312 results about "Hide markov model" patented technology

The invention relates to the technical field of sealing performance testing, in particular to a breather valve performance online testing method for oil and gas storage and transportation, which comprises the following steps: acquiring a vibration signal of a breather valve body, a multi-band acoustic signal of a valve port and a total pressure signal in a storage tank; performing multi-scale complex wavelet decomposition, and constructing a time frequency-energy correlation feature tensor; the total pressure signal and the time frequency-energy correlation characteristic tensor serve as a combined observation value and are input into a preset continuous Gaussian mixture hidden Markov model containing four hidden states of sealing, transient micro-leakage, continuous leakage and full-amount opening, and the posterior probability is calculated; and calculating to obtain a real-time leakage rate. According to the method, the opening pressure can be accurately determined, the real-time leakage rate can be quantitatively calculated after the leakage state is recognized, comprehensive and accurate quantitative online evaluation of core performance parameters of the breather valve is achieved, and the multi-source information fusion degree and the anti-interference capacity are improved.

The invention discloses a distribution line early-stage fault time sequence hidden Markov modeling and identification method and system based on multistage evolution characteristics, and belongs to the field of distribution line early-stage fault identification. Comprising the steps of obtaining a current waveform sample sequence of an early fault in a distribution line; for each current waveform sample in the sequence, extracting a multi-dimensional time-frequency feature, and constructing a feature vector of each sample; performing fault stage identification on each sample by using the first-level hidden Markov model group, and outputting a fault stage tag sequence corresponding to each sample; combining the fault stage label sequences of the current sample and a plurality of previous historical samples to form a stage label sequence window; and respectively inputting the stage label sequence window into a second-level tree line fault hidden Markov model and a second-level non-tree line fault hidden Markov model, calculating a corresponding first average log-likelihood value and a corresponding second average log-likelihood value, comparing the two average log-likelihood values, and judging whether a current sample belongs to a tree line early fault or not.

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 an operation index determination method and device for an endoscopic surgery, equipment and a storage medium, and can be applied to the technical field of surgical operation data processing. The method comprises the following steps: firstly, acquiring a surgical operation data set of a target operation object for an endoscopic surgery; the surgical operation data set comprises a plurality of pieces of surgical operation data sorted according to a time sequence. Each piece of surgical operation data comprises five-dimensional data, namely endoscope action data, endoscope type data, instrument action data, instrument type data and surgical object data. And constructing a hidden Markov model by using the surgical operation data set. In the hidden Markov model, the surgical operation described by the surgical operation data is the observed quantity, and the surgical state is the state quantity, and based on the hidden Markov model, the statistical principle is utilized to carry out quantitative evaluation on the surgical operation indexes, so that the accurate evaluation of the surgical operation when a doctor carries out the endoscopic surgery is realized, and the reference effectiveness of the operation indexes is improved.

The invention discloses a real-time early warning system for analyzing abnormal behaviors of prison prisoners based on a behavior sequence, and relates to the field of real-time early warning, and the system comprises a data collection module which is used for collecting and obtaining structured data and unstructured data in a prison area; the behavior sequence extraction module generates a feature vector of a behavior atomic unit through three-dimensional convolution spatial-temporal feature extraction, and obtains a behavior vector through feature fusion; the behavior sequence modeling module constructs a behavior sequence through a double-flow LSTM architecture; the anomaly detection module calculates the KL divergence of the behavior codes and a hidden Markov model baseline by constructing the hidden Markov model baseline, and outputs anomaly probability distribution; and the grading early warning module performs grading early warning based on the output abnormal probability distribution. The method has the advantages that by fusing multi-source data and a deep learning technology, the behavior sequence of the prisoner is analyzed in real time, intelligent early warning is performed, the supervision efficiency and safety are remarkably improved, and conversion from passive monitoring to active intervention is realized.

The invention relates to the technical field of wireless positioning, and provides a high-speed rail user positioning method and system based on trajectory compensation, and the method comprises the steps: recognizing high-speed rail users, carrying out the data preprocessing, constructing high-speed rail grids, and generating a signal fingerprint database; preliminarily screening out a plurality of candidate grids according to RSRP and TA signal features reported by high-speed rail users in real time; based on the service cell switching sequence, a hidden Markov model or particle filtering is adopted for motion trail modeling, and the advancing direction and speed of a train are recognized; introducing a multi-source information fusion and grid compensation strategy; and selecting a final positioning result from the candidate grids in a weighted voting mode, and outputting grid IDs and latitude and longitude coordinates. Through multi-source data fusion, track compensation and intelligent decision making, the positioning precision and reliability of the high-speed rail user in a complex mobile environment are remarkably improved, and the method has important technical popularization value.

The invention provides a rectal cancer patient management method and system based on postoperative rehabilitation data analysis, and relates to the field of postoperative rehabilitation management. And executing cross-scene associated feature anomaly recognition to extract a sensitive feature variable set as observation input of the hidden Markov model, driving a rehabilitation stage transition probability to calculate and output a state anomaly feature combination, comparing and positioning a real-time rehabilitation state, calculating and outputting a dynamic rehabilitation stage switching time window, and sending the dynamic rehabilitation stage switching time window to a medical end screen display. The technical problems in the prior art that rehabilitation stage division is extensive, rehabilitation stage switching prediction precision of individual users is insufficient, and rehabilitation management suitability is affected are solved. The technical effects of eliminating the rehabilitation stage division lag effect, performing rehabilitation stage switching accurate prediction and providing high-reliability time sequence feature support for rehabilitation management are achieved.

The invention discloses a pet behavior data management system and method based on big data, and relates to the technical field of pet behavior intelligent monitoring. Performing time synchronization and time sequence reconstruction; carrying out preprocessing, and carrying out key frame extraction and target detection on the video frame to obtain a pet posture track; on the basis of a state switching point of a hidden Markov model, dividing the data subjected to time sequence reconstruction into a plurality of behavior events, and combining adjacent related behavior events to form behavior blocks; extracting statistical characteristics, behavior frequency and context characteristics of each behavior block, and training an individual behavior baseline model by adopting a multi-scale sliding window and a weight attenuation strategy based on historical behavior data; and comparing the real-time behavior block with the individual behavior baseline, and carrying out anomaly monitoring and warning through an integrated classifier. According to the invention, cross-device pet behavior management and abnormal early warning can be realized, and the reliability and the intelligent level of pet health monitoring and behavior analysis are remarkably improved.

The invention discloses a new energy automobile power battery thermal runaway early warning method and system, and belongs to the technical field of new energy automobile battery safety monitoring. The method comprises the steps of multi-dimensional parameter acquisition and feature cascade fusion, battery state time sequence prediction and residual error anomaly detection based on an attention-enhanced long-short-term memory network, thermal runaway multi-stage state transition estimation based on a hidden Markov model, model parameter dynamic updating based on online adaptive learning, and dynamic state transition estimation based on the hidden Markov model. And multi-stage early warning and emergency disposal graded linkage response are carried out, and accurate early warning of thermal runaway is realized through deep coupling closed-loop cooperation among the steps.

The invention provides an industrial sensor intermittent fault detection method and device and a medium. The method comprises the following steps: acquiring a process input vector of a target industrial process at a current moment and a process output measurement value measured by a sensor to be measured; determining a process output predicted value according to the process input vector and the virtual sensor model; calculating a current residual between the process output measured value and the process output predicted value; if the current residual error exceeds a target early warning threshold value, marking an early warning signal, and generating a first fault mark under the condition that the continuous triggering times of the early warning signal on a time sequence reach a first preset times; based on the residual sequence from the historical starting moment to the current moment, the posterior probability that the sensor to be detected is in a fault state at the current moment is calculated through a hidden Markov model, and a second fault mark is generated under the condition that the posterior probability is larger than or equal to a preset probability threshold value; and if at least one of the first fault mark and the second fault mark is established, determining that the to-be-detected sensor has an intermittent fault.

The invention relates to the technical field of medical monitoring and intelligent decision making, and particularly discloses an intraoperative noninvasive real-time blood flow monitoring and medication intervention analysis system. Pulse wave oscillation harmonic component characteristics and diastolic attenuation characteristic characteristics are extracted by using variational mode decomposition and a polynomial fitting algorithm; the multi-dimensional features are fused and then input into a deep learning classification model, and accurate recognition of insufficient heart pump efficiency, abnormal peripheral vascular tension and circulation capacity imbalance states is achieved; time sequence prediction and grading early warning are carried out based on the hidden Markov model and the individualized baseline safety interval, and finally a targeted medication scheme is output through the intervention strategy knowledge base.

The invention relates to the technical field of learning education, in particular to an AR-based personalized learning education auxiliary method and system. By collecting and synchronously processing multi-modal behavior data such as eye movement, gestures and head orientation, time sequence characteristics capable of accurately reflecting the learning state of a user are constructed, and then a hidden cognitive state sequence is decoded by using a hidden Markov model and inflection points of the hidden cognitive state sequence are recognized; finally, dynamic and automatic adjustment of learning contents is realized by means of a reinforcement learning model, deep cognitive state changes can be captured from continuous and dynamic user behaviors, accurate teaching intervention is timely performed at'inflection points' of key transition of cognitive states, personalized adaptive learning path planning is realized, and the learning efficiency is improved. The pertinence and effectiveness of learning are obviously improved; the technical problem that an existing AR learning system is difficult to intervene in time at an inflection point where a user cognition state is changed during path planning, so that real personalized learning path planning is realized is solved.

The invention discloses a B5G base station T / R assembly health measurement method and system based on sparse projection and a hidden Markov model, and the method comprises the steps: collecting the multi-source operation data of a T / R assembly, and constructing a multi-dimensional operation parameter time sequence; a sparse projection matrix is constructed through covariance analysis and eigenvalue decomposition, and high-dimensional operation parameters are mapped into low-dimensional sparse health eigenvectors; training a hidden Markov model based on the feature sequence in the normal state, fitting an observation probability by using a Gaussian mixture model, and establishing a normal state reference model; calculating a KL distance between the current feature sequence distribution and the reference distribution, and mapping the KL distance into a normalized health degree index; and adaptively determining a state division threshold value by using a K-means clustering algorithm to realize health state grading of the T / R assembly. According to the method, environmental noise is effectively stripped through sparse projection, the dynamic reference model is utilized to adapt to complex working conditions, and online sensing and accurate evaluation of early weak degradation of the B5G base station assembly are realized.

The invention belongs to the technical field of image analysis and processing, and particularly relates to a flame image-based rotary furnace combustion state prediction method and system. The method comprises the following steps: acquiring a video image sequence of flames in the rotary furnace, and converting each frame of image into a YUV color space; calculating a combustion contribution degree based on the brightness component and the chromaticity component, and screening out a core flame pixel set; taking the combustion contribution degree as a weight, calculating a weighted covariance matrix, determining a confidence ellipse according to the eigenvalue and eigenvector of the matrix, taking the center of the confidence ellipse as a weighted centroid, determining a rotation angle by the eigenvector, and making the length of long and short semi-axes in direct proportion to the square root of the eigenvalue; extracting the area, eccentricity rate, rotation angle and center position of the confidence ellipse as combustion state feature vectors at the current moment; and inputting a time sequence formed by the combustion state feature vectors at the multiple moments into a pre-trained hidden Markov model, and outputting the combustion state of the rotary furnace. According to the invention, the accuracy and anti-interference capability of combustion state prediction are improved.

The invention relates to the technical field of computer data processing and mode recognition, and discloses an intelligent handwriting record analysis system supporting translation process traceability and capability assessment, which comprises the following steps: collecting multi-dimensional handwriting data generated when a user translates by using an intelligent pen; the Hidden Markov model decodes the handwriting into translation behavior primitive sequences such as smooth writing, hesitant pause and the like; automatically dividing semantic regions such as a main translation region and a draft region by a density clustering algorithm; performing space-time fusion attribution to generate a space-time attribution log for accurately recording behaviors, time and positions; and establishing a multi-dimensional capability index system containing translation efficiency, quality and strategies based on the log, and carrying out quantitative calculation and generating a visual user capability portrait. According to the method, the invisible translation cognition process is converted into traceable and quantifiable objective data, deep insight and comprehensive evaluation of the translation process are achieved, and a scientific and efficient analysis tool is provided for translation teaching and interpreter ability evaluation.

The invention discloses an intelligent analysis system for the running state of a 3C vehicle-mounted contact network, and belongs to the technical field of crossing of intelligent operation and maintenance of rail transit and industrial artificial intelligence. And the system associates the multi-source heterogeneous observation data to a specific equipment unit through an equipment centralized data binding module. And the multi-modal feature extraction and state management unit processes the data and maintains a multi-dimensional state vector of the equipment by using the Kalman filtering updating unit. And the physical-data hybrid decision maker fuses the data driving rule and the simplified physical model to output a diagnosis result. The topology analyzer performs global verification based on a mechanical transfer rule. The system further comprises a long-term health state prediction and feedback module, early failure risks are predicted through a hidden Markov model, Kalman filtering process noise is dynamically fed back and adjusted, and cooperation of long-term prediction and short-term estimation is achieved. According to the method, the technical problems of multi-source data splitting, lack of physical basis in diagnosis and incapability of predictive maintenance are solved.

The invention relates to the technical field of syndrome evolution prediction, in particular to a syndrome evolution prediction method, system and device based on multi-modal data driving and a medium, the method comprises the steps that multi-modal syndrome data is acquired and preprocessed, and the multi-modal syndrome data comprises clinical symptom data, tongue condition feature data and pulse condition feature data; extracting bidirectional time sequence characteristics in the preprocessed multi-modal syndrome data through a bidirectional long-short-term memory network, and generating a hidden state sequence; weighting the hidden state sequence by using an attention mechanism to obtain context vectors of contribution weights of different time steps; and inputting the context vector into a hidden Markov model of which the state transition matrix is constrained and corrected by the traditional Chinese medicine theory, performing syndrome state reasoning and evolution trend prediction, and outputting a prediction result. The objective of the invention is to improve the prediction precision and interpretability of syndrome evolution.

The invention relates to the technical field of sleep respiratory disease analysis, and discloses a method for analyzing obstructive sleep apnea. The method comprises the following steps: acquiring an original physiological signal flow which is output by a multi-channel sleep monitoring device and comprises a respiratory waveform, blood oxygen fluctuation, an electrocardio rhythm and a sound vibration signal; and then, carrying out adaptive window function segmentation and multi-resolution conversion on the original signal flow to generate a standardized multi-modal signal sequence. State decoding is carried out on the sequence through a hidden Markov model, and steady state physiological mode features and transient abnormal mode features are extracted. And fusing the steady state features and clinical archive data of the patient, calculating an apnea risk index, and forming an initial evaluation report. Meanwhile, a dynamic evolution path of transient abnormal mode characteristics is monitored, and a real-time pathology indicator in the signal is detected. And finally, a risk weight coefficient in the initial evaluation report is adjusted according to the real-time pathology indicator, and a more accurate optimization evaluation report is generated.

The invention discloses a personalized diet recommendation method for enteritis patients, a medium and equipment. The method comprises the following steps: continuously collecting diet image data, physiological state time sequence data and subjective symptom feedback data; performing food material component deconstruction and cooking mode identification on the diet image through a convolutional neural network, and generating a structured diet vector; analyzing physiological state time sequence data by using a hidden Markov model, and constructing an individualized physiological rhythm map; establishing a symptom triggering condition probability matrix based on a multi-modal association mining algorithm; generating an exploratory diet scheme according to the real-time physiological state of the user and the conditional probability matrix; dynamically updating the conditional probability matrix through an online reinforcement learning mechanism; and finally, periodically outputting a personalized diet taboo list and a safety exploration list. According to the method, through the dynamic correlation analysis of fusing the time sequence physiological features and the dietary components, accurate matching of diet recommendation and individual intestinal tract state fluctuation is realized, and the individuation and adaptability of diet intervention are effectively improved.

The invention discloses a household energy centralized management method and device, electronic equipment and a storage medium. The method comprises the following steps: collecting real-time observation data of a family environment to construct a multi-dimensional context feature vector; generating a dynamic state transition probability matrix and emission parameters of the hidden Markov model through a dynamic parameter mapping mechanism, and calculating initial posterior probability distribution of a family activity state by using a forward algorithm; when the information entropy of the probability distribution exceeds a threshold value, executing an active state exploration strategy, issuing an optimal perturbation instruction and correcting a posterior probability based on an active response observation evidence so as to determine a final family activity state; and based on the state loading control constraint set, solving a real-time optimization objective function containing energy efficiency and comfort penalty, and generating an equipment control instruction. According to the method, the uncertainty of state judgment is reduced by combining a dynamic model and an active exploration mechanism, and household energy cost optimization is realized on the premise of ensuring comfort.

The invention relates to the technical field of gas turbine power plant abnormity monitoring, in particular to a gas turbine power plant operation abnormity monitoring and early warning system based on artificial intelligence. The system comprises a data acquisition unit, and the data acquisition unit is used for acquiring multi-dimensional operation data of a gas turbine power plant, and obtaining a global feature expression vector by introducing a multi-modal feature fusion network based on an attention mechanism; the working condition identification unit constructs a dynamic evolution model of the gas turbine based on a long short-term memory network and a hidden Markov model, and performs time sequence coding on global feature expression vectors; and the anomaly recognition unit constructs an anomaly recognition model based on the combination of the deep auto-encoder and the time sequence convolutional network, calculates a comprehensive anomaly score, and then judges an operation anomaly state. The operation feature vectors of different subsystems are mapped to the unified dimension, invalid modals are shielded, missing modals are compensated, and the technical problem that the global state characterization precision is affected due to incomplete fusion information caused by modal failure or missing is solved.

The invention discloses a landslide early warning method, device and equipment and a medium, and the method comprises the steps: obtaining historical landslide three-dimensional displacement time sequence data of a historical landslide event; constructing a landslide state evolution model based on a Gaussian mixture model, a hidden Markov model and historical landslide three-dimensional displacement time series data; determining a current displacement state of the target landslide body based on the real-time three-dimensional displacement time sequence data of the target landslide body and a landslide state evolution model; obtaining the remaining time of the target landslide body from the current displacement state to the landslide occurrence state; and carrying out landslide early warning on the target landslide mass based on the current displacement state and the remaining time. By analyzing the displacement time sequence data of the historical landslide event and optimizing the prediction performance of the HMM model by using the GMM model, a scientific and reliable geological disaster monitoring and early warning system is constructed, real-time dynamic monitoring and accurate early warning of the landslide geological disaster are realized, and the timeliness and accuracy of early warning of the landslide geological disaster are remarkably improved.

The invention relates to the field of data processing, in particular to a carton production line monitoring method and system, and the method comprises the steps: collecting and preprocessing multi-dimensional time sequence data of a production process; constructing a standard hidden Markov model (HMM) based on historical normal data, and defining the hidden state of the HMM as a microscopic operation mode under a macroscopic process; extracting procedure context features, and constructing a procedure conformity evaluation model (PCAM) to calculate a conformity score; dynamically adjusting the emission probability of the standard HMM in combination with the score to form an improved IHMM; and calculating the log-likelihood probability of the real-time observation sequence by using the improved IHMM, and comparing the log-likelihood probability with a preset threshold to judge the production abnormality. According to the invention, the accuracy and reliability of abnormity monitoring can be effectively improved.

The invention relates to the technical field of nonferrous metal beneficiation intelligent control, and discloses a lead zinc ore flotation process state evolution modeling method. The method comprises the following steps: collecting multi-source heterogeneous data of a flotation process, preprocessing the multi-source heterogeneous data, then obtaining a plurality of key features, and constructing a feature matrix according to the plurality of key features; constructing a hidden Markov model based on the hidden state of the flotation process, inputting the feature matrix to the hidden Markov model, optimizing parameters of the hidden Markov model through a Baum-Welch algorithm, and screening evolution models in a training process in combination with a Bayesian information criterion; and acquiring real-time multi-source heterogeneous data of the flotation process and acquiring a corresponding feature matrix, inputting the feature matrix into the evolution model, and acquiring an optimal process state sequence in combination with a dimensional bit algorithm. The problem that an existing beneficiation model cannot effectively and accurately predict the flotation process state is solved.

The invention relates to the technical field of animal behavior analysis, and discloses an animal behavior accurate recording and analyzing system based on multi-mode keyboard coding. A data acquisition and synchronization module of the system acquires original key event data and generates a time sequence behavior data stream with millisecond-level precision; the coding logic engine module is used for converting the physical key sequence into a hierarchical behavior semantic coding sequence through a dynamic mode switching and key mapping technology; the behavior state modeling module is used for constructing an animal behavior state transition probability model, performing state decoding and estimation by adopting a hidden Markov model, and outputting a behavior state sequence with confidence; the behavior analysis and prediction module is used for predicting a behavior evolution trend and identifying an abnormal behavior event; and the threshold value dynamic adjustment module is used for dynamically adjusting an early warning threshold value through interaction between the normal behavior generator and the real-time discriminator aiming at an abnormal behavior event so as to finish accurate recording and risk analysis of animal behaviors.

The invention discloses a distribution cable branch box state monitoring method and system. According to the method, multi-dimensional information such as temperature, partial discharge and gas concentration of a branch box is acquired by deploying a multi-source sensor array, and dynamic feature vectors are extracted from the multi-dimensional information; the method is characterized in that a state transition model based on a hidden Markov model is constructed, and equipment operation states are divided into five hidden states including normal, concerned, abnormal, early warning and faults. And analyzing the real-time feature sequence by using an observation probability model and a Viterbi decoding algorithm, identifying the most possible current implicit state of the equipment, and predicting the future state evolution probability of the equipment. And a dual early warning mechanism of an instantaneous threshold and a state probability threshold is combined, so that comprehensive coverage from early warning to sudden faults is realized. According to the invention, the problems of high false alarm rate and lack of early degradation identification capability of traditional single-threshold monitoring are effectively solved, and accurate identification and predictive maintenance of the state of the power distribution branch box are realized.

The invention relates to the technical field of electric energy meter digital data processing, discloses a multi-sensor tamper-proof detection and evidence solidification method for an intelligent electric energy meter, and aims to solve the problems that in the prior art, the false alarm rate is high, tamper behavior recognition is inaccurate, and evidence is prone to being damaged. The method comprises the following steps: fusing four types of sensing data of magnetic field intensity, mechanical vibration, temperature and humidity and shell micro-displacement, and constructing a multi-dimensional collaborative sensing system by adopting Kalman filtering and particle filtering; in combination with a hidden Markov model and time sequence feature extraction, judging a tampering event level by using an isolated forest algorithm; a digital signature snapshot based on elliptic curve cryptography is generated for the key event, chained evidence storage is realized through a Merkel tree structure, and bidirectional verification with a cloud distributed account book is carried out; according to the technical scheme, the accuracy and robustness of tamper-proofing detection are remarkably improved, and evidence integrity, non-repudiation and system credibility are ensured.

The invention relates to a river channel sand body random model generation method and device based on hidden Markov model data filling, a medium and equipment. The method comprises the steps that logging data of a target area are collected and arranged; dividing logging data according to a logging gas-bearing interpretation conclusion, and carrying out analytical statistics on the velocity and density of longitudinal and transverse waves in the logging data; initializing an initial state probability, a state transition matrix, and a mean value and a covariance of Gaussian distribution in the hidden Markov model; inputting a three-dimensional observation vector composed of the velocity and density of the longitudinal and transverse waves into the initialized hidden Markov model for iterative training; performing down-sampling on the lithology data, then constructing a state transition probability matrix, and further constructing a lithology sequence conforming to a geological law through simulation; according to the lithologic sequence, calling a hidden Markov model to generate longitudinal wave velocity, transverse wave velocity and density corresponding to the lithologic sequence; and calculating a reflection coefficient of the synthetic stratum, setting a Ricker wavelet dominant frequency, and obtaining synthetic seismic response data through convolution.

The invention relates to the technical field of equipment control, in particular to an automatic control system and method for a pneumatic control valve, and the method comprises the steps that a displacement sensing module collects a valve element displacement signal through a magnetostrictive sensor and carries out Kalman filtering processing; the signal conditioning module fuses displacement and pressure data by adopting a fuzzy logic rule; the intelligent conversion module performs state identification by using a support vector machine algorithm; the communication gateway module realizes data protocol encapsulation and transmission; the state diagnosis module performs fault diagnosis based on a hidden Markov model; the digital twin module realizes simulation prediction through multi-domain physical modeling; the optimization decision module adopts a particle swarm optimization algorithm to generate a control instruction; and the closed-loop execution module drives the execution mechanism and feeds back displacement data. The problem that an existing pneumatic valve is insufficient in control precision due to lack of displacement feedback is solved, and real-time monitoring and high-precision closed-loop control over the position of the valve are achieved.

The invention provides a bearing temperature vibration signal fault evolution path intelligent identification method and system, and relates to the technical field of fault identification, and the method comprises the steps: carrying out the collection and time sequence alignment of bearing temperature, vibration signals and working condition parameters, and constructing a multi-source heterogeneous data matrix; multi-dimensional features are extracted after self-adaptive noise reduction processing; constructing a fault evolution mechanism causal association network by adopting a causal inference model; performing time sequence clustering based on the network to divide health state clusters, and establishing a state transition model by using a hidden Markov model; and finally, decoding by using a Viterbi algorithm to obtain an optimal fault evolution path. The method can effectively identify the bearing fault evolution law and improve the prediction precision.