12 results about "Forward algorithm" patented technology

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 discloses a breathing equipment data processing method which comprises the following steps: synchronously collecting multichannel physiological signals through a built-in sensor of breathing equipment, carrying out noise processing and data standardization, and segmenting according to a time window to form data fragments; extracting breathing amplitude, frequency and morphological variability features of each data fragment, and combining to generate a multi-dimensional dynamic feature vector sequence; constructing a breathing state modeling system based on a hidden Markov model, training model parameters through a Baum-Welch algorithm, and calculating a prospective risk transition score by using a forward algorithm; and establishing an adaptive threshold strategy based on the risk transition score, carrying out structured labeling on the original respiration data, and generating processing data containing a prospective risk early warning label. Technical support can be provided for intelligent upgrading and personalized treatment of respiratory treatment equipment, and the accuracy, predictability and clinical practicability of respiratory anomaly detection are improved.

The invention discloses a business state prediction method and device, electronic equipment and a storage medium. The system comprises the following steps: acquiring a log historical data set of a system and an initial observation probability of each log; constructing an initial hidden Markov model; according to a forward algorithm module, the initial observation probability and the model initial parameters, calculating the observation anomaly probability of any log observation sequence to obtain an anomaly probability distribution result of the log; performing weighted fusion on the abnormal probability distribution result, and determining the service state sequence length of the system according to the weighted abnormal probability distribution result; acquiring an initial service state of the system; according to the Viterbi algorithm module, the model initial parameter and the service state sequence length, performing backtracking analysis on the initial service state to obtain a target service state sequence of the system; through the target hidden Markov model, fault recovery and intervention are brought into the event and even before the event from post-event remedy, and the maintenance capability and the service continuity guarantee capability of the system are improved.

The invention provides a drill behavior detection method and system based on attack chain modeling and serialized reasoning, and the method comprises the steps: an attack chain knowledge graph comprises a tactical node and a technical node, the tactical node is used for describing a tactical stage or a tactical state, and the technical node is used for describing a specific method or means for achieving a certain tactical; modeling the attack chain knowledge graph to obtain a state transition probability matrix among different tactical nodes and an observation probability matrix that a certain event is observed under a certain tactical node; dynamically reasoning and calculating the global confidence of the current system in a certain tactical stage under the continuous time sequence by adopting an HMM forward algorithm; the hierarchical risk value is determined according to the global confidence degree, the attack chain integrity and the asset risk level of the current system in a certain tactical stage under the continuous time sequence, and the corresponding response strategy is determined according to the hierarchical risk value, so that the reliability of network attack and defense drill behavior detection is effectively improved.

The present application relates to the technical field of psychophysiological monitoring, and discloses a video psychophysiological index analysis method and system; the method comprises the following steps: positioning facial key points and partitioning from a video, extracting remote optoelectronic plethysmography pulse wave and action unit breathing characteristics, outputting heart rate and breathing signals through Kalman filtering; obtaining a heartbeat interval through peak value detection, calculating heart rate variability indicators and breathing frequency, and normalizing by establishing an individual baseline in an initial calm period; fusing emotional characteristics and normalized indicators to form an observation vector, inferring a stress state through an unsupervised model and a forward algorithm; detecting index mutation points to identify stimulus events, and adopting a gated recurrent unit network to predict excessive stress risk and grade early warning; storing multi-session data into a database, quantifying desensitization rate, tolerance and recovery capacity through linear regression, weighted scoring and model updating. The present application realizes contactless monitoring, real-time stress evaluation and early warning, and supports quantitative analysis of cross-training effects.

This invention discloses a method and apparatus for heart sound segmentation based on a Hidden Markov Model (HMM) cardiac cycle. The method involves: extracting the signal envelope from the heart sound signal using Hilbert's algorithm; obtaining the peak positions of the first heart sound (S1) and the second heart sound (S2) after Hilbert envelope extraction based on preset baselines for different leads; locating the peaks based on the cardiac cycle; extracting the diastolic phase based on an improved HMM; locating the optimal time span of heart sound signals S1 and S2 using an improved Viterbi algorithm; and segmenting the original heart sound signal using the time span and the peak positions of S1 and S2. This invention employs an improved HMM and an improved Viterbi forward algorithm to calculate the duration of heart sound intervals S1 and S2, combined with the cardiac cycle, to accurately locate the peak positions of heart sound intervals, thus improving the performance of heart sound segmentation.

The invention discloses a position privacy protection method and device based on semantic indistinguishability, equipment and a medium, and relates to the technical field of privacy protection. The method comprises the steps of firstly obtaining an interest point library, a semantic attribute library and historical movement data of a user; according to the interest point library and the semantic attribute library, obtaining a plurality of interest points similar to the semantic attributes of the actual positions as observation positions; forming an anonymous set by the actual position and the plurality of observation positions; according to historical mobile data, judging whether the anonymous set meets semantic indistinguishable constraints or not by adopting a forward algorithm of an HMM (Hidden Markov Model); a server is accessed using an anonymous set that meets semantic indistinguishable constraints. According to the method and the device, the inference attack based on the behavior pattern is simulated, so that an attacker cannot distinguish the impending (or current) behavior of the user by the anonymous position track with semantics indistinguishable, and the privacy risk caused by observing the release track is eliminated, so as to achieve the effect of resisting the attack.

The application provides a kind of based on attack chain modeling and serialization inference's exercise behavior detection method and system, this method includes: attack chain knowledge graph includes tactical node, technical node, the tactical node is used to describe tactical phase or tactical state, the technical node is used to describe the specific method or means for realizing a certain tactic;Attack chain knowledge graph is modeled, and the state transition probability matrix between different tactical nodes and the observation probability matrix observed to a certain event under a certain tactical node are obtained;The global confidence of current system under continuous time sequence in a certain tactical phase is dynamically inferred and calculated using HMM forward algorithm;The hierarchical risk value is determined according to the global confidence of current system under continuous time sequence in a certain tactical phase, attack chain integrity, asset risk level, and the corresponding response strategy is determined according to hierarchical risk value, effectively improve the reliability of network attack and defense exercise behavior detection.

This invention belongs to the technical field of microelectronic devices, and discloses a hidden Markov model forward algorithm classifier based on a memristor array and its manipulation method. The classifier includes: a memristor array, a transimpedance amplifier, an analog-to-digital converter, a digital-to-analog converter, a data distributor, a serial-to-parallel converter, and a parallel-to-serial converter; the parallel-to-serial converter and the data distributor are used to process the newly received parallel digital signal β. p1 ~β pN The serial digital signal β is obtained by sequentially inputting it to different bit lines of the first memristor array. s1 ~β sN The serial-to-parallel converter converts the serial digital signal β s1 ~β sN By simultaneously inputting different bit lines of the second memristor array in parallel, a parallel digital signal β is obtained. p1 ~β pN The input is then converted from parallel to serial to enable iterative computation. Based on this classifier, the computational efficiency of the forward algorithm for Hidden Markov Models can be improved while reducing computational power consumption.

The application belongs to the technical field of depth inversion, and provides a Rayleigh wave dispersion inversion method and system fusing physical information, which adaptively generates a multi-type layered geological model based on dynamic Markov decision according to the conditional probability relationship of the previous layer parameter; a fast vectorization Rayleigh wave forward algorithm is used to calculate the base order and first-order dispersion curve corresponding to the geological model, and construct dispersion sample data; a dispersion curve joint inversion model based on deep learning is constructed, the base order dispersion curve, the first-order dispersion curve and the corresponding mask information are taken as inputs, a loss function with physical information constraint is introduced to train the dispersion curve joint inversion model; the trained dispersion curve joint inversion model is used to process target geological data, and the layered transverse wave velocity and layer thickness parameters of the underground medium are obtained, and Rayleigh wave dispersion curve inversion is realized; the application can enhance the constraint on the real stratum relationship, and realize efficient and accurate stratum velocity and layer thickness inversion.

The application discloses a service state prediction method and device, electronic equipment and storage medium, and the system comprises: acquiring log historical data set and initial observation probability of each log of a system; constructing an initial hidden Markov model; calculating observation anomaly probability of any log observation sequence according to a forward algorithm module, initial observation probability and model initial parameters, and obtaining anomaly probability distribution result of the log; performing weighted fusion on the anomaly probability distribution result, determining service state sequence length of the system according to the weighted anomaly probability distribution result; acquiring initial service state of the system; performing backtracking analysis on the initial service state according to a Viterbi algorithm module, model initial parameters and service state sequence length, and obtaining target service state sequence of the system; and through a target hidden Markov model, fault recovery and intervention are advanced from post-repair to in-process or even pre-process, so that the maintenance capability and business continuity guarantee capability of the system are improved.

The invention relates to the technical field of psychological and physiological monitoring, and discloses a video type psychological and physiological index analysis method and system. The method comprises the following steps of: positioning facial key points from a video, partitioning, extracting remote photoelectric volume pulse waves and breathing characteristics of an action unit, and outputting heart rate and breathing signals through Kalman filtering; obtaining a heartbeat interval through peak detection, calculating a heart rate variability index and a respiratory rate, and establishing an individual baseline for normalization by using an initial calm period; fusing the emotional features and the normalized index to form an observation vector, and deducing a stress state through an unsupervised model and a forward algorithm; detecting an index mutation point to identify a stimulation event, and predicting an excessive stress risk and performing graded early warning by adopting a gating circulation unit network; and storing the multi-session data into a database, quantifying the desensitization rate, tolerance and recovery capability through linear regression, performing weighted scoring, and updating the model. According to the invention, non-contact monitoring and real-time stress evaluation and early warning are realized, and quantitative analysis of a cross-training effect is supported.