357 results about "Health indicator" patented technology

The invention discloses an intelligent fault diagnosis method fusing state monitoring and a multi-modal large model, and the method specifically comprises the steps: synchronously collecting time sequence data and a space image through a heterogeneous sensor group and monitoring equipment disposed in power grid equipment, and forming original data; based on the original data, a physical constraint feature vector is generated in combination with an equipment thermodynamic equation and a material deformation rule; performing health index prediction through the lightweight LSTM network based on the physical constraint feature vector; when detecting that the health indexes continuously decrease, clustering an HI time sequence curve by adopting a Gaussian mixture model, judging a degradation stage according to a clustering center distance, and obtaining a stage recognition result; and based on finite element simulation parameters, introducing a reinforcement learning model, optimizing the simulation parameters by taking maintenance cost minimization as a target, and outputting a predictive maintenance work order. According to the invention, intelligent fault diagnosis and accurate maintenance of the power grid equipment are realized, the fault processing efficiency and accuracy are improved, and the power failure loss is reduced.

The invention relates to a bridge and tunnel disease detection method and system based on unmanned aerial vehicle-edge computing collaboration, and solves the problem that the detection efficiency is limited due to the lack of systematic design of a collaboration mechanism of an unmanned aerial vehicle and edge computing. The method comprises the following steps that: a distributed edge computing node fuses multi-source monitoring data to obtain a health index, compares the health index with a multi-level threshold value, generates a message containing space coordinates, levels and characteristics when the health index is abnormal, and transmits the message to an edge computing center; the center screens adaptive unmanned aerial vehicles, plans an optimal path, dispatches collected data, and preliminarily screens diseases through a parallel model; determining disease complexity and types in combination with abnormal features, and establishing a collaborative detection unit to specially collect multi-source data; centimeter-level positioning is realized through BIM registration and SLAM, and an accurate detection report is generated. The method has the following effects: accurate scheduling, real-time processing and centimeter-level positioning of disease detection are realized, an intelligent detection closed loop is constructed, and the accuracy and efficiency of bridge and tunnel operation and maintenance are greatly improved.

The invention discloses an elevator generation type health state assessment method and system based on knowledge enhancement retrieval, and the method comprises the following steps: 1) collecting multi-source operation and maintenance data of an elevator, constructing an elevator health state knowledge graph containing equipment, parts, faults, health indexes and maintenance standards, and forming a structured health assessment knowledge base; 2) performing semantic analysis on operation indexes and natural language task description based on a large language model, and generating a health evidence sub-graph in combination with knowledge graph retrieval; 3) carrying out quantitative calculation on the component risk value and the complete machine comprehensive risk value under a rule engine and multi-agent framework, and constructing a memory graph to carry out historical trend analysis and similar case retrieval; and 4) generating an interpretable elevator health state evaluation report in combination with the logical reasoning graph and the standard knowledge graph. According to the method, the accuracy and traceability of elevator health assessment can be improved, and technical support is provided for predictive maintenance of the elevator.

The invention relates to the technical field of intelligent wearable health management, in particular to a health management method and device based on multi-modal data, equipment and a storage medium. Comprising the steps of obtaining multi-feature-dimension original data to construct an original data set; performing data preprocessing and feature extraction on the original data set to generate a feature vector set; performing trend capture on the feature vector set according to a time sequence to obtain a time sequence feature vector of each modal; calculating a cross-modal association weight according to each modal time sequence feature vector, outputting a joint feature vector based on the cross-modal association weight, and generating a health index value of each evaluation target dimension according to the joint feature vector; and forming a state vector by the health index value and the historical behavior data of the user, calculating action probability distribution according to the state vector, and sampling and outputting a health intervention suggestion based on the probability distribution. The problem of health assessment deviation caused by insufficient multi-modal data fusion and insufficient time sequence association mining in the prior art can be solved.

The invention relates to the technical field of data processing, and discloses a propeller fault diagnosis and predictive maintenance method and system. The method comprises the following steps: collecting current, temperature, vibration and pressure signals of a propeller, extracting characteristic parameters to construct a time sequence data set, mapping the characteristic parameters into nodes, establishing a multi-physical field coupling hyperedge to construct a time sequence incidence matrix, inputting an encoder, extracting a degradation state characteristic vector, and mapping the degradation state characteristic vector into a health index; and calculating a degradation rate function in combination with the working condition parameters, and integrating the degradation rate function to predict the remaining service life. According to the method, the high-order coupling relation and the time sequence evolution characteristics among multiple parameters of the propeller are captured through the multi-physics field coupling hypergraph time sequence encoder, the influence of different operation conditions on degradation is quantified in combination with the degradation rate model sensitive to the working condition, and the problem that the fault evolution trajectory and the remaining service life cannot be predicted is solved; and an accurate time window reference is provided for predictive maintenance.

The invention belongs to the technical field of expressway operation and maintenance, and relates to an intelligent operation and maintenance method and device for an expressway strong current system, and the method comprises the following steps: collecting and preprocessing multi-source data; constructing a state space model and defining a system state vector; obtaining an estimated state value by adopting a state estimation algorithm; performing fault anomaly detection based on the estimated state value and the multi-source data; extracting a device feature vector and calculating a health index; predicting the remaining service life of the equipment and judging the potential fault risk, and generating a maintenance work order and determining the priority when the threshold value is exceeded. According to the technical scheme of the invention, work orders are generated through multi-source data acquisition, state modeling and estimation, anomaly detection, health assessment and life prediction, so that the whole-process intelligent operation and maintenance of the highway strong current system is realized, and the real-time sensing and diagnosis capability of the operation state can be improved. And the requirements of operation and maintenance real-time performance improvement, data unified management, intelligent diagnosis enhancement and emergency disposal high efficiency are met.

The invention relates to the technical field of energy system control, and discloses a distributed cooperative fault-tolerant control method for a multi-energy-station cooling and heating system, and the method comprises the steps: enabling each energy station to measure and calculate a residual error based on a physical model and a sensor, forming a normalized health index, and carrying out the neighborhood broadcasting; carrying out robust anomaly judgment by utilizing a neighborhood median and a median absolute deviation, and isolating an abnormal site; cost coefficients are automatically generated for available sites according to equipment maneuverability, and control redistribution of minimum disturbance is solved and implemented through distributed consistency optimization; an exponential weighting updating mechanism of a residual error sequence is adopted to carry out online self-adaption on a noise baseline and a judgment threshold value so as to realize closed-loop self-calibration; when sensor abnormity, equipment errors or individual station faults occur in the multiple energy stations which work cooperatively, distributed rapid identification is achieved, faults are isolated, energy supply is redistributed with minimum disturbance, system control requirements are met, and equipment safety constraints are kept.

The invention provides a vibration-temperature correlation fault early warning method based on multi-sensor fusion, and relates to the technical field of rotating machine fault diagnosis, and the method achieves the efficient diagnosis of the rotating machine fault through the three-field coupling analysis of vibration, temperature and torque. Multi-dimensional health indexes are introduced, sensor data of different dimensions are normalized into a monitorable numerical value, and the health state of equipment is reflected in real time through a dynamically optimized weight coefficient. A core depth time sequence neural network model is combined with an attention mechanism and a Transform coding layer, the problem of long-term dependence is effectively solved, and the fault recognition precision is improved. According to the method, future innovation directions such as cross-device collaborative diagnosis, quantum computing acceleration and digital twinborn visualization are also exhibited, and the method has important industrial application value.

The invention relates to the technical field of intelligent robot health monitoring, and discloses a health monitoring management system based on an intelligent robot. The system comprises a data cleaning unit, an attribute extraction unit, a period division unit, a coordinate conversion unit and a mode analysis unit. The data cleaning unit collects multi-source health data of the robot, and generates an integrated health data set through format standardization, data mode unification, structural difference analysis and redundancy elimination according to a time sequence. The attribute extraction unit separates a monitoring timestamp, a health index code and an individual type label; a period division unit divides the integrated data set into time slices according to timestamps to form a period health data set; the coordinate conversion unit maps the health state to a standard health coordinate system by using the periodic data set and the health index code to generate a health distribution map; and the mode analysis unit classifies health change events according to the atlas, the periodic data set and the individual type labels, calculates a target periodic health activity degree and outputs a health change trend index.

The invention belongs to the crossing field of artificial intelligence and mechanical engineering, particularly relates to a micro-motor fault prediction and health management system, and aims to solve the problems that early faults of a micro-motor are difficult to recognize, degradation modeling is inaccurate and maintenance lags. The system collects multi-source data through high-density sensing, combines denoising reconstruction, composite feature extraction and time-varying weighted fusion to generate health indexes, identifies health stages by using a segmented hidden Markov model, iteratively updates residual life prediction based on a Wiener process, outputs an estimation result with a confidence interval, and links a hierarchical maintenance strategy. And continuous optimization of the model is realized through federal learning. The system improves the fault early warning accuracy and prediction reliability, and reduces the operation and maintenance cost.

The invention discloses a machine tool running state collection and evaluation method and system, and relates to the field of intelligent machine tools, and the method comprises the steps: collecting machine tool multi-dimensional data through a multi-channel collection device; performing time alignment on different sampling frequency signals by adopting a dynamic synchronization algorithm of clock drift compensation; executing noise suppression, feature extraction and compressed encoding at the edge computing node to generate local state abstract data; sending the abstract data to a central server through a distributed message queue, and realizing multi-application concurrent access based on load dynamic scheduling; and the central server performs fusion analysis on the abstract data by using a multi-modal fusion deep network model, and outputs a health degree index and an overall operation score of the key parts of the machine tool, thereby realizing intelligent evaluation and health prediction of the operation state of the machine tool. The method realizes global perception and intelligent evaluation of the machine tool operation state, and can be widely applied to operation monitoring and health management of high-end numerical control machine tools, complex machining centers and unmanned production lines.

The invention belongs to the technical field of residual life prediction, and particularly relates to a system multi-degradation-stage residual life prediction method and system based on a dynamic domain adaptive network, equipment and a storage medium. The method comprises the following steps: acquiring source domain monitoring data and target domain monitoring data of a system, and preprocessing the source domain monitoring data and the target domain monitoring data; extraction of multi-scale features and construction of health indexes; performing degradation stage division based on the IGTS; constructing a dynamic domain adaptive network containing the VBGRU and training the model; and inputting target domain monitoring data into the trained model, and outputting a residual life prediction value of the target domain system. In addition, the invention further provides a system, equipment and a storage medium for executing the method. The system not only can effectively extract the multi-scale time sequence dependence features in the system degradation process, but also can adaptively compare complete local and local features between domains, and effectively improves the generalization ability of residual life prediction in a variable working condition scene.

The invention provides an RV reducer online fault diagnosis method, and belongs to the technical field of reducer fault diagnosis based on computer data processing. The method comprises the following steps: firstly, constructing a fault data set covering multi-modal signals and multiple working conditions, and integrating vibration, current and high-frequency elastic stress wave data; performing segmentation, time-frequency conversion and multi-modal feature fusion on the original time series data to generate a three-channel time-frequency feature map, and strengthening visual expression of fault features; then constructing a domain adversarial attention neural network, and realizing cross-working-condition fault feature migration and accurate classification through adversarial training of a feature extractor, a fault classifier and a domain classifier; and finally, deploying an online diagnosis model, generating a health indicator in combination with the health state baseline model, predicting the remaining service life through a long short-term memory network, and completing fault early warning and life evaluation. According to the method, accurate fault identification and residual service life prediction under complex working conditions are realized, and reliable technical support is provided for the RV speed reducer.

The invention discloses a server health examination method and device, equipment and a medium. The method comprises the following steps: acquiring hardware layer data, system layer data, application layer data and physical environment layer data of a target server through a data acquisition component; determining a current value and a predicted change range of each health index regularly, and determining a current health level and a predicted health level range of the target server according to the current value and the predicted change range of each health index; when the current health level belongs to an abnormal level, executing a self-healing operation corresponding to the current health level; and when the predicted health level range contains an abnormal level, executing an early warning operation. According to the embodiment of the invention, real-time quantitative evaluation, abnormity prediction, early warning and automatic repair of the health state of the server can be realized, health evaluation and health pre-judgment can be comprehensively and accurately carried out on the server, and the operation stability of the server is improved.

The invention relates to the technical field of industrial automation, and particularly discloses a fault prevention and diagnosis control system based on a red-mine intelligent mining shield tunneling machine, which is characterized in that time-space synchronous acquisition and fusion of multi-source heterogeneous sensor data are realized through a red-mine operating system, and a multi-dimensional state sequence containing equipment operating parameters and health indexes is constructed; establishing a probability world model based on historical data, predicting state transition probability distribution and quantifying uncertainty; dividing a safety area according to uncertainty, and verifying action safety through multi-step rolling prediction; and iteratively updating a decision strategy by adopting a security constraint strategy optimization algorithm, and dynamically updating model parameters in combination with an online learning mechanism, so that the problems of insufficient data fusion, lagging fault prediction and insufficient exploration action security in the prior art are solved; accurate sensing of the operation state of the shield tunneling machine, early warning of faults and safe autonomous decision making are achieved, and the operation reliability and the maintenance efficiency of equipment are improved.

The invention provides a GPU (Graphics Processing Unit) hardware health prediction and active maintenance method and system, and belongs to the technical field of computing hardware maintenance and fault prediction. Based on the GPU health state data, a health degree comprehensive score of the GPU is calculated by using a preset health degree evaluation model, and a key health index in a future set time period is predicted by using a time sequence model. Calculating the fault probability of a preset type of fault risk by using a preset fault risk model based on the health degree comprehensive score of the GPU and / or the predicted key health index; and comparing the calculated fault risk probability with a preset threshold value, and executing a main preset maintenance action when the fault risk probability exceeds the preset threshold value. Through multi-source data acquisition and time sequence prediction, the potential fault risk of the GPU is found in advance, the maintenance action is actively executed, task interruption and data loss are reduced, and the reliability and availability of a GPU cluster are improved.

The invention relates to the technical field of medical data processing, in particular to an AI-based anorectal operation auxiliary system, which comprises a patient health data analysis module, a patient health data analysis module, a data processing module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, wherein the patient health data analysis module is used for classifying personal information of a patient and health data associated with anorectal diseases on the basis of basic physiological parameters of the patient, past anorectal disease history, medicine allergy history and operation records; and performing correlation analysis on the anorectal disease history and the physiological parameters of the patient to generate preoperative health feature information. According to the method, the physiological parameters of the patient and the past medical history are subjected to correlation analysis, key health indexes related to the operation can be accurately extracted, potential relations in the data can be mined, and the application effect of the data is optimized. By analyzing the combination mode of different health data, the probability of occurrence of common risks in the operation can be predicted, and the accuracy of medical decision making is enhanced. In addition, key operation steps of the surgery are matched with the health state of the patient, potential operation difficulties are recognized, and the scheme is optimized in a targeted mode.

The embodiment of the invention discloses a model training method, a life prediction method and a life prediction device, and the method comprises the steps: collecting the multi-dimensional health index related data of a target prediction object, inputting the multi-dimensional health index related data into a health index prediction related model, calculating a current comprehensive health index of the target prediction object through the health index prediction correlation model; constructing a current comprehensive health index sequence according to the current comprehensive health index of the target prediction object; and inputting the current comprehensive health index sequence of the target prediction object into the residual life prediction model so as to predict the residual service life of the target prediction object through the residual life prediction model. According to the technical scheme of the embodiment of the invention, the model prediction precision of the health index prediction correlation model and the residual life prediction model can be improved, and the precision of predicting the residual life of a certain object based on the health index prediction correlation model and the residual life prediction model is further improved.

The application relates to the technical field of data processing, and discloses an old person health state evaluation data processing system based on multi-modal data, which comprises a medical data integration module, an across-modal causal fusion processing module, a health state evolution modeling module, a backtracking evaluation report module and a decision output module. The medical data integration module acquires electronic medical record data and medical examination reports through a FHIR interface and extracts structured health indexes. The across-modal causal fusion processing module fuses monitoring data and medical texts through an image, a text and an image sandwich architecture. The health state evolution modeling module maps a health feature vector into three-dimensional state indexes of physiological functions, cognitive levels and motor abilities. The backtracking evaluation report module and the decision output module are used for backtracking evaluation and decision output. Through the image, the text and the image sandwich architecture, deep semantic fusion of multi-modal features is realized under the constraint of medical pathology rules, feature weight adaptive distribution is dynamically guided based on a medical causal graph, a high-dimensional fusion vector retaining key pathological information is generated, the semantic integration capability of health data is significantly improved, and the reliability of discrimination and decision is improved.

The invention provides a process GPU-oriented real-time fault tolerance and task migration system and method, and relates to the technical field of distributed computation.The method comprises the steps that a training task state in a GPU video memory is captured, difference data is generated through an incremental difference algorithm, and the difference data is stored in a multi-level storage system after being subjected to lightweight compression; meanwhile, GPU node health indexes are collected in real time, node states are analyzed through a time sequence prediction model, and a migration process is triggered when fault risks are predicted or preemption early warning is received; during migration, the task is paused, a final increment check point is generated, check point data is quickly transmitted to a target healthy GPU node through an RDMA network, a complete context state is reconstructed, task execution is recovered, and real-time fault tolerance and seamless migration of the GPU task are achieved. According to the method, the continuous operation reliability and the resource utilization efficiency of the GPU cluster in the long-time training task are improved.

The invention relates to the technical field of equipment health management, in particular to an electromechanical system health assessment method based on an AINN and a frequency domain causal network, and the method comprises the steps: collecting a plurality of operation parameters of an electromechanical system through a sensor, obtaining a training sample set after preprocessing, building an autoregressive neural network, and carrying out the health assessment of the electromechanical system through the autoregressive neural network; training an autoregressive neural network according to the training sample set, obtaining frequency domain representation of a parameter matrix of the trained autoregressive neural network, and calculating a generalized partial orientation coherence value; constructing a causal diagram according to the generalized partial orientation coherence value, calculating multi-dimensional health indexes based on the causal diagram, and fusing the multi-dimensional health indexes to obtain a comprehensive health index; performing change point detection on the comprehensive health indexes by adopting a PELT algorithm to obtain significant change points of the comprehensive health indexes, and obtaining comprehensive health indexes of multiple stages based on the significant change points; according to the invention, the health state evaluation effect of the electromechanical system can be improved.

The invention discloses an SSD (Solid State Disk) data maintenance method based on self-adaptive data scanning in the technical field of solid-state storage equipment, which comprises the following steps of: S1, acquiring I / O (Input / Output) data volume of an SSD in real time, and dividing the SSD into different load grades according to a preset standard; s2, dynamically adjusting a scanning mode based on the load level, wherein the scanning mode comprises a scanning range, a scanning period and a scanning bandwidth; s3, multi-dimensional health indexes of all the storage areas are scanned and collected, a total health degree score is calculated, a health degree mapping table is constructed, and risk levels are divided for all the storage areas; and S4, executing a corresponding active repair mode on each storage area according to the risk level. According to the SSD data maintenance method, the SSD performance stability is improved by distinguishing busy and idle tenses and dynamically adjusting a scanning mode; the risk is evaluated through multi-dimensional health, the active repair mode is dynamically adjusted according to the risk condition, and efficient and reliable maintenance of SSD data is achieved.

The invention discloses a livestock growth state monitoring method based on machine vision, and relates to the technical field of image processing, and the method comprises the steps: collecting a color video, a depth map, a high-frame-rate video and thermal imaging data in a sheep shed in real time through multiple types of camera devices, synchronizing a multi-device timestamp through a network time protocol, constructing a panoramic image, and carrying out the real-time monitoring of a livestock growth state. Ear tag recognition and multi-feature fusion are combined, a unique identity code is generated and stored in a database, and accurate individual recognition is achieved. Extracting health indexes such as physique parameters, activity amount, ingestion and rumination behavior characteristics, hair color and gait information, body surface temperature and respiratory rate of the sheep by adopting an image processing and behavior analysis technology; sequentially calculating a physique development index, a behavior vitality index and a health state index, performing dynamic evaluation by setting a threshold value, and judging the development, behavior and health state of the sheep in real time; for abnormal conditions, the system automatically starts corresponding monitoring and intervention strategies, so that the health of the sheep is effectively guaranteed, and the breeding management level is improved.

The invention provides a prediction method and a prediction device for the service life of a solid oxide fuel cell SOFC. The prediction method comprises the following steps: acquiring multi-source state data and obtaining a multi-domain feature set based on the multi-source state data; obtaining a first comprehensive health index sequence based on attention features in the multi-domain feature set; obtaining a sub-sequence based on the first comprehensive health index sequence, and dividing the sub-sequence into a low-frequency trend component and a high-frequency residual component; inputting the low-frequency trend component into a first data model, inputting the high-frequency residual component into a second data model, and respectively obtaining a low-frequency prediction sequence and a high-frequency prediction sequence; adjusting parameters of the first data model and the second data model by using the physical model, and predicting to obtain a physical model prediction sequence; and obtaining a residual service life prediction result of the SOFC based on the data model prediction sequence and the physical model prediction sequence. Therefore, the accuracy of predicting the service life of the SOFC can be improved.

The invention relates to the technical field of data processing, and provides an intelligent community health management system for senile chronic disease patients based on a radar map, and the system comprises the steps: regularly obtaining the monitoring data of a plurality of health indexes of the senile chronic disease patients; obtaining standardized data of each health index of the patient; determining the illness state reflecting degree of each health index of the patient; generating a radar map of each time of monitoring of the patient; determining the intervention degree of each monitoring of the patient according to the difference of the adjacent monitoring radar maps of the same patient and the change trend of the continuous monitoring radar maps; the medication adjustment degree of the patient is obtained, and then the illness state change degree of the patient is obtained; based on the disease change degree and the medication adjustment degree of the patient, obtaining health management suggestions for the patient; and the hospital timely shares the health management suggestions to the community after obtaining the health management suggestions of the patient. The objective of the invention is to solve the problem that health state monitoring management is affected by difference of radar map expressions caused by different illness states of patients.

The invention discloses an equipment fault prediction method and system, and belongs to the technical field of electric data processing. The method comprises the following steps: inputting state information of each component of equipment into a conditional generative adversarial network to generate time series data, and constructing a plurality of target maps with adjacency relations; time features and local graph features are extracted by using multi-branch convolution and a graph convolution structure of a convolution auto-encoder, and target graph features are obtained through splicing; performing channel weighting through a multi-scale attention module to obtain multi-scale image features; calculating cross attention weights among different target images and fusing the cross attention weights to obtain fused image features; and compressing the potential feature representation as a health index, constructing a health index time sequence, inputting the health index time sequence into a fault prediction model, and obtaining an estimated residual life of the equipment. According to the method, strong noise interference can be effectively filtered out, the problem of feature redundancy is solved, key degradation information is captured robustly, the equipment health state is accurately represented, and the residual life prediction precision is remarkably improved.

The invention provides a generator carbon brush residual life prediction method and system based on digital twinning, and belongs to the technical field of generator operation maintenance, and the method comprises the steps: firstly building a bidirectional mapping relation between a physical carbon brush and a digital twinning body, and synchronously storing related data; generating real-time mapping data configuration health assessment parameters based on related data association; comprehensive health indexes are calculated through health assessment parameters, various adaptation degrees, deviation degrees, fluctuation degrees and coupling influence parameters are associated, comprehensive health index time sequence data are input into a time sequence prediction process to generate a future attenuation trend curve, the remaining life is calculated, and the remaining life is transmitted to a visual interaction process and is synchronously presented. Therefore, the residual life of the carbon brush can be accurately predicted in real time, and the operation reliability of the generator is improved.

The invention provides an electric energy meter service life prediction method based on multi-source data, and belongs to the technical field of metering detection equipment. Multi-source time sequence data of an electric energy meter is collected, a multi-source time sequence data set is formed, and a training data set and an online monitoring data set are constructed; constructing an improved double-stage attention mechanism convolutional long-short term memory network as a data driving model; constructing a physical information neural network based on a failure physical equation as a physical model, and performing joint training on the data driving model and the physical information neural network; and performing health index calculation on the online electric energy meter data by adopting the bidirectional driving fusion model subjected to joint training, and outputting a residual service life prediction result based on a residual consensus prediction mechanism. The method has the advantages that the data-driven model and the physical model are subjected to interactive training on the parameter level, so that the final model has the flexible learning ability for data features and the internal compliance for physical rules, and the rationality of electric energy meter health state evaluation is improved.

The invention discloses a converter station operation and maintenance data analysis method based on big data, and relates to the technical field of data analysis, and the method comprises the following steps: obtaining converter station operation state data, and obtaining initial features; performing multi-dimensional parameter calculation according to the initial features to obtain composite features; performing abnormal state recognition according to the composite features to obtain a feature state set; performing time sequence characteristic evolution analysis according to the characteristic state set to obtain a characteristic evolution sequence; performing health index calculation according to the characteristic evolution sequence to obtain equipment health data; and performing fault correlation analysis according to the equipment health data to obtain fault correlation characteristics. Through the fault correlation analysis model, health correlation analysis and fault propagation quantification between equipment are realized, the accuracy and timeliness of fault prediction are improved, and the risk identification capability is enhanced.

The invention discloses a bridge structure health state evaluation and early warning system based on deep learning, and relates to the field of bridge structure health monitoring. The system comprises a data acquisition module, a preprocessing and feature engineering module, a deep fusion modeling module, a dynamic evaluation early warning module and an online updating module, and a closed-loop link is formed through a high-speed real-time data bus. The data acquisition module integrates multiple types of sensors to realize synchronous acquisition of multi-source heterogeneous data; the preprocessing module constructs a multi-dimensional feature vector through three-stage processing; the deep fusion modeling module adopts a double-flow hybrid neural network architecture, and constructs a health index calculation model of three-dimensional feature fusion in combination with an attention mechanism; the dynamic evaluation and early warning module realizes four-level health grading evaluation and three-level early warning based on the degradation knowledge graph; and the online updating module ensures the model precision through incremental learning. The system breaks through the limitation of a traditional fixed threshold value, structure abnormity can be predicted 3-6 months in advance, the false alarm rate is reduced by 40%, the missing report rate is smaller than or equal to 2%, and the intelligent level and safety of bridge operation and maintenance are improved.