811 results about "Health indicator" patented technology

The invention relates to an underground equipment fault early warning and diagnosis method based on big data analysis. The method is suitable for equipment operation state monitoring and intelligent diagnosis in underground operation scenes such as mines. The method comprises the following steps: collecting multi-source data such as an equipment running state, environment parameters and operation behaviors and preprocessing the multi-source data; multiple signal features are extracted and fused to construct a unified feature vector; performing health modeling by using the residual self-encoder model to generate a health index; an early warning threshold value is dynamically set through clustering analysis and Bayesian reasoning, and anomaly recognition is achieved; after early warning is triggered, fault type identification is carried out by adopting the fusion discrimination model; performing causal reasoning and maintenance suggestion generation based on the equipment fault knowledge graph; and continuously optimizing the model in combination with operation and maintenance feedback information, and constructing a closed-loop diagnosis mechanism. The method has the characteristics of high recognition precision, high response speed, explainable result and sustainable optimization of the model.

The invention discloses an industrial robot health state monitoring method and system based on digital twinning, and relates to the technical field of equipment monitoring, and the method comprises the steps: collecting the real-time state data of an industrial robot through a plurality of sensors, and enabling the real-time state data to be matched and aligned with a three-dimensional simulation model, and constructing a digital twinning model; obtaining twinborn data through the digital twinborn model after the adaptive model parameter update, and fusing the twinborn data with the real-time state data to generate a multi-source heterogeneous data set; feature data representing the health state of the industrial robot is obtained through feature selection to construct health indexes; and constructing a health state evaluation model based on the deep belief network, and taking the current health index parameters as model input to obtain an industrial robot health state evaluation result. Multi-source heterogeneous data such as vibration data and electrical data are fused, the health state of the industrial robot is monitored and evaluated in combination with digital twinning, and a reliable reference basis is provided for state monitoring and maintenance of the industrial robot.

The invention relates to the technical field of smart communities, and discloses a smart community resident health management method based on machine learning, and the method comprises the steps: continuously collecting the health feedback data of residents, such as the physical condition change after outdoor activities, carrying out the correlation analysis of the health feedback data with the previous health data and environment data, dynamically updating and optimizing a correlation model, and obtaining the health feedback data of the residents; the prediction precision of the model and the effectiveness of suggestions are improved, the relationship between the use condition of the community public space and the resident health indexes is analyzed, key public facilities and areas influencing resident health are identified, data support and decision basis are provided for community planning and facility reconstruction, and the method is suitable for popularization and application. Community resident health and environment condition analysis reports are generated regularly, and resident health index change trends are visually displayed for reference of community managers and residents. According to the invention, intelligent analysis and application of environment and health data are realized, and technical support is provided for building a healthy community.

The invention provides a Python-based automatic system configuration and monitoring method and system, and relates to the technical field of automatic system configuration and monitoring, and the method comprises the steps: constructing a multi-level service dependence model and a service node affinity matrix, generating a configuration parameter priority queue, and completing the initial configuration of a system through a distributed transaction coordinator. When the system runs, the monitoring agent program collects system running data, calculates a node health degree index and shares the node health degree index through the net-shaped sharing channel. And the core node performs data association analysis and updates a node load value and a health degree index. And when the node health degree is lower than a preset threshold value, triggering an alarm processing module, selecting an optimal node based on a node health state evaluation report to execute reconfiguration, updating a service registration center and broadcasting a configuration updating message. According to the invention, automatic system configuration and monitoring can be realized, and the system reliability and stability are improved.

The invention discloses a trackless rubber-tyred vehicle multi-terminal data fusion system, relates to the technical field of data fusion, and is used for solving the problem of poor trackless rubber-tyred vehicle multi-terminal data fusion. Comprising a path quantification module, an equipment health classification module, an alternative optimization module and a consistency verification module. According to the method, multi-terminal data fusion is carried out on a trackless rubber-tyred vehicle, a sensor dependency graph is dynamically constructed, dependency weights are calculated, a critical path is optimized, health indexes are defined according to a signal-to-noise ratio, output fluctuation and response delay, multi-dimensional abnormity is detected in combination with a sliding window and a mahalanobis distance, and a health score is generated to monitor the state of the sensor. A fault sensor is isolated, substitute data is generated through a redundant data source, the substitute data is incorporated into a fusion process after verification, a dependency graph is adjusted, a path is optimized, global consistency verification analysis is performed, different strategy management modules are triggered, a data fusion rule and task allocation are optimized, and the data fusion reliability of the trackless rubber-tyred vehicle in a complex operation environment is improved.

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 provides an industrial equipment predictive maintenance method and system based on edge computing, and the method comprises the steps: carrying out the on-site collection, cleaning, fusion, storage and analysis of mass state monitoring data generated in the operation process of industrial equipment through an edge node disposed at an industrial site, according to the method, multi-dimensional features capable of reflecting equipment health conditions are extracted, comprehensive health indexes are constructed, health state evaluation is performed through an equipment fault early warning model, and an equipment maintenance strategy is dynamically generated based on a model output result to guide maintenance personnel to overhaul or replace the equipment in advance, so that the maintenance efficiency is improved. The fault occurrence probability and the influence range are furthest reduced. According to the method, the edge computing technology is adopted, the data transmission time delay and the bandwidth pressure can be remarkably reduced, the real-time performance of fault early warning and response is improved, and the data safety is enhanced.

The invention relates to the technical field of storage optimization, in particular to a data storage management system and method based on big data, and the system comprises an efficiency monitoring module, a medium evaluation module, a decision generation module, a strategy execution module and an effect feedback module. According to the method, input and output delay, a medium health attenuation curve and a bandwidth fluctuation map are collected, a dynamic feature space is constructed, the stability of the system is judged in combination with a Lyapunov index, early warning of performance abnormity is achieved, health indexes such as an erasing cycle, a seeking error rate and a charge retention rate are fused, and the access frequency and the load pressure are combined. Calculating a compensation coefficient, improving heterogeneous medium cooperation efficiency, pre-migration evaluation cost and topology adjustment income, improving resource configuration accuracy, dynamically adjusting hot and cold data distribution and node load, enhancing system scheduling flexibility, comparing throughput, medium wear rate and synchronous delay, and quantifying stability gain brought by configuration change; and a closed-loop process from identification to feedback is constructed.

The invention provides a lithium battery health state prediction method and system based on fusion health indicators, and the method comprises the steps: obtaining lithium battery charging and discharging parameters and a data set, extracting indirect health indicators for reflecting the degradation characteristics of a battery, and verifying the reasonability of the indirect health indicators through a correlation analysis method; performing dimension reduction processing on the indirect health index by using a KPCA method to obtain first principal component data, constructing a lithium battery health state comprehensive estimation model, and further obtaining a battery health state; and decomposing an original battery capacity sequence into a local fluctuation sequence and a global degradation sequence, performing prediction by using a radial basis function neural network and Gaussian process regression, and performing superposition to obtain a battery residual life prediction value. Aiming at the problems of low accuracy, unstable model and the like in the prior art, the battery health state is measured and calculated by adopting a mode based on the fusion health indexes, the method has the advantages of high estimation precision, good application prospect and the like, and the prediction accuracy is effectively improved.

The invention discloses a health risk assessment method and early warning system based on multi-source data analysis. The health risk assessment method comprises the following steps: S1, collecting and preprocessing multi-source health data through medical detection equipment; s2, extracting key health indexes, sequence features and statistical features based on the health data set; s3, adopting a recurrent neural network model to construct a health risk assessment model; s4, optimizing structural parameters of the health risk assessment model by adopting an improved dragonfly algorithm; s5, performing performance evaluation on the health risk evaluation model by using the optimal parameter set; s6, deploying the final health risk assessment model in a health risk assessment and early warning system; and S7, when the health risk assessment result exceeds a preset risk threshold, automatically generating early warning information. According to the method, the recurrent neural network model, the improved dragonfly algorithm and the multi-source health data fusion optimization technology are combined, and health risk assessment and early warning based on multi-source data analysis are realized.

The invention relates to the technical field of motor state monitoring and fault prediction, and provides a motor residual life analysis method and system based on a support vector machine, and the method comprises the steps: building a multi-dimensional degradation feature set of a motor, and calculating a health index degradation rate based on multi-source sensor data and a failure threshold; constructing a support vector machine regression model of an adaptive kernel function, predicting a health index change track by using the model and real-time data, and generating a residual life evaluation result if a prediction deviation is within an allowable error range; otherwise, starting an incremental learning mechanism to update the training data set, dynamically adjusting kernel function parameters, and recalculating the trajectory; and if the error requirement is still not met, model regularization parameters are optimized in combination with the working condition data until the residual life evaluation result is converged. According to the method, the accuracy and dynamic adaptability of motor residual life prediction can be improved, and the robustness of the model to complex working conditions is enhanced.

Methods, systems and apparatuses for infant and child monitoring, for changing pads, and for tracking heath. One system is an intelligent changing pad system for infants and children that includes a main body with a changing surface, a scale integrated within the changing surface for measuring weight, a plurality of sensors configured to monitor health-related metrics of a human including temperature and hydration levels, a graphical user interface for displaying the human's health metrics and alerts, and a wireless communication system for transmitting the monitored data to a remote computing device.

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 provides an intelligent health risk assessment method and system, and relates to the technical field of health risk assessment, health big data is constructed, feature selection is performed through the health big data to construct a gradient boosting decision tree model, risk prediction of individual diseases is performed, and time sequence analysis of health indexes is performed according to a first health risk score. Adjusting the first health risk score, carrying out health risk interpretation based on the second health risk score, carrying out health risk hierarchical evaluation according to the personalized health optimization suggestion and the second health risk score, making a hierarchical intervention strategy, carrying out health detection on the target individual, and triggering health early warning according to an abnormal detection result. The technical problems that in the prior art, health risk assessment data sources are single, so that health assessment is not comprehensive and inaccurate, and targeted intervention strategies are lacked are solved. The technical effects of comprehensively and accurately evaluating the individual health risk and providing a targeted intervention strategy are achieved.

The invention discloses a lithium battery unsupervised health assessment and residual life prediction method based on physical knowledge, and the method comprises the steps: collecting the data of a lithium battery charging and discharging experiment sensor, and carrying out the data preprocessing; guiding the construction of a feature extraction network according to the lithium battery mechanism model, extracting unobservable signals, and enhancing the input space of a health assessment and residual life prediction algorithm; based on the lithium battery mechanism model and the degradation model, constructing a health assessment and residual life prediction algorithm based on PINN; inputting the enhanced feature data into a health assessment and residual life prediction algorithm based on PINN, and optimizing model parameters constructed by health indications; health indications fused with physical knowledge are constructed, and residual life estimation based on the health indications is realized. The method can be used for health assessment and residual life prediction when a data set is small in scale and a single sensor signal can be obtained, and a relatively accurate health assessment result is obtained on the premise of ensuring the interpretability of the algorithm.

Systems and methods that can take a plurality of user inputs from various domains, reference this data to external knowledge databases (an evidence-base), process all the variables / information, and provide back to the user: 1) a consensus evaluation of a food's healthfulness, 2) a consensus evaluation of a food's cost effectiveness for human and planetary health, 3) food and diet recommendations consistent with guidelines and user consumption patterns, and 4) predicted effects on a multitude of health outcomes, and their uncertainty, for a multitude of health and lifestyle scenarios do not yet exist. In contrast, for food evaluation, individual systems exist, but are heterogeneous and thus contradictory to one another. Likewise, for health outcomes, systems that provide advice for biomarkers, which are not health outcomes per se, do exist, albeit without providing uncertainty assessment, consistency with existing dietary guidelines, compatibility with belief structures, and / or integration with predictive health models.

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 an equipment self-healing method and device based on time-space correlation characteristics, equipment and a medium. The method comprises the steps of generating time-space correlation characteristics based on multi-modal sensing data, performing anomaly detection by utilizing the time-space correlation characteristics to obtain an anomaly score, quantifying multiple operation dimension health indexes of equipment based on the time-space correlation characteristics, fusing the multiple operation dimension health indexes to generate a comprehensive health index, identifying a fault mode based on the anomaly score and the comprehensive health index, and generating a self-healing instruction. And performing simulation verification on the self-healing instruction in the digital twin model, sending the self-healing instruction passing the verification to a control unit for execution, and generating a self-healing result. According to the method, by fusing multi-modal data and cross-modal feature analysis and combining simulation verification of the digital twin model, early recognition and self-healing intervention of equipment faults are realized, and the operation stability of the equipment is improved.

The invention discloses a rotating component fault early warning method and device based on multi-modal data fusion. The method and device are used for solving the technical problem that an existing rotating component fault early warning method is poor in early warning effect. The method comprises the following steps: acquiring multi-modal signal data, and performing preprocessing according to the multi-modal signal data by adopting a band-pass filtering algorithm to generate a multi-modal feature vector set; performing feature fusion on the multi-modal feature vector set by adopting a recurrent neural network based on an attention mechanism, and outputting a target health index; based on a preset optimization search algorithm, adopting an initial hidden Markov model to generate a target hidden state sequence according to the target health index; and adopting a preset nonlinear Wiener degradation model to generate a rotating assembly fault early warning result according to the target hidden state sequence and the multi-mode signal data.

The invention discloses a health monitoring system and method based on multi-sensor fusion. The system comprises a health index acquisition system used for acquiring various health index data through various sensors; the multi-modal data analyzing and processing system is used for analyzing and processing the obtained multi-modal health index data to generate sensing information and analyzing and processing different data to obtain corresponding health indexes; and the health index evaluation report system is used for generating a health index evaluation report according to the perception information. According to the system, multiple data sources including images, sign signals, voice interaction and the like are fused through multi-modal data, and comprehensive health assessment is achieved. Different from a traditional health monitoring system which only depends on a single signal, the system fuses various data and can provide a more comprehensive and detailed health analysis result. Four diagnosis methods of watching, smelling, asking and cutting in traditional Chinese medicine are creatively combined with a modern intelligent technology, and modern application of health monitoring is achieved.

The invention provides an industrial personal computer hardware state remote monitoring method and system, and the method comprises the steps: carrying out the synchronous collection of the specified hardware parameters of different hardware units in a target industrial personal computer, and obtaining the original collection data; the method comprises the following steps: preprocessing original collected data to generate an input vector set for hardware health state assessment; inputting the input vector set into a health assessment model, and determining a multi-dimensional health degree index corresponding to each hardware unit; comparing the multi-dimensional health degree index with a preset health threshold value, and generating a hardware abnormal state recognition result; and sending a remote control instruction to the target industrial personal computer for remote control according to the abnormal state recognition result. Through the implementation of the scheme, the key parameters of different hardware units are collected, unified structured data are formed, the health assessment model is utilized to generate the multi-dimensional health indexes, dynamic updating of the remote control strategy is finally driven, and the operation and maintenance efficiency of the industrial personal computer can be effectively improved.

The invention relates to the technical field of disease prediction, in particular to an intelligent risk prediction method based on a neural network, and the method comprises the steps: collecting blood glucose and body mass index data, carrying out the clustering analysis, and constructing a multi-risk group feature; a risk transfer track before attack is recognized by combining a historical index change path and time sequence analysis; and calculating an individual risk coefficient by using deep learning, and comparing the individual risk coefficient with a risk threshold to generate early warning information, thereby realizing dynamic accurate evaluation. According to the method, by collecting the blood glucose level and body weight index data and conducting standardization processing, risk group division based on health index clustering can be achieved, a group recognition system with health feature differentiation is constructed, and the structured understanding of the individual health trend is enhanced. And on the basis of the clustered group, extracting a fluctuation path of individual historical indexes by means of a time sequence mode, identifying key transfer characteristics before disease attack, and enhancing the traceability of the disease formation process.

The embodiment of the invention discloses an equipment health assessment method and device based on multi-dimensional data fusion and dynamic weight, and relates to an intelligent monitoring and health assessment technology of an equipment complex system in the field of equipment health management. The method comprises the following steps: describing the change condition of health parameters along with the increase of equipment storage life from the angles of the regression trend of the health parameters along with time, the incidence relation among different health parameters and the probability distribution of health parameter data, and accurately reflecting the difference between health parameter monitoring data and baseline data. A dynamic weight updating method in the whole storage process is provided, and the weights of different health parameters in the process of calculating the health indexes are scientifically calculated. Therefore, the problem that health parameter monitoring data are scarce due to the fact that the equipment cannot be frequently powered on for testing in the actual storage and use process is effectively solved, and the assessment accuracy of the health state of the equipment is improved under the condition that the monitoring data are limited.

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 data processing, and discloses an old people health state assessment data processing system based on multi-modal data, and the system comprises a medical data integration module which obtains electronic medical record data and a medical examination report through an FHIR interface, and extracts a structured health index; the cross-modal causal fusion processing module is used for fusing the monitoring data and the medical text through an image, text and image interlayer architecture; the health state evolution modeling module is used for mapping the health feature vectors into physiological function, cognitive level and athletic ability three-dimensional state indexes; an evaluation report backtracking module; and a decision output module. Through an image, text and image interlayer 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 atlas, a high-dimensional fusion vector retaining key pathology information is generated, the semantic integration ability of health data is remarkably improved, and the health data fusion efficiency is improved. And the reliability of discrimination and decision making is improved.

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 discloses a digital-analog linkage life prediction method based on nonlinear Wi engine process degradation modeling. The digital-analog linkage life prediction method comprises the following steps: selecting features with high association degree with a health state based on a Pearson's correlation coefficient; carrying out normalization and de-noising on the selected features; constructing a comprehensive health index based on multi-feature fusion; carrying out degradation modeling and life prediction on the evolution process of the constructed comprehensive health indexes by adopting a non-linear Wi-ener process; estimating values of degradation model parameters based on maximum likelihood estimation and a Bayesian parameter updating method; and optimizing model parameters, updating a weight coefficient of multi-feature fusion and a failure threshold value of degradation modeling, and obtaining a more accurate life prediction result. The invention provides a digital-analog linkage life prediction method based on nonlinear Wi-er process degradation modeling in order to solve the problems that an existing RUL prediction method based on statistical modeling does not consider the nonlinear degradation condition and feature extraction and degradation modeling links cannot form interactive linkage, so that residual life prediction is not accurate, and provides a digital-analog linkage life prediction method based on nonlinear Wi-er process degradation modeling. And high-precision life prediction and uncertainty quantification of a prediction result are realized.

The invention provides a lithium ion battery health state estimation method, and belongs to the field of battery management, and the method comprises the steps: obtaining the EIS data, the current temperature and the current SOC of a to-be-detected lithium ion battery; the EIS data are analyzed through a DRT method, a DRT curve is obtained, health indexes are extracted from the DRT curve, and the health indexes comprise the peak amplitude, the time constant corresponding to the peak value, the peak area, the full width at half maximum, the weighted average time constant, the weighted standard deviation time constant, the time constant skewness and the time constant kurtosis; combining the health index, the current temperature and the current SOC to obtain an input feature vector, inputting the input feature vector into the trained SOH estimation model, and estimating the SOH of the lithium ion battery to be measured; the invention further provides an estimation system. The eight health indexes are closely related to aging mechanisms such as SEI membrane growth, active substance loss and impedance increase of the battery, high-quality input is provided for the model, and SOH estimation precision is improved; the temperature and the SOC serve as key input, and the influence of working condition changes on EIS measurement is effectively compensated.

The invention relates to the technical field of artificial intelligence, in particular to a remote medical inquiry system based on AI recognition, which comprises a voice structure analysis module, a semantic association mapping module, a knowledge graph reasoning module, an inquiry path adjustment module and a disease traceability analysis module. According to the method, the logic relation of a description chain is accurately obtained by performing specific value mapping on the time delay and the semantic frequency of the voice information, the structural perception ability of the semantic information is enhanced, the symptom frequency and the health index trend are combined, accurate measurement is performed on abnormal fluctuation dependency, inquiry priority grading is optimized, and the key symptom recognition sensitivity is improved; through induction of a periodic fluctuation trend, formation of a dynamic evolution sequence, enhancement of the monitoring capability of health risk evolution, improvement of decision precision and optimization of a diagnosis chain, overall processing is performed through time sequence and semantic association analysis, the recognition and intervention capability of a complex health state is enhanced, and the health assessment adaptability and accuracy in multiple rounds of interaction are improved.

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.