技术领域Technical Field
本发明涉及电力系统监控与维护技术领域，尤其涉及一种配电设备健康状态评估方法及系统。The present invention relates to the technical field of power system monitoring and maintenance, and in particular to a method and system for evaluating the health status of power distribution equipment.
背景技术Background Art
随着电力需求的不断增加和电网规模的日益扩大，配电设备的健康状态评估变得越来越重要。配电设备在电力系统中承担着关键的传输和分配任务，其运行状态直接关系到电力系统的安全和稳定。传统的配电设备健康状态评估方法主要依赖于定期检修和人工经验，这些方法存在效率低、准确性差、维护成本高等问题。在现代电力系统中，随着传感技术、物联网和大数据分析技术的发展，配电设备运行数据的实时监测和智能分析成为可能，为实现更加精准的健康状态评估提供了技术基础。With the continuous increase in electricity demand and the ever-expanding scale of power grids, the health status assessment of distribution equipment has become increasingly important. Distribution equipment undertakes key transmission and distribution tasks in the power system, and its operating status is directly related to the safety and stability of the power system. Traditional methods for assessing the health status of distribution equipment mainly rely on regular maintenance and manual experience, which have problems such as low efficiency, poor accuracy, and high maintenance costs. In modern power systems, with the development of sensor technology, the Internet of Things, and big data analysis technology, real-time monitoring and intelligent analysis of distribution equipment operating data have become possible, providing a technical basis for achieving more accurate health status assessment.
然而，现有技术在实际应用中仍存在诸多技术问题。首先，传统的健康状态评估方法通常依赖单一的参数（如温度或电流）进行评估，无法全面反映设备的真实运行状态。其次，即使一些先进的评估方法使用了多参数数据，但在数据融合和特征提取方面仍存在不足，导致评估结果的可靠性和准确性不高。此外，现有技术在异常预警和故障诊断方面缺乏智能化手段，无法及时、准确地识别故障类型、位置和原因，导致设备维护和管理滞后，增加了电力系统运行的风险。However, there are still many technical problems in the practical application of existing technologies. First, traditional health status assessment methods usually rely on a single parameter (such as temperature or current) for assessment, which cannot fully reflect the actual operating status of the equipment. Secondly, even if some advanced assessment methods use multi-parameter data, there are still deficiencies in data fusion and feature extraction, resulting in low reliability and accuracy of the assessment results. In addition, existing technologies lack intelligent means in abnormal warning and fault diagnosis, and cannot identify the type, location and cause of faults in a timely and accurate manner, resulting in delayed equipment maintenance and management, and increasing the risk of power system operation.
发明内容Summary of the invention
基于上述目的，本发明提供了一种配电设备健康状态评估方法及系统。Based on the above objectives, the present invention provides a method and system for evaluating the health status of power distribution equipment.
一种配电设备健康状态评估方法，包括以下步骤：A method for evaluating the health status of power distribution equipment comprises the following steps:
S1，数据采集：通过传感器网络采集配电设备的多维度运行数据，包括电压、电流、温度、湿度、振动、声音以及环境数据，建立全面的配电设备运行数据集；S1, data collection: collect multi-dimensional operation data of power distribution equipment through sensor networks, including voltage, current, temperature, humidity, vibration, sound and environmental data, and establish a comprehensive power distribution equipment operation data set;
S2，数据预处理：对采集到的多维度运行数据进行预处理，包括数据清洗、数据校准和异常数据检测，使用统计方法剔除异常数据，以保证数据的准确性和一致性；S2, data preprocessing: preprocess the collected multi-dimensional operation data, including data cleaning, data calibration and abnormal data detection, and use statistical methods to eliminate abnormal data to ensure data accuracy and consistency;
S3，多模态特征提取：从预处理后的数据中提取多模态特征参数，包括电气特征、热力学特征、机械特征和环境特征，为后续分析提供全面的特征信息；S3, multimodal feature extraction: extract multimodal feature parameters from the preprocessed data, including electrical features, thermodynamic features, mechanical features, and environmental features, to provide comprehensive feature information for subsequent analysis;
S4，融合健康指数计算：采用多源数据融合技术，将提取的多模态特征进行加权融合，计算配电设备的综合健康指数，该健康指数反映设备的整体运行状态和健康水平；S4, fusion health index calculation: using multi-source data fusion technology, the extracted multi-modal features are weighted and fused to calculate the comprehensive health index of the distribution equipment, which reflects the overall operating status and health level of the equipment;
S5，健康状态评估模型构建：基于综合健康指数，利用深度学习算法和历史运行数据，建立配电设备的健康状态评估模型；S5, health status assessment model construction: Based on the comprehensive health index, using deep learning algorithms and historical operation data, a health status assessment model for distribution equipment is established;
S6，智能预警与故障诊断：当监测到设备健康状态异常时，生成智能预警信息，并结合历史数据和故障特征进行智能故障诊断，确定故障类型、位置和原因，生成维护建议。S6, intelligent early warning and fault diagnosis: When abnormal equipment health status is detected, intelligent early warning information is generated, and intelligent fault diagnosis is performed based on historical data and fault characteristics to determine the fault type, location and cause, and generate maintenance recommendations.
可选的，所述S1数据采集中的传感器网络具体包括：Optionally, the sensor network in the S1 data collection specifically includes:
温度传感器：安装在配电设备关键部件（如变压器、断路器）的表面或内部，用于监测配电设备温度数据；Temperature sensor: installed on the surface or inside of key components of power distribution equipment (such as transformers and circuit breakers) to monitor the temperature data of power distribution equipment;
电流传感器：安装在配电设备的电流通道上，用于测量流经配电设备的电流，得到电流数据；Current sensor: installed on the current channel of the power distribution equipment to measure the current flowing through the power distribution equipment and obtain current data;
电压传感器：安装在配电设备的输出端口，用于测量配电设备的工作电压，获取电压数据；Voltage sensor: installed at the output port of the power distribution equipment to measure the working voltage of the power distribution equipment and obtain voltage data;
振动传感器：安装在配电设备的外壳或支撑结构上，用于监测配电设备运行时的振动情况，获取振动数据；Vibration sensor: installed on the housing or supporting structure of the power distribution equipment to monitor the vibration of the power distribution equipment during operation and obtain vibration data;
声音传感器：安装在配电设备周围，捕捉设备运行时的声音特征，获取声音数据；Sound sensor: installed around the power distribution equipment to capture the sound characteristics of the equipment during operation and obtain sound data;
环境传感器：安装在设备周围，用于监测环境温度、湿度和噪声数据。Environmental sensors: Installed around the equipment to monitor ambient temperature, humidity, and noise data.
可选的，所述数据预处理具体包括：Optionally, the data preprocessing specifically includes:
S21，数据清洗：对采集到的传感器数据进行缺失值处理；S21, data cleaning: processing missing values of collected sensor data;
S22，数据校准：校准传感器的数据，使传感器在相同条件下具有一致的测量结果，确保所有传感器数据的时间戳一致；S22, data calibration: calibrate the sensor data so that the sensor has consistent measurement results under the same conditions and ensure that the timestamps of all sensor data are consistent;
S23，异常数据检测：使用统计方法检测异常值；S23, abnormal data detection: using statistical methods to detect outliers;
S24，数据标准化：将所有传感器的数据标准化，使得不同传感器的数据在同一个尺度上进行比较。S24, data standardization: standardize the data of all sensors so that the data of different sensors can be compared on the same scale.
可选的，所述多模态特征提取具体包括：Optionally, the multimodal feature extraction specifically includes:
S31，电气特征提取：电气特征包括电流波形特征和频率波动特征，从预处理后的电流数据中提取所有数据点，计算电流数据的均值和均方根值，找到预处理后的电流数据中的最大值，计算峰值因子，获取电流波形特征，从预处理后的电流数据中提取时间序列数据，进行快速傅里叶变换计算，获取频率波动特征；S31, electrical feature extraction: the electrical features include current waveform features and frequency fluctuation features. All data points are extracted from the preprocessed current data, the mean and root mean square value of the current data are calculated, the maximum value in the preprocessed current data is found, the peak factor is calculated, the current waveform features are obtained, time series data is extracted from the preprocessed current data, and fast Fourier transform calculation is performed to obtain frequency fluctuation features.
S32，热力学特征：热力学特征包括温度变化特征和热辐射特征，通过从预处理后的温度数据中提取所有数据点，计算温度数据的均值，从预处理后的温度数据中提取相邻数据点，计算温度变化率，获取温度变化特征，从预处理后的温度数据中提取温度值，计算热辐射强度，获取热辐射特征；S32, thermodynamic characteristics: Thermodynamic characteristics include temperature change characteristics and thermal radiation characteristics, which are obtained by extracting all data points from the preprocessed temperature data, calculating the mean of the temperature data, extracting adjacent data points from the preprocessed temperature data, calculating the temperature change rate, and obtaining the temperature change characteristics, extracting the temperature value from the preprocessed temperature data, calculating the thermal radiation intensity, and obtaining the thermal radiation characteristics;
S33，机械特征：机械特征包括振动频谱特征和机械应力特征，从预处理后的振动数据中提取时间序列数据，进行快速傅里叶变换计算，从快速傅里叶变换结果中提取频谱信号，计算峰值，获取振动频谱特征，从预处理后的振动数据中提取相邻数据点，计算应力变化率，获取机械应力特征；S33, mechanical characteristics: mechanical characteristics include vibration spectrum characteristics and mechanical stress characteristics. Time series data is extracted from the preprocessed vibration data, and fast Fourier transform calculation is performed. Spectral signals are extracted from the fast Fourier transform results, and peak values are calculated to obtain vibration spectrum characteristics. Adjacent data points are extracted from the preprocessed vibration data, and stress change rates are calculated to obtain mechanical stress characteristics.
S34，环境特征提取：环境特征包括湿度变化特征和环境噪声特征，从预处理后的湿度数据中提取所有数据点，计算湿度数据的均值，从预处理后的湿度数据中提取相邻数据点，计算湿度变化率，获取湿度变化特征，从预处理后的噪声数据中提取噪声功率，计算噪声强度，获取环境噪声特征。S34, environmental feature extraction: Environmental features include humidity change features and environmental noise features. All data points are extracted from the preprocessed humidity data, the mean of the humidity data is calculated, adjacent data points are extracted from the preprocessed humidity data, the humidity change rate is calculated, and the humidity change features are obtained. Noise power is extracted from the preprocessed noise data, the noise intensity is calculated, and the environmental noise features are obtained.
可选的，所述融合健康指数计算具体包括：Optionally, the fusion health index calculation specifically includes:
S41，特征归一化：对不同模态的特征进行归一化处理；S41, feature normalization: normalize the features of different modalities;
S42，特征加权：根据特征对健康状态的影响程度，给予不同的权重；S42, feature weighting: different weights are given according to the degree of influence of the features on the health status;
S43，加权融合：将归一化后的特征按权重进行加权求和，得到综合健康指数；S43, weighted fusion: the normalized features are weighted and summed according to the weights to obtain a comprehensive health index;
S44，综合健康指数解释：将计算得到的综合健康指数进行解释，用于评估配电设备的健康状态。S44, comprehensive health index interpretation: the calculated comprehensive health index is interpreted to evaluate the health status of the power distribution equipment.
可选的，所述健康状态评估模型构建具体包括：Optionally, the health status assessment model construction specifically includes:
S51，历史运行数据收集：收集配电设备历史运行数据，并提取特征数据，包括电气特征、热力学特征、机械特征和环境特征，基于提取的特征数据，计算出相应的综合健康指数；S51, historical operation data collection: collect historical operation data of distribution equipment, and extract characteristic data, including electrical characteristics, thermodynamic characteristics, mechanical characteristics and environmental characteristics, and calculate the corresponding comprehensive health index based on the extracted characteristic data;
S52，特征标注：为提取的特征数据打上健康状态标签，如正常、异常和故障；S52, feature labeling: labeling the extracted feature data with health status labels, such as normal, abnormal, and faulty;
构建特征向量：每个特征向量均包括S51中提取的特征数据和综合健康指数，每个特征向量对应一个健康状态标签（正常、异常、故障）；Construct feature vectors: Each feature vector includes the feature data extracted in S51 and the comprehensive health index, and each feature vector corresponds to a health status label (normal, abnormal, faulty);
S53，建立健康状态评估模型：通过长短期记忆网络算法，构建健康状态评估模型；S53, establish a health status assessment model: construct a health status assessment model through a long short-term memory network algorithm;
S54，模型训练：将特征向量（包括综合健康指数）作为输入，将健康状态标签作为输出，输入到模型中进行训练；S54, model training: taking the feature vector (including the comprehensive health index) as input and the health status label as output, and inputting it into the model for training;
S55，模型评估：使用准确率、精确率、召回率、F1分数评估健康状态评估模型性能；S55, Model evaluation: Use accuracy, precision, recall, and F1 score to evaluate the performance of the health status assessment model;
S56，模型部署：将训练好的健康状态评估模型部署到配电设备监控系统中，输入配电设备的实时运行数据，预测健康状态。S56, model deployment: deploy the trained health status assessment model to the power distribution equipment monitoring system, input the real-time operation data of the power distribution equipment, and predict the health status.
可选的，所述S6中生成智能预警信息包括：Optionally, generating intelligent warning information in S6 includes:
S61，实时数据输入：将配电设备的实时运行数据输入到已经训练好的健康状态评估模型中进行健康状态预测；S61, real-time data input: input the real-time operation data of the power distribution equipment into the trained health status assessment model to perform health status prediction;
S62，健康状态判断：模型输出当前设备的健康状态，包括正常、异常和故障，若健康状态被预测为异常或故障，则触发预警机制；S62, health status judgment: the model outputs the health status of the current device, including normal, abnormal and faulty. If the health status is predicted to be abnormal or faulty, the early warning mechanism is triggered;
S63，生成预警信息：在检测到异常或故障状态时，生成预警信息。S63, generating warning information: when an abnormality or a fault state is detected, generating warning information.
可选的，所述S6中智能故障诊断包括：Optionally, the intelligent fault diagnosis in S6 includes:
S64，提取特征数据：从当前异常或故障状态下的实时数据和历史数据中提取相关特征，包括电气特征、热力学特征、机械特征和环境特征；S64, extracting characteristic data: extracting relevant characteristics from the real-time data and historical data under the current abnormal or fault state, including electrical characteristics, thermodynamic characteristics, mechanical characteristics and environmental characteristics;
S65，匹配历史数据：将当前提取的特征与历史故障数据进行匹配；S65, matching historical data: matching the currently extracted features with historical fault data;
S66，确定故障类型、位置和原因：根据匹配的历史数据，确定当前故障的类型，根据历史数据中的故障位置信息，以及当前传感器数据，推断故障位置，结合历史数据中的故障原因和当前环境特征，分析故障原因。S66, determine the fault type, location and cause: determine the type of the current fault based on the matched historical data, infer the fault location based on the fault location information in the historical data and the current sensor data, and analyze the fault cause based on the fault cause in the historical data and the current environmental characteristics.
可选的，所述S6中生成维护建议包括：Optionally, generating a maintenance suggestion in S6 includes:
S67，综合分析：结合故障类型、位置和原因，进行综合分析，生成维护建议；S67, comprehensive analysis: Combine the fault type, location and cause to conduct a comprehensive analysis and generate maintenance recommendations;
S68，维护建议内容：维护建议包括故障类型（如过热、短路）、故障位置（如变压器、断路器）、故障原因（如冷却系统失效、线路老化）、紧急处理措施（如立即停机检查、降低负载）和长期维护建议（如更换老化部件、改进冷却系统）；S68, Maintenance recommendations: Maintenance recommendations include fault type (such as overheating, short circuit), fault location (such as transformer, circuit breaker), fault cause (such as cooling system failure, line aging), emergency treatment measures (such as immediate shutdown inspection, load reduction) and long-term maintenance recommendations (such as replacement of aging parts, improvement of cooling system);
S69，建议格式：生成的维护建议以标准格式呈现，便于维护人员快速理解和执行。S69, Recommendation format: Generated maintenance recommendations are presented in a standard format to facilitate maintenance personnel to quickly understand and implement them.
一种配电设备健康状态评估系统，用于实现如权利要求1-9任一项所述的一种配电设备健康状态评估方法，包括以下模块：A power distribution equipment health status assessment system, used to implement a power distribution equipment health status assessment method as claimed in any one of claims 1 to 9, comprising the following modules:
数据采集模块：通过传感器网络采集配电设备的多维度运行数据，包括电压、电流、温度、湿度、振动、声音以及环境数据；Data acquisition module: collects multi-dimensional operating data of power distribution equipment through sensor networks, including voltage, current, temperature, humidity, vibration, sound and environmental data;
数据预处理模块：对采集到的多维度运行数据进行预处理，包括数据清洗、数据校准和异常数据检测，使用统计方法剔除异常数据；Data preprocessing module: preprocesses the collected multi-dimensional operation data, including data cleaning, data calibration and abnormal data detection, and uses statistical methods to eliminate abnormal data;
特征提取模块：从预处理后的数据中提取多模态特征参数，包括电气特征、热力学特征、机械特征和环境特征；Feature extraction module: extracts multimodal feature parameters from preprocessed data, including electrical features, thermodynamic features, mechanical features, and environmental features;
综合健康指数计算模块：采用多源数据融合技术，将提取的多模态特征进行加权融合，计算配电设备的综合健康指数；Comprehensive health index calculation module: uses multi-source data fusion technology to perform weighted fusion on the extracted multi-modal features and calculate the comprehensive health index of the distribution equipment;
健康状态评估模型构建模块：基于综合健康指数，利用LSTM（长短期记忆网络）深度学习算法和历史运行数据，建立配电设备的健康状态评估模型；Health status assessment model building module: Based on the comprehensive health index, the health status assessment model of the distribution equipment is established using the LSTM (Long Short-Term Memory Network) deep learning algorithm and historical operation data;
智能预警与故障诊断模块：当监测到设备健康状态异常时，生成智能预警信息，并结合历史数据和故障特征进行智能故障诊断，确定故障类型、位置和原因，提供详细的维护建议Intelligent early warning and fault diagnosis module: When abnormal equipment health status is detected, intelligent early warning information is generated, and intelligent fault diagnosis is performed based on historical data and fault characteristics to determine the fault type, location and cause, and provide detailed maintenance suggestions
本发明的有益效果：Beneficial effects of the present invention:
本发明，通过传感器网络采集电压、电流、温度、湿度、振动、声音以及环境数据等多维度运行数据，建立全面的配电设备运行数据集。利用多源数据融合技术，将不同来源的特征数据进行加权融合，计算综合健康指数，使评估结果更加全面和准确。结合长短期记忆网络深度学习算法，对设备运行数据进行动态监测和智能分析，能够捕捉设备运行状态的细微变化，提高了评估的精度和可靠性。The present invention collects multi-dimensional operating data such as voltage, current, temperature, humidity, vibration, sound and environmental data through a sensor network to establish a comprehensive distribution equipment operating data set. Using multi-source data fusion technology, feature data from different sources are weighted and fused to calculate the comprehensive health index, making the evaluation results more comprehensive and accurate. Combined with the long short-term memory network deep learning algorithm, the equipment operation data is dynamically monitored and intelligently analyzed, which can capture subtle changes in the equipment operation status and improve the accuracy and reliability of the evaluation.
本发明，利用训练好的健康状态评估模型，对实时输入的设备运行数据进行健康状态预测。当监测到设备健康状态异常时，系统生成智能预警信息，通过实时监测和预警机制，能够及时发现配电设备潜在的运行风险，避免故障扩展，保障电力系统的安全稳定运行。The present invention uses a trained health status assessment model to predict the health status of the equipment operation data input in real time. When the health status of the equipment is abnormal, the system generates intelligent early warning information. Through real-time monitoring and early warning mechanisms, it can timely discover the potential operation risks of the distribution equipment, avoid fault expansion, and ensure the safe and stable operation of the power system.
本发明，当设备健康状态被预测为异常或故障时，系统结合历史数据和故障特征进行智能故障诊断，确定故障类型、位置和可能原因。通过匹配历史故障数据，利用相似度算法找到最相似的故障案例，推断当前故障的详细信息。系统生成详细的维护建议，包括故障类型、故障位置、可能原因、紧急处理措施和长期维护建议。通过智能故障诊断和详细的维护建议，帮助维护人员迅速识别和处理问题，提高设备的维护效率，延长设备的使用寿命，降低维护成本。In the present invention, when the health status of the equipment is predicted to be abnormal or faulty, the system combines historical data and fault characteristics to perform intelligent fault diagnosis to determine the fault type, location and possible cause. By matching historical fault data, the similarity algorithm is used to find the most similar fault case and infer the detailed information of the current fault. The system generates detailed maintenance suggestions, including fault type, fault location, possible cause, emergency treatment measures and long-term maintenance suggestions. Through intelligent fault diagnosis and detailed maintenance suggestions, maintenance personnel are helped to quickly identify and handle problems, improve equipment maintenance efficiency, extend equipment service life and reduce maintenance costs.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
为了更清楚地说明本发明或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
图1为本发明实施例的评估方法步骤示意图；FIG1 is a schematic diagram of the steps of an evaluation method according to an embodiment of the present invention;
图2为本发明实施例的评估系统流程示意图。FIG. 2 is a schematic diagram of the evaluation system flow according to an embodiment of the present invention.
具体实施方式DETAILED DESCRIPTION
下面结合附图和具体实施例对本发明进行详细描述。同时在这里做以说明的是，为了使实施例更加详尽，下面的实施例为最佳、优选实施例，对于一些公知技术本领域技术人员也可采用其他替代方式而进行实施；而且附图部分仅是为了更具体的描述实施例，而并不旨在对本发明进行具体的限定。The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.
需要指出的是，在说明书中提到“一个实施例”、“实施例”、“示例性实施例”、“一些实施例”等指示所述的实施例可以包括特定特征、结构或特性，但未必每个实施例都包括该特定特征、结构或特性。另外，在结合实施例描述特定特征、结构或特性时，结合其它实施例（无论是否明确描述）实现这种特征、结构或特性应在相关领域技术人员的知识范围内。It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).
通常，可以至少部分从上下文中的使用来理解术语。例如，至少部分取决于上下文，本文中使用的术语“一个或多个”可以用于描述单数意义的任何特征、结构或特性，或者可以用于描述复数意义的特征、结构或特性的组合。另外，术语“基于”可以被理解为不一定旨在传达一组排他性的因素，而是可以替代地，至少部分地取决于上下文，允许存在不一定明确描述的其他因素。In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.
如图1所示，一种配电设备健康状态评估方法，包括以下步骤：As shown in FIG1 , a method for evaluating the health status of a power distribution device includes the following steps:
S1，数据采集：通过传感器网络采集配电设备的多维度运行数据，包括电压、电流、温度、湿度、振动、声音以及环境数据，建立全面的配电设备运行数据集；S1, data collection: collect multi-dimensional operation data of power distribution equipment through sensor networks, including voltage, current, temperature, humidity, vibration, sound and environmental data, and establish a comprehensive power distribution equipment operation data set;
S2，数据预处理：对采集到的多维度运行数据进行预处理，包括数据清洗、数据校准和异常数据检测，使用统计方法剔除异常数据，以保证数据的准确性和一致性；S2, data preprocessing: preprocess the collected multi-dimensional operation data, including data cleaning, data calibration and abnormal data detection, and use statistical methods to eliminate abnormal data to ensure data accuracy and consistency;
S3，多模态特征提取：从预处理后的数据中提取多模态特征参数，包括电气特征（如电流波形、频率波动）、热力学特征（如温度变化、热辐射）、机械特征（如振动频谱、机械应力）和环境特征（如湿度变化、环境噪声），为后续分析提供全面的特征信息；S3, multimodal feature extraction: extract multimodal feature parameters from the preprocessed data, including electrical features (such as current waveform, frequency fluctuation), thermodynamic features (such as temperature change, thermal radiation), mechanical features (such as vibration spectrum, mechanical stress) and environmental features (such as humidity change, environmental noise), to provide comprehensive feature information for subsequent analysis;
S4，融合健康指数计算：采用多源数据融合技术，将提取的多模态特征进行加权融合，计算配电设备的综合健康指数，该健康指数反映设备的整体运行状态和健康水平；S4, fusion health index calculation: using multi-source data fusion technology, the extracted multi-modal features are weighted and fused to calculate the comprehensive health index of the distribution equipment, which reflects the overall operating status and health level of the equipment;
S5，健康状态评估模型构建：基于综合健康指数，利用深度学习算法和历史运行数据，建立配电设备的健康状态评估模型；S5, health status assessment model construction: Based on the comprehensive health index, using deep learning algorithms and historical operation data, a health status assessment model for distribution equipment is established;
S6，智能预警与故障诊断：当监测到设备健康状态异常时，生成智能预警信息，并结合历史数据和故障特征进行智能故障诊断，确定故障类型、位置和原因，生成维护建议。S6, intelligent early warning and fault diagnosis: When abnormal equipment health status is detected, intelligent early warning information is generated, and intelligent fault diagnosis is performed based on historical data and fault characteristics to determine the fault type, location and cause, and generate maintenance recommendations.
S1数据采集中的传感器网络具体包括：The sensor network in S1 data collection specifically includes:
温度传感器：安装在配电设备关键部件（如变压器、断路器）的表面或内部，用于监测配电设备温度数据；Temperature sensor: installed on the surface or inside of key components of power distribution equipment (such as transformers and circuit breakers) to monitor the temperature data of power distribution equipment;
电流传感器：安装在配电设备的电流通道上，用于测量流经配电设备的电流，得到电流数据；Current sensor: installed on the current channel of the power distribution equipment to measure the current flowing through the power distribution equipment and obtain current data;
电压传感器：安装在配电设备的输出端口，用于测量配电设备的工作电压，获取电压数据；Voltage sensor: installed at the output port of the power distribution equipment to measure the working voltage of the power distribution equipment and obtain voltage data;
振动传感器：安装在配电设备的外壳或支撑结构上，用于监测配电设备运行时的振动情况，获取振动数据；Vibration sensor: installed on the housing or supporting structure of the power distribution equipment to monitor the vibration of the power distribution equipment during operation and obtain vibration data;
声音传感器：安装在配电设备周围，捕捉设备运行时的声音特征，获取声音数据；Sound sensor: installed around the power distribution equipment to capture the sound characteristics of the equipment during operation and obtain sound data;
环境传感器：安装在设备周围，用于监测环境温度、湿度和噪声数据；Environmental sensors: installed around the equipment to monitor ambient temperature, humidity and noise data;
通过以上传感器的使用，能够实现对配电设备多维度运行数据的全面采集，为设备健康状态评估提供可靠的数据支持。Through the use of the above sensors, comprehensive collection of multi-dimensional operating data of power distribution equipment can be achieved, providing reliable data support for equipment health status assessment.
数据预处理具体包括：Data preprocessing specifically includes:
S21，数据清洗：对采集到的传感器数据进行缺失值处理，删除重复数据；S21, data cleaning: processing missing values of collected sensor data and deleting duplicate data;
用均值法填补缺失值，表示为：；Use the mean method to fill in missing values, expressed as: ;
其中，为缺失值，为有效数据点的数量；in, is a missing value, is the number of valid data points;
S22，数据校准：校准传感器的数据，使传感器在相同条件下具有一致的测量结果，确保所有传感器数据的时间戳一致；S22, data calibration: calibrate the sensor data so that the sensor has consistent measurement results under the same conditions and ensure that the timestamps of all sensor data are consistent;
传感器校准采用线性回归，表示为：；The sensor calibration uses linear regression, expressed as: ;
其中，为传感器的原始读数，为校准后的读数，和为回归系数，通过已知标准数据进行拟合确定；in, is the raw reading of the sensor, is the reading after calibration, and is the regression coefficient, which is determined by fitting known standard data;
时间戳校准采用线性插值，表示为：；The timestamp calibration uses linear interpolation, which is expressed as: ;
其中，为插值结果，为需要插值的时间点，和分别为已知数据点的时间戮，和为对应的已知数据点；in, is the interpolation result, is the time point that needs to be interpolated, and are the timestamps of known data points, and is the corresponding known data point;
S23，异常数据检测：使用统计方法检测异常值；S23, abnormal data detection: using statistical methods to detect outliers;
统计方法检测异常采用Z-score计算，表示为：；The statistical method for detecting anomalies uses Z-score calculation, which is expressed as: ;
其中，为数据点，为数据的均值，为数据的标准差，为Z-score，当时，认为数据点是异常值；in, is the data point, is the mean of the data, is the standard deviation of the data, is the Z-score, when When the data point is an outlier;
S24，数据标准化：将所有传感器的数据标准化，使得不同传感器的数据在同一个尺度上进行比较；S24, data standardization: standardize the data of all sensors so that the data of different sensors can be compared on the same scale;
Z-score标准化公式：；Z-score normalization formula: ;
其中，为原始数据，为标准化后的数据，为数据的均值，为数据的标准差；in, is the original data, is the standardized data, is the mean of the data, is the standard deviation of the data;
通过以上步骤，可以有效地对采集到的原始数据进行预处理。数据清洗确保数据的完整性和一致性，数据校准确保不同传感器的读数在相同条件下具有可比性，异常数据检测识别并剔除不合理的数据点，数据标准化将数据缩放到统一范围。这些步骤为后续的数据分析和健康状态评估提供了可靠的数据基础。Through the above steps, the collected raw data can be preprocessed effectively. Data cleaning ensures the integrity and consistency of the data, data calibration ensures that the readings of different sensors are comparable under the same conditions, abnormal data detection identifies and removes unreasonable data points, and data standardization scales the data to a uniform range. These steps provide a reliable data foundation for subsequent data analysis and health status assessment.
多模态特征提取具体包括：Multimodal feature extraction specifically includes:
S31，电气特征提取：电气特征包括电流波形特征和频率波动特征，从预处理后的电流数据中提取所有数据点，计算电流数据的均值和均方根值，找到预处理后的电流数据中的最大值，计算峰值因子，获取电流波形特征，从预处理后的电流数据中提取时间序列数据，进行快速傅里叶变换计算，获取频率波动特征；S31, electrical feature extraction: the electrical features include current waveform features and frequency fluctuation features. All data points are extracted from the preprocessed current data, the mean and root mean square value of the current data are calculated, the maximum value in the preprocessed current data is found, the peak factor is calculated, the current waveform features are obtained, time series data is extracted from the preprocessed current data, and fast Fourier transform calculation is performed to obtain frequency fluctuation features.
电流数据的平均值，计算为：，其中，是电流数据点，是数据点总数；The average value of the current data is calculated as: ,in, is the current data point, is the total number of data points;
电流数据的均方根值，计算为：，其中，是电流数据点，是数据点总数；The RMS value of the current data is calculated as: ,in, is the current data point, is the total number of data points;
电流信号的峰值因子，计算为：，其中，是电流的最大值，RMS是均方根值；The crest factor of the current signal is calculated as: ,in, is the maximum value of the current, RMS is the root mean square value;
对电流信号进行快速傅里叶变换，计算为：，其中，是时间域信号，是频域信号，是快速傅里叶变换长度；Perform fast Fourier transform on the current signal and calculate it as: ,in, is a time domain signal, is the frequency domain signal, is the fast Fourier transform length;
S32，热力学特征：热力学特征包括温度变化特征和热辐射特征，通过从预处理后的温度数据中提取所有数据点，计算温度数据的均值，从预处理后的温度数据中提取相邻数据点，计算温度变化率，获取温度变化特征，从预处理后的温度数据中提取温度值，计算热辐射强度，获取热辐射特征；S32, thermodynamic characteristics: Thermodynamic characteristics include temperature change characteristics and thermal radiation characteristics, which are obtained by extracting all data points from the preprocessed temperature data, calculating the mean of the temperature data, extracting adjacent data points from the preprocessed temperature data, calculating the temperature change rate, and obtaining the temperature change characteristics, extracting the temperature value from the preprocessed temperature data, calculating the thermal radiation intensity, and obtaining the thermal radiation characteristics;
温度数据的平均值，计算为：，其中，是温度数据点，是数据点总数；The average value of the temperature data is calculated as: ,in, is the temperature data point, is the total number of data points;
相邻温度数据点的变化率，计算为：；其中，和是相邻的温度数据点，是时间间隔；The rate of change of adjacent temperature data points is calculated as: ;in, and are adjacent temperature data points, is the time interval;
热辐射强度，计算为：，其中，是辐射强度，是斯蒂芬-玻尔兹曼常数，是温度；Thermal radiation intensity is calculated as: ,in, is the radiation intensity, is the Stefan-Boltzmann constant, is the temperature;
S33，机械特征：机械特征包括振动频谱特征和机械应力特征，从预处理后的振动数据中提取时间序列数据，进行快速傅里叶变换计算，从快速傅里叶变换结果中提取频谱信号，计算峰值，获取振动频谱特征，从预处理后的振动数据中提取相邻数据点，计算应力变化率，获取机械应力特征；S33, mechanical characteristics: mechanical characteristics include vibration spectrum characteristics and mechanical stress characteristics. Time series data is extracted from the preprocessed vibration data, and fast Fourier transform calculation is performed. Spectral signals are extracted from the fast Fourier transform results, and peak values are calculated to obtain vibration spectrum characteristics. Adjacent data points are extracted from the preprocessed vibration data, and stress change rates are calculated to obtain mechanical stress characteristics.
对振动信号进行快速傅里叶变换，计算为：，其中，是时间域信号，是频域信号，是快速傅里叶变换长度；Perform fast Fourier transform on the vibration signal and calculate it as: ,in, is a time domain signal, is the frequency domain signal, is the fast Fourier transform length;
振动信号频谱中的峰值，计算为：，其中，是频谱信号，表示最大值；The peak value in the vibration signal spectrum, calculated as: ,in, is the spectrum signal, Indicates the maximum value;
相邻应力数据点的变化率，计算为：，其中，和是相邻的应力数据点，是时间间隔；The rate of change of adjacent stress data points is calculated as: ,in, and are adjacent stress data points, is the time interval;
S34，环境特征提取：环境特征包括湿度变化特征和环境噪声特征，从预处理后的湿度数据中提取所有数据点，计算湿度数据的均值，从预处理后的湿度数据中提取相邻数据点，计算湿度变化率，获取湿度变化特征，从预处理后的噪声数据中提取噪声功率，计算噪声强度，获取环境噪声特征；S34, environmental feature extraction: environmental features include humidity change features and environmental noise features. All data points are extracted from the preprocessed humidity data, the mean of the humidity data is calculated, adjacent data points are extracted from the preprocessed humidity data, the humidity change rate is calculated, and the humidity change feature is obtained. Noise power is extracted from the preprocessed noise data, the noise intensity is calculated, and the environmental noise feature is obtained.
湿度数据的均值，计算为：，其中，是湿度数据点，是数据点总数；The mean of the humidity data is calculated as: ,in, is the humidity data point, is the total number of data points;
相邻湿度数据点的变化率，计算为：，其中，和是相邻的湿度数据点，是时间间隔；The rate of change of adjacent humidity data points is calculated as: ,in, and are adjacent humidity data points, is the time interval;
噪声强度，计算为：，其中，是噪声功率，是参考功率；Noise intensity, calculated as: ,in, is the noise power, is the reference power;
通过这些步骤，可以从预处理后的数据中提取多模态特征参数，为后续的数据融合和健康状态评估提供了丰富的信息基础。Through these steps, multimodal feature parameters can be extracted from the preprocessed data, providing a rich information basis for subsequent data fusion and health status assessment.
融合健康指数计算具体包括：The calculation of the Fusion Health Index specifically includes:
S41，特征归一化：对不同模态的特征进行归一化处理，以确保它们在同一个尺度上进行比较和融合；S41, feature normalization: normalize the features of different modalities to ensure that they are compared and fused at the same scale;
对每个特征使用Min-Max归一化处理，Min-Max归一化公式为：For each feature Use Min-Max normalization processing, the Min-Max normalization formula is:
，其中，和分别是特征的最小值和最大值； ,in, and Characteristics The minimum and maximum values of
S42，特征加权：根据特征对健康状态的影响程度，给予不同的权重；S42, feature weighting: different weights are given according to the degree of influence of the features on the health status;
确定权重：可以通过专家经验或数据驱动的方法(如主成分分析PCA)来确定每个特征的权重；Determine weights: The weight of each feature can be determined by expert experience or data-driven methods (such as principal component analysis PCA) ;
设有个特征，其权重分别为，满足：；Features features, and their weights are ,satisfy: ;
S43，加权融合：将归一化后的特征按权重进行加权求和，得到综合健康指数；S43, weighted fusion: the normalized features are weighted and summed according to the weights to obtain a comprehensive health index;
综合健康指数，计算为：；The comprehensive health index is calculated as: ;
其中，是综合健康指数，是第个归一化后的特征，是第个特征的权重；in, is a comprehensive health index. It is After normalization, It is The weight of each feature;
S44，综合健康指数解释：将计算得到的综合健康指数进行解释，用于评估配电设备的健康状态；S44, comprehensive health index interpretation: interpreting the calculated comprehensive health index to evaluate the health status of the power distribution equipment;
定义健康状态范围：根据综合健康指数的值，定义不同的健康状态范围；Defining the range of health status: Based on the comprehensive health index The value of defines different health status ranges;
例如：For example:
：健康状态良好； : Good health;
：需要注意； : Need to pay attention;
：健康状态较差，需要维护； : Poor health status, need maintenance;
通过上述步骤，可以采用多源数据融合技术，将提取的多模态特征进行加权融合，计算配电设备的综合健康指数，全面反映设备的运行状态，为设备维护和故障预警提供有力支持。Through the above steps, multi-source data fusion technology can be used to perform weighted fusion on the extracted multi-modal features, calculate the comprehensive health index of the distribution equipment, comprehensively reflect the operating status of the equipment, and provide strong support for equipment maintenance and fault warning.
健康状态评估模型构建具体包括：The construction of health status assessment model specifically includes:
S51，历史运行数据收集：收集配电设备历史运行数据，并提取特征数据，包括电气特征、热力学特征、机械特征和环境特征，基于提取的特征数据，计算出相应的综合健康指数；S51, historical operation data collection: collect historical operation data of distribution equipment, and extract characteristic data, including electrical characteristics, thermodynamic characteristics, mechanical characteristics and environmental characteristics, and calculate the corresponding comprehensive health index based on the extracted characteristic data;
S52，特征标注：为提取的特征数据打上健康状态标签，如正常、异常和故障；S52, feature labeling: labeling the extracted feature data with health status labels, such as normal, abnormal, and faulty;
构建特征向量：每个特征向量均包括S51中提取的特征数据和综合健康指数，每个特征向量对应一个健康状态标签（正常、异常、故障）；Construct feature vectors: Each feature vector includes the feature data extracted in S51 and the comprehensive health index, and each feature vector corresponds to a health status label (normal, abnormal, faulty);
S53，建立健康状态评估模型：通过长短期记忆网络算法，构建健康状态评估模型，具体步骤如下：S53, establish a health status assessment model: construct a health status assessment model through a long short-term memory network algorithm. The specific steps are as follows:
（1）定义模型架构：(1) Define the model architecture:
输入层：时间序列输入；Input layer: time series input ;
LSTM层：多个LSTM单元提取时间序列特征；LSTM layer: multiple LSTM units extract time series features;
全连接层：将LSTM层的输出映射到健康状态；Fully connected layer: maps the output of the LSTM layer to the health state;
输出层：健康状态分类（正常、异常、故障）；Output layer: health status classification (normal, abnormal, fault);
（2）构建LSTM模型；(2) Build an LSTM model;
输入维度：特征的数量；Input dimension: the number of features;
LSTM层：包含一定数量的LSTM单元；LSTM layer: contains a certain number of LSTM units;
全连接层：包含一定数量的神经元，用于健康状态分类；Fully connected layer: contains a certain number of neurons for health status classification;
激活函数：ReLU用于隐藏层，Softmax用于输出层；Activation function: ReLU for hidden layers, Softmax for output layers;
S54，模型训练：将特征向量（包括综合健康指数）作为输入，将健康状态标签作为输出，输入到模型中进行训练；S54, model training: taking the feature vector (including the comprehensive health index) as input and the health status label as output, and inputting it into the model for training;
S55，模型评估：使用准确率、精确率、召回率、F1分数评估健康状态评估模型性能；S55, Model evaluation: Use accuracy, precision, recall, and F1 score to evaluate the performance of the health status assessment model;
S56，模型部署：将训练好的健康状态评估模型部署到配电设备监控系统中，输入配电设备的实时运行数据，预测健康状态。S56, model deployment: deploy the trained health status assessment model to the power distribution equipment monitoring system, input the real-time operation data of the power distribution equipment, and predict the health status.
S6中生成智能预警信息包括：The intelligent warning information generated in S6 includes:
S61，实时数据输入：将配电设备的实时运行数据输入到已经训练好的健康状态评估模型中进行健康状态预测；S61, real-time data input: input the real-time operation data of the power distribution equipment into the trained health status assessment model to perform health status prediction;
S62，健康状态判断：模型输出当前设备的健康状态，包括正常、异常和故障，若健康状态被预测为异常或故障，则触发预警机制；S62, health status judgment: the model outputs the health status of the current device, including normal, abnormal and faulty. If the health status is predicted to be abnormal or faulty, the early warning mechanism is triggered;
S63，生成预警信息：在检测到异常或故障状态时，生成预警信息，预警信息应包括以下内容：S63, generate warning information: when an abnormal or faulty state is detected, generate warning information, which should include the following contents:
设备ID；Device ID;
监测时间；Monitoring time;
健康状态（异常或故障）；Health status (abnormal or faulty);
预警级别（如低、中、高）；Alert level (e.g. low, medium, high);
简要描述（如电流波动异常、温度过高等）。A brief description (such as abnormal current fluctuation, excessive temperature, etc.).
S6中智能故障诊断包括：Intelligent fault diagnosis in S6 includes:
S64，提取特征数据：从当前异常或故障状态下的实时数据和历史数据中提取相关特征，包括电气特征、热力学特征、机械特征和环境特征；S64, extracting characteristic data: extracting relevant characteristics from the real-time data and historical data under the current abnormal or fault state, including electrical characteristics, thermodynamic characteristics, mechanical characteristics and environmental characteristics;
S65，匹配历史数据：将当前提取的特征与历史故障数据进行匹配，使用相似度算法（如余弦相似度、欧氏距离）找到最相似的历史故障案例；S65, matching historical data: matching the currently extracted features with the historical fault data, and using a similarity algorithm (such as cosine similarity, Euclidean distance) to find the most similar historical fault case;
S66，确定故障类型、位置和原因：根据匹配的历史数据，确定当前故障的类型，例如，结合历史数据的故障类型标签，可以确定当前故障可能是过热、短路、机械故障等，根据历史数据中的故障位置信息，以及当前传感器数据，推断故障位置，例如，通过分析振动数据和电流波动数据，可以确定故障发生在变压器、断路器或其他部件上，结合历史数据中的故障原因和当前环境特征，分析故障原因，例如，温度升高可能是由于冷却系统失效，电流波动可能是由于线路老化或连接松动。S66, determine the fault type, location and cause: determine the type of the current fault based on the matched historical data. For example, combined with the fault type label of the historical data, it can be determined that the current fault may be overheating, short circuit, mechanical failure, etc. According to the fault location information in the historical data and the current sensor data, infer the fault location. For example, by analyzing the vibration data and current fluctuation data, it can be determined that the fault occurred in the transformer, circuit breaker or other components. Combined with the fault causes in the historical data and the current environmental characteristics, analyze the fault causes. For example, the temperature increase may be due to the failure of the cooling system, and the current fluctuation may be due to line aging or loose connection.
S6中生成维护建议包括：Maintenance recommendations generated in S6 include:
S67，综合分析：结合故障类型、位置和原因，进行综合分析，生成维护建议；S67, comprehensive analysis: Combine the fault type, location and cause to conduct a comprehensive analysis and generate maintenance recommendations;
S68，维护建议内容：维护建议包括故障类型（如过热、短路）、故障位置（如变压器、断路器）、故障原因（如冷却系统失效、线路老化）、紧急处理措施（如立即停机检查、降低负载）和长期维护建议（如更换老化部件、改进冷却系统）；S68, Maintenance recommendations: Maintenance recommendations include fault type (such as overheating, short circuit), fault location (such as transformer, circuit breaker), fault cause (such as cooling system failure, line aging), emergency treatment measures (such as immediate shutdown inspection, load reduction) and long-term maintenance recommendations (such as replacement of aging parts, improvement of cooling system);
S69，建议格式：生成的维护建议以标准格式呈现，便于维护人员快速理解和执行，标准格式模版如下：S69, Suggestion format: The generated maintenance suggestions are presented in a standard format to facilitate maintenance personnel to quickly understand and implement them. The standard format template is as follows:
设备ID: XYZ123；Device ID: XYZ123;
监测时间: 年-月-日 00:00；Monitoring time: year-month-day 00:00;
健康状态: 故障；Health status: Fault;
预警级别: 高；Warning level: High;
故障类型: 过热；Fault type: Overheating;
故障位置: 变压器；Fault location: Transformer;
可能原因: 冷却系统失效；Possible reasons: Cooling system failure;
紧急处理措施: 请立即停机检查冷却系统；Emergency measures: Please stop the machine immediately and check the cooling system;
长期维护建议: 考虑更换冷却系统部件，并定期检查冷却液位；Long-term maintenance advice: Consider replacing cooling system components and check coolant levels regularly;
通过以上步骤，当检测到配电设备健康状态异常时，系统可以生成智能预警信息，并结合历史数据和故障特征进行智能故障诊断，确定故障类型、位置和原因，提供详细的维护建议。这一过程不仅帮助维护人员迅速识别和处理问题，还为设备的长期稳定运行提供了有力支持。Through the above steps, when the health status of the power distribution equipment is detected to be abnormal, the system can generate intelligent early warning information, and combine historical data and fault characteristics to perform intelligent fault diagnosis, determine the fault type, location and cause, and provide detailed maintenance suggestions. This process not only helps maintenance personnel quickly identify and handle problems, but also provides strong support for the long-term stable operation of the equipment.
如图2所示，一种配电设备健康状态评估系统，用于实现上述的一种配电设备健康状态评估方法，包括以下模块：As shown in FIG2 , a power distribution equipment health status assessment system is used to implement the above-mentioned power distribution equipment health status assessment method, including the following modules:
数据采集模块：通过传感器网络采集配电设备的多维度运行数据，包括电压、电流、温度、湿度、振动、声音以及环境数据；Data acquisition module: collects multi-dimensional operating data of power distribution equipment through sensor networks, including voltage, current, temperature, humidity, vibration, sound and environmental data;
数据预处理模块：对采集到的多维度运行数据进行预处理，包括数据清洗、数据校准和异常数据检测，使用统计方法剔除异常数据；Data preprocessing module: preprocesses the collected multi-dimensional operation data, including data cleaning, data calibration and abnormal data detection, and uses statistical methods to eliminate abnormal data;
特征提取模块：从预处理后的数据中提取多模态特征参数，包括电气特征、热力学特征、机械特征和环境特征；Feature extraction module: extracts multimodal feature parameters from preprocessed data, including electrical features, thermodynamic features, mechanical features, and environmental features;
综合健康指数计算模块：采用多源数据融合技术，将提取的多模态特征进行加权融合，计算配电设备的综合健康指数；Comprehensive health index calculation module: uses multi-source data fusion technology to perform weighted fusion on the extracted multi-modal features and calculate the comprehensive health index of the distribution equipment;
健康状态评估模型构建模块：基于综合健康指数，利用LSTM（长短期记忆网络）深度学习算法和历史运行数据，建立配电设备的健康状态评估模型；Health status assessment model building module: Based on the comprehensive health index, the health status assessment model of the distribution equipment is established using the LSTM (Long Short-Term Memory Network) deep learning algorithm and historical operation data;
智能预警与故障诊断模块：当监测到设备健康状态异常时，生成智能预警信息，并结合历史数据和故障特征进行智能故障诊断，确定故障类型、位置和原因，提供详细的维护建议。Intelligent early warning and fault diagnosis module: When abnormal equipment health status is detected, intelligent early warning information is generated, and intelligent fault diagnosis is performed based on historical data and fault characteristics to determine the fault type, location and cause, and provide detailed maintenance recommendations.
本发明涵盖任何在本发明的精髓和范围上做的替代、修改、等效方法以及方案。为了使公众对本发明有彻底的了解，在以下本发明优选实施例中详细说明了具体的细节，而对本领域技术人员来说没有这些细节的描述也可以完全理解本发明。另外，为了避免对本发明的实质造成不必要的混淆，并没有详细说明众所周知的方法、过程、流程、元件和电路等。The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, and those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.
以上所述仅是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以作出若干改进和润饰，这些改进和润饰也应视为本发明的保护范围。The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410979956.4ACN118503636B (en) | 2024-07-22 | 2024-07-22 | Power distribution equipment health state assessment method and system |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410979956.4ACN118503636B (en) | 2024-07-22 | 2024-07-22 | Power distribution equipment health state assessment method and system |
| Publication Number | Publication Date |
|---|---|
| CN118503636Atrue CN118503636A (en) | 2024-08-16 |
| CN118503636B CN118503636B (en) | 2024-11-19 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410979956.4AActiveCN118503636B (en) | 2024-07-22 | 2024-07-22 | Power distribution equipment health state assessment method and system |
| Country | Link |
|---|---|
| CN (1) | CN118503636B (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118657404A (en)* | 2024-08-16 | 2024-09-17 | 广东电网有限责任公司茂名供电局 | A multi-modal power grid health assessment and analysis method |
| CN118657517A (en)* | 2024-08-19 | 2024-09-17 | 溯源电气(泉州)有限公司 | Intelligent data collection and transmission method for electrical equipment based on the Internet of Things |
| CN118690313A (en)* | 2024-08-26 | 2024-09-24 | 大唐可再生能源试验研究院有限公司 | Wind turbine operation and maintenance decision system based on artificial intelligence |
| CN118690308A (en)* | 2024-08-23 | 2024-09-24 | 北京理工大学 | Anomaly detection method and system for complex industrial systems embedded with physical knowledge |
| CN118859037A (en)* | 2024-09-23 | 2024-10-29 | 国网河南省电力公司新乡供电公司 | A method for power equipment fault analysis based on multi-source data fusion |
| CN118914725A (en)* | 2024-10-08 | 2024-11-08 | 北京融信数联科技有限公司 | Fault diagnosis and early warning method, system and medium of energy storage management system |
| CN118937970A (en)* | 2024-10-09 | 2024-11-12 | 山东海纳智能装备科技股份有限公司 | A mine circuit fault self-diagnosis method and system |
| CN119000053A (en)* | 2024-10-25 | 2024-11-22 | 天普能源装备(天津)有限公司 | Multi-flywheel automatic test monitoring system |
| CN119027102A (en)* | 2024-10-23 | 2024-11-26 | 中煤科工集团信息技术有限公司 | Coal mining machine equipment health management method and device |
| CN119059385A (en)* | 2024-11-04 | 2024-12-03 | 江苏省工业互联网发展研究中心 | An intelligent elevator management system based on edge computing and computing network integration |
| CN119129862A (en)* | 2024-11-15 | 2024-12-13 | 北京网藤科技有限公司 | A method for equipment management integrating power dispatching |
| CN119125941A (en)* | 2024-11-08 | 2024-12-13 | 珠海腾盾科技有限公司 | Emergency power supply status monitoring method and system based on multimodal data |
| CN119150210A (en)* | 2024-11-19 | 2024-12-17 | 陕西思极科技有限公司 | Low-voltage power distribution network running state detection method based on fusion terminal |
| CN119150207A (en)* | 2024-11-18 | 2024-12-17 | 绍兴文理学院 | Robot equipment health state assessment method and system |
| CN119199413A (en)* | 2024-09-10 | 2024-12-27 | 深圳市亿新达科技有限公司 | Capacitive voltage transformer insulation fault diagnosis and analysis method and system |
| CN119224630A (en)* | 2024-11-28 | 2024-12-31 | 深圳市南霸科技有限公司 | Detection method and system of emergency starting power supply |
| CN119298399A (en)* | 2024-12-12 | 2025-01-10 | 云科(山东)电子科技有限公司 | A monitoring device for power distribution station room of transmission and transformation network based on Internet of Things |
| CN119293664A (en)* | 2024-10-08 | 2025-01-10 | 厦门多多云技术创新研究院有限公司 | A device operation evaluation method based on multi-source data fusion |
| CN119298395A (en)* | 2024-12-06 | 2025-01-10 | 北京北方永达智能电气有限公司 | Visual intelligent monitoring system, method, computing device and storage medium for power distribution cabinet |
| CN119291342A (en)* | 2024-10-17 | 2025-01-10 | 内蒙古电力(集团)有限责任公司包头供电分公司 | Data collection method for power equipment status monitoring based on Beidou navigation system |
| CN119312250A (en)* | 2024-09-30 | 2025-01-14 | 国网江苏省电力有限公司苏州供电分公司 | Substation main equipment self-test system based on multi-source heterogeneous data fusion technology |
| CN119323003A (en)* | 2024-10-08 | 2025-01-17 | 中广核风电有限公司 | Power transmission and transformation equipment fault early warning system based on-line monitoring |
| CN119337319A (en)* | 2024-12-05 | 2025-01-21 | 四川智慧高速科技有限公司 | A method for monitoring electromechanical equipment in highway tunnels |
| CN119335383A (en)* | 2024-12-23 | 2025-01-21 | 浙江万胜智能科技股份有限公司 | A method and system for monitoring and warning abnormal state of a measuring switch |
| CN119377869A (en)* | 2024-12-31 | 2025-01-28 | 华雁智能科技(集团)股份有限公司 | Method, device, electronic equipment and storage medium for detecting abnormal environment in power distribution room |
| CN119397455A (en)* | 2024-12-31 | 2025-02-07 | 浙江驰普电气有限公司 | Intelligent detection device and detection method for pole-mounted circuit breaker |
| CN119416113A (en)* | 2024-10-25 | 2025-02-11 | 北京瑞智德信息技术有限公司 | A method and system for monitoring energy equipment health based on big data |
| CN119416101A (en)* | 2025-01-09 | 2025-02-11 | 陕西华添聚能科技有限公司 | Data analysis method and system for multi-energy complementary distribution system based on deep learning |
| CN119477283A (en)* | 2024-11-25 | 2025-02-18 | 辽东学院 | Equipment health management system, method and computer equipment based on artificial intelligence |
| CN119493402A (en)* | 2024-10-29 | 2025-02-21 | 浙江百灵智能科技有限公司 | A loom data monitoring method, system, computer equipment and storage medium |
| CN119509974A (en)* | 2024-11-27 | 2025-02-25 | 徐州永昶轴承有限公司 | A method and system for detecting the running state of a motor bearing |
| CN119556056A (en)* | 2024-11-19 | 2025-03-04 | 国网山东省电力公司信息通信公司 | A distribution line temperature diagnosis method and system based on intelligent Internet of Things |
| CN119556133A (en)* | 2024-12-10 | 2025-03-04 | 徐州科亚机电有限公司 | Motor fault detection method and system |
| CN119587279A (en)* | 2024-11-25 | 2025-03-11 | 重庆理工大学 | Intelligent stretcher system and data processing method |
| CN119622664A (en)* | 2025-02-13 | 2025-03-14 | 北京合锐赛尔电力科技股份有限公司 | A multi-modal fusion monitoring system for intelligent switch cabinet |
| CN119644950A (en)* | 2024-12-06 | 2025-03-18 | 江门高迪食品有限公司 | Operation and maintenance processing method and system applied to solid beverage intelligent production control platform |
| CN119671545A (en)* | 2024-12-24 | 2025-03-21 | 国能浙江南浔天然气热电有限公司 | A production management system for power plants |
| CN119692568A (en)* | 2025-02-25 | 2025-03-25 | 南京协澳智能控制系统有限公司 | Health management prediction system based on the whole life cycle of power high-voltage circuit breaker equipment |
| CN119722022A (en)* | 2024-12-04 | 2025-03-28 | 杭州磐升润妍生物科技有限公司 | Information service system based on big data |
| CN119719933A (en)* | 2025-02-25 | 2025-03-28 | 福建乐摩物联科技股份有限公司 | A method, system, device and medium for monitoring the operation status of a shared massage chair |
| CN119742917A (en)* | 2024-12-02 | 2025-04-01 | 南方电网数字电网科技(广东)有限公司 | A method and system for monitoring power transmission equipment based on the edge |
| CN119778195A (en)* | 2024-12-31 | 2025-04-08 | 河北大唐国际唐山热电有限责任公司 | A wind turbine condition monitoring and maintenance decision support system |
| CN119830191A (en)* | 2025-03-17 | 2025-04-15 | 中冶建筑研究总院有限公司 | Intelligent dust remover fault and health state diagnosis management method, device, equipment and storage medium |
| CN119831112A (en)* | 2025-03-15 | 2025-04-15 | 北京北方永达智能电气有限公司 | Preloaded equipment compartment fault prediction method based on multimode data analysis |
| CN119848516A (en)* | 2025-03-20 | 2025-04-18 | 豪越科技有限公司 | Intelligent algorithm-based system operation abnormity monitoring and analyzing method |
| CN119848470A (en)* | 2025-03-19 | 2025-04-18 | 广东电网有限责任公司 | Multi-mode data fusion-based power grid equipment health state prediction method and system |
| CN119862507A (en)* | 2024-12-03 | 2025-04-22 | 广昌县中广创新电子科技有限公司 | Health condition detection system for electronic components |
| CN119917910A (en)* | 2024-12-25 | 2025-05-02 | 湖北华中电力科技开发有限责任公司 | A multi-modal intelligent evaluation method for health status of smart park equipment, electronic equipment and medium |
| CN119941046A (en)* | 2025-01-23 | 2025-05-06 | 成都越动无限信息科技有限公司 | A performance appraisal method and system based on security operation and maintenance platform |
| CN119944945A (en)* | 2024-12-20 | 2025-05-06 | 南京朝上电子科技有限公司 | A power failure monitoring system |
| CN119941451A (en)* | 2025-04-08 | 2025-05-06 | 江苏大淀能源科技有限公司 | Intelligent optimization method of household energy ecology driven by electricity meter data fusion |
| CN119944976A (en)* | 2025-04-07 | 2025-05-06 | 浙江晴天太阳能科技股份有限公司 | Photovoltaic-storage-diesel fusion power supply system and its cellular monitoring and power dispatching method |
| CN119959781A (en)* | 2025-04-10 | 2025-05-09 | 深圳市金麒麟电源技术有限公司 | A method and system for battery pack health prediction |
| CN119995161A (en)* | 2025-03-17 | 2025-05-13 | 广东电网有限责任公司 | A distribution network state assessment method and system based on artificial intelligence |
| CN119984536A (en)* | 2025-04-17 | 2025-05-13 | 杭州宇诺电子科技有限公司 | A low-voltage multi-channel wireless temperature measurement method and device based on artificial intelligence |
| CN119990887A (en)* | 2025-01-21 | 2025-05-13 | 贵州黔源电力股份有限公司 | Method and system for monitoring health status of hydropower units based on machine learning |
| CN119992808A (en)* | 2025-04-15 | 2025-05-13 | 航天云网数据研究院(广东)有限公司 | Abnormal early warning system for equipment health monitoring |
| CN120084459A (en)* | 2025-02-13 | 2025-06-03 | 山东心动澎湃信息科技有限公司 | A heat meter online fault diagnosis method and system |
| CN120105312A (en)* | 2025-05-06 | 2025-06-06 | 交通运输部天津水运工程科学研究所 | Remote online monitoring method and system based on machine vision and artificial intelligence |
| CN120127840A (en)* | 2025-05-12 | 2025-06-10 | 浙江金茂科技有限公司 | Smart electricity safety supervision system based on the Internet of Things |
| CN120127838A (en)* | 2025-05-07 | 2025-06-10 | 哈尔滨智汇信息科技有限公司 | An intelligent power monitoring and alarm system based on 5G network |
| CN120163575A (en)* | 2025-05-19 | 2025-06-17 | 广东联想懂的通信有限公司 | AI-based pre-perception analysis method for health status of IoT device terminals |
| CN120197063A (en)* | 2025-05-26 | 2025-06-24 | 自然语义(青岛)科技有限公司 | Industrial equipment fault prediction and diagnosis system based on deep learning |
| CN120217136A (en)* | 2025-05-28 | 2025-06-27 | 上海君世电气科技有限公司 | A transformer operating status evaluation system and method |
| CN120318011A (en)* | 2025-06-17 | 2025-07-15 | 莱芜职业技术学院 | An intelligent manufacturing system based on digital twin |
| CN120314685A (en)* | 2025-04-29 | 2025-07-15 | 浙江宏仁电气有限公司 | A remote monitoring method, system, device and medium for metering box based on artificial intelligence |
| CN120342084A (en)* | 2025-06-17 | 2025-07-18 | 广东正超电气有限公司 | A multi-dimensional diagnosis method for box-type substation based on multi-network fusion and intelligent algorithm |
| CN120338088A (en)* | 2025-06-19 | 2025-07-18 | 慈溪市输变电工程有限公司 | A method and system for constructing a rule base for automatically generating equipment safety measures |
| CN120374097A (en)* | 2025-06-26 | 2025-07-25 | 温州建力电器有限公司 | Power distribution intelligent operation and maintenance management method and system based on data processing |
| CN120385396A (en)* | 2025-06-30 | 2025-07-29 | 上海常颖科技有限公司 | An intelligent monitoring and early warning system for the safety status of electrical cabinets |
| CN120489255A (en)* | 2025-07-21 | 2025-08-15 | 安徽继远软件有限公司 | A system and method for monitoring the dynamic environment and equipment status of a high-voltage distribution station |
| CN120496302A (en)* | 2025-07-11 | 2025-08-15 | 深圳市思友科技有限公司 | Intelligent security management method and system based on multi-sensor fusion |
| CN120507004A (en)* | 2025-07-18 | 2025-08-19 | 三峡智控科技有限公司 | State detection method and system for industrial equipment |
| CN120509001A (en)* | 2025-07-22 | 2025-08-19 | 南京迅集科技有限公司 | Industrial equipment fault prediction and health management method based on multi-sensor fusion |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180284747A1 (en)* | 2016-05-09 | 2018-10-04 | StrongForce IoT Portfolio 2016, LLC | Methods and systems for optimization of data collection and storage using 3rd party data from a data marketplace in an industrial internet of things environment |
| CN110598907A (en)* | 2019-08-13 | 2019-12-20 | 中国电力科学研究院有限公司 | Intelligent diagnosis method and system for health state of power distribution network |
| CN112488541A (en)* | 2020-12-04 | 2021-03-12 | 深圳先进技术研究院 | Multi-mode fault early warning method and system for electric equipment in construction site |
| CN112686408A (en)* | 2021-01-07 | 2021-04-20 | 中海石油(中国)有限公司 | Transformer maintenance decision-making method based on multi-source information fusion and risk assessment |
| CN115270860A (en)* | 2022-07-18 | 2022-11-01 | 国网信息通信产业集团有限公司 | A kind of transformer abnormality diagnosis method, system and diagnosis equipment |
| CN117559651A (en)* | 2023-11-22 | 2024-02-13 | 西安热工研究院有限公司 | Wind farm remote monitoring and intelligent fault diagnosis system |
| CN117630758A (en)* | 2024-01-24 | 2024-03-01 | 国能大渡河检修安装有限公司 | Method and system for monitoring health state of power station transformer |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180284747A1 (en)* | 2016-05-09 | 2018-10-04 | StrongForce IoT Portfolio 2016, LLC | Methods and systems for optimization of data collection and storage using 3rd party data from a data marketplace in an industrial internet of things environment |
| CN110598907A (en)* | 2019-08-13 | 2019-12-20 | 中国电力科学研究院有限公司 | Intelligent diagnosis method and system for health state of power distribution network |
| CN112488541A (en)* | 2020-12-04 | 2021-03-12 | 深圳先进技术研究院 | Multi-mode fault early warning method and system for electric equipment in construction site |
| CN112686408A (en)* | 2021-01-07 | 2021-04-20 | 中海石油(中国)有限公司 | Transformer maintenance decision-making method based on multi-source information fusion and risk assessment |
| CN115270860A (en)* | 2022-07-18 | 2022-11-01 | 国网信息通信产业集团有限公司 | A kind of transformer abnormality diagnosis method, system and diagnosis equipment |
| CN117559651A (en)* | 2023-11-22 | 2024-02-13 | 西安热工研究院有限公司 | Wind farm remote monitoring and intelligent fault diagnosis system |
| CN117630758A (en)* | 2024-01-24 | 2024-03-01 | 国能大渡河检修安装有限公司 | Method and system for monitoring health state of power station transformer |
| Title |
|---|
| 杨春波;陶青;张健;程志友;凡远柱;: "基于综合健康指数的设备状态评估", 电力系统保护与控制, vol. 47, no. 10, 14 May 2019 (2019-05-14), pages 104 - 109* |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118657404A (en)* | 2024-08-16 | 2024-09-17 | 广东电网有限责任公司茂名供电局 | A multi-modal power grid health assessment and analysis method |
| CN118657517A (en)* | 2024-08-19 | 2024-09-17 | 溯源电气(泉州)有限公司 | Intelligent data collection and transmission method for electrical equipment based on the Internet of Things |
| CN118690308A (en)* | 2024-08-23 | 2024-09-24 | 北京理工大学 | Anomaly detection method and system for complex industrial systems embedded with physical knowledge |
| CN118690313A (en)* | 2024-08-26 | 2024-09-24 | 大唐可再生能源试验研究院有限公司 | Wind turbine operation and maintenance decision system based on artificial intelligence |
| CN119199413A (en)* | 2024-09-10 | 2024-12-27 | 深圳市亿新达科技有限公司 | Capacitive voltage transformer insulation fault diagnosis and analysis method and system |
| CN118859037A (en)* | 2024-09-23 | 2024-10-29 | 国网河南省电力公司新乡供电公司 | A method for power equipment fault analysis based on multi-source data fusion |
| CN118859037B (en)* | 2024-09-23 | 2025-01-21 | 国网河南省电力公司新乡供电公司 | A method for power equipment fault analysis based on multi-source data fusion |
| CN119312250A (en)* | 2024-09-30 | 2025-01-14 | 国网江苏省电力有限公司苏州供电分公司 | Substation main equipment self-test system based on multi-source heterogeneous data fusion technology |
| CN118914725B (en)* | 2024-10-08 | 2025-01-07 | 北京融信数联科技有限公司 | Fault diagnosis and early warning method, system and medium for energy storage management system |
| CN118914725A (en)* | 2024-10-08 | 2024-11-08 | 北京融信数联科技有限公司 | Fault diagnosis and early warning method, system and medium of energy storage management system |
| CN119323003A (en)* | 2024-10-08 | 2025-01-17 | 中广核风电有限公司 | Power transmission and transformation equipment fault early warning system based on-line monitoring |
| CN119293664A (en)* | 2024-10-08 | 2025-01-10 | 厦门多多云技术创新研究院有限公司 | A device operation evaluation method based on multi-source data fusion |
| CN118937970A (en)* | 2024-10-09 | 2024-11-12 | 山东海纳智能装备科技股份有限公司 | A mine circuit fault self-diagnosis method and system |
| CN119291342A (en)* | 2024-10-17 | 2025-01-10 | 内蒙古电力(集团)有限责任公司包头供电分公司 | Data collection method for power equipment status monitoring based on Beidou navigation system |
| CN119027102A (en)* | 2024-10-23 | 2024-11-26 | 中煤科工集团信息技术有限公司 | Coal mining machine equipment health management method and device |
| CN119000053A (en)* | 2024-10-25 | 2024-11-22 | 天普能源装备(天津)有限公司 | Multi-flywheel automatic test monitoring system |
| CN119416113A (en)* | 2024-10-25 | 2025-02-11 | 北京瑞智德信息技术有限公司 | A method and system for monitoring energy equipment health based on big data |
| CN119000053B (en)* | 2024-10-25 | 2025-02-07 | 天普能源装备(天津)有限公司 | A multi-flywheel automatic test monitoring system |
| CN119493402A (en)* | 2024-10-29 | 2025-02-21 | 浙江百灵智能科技有限公司 | A loom data monitoring method, system, computer equipment and storage medium |
| CN119059385A (en)* | 2024-11-04 | 2024-12-03 | 江苏省工业互联网发展研究中心 | An intelligent elevator management system based on edge computing and computing network integration |
| CN119125941A (en)* | 2024-11-08 | 2024-12-13 | 珠海腾盾科技有限公司 | Emergency power supply status monitoring method and system based on multimodal data |
| CN119129862A (en)* | 2024-11-15 | 2024-12-13 | 北京网藤科技有限公司 | A method for equipment management integrating power dispatching |
| CN119150207A (en)* | 2024-11-18 | 2024-12-17 | 绍兴文理学院 | Robot equipment health state assessment method and system |
| CN119150210A (en)* | 2024-11-19 | 2024-12-17 | 陕西思极科技有限公司 | Low-voltage power distribution network running state detection method based on fusion terminal |
| CN119556056A (en)* | 2024-11-19 | 2025-03-04 | 国网山东省电力公司信息通信公司 | A distribution line temperature diagnosis method and system based on intelligent Internet of Things |
| CN119587279A (en)* | 2024-11-25 | 2025-03-11 | 重庆理工大学 | Intelligent stretcher system and data processing method |
| CN119477283A (en)* | 2024-11-25 | 2025-02-18 | 辽东学院 | Equipment health management system, method and computer equipment based on artificial intelligence |
| CN119509974A (en)* | 2024-11-27 | 2025-02-25 | 徐州永昶轴承有限公司 | A method and system for detecting the running state of a motor bearing |
| CN119224630A (en)* | 2024-11-28 | 2024-12-31 | 深圳市南霸科技有限公司 | Detection method and system of emergency starting power supply |
| CN119742917A (en)* | 2024-12-02 | 2025-04-01 | 南方电网数字电网科技(广东)有限公司 | A method and system for monitoring power transmission equipment based on the edge |
| CN119862507A (en)* | 2024-12-03 | 2025-04-22 | 广昌县中广创新电子科技有限公司 | Health condition detection system for electronic components |
| CN119722022A (en)* | 2024-12-04 | 2025-03-28 | 杭州磐升润妍生物科技有限公司 | Information service system based on big data |
| CN119337319A (en)* | 2024-12-05 | 2025-01-21 | 四川智慧高速科技有限公司 | A method for monitoring electromechanical equipment in highway tunnels |
| CN119298395A (en)* | 2024-12-06 | 2025-01-10 | 北京北方永达智能电气有限公司 | Visual intelligent monitoring system, method, computing device and storage medium for power distribution cabinet |
| CN119644950A (en)* | 2024-12-06 | 2025-03-18 | 江门高迪食品有限公司 | Operation and maintenance processing method and system applied to solid beverage intelligent production control platform |
| CN119556133A (en)* | 2024-12-10 | 2025-03-04 | 徐州科亚机电有限公司 | Motor fault detection method and system |
| CN119298399A (en)* | 2024-12-12 | 2025-01-10 | 云科(山东)电子科技有限公司 | A monitoring device for power distribution station room of transmission and transformation network based on Internet of Things |
| CN119944945A (en)* | 2024-12-20 | 2025-05-06 | 南京朝上电子科技有限公司 | A power failure monitoring system |
| CN119335383A (en)* | 2024-12-23 | 2025-01-21 | 浙江万胜智能科技股份有限公司 | A method and system for monitoring and warning abnormal state of a measuring switch |
| CN119671545A (en)* | 2024-12-24 | 2025-03-21 | 国能浙江南浔天然气热电有限公司 | A production management system for power plants |
| CN119917910A (en)* | 2024-12-25 | 2025-05-02 | 湖北华中电力科技开发有限责任公司 | A multi-modal intelligent evaluation method for health status of smart park equipment, electronic equipment and medium |
| CN119778195A (en)* | 2024-12-31 | 2025-04-08 | 河北大唐国际唐山热电有限责任公司 | A wind turbine condition monitoring and maintenance decision support system |
| CN119377869A (en)* | 2024-12-31 | 2025-01-28 | 华雁智能科技(集团)股份有限公司 | Method, device, electronic equipment and storage medium for detecting abnormal environment in power distribution room |
| CN119778195B (en)* | 2024-12-31 | 2025-09-12 | 河北大唐国际唐山热电有限责任公司 | Wind turbine generator system state monitoring and maintenance decision support system |
| CN119397455A (en)* | 2024-12-31 | 2025-02-07 | 浙江驰普电气有限公司 | Intelligent detection device and detection method for pole-mounted circuit breaker |
| CN119416101A (en)* | 2025-01-09 | 2025-02-11 | 陕西华添聚能科技有限公司 | Data analysis method and system for multi-energy complementary distribution system based on deep learning |
| CN119990887A (en)* | 2025-01-21 | 2025-05-13 | 贵州黔源电力股份有限公司 | Method and system for monitoring health status of hydropower units based on machine learning |
| CN119941046A (en)* | 2025-01-23 | 2025-05-06 | 成都越动无限信息科技有限公司 | A performance appraisal method and system based on security operation and maintenance platform |
| CN119622664A (en)* | 2025-02-13 | 2025-03-14 | 北京合锐赛尔电力科技股份有限公司 | A multi-modal fusion monitoring system for intelligent switch cabinet |
| CN120084459A (en)* | 2025-02-13 | 2025-06-03 | 山东心动澎湃信息科技有限公司 | A heat meter online fault diagnosis method and system |
| CN119719933A (en)* | 2025-02-25 | 2025-03-28 | 福建乐摩物联科技股份有限公司 | A method, system, device and medium for monitoring the operation status of a shared massage chair |
| CN119692568A (en)* | 2025-02-25 | 2025-03-25 | 南京协澳智能控制系统有限公司 | Health management prediction system based on the whole life cycle of power high-voltage circuit breaker equipment |
| CN119831112A (en)* | 2025-03-15 | 2025-04-15 | 北京北方永达智能电气有限公司 | Preloaded equipment compartment fault prediction method based on multimode data analysis |
| CN119995161A (en)* | 2025-03-17 | 2025-05-13 | 广东电网有限责任公司 | A distribution network state assessment method and system based on artificial intelligence |
| CN119830191A (en)* | 2025-03-17 | 2025-04-15 | 中冶建筑研究总院有限公司 | Intelligent dust remover fault and health state diagnosis management method, device, equipment and storage medium |
| CN119995161B (en)* | 2025-03-17 | 2025-09-09 | 广东电网有限责任公司 | A distribution network state assessment method and system based on artificial intelligence |
| CN119848470A (en)* | 2025-03-19 | 2025-04-18 | 广东电网有限责任公司 | Multi-mode data fusion-based power grid equipment health state prediction method and system |
| CN119848470B (en)* | 2025-03-19 | 2025-07-08 | 广东电网有限责任公司 | Multi-mode data fusion-based power grid equipment health state prediction method and system |
| CN119848516A (en)* | 2025-03-20 | 2025-04-18 | 豪越科技有限公司 | Intelligent algorithm-based system operation abnormity monitoring and analyzing method |
| CN119944976A (en)* | 2025-04-07 | 2025-05-06 | 浙江晴天太阳能科技股份有限公司 | Photovoltaic-storage-diesel fusion power supply system and its cellular monitoring and power dispatching method |
| CN119941451A (en)* | 2025-04-08 | 2025-05-06 | 江苏大淀能源科技有限公司 | Intelligent optimization method of household energy ecology driven by electricity meter data fusion |
| CN119941451B (en)* | 2025-04-08 | 2025-06-06 | 江苏大淀能源科技有限公司 | Household energy ecological intelligent optimization method driven by electric energy meter data fusion |
| CN119959781A (en)* | 2025-04-10 | 2025-05-09 | 深圳市金麒麟电源技术有限公司 | A method and system for battery pack health prediction |
| CN119992808A (en)* | 2025-04-15 | 2025-05-13 | 航天云网数据研究院(广东)有限公司 | Abnormal early warning system for equipment health monitoring |
| CN119992808B (en)* | 2025-04-15 | 2025-09-05 | 航天云网数据研究院(广东)有限公司 | Abnormal early warning system for equipment health monitoring |
| CN119984536A (en)* | 2025-04-17 | 2025-05-13 | 杭州宇诺电子科技有限公司 | A low-voltage multi-channel wireless temperature measurement method and device based on artificial intelligence |
| CN120314685A (en)* | 2025-04-29 | 2025-07-15 | 浙江宏仁电气有限公司 | A remote monitoring method, system, device and medium for metering box based on artificial intelligence |
| CN120105312A (en)* | 2025-05-06 | 2025-06-06 | 交通运输部天津水运工程科学研究所 | Remote online monitoring method and system based on machine vision and artificial intelligence |
| CN120127838A (en)* | 2025-05-07 | 2025-06-10 | 哈尔滨智汇信息科技有限公司 | An intelligent power monitoring and alarm system based on 5G network |
| CN120127840A (en)* | 2025-05-12 | 2025-06-10 | 浙江金茂科技有限公司 | Smart electricity safety supervision system based on the Internet of Things |
| CN120163575A (en)* | 2025-05-19 | 2025-06-17 | 广东联想懂的通信有限公司 | AI-based pre-perception analysis method for health status of IoT device terminals |
| CN120197063A (en)* | 2025-05-26 | 2025-06-24 | 自然语义(青岛)科技有限公司 | Industrial equipment fault prediction and diagnosis system based on deep learning |
| CN120197063B (en)* | 2025-05-26 | 2025-09-19 | 自然语义(青岛)科技有限公司 | Industrial equipment fault prediction and diagnosis system based on deep learning |
| CN120217136A (en)* | 2025-05-28 | 2025-06-27 | 上海君世电气科技有限公司 | A transformer operating status evaluation system and method |
| CN120318011A (en)* | 2025-06-17 | 2025-07-15 | 莱芜职业技术学院 | An intelligent manufacturing system based on digital twin |
| CN120342084A (en)* | 2025-06-17 | 2025-07-18 | 广东正超电气有限公司 | A multi-dimensional diagnosis method for box-type substation based on multi-network fusion and intelligent algorithm |
| CN120338088A (en)* | 2025-06-19 | 2025-07-18 | 慈溪市输变电工程有限公司 | A method and system for constructing a rule base for automatically generating equipment safety measures |
| CN120374097A (en)* | 2025-06-26 | 2025-07-25 | 温州建力电器有限公司 | Power distribution intelligent operation and maintenance management method and system based on data processing |
| CN120385396A (en)* | 2025-06-30 | 2025-07-29 | 上海常颖科技有限公司 | An intelligent monitoring and early warning system for the safety status of electrical cabinets |
| CN120496302A (en)* | 2025-07-11 | 2025-08-15 | 深圳市思友科技有限公司 | Intelligent security management method and system based on multi-sensor fusion |
| CN120507004A (en)* | 2025-07-18 | 2025-08-19 | 三峡智控科技有限公司 | State detection method and system for industrial equipment |
| CN120489255A (en)* | 2025-07-21 | 2025-08-15 | 安徽继远软件有限公司 | A system and method for monitoring the dynamic environment and equipment status of a high-voltage distribution station |
| CN120489255B (en)* | 2025-07-21 | 2025-09-26 | 安徽继远软件有限公司 | A system and method for monitoring the dynamic environment and equipment status of a high-voltage distribution station |
| CN120509001A (en)* | 2025-07-22 | 2025-08-19 | 南京迅集科技有限公司 | Industrial equipment fault prediction and health management method based on multi-sensor fusion |
| CN120509001B (en)* | 2025-07-22 | 2025-09-30 | 南京迅集科技有限公司 | Industrial equipment fault prediction and health management method based on multi-sensor fusion |
| Publication number | Publication date |
|---|---|
| CN118503636B (en) | 2024-11-19 |
| Publication | Publication Date | Title |
|---|---|---|
| CN118503636B (en) | Power distribution equipment health state assessment method and system | |
| KR102604708B1 (en) | Switchboard diagnosis system based on artificial intelligence and switchboard diagnosis method based on artificial intelligence | |
| KR102092185B1 (en) | Platform for analyzing electric motor health and analysis method using the same | |
| CN118300273B (en) | Cable room fault warning system based on multi-field coupling | |
| CN119716416B (en) | Power cable joint insulation state intelligent monitoring method based on complex environment | |
| CN119204689B (en) | A wireless cable temperature measurement system and method thereof | |
| CN118364381B (en) | Direct-current power supply early warning operation and maintenance method and system based on gradual change type data statistical analysis | |
| CN117134503B (en) | State monitoring method and system for large-scale power supply device | |
| CN118653969B (en) | Real-time detection system and early warning method for lubrication system of wind power generation equipment | |
| CN117972600A (en) | A method for abnormality detection of key components of wind turbines based on multi-dimensional fault feature learning | |
| CN119395568B (en) | An intelligent detection system for battery failures in new energy vehicles | |
| CN117629422B (en) | Temperature measurement method and system for photovoltaic module | |
| CN119312205B (en) | Intelligent fire-fighting equipment fault identification method and system | |
| CN118610903A (en) | A gateway electric energy metering device and a method for judging metering error thereof | |
| CN119310422A (en) | Insulation detection system supporting DC high voltage energy storage system | |
| CN119494225B (en) | A data processing method and system for digital twin oil and gas pipeline | |
| CN119147870B (en) | Photovoltaic energy storage inverter life test and evaluation system | |
| CN119475246A (en) | A method for monitoring the operation status of a smart electric energy meter | |
| CN114492636A (en) | A Acquisition System of Transformer Winding Status Signal | |
| CN114280434A (en) | Quantitative analysis method and system for degradation degree of composite insulator | |
| He et al. | Fault Diagnosis and Prediction of Large Power Model Driven by Multi-Modal Data | |
| CN118041233B (en) | Photovoltaic power station equipment fault detection method and system | |
| CN118604681B (en) | Short circuit diagnosis method and system based on 4G Internet of things | |
| CN120567040B (en) | Real-time monitoring system for electric energy quality of photovoltaic power station | |
| CN120414898B (en) | A power operation fault monitoring and early warning system based on digital twin |
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address | Address after:No. 5, Zhongxin West Road, Chashan Street, Ouhai District, Wenzhou City, Zhejiang Province, 325000 Patentee after:WENZHOU VOCATIONAL & TECHNICAL College Country or region after:China Address before:325000 Chashan Higher Education Park and No. 81 Jiangjiaqiao, Ouhai District, Wenzhou City, Zhejiang Province Patentee before:WENZHOU VOCATIONAL & TECHNICAL College Country or region before:China | |
| CP03 | Change of name, title or address |