11 results about "Levenshtein distance" patented technology

A method for improving chatbot accuracy includes receiving an input test file containing chatbot interaction data, the data including utterances, intended intents, and intended responses. For each interaction, a predicted intent and response can be generated using an AI chatbot trained on prior interaction data. The method determines the similarity between the predicted and intended responses / intents by calculating the Levenshtein distance and comparing it to a predetermined threshold. Keywords from failed interactions are extracted through natural language processing to identify failure themes. Machine learning decision tree classification can analyze patterns in failed interactions to generate solution recommendations. The method can generate an interactive dashboard displaying historical performance trends, accuracy metrics for specific domains, visualizations of high-frequency keywords, and confidence distributions for failed interactions. This automated regression testing method can effectively identify and resolve chatbot performance issues while maintaining response quality.

The application discloses a kind of IP network attack group classification method based on behavior sequence, its steps are as follows: S101, obtain the malicious IP behavior data set of operator security protection platform;S102, data cleaning, and dictionary coding is carried out to behavior sequence, generate behavior sequence data;S103, to behavior sequence data, behavior frequent item calculation is carried out;S104, Levenshtein distance similarity calculation is carried out to behavior sequence, similarity range is divided into four intervals, for the IP value in the corresponding range in each interval, four intervals are used as characteristic field;S105, the appearance frequency of each instruction in all IP behavior sequences is counted as characteristic field;S106, all characteristic fields are handled by feature engineering, and are arranged into model input format;S107, using cluster analysis, obtain IP network attack group classification.The application realizes IP network attack group classification, and then solves the problem that existing technology cannot be applied to IP group classification scene without communication or associated attribute.

The application relates to the technical field of data security, in particular to a data asset classification and grading and dynamic management system of authority, which comprises an asset parameter acquisition module, a classification feature discrimination module, a grading weight calculation module and a dynamic adaptation module of authority. In the application, distributed crawlers are combined with regular expressions to automatically collect metadata and path parameters, naming parameters and content feature parameters are separated to generate a structured parameter set, Levenshtein distance is used to quantify the naming similarity, chi-square test is used to verify the content character distribution feature, a double-checking mechanism of naming similarity and distribution feature is constructed, the entropy weight method is used to dynamically distribute the sensitivity, access frequency and data volume weight, the grading parameters are generated through linear superposition, the RBAC model is combined with the Dijkstra algorithm to verify the legality of the access path topology, the over-authorization access is blocked, and the dynamic adaptation capability of heterogeneous data classification and grading and authority control is improved.

This invention discloses an iterative chart code generation method and apparatus based on a chart and a large-scale code correction model, belonging to the field of chart code generation technology. The method includes: based on a code error classification system, performing data mutation using a multimodal large-scale model based on the original chart code pairs, and constructing an original dataset; based on the code error classification system and the row-level Levenshtein distance algorithm, adding correction prompts to the original dataset, and constructing a training dataset; based on the LLaMA-Factory fine-tuning framework and the GRPO training strategy, performing two-stage training on the large-scale code correction model based on the training dataset to obtain an optimized large-scale code correction model; and based on the optimized large-scale code correction model and the large-scale chart code generation model, iteratively generating code based on a target reference chart to obtain the target generated code. This invention is a high-precision, interpretable, and iterative chart code generation method based on charts.

The application relates to the technical field of industrial manufacturing, and discloses a method and system for improving the completion parameters of understanding user input, which comprises the following steps: inputting an industrial standardized file, generating a semantic label, reasoning through the industrial standardized file and the semantic label, obtaining an optimized industrial standardized file, and obtaining an industrial rule set and a text parameter set input by a user. The application realizes accurate semantic label generation based on a WordNet synonym set, solves the polysemy ambiguity problem, calculates the similarity between a candidate parameter and a user conversation through an improved Levenshtein distance algorithm and a compensation mechanism, accurately captures the potential demand of the user, improves the consistency with the actual input intention of the user, multiplies the initial weight value, the click rate and the time decay factor to sort the candidate parameters, balances the instant production demand and the historical experience in the industrial scene, and significantly improves the accuracy, adaptability, efficiency and interpretability in four dimensions.

The invention discloses a polarization code belief propagation decoding method for correcting insertion and deletion errors, and relates to the field of digital communication error control coding. The method comprises the following steps: mixing information bits with the length of 1 and all-zero frozen bits with the length of 1 according to position indexes of preset information bits and fixed bits to obtain a bit sequence; encoding the bit sequence to obtain a sending sequence with the length of 1; the sending sequence is transmitted through an IDS channel to obtain a receiving sequence with the length of the IDS channel; and carrying out insertion and deletion error correction processing on the receiving sequence through a BP decoder based on a weighted Lelwstein distance, and outputting an information sequence estimated value. According to the method, the drift distance is introduced, and the weighted Levinstein distance is adopted as probability measurement, so that the problem that insertion and deletion errors cannot be corrected by a traditional BP decoding algorithm is solved. The inherent problem of an SC framework is also avoided, and the error correction capability is improved; and through parallel operation, the decoding time delay is reduced, and the unit time processing capacity is improved.

The application provides a text difference degree calculation method and system, and the method comprises the following steps: obtaining the length of a first text and the length of a second text; in the case that the length of the first text and the length of the second text satisfy a first preset condition, calculating the difference degree between the first text and the second text according to a target Levenshtein distance between the first text and the second text, the length of the first text and the length of the second text. The application calculates the difference degree between the texts to be compared based on the length of the texts to be compared and the target Levenshtein distance, solves the problems of slow manual comparison speed and complicated operation in the case that the amount of text data is very large, improves the calculation efficiency of the difference degree of the texts to be compared, and enables the user to quickly understand the difference degree between the texts to be compared.

The application discloses a kind of cross-system document intelligent coding method and system based on dynamic rule engine, by obtaining the metadata information of to-be-coded document, call dynamic rule engine from configurable rule base and match coding rule, rule engine supports multi-dimensional condition matching and priority dynamic calculation, and provide visual configuration interface to realize the real-time adjustment and version management of rule;Adopt three-level mapping strategy to convert the metadata of PDMS, BIM, SCADA and other heterogeneous systems into unified equipment and facility list ID without loss, combined with improved Levenshtein distance algorithm to calculate the similarity of equipment name, form the dual conflict detection mechanism of equipment ID accurate matching and equipment name similarity calculation, trigger artificial review when conflict is detected, review result is fed back to knowledge base to realize self-learning optimization, while establishing coding life cycle management;The application realizes the automation, intelligentization and traceability of cross-system document coding, significantly improves coding consistency and standard adaptation efficiency.

An electronic device for record linkage including a memory storing one or more instructions, and a processor that executes the one or more instructions to generate one or more vectors of one or more strings from a reference database based on a modified Levenshtein distance, and generate a vector database for spelling similarity based on the one or more vectors. The modified Levenshtein distance is based on one or more parameters, including at least one of: a first number of insertions, a second number of deletions, a third number of replacements, or a fourth number of matches, one or more of the one or more parameters including a predefined weight, and one or more fixed strings.

FIELD: computer technology.SUBSTANCE: method for multilingual training of language models using reinforcement learning based on human feedback using synthetic gaze trajectories, comprising the steps of feeding multilingual text to a gaze prediction model based on a multilingual eye movement corpus and a multilingual BERT model to generate a fixation sequence, computing visual attention features including first-pass regression rate, skip rate, first-pass and total fixation counts, and normalized Levenshtein distance, generating a gaze-aware reward model by projecting features into a latent space through a fully connected network, distributing rewards to tokens proportional to fixation probabilities, and optimizing the policy using PPO or GRPO algorithms with a modified advantage that takes into account the Levenshtein distance.EFFECT: multilingual training in thirteen languages without collecting real eye tracking data, achieving the accuracy of reproducing the features of visual attention.6 cl

The invention belongs to the technical field of knowledge graph completion, and discloses an embedded model architecture search method based on a double-layer proxy assisted genetic algorithm. A local semantic enhancement module, an interactive semantic enhancement module, a global semantic enhancement module and a convolution module are designed, and a pooling module extracts adjacent semantics, differentiated semantics under different relations and global compensation semantic features respectively; then, semantic information flow and complementation are promoted through semantic enhancement module cascading, and deep fusion of multi-granularity semantic features is achieved; an embedded model architecture search method SAGA-KGE based on a double-layer proxy assisted genetic algorithm is provided; in order to enhance the population diversity in the search process, an elite environment selection method based on Levenshtein Distance is provided, and the search range of the genetic algorithm is expanded under the condition that elite individuals are reserved; in order to reduce computing resource overhead in the search process, a'classification + regression 'double-layer agent model is designed to replace an expensive fitness evaluation function, and consumption of computing resources is reduced.