safety and reliability of the power system.

[0003] The prior art has the following deficiencies:

[0004] In the intelligent early warning and maintenance management of power equipment based on AI Agent, there may be implicit biases in the input data of the AI ​​model. Since power equipment data is usually collected by a variety of sensors and the data sources are complex, some sensors may introduce systematic deviations due to aging, environmental interference or configuration errors. These deviations may be ignored or mistaken for normal data during model training, thus affecting the accuracy of the prediction rules. Once the AI ​​model builds prediction rules based on deviated data, it may lead to false alarms or missed alarms, especially when potential faults occur in key equipment (such as transformers or high-voltage circuit breakers). This implicit bias may weaken the system's early warning capabilities, fail to identify key faults, and may eventually cause sudden equipment failures, resulting in large-scale power outages or secondary disasters, seriously threatening power supply safety and social order.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent early warning and maintenance management system for power equipment based on AI Agent. Through multi-source data correction and dynamic weight distribution technology, the data quality problem caused by the implicit bias of the sensor is effectively solved. The accuracy and real-time performance of data correction are ensured by using adaptive weight distribution and particle swarm optimization algorithms, which significantly reduce false positives and false negatives. In combination with the feature enhancement and denoising technology of deep learning, the denoising variational autoencoder (D-VAE) and the generative adversarial network (GAN) are used for collaborative optimization to achieve accurate extraction of key features and noise elimination, thereby improving data processing accuracy. Through dynamic prediction models and intelligent early warning generation methods, reinforcement learning, long short-term memory networks (LSTMs) and hierarchical attention mechanisms are used to accurately predict potential faults and generate graded early warnings, optimize maintenance strategies, and greatly improve the reliability and safety of the power system to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: an AI Agent-based intelligent early warning and maintenance management system for electric power equipment, which is used to solve the problem of implicit deviation in the operation data of electric power equipment caused by sensor aging, environmental interference or configuration errors, and is characterized in that it includes a data preprocessing and weight correction module, a feature extraction and denoising module, a dynamic prediction and fault identification module, a time series analysis and intelligent early warning module, and a fault simulation and maintenance optimization module;

[0008] The data preprocessing and weight correction module preprocesses the operating data of the power equipment, uses an adaptive weight allocation algorithm to perform weighted analysis on multi-source sensor data, identifies and corrects sensor data with significant deviations, and dynamically adjusts the weight of abnormal data by building a sensor consistency detection model, thereby reducing the impact of deviations on the overall accuracy of the data set;

[0009] The feature extraction and denoising module uses a deep learning-based autoencoder network to extract high-dimensional features of the running data, and combines it with a denoising variational autoencoder to eliminate implicit biases while ensuring the integrity of key features, providing high-quality input data for subsequent model training;

[0010] The dynamic prediction and fault identification module builds a dynamic prediction model based on the reinforcement learning algorithm. It optimizes the model parameters and prediction rules in real time for the corrected and denoised equipment data. It dynamically adjusts the model's sensitivity to the state of key equipment through the reward function, and improves the model's ability to identify potential fault modes.

[0011] The time series analysis and intelligent warning module uses a long short-term memory network based on time series pattern recognition to analyze the time series characteristics of equipment data, generate multi-level warning signals, and optimize the balance between false alarms and missed alarms through dynamic adjustment of warning level thresholds;

[0012] The fault simulation and maintenance optimization module simulates the possible fault development paths of the equipment based on the generative adversarial network, combines the power system operation constraints, generates optimal maintenance decision suggestions, and formulates targeted maintenance plans through collaborative evaluation with the expert system to prevent sudden equipment failures or large-scale power outages.

[0013] Preferably, the adaptive weight allocation algorithm includes the following specific steps:

[0014] By performing historical statistical analysis on multi-source sensor data, the long-term mean and standard deviation of sensor output values ​​are calculated;

[0015] Use Mahalanobis Distance to determine the degree of deviation between the current data point and the historical data;

[0016] Combined with the health assessment model of sensor equipment, a lower weight is given to abnormal sensors, while increasing the weight of normal sensor data;

[0017] Based on real-time optimization algorithms (such as particle swarm optimization), the weight factor of each sensor is dynamically adjusted to ensure that the comprehensive data has high robustness and accuracy. This method effectively identifies and corrects abnormal data through multi-level adjustments, significantly reducing the risk of erroneous analysis caused by deviations.

[0018] Preferably, the adopted denoising variational autoencoder (D-VAE) further includes the following optimization measures:

[0019] By introducing regularization terms to constrain the hidden layer representation, the model can avoid overfitting abnormal data;

[0020] The generator of the generative adversarial network is used to verify the denoising results to ensure the authenticity and integrity of the extracted features;

[0021] A multi-task learning framework is used to perform key feature classification and fault labeling while denoising to enhance the applicability of the model;

[0022] By dynamically adjusting the weight of the reconstruction loss function during the training process, the model's ability to extract weak signal features is optimized. This optimization measure further improves the device's ability to process data in complex environments and effectively addresses the challenge of implicit bias.

[0023] Preferably, the dynamic prediction model training combines the following reinforcement learning strategies:

[0024] Using a mechanism based on Prioritized Experience Replay, data with high contribution to key equipment status is selected for model update;

[0025] Use the Deep Q-Network (DQN) algorithm to optimize decision-making for high-risk states in equipment operation;

[0026] Build a reward function for key equipment status data to reward model behaviors that detect potential failures early;

[0027] Multi-Agent Reinforcement Learning is used to simulate and analyze the complex device interaction process to further improve the dynamic adaptability of the prediction model. Through this reinforcement learning strategy, the flexibility and robustness of the prediction model in complex scenarios are significantly enhanced.

[0028] Preferably, the temporal pattern recognition is further combined with the following optimization strategies:

[0029] The Hierarchical Attention Mechanism is used to enhance the LSTM network’s ability to model long-term dependencies.

[0030] Use the Multi-Head Attention mechanism to enhance the capture of key temporal features;

[0031] Process time series data based on the Differential Privacy method to prevent privacy leakage caused by data sensitivity;

[0032] By dynamically adjusting the threshold strategy of the early warning signal, the model's response speed and accuracy to different levels of faults are optimized, thus achieving the best balance between false positives and missed negatives. This optimization strategy significantly improves the effect of time series analysis and enhances the credibility of multi-level early warning signals.

[0033] Preferably, the maintenance strategy optimization process includes the following detailed steps:

[0034] Predict the probability of equipment failure under different operating conditions based on the GAN generator, and evaluate the reliability of the prediction results in combination with the discriminator;

[0035] Conduct weighted analysis on power equipment maintenance costs and downtime losses, and build a multi-objective optimization model for maintenance decision-making;

[0036] The maintenance suggestions are graded and outputted through the fuzzy logic system to suit different levels of urgency;

[0037] The generated maintenance strategy is verified and adjusted in combination with the domain expert knowledge base to ensure its feasibility and rationality in practical applications. This process provides efficient and accurate maintenance decision-making solutions through a comprehensive optimization and verification mechanism.

[0038] Preferably, the system includes a self-learning mechanism that enables continuous optimization of the model in the following two aspects:

[0039] The Federated Learning framework is used to integrate the operation data of power equipment in multiple regions to improve the generalization performance of the model while avoiding the privacy and security risks brought by the centralized storage of data sets.

[0040] Online learning technology is introduced to dynamically update model parameters using the latest equipment operation data, and historical data is weighed against real-time data to maintain the real-time and stability of the model. This self-learning mechanism greatly improves the adaptability and long-term performance of the AI ​​system, providing strong support for intelligent equipment warning and maintenance management.

[0041] Preferably, the system supports integration with the power system dispatching platform, specifically including:

[0042] Achieve seamless connection with the dispatching platform through standardized interface protocols, and transmit equipment status data and early warning signals in real time;

[0043] Use edge computing technology to perform real-time analysis at the data end close to the device to reduce data transmission delays and network bandwidth usage;

[0044] After the fault warning is generated, dispatch suggestions are automatically pushed, including load distribution adjustment and emergency power outage plan, to ensure the stability of the system. Through this integrated approach, the overall coordination and emergency handling capabilities of the power system are effectively improved.

[0045] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0046] The present invention effectively solves the problem of implicit deviation introduced in power equipment data due to sensor aging, environmental interference or configuration errors through the method of multi-source data correction and dynamic weight allocation. Through the adaptive weight allocation algorithm and Mahalanobis distance deviation detection mechanism, the present invention can dynamically adjust the weight of sensor data, identify and correct abnormal data, and avoid the misuse of deviation data as normal samples to input AI models. In addition, combined with the global optimization capability of the particle swarm optimization algorithm, the accuracy and real-time performance of the data correction process are ensured, providing high-quality data support for the training and prediction of AI models. Through this method, the probability of model false alarms or omissions caused by implicit biases is significantly reduced, and the reliability of the intelligent early warning system is improved.

[0047] The present invention realizes accurate processing of power equipment operation data and extraction of key features through feature enhancement and denoising technology based on deep learning. Through the collaborative optimization of denoising variational autoencoder (D-VAE) and generative adversarial network (GAN), the present invention can effectively eliminate noise in the data and extract high-dimensional features to ensure that the extracted features are authentic and reliable. Especially in the case of complex and diverse data, the deep learning model simultaneously completes feature classification and fault labeling through a multi-task learning framework, providing more comprehensive feature support for subsequent predictions. In addition, the mechanism of dynamically adjusting the weights of the reconstruction loss function further optimizes the model's ability to extract weak signals and key features. This solution greatly improves the processing accuracy of power equipment operation data and provides important guarantees for equipment status analysis in complex environments.

[0048] The present invention realizes accurate prediction and graded warning of potential faults of power equipment through dynamic prediction models and intelligent warning generation methods. The reinforcement learning algorithm is combined with the deep Q network (DQN) to optimize the sensitivity of the AI ​​model to the status of key equipment, which can detect potential risks at an early stage and dynamically adjust the prediction rules to adapt to environmental changes. At the same time, by analyzing the time series characteristics of equipment operation through the long short-term memory network (LSTM), and combining the hierarchical attention mechanism and dynamic threshold adjustment strategy, the present invention can effectively balance false alarms and missed alarms, and ensure the reliability and real-time nature of the warning signal. After the warning is generated, the fault development path is simulated by combining the generative adversarial network, and the maintenance strategy is optimized through the expert system, which effectively reduces the equipment downtime and maintenance costs, thereby comprehensively improving the stability and safety of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0050] Figure 1 This is a module schematic diagram of the AI ​​Agent-based intelligent early warning and maintenance management system for power equipment of the present invention. DETAILED DESCRIPTION

[0051] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0052] The present invention provides Figure 1The AI ​​Agent-based intelligent early warning and maintenance management system for electric power equipment shown is used to solve the problem of implicit deviation in the operation data of electric power equipment caused by sensor aging, environmental interference or configuration errors. It is characterized by including a data preprocessing and weight correction module, a feature extraction and denoising module, a dynamic prediction and fault identification module, a time series analysis and intelligent early warning module, and a fault simulation and maintenance optimization module;

[0053] The data preprocessing and weight correction module preprocesses the operating data of the power equipment, uses an adaptive weight allocation algorithm to perform weighted analysis on multi-source sensor data, identifies and corrects sensor data with significant deviations, and dynamically adjusts the weight of abnormal data by building a sensor consistency detection model, thereby reducing the impact of deviations on the overall accuracy of the data set;

[0054] The feature extraction and denoising module uses a deep learning-based autoencoder network to extract high-dimensional features of the running data, and combines it with a denoising variational autoencoder to eliminate implicit biases while ensuring the integrity of key features, providing high-quality input data for subsequent model training;

[0055] The dynamic prediction and fault identification module builds a dynamic prediction model based on the reinforcement learning algorithm. It optimizes the model parameters and prediction rules in real time for the corrected and denoised equipment data. It dynamically adjusts the model's sensitivity to the state of key equipment through the reward function, and improves the model's ability to identify potential fault modes.

[0056] The time series analysis and intelligent warning module uses a long short-term memory network based on time series pattern recognition to analyze the time series characteristics of equipment data, generate multi-level warning signals, and optimize the balance between false alarms and missed alarms through dynamic adjustment of warning level thresholds;

[0057] The fault simulation and maintenance optimization module simulates the possible fault development paths of the equipment based on the generative adversarial network, combines the power system operation constraints, generates optimal maintenance decision suggestions, and formulates targeted maintenance plans through collaborative evaluation with the expert system to prevent sudden equipment failures or large-scale power outages.

[0058] Implementation method 1: multi-source data correction and dynamic weight allocation;

[0059] In the implementation of multi-source data correction and dynamic weight allocation, we proposed a systematic correction and allocation method to address the diversity and potential hidden bias of power equipment operation data. The operation data of power equipment is often collected by multiple sensors in a collaborative manner. These sensors may cause data deviations due to differences in age, working environment, and equipment design. For example, some aging sensors may continuously output low-value data, while sensors subject to electromagnetic interference may intermittently output abnormal values. To address these challenges, the data of each sensor is first analyzed independently, and historical statistical characteristics (such as mean and standard deviation) are used to determine its long-term stability and recent fluctuations.

[0060] Specifically, the Mahalanobis distance algorithm is used to quantify the degree of deviation between the current data points of each sensor and the historical data distribution. The calculation of this distance takes into account the overall distribution and covariance structure of the data, so that abnormal points can be effectively identified. When some data points of a sensor show abnormal deviations, the system will temporarily reduce the weight of the sensor to prevent abnormal data from having an adverse effect on the overall analysis results. At the same time, for sensors with persistent deviations, we have introduced a health assessment model to adjust its weight factor in real time based on parameters such as the sensor's working time and environmental conditions to ensure the rationality of data weight distribution.

[0061] In order to further improve the optimization effect of dynamic weight allocation, this implementation adopts the particle swarm optimization algorithm (PSO) to globally optimize the weight adjustment process. The algorithm simulates the search behavior of particles in multi-dimensional space to find the optimal solution in the multi-objective optimization problem of power data weight adjustment. During the particle search process, the objective function comprehensively considers the consistency, stability and importance of the data, dynamically adjusts the sensor weight, and ensures that the contribution of each sensor data meets the overall system goal.

[0062] In addition, in order to enhance the adaptability of the system, the correction module and the dynamic weight allocation module are designed to run online in real time. Real-time processing can quickly capture the state changes of sensors, such as signal fluctuations caused by sudden failure of a sensor or drastic changes in the environment. Through these measures, this implementation not only eliminates the data deviation caused by sensor aging or environmental interference, but also ensures the accuracy of data fusion, providing high-quality input data for the training of AI models. Overall, the multi-source data correction and dynamic weight allocation method has important application value in the intelligent early warning system of power equipment, laying a solid foundation for subsequent analysis.

[0063] Implementation method 2: Feature enhancement and denoising based on deep learning;

[0064] The core of this implementation is to use deep learning technology to solve the problem of implicit bias in power equipment operation data, and to improve the credibility and applicability of data through feature enhancement and denoising. Specifically, power equipment operation data often contains a large number of high-dimensional, diverse and noise-affected signals, which may hide important features related to equipment failures. Traditional signal processing methods may find it difficult to effectively identify and extract these features, so we use the denoising variational autoencoder (D-VAE) in the deep learning model for processing.

[0065] The denoising variational autoencoder is an unsupervised learning model with the goal of noise reduction. It generates high-quality noise-free features by learning the distribution of input data. In practical applications, the operating data of the power equipment is first divided into multiple small time windows and input into the D-VAE model. The encoder part extracts the high-dimensional features of the input data through multiple convolutional layers, and the decoder part removes the noise and restores the original signal in a reconstructed manner. During the training process, the regularization term is introduced to constrain the representation of the hidden layer, so that it can not only capture the key features, but also suppress the interference of noise on the model.

[0066] To further verify the authenticity of the denoising results, this implementation combines the Generative Adversarial Network (GAN) for auxiliary optimization. The generator part of GAN simulates the noise-free signal, and the discriminator part distinguishes the difference between the denoised signal and the real signal. Through this adversarial mechanism, the denoising effect of D-VAE is ensured to be more accurate and the extracted features are more authentic. In addition, while denoising, the model also performs key feature classification and fault labeling through a multi-task learning framework, further enhancing the practicality of the denoising process and the accuracy of fault diagnosis.

[0067] In the feature extraction process, in order to ensure the model's sensitivity to weak signals and the ability to extract key features, this implementation method dynamically adjusts the weight of the reconstruction loss function to optimize the model's learning objectives. Combined with the above method, the noise-free features finally extracted can not only reflect the operating status of the equipment, but also reveal early signals of potential faults, providing high-quality data support for AI prediction models. This feature enhancement and denoising method based on deep learning significantly improves the accuracy of power equipment data analysis and provides important guarantees for equipment management in complex environments.

[0068] Implementation method three: Dynamic prediction model and intelligent warning generation;

[0069] In the implementation method of dynamic prediction model and intelligent warning generation, a combination of reinforcement learning and time series analysis is used to achieve efficient prediction and graded warning of potential risks for potential failures of power equipment (especially key equipment such as transformers and high-voltage circuit breakers).

[0070] First, in order to dynamically model the operating status of the equipment, this implementation method introduces a dynamic prediction model based on reinforcement learning. The model uses the Deep Q-Network (DQN) algorithm to build a value function of the equipment status by learning the equipment operation data. The model uses the priority experience replay mechanism to focus on learning high-risk samples in historical data to improve the ability to identify critical states. At the same time, a special reward function is designed for the status of critical equipment to reward the model for early detection of potential faults, thereby optimizing the model's learning strategy. During the training process, the system also uses multi-agent reinforcement learning technology (Multi-Agent Reinforcement Learning) to simulate the interaction between complex power equipment to further improve the model's ability to identify systemic problems.

[0071] Secondly, in order to achieve accurate intelligent early warning, this implementation adopts Long Short-Term Memory (LSTM) network for time series analysis. LSTM identifies potential time series patterns by capturing the long-term dependencies of power equipment operation data, and further amplifies the impact of key features in combination with the hierarchical attention mechanism. In order to ensure the rationality of multi-level early warning signals, the system introduces a dynamic threshold adjustment strategy to adjust the early warning threshold in real time according to changes in the equipment operating environment, thereby effectively reducing the false alarm and missed alarm rates. In addition, in order to optimize the classification results of the early warning signal, this implementation combines differential privacy protection technology to enhance the robustness of the early warning algorithm under the premise of protecting data sensitivity.

[0072] Finally, the results of the intelligent early warning generation are further used to optimize the maintenance strategy. The development path of potential equipment failures is simulated by generative adversarial networks (GANs), and maintenance recommendations are evaluated and verified by expert systems to generate the optimal maintenance plan that meets actual needs. This method can not only accurately predict potential equipment failures, but also formulate targeted maintenance measures according to different risk levels, thereby comprehensively improving the reliability and application value of the intelligent early warning system for power equipment.

[0073] The present invention effectively solves the problem of implicit deviation introduced in power equipment data due to sensor aging, environmental interference or configuration errors through the method of multi-source data correction and dynamic weight allocation. Through the adaptive weight allocation algorithm and Mahalanobis distance deviation detection mechanism, the present invention can dynamically adjust the weight of sensor data, identify and correct abnormal data, and avoid the misuse of deviation data as normal samples to input AI models. In addition, combined with the global optimization capability of the particle swarm optimization algorithm, the accuracy and real-time performance of the data correction process are ensured, providing high-quality data support for the training and prediction of AI models. Through this method, the present invention significantly reduces the probability of model false alarms or omissions caused by implicit biases, and improves the reliability of the intelligent early warning system.

[0074] The present invention realizes accurate processing of power equipment operation data and extraction of key features through feature enhancement and denoising technology based on deep learning. Through the collaborative optimization of denoising variational autoencoder (D-VAE) and generative adversarial network (GAN), the present invention can effectively eliminate noise in the data and extract high-dimensional features to ensure that the extracted features are authentic and reliable. Especially in the case of complex and diverse data, the deep learning model simultaneously completes feature classification and fault labeling through a multi-task learning framework, providing more comprehensive feature support for subsequent predictions. In addition, the mechanism of dynamically adjusting the weights of the reconstruction loss function further optimizes the model's ability to extract weak signals and key features. This solution greatly improves the processing accuracy of power equipment operation data and provides important guarantees for equipment status analysis in complex environments.

[0075] The present invention realizes accurate prediction and graded warning of potential faults of power equipment through dynamic prediction models and intelligent warning generation methods. The reinforcement learning algorithm is combined with the deep Q network (DQN) to optimize the sensitivity of the AI ​​model to the status of key equipment, which can detect potential risks at an early stage and dynamically adjust the prediction rules to adapt to environmental changes. At the same time, by analyzing the time series characteristics of equipment operation through the long short-term memory network (LSTM), and combining the hierarchical attention mechanism and dynamic threshold adjustment strategy, the present invention can effectively balance false alarms and missed alarms, and ensure the reliability and real-time nature of the warning signal. After the warning is generated, the fault development path is simulated by combining the generative adversarial network, and the maintenance strategy is optimized through the expert system, which effectively reduces the equipment downtime and maintenance costs, thereby comprehensively improving the stability and safety of the power system.

[0076] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0077] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0078] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0079] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0080] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0081] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0082] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0083] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0084] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0085] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. The intelligent early warning and maintenance management system for power equipment based on AI Agent is characterized by: It includes data preprocessing and weight correction module, feature extraction and denoising module, dynamic prediction and fault identification module, timing analysis and intelligent early warning module, and fault simulation and maintenance optimization module; The data preprocessing and weight correction module preprocesses the operating data of the power equipment, uses an adaptive weight allocation algorithm to perform weighted analysis on multi-source sensor data, identifies and corrects sensor data with significant deviations, and dynamically adjusts the weight of abnormal data by building a sensor consistency detection model to reduce the impact of deviations on the overall accuracy of the data set; The feature extraction and denoising module uses a deep learning-based autoencoder network to extract high-dimensional features of running data, and combines it with a denoising variational autoencoder to eliminate implicit biases, providing high-quality input data for subsequent model training; The dynamic prediction and fault identification module builds a dynamic prediction model based on the reinforcement learning algorithm. It optimizes the model parameters and prediction rules in real time for the corrected and denoised equipment data. It dynamically adjusts the model's sensitivity to the state of key equipment through the reward function, and improves the model's ability to identify potential fault modes. The time series analysis and intelligent warning module uses a long short-term memory network based on time series pattern recognition to analyze the time series characteristics of equipment data, generate multi-level warning signals, and optimize the balance between false alarms and missed alarms through dynamic adjustment of warning level thresholds; The fault simulation and maintenance optimization module simulates the potential fault development path of the equipment based on the generative adversarial network, combines the power system operation constraints, generates optimal maintenance decision suggestions, and formulates targeted maintenance plans through collaborative evaluation with the expert system.

2. The AI ​​Agent-based intelligent early warning and maintenance management system for power equipment according to claim 1 is characterized in that: The adaptive weight allocation algorithm includes the following specific steps: By performing historical statistical analysis on multi-source sensor data, the long-term mean and standard deviation of sensor output values ​​are calculated; Use Mahalanobis distance to determine the degree of deviation between the current data point and the historical data; Combined with the health assessment model of sensor equipment, low weight is given to abnormal sensors, while increasing the weight of normal sensor data; The weight factor of each sensor is dynamically adjusted based on a real-time optimization algorithm to ensure the robustness and accuracy of the integrated data.

3. The AI ​​Agent-based intelligent early warning and maintenance management system for power equipment according to claim 1 is characterized in that: The adopted denoising variational autoencoder further includes the following optimizations: By introducing regularization terms to constrain the hidden layer representation, the model can avoid overfitting abnormal data; The generator of the generative adversarial network is used to verify the denoising results to ensure the authenticity and integrity of the extracted features; A multi-task learning framework is used to perform key feature classification and fault labeling while denoising to enhance the applicability of the model; By dynamically adjusting the weight of the reconstruction loss function during the training process, the model's ability to extract weak signal features is optimized.

4. The AI ​​Agent-based intelligent early warning and maintenance management system for power equipment according to claim 1 is characterized in that: Dynamic prediction model training combines the following reinforcement learning strategies: Using a mechanism based on priority experience playback, data with high contribution to key equipment status is selected for model update; Use deep Q network algorithm to optimize decision making for high-risk states in equipment operation; Build a reward function for key equipment status data to reward model behaviors that detect potential failures early; Multi-agent collaborative learning is used to simulate and analyze the complex equipment interaction process to further improve the dynamic adaptability of the prediction model.

5. The AI ​​Agent-based intelligent early warning and maintenance management system for electric power equipment according to claim 1 is characterized in that: Temporal pattern recognition is further combined with the following optimization strategies: The LSTM network’s ability to model long-term dependencies is enhanced through a hierarchical attention mechanism. Use multi-head self-attention mechanism to enhance the capture of key temporal features; Processing time series data based on differential privacy protection methods.

6. The AI ​​Agent-based intelligent early warning and maintenance management system for power equipment according to claim 1 is characterized in that: The maintenance strategy optimization process includes the following detailed steps: Predict the probability of equipment failure under different operating conditions based on the GAN generator, and evaluate the reliability of the prediction results in combination with the discriminator; Conduct weighted analysis on power equipment maintenance costs and downtime losses, and build a multi-objective optimization model for maintenance decision-making; The maintenance suggestions are graded and outputted through the fuzzy logic system to suit different levels of urgency; The generated maintenance strategy is verified and adjusted in combination with the domain expert knowledge base to ensure its feasibility and rationality in practical applications.

7. The AI ​​Agent-based intelligent early warning and maintenance management system for electric power equipment according to claim 1 is characterized in that: The system includes a self-learning mechanism that enables continuous optimization of the model in two aspects: Integrate multi-regional power equipment operation data through a federated learning framework to improve the generalization performance of the model while avoiding the privacy and security risks brought by centralized data storage; Online learning technology is introduced to dynamically update model parameters using the latest equipment operation data, and historical data is weighed against real-time data to maintain the real-time and stability of the model.

8. The AI ​​Agent-based intelligent early warning and maintenance management system for electric power equipment according to claim 1 is characterized in that: The system supports integration with the power system dispatching platform, including: Achieve seamless connection with the dispatching platform through standardized interface protocols, and transmit equipment status data and early warning signals in real time; Use edge computing technology to perform real-time analysis at the data end close to the device to reduce data transmission delays and network bandwidth usage; After a fault warning is generated, dispatch recommendations are automatically pushed, including load distribution adjustments and emergency power outage plans, to ensure system stability.

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