775 results about "Problem solve" patented technology

The invention discloses an aerospace intelligent manufacturing large model construction method, which comprises the steps of collecting original data, performing preprocessing and data association, and constructing an aerospace intelligent manufacturing database; establishing a knowledge acquisition and structured conversion assembly line, a multi-dimensional associated domain knowledge graph, a knowledge quality control system and a dynamic updating mechanism, and constructing a professional knowledge base; aligning the cross-modal manufacturing data to generate a corpus; combining base general large model pre-training, injecting terminology semantics and multi-modal association capability, and completing knowledge migration; based on the pre-trained aerospace intelligent manufacturing large model, constructing an aerospace manufacturing cognitive agent, and forming a complex engineering problem solving framework; professional ability is optimized through a two-stage progressive multi-task training strategy, and dynamic adaptation of a production environment is realized in combination with an online learning and incremental updating mechanism. The intelligent level of aerospace intelligent manufacturing is remarkably improved.

Searches based on an incoming ticket identify quality ticket enrichment data using a vector database. Language model prompts target particular kinds of quality ticket data. The incoming quality ticket, or a search result ticket, or both, are enriched using enrichment data, such as a user intent identification, a workaround suggestion, a resolution description, a target audience description, a relevance description, an impact description, a description of missing resolution facilitation information, an association between the incoming quality ticket and the search result ticket, a user sentiment identification, a tag suggestion, or a feedback utility estimate. The enrichment reduces engineering and support burdens, and facilitates faster more effective resolution of the problem or the request that is stated or implied in the incoming quality ticket. Duplicate tickets are merged or removed. Tickets are prioritized. Missing problem resolution information is identified and requested sooner.

The invention discloses a multi-agent dynamic arrangement method based on multi-modal analysis and adaptive retrieval, and relates to the technical field of artificial intelligence and information retrieval. Comprising the steps of S1, converting a text, an image, structured data and voice content input by a user into a unified multi-mode semantic representation, S2, converting the unified multi-mode semantic representation into a specific execution process, and S3, automatically scheduling a reasoning agent, a knowledge obtaining agent and an execution agent according to DAG nodes, task elements and available resources, and obtaining the task elements and the execution agent according to the reasoning agent, the knowledge obtaining agent and the execution agent. S4, after task process construction and agent arrangement are completed, dynamic retrieval, evidence convergence and strategy optimization are carried out on information requirements related to a user task, so that a reasoning agent obtains complete knowledge support with consistent context, and S5, knowledge evidence is combined with a task process, so that the task process is completed. The method comprises the following steps: step S6, implementing problem solving, strategy generation and task closed-loop execution through a reasoning agent, step S6, performing actual operation on a target task by an execution agent according to an executable instruction sequence output by the reasoning agent, and outputting a result, and step S7, performing result verification according to an output result returned by the execution agent, and the correctness, integrity and consistency of an output result are examined through rule verification, model evaluation and evidence alignment.

The invention relates to the technical field of program exception positioning, in particular to an AI-based data interface program exception fault pre-positioning algorithm, which comprises the following steps: S1, data stream pre-analysis; s2, capturing an abnormal signal; s3, performing abnormal clustering and feature extraction; s4, interface behavior modeling is carried out; s5, abnormal traceability positioning; s6, generating a self-adaptive repair suggestion; s7, performing feedback circulation and continuous learning; and S8, performing visualization and report generation. Through the steps of data stream classification, key field extraction, behavior modeling and the like, the system can accurately capture abnormal signals in mass data, meanwhile, an abnormal mode is visually displayed through clustering analysis and a dimension reduction technology, a reliable foundation is laid for subsequent positioning and repairing, and in addition, the method has the advantages of being high in practicability and easy to popularize. The abnormal source is quickly positioned by adopting methods such as topological sorting and causal network reasoning, the false alarm rate and the missing report rate in the positioning process are effectively reduced by combining a sliding window and time sequence analysis, and the problem solving efficiency is greatly improved.

The invention discloses a warehouse level code problem solving system based on a large language model, and relates to the technical field of software engineering, a source code file is extracted from a code warehouse, a static analysis tool is utilized to analyze codes, a warehouse level code graph is constructed, and then code entities and dependency relationships thereof are analyzed; based on the warehouse-level code graph, combining historical problem solving data, and constructing a dynamic evolution knowledge graph through continuous learning; through a context retrieval module, according to the context information of the current problem, dynamically retrieving related solution strategies and generating corresponding code patches, complementing or reviewing suggestions; the validity of the generated patch is verified through static analysis, unit testing and integration testing, and a problem solving strategy is optimized according to feedback of a developer. The problem solving efficiency and accuracy can be improved, the development cost is reduced, the solving process is gradually optimized through an intelligent feedback mechanism, a closed-loop feedback mechanism is formed, and therefore the increasingly complex software development requirements are met.

The invention relates to the technical field of artificial intelligence, and discloses an AI intelligent auxiliary question answering system for student practice. According to the system, various interactive data such as texts, voices and handwritten formula images of students are collected through a multi-modal data acquisition module, semantic feature vectors of all modals are generated through a multi-dimensional feature analysis module, and then the semantic feature vectors are mapped to a unified semantic space through a cross-modal fusion module. The double-layer graph construction module constructs a domain knowledge graph and a learning behavior graph, and the dynamic reasoning and recommendation module generates a personalized problem solving path recommendation and knowledge point completion strategy based on fusion semantic representation and a graph library. In addition, the system also has the functions of interactive behavior log anomaly detection, path backtracking, knowledge forgetting curve prediction and the like. The system can comprehensively understand questions of students, provides personalized question answering service, and effectively improves the learning efficiency and knowledge mastering degree of the students.

A tree-of-thought (ToT) system is presented that improves problem-solving capabilities of machine learning models, such as auto-regressive large language models (LLMs). The TOT system can solve complex reasoning tasks through trial and error. In this process, the system explores the solution space through a tree-like thought process, allowing for backtracking when necessary. The system augments an LLM with additional modules including a prompter agent, a checker module, a memory module, and a ToT controller. These modules engage in a multi-round conversation with the LLM. The memory module records the conversation and state history of the problem-solving process, which allows the system to backtrack to the previous steps of the thought-process and explore other solution paths. This new system can be applied to a blockchain and / or a distributed computing system.

A method for problem inference based on multi-modal generative artificial intelligence includes receiving question information including an image and text, generating formal languages by parsing the image and text of the question information, respectively, based on a pre-constructed problem solving template, generating text-based intermediate inference information for the question information by inputting the generated formal language to a formal language inference unit, generating image-based inference information by inputting the text-based intermediate inference information, the text included in the question information (hereinafter referred to as “text question information”), and the image included in the question information (hereinafter referred to as “image question information”) to a multi-modal image generation model, and generating text-based inference information by inputting the text-based intermediate inference information, the image-based inference information, and the text question information to a multi-modal text generation model.

The invention provides an intelligent customer service dialogue generation optimization method and system based on a knowledge graph, and relates to the technical field of knowledge graphs, and the method comprises the steps: employing a bidirectional long-short-term memory network to recognize the entity and intention of a user question; performing multi-hop query in the knowledge graph based on the question entity to obtain an initial knowledge sub-graph; constructing a user feature vector containing a dialogue state, problem solving and service preference; performing hierarchical reconstruction on the initial knowledge sub-graph based on the user feature vector; calculating the structural similarity of the domain knowledge graph, and extracting a general knowledge organization mode; decomposing the reconstructed knowledge sub-graph into a structure template and a content filling item, and determining a knowledge mapping relation between fields to obtain a migration optimization knowledge graph; and generating candidate replies based on the optimized knowledge graph and the initial intention, evaluating the accuracy, suitability and correlation of the candidate replies, and selecting the optimal reply output.

The invention discloses a feature classifier-based thinking chain reasoning mathematical problem solving method and system. The method comprises the following steps of: (1) acquiring a mathematical problem set, and selecting forward and reverse thinking chain examples to splice to form a new set; (2) respectively inputting the new set elements into a model for reasoning to construct a word-level reasoning path spanning tree, and selecting an attention weight matrix after pooling difference processing as node features for storage; (3) traversing all the generated reasoning path spanning trees, and screening nodes meeting requirements to construct a feature classifier training set; (4) training a feature classifier by using a support vector machine algorithm; and (5) through the trained feature classifier, participating in path selection in a pre-training language model reasoning process, and obtaining a relatively accurate reasoning process and answers. By means of the method, adjustment and control of finer granularity of the reasoning path of the pre-training language model can be achieved, accurate reasoning solving of the pre-training language model on mathematical problems is facilitated, and the generalization level of the pre-training language model is improved.

The invention discloses a method for training and evaluating a large model in the field of table data analysis. Screening and scoring the obtained table and text data; compiling a plurality of sets of different prompt words, and making'question 'data and'question-answer' data pairs under a table data scene as basic training samples; performing data enhancement on the generated'question 'and'question-answer' sample pairs; collecting and screening common text questions and answers, logic mathematics and code generation task data, selecting a proper data ratio, and performing all-parameter instruction alignment training on the base model; collecting and making an evaluation data set, compiling an evaluation script, and running to obtain a corresponding evaluation index; and iteratively optimizing the training data set according to the evaluation index and the feedback of the evaluation set until the model reaches an expected index. According to the invention, the large model in the field of table data analysis obtained by training not only has strong table question and answer ability, but also has strong general problem solving ability.

The invention relates to a knowledge graph question answering method combining a large model and a graph neural network, which comprises a question decomposition module, a sub-question solving module and an answer reasoning module, the method is used for solving the problem that the size of a problem sub-graph is difficult to determine in a multi-hop problem in a graph neural network method, a sub-problem solving module is composed of a problem sub-graph retrieval stage and a reasoning path construction stage, in the problem sub-graph retrieval stage, a similarity definition mode is given, calculation is carried out through a Personalized PageRank algorithm, and the problem sub-graph retrieval stage is used for solving the problem sub-graph size. And meanwhile, in order to improve the accuracy of GNN answering, the question sub-graphs of the subsequent questions are combined with the question sub-graphs of the previous questions, so that the dynamic expansion of the question sub-graphs is realized. In the inference path construction stage, candidate answers are obtained through a pre-trained graph neural network, and then an inference path is constructed by using a BFS algorithm. And finally, the reasonability of the reasoning path is judged through an answer reasoning module so as to give sub-question answers. The invention provides a knowledge graph question and answer framework combining a large model and a graph neural network, and the performance of the model on a multi-hop problem is effectively improved.

The invention provides a large model active tool calling method and system based on multi-step reasoning, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting tool information, generating a structured tool description set, automatically generating an executable code sample and corresponding natural language explanation based on the tool description set, and constructing a reverse training data set; the method comprises the following steps of: marking tool use necessity tags in a real problem, prompting a large model to generate a multi-step reasoning path combined with a natural language and codes, verifying and filtering the correctness of the reasoning path, and constructing a forward training data set; and performing three-stage training on the large model based on the reverse training data set and the forward training data set. According to the method, the problem that the active tool calling capability of a large model is insufficient is solved, and the code execution rate and problem solving efficiency of a complex reasoning task are improved.

The embodiment of the invention provides a legal affair information processing method and system fusing a knowledge graph and a large language model, and belongs to the technical field of knowledge graphs. The method comprises the steps that existing information related to the legal field is collected, and data preprocessing is conducted on the existing information; entity recognition and relation extraction are carried out based on the preprocessed existing information, and a knowledge graph is constructed based on the relation between the entities; collecting a legal affair information processing instruction of a user, and performing user intention recognition based on the legal affair information processing instruction; and based on the identification intention and the legal affair information processing instruction, generating result prediction information through the knowledge graph, and pushing the result prediction information to a user side. According to the scheme, deep analysis and intelligent processing of legal problems can be achieved, the accuracy of information retrieval and the reliability of legal suggestions are improved, and therefore decision making of legal practitioners and legal problem solving of users are better supported.

The invention relates to the technical field of express service management, and discloses an express service problem solving method and system based on a knowledge base management system, and the method comprises the steps: firstly obtaining the form and semantic features of a package, obtaining an atlas node state matrix through the operation of structural analysis, abnormal mark matrix generation, mapping relation establishment and the like, and obtaining multi-stage calibration parameters according to the atlas node state matrix; constructing a cross-modal mapping function cluster to obtain a parcel feature matrix, designing a knowledge base retrieval sorting mechanism to generate a knowledge recommendation list, and finally generating a solution based on the list and feeding back the knowledge base; according to the method, through multi-modal data processing and application of the knowledge base, the accuracy and efficiency of express service processing are remarkably improved, the practicability and adaptability of the knowledge base are enhanced, and powerful support is provided for intelligent development of express services.

The invention relates to a stipulation processing method and system for mathematical subjective question answering steps. The method comprises the steps of performing deep semantic understanding on problem solving information of mathematical subjective problems based on a pre-training language model of a Transform architecture, generating semantic representation information, and segmenting the problem solving information into semantically coherent atomic steps based on the information; matching the atomic steps with a knowledge graph to obtain a first matching result, and determining a connection relationship between the atomic steps based on the knowledge graph; based on the connection relation of the atomic steps and the atomic steps, constructing an answer step graph, and performing reasoning through a graph neural network to obtain a reasoning result; a reference answer is generated according to the reasoning result, semantic matching is conducted on the atomic step and the reference answer, a second matching result is obtained, and the result is used for indicating generation of correction feedback. According to the method, through semantic understanding, the knowledge graph and the graph neural network, automatic and accurate answer step analysis can be realized, and the question solving efficiency and the feedback quality are improved.

Certain aspects provide techniques and apparatus for executing queries in a computing system using machine learning models. An example method generally includes receiving a plan to satisfy a request in the computing system and event log data associated with execution of the plan. The plan generally specifies a first plurality of function calls at a first level of granularity. Using a plan refinement machine learning model, a refined plan is generated when the event log data indicates that execution of the generated plan results in one or more execution errors and the one or more execution errors are solvable. Generally, the refined plan specifies a second plurality of function calls at a second level of granularity, the second level of granularity being finer than the first level of granularity.

The invention relates to the technical field of teaching systems, in particular to a teaching information processing method and system based on artificial intelligence, and the method comprises the following steps: S1, carrying out the collection and fusion of multi-modal data, and carrying out the fusion of the data through a time warping dynamic alignment algorithm; s2, cognitive state modeling: introducing a dynamic forgetting gate mechanism on the basis of a traditional LSTM, quantifying the half-life effect of knowledge points, and obtaining a deviation degree delta gt between a problem solving path of a student and an expected optimal path; 0.4, an alarm is triggered; and S3, a dynamic knowledge topological component dynamically updates a relation weight between knowledge points based on a graph neural network, and an improved Sweller cognitive load index is used. The method solves the problem that when students have unexposed understanding deviation in an algebraic'factorization 'link (such as a'square difference formula' and a'complete square formula '), a system still mechanically advances to a'polynomial operation' module to cause chain accumulation of knowledge vulnerabilities and finally cause systematic learning collapse.

A computer implemented method determines a solution to a Master Problem, MP, the MP derived from a Mixed-Integer Program, MIP, problem and associated with a Sub-Problem, SP. The method comprises determining, using a quantum computer, a solution to a first version of the MP. The first version of the MP comprises a first initial constraint. The solution to the first version of the MP comprising: a first value for the group of one or more respective integer variables. The SP is run, using the one or more first values, to determine either: a third value, corresponding to the dual problem, for a solution for the SP; a fourth value corresponding to an extreme ray. Using the third value or fourth value, a further constraint for a second version of the MP is generated. The second version of the MP is generated by replacing the first initial constraint with the further constraint. A further solution to the second version of the MP is determined, using the quantum computer.

The invention provides a mathematical question and answer process teaching system, which comprises a user interaction module, a question analysis and standardization module, a cognitive diagnosis engine, a generative teaching module, a mathematical knowledge graph database and a user portrait database, teaching content and guiding questions are presented to the user in a multi-mode form; the question analysis and standardization module is used for performing semantic understanding and structured analysis on mathematical questions and generating standardized question description and at least one standard question solving path; according to the method, the structured reasoning of the knowledge graph is combined with the natural language generation ability of the generative AI, so that a closed loop from'problem solving 'to'diagnosis' to'heuristic teaching 'is realized, thinking blockage points of students can be accurately positioned and personalized tutoring can be provided like human teachers, and the learning efficiency and the autonomous thinking ability are remarkably improved.

The invention discloses a data processing method and device, a storage medium and electronic equipment, and relates to the technical field of artificial intelligence, first type memory information is obtained according to the current session context with a user, a model can be ensured to understand the background of a current problem, the problem background is supplemented by using second type memory information, namely the historical session record of the user, and the user experience is improved. According to the method, the service knowledge information related to the question is retrieved, namely the target knowledge information, the target model generates the comprehensive reply according to the information, the context of the instant session is considered, and the personalized preference of the historical session and the current service rule are combined, so that the generated reply information is more comprehensive, and the reply efficiency is improved. Therefore, incoherence or inaccuracy caused by forgetting important details is avoided, and the technical effects of improving the accuracy of problem solving and enhancing individuation and satisfaction of user experience are achieved.

The invention belongs to the technical field of computers, and relates to a system exception positioning notification method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining complete log information; log extraction parameters are obtained, target log information is obtained through screening, multi-channel log analysis and integration are carried out, and a target analysis result is obtained; obtaining abnormal feature information and abnormal stack information according to the target analysis result, obtaining code features based on the abnormal feature information, and accessing a code warehouse based on the abnormal stack information and the code features to position abnormal codes and positions thereof; obtaining code replacement historical data of the abnormal code to position developer information before the abnormality occurs, and taking a corresponding developer as a target object; and rating the exception according to the target analysis result, generating notification information according to the rating, the exception code and the position of the exception code, and sending the notification information to the target object. According to the invention, targeted notification and reminding can be carried out, and the abnormal problem solving efficiency is improved.

The invention provides an intelligent assistant method and system based on a large model, relates to the field of large model application, is used for providing a question and answer result for a user question, and comprises the following steps: constructing an entity recognition cue word, and recognizing an entity in the user question through the large model; constructing intention recognition and tool selection prompt words, recognizing user intentions in the user questions through a large model, and determining tools to be used; based on the assembled user questions and entities, related information is retrieved from a pre-constructed vector knowledge base, and a large model is combined with the retrieved related information to enhance and generate an answer text; processing the answer text according to the user intention and the tool, and outputting a final question and answer result; according to the method, the limitation that a traditional intelligent assistant cannot deeply understand user semantics and only can mechanically carry out rule matching is solved, and the requirements of the user can be more accurately grasped to provide a more accurate problem solving strategy.

The invention discloses a student comprehensive ability evaluation system based on artificial intelligence, and belongs to the technical field of artificial intelligence student learning evaluation. Aiming at the problems of single dimension and poor evaluation effect in the existing student evaluation system, the system not only can provide personalized feedback and improvement suggestions for students and help the students to improve team cooperation, communication and problem solving capabilities, but also can provide accurate teaching evaluation and decision support for teachers and education managers, so that the students can have a better teaching effect. And intelligent upgrading of a teaching mode is promoted. By promoting digital transformation of education evaluation, the system provides an innovative solution for constructing a more scientific, fair and efficient learning evaluation system, and assists high-quality development of education.

The invention discloses an intelligent agent system and a control method thereof, and relates to the technical field of natural language processing. The system comprises an execution layer, a planning layer and an auditing layer, the planning layer identifies and extracts innovation task meta-information, constructs a task tree to complete operations such as hierarchical clustering, and determines a task execution path; the execution layer calculates the semantic similarity between the content of the knowledge graph and the user innovation question, determines the optimal question and answer, extracts heuristic information, and decomposes the heuristic information into sub-questions to construct a dependency graph; generating an answer set based on the graph solving sub-problems, and integrating answers by using a genetic algorithm to obtain an overall solution; and the auditing layer performs multi-dimensional scoring on the scheme, and determines a structured scheme report and optimization suggestions for users to use according to a scoring result. The method has the capabilities of structured reasoning, problem recursive decomposition and scheme closed-loop optimization, can generate a multi-dimensional evaluation result, and efficiently realizes systematic modeling of a clear path for analogy heuristic information support problem solution.

The invention relates to the technical field of artificial intelligence education, and discloses an AI intelligent education tutoring system based on a thinking chain and a retrieval enhancement generation technology. Transparent generation and dynamic verification of a problem solving logic chain are realized through a CoT-RAG fusion architecture, and the interdisciplinary confusion rate is reduced in combination with a knowledge graph technology of subject boundary isolation; the non-question-bank-dependent generation engine creates questions in real time based on semantic association, and the coverage rate is increased; federated learning and differential privacy technologies are integrated, and the safety of user data is ensured while the generalization ability of the model is improved; the learning behavior analysis unit dynamically optimizes a knowledge recommendation path through time sequence modeling, and cooperates with the interactive logic correction module to form a self-evolution closed loop. Finally, an intelligent education system which is high in interpretability, high in professional precision and compliant in privacy is constructed, and the learning efficiency and the interdisciplinary problem solving capability are remarkably improved.

The invention discloses a signal timing scheme automatic generation system based on multiple algorithms, and the system comprises an index data statistics module which is used for carrying out the statistics of a plurality of preset index data in obtained traffic big data in any traffic region which is delimited in advance, and obtaining a statistical result of the index data; the index algorithm model management module is used for performing result representation on any index in the statistical result by using a corresponding specific index algorithm model; the optimization model generation module is used for constructing a multi-algorithm signal timing optimization model for a plurality of index algorithm models corresponding to the plurality of indexes; and the problem solving module is used for solving the optimization model and generating a signal timing scheme of the target traffic area according to a solving result. According to the invention, through integration of the multi-algorithm model, automatic generation of the traffic signal timing scheme is realized, the efficiency and effect of traffic management are improved, and powerful technical support is provided for sustainable development of urban traffic.

The invention discloses an electric power field credible question-answering system based on a knowledge graph and multiple agents, and belongs to the technical field of intelligent power grids. The electric power field credible question-answering system comprises the steps that an existing knowledge base is stored through a vector knowledge base, the knowledge graph and a relational database, and then a multi-agent system is constructed based on a large language model; a complex problem is decomposed into a plurality of sub-problems by adopting a logic form language in real-time dialogue, solving is carried out based on operators such as planning, reasoning and retrieval, symbolization and structuring of a problem solving process are realized, knowledge is aligned and integrated by using a semantic reasoning technology, a semantic relationship between the knowledge is identified and established, and a real-time dialogue result is obtained. And realizing credible questions and answers in a manner of recalling related knowledge. According to the method, the deep fusion of the unstructured text, the structured data and the semantic relationship is realized by constructing the multi-modal knowledge storage system, the knowledge coverage rate and the semantic consistency are remarkably improved, and the understanding and response capabilities of the system to compound problems are improved.

The invention provides an intelligent operation and maintenance method and device and a storage medium. The method comprises the following steps: constructing an operation and maintenance knowledge graph; obtaining question data input by a user, and determining a first intention classification result and a first scene classification result corresponding to the question data; based on the first intention classification result, obtaining target key information corresponding to the problem data through an operation and maintenance knowledge graph; determining recommendation information corresponding to the problem data based on the first scene classification result; and inputting the target key information and the recommendation information into the large language model to obtain answer data of the question data. Therefore, the target key information and the recommendation information of the question data are input into the large language model to obtain the answer data of the question data, so that the answer data better meets the actual requirements of the user, the pertinence and effectiveness of auxiliary decision making are enhanced, the operation and maintenance accuracy is improved, efficient transmission of operation and maintenance knowledge is promoted, and the user experience is improved. And team cooperation and problem solving processes are accelerated.

The invention relates to a method for improving the reasoning ability of a large language model based on Monte Carlo tree search. The method comprises the following steps that S1, a mathematical question set and corresponding standard answers are preset; s2, taking each step for solving a mathematical problem as a tree node, and generating a Monte Carlo tree in the whole problem solving process; s3, generating training data based on Monte Carlo tree search; s4, scoring the generated training data; and S5, constructing a loss function according to scores obtained by scoring, optimizing the loss function and updating parameters of the large language model. The reasoning ability of the large language model can be improved.