39 results about "Viterbi algorithm" patented technology

The invention relates to a rotary machine composite fault detection method and system under a time-varying working condition, and belongs to the technical field of rotary machine fault diagnosis. The method comprises the steps of collecting a non-stationary vibration signal under a variable speed working condition, fusing a third-order Renyi entropy and kinematics prior to construct an adaptive time-frequency enhancement model, and outputting weighted time-frequency distribution; global optimization is carried out through a priori constraint Viterbi algorithm, and an instantaneous frequency ridge line is extracted under the condition of no rotating speed sensor; performing angular domain resampling by taking the ridge line as a phase reference, and converting a non-stationary signal into an angular domain stationary signal; constructing an extended difference mode decomposition convex optimization model, and iteratively solving an optimal difference spectrum; and determining a frequency spectrum segmentation threshold value based on self-adaptive change point analysis, performing decoupling separation and time domain reconstruction on the optimal difference spectrum, and completing composite fault diagnosis through feature comparison. The method does not need an external tachometer, can adaptively extract and separate the composite fault features under the variable speed working condition, is high in diagnosis accuracy, and is good in engineering application value.

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.

The application provides a bearing temperature and vibration signal fault evolution path intelligent identification method and system, relates to the technical field of fault identification, and comprises the following steps: collecting and time-aligning bearing temperature, vibration signals and working condition parameters to construct a multi-source heterogeneous data matrix; extracting multi-dimensional features after adaptive noise reduction processing; constructing a fault evolution mechanism causal correlation network by using a causal inference model; performing time series clustering to divide health state clusters based on the network and establishing a state transition model by using a hidden Markov model; and finally applying a Viterbi algorithm to decoding to obtain an optimal fault evolution path. The method can effectively identify the bearing fault evolution law and improve the prediction accuracy.

This invention discloses an automatic construction method for catenary support equipment archives based on multi-frame visual sequence alignment, belonging to the field of railway operation and maintenance technology. The method includes: acquiring trackside inspection videos; segmenting the frame data sequence based on the area change trajectory of the pole boundary frame to obtain several pole trajectories compressed into candidate pole nodes; extracting representative frames, kilometer marker area characters, license plate area characters, and pole type labels to obtain a candidate pole node sequence arranged by time; acquiring pre-stored real structured trackside data; constructing a key point dictionary based on the uniqueness of kilometer markers; generating a real pole number sequence arranged in line order; scoring all video frames covered by the candidate pole nodes frame by frame; using the Viterbi algorithm to solve the globally optimal alignment path from the candidate pole node sequence to the real pole number sequence; and generating catenary support equipment archives. This invention solves the problems of low accuracy in catenary support equipment archive construction, inability to effectively handle missed detections, false detections, and disordered sequences.

The application provides a farmland feature extraction method based on texture analysis, and belongs to the technical field of remote sensing data processing.The application extracts multi-direction and multi-scale gray level co-occurrence matrix texture feature parameters by constructing a block integral graph to form an initial texture feature vector, constructs a Riemann kernel function matrix by using Riemann geodesic distance calculation, and realizes texture feature dimension reduction by principal component projection in a Riemann manifold tangent space.The reduced texture feature vector is encoded as a genetic algorithm chromosome individual, and the optimal texture feature subset is obtained by fitness evaluation and evolution operation.The optimal texture feature subset of multi-temporal images is used to construct a hidden Markov model, and the optimal state sequence path is searched by using the Viterbi algorithm to realize farmland feature time sequence recognition.After morphological post-processing, farmland feature data is output, and the problem that high-dimensional feature redundancy leads to low feature classification accuracy during farmland remote sensing image texture feature extraction is solved.

The present application belongs to the technical field of radar, and particularly relates to a bird wing-flapping frequency extraction method based on Doppler radial acceleration. The present application firstly tracks the instantaneous frequency trajectory of the time-frequency analysis result by using the Viterbi algorithm, dynamically searches for the best path, and thus effectively extracts the signal instantaneous frequency; then performs differential processing, converts the instantaneous Doppler information into Doppler radial acceleration; and finally sets an adaptive threshold, counts the wing-flapping times of the bird, and thus realizes the extraction of the wing-flapping frequency of the bird. The present application proposes a bird wing-flapping frequency extraction method based on Doppler radial acceleration, which provides an effective means for the extraction of the wing-flapping frequency of the bird. Compared with the existing bird wing-flapping frequency extraction method, the present method can greatly improve the success rate and precision of the extraction of the wing-flapping frequency of the bird under the condition of low signal-to-noise ratio, and will be helpful for the bird target recognition of the radar.

The invention belongs to the technical field of causal traceability, and particularly discloses a complex event relation causal traceability method based on subgraph evaluation, which comprises the following steps of: firstly, performing data preprocessing on input event relation data by a system, constructing a directed graph by utilizing purified data, and then, according to a received target tail event needing to be traced, establishing a source of the target tail event; performing multi-path hierarchical backtracking search in the directed graph to obtain a candidate source event set and a corresponding optimal path, then extracting causal relationship sub-graphs corresponding to different source events, then performing sub-graph evaluation, performing analysis to obtain corresponding event development context sub-graphs and key nodes in a causal evolution path, and finally obtaining the event development context sub-graphs and the key nodes in the causal evolution path. And causal tracing in the complex event relation network is completed. According to the method, the causal traceability efficiency of the complex event relation network can be improved according to the thought of the Viterbi algorithm and in combination with subgraph evaluation, and comparison and screening of the optimal source node and the corresponding optimal development path are realized by constructing a complete cause subgraph quality evaluation system.

The application discloses a skipping counting detection method based on laser radar area recognition, and relates to the technical field of motion monitoring, which comprises the following steps: continuously scanning each effective detection ROI area to obtain original point cloud data, and acquiring time sequence point cloud data through preprocessing; training a multiple hidden Markov model based on the time sequence point cloud data, and independently modeling the state of a rope in parallel, and solving the optimal state sequence of each rope through a Viterbi algorithm; based on the optimal state sequence, applying Bayesian filtering to dynamically optimize cycle parameters, and acquiring a complete counting sequence through interpolation counting; performing event counting statistics on the complete counting sequence, aligning through a time window, mapping the effective detection ROI area, and outputting the independent counting value of each person. Through the parallel independent modeling mechanism of the multiple hidden Markov model, the application realizes accurate separation and recognition of a multi-person skipping scene; through the Viterbi algorithm, the optimal state sequence is solved, and the recognition accuracy and stability are maintained.

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.

Systems and processes are disclosed for a multi-layered approach to fraud prevention by leveraging a machine learning engine integrated with the Session Initiation Protocol (SIP) to attempt caller identification before transitioning to a voice call, allowing for the potential blocking of unwanted calls. If SIP-based identification remains inconclusive, an anomaly detection engine employing the Viterbi algorithm analyzes the caller's speech patterns during voicemail messages. The Viterbi algorithm converts spoken language into text, identifying suspicious characteristics such as unusual speech patterns, inconsistencies, and keywords associated with scams. If suspicious characteristics are detected, the system automatically blocks callback attempts and notifies the customer of potential spam or unwanted calls. This proactive approach addresses both live and recorded fraudulent calls, enhancing the security of telecommunications by preventing fraudulent interactions before they can cause harm. The system continuously learns from new data, adapting to evolving fraud tactics, providing robust, long-term protection for telecommunications users.

This invention relates to the field of safety detection technology, specifically to a multi-source fusion closed-loop safety monitoring method for salt cavern gas storage facilities applicable to high-frequency injection and production conditions. In this invention, the Mankendall trend test outputs trend direction labels and significant trend indicators on the cavity pressure change value sequence, the annular pressure change value sequence, and the event increment value sequence. The trend direction labels are combined with the consistent out-of-bounds item count to form a stability direction discrimination sequence. The Viterbi algorithm outputs a time step hidden state label sequence based on the stability direction discrimination sequence and completes the location of continuous consistent sections. The location of continuous consistent sections separates short-term fluctuations from persistent anomalies and reduces the frequency of triggering actions under high-frequency injection and production conditions. The hidden state label sequence and the dominant direction of the section are mapped to the differential pressure reduction amount, the upper limit contraction amount, and the pressure stabilization extension amount to form an injection and production control action set. The action parameters are bound to the number of execution cycles and solidified into the execution trajectory of the operating state.

This invention provides a method and apparatus for extracting traffic networks from high-noise trajectory data. Based on the density raster map of the trajectory data, an iterative path extraction process is executed to extract multiple path centerlines to obtain the traffic network. In the path extraction process, a Markov probability model is used to model the tracking process, and the tracking step size is adaptively calculated based on the curvature of the trajectory lines to address the Markov chain interruption problem, thus providing stronger robustness when tracking path structures with different widths and curvatures. Then, for each local path center point candidate point set, the deviation of the candidate points from the path center is adaptively quantified based on the density raster map to solve the path center estimation problem under uneven trajectory distribution. Finally, the Viterbi algorithm is introduced to account for local measurement errors caused by noise, and the path centerlines are accurately derived with optimal global joint probability, overcoming the problem of complex parameter settings faced by existing technologies in different scenarios and improving the accuracy of extraction.

The application discloses a kind of based on improved hidden Markov model line network matching method, device and medium, by obtaining bus line network data and road network data, and the bus line network data and road network data are preprocessed, obtain the point set to be matched of bus line network and the section set of road network, the point set to be matched of bus line network and the section set of road network are input into the fitting hidden Markov model of pre-set, the emission probability and transition probability of all candidate points corresponding to all points to be matched of the bus line network are calculated, the shortest path between all candidate points of any adjacent point to be matched of the bus line network is calculated using Viterbi algorithm tracing source, obtain the matching result of bus line network and road network.Therefore, the application embodiment can improve the matching accuracy of bus line network and road network.

This application discloses a method, apparatus, equipment, medium, and product for technical condition assessment based on matter-element theory and Hidden Models (HMM), relating to the field of equipment technical condition assessment. The method includes: acquiring technical condition parameter data of a marine centrifugal pump; determining a technical condition assessment sequence group based on the technical condition parameter data using a technical condition assessment model for the marine centrifugal pump; selecting a predetermined number of technical condition assessment sequences within the technical condition assessment sequence group to train and re-evaluate the HMM, and then selecting the remaining technical condition assessment sequences for optimization using the Viterbi algorithm to determine the optimal assessment path; the optimal assessment path is used to assess the technical condition of the marine centrifugal pump. This application effectively reflects the dynamism and stability in technical condition assessment, providing a new approach for realizing equipment technical condition assessment.

The invention discloses a power grid dispatching knowledge processing method based on a DistilBERT-LSTM-HMM model, and the method comprises the steps: carrying out the character extraction of an image format document through an OCR technology, and obtaining text data; performing data cleaning on the extracted text to remove irrelevant contents and format errors; synonym replacement is performed by applying GloVe word vectors, so that the diversity of texts is enhanced, and rich semantic information is provided for subsequent model training; performing semantic embedding on the text by using DistilBERT, generating high-quality word vector representation of each word, and capturing deep semantic information; inputting an LSTM model, and capturing a context and a long-range dependency relationship in the text to identify event sequence and time sequence information in the power dispatching document; the tag sequence is optimized through the HMM, it is ensured that the sequence relation between tags is reasonable, the optimal path is selected through the Viterbi algorithm, and automatic labeling and knowledge processing of the power dispatching document are completed. According to the method, the understanding accuracy of the power dispatching document can be effectively improved, manual intervention is reduced, and the working efficiency is improved.

The application relates to the technical field of non-ferrous metal ore dressing 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, obtaining a plurality of key features after pre-processing the multi-source heterogeneous data, and constructing a feature matrix according to the plurality of key features; constructing a hidden Markov model based on the implied state of the flotation process, inputting the feature matrix into the hidden Markov model, optimizing the parameters of the hidden Markov model through a Baum-Welch algorithm, and screening an evolution model in the training process in combination with a Bayesian information criterion; obtaining real-time multi-source heterogeneous data of the flotation process and obtaining a corresponding feature matrix, inputting the feature matrix into the evolution model, and obtaining an optimal process state sequence in combination with a Viterbi algorithm. The method solves the problem that existing ore dressing models cannot effectively and accurately predict the state of the flotation process.

This application relates to a motion capture method, device, equipment, and medium for ice hockey passing and control training. The method includes: acquiring raw motion data from an inertial measurement unit (IMU), synchronizing and cleaning it to obtain a synchronization data matrix; constructing an initial spatiotemporal graph using IMUs as nodes and spatial relationships as edges; generating weight information using a LASSO feature selector, and weighting it to obtain an updated spatiotemporal graph; inputting the updated spatiotemporal graph and weights into a graph neural network for synchronous inference of motion segmentation, category, and quality assessment information; decoding the motion boundaries and category labels using the Viterbi algorithm; inputting the data into a scoring regression model to calculate a technical quality score, and outputting the motion analysis results. This method can accurately capture ice hockey passing and control motions, achieving integrated motion segmentation, classification, and quality assessment, thus improving training analysis efficiency.

The invention relates to the field of mineral exploration, and discloses a uranium reservoir sedimentary facies belt automatic identification and division method based on logging data constraint. Generating initial feature vectors of the nodes; inputting the layer control well bitmap into a map attention network, and performing spatial feature fusion on the initial feature vector of the node to obtain a fused feature vector; dynamically generating a geological transition probability matrix for each well; calculating the emission probability of each depth point according to the logging data and the fusion feature vector; and decoding an optimal sedimentary facies belt sequence as a division result by adopting a Viterbi algorithm in combination with the emission probability and the geological transition probability matrix. According to the method, a layer control well position map is constructed, a well pattern relationship is introduced into a model, and adaptive weighted fusion is performed on inter-well information by using a map attention network, so that inter-well transverse geological relevance is effectively brought into a sedimentary facies identification process, and transverse continuity and geological rationality of a division result are improved.

The application discloses a multi-temporal SAR coherent change detection image false alarm suppression method and system, and relates to the technical field of synthetic aperture radar image processing. The method establishes a hidden Markov model suitable for multi-temporal SAR coherent change detection, defines an initial state probability vector, a state transition probability matrix and an emission probability density function; performs radiation calibration, sub-pixel registration and coherent coefficient calculation on multi-temporal SAR images to obtain a coherent coefficient observation sequence; obtains optimal hidden states of each pixel by using a Viterbi algorithm through initialization, recursive updating, termination judgment and backtracking; distinguishes states according to the time sequence characteristics of unchanged areas, target changes and CCD clutter, realizes matching of transient targets and persistent clutter according to state transition probability configuration, and finally removes clutter to complete false alarm suppression. The application can reduce the false alarm rate while maintaining the sensitivity of subtle target detection, and improve the accuracy and robustness of multi-temporal SAR coherent change detection.

The invention discloses a high-speed parallel Viterbi decoder based on a CUDA (Compute Unified Device Architecture) and a decoding method. The decoder comprises an input cache module, a parallel data de-puncturing module, a parallel measurement calculation module, a backtracking path processing module and an output control module, and multi-level parallel processing is realized by utilizing a CUDA (Compute Unified Device Architecture) of a GPU (Graphics Processing Unit). The method comprises the following steps: segmenting a receiving sequence and then distributing the segmented receiving sequence to GPU thread blocks; rapidly reading a state transition matrix and an output matrix by adopting a pre-calculation table look-up strategy; adopting a parallel prefix scanning algorithm to accelerate state measurement calculation; and reducing the global memory access delay by using the shared memory. According to the invention, the performance bottleneck of the traditional serial Viterbi algorithm is broken through, on the premise of keeping the decoding precision, the throughput rate is obviously improved, the real-time processing of multi-code-rate and multi-code-stream convolutional codes is supported, and a remarkable acceleration effect can be obtained compared with CPU (Central Processing Unit) implementation. The method is especially suitable for high-throughput ACM scenes, such as 5G communication and satellite navigation, requiring real-time processing of multiple code streams and long constraint length convolutional codes.

This invention discloses a medical named entity recognition method and apparatus based on context-aware spectral units and multi-scale networks, belonging to the fields of natural language processing and medical information processing technology. The technique first preprocesses the original medical text to obtain a standardized lexical sequence, encodes it using a pre-trained semantic model to obtain a context semantic vector sequence, and then extracts enhanced sequence features through a bidirectional gated recurrent unit network. These features are then input into a multi-scale context feature extraction network containing three parallel feature extraction units. Each unit performs feature projection, relevance calculation, weighted fusion, and bipolar rectification according to different window sizes. After fusing the multi-scale features, the sequence is passed through a fully connected classification layer and a conditional random field decoding layer, combined with the Viterbi algorithm to obtain the optimal named entity label sequence. This technique also constructs a recognition system including modules such as preprocessing, semantic encoding, and multi-scale feature extraction, suitable for named entity recognition of medical texts such as electronic medical records and medical literature.

This invention relates to an AED emergency response decision optimization method based on federated learning and digital twins, belonging to the field of intelligent medical technology. It acquires multi-source sensing data from AED emergency cabinets, decodes the multi-source sensing data online using a Hidden Markov Model, and uses the Viterbi algorithm to infer the real availability status of the AED in real time. Digital twins are created for each AED, emergency vehicle, and personnel, synchronizing the real-world status and integrating real-time traffic conditions, weather information, and personnel location to predict the estimated arrival time of the AED and emergency personnel. A federated learning framework is used to train a deep reinforcement learning model, with patient location, AED availability, personnel location, and predicted time as the state space, AED selection and personnel assignment as the action space, and actual response time as the reward function, generating a globally optimal scheduling strategy. This invention effectively improves the accuracy of AED status perception and the timeliness of response time prediction while protecting data privacy.

The application discloses a model parameter automatic selection method and system based on a hidden Markov model. The method comprises the following steps: in response to a selection instruction input by a user in a visual operation interface, a selected region in the model is determined, and a selected region set is constructed; a hidden state set is constructed, wherein the hidden state set contains adjustable parameters of the model; a state transition probability matrix and an observation probability matrix are obtained; a hidden state sequence is calculated based on the selected region, the obtained state transition probability matrix and the observation probability matrix by using a Viterbi algorithm to obtain a predicted parameter combination. The technical scheme of the application fixes expert experience in the state transition matrix and the observation probability matrix based on the hidden Markov algorithm. When a user selects certain regions, a parameter combination is automatically predicted and prompted, the user is guided, the experience threshold for use is reduced, and the efficiency of parameter selection by technical personnel is improved.

The invention discloses a vehicle positioning method and system for a multi-level overpass bridge scene. The method comprises the following steps: acquiring real-time positioning data including a three-dimensional position and a positioning state; acquiring a three-dimensional road network model based on the three-dimensional position; determining a candidate lane screening range by taking the positioning state as an adaptive factor and screening a candidate lane set; calculating an observation probability based on a three-dimensional space relationship between the vehicle observation position and the candidate lane; calculating a transition probability based on a three-dimensional topological connection relationship between the candidate lane pairs at adjacent moments; and searching an optimal path through a Viterbi algorithm by using the observation probability and the transition probability, and outputting a lane where the vehicle is located and a matching position. According to the method, the hidden Markov model is expanded to a three-dimensional space, the calculation complexity is reduced through adaptive screening, the matching precision is improved through multi-dimensional feature fusion, and the method is suitable for three-dimensional traffic scenes such as viaducts and tunnels.

This invention relates to the field of disease analysis technology, specifically to a method and system for configuring pesticides for Chinese medicinal herbs based on disease types. In this invention, the evolution of the occurrence period is jointly defined by the stage sequence, adjacent transfer, number of backtracking, and dwell length constraints. The Viterbi algorithm accumulates the path probability according to the inspection sequence number and selects the appropriate path. The start and end sequence numbers of the window and the backtracking segment are synchronously written into the prevention and control window set. The prevention and control window is driven by the symptom sequence rather than a fixed calendar. The prevention and control window set is linked to the expansion of candidate formulations. The lower limit of the application interval days, the safety interval margin, the registration upper limit, and the taboo matrix are multi-dimensional hard constraints executed in the same round of screening. The execution is interrupted when the candidate list for mitigation is empty due to the backtracking and rescreening at the grade level. The greedy algorithm selects items and marks the switching points based on the difference in the disease index and the weighted values ​​of residual penalty, pesticide damage penalty, and resistance penalty, improving the executability and compliance consistency of the preparation steps, while reducing the probability of repeated application and application beyond the interval.

The invention relates to a protein allosteric path identification method AlloPath based on transfer entropy and a hidden Markov model, and belongs to the technical field of bioinformatics and protein structure analysis. The method comprises the following four steps: firstly, constructing a Gaussian network model (GNM) for a target protein; secondly, calculating a transfer entropy (TE) between residues through a Gaussian network model so as to quantify an information transmission amount between the residues; secondly, calculating two parameters including the transition probability and the emission probability in a hidden Markov model (HMM) based on the transition entropy; and finally, identifying the optimal allosteric path by adopting HMM and Viterbi algorithms, including initialization, recursive calculation, termination and backtracking processes. The method only depends on a protein static topological structure, and the continuous allosteric path with a clear direction can be efficiently recognized. The test result of the method on three classic allosteric proteins of hPTP1E PDZ2, Caspase-1 and CheY shows that the prediction path of the method is highly consistent with experimental and theoretical data. The invention provides key technical support for protein allosteric mechanism research and allosteric drug design.

This invention discloses a method for staging TUG (Turn-Off) test actions based on a kinematically constrained Hidden Markov Model (HMM), relating to the fields of medical rehabilitation assessment and action recognition technology. The method includes: acquiring and preprocessing human IMU data collected by an IMU sensor during a timed stand-up-walk test; extracting the slope of linear regression of acceleration and the maximum absolute value of angular velocity using a sliding window, and fusing them to generate observation features; simultaneously calculating the human torso tilt angle through integration; discretizing the observation features to generate an observation sequence; constructing a HMM containing six TUG action states; introducing angle interval matching weights to improve the Viterbi algorithm for decoding the observation sequence; correcting the path probability by judging the angle matching degree; and finally outputting the action state sequence to achieve automatic six-stage staging of the TUG test. This invention improves the accuracy of action staging and reduces computational complexity by introducing kinematic prior knowledge to constrain the HMM decoding process.

This invention discloses an intelligent evaluation system for study tours based on intelligent agents, including a multimodal data acquisition module, a data intelligent analysis module, a comprehensive analysis and processing module, and an intelligent evaluation output module. This invention relates to the field of intelligent evaluation technology and solves the technical problem that study tour evaluation is currently hampered by an overemphasis on results and a neglect of process and effectiveness, making it difficult to meet the needs of quality education for process-oriented, comprehensive, and personalized evaluation. This invention, by relying on a knowledge graph and dynamic weight model in the field of study tour evaluation, achieves the adaptation of indicator dimensions to themes / grades, the adjustment of granularity to data richness, and the dynamic optimization of weights to objectives / features. It completely solves the problem of the rigid, one-size-fits-all approach of traditional indicators. Through a three-dimensional trajectory model of space-time-behavior, it depicts the entire study tour process and combines a hidden Markov model and the Viterbi algorithm to decode behavioral sequences, achieving intelligent identification and quantitative analysis of behavioral patterns and improving the accuracy of behavioral pattern recognition.

The invention provides a path fitting method and system based on trajectory data and a storage medium. The method comprises the following steps: acquiring road network data and trajectory data; based on the road network data and the trajectory data, candidate road sections corresponding to the trajectory points are determined; for each track point, inputting the determined position information of the corresponding candidate road section and the related information of the track point in the track data into a pre-trained path fitting model, and outputting to obtain an observation probability corresponding to the track point and a transition probability between the candidate road section corresponding to the track point and the candidate road section corresponding to the adjacent track point; wherein the pre-trained path fitting model comprises a hidden Markov model and a gradient boosting model, and the output of the hidden Markov model is used as the input of the gradient boosting model; and a matched candidate road section sequence is obtained through fitting based on the observation probability and the transition probability by using a Viterbi algorithm. According to the method, the driving state of the vehicle can be dynamically captured, and real-time optimization is realized while the fitting precision is met.

This invention provides a method, apparatus, and application for generating bus stop announcements based on passenger profiles and ETA data. It supplements bus route data that lacks GPS positioning information by using historical passenger profiles and ETA data to determine the stop announcement times. The solution obtains a first probability of a transaction cluster occurring at a specified stop based on the passenger profile within the historical transaction cluster, a second probability based on the corresponding ETA data, and a third probability using the Viterbi algorithm. The earliest transaction time of the transaction cluster corresponding to the highest third probability value is used as the announcement time for the specified stop. This method accurately supplements missing stop announcements even when some GPS positioning data is missing, improving the accuracy of simulated bus stop announcements.