162 results about "State sequence" patented technology

The invention relates to the technical field of exception prediction, in particular to a storage cabinet exception trend prediction system based on time series data analysis, which comprises a state monitoring module, an interval sensing module, a path reconstruction module, a symptom activation module and an evolution prediction module. According to the method, the state vectors including the temperature, the voltage, the current and the door lock state are constructed and combined with the timestamp information to form the time sequence data sequence, and the dynamic expression mode of state change is established; a jump characteristic is analyzed by using a ratio of a time interval to a state change amplitude, a short-time disturbance path and a trend evolution path are distinguished by combining a jump rate statistical index, and an evolution activation signal is identified based on trend maintenance and non-fallback characteristics. On the basis, a neural network structure with long-time dependent learning ability is introduced to capture an aperiodic thermal anomaly trend in a state sequence, and the accuracy and timeliness of anomaly recognition are improved through multi-dimensional parameter cooperative processing and path construction logic.

The invention provides a material detection operation behavior identification method based on multi-modal perception, and belongs to the field of artificial intelligence and electric power operation and maintenance detection, and the method comprises the steps: carrying out the feature extraction of the preprocessed visual, motion and voice modal data, and obtaining visual, motion and voice modal features; the visual, action and voice modal features are input into the trained fine-grained behavior segmentation network, the multi-modal input layer is used for carrying out weighted fusion on the visual modal features, the action modal features and the voice modal features to obtain multi-modal global feature representation, and the time sequence modeling layer is used for carrying out frame-by-frame analysis and labeling on the multi-modal global feature representation to obtain the multi-modal behavior segmentation network. The hidden state sequence is output, and the classification output layer is used for classifying the hidden state sequence and recognizing behavior categories; matching and aligning the behavior type with a preset standard operation instruction, and judging whether the operation behavior is abnormal or not; the invention further provides an identification system. And accurate identification and compliance evaluation of the operation process are realized.

The invention discloses a ship network abnormal behavior detection system based on multi-protocol deep analysis, and belongs to the technical field of ship network security. According to the ship network abnormal behavior detection system integrating multi-protocol semantic analysis, communication chain modeling, state awareness and behavior scoring, a unified semantic intermediate expression structure is constructed, fields of various heterogeneous protocols are abstracted into standard semantic units, behavior portraits are established in combination with a communication topological graph and an equipment state sequence, and the behavior portraits are subjected to state awareness and behavior scoring. And multi-dimensional anomaly recognition and hierarchical response are realized based on a weighted scoring mechanism, so that the blank in the aspects of cross-protocol fusion recognition and behavior modeling closed-loop detection in the prior art is filled, and the security risk recognition requirements of complex data and frequent switching in a ship scene are met.

The invention discloses a short video content intelligent generation method based on deep learning, and the method comprises the following steps: obtaining short video data and candidate material frame data, and organizing and generating a candidate material frame sequence; inputting the candidate material frame sequence into an improved Slot-VAE model to generate an object-level potential slot representation set; inputting the short video data into a shot-level semantic coding network, and constructing a double-path latent variable structure; establishing an object-level slot evolution module for time sequence modeling; executing joint training, and updating parameters of the model and the network; inputting the target short video data into a shot-level semantic coding network, and generating a target shot-level semantic slot set and a target object-level slot time sequence potential state sequence; and generating a target short video frame sequence in the object-level decoding network and executing post-processing to generate a short video content intelligent generation result. According to the invention, the lens semantic consistency and the object time sequence continuity are improved.

The invention relates to the technical field of encrypted communication, and discloses an intelligent analysis method and system based on multi-source data, and the method comprises the steps: obtaining multi-dimensional metadata of an encrypted communication flow, and generating a feature vector set; calculating a state transition probability of a communication session by using a Markov chain model, dividing the communication session according to a time window and mapping the communication session to a discrete state, and generating a session state evolution sequence; analyzing a session state evolution sequence based on a hierarchical topic model, mapping the state sequence to a behavior topic through potential Dirichlet allocation, mapping the behavior topic to an intention category through a hierarchical Dirichlet process, and outputting a communication intention category and a confidence score; according to the method, the limitation that static feature analysis cannot reflect the complete life cycle of the session is overcome, the problem of semantic gaps caused by limited metadata information dimensions is solved, and the accuracy and robustness of intention recognition are improved.

A system uses a machine learning based model such as a neural network for transcribing audio inputs. The system receives a set of audio inputs representing utterances of a conversation. For each conversation, the system determines a dialogue state for each utterance. The system uses a hierarchical language model for transcribing audio inputs of an online conversation using the received conversations. The hierarchical language model includes a top-level language model and a plurality of lower-level language model. The training is performed by (1) training the top-level language model using sequences of corresponding dialogue state, each sequence of dialogue states for a conversation, and (2) for each dialogue state, training a lower-level language model using utterances having that dialogue state. The system executes the hierarchical language model to transcribe audio input of new conversations.

The invention relates to the technical field of learning education, in particular to an AR-based personalized learning education auxiliary method and system. By collecting and synchronously processing multi-modal behavior data such as eye movement, gestures and head orientation, time sequence characteristics capable of accurately reflecting the learning state of a user are constructed, and then a hidden cognitive state sequence is decoded by using a hidden Markov model and inflection points of the hidden cognitive state sequence are recognized; finally, dynamic and automatic adjustment of learning contents is realized by means of a reinforcement learning model, deep cognitive state changes can be captured from continuous and dynamic user behaviors, accurate teaching intervention is timely performed at'inflection points' of key transition of cognitive states, personalized adaptive learning path planning is realized, and the learning efficiency is improved. The pertinence and effectiveness of learning are obviously improved; the technical problem that an existing AR learning system is difficult to intervene in time at an inflection point where a user cognition state is changed during path planning, so that real personalized learning path planning is realized is solved.

The invention provides a multi-modal dialogue emotion recognition method based on emotion memory enhancement, and relates to the technical field of emotion recognition, and the method comprises the steps: extracting the multi-modal features of a dialogue; respectively embedding speaker representation embedding vectors into the multi-modal features of the dialogue to obtain multi-modal sequence features; performing time sequence modeling on the multi-modal sequence features by adopting an extended long and short-term memory network to obtain a multi-modal hidden state sequence; inputting the multi-modal hidden state sequence into a preset memory module to obtain a multi-modal memory sequence; performing cross-modal alignment and fusion on the multi-modal memory sequence to obtain a cross-modal fusion feature sequence; and performing time sequence modeling on the cross-modal fusion feature sequence, and mapping an output sequence after secondary time sequence modeling to obtain a final sentiment classification result of the dialogue. The accuracy of emotion recognition in the prior art is effectively improved.

The invention relates to the field of man-machine interaction, and discloses a man-machine interaction method and system based on a large language model, and the method comprises the steps: extracting original semantic features from the current input of a user; obtaining a context representation of the current dialogue; extracting a historical emotional state sequence from the multi-round dialogue historical record; generating a current user emotion representation vector based on the context representation of the current dialogue and the historical emotion state sequence; constructing a hierarchical memory structure based on the current user emotion representation vector and the context representation of the current dialogue; adjusting the attention degree of the historical dialogue content stored in the semantic memory component according to the hierarchical memory structure; and generating a response sequence through a large language model based on the adjusted attention degree, the hierarchical memory structure and the context representation of the current conversation, and outputting the response sequence to the user. According to the technical scheme, the problems of stiff switching and dialogue breakage during emotion turning of a traditional dialogue system are solved.

The invention discloses a threat entity recognition and intelligence processing method based on BERT assistance, and the method comprises the following steps: S1, collecting and preprocessing threat intelligence text data, and constructing an input corpus data set; s2, inputting to a BERT encoder, extracting context semantic vectors, and obtaining a semantic feature vector sequence; s3, inputting the semantic feature vector sequence into a gating recursion unit, modeling time sequence features, and outputting a hidden state sequence; s4, based on the hidden state sequence, performing threat entity label labeling through a decoding layer; s5, adopting a dung beetle optimization algorithm to optimize BERT and gating recursive unit parameters; and S6, performing reasoning prediction by using the optimal parameter, and outputting and storing structured threat entity information. According to the method, the BERT encoder, the gating recursion unit and the improved dung beetle optimization algorithm are fused, so that high-precision recognition and structured extraction of the threat entities in the threat intelligence text are realized.

This application discloses an intelligent method and apparatus for detecting active speech with sentence segmentation based on multi-state temporal modeling. The method includes: receiving audio signals from at least one channel; extracting acoustic feature sequences from the audio signals using a target speech recognition model corresponding to the number of channels; determining the probability distribution of each speech frame corresponding to the acoustic feature sequences belonging to different speech activity states, obtaining a state sequence corresponding to each channel, wherein the speech activity state includes at least one of the following: initial silence state, speech state, intra-turn pause silence state, and inter-turn sentence segmentation silence state; and determining the time of sentence segmentation in the audio signal based on the state sequence. This application solves the technical problem of erroneous sentence segmentation in speech activity detection based on a fixed silence threshold in related technologies.

The invention relates to an IEC61499 intelligent model segmentation training method based on DCU state enhancement, and belongs to the technical field of computer distribution. The method comprises the steps of constructing an intelligent model state enhancement module, transplanting an intelligent model to a DCU accelerator card, adjusting the number of attention heads through state vector complexity, carrying out parallel calculation, fusing and optimizing a multi-head attention matrix, sampling an optimal historical state based on Euclidean distance, and obtaining an enhanced state sequence in combination with a current state. Providing optimization state representation for segmentation learning; taking the enhanced state sequence as initial input, constructing and training a reinforcement learning agent, and enabling the reinforcement learning agent to learn and recognize an optimal segmentation strategy of the intelligent model; and deploying the trained intelligent agent, and performing intelligent splitting on the intelligent model according to the calculation load and the communication cost between the acceleration cards to realize distributed parallel training. The invention aims to solve the technical problems of unbalanced resource allocation, low model segmentation efficiency and redundant fixed attention mechanism calculation in a dynamic environment in the prior art.

The invention relates to the technical field of hoisting control, in particular to a hoisting control system and method for modularized fast-installed electrical equipment, which forcibly limits the sequence relation of the whole clamping and propelling process through a finite-state machine, so that the propelling direction, segmented propelling, syn-position recording and propelling and maintaining switching of a clamping component are all controlled by state constraint; through Markov decision, subsequent state transfer is associated only according to current state information in a holding stage, so that reverse displacement change in the holding stage is not judged by a single-point threshold value any more, but is continuously associated in a state sequence form, the conditions of syn-position jumping, advanced holding and repeated advancing in clamping action are avoided, and the clamping efficiency is improved. The development trend of reverse displacement is described through inter-state transfer records, so that formation of a keeping interval is established on the basis of continuous state consistency, continuous transfer association based on the current state is formed in the keeping stage, and the clamping process has controllability in time dimension and space dimension at the same time.

The invention relates to a rotating position coding design method suitable for large-model long context understanding, which comprises the following steps of: performing linear mapping on a hidden state sequence in a large-language model to obtain a query vector sequence, a key vector sequence and a value vector sequence, and copying one query vector sequence, wherein a self-attention layer comprising multiple attention heads is arranged in the large language model; performing traditional rotation position coding on the query vector sequence and the key vector sequence to obtain a query vector sequence and a key vector sequence with position codes; based on the copied query vector sequence, rotation is carried out, then traditional rotation position coding is carried out, and a query vector sequence of position coding after rotation is obtained; and based on the query vector sequence of the position code after rotation, the query vector sequence with the position code, the key vector sequence and the value vector sequence, carrying out self-attention operation to obtain an output result of a self-attention layer, and completing the design process. The method has a good effect in both long and short tasks.

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

The invention provides a classroom behavior analysis method based on posture and emotion recognition and related equipment, and relates to the field of artificial intelligence, and the method comprises the steps: synchronously collecting an image sequence and an audio stream through terminal equipment disposed in a classroom, and carrying out the feature extraction; obtaining a time sequence feature vector comprising a human skeleton key point sequence and an emotional state sequence; if it is detected that the time sequence feature vector meets a preset event triggering rule, marking a time point meeting any event triggering rule to obtain a triggering signal; intercepting one or more behavior fragments corresponding to the trigger signal to generate a corresponding structured behavior tag; and outputting the structured behavior tag to a teaching analysis platform for statistics, visually displaying and generating a teaching analysis report. By implementing the method, the data processing and analysis efficiency can be improved on the premise of not sacrificing the key behavior analysis depth and accuracy.

The invention belongs to the technical field of data processing, and provides a BIM data intelligent matching and conflict detection method, and the method comprises the steps: generating a fusion feature of a component through fusing a geometric topology feature and a semantic constraint feature; through alignment and coupling of the installation state sequence and the compliance state sequence, a space-time state sequence of the component is formed, so that the construction constraint graph can fit the actual dynamic change of construction; the method comprises the following steps: injecting a space-time state sequence into entity nodes by taking components as the entity nodes and a design specification and a construction process as constraint nodes, constructing a construction constraint graph, and predicting a conflict propagation path between the components through a time sequence graph neural network; the prediction result of the conflict propagation path is subjected to actual construction verification, the verified prediction result is subjected to influence tracing and grading, and the comparison data of the actual construction result and the prediction result is combined to adjust the parameters of the time sequence diagram neural network and the edge weight of the construction constraint diagram, so that the prediction accuracy of the conflict propagation path is further improved.

The invention discloses a power distribution equipment state detection method and device based on multi-modal data fusion, terminal equipment and a storage medium, and belongs to the field of power distribution equipment. Performing feature analysis on the acquired multi-modal data by using a time sequence convolutional network, a residual neural network and a Transform network to obtain a local abnormal fluctuation feature, an infrared temperature spatial distribution feature and a video global feature representation; performing feature splicing on the local abnormal fluctuation feature, the infrared temperature spatial distribution feature and the video global feature representation to obtain a fusion feature, and inputting the fusion feature to a long-short term memory network to obtain a historical hidden state sequence; and determining the equipment state of the to-be-detected power distribution equipment according to the historical hidden state sequence, the fusion features and edge equipment deployed in the local network domain of the equipment. By implementing the application, the problem of low accuracy of state analysis of the power distribution equipment caused by only depending on single modal data in the prior art can be solved.

The embodiment of the invention provides a robot driving control method and device, electronic equipment and a storage medium, and belongs to the technical field of nonlinear system control. The method comprises the following steps: constructing a state prediction model according to a first observation state sequence, a second observation state sequence and a historical control input sequence, and calling the state prediction model to perform state error prediction based on the first observation state sequence, the second observation state sequence and the historical control input sequence, performing parameter adjustment on the state prediction model according to the predicted observation state error sequence and a preset dimension reduced-order matrix to obtain an error compensation state space model; based on the first given state data and the first control input data of the current time, an error compensation state space model is called to conduct state prediction, second given state data corresponding to the to-be-predicted time is obtained, and the target robot is driven and controlled according to the second given state data. According to the embodiment of the invention, the driving control accuracy of the robot can be improved.

The invention belongs to the technical field of large language models, and particularly discloses a large language model illusion detection method and system, a terminal and a medium. Comprising the steps of obtaining a middle hidden state of each token in a process of generating an answer by a large language model, and organizing and forming a hidden state sequence according to a generation sequence; performing time sequence preprocessing on the hidden state sequence to complete the preprocessing of the sequence; inputting the pre-processed hidden state sequence and a dynamic feature formed by a difference vector, a trend drift degree or local fluctuation intensity into a dynamic sequence modeling network, and enabling the network to extract a time dependence feature reflecting an internal state change mode based on an evolution relation of a hidden state along with a generation process; and generating an illusion risk score according to the comprehensive time sequence characteristics output by the dynamic sequence modeling network, and determining that the answer contains the illusion content when the score exceeds a preset threshold. The reliability of the output content of the large language model can be improved.

This invention discloses a spoken language understanding method based on multi-view expert fusion and interest lexical selection, belonging to the field of natural language understanding and semantic parsing technology. The method includes: extracting hidden state sequences from the input utterance using a shared encoder; constructing a multi-view expert fusion module containing utterance view experts, block view experts, and lexical view experts to generate multi-granularity expert features; generating intent aggregation features and slot aggregation features through a task decoupling gating mechanism; performing interest lexical selection on the intent aggregation features, calculating lexical importance scores, generating weighted intent representations, and predicting multi-intent labels; and inputting the slot aggregation features into a decoder with diagonal mask constraints to generate a position-aligned slot label sequence. This invention enhances the model's dynamic focusing ability on key intent signals and can be applied to intelligent dialogue systems, virtual assistants, and vertical domain semantic parsing scenarios.

The invention discloses an active intelligent dialogue pushing method and system based on user state perception, and belongs to the technical field of computers. The method comprises the steps of obtaining a trigger event; extracting multi-dimensional state information of the user according to the trigger event and the historical interaction data to obtain a structured snapshot; generating an action sequence according to the plurality of structured snapshots; searching according to the action sequence to obtain candidate contents; and screening high-score contents from the candidate contents for pushing. According to the user behavior analysis and active dialogue decision-making mechanism based on the time sequence state snapshots, the state timeline is formed by continuously generating the user state snapshots containing multi-dimensional information (demands, emotions and trends), the active dialogue opportunities are intelligently recognized based on the change mode of the state sequence, the information is planned, and the action sequence is obtained. And high-quality personalized dialogue content is generated.

The application provides a large model-based automatic text generation and content creation method and system, wherein the method comprises: obtaining material text data in a multi-turn dialogue scene, creation theme information, creation history state and current environment data; jointly processing the material text data and the creation theme information, and constructing text structure information according to a joint result; determining a core description element based on the text structure information; adopting a memory-enhanced attention mechanism to fuse the environment data, the creation history state and the core description element, and generating a hidden state sequence; and generating an automatic text conforming to the current dialogue scene based on the hidden state sequence. The application improves the context coherence and theme fitting degree of an intelligent dialogue system in a complex environment.

The invention relates to the technical field of operator overflow control, in particular to an AI processor operator overflow optimization method and system.The method comprises the steps that a finite automaton is introduced into numerical values for state combination and sorting, a continuous state sequence covering an operator execution stack is generated, and an intermediate result obtains a state number carrying bit width section information and stack position information at the same time; the method comprises the following steps of: setting a section to which an intermediate value of each operator belongs to a discrete identifier in a fixed-point space, executing weighted truncation reduction transformation by introducing a graph neural network, and deducing a clipping weight under topological connection constraints among the operators, so that the intermediate value of each operator obtains differential correction strength in a longitudinal accumulation direction and a transverse link direction at the same time; an operator correction set is generated by guiding an intermediate numerical value through a clipping weight, so that the effective dynamic range utilization rate after right shift and truncation are matched and reduced is improved, and the stability and repeatability of a reasoning process in a long-chain accumulation scene and the tracking capability of an abnormal calculation behavior are enhanced.

The invention provides a task-based dialogue data generation method and device, a medium and electronic equipment, and the method comprises the steps: obtaining a state transition matrix which represents the probability of transferring from any state node of a task-based dialogue to another state node; performing sampling processing on the state transition matrix to obtain a state sequence of a plurality of sampling paths; processing each state sequence by using a large language model and corresponding cue words to generate a plurality of dialogue contents, and scoring each dialogue content to obtain a plurality of dialogue content scores, the dialogue contents being in one-to-one correspondence with the dialogue content scores; and performing multiple iterations on the state transition matrix based on the dialogue content score, and generating task-type dialogue data by adopting the iterated state transition matrix meeting the iteration stop condition, thereby solving the problem of insufficient training caused by lack of high-quality data of the task-type dialogue system.

The invention provides a disease progress probability prediction method and device based on a Monte Carlo-Markov chain, and relates to the technical field of disease progress prediction. The method comprises the following steps: acquiring multi-modal historical follow-up visit data of a patient to be tested, and constructing a state space with transfer constraint; extracting and checking time series data according to the state space; performing Monte Carlo sampling on the time sequence data, establishing a non-homogeneous transfer model and forming an individualized non-homogeneous transfer kernel set; completing path interpolation and probability filling of missing fragments based on the transfer kernel set to obtain continuous complete state sequence data; a Markov chain Monte Carlo algorithm is used for parameter updating, an individualized transfer model subjected to posteriori updating is obtained, a calibration probability result is obtained, and dynamic prediction and uncertainty evaluation of individualized disease progression are achieved. According to the method, the problems of inaccurate state determination, single transfer modeling, incomplete observation and insufficient prediction calibration in the prior art are solved.

The invention discloses a robot teleoperation control method based on non-delay data training in a delay environment, and the method comprises the following steps: S1, collecting an offline data set obtained when a robot executes a task in a non-delay environment, the offline data set comprising a plurality of tracks, and each track comprising a state sequence, an action sequence and a reward sequence; s2, reconstructing a state sequence and an action sequence in the offline data set to generate an information state sequence suitable for a delay environment; s3, performing state estimation on the information state sequence to obtain a prediction state sequence; s4, based on the information state sequence and the prediction state sequence, an offline reinforcement learning algorithm is adopted to train a strategy model, and the strategy model is used for outputting an action instruction for controlling the robot based on the prediction state sequence in a delay environment; and S5, controlling the robot to execute a teleoperation task by adopting the trained strategy model in a delayed environment. According to the invention, safe, efficient and reliable control of robot teleoperation in a delayed environment is realized.

The application discloses an interface fault early warning method based on state sequence statistical characteristics and a server, and relates to the field of operation and maintenance.The method comprises the following steps: collecting the physical layer, IPv4 layer and IPv6 layer states of each interface of a network device, and aligning the states in time sequence to form a multilayer state sequence. Intermittent jitter interfaces with frequent state changes and high availability are identified. Each state change is analyzed one by one, and it is judged whether each layer changes synchronously according to a preset time window, and marked as multilayer linkage or single layer independent change. The linkage change frequency and linkage synchronization rate are calculated according to a statistical unit, and two types of time sequence are generated. When the linkage synchronization rate in the observation window continuously exceeds the threshold value, the linkage change frequency sequence is linearly fitted to obtain a deterioration rate. If the deterioration rate is positive and exceeds the threshold value, the remaining available time window is calculated according to the rate, the current frequency and the unavailable frequency threshold value, and time prediction and fault early warning are output. By implementing the application, the ability to judge the continuous deterioration of the interface health condition can be improved.

The application relates to the technical field of operator overflow control, in particular to an AI processor operator overflow optimization method and system, state combination and sequencing are performed on a numerical value introduction finite automaton, a continuous state sequence covering an operator execution stack is generated, a state number carrying bit width section information and stack position information at the same time is obtained for an intermediate result, a section to which an operator intermediate value belongs is provided with discrete identification in a fixed point space, a graph neural network is introduced to perform a weighted truncation reduction transformation, a clipping weight is derived under a topological connection constraint between operators, each operator intermediate value is simultaneously provided with differentiated correction strength in a longitudinal accumulation direction and a horizontal link direction, the intermediate value is guided to generate an operator correction set through the clipping weight, the utilization rate of an effective dynamic range after right shift and truncation cooperation reduction is improved, and the stability, repeatability and tracking ability for abnormal calculation behavior of an inference process in a long chain accumulation scene are enhanced.

The invention relates to the technical field of information security, in particular to a sequential cipher security analysis method for a composite structure, which comprises the following steps of: acquiring round function parameters of a target cipher algorithm, nonlinear component definition and a key scheduling rule, and generating a standardized configuration data set; dividing the complete algorithm into a plurality of independent low wheel structures along the boundary of the nonlinear component according to the wheel function parameters, and constructing a directed graph data model containing state transition constraints to compress a state space; calling a password component vulnerability feature library to quantify a sub-structure active S box distribution abnormal value and a linear diffusion layer vulnerability path, and generating a risk weight priority ranking list; and inputting the state sequence data into the target application environment simulation platform, and generating a reproducible vulnerability report when the output deviation exceeds a security threshold. Through hierarchical compression, risk guidance and multi-environment verification, the technical defect of insufficient path coverage of a high-round-number lightweight algorithm in a resource-limited scene is solved.