6073 results about "Acquisition apparatus" patented technology

The invention provides a multi-source data fusion diagnosis method and system for new energy station equipment, and relates to the technical field of intelligent operation and maintenance of power equipment, and the method comprises the steps: collecting equipment operation data, and carrying out the preprocessing to obtain standardized data; transient features and drift features are extracted, a transient fault mode and a degradation fault mode are identified, and a dual-frequency-domain fault feature library is established; calculating a power transmission fluctuation value between devices, analyzing energy transmission efficiency, positioning a fault source device and identifying a fault propagation path; establishing a fault diagnosis evaluation index for grading diagnosis, and evaluating a fault development trend; and generating and executing a maintenance decision scheme. According to the invention, through multi-source data fusion analysis, accurate diagnosis and early warning of equipment faults of the new energy station are realized, and the equipment operation and maintenance efficiency is improved.

The invention provides an intelligent auxiliary diagnosis and maintenance method and system based on multi-path recall, and relates to the technical field of fault processing, and the method comprises the steps: collecting equipment operation data, extracting and enhancing time sequence features after preprocessing, constructing a feature dependency graph, and generating a state feature vector. And carrying out fault feature recall by using the multi-path recall diagnosis network, fusing to generate a fault propagation map, and further generating a fault diagnosis result. And finally, generating a preliminary maintenance scheme based on a fault diagnosis result and the equipment state transition probability matrix, and optimizing the maintenance scheme by adopting a hierarchical reinforcement learning method in combination with a historical case and a fault propagation map to generate an optimal maintenance scheme. According to the invention, the accuracy of fault diagnosis and the effectiveness of a maintenance scheme can be improved, and the equipment maintenance cost is reduced.

The invention belongs to the technical field of electromechanical equipment operation and maintenance, and discloses an electromechanical equipment self-adaptive intelligent early warning system based on multi-source sensing data. The system is composed of a multi-source sensing module, an edge data acquisition and preprocessing module, a data cleaning and multi-dimensional feature extraction module, an equipment state dynamic modeling module, an intelligent fault prediction and trend analysis module, a self-adaptive early warning threshold generation and dynamic adjustment module, and an intelligent decision and remote cooperation module. The system is composed of a multi-source sensing module, an intelligent low-carbon operation and maintenance management and control module and a digital twin system integration and full-period mapping module, multiple sensors are deployed through the multi-source sensing module to acquire multi-dimensional data of equipment, cleaning and calibration are performed through the edge data acquisition and preprocessing module, deep processing is performed through the data cleaning and multi-dimensional feature extraction module, and multi-dimensional feature extraction is performed through the multi-source sensing module. The data integrity and accuracy are ensured; and data are quickly transmitted among the modules, so that the monitoring system can accurately present the running state of the equipment in real time.

The invention provides an equipment group collaborative fault prediction analysis method and system for a complex industrial process, and relates to the technical field of fault prediction, and the method comprises the steps: collecting equipment group data, and reconstructing a multi-scale time sequence feature; constructing a quantum walking enhanced dynamic association map; extracting spatial-temporal features and mapping the spatial-temporal features to a Riemannian manifold space; identifying an abnormal mode and generating hidden variable probability distribution; constructing a fault evolution model driven by a non-equilibrium state statistical fluctuation theory; and solving the cooperative field equation set to obtain a control parameter sequence. The method can effectively improve the accuracy and reliability of equipment group collaborative fault prediction and reduce the maintenance cost.

The invention relates to the technical field of operation and maintenance scenes, in particular to a fault diagnosis prediction method and system based on an operation and maintenance scene, and the method comprises the steps: collecting equipment data, and constructing a scene fault diagnosis model; performing nonlinear fusion on the collected operation and maintenance data and historical maintenance records to obtain a real-time data fusion result; constructing an adaptive prediction algorithm based on interaction of a scenarized fault diagnosis model and real-time data fusion; and establishing a multi-level warning system according to a prediction result. According to the method, through multi-dimensional data acquisition, nonlinear data fusion, adaptive algorithm prediction and a multi-level warning system, accurate prediction and effective prevention of equipment faults are realized. Experimental results show that compared with a traditional method and a general machine learning method, the method has remarkable advantages in the aspects of early warning accuracy, false alarm rate, early warning time in advance and the like, meanwhile, the maintenance cost is effectively reduced, the downtime of equipment is shortened, and the method has good application prospects.

The invention provides a prediction model construction method and system for personalized equipment operation and maintenance, and relates to the technical field of data processing, and the method comprises the steps: collecting equipment operation data and environment data, extracting the change characteristics of the equipment operation state, carrying out the correlation analysis of the environment data based on historical equipment fault records, and obtaining a prediction model. Performing normalization processing, performing time sequence analysis, generating a joint feature matrix, segmenting the joint feature matrix into time slices of a fixed window according to a time sequence, classifying the time slices into a normal state and an abnormal state, performing feature contribution degree analysis, generating a feature weight matrix, and performing feature extraction; re-combining the equipment features in the time slice feature data set with the environment factors according to feature weights, generating fusion feature data layer by layer, performing supervised learning training on the fusion feature data, constructing an equipment prediction model, and outputting the equipment prediction model as a prediction model of personalized equipment operation and maintenance; according to the invention, the operation and maintenance effect of personalized equipment is improved.

The invention provides a GPU computing power scheduling method based on a one-cloud multi-core heterogeneous computing power platform, and the method comprises the following steps: S1, carrying out heterogeneous resource registration and modeling, accessing hardware equipment containing multiple types of GPUs through a resource registration module, collecting the equipment model, the video memory capacity and performance index data, and carrying out heterogeneous resource modeling; constructing a resource feature database containing a topological relation, and supporting hybrid access of chips; s2, virtualized resource reconstruction: pooling a physical GPU into virtual GPU resources by adopting a hardware abstraction layer technology, realizing video memory isolation and calculation unit division through a containerization technology, and configuring each virtual GPU instance with an independent drive stack and a security sandbox; and S3, submitting a multi-modal task, receiving a CUDA / OpenCL calculation task submitted by a user, analyzing task demand parameters including a calculation core number, a video memory occupation amount and a data throughput threshold, and generating a task descriptor containing a priority label.

The invention relates to an intelligent industrial equipment state monitoring device and method, and the method comprises the steps: installing a vibration sensor, an acoustic sensor and an image sensor at key parts of industrial equipment, and comprehensively collecting the operation data of the equipment. In the data preprocessing stage, vibration and acoustic signals are filtered and denoised, and image data are denoised and enhanced. Extracting multi-dimensional characteristic parameters from three aspects of vibration, acoustics and images; a random forest algorithm is adopted for model training, and the method comprises the steps of data preparation, segmentation, decision tree construction, voting mechanism, majority voting, model verification and optimization, testing and the like. In the model identification stage, the real-time features are input into the trained model, and the equipment state is determined through decision tree prediction and voting summarization. And when an abnormal state is detected, setting a multi-level alarm response according to the probability, and generating a maintenance suggestion. According to the method, the whole-process intelligent monitoring of the industrial equipment is realized, and the fault identification accuracy and the maintenance efficiency are improved.

The invention provides an advertisement recommendation method and system fused with user dynamic behavior modeling. The method comprises the following steps: acquiring a cross-device operation record of a user on multiple terminal devices, and synchronously acquiring a device unique identification code, high-frequency click area data and an operation timestamp; and constructing a space-time relation graph based on combined analysis of the equipment switching time interval and the click behavior, and generating a feature set reflecting the behavior correlation degree. The trajectory similarity is calculated at the edge calculation node through hardware acceleration, and migration correlation parameters are generated. And inputting the space-time relation graph into a behavior analysis model to extract coherent behavior characteristics, and forming a multi-dimensional scene portrait in combination with geographic position change and interface element distribution. And finally, through identifying a user cross-device interest migration mode, dynamically screening advertisement contents matched with the current scene, and realizing accurate pushing strategy optimization based on behavior continuity. According to the technical scheme provided by the invention, the accuracy of cross-terminal advertisement recommendation and the scene response speed are remarkably improved.

The invention discloses a multi-working-condition-oriented equipment fault diagnosis method based on unbalanced learning, and the method comprises the following steps: collecting the operation data of equipment under different working conditions through a plurality of sensors, carrying out the preprocessing, feature extraction and feature selection of the collected original operation data, and obtaining a feature set; processing the feature set by adopting an unbalanced learning technology and a cost-sensitive learning algorithm to obtain a balanced data set; constructing a multi-working-condition fault diagnosis model based on learning, and optimizing model parameters by adopting a method of combining cross validation and grid search according to a balanced data set in a model training process; deploying a multi-working-condition fault diagnosis model obtained through training, carrying out the online diagnosis of equipment faults, and continuously updating and optimizing the model through a continuous learning mechanism. According to the method, the unbalanced learning technology is applied to multi-working-condition fault diagnosis, the accuracy and robustness of diagnosis can be remarkably improved, and real-time monitoring and predictive maintenance of the equipment health state are achieved.

The invention relates to the technical field of industrial Internet of Things, in particular to a predictive maintenance method for intelligent factory Internet of Things equipment, which comprises the following steps of: acquiring equipment operation parameters, environment monitoring data and historical maintenance records, constructing a multi-dimensional feature data set, extracting equipment degradation features by adopting a topological graph attention mechanism and a Bayesian network, and establishing a multi-dimensional feature data set; the method realizes equipment health state modeling and fault probability prediction, combines a dynamic adjacency matrix and a multi-objective optimization algorithm, comprehensively optimizes maintenance cost, equipment fault risk and associated equipment influence, dynamically generates an optimal maintenance plan, carries out constraint optimization based on a mixed integer programming method, automatically generates a maintenance instruction sequence, and achieves the optimal maintenance of the equipment. Tasks are issued through the computerized maintenance management system, the PLC control system and the industrial Internet of Things gateway, and the execution state is monitored and maintained in real time. The intelligent level of equipment maintenance is effectively improved, non-planned shutdown is reduced, and the equipment reliability and the production efficiency are improved.

The invention provides a tunnel construction monitoring analysis method and system based on AI video monitoring, and relates to the technical field of artificial intelligence. A tunnel construction original video stream data set is acquired through a multi-view video acquisition device, and a spatio-temporal feature sequence is generated through spatio-temporal feature fusion processing; the spatiotemporal feature unit comprises a pixel region movement track, a personnel wearing identifier and a construction equipment contour feature, then extracting dynamic features of the spatiotemporal feature sequence to obtain a dynamic spatiotemporal feature set, and executing construction personnel statistical processing based on the dynamic spatiotemporal feature set; generating a hole entering personnel quantity statistical result, a personnel distribution statistical result of each construction part and a personnel identity matching result, performing work efficiency comparative analysis according to the personnel statistical result and a preset construction progress plan, and generating a construction progress deviation index and resource allocation optimization strategy; and when the progress deviation index exceeds the first threshold value, early warning is triggered, and a strategy is output to the management terminal, so that effective monitoring and management of tunnel construction are realized.

The invention relates to the technical field of ships, in particular to a fault monitoring system and method for a ship power system, and the system comprises a multi-source data collection module which carries out the real-time collection of the operation parameters, environment parameters and equipment state parameters of the ship power system through a collection device. The fault early warning module is used for predicting the development trend of the fault after the fault diagnosis is completed; the early warning decision module generates early warning information of different levels according to the state evaluation result, the fault diagnosis result and the RUL prediction result, gives targeted operation and maintenance decision suggestions, and pushes the suggestions to a ship cockpit, a shore-based operation and maintenance center and an operation and maintenance personnel mobile terminal; 24-hour uninterrupted multi-parameter acquisition of the ship power system is realized through the multi-source sensor network, three types of parameters of operation, environment and state are covered, acquisition delay is reduced, and the problems of poor timeliness and incomplete parameter coverage of traditional manual inspection are solved.

The invention provides a virtual power plant platform source network load storage equipment real-time monitoring and optimization method and system, and relates to the technical field of virtual power plant intelligent optimization scheduling, and the method comprises the steps: collecting equipment operation data, carrying out the noise reduction and feature compression to obtain feature data, and recognizing the equipment fault type based on the feature data; carrying out decoupling reconstruction on the feature data in time and space dimensions to obtain an equipment incidence matrix, constructing an equipment topological graph based on the equipment incidence matrix and a credible fault type, carrying out bidirectional tracking on the equipment topological graph according to a fault diffusion probability, and generating a fault propagation path; grading and sorting the equipment on the fault propagation path, and outputting a fault influence prediction result and an equipment priority list; and carrying out cooperative scheduling optimization on the equipment in the virtual power plant by adopting a hierarchical reinforcement learning algorithm to realize cooperative scheduling optimization of the equipment in the virtual power plant.

The invention discloses a thermal power plant equipment management method and system based on data fusion, and relates to the technical field of equipment management, and the method comprises the steps: collecting the real-time data of equipment through a sensor, and evaluating the health state of the equipment through a data fusion algorithm; based on the real-time operation data of the equipment, the intelligent rule engine dynamically adjusts the evaluation standard; predicting a potential fault and generating a maintenance priority through an intelligent fault prediction algorithm by using historical data and a health state evaluation result; and according to fault prediction and an equipment state, scheduling maintenance resources are optimized, and timely maintenance and repair of key equipment are ensured. According to the thermal power plant equipment management method based on data fusion provided by the invention, comprehensive monitoring of the equipment operation state is realized, and the accuracy and comprehensiveness of data are remarkably improved by combining a multi-stage data fusion technology based on dynamic weighted average and principal component analysis.

The invention discloses a GIS partial discharge intelligent diagnosis system and method based on one-dimensional ultrahigh frequency signal analysis, and relates to the technical field of power electrical equipment intelligent monitoring, and the system comprises a signal collection and preprocessing module which is used for collecting ultrahigh frequency signals of GIS equipment and obtaining preprocessed signal data through a dynamic threshold algorithm; the discharge initial judgment module is used for performing multi-dimensional sequential judgment to eliminate interference discharge data so as to obtain effective discharge signal data; the feature extraction module is used for performing time domain kurtosis and pulse width analysis, frequency domain energy distribution analysis and time-frequency domain wavelet entropy calculation based on the multi-dimensional features of GIS partial discharge, and generating an optimized feature subset; and the type identification module is used for identifying the partial discharge type by using the integrated learning model to obtain a diagnosis result. According to the invention, the problem of unstable recognition accuracy caused by insufficient signal preprocessing, single feature representation and single classification algorithm in the prior art is solved.

The invention discloses a saliency target detection method for an intelligent auxiliary driving system, and the method comprises the following steps: 1, obtaining a driving scene image based on a driver visual region in real time through a vehicle-mounted image collection device, and carrying out the preprocessing of the image; step 2, performing bottom-layer multi-scale feature extraction, interaction and global enhancement of feature information on the backbone network, and extracting rich multi-scale feature information; step 3, the neck network comprises an AESwinTransform attention module, and a cross-layer feature guide fusion module and a multi-level feature interaction fusion module in the TFG network, and the multi-level features extracted from the backbone network are fused together; step 4, detecting and classifying the feature map; and 5, inputting the adversarial sample and the normal sample into the model in a mixed manner, and performing adversarial training by using the trained model. According to the method, the detection precision of the small saliency target can be improved, the generalization ability and robustness of the model in a complex environment are improved, and the interference of irrelevant scene information is reduced.

The invention relates to the technical field of concrete member detection, and discloses a concrete member defect identification and labeling system, which comprises an image acquisition equipment matching module, a defect feature analysis module and a real-time labeling regulation and control module, and a defect classification priority judgment module capable of being additionally arranged. The image acquisition equipment matching module calculates and matches the optimal equipment through the adaptive characteristic value based on the image resolution, the equipment acquisition precision, the working distance and the illumination compensation parameter; the defect feature analysis module performs quantitative analysis on features such as textures, crack forms and hole distribution of zoning images by using algorithms such as multi-scale image segmentation and frequency domain transformation; the real-time labeling regulation and control module dynamically adjusts the labeling position according to the defect position offset, the size change rate and the illumination fluctuation parameters; and the defect classification priority judgment module divides defect grades according to crack width, hole density and the like. The system improves the automation level and accuracy of concrete member defect detection, and is suitable for constructional engineering member quality detection.

The invention discloses a new energy power generation equipment health management platform based on a large model, and relates to the technical field of power generation equipment health management. The system comprises a multivariate data acquisition and region identification module, a data preprocessing and region adaptation unit, a region perception large model analysis module, an intelligent operation and maintenance decision and scheduling module and a spare part inventory optimization sharing module. The multivariate data acquisition and area identification module deploys multiple types of sensors at key equipment parts of photovoltaic power generation stations in different areas, acquires equipment operation parameters and environment state data, endows the areas with unique codes and feature tags, and ensures that the data are traceable and are associated with area characteristics; according to the invention, the health management efficiency and accuracy of the new energy power generation equipment are significantly improved; the system not only realizes real-time monitoring and fault early warning of the equipment state, but also reduces the maintenance cost and improves the system operation efficiency by optimizing the operation and maintenance resource configuration and intelligent spare part inventory management.

The invention relates to the technical field of intelligent operation and maintenance and data processing of an electric power system, in particular to an electric power inspection system and method. The method comprises the following steps: collecting equipment state data, constructing a time sequence evolution characteristic of a three-order state transition tensor field modeling equipment space state, and identifying short-term high-risk equipment based on a weighted abnormal score and a dynamic threshold strategy; a historical state peak value is recorded through a state-time matrix fusion exponential decay model, a forgetting risk factor is calculated in combination with the current silencing degree, and equipment which is seriously abnormal but is silencing recently is actively rechecked; constructing a dynamic evolution diagram structure, optimizing an inspection path by adopting a class genetic perturbation mechanism, and scheduling multiple teams in parallel through task cluster division and queue matching; and establishing an equipment abnormity linkage propagation model to predict a cascade risk, and automatically matching an optimal disposal scheme in combination with a strategy template library. According to the method, closed-loop control from anomaly recognition, path optimization to strategy execution is formed, and the accuracy and timeliness of power operation and maintenance are improved.

The invention belongs to the technical field of computers, and particularly relates to an Internet of Things security data real-time monitoring and protection method and system, and the method comprises the steps: collecting equipment communication and state data through an edge agent, and analyzing and extracting standardized metadata; constructing an equipment behavior contour vector based on a sliding window, and dynamically maintaining a global equipment topological graph; triggering a primary alarm in combination with behavior deviation detection and topology abnormity; outputting a threat score and an attack intention through rule matching and Bayesian network double-engine collaborative reasoning; and executing automatic response according to grading, and feeding back and correcting a behavior baseline to realize closed-loop optimization. The system comprises a data acquisition module, a protocol analysis module, a behavior modeling module, a topology maintenance module, an anomaly detection module, a collaborative reasoning module, an automatic response module and a baseline correction module. Through full-link real-time modeling and cross-device collaborative analysis, the attack detection rate is significantly increased to 98% or above, the false alarm rate is lower than 2%, the response delay is controlled within 800 milliseconds, and the security and adaptive ability of the Internet of Things system are enhanced.

The invention relates to the technical field of geological disaster monitoring, in particular to a geological disaster activity target monitoring method based on dynamic visual tracking, which comprises the following steps of: adjusting multiple shooting parameters of a camera according to illumination intensity difference in an environment based on surface image acquisition equipment, and recording time points and geographical location information of image shooting in real time; a sequence of geo-tagged images is generated. According to the method, camera parameters are adjusted according to real-time illumination conditions, the quality of surface images is optimized, the monitoring and recognition capability of surface cracks and deformation is improved, and data support is provided for prediction and type recognition of geological disasters through multi-time-point image analysis and calculation of displacement speed and sedimentation rate, so that early warning is more accurate and targeted, and the early warning efficiency is improved. According to the method, the monitoring data and the topographic features of the target area are combined, the disaster type is accurately recognized, the development trend is predicted, dynamic monitoring and pre-judgment of geological disaster activities are achieved, the effectiveness of prevention measures is enhanced, and potential threats to human life and property are reduced.

A system and method for carbon emission monitoring in a highway construction period, belonging to the technical field of traffic carbon emission monitoring. The system for carbon emission monitoring in a highway construction period comprises an acquisition device (11), a transmission device (12), a data analysis device (13) and a data storage device (14), the acquisition device (11) being used for acquiring first-type data of a preset area in a highway construction period, and the first-type data comprising energy consumption measurement data and exhaust emission data, the transmission device (12) being in communication connection with the acquisition device (11) and the data analysis device (13) and being used for transmitting the first-type data to the data analysis device (13), the data analysis device (13) being used for performing analysis and calculation on the first-type data to obtain first carbon emission data corresponding to the first-type data, and the data storage device (14) being in communication connection with the data analysis device (13) and being used for storing the first carbon emission data. The system for carbon emission monitoring in a highway construction period achieves automatic acquisition, calculation and analysis of real-time carbon emissions in a highway construction process.

The invention discloses a robot inspection method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: collecting environment information of a target inspection space through information collection equipment, and constructing an environment map of the target inspection space through the environment information; determining the position information of the target inspection object and the position information of the obstacle in an environment map; determining an optimal shooting point location related to the target inspection object according to the position information of the target inspection object and the position information of the obstacle, and generating an inspection path related to the target inspection object; moving to the optimal shooting point position according to the inspection path to shoot the target inspection object to obtain an inspection image about the target inspection object; and sending the inspection image to an upper system for inspection analysis to obtain a robot inspection result. By applying the technical scheme provided by the invention, more efficient and intelligent article inspection and management are realized, the article safety can be effectively guaranteed, and the labor cost is reduced.

The invention relates to the technical field of environment monitoring and early warning, in particular to a road and bridge pavement environment monitoring and early warning method and system. The method comprises the following steps: arranging acquisition equipment for acquiring pavement environment data, wherein the pavement environment data comprises pavement ice and snow state data; the collected road surface environment data are preprocessed; training an environment monitoring and early warning model based on the road historical environment data to obtain a trained environment monitoring and early warning model, and inputting the preprocessed environment data into the environment monitoring and early warning model for monitoring and early warning; comparing a monitoring and early warning analysis result with a preset threshold value, and determining whether early warning needs to be sent out according to a comparison result; meanwhile, when ice and accumulated snow are detected to appear on the road surface, the position and the size of the ice and the accumulated snow are marked in visual equipment. The system comprises a data acquisition module, a model training module and an early warning judgment module. According to the invention, a comprehensive and efficient road and bridge pavement environment monitoring and early warning system is formed.

The invention relates to the technical field of intelligent monitoring, and discloses an equipment state intelligent monitoring platform based on data fusion and the Internet of Things technology, which is based on a scene feature quantitative capture module, uses a multi-scene adaptive sensor to collect the physical state and scene factors of equipment, constructs a two-dimensional feature vector through modal completion and space-time alignment, and carries out real-time monitoring on the two-dimensional feature vector. A scene label is generated in combination with dynamic threshold matching, a data fusion parameter dynamic adjustment module solves the problem of fusion layer dynamic adaptation deficiency by means of a structured collaborative weight algorithm and multi-modal hybrid filtering, and a model lightweight fine adjustment module optimizes parameters according to difference recognition, output layer fine adjustment and federal aggregation processes and performs incremental issuing. The scene constraint type decision module quantifies cost by means of labels and generates work orders by means of a multi-objective optimization algorithm, and the feedback optimization module adjusts and solidifies parameters through three-dimensional evaluation and reinforcement learning, and realizes cross-scene accurate monitoring of multi-field equipment in combination with unique binding of equipment identities and storage visualization of a quality supervision platform.

The invention relates to the technical field of electrical fault detection, in particular to an electromechanical equipment health state diagnosis method and device based on machine learning, and the method comprises the following steps: collecting the current, voltage and temperature parameters of equipment, calculating the real-time average value and standard deviation of each parameter through numerical processing, and obtaining a parameter monitoring result; and monitoring a result based on the parameters. The current, voltage and temperature parameters of the equipment are collected in real time, numerical processing is carried out on each parameter, the real-time average value and the standard deviation are calculated, and accurate basic data support is provided for state monitoring. Based on the monitoring data, parameter abnormity is judged through threshold analysis, abnormal changes can be dynamically recognized in the equipment operation process, and the timeliness and accuracy of fault discovery are improved. In combination with past performance data, the future change trend of parameters is predicted through a regression algorithm, early warning for equipment operation can be formed, and problem expansion or out-of-control is avoided.

The invention relates to the technical field of abnormal state prediction, provides a multi-dimensional time sequence equipment abnormal state prediction method and system, and solves the problems of high false alarm rate and risk prediction inaccuracy in the prior art. The method comprises the following steps: collecting a multi-node operation state data set and communication transmission layer time sequence offset information of an equipment distributed system; jitter and delay association processing is carried out on the data, and a communication stability feature set is generated by quantizing space-time correlation of a jitter extreme value and delay fluctuation; obtaining physical layer deformation and temperature drift data of the communication cable, analyzing and generating a physical layer disturbance feature sequence based on optical signal feature offset, and converting the physical layer disturbance feature sequence into channel abnormal strength features; and cross-level feature coupling is carried out on the communication stability feature and the channel abnormal strength feature, and a communication node failure or data transmission abnormal risk is predicted based on a coupling result. According to the invention, millisecond-level linkage prediction of equipment-level physical disturbance and system-level communication risks is realized, the fault false alarm rate is reduced, and the cascade failure risk is blocked.

The invention relates to the technical field of sound anomaly detection, in particular to a sound anomaly detection method and device based on a Transform model, equipment and a medium, and the method comprises the steps: collecting a sound signal during the operation of the equipment through a data collection interface, and obtaining an original sound signal; resampling is carried out on the collected sound signals, and normalization processing is carried out on the resampled data; mel-frequency cepstrum coefficient features are extracted from the sound signals after normalization processing, and the sound signals after normalization processing are input into a pre-training module to output high-dimensional features including time sequence and semantic information; splicing the Mel-frequency cepstrum coefficient features with the high-dimensional features to form a comprehensive feature vector; inputting the comprehensive feature vector into a support vector machine model, and performing abnormal sound recognition through a trained classification hyperplane; and detected abnormal information is fed back to the user in real time. Multi-feature fusion enables the model to identify abnormal sound more accurately.

The invention relates to the technical field of power equipment operation and maintenance management, in particular to a power equipment asset health management and predictive maintenance service system which comprises a data acquisition and integration module, a feature engineering module, a health assessment and prediction engine maintenance decision and early warning module and a service interface module. The data acquisition and integration module acquires equipment operation parameters through multiple types of sensors, and associates pre-stored equipment asset information to generate an equipment comprehensive data stream; the feature engineering module cleans and standardizes the equipment comprehensive data stream, and constructs a space-time correlation feature matrix; the health assessment and prediction engine comprises a health state assessment unit and a fault prediction unit, the health state assessment unit outputs a health index HI by using a gradient boosting decision tree, and the fault prediction unit outputs a fault probability and a remaining service life RUL in a future preset time period; and the maintenance decision and early warning module generates a grading early warning signal and a maintenance strategy scheme. The intelligent level of operation and maintenance of power equipment is improved, reliable operation of the equipment is guaranteed, and the operation and maintenance cost is reduced.