33 results about "Sequence segmentation" patented technology

The invention provides a self-adaptive control method for improving new energy power generation grid-connected stability of a micro-grid, and the method comprises the steps: carrying out the description according to a corrected phase change trend, fusing the real-time feedback data of voltage deviation, generating a reactive compensation instruction sequence segmentation adjustment scheme, and determining a preliminary compensation capacity dynamic distribution strategy; the microgrid system is regulated and controlled in real time through the optimized control instruction sequence in combination with the power grid stability constraint condition, the actual execution effect of the compensation action is obtained, and whether the system voltage deviation meets the stability requirement or not is judged; according to the stability requirement judgment result of the system voltage deviation, the execution effect is fed back to a micro-grid control layer, a closed-loop mechanism is adopted to carry out iterative updating on the instruction sequence segmentation adjustment scheme, and a final reactive compensation regulation and control scheme is determined.

The application discloses a kind of multi-group one-dimensional frequency energy density spectrum ratio measurement precision evaluation method and system, belong to marine observation technical field, comprising: the training data sample of establishing including M group synchronous observation sea wave one-dimensional frequency energy density spectrum sequence;21 similarity evaluation value sequences are constructed;21 similarity evaluation value sequences are sorted according to the order of evaluation value from big to small, and at least two dimensions in sequence segmented average, overall average, fluctuation amplitude, fluctuation rate, distribution concentration and dispersion degree are analyzed, to determine the comprehensive weight coefficient of 21 similarity evaluation values;The one-dimensional frequency energy density spectrum sequence of two synchronous observations to be evaluated is obtained, and the corresponding 21 similarity evaluation values thereof are calculated;The final similarity evaluation value NRD is calculated, to evaluate the ratio measurement precision of two to be evaluated sequences.The application constructs a multidimensional evaluation system, can comprehensively reflect the similarity of two sea wave spectrum sequences.

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.

The invention discloses a bit sequence preprocessing method and an OOK symbol generation method and device, and belongs to the technical field of Internet of Things. The method comprises the following steps: acquiring a first bit sequence of which the length is a first number L; under the condition that the first number L is not the integral multiple of the second number M, processing the first bit sequence into a second bit sequence of which the length is a third number L '; wherein the second bit sequence is used for being divided into at least one sequence segment, the length of which is a second number M, for OOK modulation, and M OOK symbols corresponding to each sequence segment are obtained.

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.

The invention relates to a parameter reduction meta learning load prediction method based on sequence segmentation and a bidirectional GRU network. According to the method, knowledge migration from source load data to a target prediction task is realized based on meta-learning, and the prediction effect under the conditions of small samples and distribution drift is improved. Firstly, a task data division method based on decomposition characteristic time sequence distribution similarity quantization DP is provided, it is ensured that each task has unique time sequence characteristics, and the meta-learning generalization ability is improved. Secondly, feature gradient parameter reduction is introduced into inner layer circulation of meta-learning, and the memory overfitting problem is relieved; besides, a bidirectional GRU network is introduced as a basic model, the long-term and short-term dependency capture capability of the sequence is enhanced, the network structure is kept simple, model parameters can quickly adapt to a target task under limited training data, and good prediction performance is achieved under the conditions of small samples and distribution drift.

This application relates to the field of generator fault identification technology, and discloses a method and apparatus for early fault identification of steam turbine generators based on vibration. The method includes: acquiring training samples; extracting multiple continuous vibration signal segments from each vibration signal sequence sample, constructing grayscale images based on each continuous vibration signal segment, and generating a grayscale image set; training an initial convolutional neural network classifier using an unsupervised sequence segmentation method on each grayscale image set to obtain a target convolutional neural network classifier. The unsupervised sequence segmentation method generates pseudo-labels by automatically determining the optimal early fault occurrence time for each grayscale image set, and uses the pseudo-labels to train the initial convolutional neural network classifier; acquiring the vibration signal to be identified in real time, dynamically constructing the vibration signal segment to be identified based on the acquired vibration signal, constructing the grayscale image to be identified based on it, and inputting the grayscale image to be identified into the target convolutional neural network classifier to determine whether a fault exists.

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.

The invention relates to the technical field of generator fault identification, and discloses a vibration-based turbonator early fault identification method and device, and the method comprises the steps: obtaining a training sample; intercepting a plurality of continuous vibration signal segments from each vibration signal sequence sample, and constructing a grayscale image according to each continuous vibration signal segment to generate a grayscale image set; and training the initial convolutional neural network classifier by using each grayscale image set and adopting an unsupervised sequence segmentation method to obtain a target convolutional neural network classifier, and generating a pseudo-tag by automatically determining the optimal early fault occurrence time for each grayscale image set according to the unsupervised sequence segmentation method. Training an initial convolutional neural network classifier by using the pseudo labels; the method comprises the steps of obtaining a to-be-recognized vibration signal in real time, dynamically constructing a to-be-recognized vibration signal segment based on the obtained vibration signal, constructing a to-be-recognized grayscale image according to the to-be-recognized vibration signal segment, inputting the to-be-recognized grayscale image into a target convolutional neural network classifier, and determining whether a fault exists or not.

The invention relates to the technical field of ground surface settlement monitoring, and discloses a ground surface real-time monitoring system based on a shield construction tunnel. A data acquisition unit of the system obtains a displacement measurement value from an earth surface sensor and generates time sequence data; the sequence segmentation unit analyzes mutation points in the sequence and segments the sequence into a plurality of data segments according to the mutation points; an exception marking unit performs statistical test on each data segment to identify an exception data segment set; the trend evaluation unit performs trend line fitting on the abnormal set and evaluates the fluctuation degree of fitting deviation; the matrix conversion unit organizes the time sequence data into a specific matrix structure and performs matrix decomposition; a feature derivation unit derives feature parameters from the decomposition result; and the monitoring control unit adaptively adjusts a monitoring strategy according to the characteristic parameters and generates a settlement report. According to the system, accurate and self-adaptive monitoring and early warning of ground surface settlement are realized through multi-stage data processing and intelligent analysis.

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.

The application discloses a kind of multi-source data's reliable alignment method, device, equipment, storage medium and product.Identify the discontinuous fragment of the measurement data sequence of each sensing device and associate sensing device identification, obtain measurement data variation section set;Determine the data channel of each associated sensing device based on sensing device identification and determine overlapping path section through network diagnosis, generate path network atlas;The target measurement data sequence that flows through each overlapping path section is segmented and processed to obtain target data segment, and different target data segments that flow through each overlapping path section at the same time are subjected to data flow consistency analysis to determine an interference channel list;Each target data segment in the target measurement data sequence via the interference channel is subjected to validity determination, and the effective data segment and the measurement data sequence of the non-interference channel are aligned and combined according to time to obtain multi-source reliable alignment data.It ensures data quality, avoids distortion information mixed into fusion results, and improves the accuracy and reliability of downstream decision-making.

This application relates to the field of image processing technology, specifically to a high-voltage power supply DNA sequencing visual inspection method and system. The method includes: acquiring a four-channel DNA electrophoresis image using Sanger sequencing technology; acquiring contours in the DNA electrophoresis grayscale image; constructing a DNA local density confidence coefficient and local DNA density parameters for each pixel based on the grayscale values ​​of each pixel within a channel and its neighboring pixels; acquiring the density run matrix for each pixel; constructing a DNA density distribution heterogeneity coefficient and channel tailing effect evaluation parameters for each pixel, and based on these, constructing a sequencing segmentation probability coefficient for each pixel; acquiring a decision threshold for each pixel; using the center of each contour as a seed point, and acquiring each DNA fragment based on an adaptive threshold using a region growing method, thereby obtaining the DNA sequence. This application can improve the accuracy of detecting the base sequence of a DNA sequence.

The application provides an ultra-long sequence processing method based on an improved Transform model, and the method is implemented based on a WSformer model which is constructed by improving the Transform model, and specifically comprises the following steps: S1, pre-training of the WSformer model; S1.1, an ultra-long sequence is divided into small sequences by using a sequence segmentation method; S1.2, feature extraction based on a double-layer attention mechanism; word-level feature extraction and sentence-level feature extraction are performed by using the double-layer attention mechanism, and feature encoding of the entire sequence is obtained; S2, improved position vector coding; in the coding process of step S1.2, the position coding of the word is adjusted by using a trigonometric function, and the coding of the position vector is realized. The method can effectively reduce the time cost of long text sequence coding, more accurately depict the position relationship between words, realize effective modeling of long sequence texts, and improve the prediction accuracy and the calculation efficiency.

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 invention discloses an archive data structured extraction method based on machine learning, and the method comprises the following steps: carrying out the normalization processing, noise suppression and sequence segmentation of an archive text, and obtaining a processable text sequence; the method comprises the following steps: generating a plurality of candidate field fragments in a text sequence, extracting multi-level features for each fragment, carrying out joint evaluation on boundary combination and feature representation of the field fragments by introducing a minimum description length criterion, determining an optimal fragment division result, generating compressed feature representation, and constructing sequence input by taking the field fragments as an integral unit. And utilizing a semi-Markov conditional random field model to carry out overall modeling and joint decoding on the variable-length field to obtain a corresponding relationship between the field tag and the field fragment, and finally generating a structured archive data result. According to the method, the dependence on manual rules can be reduced, and the accuracy and adaptability of structured extraction of the archive data are improved.

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.

The invention provides a natural language processing method based on a self-adaptive double-memory hybrid architecture, which comprises the following steps of: embedding conversion: acquiring an input text lexical element sequence, converting the input text lexical element sequence into embedding representation through an embedding layer, and providing input for a self-adaptive double-memory hybrid architecture model; complexity evaluation: based on the embedded representation, a sequence complexity evaluator of a dynamic memory layering mechanism is adopted; memory allocation; sequence segmentation; performing short-term processing; middle-stage treatment; long-term treatment; bidirectional fusion; performing forward optimization; outputting a task; precise modeling is kept in a key short-term window, an efficient mechanism is adopted in a medium-and-long-term window, optimal balance of efficiency and performance is achieved, a three-layer memory system is adopted, active information is captured in a short term, semantic association is maintained in a medium term, historical knowledge is compressed for a long term, multi-scale memory organic unification, two-way feedback and self-adaptive fusion are achieved, and the method is suitable for large-scale application. Different mechanisms are complementary to each other and are dynamically coordinated, and optimal configuration is automatically achieved in different task scenes.

The invention relates to the technical field of natural language processing, and discloses a recruitment information and resume matching method, which comprises the following steps: firstly, converting a position description text and a resume text into vector sequences, respectively inputting the vector sequences into a recurrent neural network for encoding, aggregating position side codes into position vectors, and extracting a hidden state sequence from a resume side; segmenting the hidden state sequence according to window parameters, and obtaining collapse measurement in each segment through state derivative calculation and matrix operation; generating a time weight according to collapse measurement and adjustment parameters, and performing weighted aggregation on the hidden state sequence to obtain a resume vector; and finally, calculating the similarity between the resume vector and the position vector to obtain a matching score, comparing the matching score with a threshold parameter, and outputting a resume matching or resume mismatching result to realize inhibition and high-precision semantic matching of the resistant resume.

The invention relates to the technical field of image processing, in particular to a high-voltage power supply DNA sequencing visual detection method and system.The method comprises the steps that a Sanger sequencing technology is adopted to obtain DNA electrophoretograms of four channels; obtaining each contour in the DNA electrophoresis grayscale image; constructing a DNA local density confidence coefficient and a local DNA density parameter of each pixel point according to the gray values of each pixel point and a neighborhood pixel point in the channel; acquiring a density run-length matrix of each pixel point; constructing a DNA density distribution heterogeneity coefficient and a channel trailing effect evaluation parameter of each pixel point, and constructing a sequencing segmentation probability coefficient of each pixel point based on the DNA density distribution heterogeneity coefficient and the channel trailing effect evaluation parameter; obtaining a decision threshold value of each pixel point; and taking the center of each contour as a seed point, obtaining each DNA fragment according to a region growing method based on an adaptive threshold value, and further obtaining a DNA sequence. According to the invention, the accuracy of detecting the base sequence of the DNA sequence can be improved.

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.

The invention relates to a camera track generation method, which comprises the following steps: S1, condition input preprocessing is carried out during reasoning, and the condition input preprocessing comprises RGB and RGBD depth map processing, audio processing and text processing; s2, constructing a diffusion model architecture based on a multi-modal condition DiT; s3, setting a time sequence alignment text attention bias mechanism for fragment perception; s4, setting a long sequence segmentation generation mechanism; s5, a multi-mode CFG coordination strategy is set; and S6, training a loss function, wherein the total loss is the weighted sum of diffusion loss, translation speed loss, acceleration loss, rotation smooth loss and rotation legality loss. The camera pose motion trail generated by the invention can be used for controlling the lens of a video generated by AI.

The invention discloses an unmanned aerial vehicle detection and classification method based on deep learning. A received unmanned aerial vehicle radio frequency signal is processed to generate sparse enhanced MVDR spectrum features; based on an improved self-attention learning feature extraction and coding module and a convolutional coding module, a SunnyitUAVCNN neural network is constructed; sequentially carrying out sequence segmentation, linear embedding and spectrum physical position coding on the sparse enhanced MVDR spectrum features, carrying out feature hierarchical extraction and enhancement through the constructed neural network, and constructing a residual structure to enhance feature representation; and finally, introducing a classification confidence weighting mechanism, performing dimension compression and probability mapping on the feature matrix processed by the neural network, calculating classification confidence, and comparing the classification confidence with a preset threshold to complete unmanned aerial vehicle detection classification. According to the invention, the problem that unmanned aerial vehicles with unknown models or signal disguise cannot be effectively identified in the prior art is solved.

The invention belongs to a prediction method, provides a multi-monitoring-point grounding current prediction method and a related device, and aims to solve the technical problem that an existing multi-monitoring-point current prediction method is insufficient in prediction precision and robustness, and the multi-monitoring-point grounding current prediction method comprises the following steps: extracting a multi-dimensional reinforcement learning state from a sample after segmentation of a multi-monitoring-point continuous current time sequence; dynamically outputting an optimal time mask rate and an optimal space mask rate, enabling the optimal time mask rate and the optimal space mask rate to act on a sample to obtain an input tensor after space-time double mask, and extracting space-time representation from the input tensor after space-time double mask; according to the method, the prediction precision and robustness can be remarkably improved, and reliable technical support is provided for stable prediction and fault early warning of the multi-monitoring-point grounding current.

The invention provides a data analysis method and system based on user behavior sequence segmentation modeling, and relates to the technical field of data processing, and the method comprises the steps: obtaining 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, and dividing the data with the same unified customer identifier into the same user data group; obtaining a standard timestamp of each customer interaction record in the user data set, and sorting the customer interaction records in the user data set according to the sequence of the standard timestamps to obtain sorted customer interaction records; and connecting the sorted customer interaction records in sequence to form a behavior event list arranged according to a time sequence, and determining the behavior event list as a user behavior sequence. According to the method, unified collection standardization of the global behavior data of the multi-service system is realized, and the accuracy of user behavior mining is improved.

The invention discloses a license plate recognition method and system based on feature fusion segmentation and splicing, and relates to the technical field of image recognition. The method comprises the following steps: constructing a license plate recognition network which comprises a feature extraction module, a feature fusion module, a feature segmentation and splicing module and a recognition sequence output module; preprocessing the collected license plate image to obtain a standard image; performing multi-level feature extraction through a feature extraction module to obtain a multi-feature sequence; a feature fusion module performs splicing and fusion in a channel dimension to generate a fusion feature map; performing sequence segmentation and splicing through a feature segmentation and splicing module to obtain a recombined feature map; and inputting the recombined feature map into a recognition sequence output module, and outputting a license plate recognition result. The technical problem that the recognition accuracy and generalization performance are insufficient due to the fact that the license plate recognition technology cannot effectively capture the correlation features between the uplink characters and the downlink characters when facing double-layer license plates is solved, and the technical effect of improving the accuracy and robustness of license plate recognition through multi-level feature extraction and intelligent feature fusion is achieved.

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.