267 results about "Beam search" patented technology

Certain aspects of the present disclosure provide techniques and apparatus for efficiently generating a response to a query input in a generative artificial intelligence model. An example method generally includes generating, based on an input prompt and using a first machine learning model, a set of tokens including one or more subsets of tokens. Each respective subset of the one or more subsets corresponds to a respective portion of a response to the input prompt and includes a fixed number of tokens corresponding to a beam width for a beam search through the set of tokens. The set of tokens is output to a second machine learning model for verification, and information identifying a selected sequence of tokens from the generated set of tokens is received from the second machine learning model. The selected sequence of tokens is output as the response to the input prompt.

The invention provides a personalized teaching method and system based on generative artificial intelligence. The method comprises the following steps: constructing a knowledge graph and a teaching strategy template library of a target course; constructing a feature enhancement matrix; calculating node activation intensity, generating a plurality of candidate learning paths through a beam search algorithm, and screening an optimal learning path in combination with a preset teaching strategy template; using the generative model to generate standardized teaching content adaptive to the teaching strategy; dynamically optimizing a teaching scheme through a neuroplasticity model; and carrying out teaching adjustment based on the optimized teaching scheme. Standardized teaching content is generated by utilizing the generative model, the teaching content quality is improved, the teaching content is closely matched with a learning path and a teaching strategy of a learner, meanwhile, a teaching scheme is optimized through the neuroplasticity model, teaching parameters and cognitive resource allocation are adjusted in time according to real-time feedback and cognitive states of the learner, and the teaching efficiency is improved. Limited cognitive resources are reasonably distributed to key parts of teaching contents, and the teaching effect is improved.

The invention discloses a knowledge graph multi-hop reasoning-based complex manufacturing process intelligent planning decision method, which comprises the following steps of: 1, constructing a manufacturing process knowledge graph to obtain a triple set, generating a query-path pair sequence, and mapping entities and relationships in the query-path pair sequence into low-dimensional vectors through an embedded layer; 2, performing expansion causal convolution calculation on the query-path pair sequence through a shallow feed-forward convolution network module, extracting key process features and potential correlation between processes, and generating a feature vector fusing the potential process correlation; 3, performing rotation position coding and causal mask processing on the feature vector output in the step 2 by adopting a parallel multi-query attention module, and realizing cross-process dependency relationship modeling through a multi-head attention mechanism; 4, generating a global optimal process scheme through beam search; and on the basis of a rule-guided dynamic aggregation iteration strategy, mixing a process path generated by the model with an original training set, and updating parameters.

The invention discloses a non-point source pollution emergency bypass decision control method, particularly relates to the technical field of automation and process control, and aims to solve the problems that existing scheduling cannot align multi-source measurement and evaluate credibility in minutes under a unified time base, and is lack of risk measurement integrated with a tide level phase and a moisture regain path and same-layer gating. The bypass rhythm is easy to misjudge, and the oscillation is difficult to audit. A minute-level risk density is constructed through multi-source alignment and credibility grading under a unified time reference, injected tide level phases and moisture regaining paths, bypass rhythms are constrained by same-layer gating and tide windows, and beam search, boundary arbitration and fixed window closed-loop correction are matched, so that the risk density is improved. Therefore, under the superposition of rainstorm and tide, decision making and execution are stably limited in a non-backflow red line, bearing and equipment limit, misjudgment and oscillation are remarkably reduced, the problems of unavailability and difficult auditing caused by multi-source asynchronization and moisture regain coupling are solved, intelligent decision support is provided for non-point source pollution treatment, and it is ensured that the treatment technology is accurate and efficient.

The invention belongs to a decoding method, and provides a multi-modal brain-computer interface decoding method and a related device for solving the technical problems that an existing non-intrusive brain language decoding method is insufficient in adaptability in global context and weak in generalization performance in a cross-subject scene, and multi-modal neural feature collaborative enhancement is difficult to achieve. And determining the called execution module. The execution module comprises a feature extraction module, a cross-subject standardization module, a multi-mode semantic fusion module, a language recognition module and a semantic consistency module. By obtaining the unified semantic representation and combining the beam search algorithm, the fairness and universality in different language groups are remarkably improved, multiple modes can be supported, and the brain signal decoding precision and robustness are improved. In addition, cross-subject semantic representation generalization can be realized, and individual specificity is effectively reduced.

The invention belongs to the technical field of dialogue generation, and relates to a digital intelligent carrier pigeon WeChat intelligent dialogue generation method based on deep learning. Dialogue data are obtained, and semantic anomaly detection is carried out through a regular expression and a BERT model in combination with a public corpus; organizing dialogue data in a mode of constructing a tree structure and introducing a three-dimensional timestamp; for emotion information of emoticons, text and visual features of expressions are combined through a hybrid encoder, a dynamic attention mechanism is adopted, weighting and memory length adjustment are carried out according to importance of historical statements, after user portrait features are introduced, user features and dialogue codes are fused, and therefore, the emotion information of the expressions is obtained. In the generation stage, through a Transform-based model and a Beam Search algorithm, a diversity penalty term is introduced in the generation process, and generation parameters are dynamically adjusted according to a user portrait, so that the diversity and security of generated dialogues are ensured, the precision and coherence of a dialogue generation model are improved, and the technical problems of context coherence and personalized generation are effectively solved.

The invention relates to a lyric generation method and system based on a large language model and thinking chain reinforcement learning. The method comprises the steps of designing a thinking chain path for lyric creation example data, and performing parameter adjustment on a large language model; performing multi-round reasoning on each paragraph through the adjusted large language model to obtain each thinking path; scoring each thinking path based on a process reward model; and selecting a thinking path with the highest score as a positive sample by using a Monte Carlo tree search algorithm, selecting other paths as negative samples by using a beam search algorithm, and training a lyric generation model. Model fine tuning is carried out by designing a thinking chain path, and the ability of the model to generate a lyric thinking path can be enhanced; each paragraph is subjected to multi-round reasoning through a large language model, various different thinking paths are generated, the individuation, logicality and emotional expression of lyric generation can be enhanced, and therefore a music creation text closer to actual requirements is provided.

The invention provides a method and device for determining a code patch, equipment and a storage medium, and the method comprises the steps: generating a test case for a set fragment in a to-be-tested file by executing a first large language model; running the test case for the to-be-tested file; if the error description of the test failure is obtained, inputting the error description and the problem code block into a second large language model to obtain at least one code patch for the problem code block; wherein the at least one code patch is obtained by the second large language model according to at least one code repair strategy through a cluster search mode; the at least one code patch is in one-to-one correspondence with the at least one code repair strategy; a final patch is determined from the at least one code patch and the set segment is replaced. According to the method, the test case is automatically generated through the first large language model, the patch is automatically generated for the problem code block through the second large language model, and the automation degree and efficiency of the test are improved.

The invention discloses an intelligent task planning method and device based on environment perception, equipment and a medium, and the method comprises the steps: fusing abstract environment information into a large language model, and carrying out the fine adjustment of the large language model, so as to enhance the environment perception capability of the large language model; inputting a task target and an environment state table into the fine-tuned large language model, and generating a multi-step action sequence with the highest probability through a step-by-step beam search strategy; according to the generated action sequence, the environment data state table is updated by simulating interaction between the robot agent and other objects, and therefore interaction tracks corresponding to the steps are generated. According to the method, the model is adjusted into an environment perception large language model fine tuning structure, so that higher accuracy and practicability are brought to intelligent task planning; the large language model can explore a wider search space by utilizing a step-by-step beam search strategy, so that more comprehensive search is beneficial to generating a more reasonable and effective plan.

The invention discloses a method and a system for detecting a jailbreak attack of a large language model, which are applied to discovering potential security risks of the large language model, and the method comprises the following steps: constructing a jailbreak attack-malicious query data set of the large language model; an automatic antagonism suffix generation method fusing cluster search and a gradient descent algorithm is provided; the detection system of the prison break attack large language model is constructed by training, testing and evaluating the validity of the resistant suffixes based on a prison break attack data set, and comprises a malicious query data set display unit, a detection validity verification unit, a comparison and analysis unit before and after an attack and a model defense strategy unit. The method is helpful for discovering defects of a large language model alignment mechanism, so that a more powerful large language security model is designed and constructed.

The invention discloses a document retrieval query rewriting method and system based on a large language model, and the method comprises the steps: collecting a query rewriting data set, carrying out the correlation filtering and retrieval increment, and retaining a data sample highly related to a query rewriting task; an auxiliary task data set highly related to the query rewriting task is collected, and a multi-task SFT data set is constructed; constructing a self-supervised fine tuning model based on GPT-2, taking query as input, taking query rewriting as output, and training the self-supervised fine tuning model by using a multi-task SFT data set; a self-supervised fine tuning model is regarded as an intelligent agent, and target alignment is carried out based on reinforcement learning, so that the self-supervised fine tuning model better conforms to the user intention when generating query rewriting; a plurality of candidate rewrites are generated for each query using a beam search algorithm, a self-supervised trim model of target alignment is entered, and a set of related documents is retrieved. According to the method, the retrieval relevance is improved, and the user satisfaction is improved.

The invention discloses a method for generating an image description text based on a large model, which relates to the technical field of image processing, and comprises the following steps: an image preprocessing step: dividing a target level through semantic segmentation and extracting key visual information by adopting hybrid denoising and self-adaptive normalization; a feature extraction step: fusing the multi-scale visual features and the semantic features, and generating a high-dimensional fusion feature vector through cross-modal alignment; a large model initialization and adaptation step: loading the pre-training model and performing incremental fine tuning, and dynamically adjusting the Prompt template; a text generation step: generating candidate texts through logic constraint and beam search; and a text optimization adjustment step of outputting a final description text based on multi-dimensional evaluation and user preference iterative correction. According to the method, the semantic matching degree, logic coherence and common sense accuracy of image description are improved, multi-element scenes are adapted through dynamic adaptation and iterative optimization, and high-quality text description support is provided for high-precision and diversified scenes.

The invention discloses a network attack path deduction method and device based on a large language model, equipment and a medium, and relates to the technical field of network security, and the method comprises the steps: generating each initial deduction result corresponding to a current abnormal network activity through employing a target large language model and a beam search algorithm; verifying each initial network attack deduction path by using a target knowledge graph to obtain a target confidence degree corresponding to each initial network attack deduction path; and determining a target network attack path with the highest target confidence from the initial network attack deduction paths, and determining an initial deduction result corresponding to the target network attack path as a target deduction result so as to display the target deduction result by using a time sequence animation. The initial network attack deduction path is generated by using the large language model, and the target network attack deduction path is determined based on the knowledge graph, so that the problem that complex and changeable attack techniques cannot be adapted due to rule engine stiffness is solved.

The invention provides a text error correction method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence, and the method comprises the steps: inputting a target text into a plurality of error correction components to obtain a to-be-corrected result set, and aiming at any to-be-corrected word of the to-be-corrected result set, performing error correction on the to-be-corrected word; modifying the target text by using all the error correction recommendations of any word to be corrected to generate a candidate text set, so as to screen out a reserved text from the candidate text set; and continuously carrying out error correction on another word to be subjected to error correction in each reserved text until all words to be subjected to error correction in the result set to be subjected to error correction are traversed, and determining a recommended text from the finally obtained reserved texts. According to the text error correction method based on beam search multi-model integration provided by the invention, the error correction capability can be improved to the greatest extent by integrating the advantages of a plurality of error correction components.

The invention discloses a wafer scheduling method and semiconductor process equipment. The method comprises the following steps: determining a first candidate action sequence according to an objective function value of each action sequence obtained by cluster search; determining a second candidate action sequence which reaches the process chamber fastest in all action sequences corresponding to the same new wafer; in response to the fact that the number of actions in the candidate action sequence reaches an action number threshold value, determining a target action sequence according to the target function value; in response to the fact that a first action of the target action sequence meets a preposition constraint, the first action of the target action sequence is executed in a simulation mode, and the preposition constraint is that new wafers executing different tasks are taken out from the loading and unloading positions according to the sequence of reaching the process chamber; and in response to completion of simulation scheduling of all wafers, outputting an action scheduling sequence including sequential simulation execution. According to the invention, the wafer can be processed as soon as possible. The development period is short, the maintenance cost is low, and the result is more accurate and reliable.

A computer-implemented method for optimizing a video encoder. The method includes the step of defining a plurality of CUs of the video encoder that correspond to a plurality of stages. The plurality of stages includes at least a first stage, and a second stage immediately after the first stage. The method further includes the steps of providing a decision space of encoding parameters of the video encoder, and defining, for the second stage, a subspace being a subset of the decision space. The subspace contains b1 optimal decision paths from the first stage to the second stage, wherein b1 is defined as a beam size for the first stage. The b1 optimal decision paths are the b1 decision paths that have lowest accumulated cost among all decision paths from the first stage to the second stage.

The invention discloses an optimization method and device for a natural language processing model, and relates to the technical field of information security, and the method comprises the steps: reducing the text confusion degree through employing an iterative beam search mode, and guaranteeing the text quality at the same time, i.e., carrying out the quantitative evaluation and screening of readability and usefulness in a processing process, and improving the processing efficiency. And performing model fine tuning by using the optimized and transformed mixed data to reduce the influence of harmful texts in the third-party data on the model security alignment, thereby improving the security performance and the alignment degree of the large model, and solving the problem that the security alignment of the large language model cannot be effectively coped with in the related technology, and the security alignment efficiency is improved. The technical problem that possible harmful information damage in the third-party data is difficult to fundamentally reduce is solved, and the technical effects that the safety performance and the alignment degree of the large language model are improved, it is ensured that the large language model can give safe and reliable response when facing the data safety problem, and harmful information is prevented from being provided are achieved.

A method for updating a question-answer mechanism includes obtaining, by a data system implementing the question-response mechanism, an input query, in response to the input query: generating a token sequence associated with the input query by applying a beam search algorithm on generated bound knowledge graphs, applying the input query to a large language model (LLM) of the question-response mechanism to obtain first outputs, applying the generated token sequence to a graph neural network (GNN) to obtain second outputs, applying fusion layers on the first outputs and the second outputs to generate a response associated with the input query and generated token sequence, and performing a remediation using the generated response, wherein the remediation comprises updating the LLM and the GNN based on the generated response.

The invention discloses an intelligent fast beam training method for low-altitude millimeter wave communication, and belongs to the technical field of wireless communication. The method comprises the following steps: establishing a channel and transmission model of the low-altitude millimeter wave cellular-free large-scale MIMO system; in the channel estimation stage, a pilot signal is used for estimating a channel and preliminarily positioning the position of a user; in the beam training stage, firstly, customized three-dimensional beam codebook design is carried out based on obtained positioning information; and then, an enhanced multi-agent depth Q network algorithm is adopted to efficiently carry out beam search in the designed customized codebook, so that accurate three-dimensional beam alignment is realized. According to the method, the customized codebook and the intelligent learning algorithm are combined, the beam forming process is effectively optimized, the complexity and overhead of beam training are remarkably reduced, the convergence speed is increased, the system performance and reliability of downlink communication are improved, and the method is suitable for a high-maneuverability low-altitude unmanned aerial vehicle communication scene.

The invention discloses a robot control system and method based on AI voice, and belongs to the technical field of robot intelligent control. The system comprises a local voice processing module, a dialogue management and context understanding module, an instruction mapping and generating module and a robot communication and control module. The local voice processing module is integrated with a 16 kHz sampling ASR engine, voice-to-text conversion is realized through a 6-layer LSTM network and beam search decoding, and the TTS engine adopts a WaveNet architecture to synthesize voice. A 7 billion parameter large language model is arranged in the dialogue management module, a 32-layer Transform decoder architecture is adopted, 10 rounds of dialogue history is maintained, and intention recognition and entity extraction are achieved through a multi-head self-attention mechanism. And the instruction mapping module performs trajectory planning by adopting quintic polynomial interpolation and quaternion spherical linear interpolation. According to the invention, full-link localization processing is realized, the speech recognition delay is 180 milliseconds, the position error is less than 0.5 mm, and the problems of high delay, high privacy risk and weak complex instruction understanding ability in the prior art are effectively solved.

To provide native hardware support for complex operations such as a beam search operation, an integrated circuit device can be implemented with multiple computational circuit blocks coupled in series to form a pipeline. Each computational circuit block includes an arithmetic logic unit (ALU) circuit having a first numeric input, a second numeric input, a primary result output, and a secondary output. The ALU circuit is programmable to perform an arithmetic operation on the first numeric input and the second numeric input to generate the primary result output, and provide one of the first numeric input or the second numeric input to the secondary output, which is fed back to the ALU circuit.

The invention discloses a simulation method and system for students with different cognitive levels based on a large language model. According to the method, the cognitive prototype is constructed based on the past learning records of the students, and the mastering degrees of the students on different knowledge concepts are clearly expressed through the knowledge graph. The cognitive prototype can be used for accurately predicting the performance of the student in a new task, including whether the problem can be solved or not and specific errors possibly caused. Meanwhile, a self-evaluation-self-optimization loop process based on beam search is introduced, and a student answer consistent with a prediction behavior is iteratively generated, so that student simulation which is more real, natural and consistent in cognition is realized on the premise that additional training is not needed. Related results show that the staged and cognitive prototype-driven student simulation framework provided by the invention is superior to the existing mainstream method in multiple dimensions, and behavior characteristics of students with different cognitive levels can be reproduced more truly.

The invention is suitable for the technical field of gene sequencing, and provides a nanopore sequencing signal identification method and system based on Transform, and the method comprises the following steps: carrying out normalization processing on a nanopore sequencing signal, and segmenting the nanopore sequencing signal into signal windows with fixed lengths; signal features are extracted through a Transformer encoder comprising a convolutional layer and a multi-head self-attention module; generating a prediction probability of a current base through a cross attention mechanism by adopting an autoregressive Transform decoder and combining the signal features and context information of a decoded base sequence; iteratively generating an optimal base sequence based on a beam search algorithm, and optimizing decoding efficiency in combination with key value cache; and carrying out overlapping region fusion and quality control on a segmented decoding result, and outputting a final sequence. According to the method, the precision and reliability of nanopore sequencing in complex biological sequence analysis are remarkably improved, and the recognition accuracy on multiple gene sequencing data sets is higher than that of an existing CTC-based model.

The disclosure relates to a communication technique for a 5G communication system for supporting a higher data transfer rate after a 4G system, and a system therefor. The disclosure may be applied to an intelligent service (for example, a smart home, a smart building, a smart city, a smart car or connected car, healthcare, digital education, retail business, and a service relating to security and safety), based on a 5G communication technology and an IoT-related technology. The disclosure relates to a method and device for performing beam searching in a mobile communication system using beamforming.

The invention discloses a medical text condition generation method under rule constraints, and belongs to the technical field of natural language processing and medical information. According to the method, a medical rule knowledge graph is constructed, graph neural network coding is adopted, and medical business rules are converted into constraint representation; designing a rule-perceived attention mechanism, and fusing medical rule constraints in a generation process; constructing a constraint-aware Transform decoder, and ensuring the generation normalization by adopting an improved beam search algorithm; and establishing a multi-level quality control system, and performing three-dimensional evaluation from local consistency, global consistency and rule conformity to generate a result. According to the method, the technical problem that existing medical text generation cannot meet complex rule constraints while guaranteeing fluency is solved, the rule coincidence rate reaches 95% or above, the quality score is improved by 18-25% compared with an existing method, and the method is excellent in performance in application of multiple medical departments.

A method for performing time constrained queries for event sequence visualization may include receiving an event sequence dataset including event sequences. Each event sequence includes at least two sequentially occurring events. The method further includes generating a forward index having event pattern sets that include pattern occurrence data for each event sequence positioned according to a fixed positional order. The method includes generating a time constraint search structure, such as a B−tree, B+tree, or table, that includes keys and pointer values. The method also includes inserting, into the search structure, a sequence identifier corresponding to each event sequence based on the fixed positional order and time interval data for the at least two sequentially occurring events of each event sequence. The method may also include searching the search structure to identify key values satisfying a received time interval constraint, and outputting the sequence identifiers corresponding to the identified key values.

The invention discloses a text-to-instruction system based on fuzzy pinyin matching and parameter robust analysis. The text-to-instruction system comprises an input preprocessing module, a text-to-instruction conversion module and a text-to-instruction conversion module, the fuzzy pinyin matching module is used for uniformly transferring the standardized text and a pre-stored instruction template into a silent pinyin sequence, and constructing a weighted editing distance matrix based on a preset pinyin character replacement cost; the parameter alignment search module is used for executing beam search on the weighted editing distance matrix and outputting a plurality of candidate matching paths with the minimum cost and a corresponding parameter fragment set; the parameter robust distribution module is used for performing legality and consistency checking on parameters in the candidate matching paths, completing parameter standardization and performing error correction in combination with a parameter candidate dictionary; the instruction output module is used for encoding the template identifier and the parameter key value pair obtained through robust distribution into a structured instruction and outputting the structured instruction; according to the method, text-to-instruction conversion can be completed in real time.

Proposed are an apparatus and a method for NCR-based beam search in a wireless communication system. A beam search procedure performed by a network-controlled repeater (NCR) in a wireless communication system includes performing carrier sensing under control of a base station to set a communication-possible area, limiting a beam candidate group on the basis of the communication-possible area to shorten the time taken for beam search, and continuing the carrier sensing even after the beam search procedure is initiated, to update the beam candidate group.

The invention provides a cargo loading scheme determination method, electronic equipment and a storage medium, and relates to the technical field of logistics management. The method comprises the steps of obtaining loading space parameters and attribute information corresponding to each to-be-loaded cargo; sorting the to-be-loaded goods according to the attribute information corresponding to each to-be-loaded goods to obtain a goods sequence; according to the cargo sequence, the loading space parameters and the attribute information corresponding to each to-be-loaded cargo, adopting a beam search algorithm and a maximum spare space algorithm to obtain an initial loading scheme; according to the initial loading scheme, the loading space parameters and the attribute information corresponding to each to-be-loaded cargo, the initial loading scheme is destroyed and repaired through a self-adaptive large-field search algorithm, a final loading scheme is obtained, and the final loading scheme is used for indicating the final loading position of each to-be-loaded cargo in the loading space. Therefore, the loading rate of the loading scheme is improved.

The invention discloses a monomolecular structure design and generation method based on a large language model, and relates to the crossing field of monomolecular electronics and artificial intelligence. In order to solve the problems of low single molecule design efficiency and insufficient data, SingMolT5 is obtained through field fine tuning on the basis of MolT5, and generation from a natural language to a molecular SMILES is realized. According to the method, data are collected from literatures in the single molecule field and an existing disclosed molecule library, a fine tuning data set containing 329 high-quality instructions is constructed, and the fine tuning data set comprises three basic data types including a molecular skeleton, an anchoring group and molecules. The model is initialized by a MolT5 check point, and a cross entropy loss function is used in the fine tuning process; reasoning, generating and using a beam search strategy; the follow-up evaluation indexes comprise BLEU, Levenshtein, MACCS, RDK, Morga and effectiveness. The method provides an innovative technical path for'low experience and data dependent type single molecule design 'in the field of single molecule electronics, and is particularly suitable for aided design of a single molecule device core function unit and a single molecule sensing probe molecule, namely, a target molecular structure can be quickly converted through a natural language, the design threshold of researchers is reduced, and the design efficiency is improved. And a traceable technical basis can be provided for screening and iteration of a monomolecular structure based on a high-quality data set and rigorous evaluation logic.