13 results about "Sequence segmentation" patented technology

The application discloses a kind of communication power supply operating state intelligent monitoring methods based on machine learning, comprising the following steps: S1, obtains multiple-source monitoring data and pre-processes;S2, sequence segmentation is carried out using sliding time window and statistical feature vector is extracted, forms state variable set;S3, each state variable is encoded to construct state node and establish weighted directed connection, and construct operating state evolution diagram;S4, state transition matrix is constructed, main transition feature is extracted and low rank is approximately reconstructed, sequence learning is carried out to construct state update function;S5, to state node, multiple-step recursion is carried out, and the Euclidean distance between recursive state and risk boundary is calculated, to determine state monitoring result;S6, error is calculated and state update function parameter is iteratively updated.The application can realize the multiple-step recursion prediction and risk trend identification of communication power supply operating state, improve the accuracy and stability of communication power supply operating state monitoring.

This invention discloses a multi-source electromagnetic noise suppression method based on noise classification and deep learning, belonging to the field of geophysical electromagnetic exploration technology. The method includes: segmenting and preprocessing the original electromagnetic observation sequence; using an improved U-Net network with an encoder embedded in a Mamba time-series modeling module for low-frequency noise suppression; identifying strong noise types in the data segments using a ROCKET classifier; based on the classification results, calling a second improved U-Net network trained for the corresponding noise type for class-based targeted denoising; and finally, splicing the data segments to obtain complete, high-quality data. This invention, through a phased processing framework of "low-frequency pre-suppression—noise classification—class-based denoising," combined with the strong time-series modeling capabilities of the Mamba module and the efficient classification performance of ROCKET, significantly improves the suppression accuracy and signal fidelity for complex, multi-source electromagnetic noise, and is particularly suitable for processing ground and airborne electromagnetic exploration data.

This invention belongs to the field of acoustic sensing demodulation, specifically a demodulation method for a distributed fiber optic acoustic wave sensing system based on FPGA time-sequence segmentation. This invention uses an FPGA as the processing module for signal demodulation. On the FPGA, three signals with a 120-degree phase difference output from the distributed fiber optic system are acquired. Then, time-sequence segmentation is achieved by FIFO buffering of the spatial point data, ensuring data alignment at each spatial point during signal demodulation. Finally, moving average and other signal demodulation processes are performed on the data at each spatial point in the time domain. This invention requires only a small amount of data buffering to achieve demodulation of the entire system, reaching the theoretical maximum distance for signal demodulation. Ultimately, it achieves signal demodulation of a long-distance distributed fiber optic acoustic wave sensing system with high repetition rate under low resource consumption, providing a new approach and method for hardware-based signal demodulation of distributed fiber optic sensing systems.

This invention relates to the field of fault location technology, and discloses a method and system for rapid fault location in operation and maintenance. The method includes: acquiring operational data and denoising it to obtain a denoised data sequence; segmenting the sequence and identifying anomalies, determining the anomaly range through trend offset, and obtaining anomaly markers; if the waveform distortion metric of the anomaly marker exceeds a threshold, obtaining the anomaly location through time series analysis; extracting periodic sequences, grouping similar behaviors among devices to obtain associated device clusters; constructing a causal graph among devices, obtaining a list of potential anomalous devices through propagation path backtracking and node dependency sorting; assessing the impact degree of each device and identifying the faulty device; and obtaining the final fault location based on the topology information of the faulty device through cross-cluster association decomposition, causal edge weight adjustment, and graph structure simplification. This method can accurately and quickly locate the source of a fault in complex network environments with severe noise interference and tight device coupling.

This invention discloses a path tracking method for tracked vehicles based on a liquid time constant neural network, belonging to the field of intelligent control technology for tracked vehicles. The method includes: establishing an expert demonstration database based on MPC (Multi-Process Control) and collecting path tracking error and drive motor torque data; performing robust scaling normalization and long sequence segmentation preprocessing on the data; building an LTCNN model with an NCP (Non-Conceptual Processing) architecture in the PyTorch framework and training it using the AdamW optimizer through MPC behavior cloning; inputting the real-time path tracking error into the trained model and outputting the drive motor torque to control the vehicle to track the desired path. This invention ensures path tracking accuracy through MPC behavior cloning, achieving a lateral error RMSE of 0.089m under small curvature conditions and 0.13m under large curvature conditions; the forward inference of LTCNN improves real-time performance, saving 34.8% of runtime compared to MPC; data preprocessing and the NCP architecture enhance the model's robustness and interpretability, resulting in smoother output torque, making it suitable for path tracking of tracked vehicles in complex off-road environments.

The invention provides a rapid gas detection method, system and equipment for an electronic nose and a storage medium, and the method comprises the following steps: carrying out sequence segmentation on an input gas sensor signal, and extracting a time sequence feature; the time sequence features are embedded into the potential space, embedded information is obtained, and the embedded information carries the local time sequence features of the gas sensor signals; carrying out global mode feature extraction and pooling fusion on the embedded information, and integrating global features of the gas sensor signals to obtain gas signal characterization; the gas signal characterization is mapped to a semantic space, a gas classification is identified, and a corresponding gas concentration is determined. Embedded information rich in gas semantic information is obtained through sequence segmentation, signal redundancy and noise can be reduced, and overfitting is prevented. Therefore, the method can realize high-precision rapid gas classification and concentration estimation in the gas detection process.

The present application relates to the technical field of database concurrency control, and particularly relates to a multi-instance concurrent sequence segment competition method, system and computer readable storage medium based on a TDSQL database. The method first acquires sequence segment information corresponding to a current instance from a sequence list; then calculates a target sequence segment range based on the sequence segment information, and occupies the target sequence segment in the TDSQL database through an update operation with an original value condition, the update operation including a conditional judgment of whether the starting value of the current sequence segment is consistent with a pre-stored value; finally, it is judged whether the update operation successfully occupies the target sequence segment, the successfully occupied sequence segment is cached to the local instance, and an incremental sequence number is generated one by one through a thread safety mechanism. The present application realizes the generation of a globally unique and strictly incremental sequence number in a multi-instance concurrent environment under the constraint of only using a TDSQL database.

The application discloses a kind of based on noise classification and deep learning's multi-source electromagnetic noise suppression method, belong to geophysical electromagnetic detection technical field.The method includes: original electromagnetic observation sequence is segmented and preprocessed;Low-frequency noise is suppressed using the improved U-Net network of encoder embedding Mamba time series modeling module;The strong noise type in data segment is identified by ROCKET classifier;According to the classification result, the second improved U-Net network trained for the corresponding noise type is called to carry out class-oriented denoising;Finally, complete high-quality data is obtained by splicing data segments.The present application uses the processing framework of "low-frequency pre-suppression-noise classification-class denoising", combines the strong time series modeling capability of Mamba module and the high-efficiency classification performance of ROCKET, significantly improves the suppression accuracy and signal fidelity of complex, multi-source electromagnetic noise, especially suitable for ground and air electromagnetic exploration data processing.

This application discloses a temporal segmentation method, apparatus, device, and storage medium for image data. First, a set of continuous temporal image data is acquired, and a frame is randomly selected as the first target image frame. Then, a pre-trained segmentation model based on the U-Net architecture is used to generate a mask for this frame, resulting in a target segmentation mask. Next, the generated target segmentation mask is used as a cue, and the temporal image data is used as the tracking object. This is then input into a finely tuned SAM2 model for real-time inference to obtain a full temporal segmentation mask sequence. This application constructs a collaborative processing flow by integrating the high-precision single-frame segmentation capability of U-Net with the temporal propagation capability of the SAM2 model, thereby improving the accuracy of target association while ensuring consistency between segmentation accuracy and long-term tracking, achieving a balance between target association accuracy and segmentation accuracy.

The application provides a data analysis method and system based on user behavior sequence segmentation modeling, and relates to the technical field of data processing.The method comprises the following steps: acquiring multi-source heterogeneous original customer behavior data; performing quality cleaning on the original customer behavior data to obtain standardized customer behavior data; identifying a unified customer identifier from the standardized customer behavior data, dividing data with the same unified customer identifier into the same user data group; acquiring standard timestamps of each customer interaction record in the user data group, sorting the customer interaction records in the user data group according to the sequence of the standard timestamps to obtain sorted customer interaction records; sequentially connecting the sorted customer interaction records to form a behavior event list arranged in chronological order, and determining the behavior event list as a user behavior sequence.The application realizes unified collection and standardization of global behavior data of multi-service systems, and improves the accuracy of user behavior mining.

This application discloses a method, system, terminal, and storage medium for training large language models using reinforcement learning, relating to the field of large language model technology. The method includes: acquiring a training question-and-answer dataset, including training questions, standard answers, and answer formats; obtaining the backbone answer sequence of the training questions using the large language model to be trained and using it as the answer sequence to be processed; performing branch processing on the answer sequence to be processed to obtain branch answer sequences; identifying high-entropy points during branch processing and generating the next layer's answer sequence to be processed; backtracking and concatenating the branch answer sequences to obtain the concatenated answer sequence corresponding to the backbone answer sequence; calculating the normalized reward function value of the concatenated answer sequence; calculating the path weight of the high-entropy points; segmenting the concatenated answer sequence based on the high-entropy points; determining the dominance function value of the sequence segment based on the path weight of the high-entropy points and the normalized reward function value to obtain the dominance function value of the concatenated answer sequence; and updating the gradient of the large language model. This can improve the model training effect.

This invention relates to a method and system for identifying the operating state of a pole-mounted circuit breaker based on action time sequence segmentation and transfer chaotic features. It achieves high-precision time sequence segmentation by synchronously acquiring triaxial Hall signals and vibration signals, and improves adaptability to noise environments by combining transfer learning to optimize phase space reconstruction parameters. It also employs a stage-sensitive attention mechanism to fuse multi-source features, solving the technical problems of large time sequence errors, poor feature robustness, and high computational requirements of traditional methods. It has the advantages of improving segmentation accuracy, enhancing feature robustness in noisy environments to improve accuracy, and reducing computational requirements to enable real-time diagnosis of embedded devices.