537 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 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 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 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.

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 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 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 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.

An adaptive dependency replay control system for use in a latency-sensitive ad serving backend, comprising: a microcontroller-based replay control engine configured to receive ad serving requests and interface with a variety of downstream microservices; a latency monitoring unit operatively coupled to the microcontroller, the latency monitoring unit continuously sampling and maintaining real-time latency histograms for each downstream microservice over sliding time windows; a health status aggregator communicatively coupled to the retry control engine, the aggregator configured to receive service-level health indicators, including, but not limited to, HTTP status codes, circuit breaker states, request timeout counters, and error rate thresholds; a retry decision processing unit stored in a memory accessible to the microcontroller, wherein the matrix can generate a retry action vector based on one or more of the following factors: dependency health, request priority, estimated ad impression value, and system resource metrics; a policy execution engine configured to evaluate retry policies expressed in a domain-specific retry policy language, wherein the execution engine resolves the policies into bytecode rules that are executed by the retry control engine in real time on a per-request basis; and at least one fallback path generator capable of returning an approximate or synthetic response instead of retrying a degraded dependency, where the fallback path is selected based on runtime evaluation of the policy conditions and a calculated retry confidence value.

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 relates to the technical field of mine fans, in particular to a mine fan fault recognition and detection system and method, and the method comprises the steps: collecting mine fan data, and obtaining time-frequency domain features and working condition features; a CNN-based fault diagnosis model is constructed and trained, and fault identification is carried out; meanwhile, building a health index of the mining fan, evaluating a health state, building a health index of the mining fan, building a mining fan residual life prediction model based on a long short-term neural network LSTM model, and predicting the residual life of the mining fan; in order to adapt to a new fault type, an incremental learning method is adopted to dynamically update a diagnosis model, and a knowledge distillation technology is utilized to learn a new fault. According to the method, multi-source heterogeneous data are comprehensively utilized, the model can be dynamically updated, the evaluation health state of the mining fan is quantified, and a comprehensive solution is provided for reliable operation of the mining fan.

The invention belongs to but is not limited to the technical field of state monitoring, and particularly relates to a multi-feature fusion power field effect transistor online state monitoring and evaluation method and system, which are used for understanding the aging mechanism of a device by selecting a proper aging signal and calculating the features of the aging signal. Then, effective feature information strongly correlated with the life sequence is screened out by using a mutual information correlation coefficient algorithm; next, dimension reduction fusion is performed on the screened features by using a KPCA polynomial kernel function algorithm, so that a health index is constructed, and the degradation state of the device is reflected; and monitoring and evaluating the health state of the device by using CNN, XGBoost and RF classifiers according to the constructed health indexes so as to determine the health state of the device. In addition, the RF classification model with the best classification effect is deployed on the DSP development board, the performance of the prediction model is further optimized, and the prediction time of the model is shortened. According to the method, the health state of the power device is described by integrating the multi-feature information.

The invention discloses a motor bearing remaining service life prediction method and system using health indexes, and belongs to the technical field of automation. According to the method, time domain, frequency domain and time-frequency domain characteristic extraction is carried out on a vibration signal sequence, and sensitive characteristics of a degradation process can be accurately reflected through monotonicity and time correlation screening. And performing principal component analysis and dimension reduction on the screened features, and selecting the maximum principal component as a health index. And calculating an initial degradation point of the health index and dividing the data set. When residual service life prediction is carried out, an input signal is decomposed into a trend part and a residual error part, different models are used for feature extraction, and finally a residual service life prediction result is output. Through the steps of health index construction, signal decomposition, feature extraction, residual service life prediction and the like, the degradation stage can be accurately divided, meanwhile, accurate feature extraction and service life prediction can be carried out on data of the degradation stage, and the service life prediction accuracy is improved.

The invention provides a rolling bearing digital twinning dynamic evolution method and system based on continuous learning, and belongs to the technical field of bearing life prediction. The method comprises the following steps: processing a bearing monitoring signal through short-time Fourier transform to generate standardized time-frequency data; constructing health indexes based on the index degeneration function and dividing health levels; training a life prediction model by using the extended LSTM network and taking the time-frequency data as input; real-time data is collected through a fixed time window to predict the life, and when the root-mean-square error of a predicted value and an actual value exceeds the limit, the edge device is triggered to upload new data; evaluating parameter importance in combination with a Fisher information matrix, and dynamically adjusting a regularization intensity updating model; and monitoring the data standard deviation in real time, triggering shutdown when the data standard deviation exceeds the limit, otherwise, predicting the remaining life by updating the model, and stopping when the remaining life reaches the threshold value. According to the method, dynamic evolution of the digital twin model is realized through continuous learning, and the industrial equipment state monitoring and predictive maintenance capability is effectively improved.

The invention relates to the technical field of health monitoring, in particular to a health monitoring method and device based on the Internet of Things, and the method comprises the steps: obtaining a historical health monitoring data set according to an Internet of Things sensor network; dividing the monitoring area into a multi-scale space-time health state grid; and performing state classification through the historical health monitoring data set according to a density peak clustering algorithm to obtain a health state mode. According to the method, the monitoring area is divided into the multi-scale space-time health state grids, and the mapping relation between the environment parameters and the health indexes is combined, so that the microenvironment-health association which is difficult to find by a traditional monitoring system can be captured, for example, in a ward scene, the monitoring accuracy is improved; the phenomenon that the infection risk of a patient is increased due to poor air circulation in a ward can be accurately identified, so that environmental parameters are optimized in a targeted manner; and through collaborative analysis of a global channel and a local channel, a group trend and individual anomaly can be captured.

The invention relates to the technical field of image detection, in particular to a pest and disease identification method and system based on unmanned aerial vehicle remote sensing. The method comprises the following steps: acquiring health indexes of each plant; marking suspicious points; spraying preset hormones to the suspicious points; after a first preset time interval, obtaining the health index of the hormone-induced plant again, and distinguishing whether the plant is pathological abnormality or physiological abnormality according to the change of the health index; on the basis of the pathology and physiology distinguishing result, a definite diagnosis threshold value in a preset range of the suspicious point is adjusted, so that when the plant at the suspicious point is pathologically abnormal and the health indexes of other plants in the preset range adjacent to the suspicious point are abnormal, the condition that the other plants are pathologically abnormal can be detected more sensitively; and using the adjusted pest and disease damage early warning threshold value to identify pest and disease damage conditions of plants of which suspicious points are adjacent to a preset range. According to the invention, the pest and disease states of crops can be detected more accurately.

The invention belongs to the technical field of industrial equipment health monitoring and predictive maintenance, and discloses a machine tool feed shaft precision degradation predictive maintenance method based on a continuously optimized Mamba network. According to the method, a vibration signal of a machine tool feed shaft is collected through a three-way vibration sensor, and a low-dimensional feature vector is constructed after baseline correction, filtering noise reduction and feature extraction of an auto-encoder. The method comprises the following steps: pre-training a Mama network model by using full life cycle data of a test platform bearing, capturing a state evolution law in combination with a dynamic parameter generation mechanism, and discretizing a state-space equation through a zero-order retention rule to realize efficient time sequence modeling. In actual monitoring, the model predicts a future feature sequence based on part of input data, calculates health indexes and compares the health indexes with multi-stage thresholds, and four-stage state judgment of health / sub-health / initial degeneration / serious degeneration is achieved. A continuous learning mechanism is further introduced, model parameters are dynamically updated through experience playback and small learning rate fine adjustment, and working condition changes and novel fault modes are adapted.

The invention provides an equipment edge intelligent early warning method and system based on multi-dimensional data association, and the method comprises the steps: obtaining a current actual health index parameter of a health index of target equipment, and obtaining a current working condition index parameter of at least one working condition index associated with the health index, the health index is used for evaluating the health condition of the target equipment, and the working condition index is an index capable of influencing the parameter size of the health index; inputting each working condition index parameter into a pre-trained health index parameter prediction model, and outputting a reference health index parameter by the health index parameter prediction model; determining a target health evaluation parameter of the target device based on a difference value between the actual health index parameter and the reference health index parameter; and if the target health evaluation parameter is greater than a preset threshold, generating first abnormal early warning information, and sending the first abnormal early warning information to operation and maintenance personnel. According to the technical scheme, the accuracy of performing abnormal early warning on the transformer substation can be improved.

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 belongs to the technical field of high-temperature experimental equipment, and discloses an integrated intelligent program control and multiple safety protection experimental box-type resistance furnace which comprises a furnace body, a sensor suite and a controller, the controller runs an intelligent control program based on neural network model predictive control, namely NN-MPC, and the intelligent control program is connected with the sensor suite. The method is used for realizing accurate tracking of a complex temperature control curve and optimization of energy consumption. Meanwhile, the controller also runs an active health management program based on prediction and health management, namely PHM, and the program predicts the residual service life, namely RUL, by monitoring health indexes such as resistance of the heating element in real time, so that early warning and predictive maintenance of faults are realized; the health state information of the PHM module is fed back to the NN-MPC module in real time, so that the control system can adapt to aging of the equipment, and the control performance, the energy efficiency, the operation reliability and the overall safety of the equipment are remarkably improved.

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.