21 results about "Hidden Markov model" patented technology

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 relates to the technical field of emergency command, in particular to an emergency command method and system based on visualization, and the method comprises the following steps: S1, data collection and preprocessing; s2, data fusion and feature extraction; s3, carrying out three-dimensional visual modeling; s4, performing decision deduction and path planning; and S5, dynamic feedback and optimization are performed, interference of data noise and abnormal values is reduced by using a sliding window Gaussian filtering algorithm and artificial data verification based on a hidden Markov model, a risk assessment model is constructed for risk assessment, weights are determined through an analytic hierarchy process, multiple factors are comprehensively considered, risk assessment is more accurate, and the risk assessment efficiency is improved. An accurate decision-making basis is provided for commanders, and resource waste and rescue delay caused by blind decision making are avoided.

A distributed environmental control system comprises control nodes that communicate via wireless mesh networks to predict user movement and automate loads without centralized coordination. Each node executes a local inference engine (Markov model, HMM / DBN, or reinforcement learning policy) to predict occupancy patterns, broadcasts predictions to neighbors, and makes autonomous actuation decisions based on local and shared data. When multiple nodes control loads in a shared zone, a Runtime Actuation Authority mechanism designates a single decision-maker based on most recent manual interaction, with authority epochs and deterministic total order resolution ensuring distributed consistency. The system prevents feedback loops via causal action identifiers, logical timestamps, and recent action caches. Sensor data is transmitted as compressed waveform encodings. A deterministic policy layer enforces safety and comfort constraints. The reinforcement learning embodiment learns household preferences from user feedback, adapting actuation behavior to optimize satisfaction, comfort, and energy efficiency while maintaining safety guarantees.

The application discloses a kind of monomer battery consistency detection algorithm based on Internet of Vehicles big data platform, including the following steps: S1, receive data and pre-process;S2, utilize trained hidden Markov model to judge the voltage interval section to be detected;S3, extract the subsequence near each monomer voltage sequence feature point, calculate the Mahalanobis distance between corresponding subsequence;S4, do anomaly detection to the feature matrix formed by characteristic subsequence with isolated forest algorithm;S5, respectively to the highest monomer voltage sequence and the lowest monomer voltage sequence and average voltage sequence do KS test;S6, calculate the Wasserstein distance between abnormal monomer voltage sequence and average monomer voltage sequence;Avoid relying on the error, noise and time delay of the voltage measurement value of some single point moment, can eliminate the noise and error of single point moment, avoid the interference of a large number of same subsequence in full time sequence distance calculation, greatly reduce the calculation amount compared with full time sequence distance calculation and basic shapelet search algorithm based on brute force search.

This invention relates to the field of big data analysis and processing technology, and discloses a method for dynamic correlation analysis of roadbed compaction trajectory and compaction quality. The method includes: Step 1: collecting the position information, attitude information, and vibration response information of the road roller during the roadbed compaction process to obtain a set of hidden state samples and a set of observation sequence samples of corresponding partial trajectory analysis units; Step 2: executing a joint dynamic correlation analysis algorithm based on Hidden Markov Model (HMM) and manifold learning; Step 3: using the joint dynamic correlation analysis model to process the observation sequence of real-time trajectory analysis units constructed from the real-time collected data, and combining it with the compaction manifold embedded coordinates to generate a dynamic compaction quality grade distribution map covering the roadbed construction area. This invention not only improves the continuity and stability of compaction quality assessment, but also enhances the responsiveness to changes in construction conditions.

The invention relates to the cross technical field of bioinformatics and cell molecular biology, and discloses a breast cancer FOXO3 gene dynamic distribution metering method based on a subcellular localization means. Comprising the following steps: acquiring FOXO3-related single-type basic amino acid data, two-cluster basic amino acid data and phosphorylation-induced amino acid data acquired by a data acquisition module; processing the amino acid data based on a recessive Markov model, and predicting the amino acid sequence of the FOXO3 gene; analyzing an amino acid sequence through a machine learning method, providing a potential position and confidence of a nuclear localization signal, and predicting nucleoprotein and an NLS region; recursively deducing the dynamic distribution range of the FOXO3 gene in the cell nucleus or cytoplasm based on the prediction result of the NLS region; after NLS is predicted by a bioinformatics tool, the subcellular localization function of the FOXO3 gene is subjected to experimental verification in combination with a fluorescence localization experiment so as to confirm the dynamic distribution range.

The present invention relates to a method for non-invasively assessing the risk of uniparental disomy and related diseases in a pregnant fetus. Specifically, the present invention provides a detection device for determining fetal uniparental disomy (UPD), characterized in that the device comprises: (a) a data input module; (b) a quality control module; (c) an analysis and calculation module; and (d) an output module. The analysis and calculation module includes copy number analysis and allele fraction analysis; and the allele fraction analysis includes detection of fetal uniparental disomy based on a hidden Markov model. As an auxiliary diagnostic method, the detection device of the present invention can detect fetal uniparental disomy and related disease risks with high sensitivity and specificity.

The invention discloses an inertial navigation system error correction method fusing template matching and a hidden Markov model. The method comprises the following steps: constructing original map data into an R-tree index structure; key frames of the INS points are selected in a self-adaptive threshold value mode, and redundant INS points are screened out; detecting curve features of key frames in the INS trajectory by adopting a sliding window mechanism; creating a template image according to the INS trajectory, generating a road network image by using road section information in the R-tree index structure, and performing template matching processing on the road network image by adopting sliding search; when a plurality of candidate correction values are generated in template matching, a matching process is optimized through an HMM algorithm; and updating a correction value obtained after the matching process is optimized in a recursion mode to obtain a corrected INS position. According to the embodiment of the invention, the inertial navigation information is accurately matched with the map without depending on an external sensor, meanwhile, the transverse error and the longitudinal error of the INS are corrected, and the real-time requirement is met.

The invention relates to the technical field of mine intelligent management and emergency response, and particularly provides a mining emergency material intelligent distribution system oriented to complex geological conditions, and the system comprises a data collection module which is used for collecting multi-dimensional sensing data and emergency material demand information of a mine geological environment in real time, and sending the data to a data processing module; the multi-dimensional sensing data comprises rock stratum stress, fracture development degree and underground water seepage parameters, and the geological evaluation module is used for carrying out dynamic geological risk grade evaluation on the multi-dimensional sensing data based on an improved hidden Markov model to generate a real-time geological threat probability distribution diagram; the mining emergency material intelligent distribution system facing the complex geological condition breaks through the limitation of path rigidity and sensing lag of a traditional system, achieves safe, efficient and intelligent distribution of emergency materials in the complex environment of a mine, provides more scientific and reliable technical support for mine emergency rescue, and is suitable for popularization and application. And the mine safety guarantee capability and the accident response efficiency are obviously improved.

This invention relates to the field of CNC precision machining technology, and discloses a multi-parameter adaptive CNC precision turning method and system. The method includes: using the pulse sequence per revolution of the spindle encoder as a global time reference, performing physical feature event anchor point matching time alignment on multi-sensor data; retrieving hidden Markov model parameters of similar materials based on material property distance vectors and performing migration correction, recording the migration correction amount as a material offset baseline vector; distinguishing between normal feature offsets caused by material switching and real sensor anomalies by cosine similarity comparison, and generating a corrected conflict degree matrix; generating a robust wear stage probability distribution based on credibility weighted fusion, driving multi-parameter adaptive adjustment of feed rate, spindle speed and depth of cut.

The application discloses a kind of road network trajectory matching method, device and equipment, the method can include: based on the corresponding relation of single-trip trajectory data and road network data, in road network data, determine the sub-road network data corresponding to single-trip trajectory data;Sub-road network data is split based on split length threshold value, and topological relationship reconstruction is carried out to the sub-road network data after splitting;Based on hidden Markov model, the trajectory point included in single-trip trajectory data and the reconstructed sub-road network data are matched to realize road network trajectory matching.The method starts from road network data itself, reconstructs road network structure and topological relationship, and then constructs the topological relationship of sub-road network data more suitable for trajectory point state, so as to improve the upper limit of HMM algorithm matching ability, improve the accuracy of map matching.

The embodiment of the invention provides an emergency steering auxiliary method and device, equipment, a storage medium and a program product. The method comprises the steps that operation data related to a driver in the vehicle driving process are obtained in real time, and the operation data comprise one or more of the steering wheel rotating angle, the steering wheel rotating angle speed and the hand torque; in response to the operation data and the hidden Markov model, determining that the driver enters the emergency steering state at present, and identifying whether the driver completes emergency avoidance operation or not; in response to the fact that the emergency avoidance operation is completed, determining a vehicle contour envelope track associated with the emergency avoidance operation and at least one piece of obstacle information; and in response to determining that avoidance is not successful currently based on the vehicle contour envelope trajectory and the at least one piece of obstacle information, performing an intervention avoidance operation through the emergency steering assist system. Finally, the system intervenes in assistance only under necessary and safe conditions, emergency avoidance is accurately realized on the basis of not influencing the driving experience of a driver, and the driving safety is improved.

The application relates to the technical field of energy management, in particular to a photovoltaic inverter applied to a photovoltaic micro-grid of an isolated island, which comprises an energy state classification module; based on photovoltaic grid electricity and weather data, the energy state classification module extracts temperature and light intensity parameters, carries out data classification, and generates a state classification index. The application predicts the energy state in the photovoltaic micro-grid of the isolated island through a hidden Markov model, and improves the accuracy of energy management. The model analyzes energy output according to historical and real-time weather data, can adjust the energy storage system and the inverter output in advance when the energy output fluctuates due to weather changes, allows the system to actively store energy during the energy supply peak period, saves the use when low output is predicted, balances the energy supply and demand, and maximizes the energy efficiency. In addition, an AC / DC complementary dynamic adjustment system is driven by real-time monitoring data, and automatically selects an energy conversion path.

The application discloses a kind of based on digital twinning self-flowing water plant pump station group linkage control system, it is related to self-flowing water plant water supply control technical field;Including data acquisition module, water plant water supply analysis module, digital twinning module and linkage control module.By real-time acquisition water supply data, water supply demand prediction, pipe network path complex value calculation and pump station load assessment;Based on analysis result, establish digital twinning model, adopt hidden Markov model to deduce the system state evolution under different scheduling strategy, and utilize multi-objective evolutionary algorithm to water supply stability, energy consumption economy and equipment operation balance are carried out collaborative optimization, output optimal linkage control result.The application realizes the feedforward linkage control of pump station group, improves the response speed to demand fluctuation, reduces pipe network pressure fluctuation, reduces operating energy consumption, balances equipment load, avoids potential operation risk, while guaranteeing water supply safety, economic efficiency and reliability are considered.

The application relates to a business execution method and device, computer equipment and a storage medium thereof, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring user voice input by a user and containing a target business function, the target business function being at least one optional business function in a business system; extracting a function voice segment of the target business function from the user voice through a hidden Markov (HMM) model; the HMM model being obtained through training according to function voice data of each optional business function supported by the business system; analyzing at least one to-be-executed business corresponding to the function voice segment; acquiring a target business link corresponding to each to-be-executed business, and executing each to-be-executed business based on the target business link. According to the application, the user does not need to spend a lot of time learning the triggering method of each function in the system, and the time consumption of the user is reduced.

The application discloses a kind of three-dimensional packing machine multi-parameter associated modeling methods based on dynamic coupling weight, comprising: the synchronous acquisition and working condition marking of multi-source time series data are executed to three-dimensional packing machine;Multi-source time series data are executed based on local feature and cross feature extraction of sliding window;According to the basis feature set, working condition recognition and state division based on hidden Markov model are executed;The construction and self-adapting weighting of dynamic coupling weight network are executed;The construction and sparse projection optimization of dynamic weighted feature space are executed;The construction and online updating mechanism of support vector regression model are executed.The three-dimensional packing machine multi-parameter associated modeling method based on dynamic coupling weight of the application, by introducing dynamic coupling weight mechanism, accurately depicts the nonlinear interaction between multiple parameters under different working conditions, improves the accuracy and interpretability of quality prediction model, and provides reliable data-driven model for real-time quality early warning and process parameter collaborative optimization.

The invention relates to the technical field of big data analysis and processing, and discloses a roadbed rolling track and compaction quality dynamic correlation analysis method, which comprises the following steps of: 1, acquiring position information, attitude information and vibration response information of a road roller in a roadbed rolling process, obtaining a hidden state sample set and a corresponding partial trajectory analysis unit observation sequence sample set; 2, executing a combined dynamic correlation analysis algorithm based on a hidden Markov model HMM and manifold learning; and step 3, processing a real-time trajectory analysis unit observation sequence constructed by real-time acquisition data by using a joint dynamic correlation analysis model, and generating a dynamic compaction quality grade distribution map covering a roadbed construction area by combining a compaction manifold embedding coordinate. According to the method, the continuity and the stability of compaction quality evaluation can be improved, and the capability of responding to the change of the construction state can be enhanced.

The application discloses a kind of movement and cognitive ability evaluation method, system and device, method includes: obtaining the real-time motion information of user, including real-time joint angle information, real-time gait information, real-time attention information and real-time decision speed information;Physiology-behavior coupling model based on hidden Markov model is constructed, and joint evaluation index is generated by input real-time motion information;Real-time motion information is extracted using hierarchical feature extraction model, and motion feature vector and cognitive feature vector are obtained;Characteristic vector is fused with joint evaluation index, and abnormal state information containing movement anomaly and cognitive anomaly is output;Through dynamic threshold model detection behavior anomaly and combine preset feedback rule generates real-time feedback instruction, finally according to instruction dynamic adjustment user training task parameter.The method realizes the collaborative monitoring and adaptive training control of movement and cognitive ability by multidimensional data fusion and dynamic evaluation mechanism.

The application discloses a kind of home security intelligent early warning device, system and method, it is related to intelligent security technology field.The device includes static electric field perception module, vibration detection module, airflow analysis module, feature fusion module, target identification module, risk assessment module, early warning control module and response execution module.Through distributed high impedance antenna array detection space static electric field change, piezoelectric sensor gathers building structure vibration, hot flow sensor monitors airflow disturbance, realizes the collaborative perception of three-dimensional physical field.Adopting space-time alignment and adaptive weighted fusion technology generates multidimensional feature vector, combined with mahalanobis distance matching and hidden markov model carries out target identification and behavior analysis, assesses threat risk and executes graded early warning response by comprehensive multi-factor.The application realizes the technical upgrading from single detection to multidimensional perception, from passive record to active early warning, sensor stealth deployment in home facilities, improve detection accuracy while protecting user privacy.

The application discloses a kind of multi-source heterogeneous battery time series data standardization processing and analysis method, comprising: input lithium battery related multi-source heterogeneous data, construct based on standard metadata target extraction effective metadata, calculate information acquisition degree coefficient;Based on lithium battery data terminology mapping dictionary, standard field naming is carried out, and the average sampling frequency of data is calculated;Calculate field coverage rate and frequency comprehensive index coefficient, output field standardization and time alignment CSV data stream;Variational autoencoder VAE is constructed, and battery multidimensional time series feature is mapped into low-dimensional distribution in implicit layer, and data availability coefficient is calculated;Hidden Markov model HMM is used to describe the hidden state sequence of battery real operation condition, and data content coefficient is calculated;The information entropy of each evaluation feature is calculated, to obtain the objective weight of each evaluation feature, the comprehensive score of data quality is calculated using objective weight, and the quality of data is evaluated.

The application discloses a kind of sow lactation behavior fine-grained identification method based on computer vision, steps are as follows: collecting sow and piglet's down video during lactation period;Establish sow lactation behavior fine-grained classification data set;Train sow lactation behavior fine-grained classification behavior recognition model;Long section monitoring video is input into behavior recognition model, and the behavior category label sequence corresponding to sow lactation behavior fine-grained classification is generated;Using the prior knowledge of sow lactation behavior, combined with hidden Markov model, the problem of sow lactation behavior fine-grained classification is modeled, and the category label sequence is corrected using Viterbi algorithm, and the classification results of four categories of piglet weaning, lactation, interrupted lactation and non-lactation state are output.The application can avoid the shielding interference of sow delivery bed limiting fence as much as possible, has strong robustness, and consumes less computing resources, runs faster, and is suitable for video monitoring system in actual breeding environment.