971 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 discloses an unmanned aerial vehicle cluster task planning algorithm based on hierarchical multi-agent deep reinforcement learning and an evaluation method thereof, belongs to the technical field of unmanned aerial vehicles, and solves the problems of low online solving efficiency, large calculation amount, poor stability and high efficiency generally existing in a conventional unmanned aerial vehicle cluster cooperative task planning problem solving algorithm. And the method is difficult to adapt to complex dynamic scenes. The method comprises the following steps: firstly, respectively establishing a Markov decision process model of a cluster task allocation problem part and a Markov decision process model of a track planning problem part; secondly, a layered deep reinforcement learning framework based on multi-agent deep reinforcement learning is provided, and synchronous solving of task allocation and route planning association coupling problems is achieved through coupled training and a layered solving mode; in addition, an unmanned aerial vehicle cluster combat simulation environment is established based on UE4 and Airsim, evaluation indexes are designed, and the result shows that the method has good performance in cluster task planning, and the intelligent and actual combat level of cluster combat can be effectively improved.

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 relates to a vehicle-mounted edge computing network scheduling method based on information age, and belongs to the field of mobile edge computing. The method is oriented to a vehicle-mounted edge computing network in which a fixed server and a vehicle-mounted mobile server coexist, and aims to minimize the long-term average information age of the system by selecting a device node combination scheduled by the server in each time slot. The position constraint, the link conflict constraint and the server computing resource constraint of a vehicle-mounted mobile server are considered, an optimization problem is modeled as a Markov decision process, and a deep breach Q network is improved by using a bifurcation structure and an action mask to obtain a scheduling strategy of an equipment node. According to the method, the bifurcation structure and the action mask are introduced into the deep breach Q network, so that the problem solving complexity is reduced, and the data delivery freshness of the vehicle-mounted edge computing network system is guaranteed.

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.

According to the intelligent alarm method and system based on the Transform variational auto-encoder network, input data are mapped to a potential space in combination with long-term dependence capture, a self-attention mechanism and parallel processing capacity of the Transform, and output data are sampled from the potential space, so that the accuracy and real-time processing capacity of anomaly detection are improved. According to the method, the Transform network and the VAE network are combined, and the modeling capability of the database sequence data is remarkably improved by utilizing the long-term dependence capture and self-attention mechanism of the Transform. On the basis of a TVAE model, an AI large model and a local knowledge graph are combined, deep analysis is carried out on an anomaly detection result, potential root causes are extracted, and a solution is provided. Through the strong learning ability of the AI large model and the rich background information of the knowledge graph, the root cause of the abnormality can be quickly identified and positioned, so that an efficient and accurate solution is provided, and the processing efficiency of operation and maintenance personnel and the accuracy of problem solving are improved.

The invention discloses a large model reasoning ability enhancing method and system based on semantic execution, and the method comprises the steps: obtaining problem description information, adding computable guide part information, and inputting a large language model to output cue words; analyzing and outputting a problem solving step by the large language model according to the cue word; transmitting the problem solving step to a calculation container, and outputting a correct execution step; and integrating all the correct execution steps to generate a reasoning result. According to the method, the problem description input by the user is converted into the computable content, the problem solving efficiency is improved, the computable content is decomposed into a plurality of steps for solving, the feasibility of each solving method is verified through the middleware, the correct execution steps of problem solving are calculated through the semantic model and the calculation container, and the problem solving efficiency is improved. And the problem solving accuracy is improved.

The embodiment of the invention provides a method and device for generating a question solving model for solving a plane geometry question. The method for generating the question solving model for solving the plane geometry question comprises the steps that a plurality of questions to be solved are acquired; the to-be-answered questions are questions for plane geometry questions; obtaining answering information of a plurality of to-be-answered questions by using a large language model; the answering information comprises video data used for generating an explanation video of the question to be answered; obtaining training data based on the plurality of questions to be answered and the corresponding answering information containing the video data; training a problem solving model by using the training data to obtain a trained problem solving model; the trained problem solving model is used for generating explanation video data, and the explanation video data is used for generating an explanation video for explaining a problem about the plane geometry problem. The explanation video for explaining the plane geometry problem can be quickly and accurately generated by using the trained solution model, so that the manual operation is reduced, and the efficiency of generating the solution video is 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 a robot customer service question intelligent processing method and system. The method comprises the steps that user questions are acquired, and feature extraction and integration are conducted on the user questions; constructing a user dynamic causal reasoning model, and generating a multi-level problem solving strategy according to user problems; constructing a self-adaptive response strategy generation model according to the generated multi-level problem solving strategy and the integrated feature vector to generate a response strategy most adaptive to the current corresponding context; executing the response strategy and judging whether to perform learning triggering according to the feedback of the user; performing real-time adaptive learning and optimization on the adaptive response strategy generation model through user feedback and historical user feedback; and analyzing the long-term trend of the historical user feedback and the model parameters of the adaptive response strategy generation model after multiple times of optimization, performing global optimization in combination with the long-term trend, and generating a final response strategy. The method and the device can be excellently expressed in a complex user service scene.

The invention provides a customer service method, device and program product based on context semantic understanding, and relates to the technical field of Internet, the method comprises the following steps: in response to a received current dialogue text of a user, inputting the current dialogue text into a preset intention entity recognition model, performing intention recognition and entity recognition on the current dialogue text, and obtaining a preset intention entity recognition model; obtaining a user intention and a text entity; storing the user intention, the text entity and the current dialogue text as a current dialogue state, and updating a historical dialogue state corresponding to the user; determining a next customer service action based on the historical dialogue state through a preset dialogue strategy; the preset dialogue strategy comprises a second dialogue strategy based on rule matching and / or a first dialogue strategy based on deep learning; and executing the next customer service action and generating a corresponding customer service dialogue text to be returned to the user. According to the method and the device, the problem of relatively low efficiency of intelligent customer service business can be solved. The problem solving rate can be improved, and the upstream and downstream efficiency of intelligent customer service 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.