446 results about "Hide markov model" patented technology

The invention discloses a data security dynamic evaluation system and a protection method, belongs to the technical field of information security, and is used for solving the problem of terminal equipment access control and behavior risk dynamic collaborative protection. The method comprises the following steps: generating fingerprints through terminal equipment features, verifying authorization, inputting access feature slices into a time sequence model by authorized terminal equipment, generating a trust score based on cosine similarity of a behavior sequence and a prediction sequence, and constructing a Markov model to dynamically select an authentication mode according to the trust score; calculating a risk index by combining network and geographic information, and distributing the risk index to a real or virtual environment; and the unauthorized terminal equipment distributes the virtual environment after registration. Differentiated monitoring is carried out, a time sequence library is established in the virtual environment, and doubt scores are generated through sequence matching; the state deviation of the real environment is calculated through a hidden Markov model, and the risk score is generated by fusing the multi-dimensional features. And based on the result management authority, the suspicion terminal equipment isolates and shrinks the authority, the compliance terminal equipment authenticates and migrates, and the access is regulated and controlled according to the minimum privilege principle and the time window mechanism.

The invention relates to the technical field of multi-modal data, provides a pilot competency dynamic evaluation method, system and device based on multi-modal data and a storage medium, and solves the problems of low accuracy of pilot competency evaluation and poor pertinence of training guidance. The method comprises the steps of collecting flight control data, physiological signal data, psychological assessment data, subjective scale data and international civil aviation organization core competency information, performing alignment fusion on multi-source data by adopting a time synchronization algorithm, and identifying an attention fixation mode based on a hidden Markov model. And constructing a workload index by fusing a subjective scale and a physiological entropy value, inputting the multi-modal features into a pre-trained competency assessment model, outputting three levels of psychological assessment indexes including a basic ability layer, a dynamic presentation layer and a risk early warning layer, and finally generating a personalized training report. According to the invention, the accuracy of pilot competency evaluation and the pertinence of training guidance are improved.

The invention relates to the technical field of early warning decision making, in particular to an agricultural disaster early warning decision making method and system, and the method comprises the following steps: obtaining a structure parameter set through a multispectral sensor, carrying out the smooth processing of a sliding window, marking the direction consistency, inputting a hidden Markov model, calculating the transition probability, recognizing a stage trigger point, and outputting a label. And matching a physiological and meteorological time sequence to generate a response mapping table, extracting a trend overlapping section mark impact window, judging a disaster grade in combination with multi-factor weighted sorting, and outputting an early warning instruction. According to the invention, through continuous observation of growth parameters and smooth processing of time sequence data, analysis of dynamic consistency of structural indexes, identification of growth mutation, alignment of physiological and meteorological factors, extraction of trend overlapping sensitive areas, and superposition of residual errors to lock risk time windows, the sensitivity and pertinence of disaster early warning are improved, and the risk of misinformation and missing report is reduced. Fine data support is provided for management, and risk management and control scientificity and initiative are enhanced.

The invention relates to the technical field of biomedicine, and discloses a female private rehabilitation training system integrating a breathing method and neural motor control, and the system comprises a data collection module which is used for collecting physiological data of a user; the personalized training scheme generation module is used for generating a personalized nerve-muscle-fascia training scheme; the nerve-muscle synchronous resonance training module is used for constructing a time sequence matching model and generating an adaptive training dynamic control instruction; the space-time adaptability training module is used for adjusting training duration, intensity and stimulation frequency; the multi-dimensional nerve-muscle feedback fusion module is used for constructing a feedback response model; and the fascia synchronous activation and relaxation module is used for coordinating the tension relationship between the fascia and the muscle system. According to the method, feature matching is carried out on an electric activation heat map matrix through the convolutional neural network, analysis of a myoelectricity response state transition probability is carried out in combination with a hidden Markov model, and stimulation parameters and respiratory rhythm are optimized in real time according to a training process.

The invention relates to the technical field of image state recognition, in particular to an unhooking and rehooking AI accurate recognition grabbing system and method based on pattern recognition, in the system, node construction is conducted through contour changes, boundary difference values and gray level dynamic states of a hook assembly in an image sequence frame, edge displacement accumulation analysis is combined, meanwhile, through a graph neural network, an image sequence frame is obtained, and the image sequence frame is obtained. Cosine values and coordinate difference values between nodes are subjected to combined comparison, and a path hopping sequence is constructed, so that the response sensitivity to state abrupt change is enhanced, a key path of morphological evolution can still be stably extracted under the condition of complex background interference or local shielding, the anti-interference performance and fault tolerance of space path identification are effectively improved, and the space path identification accuracy is improved. Statistical modeling is further carried out on state rate sudden change points through a hidden Markov model, paragraph merging and invalid fragment removing operation are carried out on abnormal point segments by matching a standard state mode, a state label sequence is constructed, and accurate division of high-confidence and multi-segment continuous states is achieved.

The invention discloses a mining equipment energy consumption prediction method, and relates to the technical field of mining equipment energy consumption prediction.The mining equipment energy consumption prediction method comprises the steps that through multi-dimensional data collection, operation, process and environment parameters are collected through a sensor cluster; noise reduction is carried out through a generative adversarial network in combination with empirical mode decomposition, and data is restored through a space-time interpolation network; identifying working conditions and extracting features by means of a hidden Markov model in combination with an attention mechanism; a cross-device transfer learning framework is constructed, and a cloud training general model is combined with local data fine tuning; the edge end deploys a lightweight model for real-time prediction, and the cloud end generates a global energy-saving strategy; through digital twinborn visualization, a model and a strategy are automatically corrected based on SHAP value analysis. The equipment idling rate is reduced; the unit energy consumption of the crushing link is reduced; the abnormal response time is shortened; and the prediction precision and the system adaptability are remarkably improved.

The invention relates to the technical field of network security, and discloses a response processing method and system for a network security event, and the method comprises the steps: carrying out the feature extraction and time sequence behavior clustering of network flow data, and obtaining a behavior segment and a corresponding time sequence feature; according to the behavior segments and the time sequence features, a hidden Markov model is adopted, and the causal relationship confidence degree between the adjacent behavior segments is calculated; according to the behavior fragment and the causal relationship confidence coefficient, attack chain reconstruction and key path analysis are carried out to obtain an attack key path; inputting the attack key path into a pre-threat mode library for threat matching to obtain an attack intention prediction result; and generating a dynamic response strategy according to the attack intention prediction result, and executing the dynamic response strategy by adopting a streaming processing engine. According to the method, the attack detection precision and the defense response efficiency in a complex attack scene can be remarkably improved, and a self-adaptive and reliable technical scheme is provided for network security protection, so that the security and the stability of network operation are ensured.

The invention provides a method for identifying a power grid frequency signal assessment standard, and the method comprises the steps: constructing a system architecture composed of a master station, a substation and a communication network, and achieving the initialization of equipment through a finite-state machine; the method comprises the following steps: collecting power grid frequency, power generation side frequency difference and communication state multi-dimensional data, processing signals by applying median filtering, a sliding window and an EWMA algorithm, introducing fuzzy logic to evaluate a system state, and dynamically adjusting a response strategy; an improved PID algorithm is adopted to track frequency change, feed-forward compensation and PSO parameter optimization are combined, a hidden Markov model is utilized to predict a communication fault, and local mode switching is triggered in advance; main station, mixing and local three-state switching is carried out according to communication states and load changes, and a frequency modulation dead zone is automatically adjusted according to power grid loads through a dynamic threshold value self-adaptive algorithm; the control effect is verified by using a digital twinning technology; and the safety and reliability of the system are guaranteed by optional quantum encryption and active-active redundancy design. The whole scheme effectively reduces the power loss of power generation enterprises, and maintains the safe and stable operation of a power grid.

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 provides an intelligent control method and system for a refrigeration house, and relates to the field of control. A refrigeration house space thermodynamic diagram model is constructed based on temperature measuring points, and a historical temperature sequence, a house door opening and closing state sequence, cargo warehouse-in and warehouse-out records and external environment parameters are obtained; predicting a temperature change track of each node in a future control time domain by using a space-time diagram convolutional network; establishing an equipment health state evaluation model based on a hidden Markov model, evaluating the health state of each refrigeration compressor unit and quantifying the health state into an equipment health index; and solving the rolling optimization objective function in the control time domain to obtain an optimal start-stop time sequence and operation power combination instruction of each refrigeration compressor unit and auxiliary equipment, and issuing the combination instruction to a corresponding field controller for execution. The temperature change of each area in the refrigeration house can be accurately predicted, the operation load can be reasonably distributed according to the health condition of the unit, the service life of equipment is effectively prolonged, and the sudden failure risk and the maintenance cost of the whole life cycle are reduced.

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 discloses a digital archive multi-protection-based data security transmission method, and relates to the field of data security transmission, and the method comprises the steps: generating a random seed in real time based on a quantum state, carrying out the iterative blocking of the quantum seed through combining with Logistic chaotic mapping, outputting variable-length secret key fragmentation, carrying out the symmetric encryption of archive data through employing a lightweight SM4 algorithm, and carrying out the encryption of the archive data. Obtaining encrypted data of the fragments; a behavior anomaly probability is calculated through a hidden Markov model, a risk score is calculated through a safety interval, and when a score value exceeds a safety threshold value, a grading response is triggered; key fragments are generated by adopting n nodes, at least k fragments can reconstruct signatures to monitor the node state of the block chain, and when the node anomaly rate exceeds a node threshold value, a three-layer architecture is automatically switched. The method has the advantages that an active multi-protection system is constructed through a quantum dynamic key, risk-driven hierarchical response and block chain disaster recovery switching, and quantum attack resistance and dynamic adaptation to transmission of network risks are realized.

The invention discloses an intelligent fault diagnosis and early warning method for an electrical automatic composting process system, and relates to the technical field of intelligent monitoring. Sensor data in a composting process is collected, high-frequency noise is removed by adopting sliding window filtering, and a timestamp is synchronized to align multi-dimensional signals; based on a hidden Markov model, dividing composting stages of temperature rise / high temperature / decomposition period, and extracting time domain and frequency domain features for different working conditions; and an online K-means + + algorithm is adopted to initialize a clustering center. According to the method, multi-dimensional sensor data such as temperature, humidity and oxygen content in the composting process are collected in real time, the online K-means + + algorithm and the density peak value clustering technology are utilized to dynamically adapt to the time-varying characteristics of composting parameters, outliers caused by sensor drift or dust interference are effectively filtered, the sensitivity of a model to noise data is remarkably reduced, and the method has the advantages of being high in reliability and high in reliability. It is ensured that the composting system can still operate stably under complex working conditions, and the risk of fermentation interruption or material decay caused by parameter abnormity is reduced.

The invention provides a scanning method and system for hidden asset identification in an electric power industrial control network, and aims to solve the problem that silent, non-registered or illegal access equipment is difficult to discover by the existing asset surveying and mapping means. According to the method, multiple protocol induced detection messages are injected into a target network, equipment response data are collected, feature vectors are extracted, and abnormal equipment behaviors are identified by using an unsupervised clustering and anomaly detection algorithm; and meanwhile, in combination with a graph neural network and a hidden Markov model, modeling is performed on a network communication chain structure and a historical behavior sequence, and potential hidden asset nodes are deduced. According to the method, active discovery and risk reasoning of hidden assets can be realized on the premise of not influencing industrial control services, and the method is suitable for industrial control scenes such as transformer substations and dispatching centers in the power industry and has high safety, intelligence and feasibility.

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

The invention discloses a spine tactile sensing system and method based on a multi-modal sensor, and the method comprises the steps: obtaining an original tactile signal, carrying out the preprocessing of the original tactile signal, carrying out the feature extraction based on the preprocessed original tactile signal, and obtaining a spatial distribution feature and a time dynamic feature; on the basis of tail end coordinates collected by forward kinematics of the tail end of the mechanical arm, space mapping of the tactile signals and the spine anatomical structure is constructed, and anatomical position features are obtained on the basis of the preprocessed original tactile signals and the space mapping; constructing task-oriented feature vectors based on the spatial distribution features, the anatomical position features and the time dynamic features, wherein the task-oriented feature vectors comprise a sliding state recognition feature vector and a spine ordinal number positioning feature vector; and constructing a multi-stage adaptive evolution hidden Markov model, and realizing spine ordinal number positioning in combination with the task-oriented feature vector. According to the method, through multi-dimensional feature fusion and dynamic model adjustment, anatomical prior knowledge and real-time tactile signals are combined, and spine segments are accurately recognized.

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 discloses a talent recommendation system for matching local productivity based on vocational education, and relates to the technical field of talent recommendation. Comprising a dynamic industry portrait construction module, an expert auditing and correcting module, a personalized path planning module, a talent growth prediction module, a path adjustment module, a talent recommendation module, a data storage module, an interactive interface module and a system management module. The dynamic industry portrait construction module predicts industry trend data by introducing a time sequence hidden Markov model of an attention mechanism, and the dynamic industry portrait construction module and the expert auditing and correcting module integrate undisclosed project information supplemented by Bayesian reasoning based on a knowledge graph completion algorithm. And generating a dynamic industry knowledge graph based on a time decay factor through a graph convolutional network feature fusion technology. According to the invention, the dynamic industry portrait construction module and the expert auditing and correcting module are arranged, so that the problem that the industry information lags behind and the local industry dynamic change cannot be reflected in real time in the existing talent recommendation system can be solved.

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 relates to the technical field of equipment monitoring, in particular to a loss and fatigue monitoring method for water gate opening and closing equipment. The method comprises the following steps that multi-mode time sequence data are synchronously collected through a vibration sensor, a temperature sensor and an acoustic emission sensor, and the multi-mode time sequence data comprise a vibration signal, a temperature signal and an acoustic emission signal; performing wavelet packet decomposition on the vibration signal, and extracting an energy ratio of a preset frequency band as a vibration energy feature; calculating a temperature rise rate and a local range of a sliding window of the temperature signal as temperature characteristics; extracting a Mel-frequency cepstrum coefficient from the acoustic emission signal as an acoustic feature; the vibration energy characteristics, the temperature characteristics and the acoustic characteristics are spliced into a multi-dimensional time sequence characteristic matrix according to time steps. According to the method, the maximum probability state transition path is solved by using the hidden Markov model, and accurate identification and prediction of the equipment loss and the fatigue state are realized.

The invention belongs to the technical field of customer service work order processing, and discloses an event priority ranking distribution method and system, and the method comprises the steps: converting historical multi-round dialogue data into a numeralized historical work order emotion score sequence set; the Hidden Markov model is trained by using a Boem-Weili algorithm, and core dynamic parameters of customer emotional evolution are learned; an optimal risk score threshold value after calibration is obtained through calibration; for each work order in progress, the probability distribution of each current hidden state is deduced in real time, and a forward-looking negative upgrading risk score is calculated; and when the negative upgrading risk score exceeds the calibrated risk score threshold, automatically triggering priority adjustment, and generating an updated work order record. According to the invention, a quantifiable framework based on probability prediction is provided, and customer support is converted from passive and post-response type work order processing into an active and pre-warning type risk management normal form.

The invention provides an image video retrieval method based on a domain fine-tuning large language model, which comprises the following steps: performing fine-tuning on a pre-training model to obtain a fine-tuning pre-training model for intention classification and keyword extraction; performing dynamic iteration screening on an optimal prompt template through Monte Carlo tree search in combination with a hidden Markov model (HMM); performing noise filtering on the keyword list, and predicting category labels of the filtered keywords through a conditional random field model to obtain a keyword enhancement set; combining with the user intention to generate a query condition, and obtaining a candidate resource set; and according to the similarity between the user query text and the candidate resource set, and in combination with the optimal prompt template, obtaining the resource path with the highest matching score between the user query and the candidate resource, and obtaining the retrieved image or video, so that the identification deviation possibly occurring when a general model processes proper nouns and terminologies can be effectively solved, and the user experience is improved. And the retrieval accuracy and response speed are improved, so that the retrieval accuracy and professional adaptability are improved.

The invention discloses an energy facility operation state remote monitoring and early warning system, and relates to the technical field of energy facility operation state monitoring, in the system, a distributed sensor network collects multi-type state time sequence data of each node of an energy facility; the edge computing gateway performs data cleaning and feature extraction on the received data; the cloud platform is used for performing quantum dynamic encryption and transmission on the feature data packet to obtain feature plaintext data, calculating a current feature parameter coupling coefficient based on a hidden Markov model and a Bayesian network, and further determining an equipment health degree index and a health risk level of each node of the energy facility based on a neural network; and determining corresponding prompt information according to the health risk level, wherein the prompt information comprises an early warning signal, an equipment topological association graph and associated equipment co-processing suggestions. According to the invention, the monitoring efficiency can be improved, the quality of the collected data is ensured, the accuracy of early warning is ensured, the data security is ensured, and the operation management level of energy facilities is improved.

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 discloses an electric vehicle charging load scene clustering method and system, and belongs to the technical field of load prediction and data analysis, and the method comprises the steps: collecting the historical time series data of an electric vehicle charging load, and constructing a feature vector; training under multiple confidence levels to obtain a probability distribution model of the charging load; generating a plurality of charging load time sequence scenes; performing dynamic modeling on each scene by applying a Gaussian hidden Markov model, extracting a state transition matrix, a state mean value and an initial state probability parameter, and combining the state transition matrix, the state mean value and the initial state probability parameter into a feature vector representing dynamic features of the scene; clustering analysis is carried out on the feature vectors representing the dynamic features of the scenes, and the scene closest to the clustering center is selected from each cluster to serve as a typical scene to be output. By accurately describing the uncertainty of the charging load, a charging load time sequence scene conforming to statistical distribution is generated, and a reliable and efficient decision basis is provided for power grid dispatching, planning and risk assessment.