133877 results about "Testing Methods" patented technology

The invention relates to the technical field of cable operation state detection, and particularly discloses a multi-source data driven cable operation state comprehensive evaluation method, which comprises the following steps of S1, adopting a layered distributed sensing network architecture, and deploying three types of core sensors at key nodes of a cable, through space-time calibration of the multi-source heterogeneous sensor, data consistency is improved, fusion deviation is eliminated, the problem of data islands of a traditional system is solved, and a precise evaluation foundation is laid; noise suppression and dynamic correlation modeling are adopted, environmental interference is stripped, a vibration and displacement coupling relation is quantified, limitation of a single parameter is broken through, heterogeneous fault features are captured, and evaluation comprehensiveness and sensitivity are improved; a self-adaptive threshold mechanism is constructed based on environment weight and historical data, the bottleneck of a fixed threshold is broken through, an evaluation standard is corrected along with equipment aging and environment change, misjudgment is avoided, and diagnosis robustness in different scenes is enhanced.

The invention discloses a mobile phone anti-theft system and method based on a camera and a GPS (Global Position System) of a mobile phone. The mobile phone anti-theft system comprises a mobile phone CPU (Central Processing Unit), a microprocessor, a camera driving module, an input module and an unlocking detection module, wherein the input module is connected with the unlocking detection module; the unlocking detection module is connected with the mobile phone CPU; and the mobile phone CPU is connected with the camera driving module through the microprocessor. Compared with the prior art, the mobile phone anti-theft system and method provided by the invention have the advantages as follows: an unlocking operation is carried out when the mobile phone is used after the mobile phone is lost or stolen, and a background operation program which is installed in the mobile phone is triggered and started when the unlocking operation is inaccurate; and a user who currently uses the mobile phone is positioned and photographed through a GPS module and the camera of the mobile phone; meanwhile, obtained information is sent to a mailbox or other addresses which are specified by the user in time, so that the user is convenient to alarm and can find the mobile phone back accurately in time, and the problems of the mobile phone loss and personnel information release of the user can be solved.

The present invention relates to the technical field of stomatology. Provided is an auxiliary dental implant generation method based on a diffusion model. The method in the present invention comprises: acquiring oral CBCT image data of historical patients, preprocessing the oral CBCT image data of the historical patients to obtain a CBCT image dataset, using the CBCT image dataset to train a multi-task segmentation network, and using the segmentation network to obtain an intraoral tissue segmentation result; using the intraoral tissue segmentation result to train detection networks from the three dimensions of a cross-sectional plane, a coronal plane and a sagittal plane, respectively; using the detection networks to obtain detection results in the three directions of the cross-sectional plane, the coronal plane and the sagittal plane; fusing the detection results in the three directions of the cross-sectional plane, the coronal plane and the sagittal plane, and using a majority voting algorithm to construct a three-dimensional bounding box, so as to acquire an edentulous area; and using the intraoral segmentation result and the edentulous area as prompt information to guide, by means of an iterative process, a network to generate a post-implantation effect. The implantation effect obtained by the present invention is highly accurate, thereby providing a more precise auxiliary tool for stomatology.

The invention discloses a method for controlling a mobile phone music player. The method includes the steps of enabling a user to set the corresponding relation between the moving directions of a mobile phone and control motions, when music is played in the background, automatically starting an acceleration sensor of the mobile phone, enabling the user to shake the mobile phone, enabling the acceleration sensor to detect the mobile phone shaking directions by the user, and carrying out control over the mobile phone music player according to the detected directions and the corresponding relation set by the user. The method for controlling the mobile phone music player can perceive the different mobile phone shaking directions by the user through the acceleration sensor so as to carry out the corresponding song switching operations or the corresponding volume adjustment operations, and the control of the user over the music player is greatly facilitated. In addition, due to the fact that the corresponding relation between the moving directions and the control motions can be preset by the user, user experience is strengthened.

The local intrinsic dimensionality (LID) for a diffusion model with respect to a particular data sample is determined by using the diffusion model's diffusion process to apply noise to a data sample and evaluate how the estimated log probability of the data sample changes at different levels of noise. Particularly, the differential of change in noise to change in log probability can be used to determine the local intrinsic dimensionality. This may be determined by evaluating the log probability at several noise levels and determining a slope of the difference. In additional examples, the differential is evaluated directly at a selected noise level. The selected noise level can be optimized by calculating the estimated LID for various data samples at a variety of noise levels and selecting the LID that corresponds to a “knee” where the estimated LID sharply changes.

The present invention relates to the technical field of power system equipment and control. The method of the present invention comprises: installing sensors to acquire data for online real-time monitoring, and constructing a comprehensive condition online monitoring model for comprehensive data analysis; comprehensively evaluating the health condition of a generator on the basis of a comprehensive analysis result, and identifying fault causes; and constructing a generator comprehensive data intelligent monitoring and dynamic early warning model to perform insulation degradation trend analysis and prediction on the generator. In the present invention, by comprehensively monitoring the condition of a turbine generator, various key indicators of the generator are captured in real time, and trends and patterns underlying the data are revealed, providing technical support for accurately evaluating the health condition of the generator; and continuous monitoring for the insulation condition of the generator allows for proactive identification of potential risks, thereby providing decision-making support for preventive maintenance, helping operators take measures promptly, preventing faults, improving the reliability and safety of the generator.

A handheld fan includes a housing, a fan module, a power module, and a retractable cable module. The fan module is disposed in the housing, the power module is disposed in the housing and configured to supply power to the fan module, the retractable cable module is disposed in the housing and includes a cable retractable relative to the housing to be withdrawn into the housing or extended out of the housing, and the cable is configured to connect to an external electronic device, and supply power to the external electronic device through the power module. When a user carries the handheld fan and has a charging need, the cable is capable of being pulled out of the housing of the handheld fan, and connected to the external electronic device including a mobile phone. The power module is configured to supply power to the external electronic device.

The invention provides a Docker Registry management optimization mode. The Docker Registry management optimization mode comprises the steps that based on Docker Registry management requirements, a Docker Registry native management mode corresponding to the management requirements is determined; based on the management requirements, a corresponding optimization management mode is determined; optimization is conducted on the native management mode through the optimization management mode, and corresponding Docker Registry management operation is conducted through an optimized operational order. According to the embodiment, the problems that the native Docker Registry control operational mode is unfriendly, management operation is complex, and the network operation and maintenance cost is too high are solved. For example, storage catalogs of mirrored documents are changed to cluster-type share mirrored storage equipment from a local storage area, namely, single-machine mirrored storage is changed to cluster-type mirrored storage, and on the condition of large-scale cluster-type machinery equipment, the mirrored document maintenance cost can be greatly lowered. For another example, by means of the mode that multiple Registry service operation is simplified, the operating difficulty of Docker Registry management is lowered, and the network maintenance operational experience is improved.

The invention discloses a power transmission line connection fitting fault detection method based on acoustics and electromagnetic induction, and belongs to the technical field of intelligent inspection and nondestructive detection of power transmission lines. A multi-mode sensor is carried by an unmanned aerial vehicle, ultrasonic wave, vibration, images and power frequency electromagnetic field data are synchronously collected, and a multi-dimensional original data set is constructed. Signal quality is improved by adopting wavelet noise reduction, beam forming and sound image fusion technologies, and flight vibration and electromagnetic interference are effectively suppressed by combining an adaptive filtering algorithm and a physical shielding structure. And acoustic, image and electromagnetic characteristics are extracted and normalized and fused, a dynamic threshold reference library is established, and intelligent grading discrimination of abnormity, defects and faults is realized through multi-stage early warning logic. A detection result automatically generates a report and is mapped to a three-dimensional line model, and operation and maintenance system linkage is supported. According to the method, synchronous identification of surface and internal defects is realized, the anti-interference capability is high, the detection accuracy is high, the inspection efficiency and safety are remarkably improved, and the method is suitable for intelligent operation and maintenance of the high-voltage transmission line.

The invention discloses an intelligent cable fault diagnosis and positioning method and system, and relates to the technical field of intelligent operation and maintenance of a power system. The method is used for accurately identifying and positioning high-resistance faults and external damage. According to the method, electric field, current, temperature and vibration signals are synchronously collected, a multi-source fusion enhanced signal flow is constructed, and multi-physical field features are extracted; based on a coupling mechanism of an electromagnetic-thermal field and a mechanical-electric field, generating a fault type label and a space coordinate; executing targeted impedance correction for different fault types, establishing a dynamic topology network, and inputting a time-space diagram neural network to output a preliminary positioning result; and multi-source verification is carried out by further fusing salinity dielectric, a harmonic thermal field, a vibration electric field and stress topological information, a high-confidence-coefficient fault positioning result is finally output, a closed-loop diagnosis mechanism is formed, and the fault recognition accuracy and the system adaptability under complex working conditions are improved.

The invention discloses a multi-mechanical-arm space-time synchronization control method for coiled pipe welding, and belongs to the technical field of automation control, and the method comprises the steps: in a primary laser and TIG hybrid welding execution stage, carrying out real-time data acquisition on a welding area of a workpiece, carrying out real-time deviation detection, and once real-time welding deviation is detected, carrying out time-space synchronization on the welding area of the workpiece; a local correction mechanism is triggered immediately, and deviation is prevented from being accumulated in the single welding process; after one-time welding is completed, the workpiece enters a transition area, global contour reconstruction is conducted on a welded section of the workpiece through three-dimensional scanning equipment, global deviation recognition is conducted, and unprocessed hysteresis deformation and accumulative errors are covered and corrected in real time; on the basis of the analysis result of the global deviation recognition, double correction is conducted on secondary welding, and cooperative control over space track correction and technological parameter adaptation is achieved; and after secondary welding is completed, the correction effect is evaluated through a double-layer evaluation mechanism, and the stability of the correction process is ensured.

The invention relates to the technical field of intelligent manufacturing, discloses a papermaking equipment fault tracing method and system based on a process knowledge graph, and discloses the papermaking equipment fault tracing method and system based on the process knowledge graph. The method and the system comprise data acquisition and preprocessing, papermaking process knowledge graph construction, fault event detection and matching, fault tracing and propagation path reasoning, and maintenance scheme recommendation and optimization. The method overcomes the limitation that the traditional method is difficult to capture the cross-equipment, cross-process and cross-time sequence deep causal association and fault propagation path of the papermaking equipment. By constructing a comprehensive papermaking process knowledge graph, equipment operation state data, process parameter data, production quality data, equipment structure principle, process flow knowledge, fault mode knowledge, maintenance experience and other heterogeneous knowledge are subjected to deep fusion and semantic association, so that the system can exceed the correlation of the data surface; and an internal mechanism and a propagation chain of the fault are deeply excavated.

The invention discloses a stamping part forming precision dynamic monitoring system based on digital twinning, and particularly relates to the field of general monitoring and adjusting systems, and the stamping part forming precision dynamic monitoring system comprises a digital twinning modeling module, a twinning simulation prediction module, an intelligent function coupling decision module and a physical execution closed loop feedback module; the digital twinning modeling module collects stamping related data through a multi-source sensor, and constructs and dynamically updates a geometric, physical and behavior three-level digital twinning body after preprocessing; the twinborn body simulation prediction module is based on three-stage twinborn bodies and is combined with parameter initialization, double-source simulation, result fusion and threshold decision to realize pre-judgment of forming precision; the intelligent function coupling decision-making module generates a targeted regulation and control instruction through a basic regulation and control function and a dynamic coupling mechanism according to the precision out-of-tolerance signal and the physical data; and the physical execution closed-loop feedback module completes instruction execution, state perception and model correction through iterative loop, realizes dynamic monitoring of the forming precision of the stamping part, and improves the forming precision stability and the production efficiency of the stamping part.

The invention relates to the technical field of growth state evaluation, and discloses a crop growth state evaluation method and system based on multi-dimensional monitoring. The method comprises the steps of collecting multi-source remote sensing data of a farmland area according to a crop growth period, and performing topographic correction on the multi-source remote sensing data to obtain target vegetation data; based on the multi-source remote sensing data, farmland plot boundaries are extracted, and a farmland space association graph is constructed; inputting the target vegetation data and the farmland space association graph into an elevation perception graph convolutional network for elevation feature analysis, and calculating to obtain a crop abnormal growth index; and generating a growth state evaluation result based on the target vegetation data and the crop abnormal growth index. According to the method, crop growth abnormity caused by regional factors can be accurately identified, so that the accuracy of evaluation results under different terrain and environmental conditions is ensured.

The invention provides a fatigue life prediction and residual life evaluation method for a high-power heavy-duty gearbox, and belongs to the technical field of intelligent operation and maintenance based on computer data processing. Comprising the following steps: acquiring dynamic data in an operation process, and performing multi-scale decomposition to form multi-source multi-scale data; inputting the multi-source multi-scale data into a designed multi-scale fatigue feature extraction module and a health state prediction module to obtain a multi-scale health index sequence and a health state label; establishing a fatigue damage evolution model, introducing the generated health index sequence for self-adaptive updating, outputting a comprehensive damage value, performing staged evaluation of fatigue degradation to obtain a damage label set, and performing multi-scale health index sequence and health state labels as well as the comprehensive damage value and the damage label set to obtain a multi-scale health index sequence and health state labels; inputting into a designed double-source fusion fatigue life prediction model, and outputting residual life prediction quantity; according to the invention, high-precision prediction and residual life evaluation of the fatigue life of the high-power heavy-duty gearbox are realized.

The invention relates to the field of wastewater treatment, and provides an industrial wastewater membrane process intelligent optimization method and system based on multi-dimensional data modeling. The method comprises the following steps: acquiring wastewater quality parameters of a target scene and a production line equipment state in real time through a multi-dimensional sensor network, and constructing a digital twinborn model of a wastewater treatment system in the target scene; the membrane surface pollution type of each node in the wastewater treatment system is identified through a digital twin model, and the inner membrane pollution evolution condition in the future preset duration is predicted; a multi-strategy evolutionary reinforcement learning algorithm is adopted, the wastewater quality parameters, the production line equipment state and the membrane pollution prediction result are combined, multi-target dynamic optimization is conducted on a wastewater treatment system through a multi-agent collaborative decision-making algorithm, an optimization strategy is generated based on the multi-target dynamic optimization result, and membrane process operation parameters and production control parameters are dynamically adjusted. According to the method, real-time intelligent adjustment of membrane process operation can be realized, the stability of membrane process operation is guaranteed, and the treatment efficiency of membrane process operation is improved.

The invention relates to the technical field of circuit detection, discloses a power transmission line thermochromic wire clamp heating early warning method and system, effectively solves the problem of data acquisition distortion in a strong electromagnetic environment, and improves the accuracy of state evaluation through multi-source data fusion. The dynamically adjusted early warning model reduces the risk of false alarm and missing alarm caused by equipment aging, the intelligent decision support module shortens the fault handling response time, the data closed-loop mechanism ensures the reliability of the system in the whole life cycle, and the reliability of the system in the whole life cycle is improved through multi-sensor cooperative monitoring and edge calculation processing. And the influence of environmental factors on data acquisition is reduced. The two-channel transmission architecture guarantees the data transmission integrity under different network conditions, the CRC verification mechanism effectively recognizes and corrects transmission errors, a high-quality data basis is provided for a subsequent early warning model, the abnormal data recollection mechanism avoids data missing caused by single collection failure, and continuous and stable operation of the monitoring system is ensured.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a fault diagnosis and recovery verification method and device, equipment and a medium. Inputting the candidate event and the context state into an intelligent diagnosis model to obtain a diagnosis result and a fault type, selecting a recovery path in a recovery strategy library based on the diagnosis result, generating a recovery execution instruction, creating a verification experiment based on the instruction, and injecting drill data to obtain verification data, and forming a verification conclusion according to the verification data, associating a recovery execution instruction, and issuing the instruction in the execution environment to complete recovery operation. According to the method, through multi-source monitoring data processing, intelligent diagnosis model reasoning, strategy library path selection, verification experiment verification and execution environment automatic recovery, a full-link automatic process from detection, diagnosis, recovery to verification is constructed, and accurate diagnosis and dynamic recovery in a complex environment are achieved.

The invention discloses a building engineering quality monitoring system, and relates to the technical field of building engineering monitoring, the building engineering quality monitoring system comprises a collection module, an analysis module, a monitoring module and an early warning module, the collection module collects first quality monitoring data of building engineering and obtains historical monitoring data, and transmits the data to the analysis module; performing feature extraction and data analysis on the first quality monitoring data to obtain second quality monitoring data, storing historical monitoring data and presetting quality standard data, transmitting the second quality monitoring data and the quality standard data to a monitoring module, performing dynamic comparison on the real-time second quality monitoring data and the preset quality standard data, and outputting the result. The method comprises the steps of generating quality anomaly feature parameters, transmitting the quality anomaly feature parameters to an early warning module, matching a preset early warning strategy according to the quality anomaly feature parameters, sending out graded early warning signals, and carrying out multi-dimensional early warning by integrating multi-dimensional data acquisition and analysis, dynamic feature index extraction and dynamic standard construction, so that the engineering quality monitoring accuracy and the management and control timeliness are improved.

A wearable device configured to secure to skin of a user and noninvasively measure body temperature of the user can include first and second pairs of temperature sensors configured to generate one or more signals responsive to detected thermal energy, a thermally conductive element positioned at least partially between the second pair of temperature sensors, and one or more hardware processors configured to receive the one or more signals generated by each of said first and second pairs of temperature sensors and determine one or more body temperature values of the user based on at least comparisons between different ones of the first and second pairs of temperature sensors. In some implementations, the wearable device includes thermally conductive probes for transmitting thermal energy toward ones of the first and second pairs of temperature sensors and a substrate positioned between the probes and the skin.

The invention provides a water conservancy project safety monitoring method and system based on data processing, and relates to the technical field of monitoring, and the method comprises the steps: obtaining and carrying out the multi-dimensional preprocessing of water conservancy project multi-source heterogeneous monitoring data through the deployment of a sensor network, and extracting multi-scale space-time fusion features from the data; performing structural state modeling, anomaly prediction, risk assessment and early warning by using a long-short-term memory neural network model integrated with a multi-head attention mechanism; and intelligent suggestions oriented to maintenance decisions are generated, so that comprehensive, accurate and prospective evaluation and early warning of the structural state of the water conservancy project are finally realized, the exception identification and risk prediction capabilities are effectively improved, the false alarm rate is reduced, refined and initiative intelligent maintenance decisions are provided, resource allocation is optimized, and the service life of the project is prolonged.

The invention discloses an intelligent management system for nuclear power plant personnel situation prediction and risk assessment, and relates to the field of intelligent safety management systems, and the system comprises a data collection unit, a multi-dimensional situation awareness unit, a risk prediction and assessment unit, an intelligent decision intervention unit and a visual interaction unit. And multi-source data acquisition, real-time situation construction, dynamic risk prediction and evaluation, intelligent early warning intervention and information visualization are realized. According to the invention, real-time monitoring, dynamic risk prediction and intelligent management of the safety state of the operating personnel can be realized, and the defects of real-time monitoring, dynamic prediction and intelligent management of the operating personnel in a high-risk area in the prior art are overcome, so that the safety management level is improved, the life safety is guaranteed, and the accident occurrence probability is reduced.

The invention relates to the technical field of sensor calibration, in particular to an environment adaptive multi-dimensional calibration method for an excavation type intelligent sensor, which comprises the following steps: acquiring temperature, humidity and gas data through environment monitoring, comparing with a threshold value, marking an abnormal state, evaluating the influence of each parameter on a sensor signal and analyzing fluctuation amplitude and intensity; combining sensor sensitivity and response rate to screen key factors, extracting factor fluctuation trend, optimizing signal disturbance weight and response path, correcting signals in real time, updating a calibration parameter set, monitoring signal values based on multi-dimensional parameters, and adjusting output to maintain a stable range. According to the invention, temperature and humidity and gas abnormity are dynamically identified through threshold comparison, a factor model is constructed based on sensitivity weight, a multi-dimensional calibration path is generated, a parameter set is updated in real time to decouple environment sudden change and sensor drift, signal self-adaptive convergence is realized through closed-loop control, multi-parameter interference errors are reduced, and the bottleneck of calibration delay is broken through. And the time-varying working condition data stability is ensured.

The invention discloses an intelligent operation and maintenance management system and method based on a charging pile, and belongs to the technical field of fault early warning, and the method comprises the steps: building a unified time sequence operation data matrix through collecting multi-source state data generated in the operation process of the charging pile; key features are extracted to construct feature vectors, and a multi-classification neural network model is utilized to evaluate a health state; a micro-degradation evolution path model is constructed in combination with the health trend in the continuous observation period, and a fault prediction curve is generated; performing similarity matching on a prediction result and a fault prior curve library, calculating a risk weight coefficient, identifying potential fault nodes and outputting an early warning list; constructing a regional task scheduling graph based on the high-risk pile position, fusing geographic position, power level and residual life information, and optimizing to generate an operation and maintenance path and a resource configuration scheme; according to the method, the fault prediction accuracy and operation and maintenance efficiency of the charging pile can be remarkably improved, and intelligent operation and maintenance and response optimization are realized.

The invention discloses an error compensation inductance sensor calibration method and system, and the method comprises the steps: controlling a macro-micro composite driving platform to drive a moving part provided with a to-be-calibrated inductance sensor, and enabling the moving part to carry out the continuous displacement movement relative to a fixed standard target surface; measuring the actual displacement of the moving part to obtain a standard displacement sequence; the actual output voltage of the inductive sensor to be calibrated in the movement process is synchronously collected, and an actual measurement value sequence is formed; solving measurement deviation, generating a measurement deviation sequence, and dividing the sequence into not less than three compensation intervals according to a displacement range; a compensation value lookup table is established in each compensation interval, and calibration parameters used for correcting zero offset, gain drift and nonlinear characteristics are generated. Displacement measurement and sensor output synchronous acquisition are combined, zero offset, gain drift and non-linear errors can be corrected in a targeted mode, and compensation fineness and adaptability are improved.

The invention discloses a method for intelligently regulating and controlling production parameters in a fruit concentrated juice production process, which comprises the following steps of: acquiring multi-dimensional process parameters such as temperature, pressure, flow, concentration, equipment state and the like in real time through a multi-channel sensor network, and forming a standardized data sequence after filtering, normalization and drift correction; extracting stage features by using technologies such as a sliding window and Fourier transform, and inputting the stage features into the lightweight classification model to realize production stage identification; in combination with an identification result, dynamically calling a corresponding multi-target optimization sub-model, and realizing nonlinear prediction and optimal solution selection of process parameter setting by adopting an LSTM and a multi-target genetic algorithm; on the basis of real-time feedback, the performance of the model is automatically evaluated, self-adaptive adjustment and optimization of the optimization algorithm are achieved through reinforcement learning and an incremental updating mechanism, multi-target collaborative optimization, self-adaptive adjustment and model switching in the production process can be achieved, and the consistency of production efficiency and product quality is improved.

A head and neck assembly for a humanoid robot, including a head portion having an exterior surface defining an overall shape resembling a human head; a neck portion extending from the head portion; a head housing assembly enclosing the head portion and neck portion; an electronics assembly contained within the head housing assembly; and a head actuator assembly configured to move the head portion relative to a torso of the humanoid robot.

The invention discloses an integrated collaborative management method for construction site risk operation data, and belongs to the technical field of data management. The method comprises the following steps: constructing and calibrating a space-time risk reference database; deducing a potential risk conduction link set, and generating an associated intervention knowledge base; during operation, through multi-sensor cooperative verification, an abnormal signal is confirmed as a risk event; matching the risk event with a conduction link to calculate a risk upgrade level and dynamically adjust an early warning threshold; and when the early warning is triggered, generating and issuing a dynamic collaborative response instruction, and feeding back a processing result to correct the reference database to form a management closed loop. According to the method, the technical means of constructing the space-time risk reference, deducing the conduction link, cooperatively verifying the risk and dynamically regulating and controlling the threshold are adopted, so that the predictability of project risk management and control, the efficiency of resource cooperative scheduling and the scientificity of overall management decision are improved.

A method for synchronizing audio reproduction in collocated end devices is presented. Each of the devices auto-correlates using noise to determine a threshold prior to the antenna receiving an audio signal. When the devices receive a common audio signal, they provide audio outputs. Each device cross-correlates its audio output with the audio outputs of the other devices. The timing of the audio output of each device is then adjusted such that the audio outputs of all of the devices align temporally with the lagging or leading device.

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.