220 results about "Problem solve" patented technology

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.

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 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 discloses an energy storage power station capacity measuring and calculating method considering the power grid bearing capacity and multiple application scenes. The method comprises the following steps: firstly, performing fitting correction on a scene by adopting a scene aggregation correction technology based on least square fitting; and constructing an energy storage power station capacity optimal measurement and calculation model considering the power grid bearing capacity and multiple application scenes, finally carrying out linear transformation on the energy storage power station capacity optimal measurement and calculation model, and solving by adopting commercial optimization software. According to the calculation model, multiple application scenes are introduced, and peak regulation, frequency modulation and standby multiple dimensions are considered, so that the operation and income calculation of the energy storage power station are more comprehensive, the behavior of investing the energy storage power station on the user side can be better simulated, and the influence of energy storage access on the power grid can be better evaluated. According to the scene aggregation correction technology, fitting correction is carried out on the scene by introducing the least square method, more efficient and accurate scene expression is achieved, and the problem solving scale and solving difficulty are reduced.

There is provided a system and methods to determine an improved solution to a Mixed Integer Problem (MIP) using a quantum-assisted MIP solver. The methods are performed by a digital processor in communication with a quantum processor. Methods include: selecting at least one feasible solution determined by an MIP solver, determining a first sub-problem of the MIP based on the at least one feasible solution; casting the first sub-problem as Binary Quadratic Models (BQMs); solving the BQMs using the quantum processor to generate sample solutions; determining a second sub-problem based on at least the sample solutions, and obtaining a current solution to the MIP by evaluating the second sub-problem; and updating an incumbent solution if the current solution improves over the current incumbent solution. The quantum-assisted MIP solver uses hybrid crossover and mutation heuristics to improve the convergence time and accuracy of solutions obtained using Branch-and-Cut solvers.

The invention discloses a rich text multi-dimensional difference analysis and accurate revision method and system based on AI, and relates to the technical field of artificial intelligence and data analys.The rich text multi-dimensional difference analysis and accurate revision method comprises the steps that firstly, a rich text file is analyzed, a document structure is reconstructed, vectorization and key information labeling are conducted on semantic blocks, and structuralization and semantic enhancement of a text are achieved; the method comprises the following steps of: firstly, carrying out text surface layer, semantic deep layer and structure association dimension difference analysis in parallel, then carrying out system knowledge graph-based revision influence degree evaluation on the basis of difference analysis, tracing associated terms through penetrating analysis, calculating an influence degree score and early warning potential conflicts, and automatically generating accurate revision suggestions; visual difference preview and one-key generation of a revised draft are provided, a closed loop from problem discovery to problem solving is realized, and the efficiency and accuracy of system revision are greatly improved.

To enable a manager to accurately recognize information effective for improving an organization without spending as much effort as possible in the field of improving the organization. [Solution] The information processing system has the following functions: using the functions of language model 4, to conduct specific interviews with members of an organization to elicit the organization's challenges and solutions to those challenges; a function to extract problem-solution pairs, which are combinations of problem answers that mention the challenges and solution answers that mention the solutions, from the results of the specific interviews; a function to classify each of the problem-solution pairs based on the specific interviews conducted with multiple members into one of multiple problem categories; and a function to provide information based on the problem-solution pairs belonging to each problem category to the administrator for each problem category.

The embodiment of the invention provides a problem solving large model training method and device, equipment, a medium and a product, and the scheme can comprise the steps: obtaining a first training sample comprising a first sample problem and a first standard answer, the problem type of the first sample problem can comprise a plurality of graph-containing problem types, the first standard answer comprises a plurality of derivation steps and a final result. Character role information, question type information and guiding information for guiding the pre-trained large model to perform answer derivation according to specific question solving logic corresponding to the question type information are fused into the built prompt word information used for solving the first sample question. Inputting the first cue word information into a pre-trained large model to obtain a first prediction answer; and according to the first standard answer and the first predicted answer, performing fine tuning training on the pre-trained large model to obtain a trained problem solving large model. The perceptual ability of a large model to graphs in topics with graphs is improved, and the accuracy of solving the topics with graphs is improved.

The invention relates to the technical field of natural language processing, and provides a multi-agent collaborative question answering method and system based on Bayesian Nash equilibrium, and the method comprises the steps: obtaining a user query instruction, generating a reasoning strategy, calling a plurality of agents, enabling each agent to convert a current observation state into a belief state through a belief network according to the current observation state, action parameters are generated, the large model is controlled to execute a text generation task, each agent maintains a value estimation network, and the value estimation networks take the belief state and the action parameters as input to generate instant rewards; aggregating the results of executing the text generation task by each agent to obtain a final answer; the belief state of each agent is aggregated into a unified group belief representation through a belief encoder, comprehensive loss is generated through a centralized hybrid network, and parameters of each agent are updated. Stronger consensus achievement capability and higher problem solving quality are shown in complex reasoning and planning tasks.

The invention discloses a dynamic tool selection system and method based on multi-stage training, and belongs to the field of natural language processing. The method comprises the following steps: acquiring a labeling data set in a specific vertical field through a high-confidence training sample construction module, preprocessing the labeling data set to obtain different types of complex problem sets, generating a positive sample tool and a negative sample tool for each complex problem, and performing reasoning to obtain a high-quality training sample set; a progressive training module is used for multi-stage training of a model, and the model comprises a supervised fine-tuning training stage, a comparative learning stage and a reinforcement learning stage; and performing multi-dimensional evaluation on the trained model by taking the accuracy rate, the recall rate and the F1 score as evaluation indexes through a multi-dimensional evaluation module. Through the synergistic effect of the modules, the question answering ability of the model in the vertical field is effectively improved, particularly, a remarkable breakthrough is made in the aspect of tool selection accuracy, and a reliable technical scheme is provided for application of an intelligent question answering system in the professional field.

The invention discloses a learner error concept diagnosis method, system and device based on reverse reasoning chain generation and a storage medium, and belongs to the technical field of error concept diagnosis. The method comprises the steps of obtaining learning interaction data; inputting the learning interaction data into a pre-trained error concept diagnosis model to obtain a student error concept diagnosis result; wherein the error concept diagnosis model comprises a standard reasoning chain generation module, an error reasoning chain generation module and a diagnosis module; based on the question and the standard answer, a standard reasoning chain generation module generates a standard answer reasoning chain; based on the question and the student error answer, an error reasoning chain generation module generates an error answer reasoning chain through an attention mechanism, a neural network layer and a pre-acquired error question knowledge set; and the diagnosis module performs sequence comparison on the standard answer reasoning chain and the error answer reasoning chain to obtain a student error concept diagnosis result. According to the invention, accurate positioning of key error steps of question solving of students is realized.

An integrated system for generating innovative solutions is disclosed. The system comprises a literature review AI agent, a knowledge graph, and a TRIZ Module. The AI agent continuously analyzes diverse information sources, feeding data into the knowledge graph. The knowledge graph organizes and connects information, identifying patterns and relationships. The TRIZ Module interacts with both components to identify contradictions and generate innovative solutions. This system accelerates innovation by processing vast amounts of information, recognizing cross-disciplinary patterns, and applying systematic problem-solving techniques.

The invention provides a resource allocation method in task unloading, relates to the technical field of network and communication, and solves the technical problem of relatively high time consumption of joint optimization of channel resources, computing resources and power consumption control in a task unloading process. According to the technical scheme, the method comprises the following steps of S1, task unloading problem modeling; s2, realizing an unloading decision; and S3, designing and realizing a resource allocation algorithm. According to the allocation method, the search behavior can be dynamically adjusted according to the real-time performance of the particles, and exploration and development of resource allocation problem solving are dynamically balanced; according to the scheme, the time overhead performance of program calculation is reduced, and a high target utility function value can be maintained.

The invention discloses a vehicle path problem solving method, system and equipment and a medium, and particularly relates to a vehicle path problem solving method based on clustering decomposition and graph matching, which comprises the following steps: S1, receiving a large-scale vehicle path problem instance to be solved, modeling into a weighted undirected graph, and combining a series of constraint conditions and optimization targets; s2, constructing an offline knowledge base with a diversified structure, inputting a group of preset parameters in the step, and outputting a knowledge base stored in the memory; s3, aiming at the large-scale vehicle path problem instance to be solved, executing initialization operation to generate a global initial solution; and S4, by taking the global initial solution as a starting point, executing an iterative local search framework to carry out deep optimization on the solution until a preset termination bar is met. The technical problems that in the prior art, when a large-scale complex logistics network is processed, the calculation time consumption is long, the planning cost is high, and the result is unstable are solved.

The invention discloses a text analysis method for intelligent paper marking based on artificial intelligence, and provides an integrated solution for business scene problems of handwritten content recognition, answer correction, wrong question analysis and personalized tutoring in student homework. A correction result is generated by comparing a machine learning model with a standard answer, knowledge points are analyzed through an error generation module aiming at errors, similar exercises are created, meanwhile, a detailed problem solving guide text is generated, and finally a feedback document containing error review and tutoring suggestions is integrated. And furthermore, model parameters are optimized through a dynamic adjustment mechanism, so that the correction accuracy is improved. According to the invention, homework correction and personalized tutoring are seamlessly connected, full-process automation from identification to feedback is realized, education efficiency and learning pertinence are significantly improved, and accurate learning support is provided for students.

The invention discloses an equipment maintenance task optimization scheduling method based on constraint grouping. The method comprises a constraint grouping correlation theory, a multi-objective optimization model and a solution method based on constraint grouping. The constraint grouping correlation theory comprises a constraint grouping definition and principle, constraint type division and quantification, constraint grouping priority ranking and constraint grouping determination flow; the multi-objective optimization model comprises a multi-objective optimization function, constraint conditions and dynamic optimization and uncertainty modeling; the solution method based on constraint grouping comprises a hierarchical constraint grouping mechanism, an improved NSGA-II algorithm with dynamic updating and scheduling scheme output under uncertainty. The invention relates to the technical field of equipment maintenance task scheduling. According to the equipment maintenance task optimization scheduling method based on constraint grouping, cooperation of constraint grouping sub-problem solving and global optimization is shown, the method is suitable for an uncertain and multi-constraint equipment maintenance scheduling scene, and the problems that a traditional algorithm is too many in dimension and difficult to solve are solved.

The invention provides a multi-modal large model combined question analysis and explanation generation method and system, and relates to the technical field of multi-modal learning, and the method comprises the steps: carrying out the standardization processing of a multi-modal input question, and obtaining the standardized question data; separating structured question information to perform deep feature extraction, driving three-level knowledge structure linkage matching of the question solving thought library, and generating a plurality of initial question solving paths; performing knowledge blind area coverage screening, and positioning a target problem solving path; recursive knowledge point reinforcement is carried out, and a non-blind area credible problem solving chain is generated; step-by-step analysis logic derivation is carried out, and a target analysis logic framework is output; and performing cross-modal directional rendering according to real-time explanation modal selection, and delivering and outputting a multi-modal analysis explanation stream. The technical problems that in the prior art, deep understanding of personalized requirements and knowledge mastering levels of users is lacked, customized answers cannot be provided for the users, and the learning effect of the users and tool practicability are affected are solved.

The application is a QUBO problem solving method based on analog computing circuit, belonging to the field of analog computing and integrated circuit.The method combines the mathematical mapping of QUBO problem with the physical evolution process of analog computing circuit, maps the quadratic term matrix in QUBO problem into the product of resistance value in programmable resistance memory array and output current of current source module, maps the linear coefficient vector into threshold voltage of each row comparator, and takes the output voltage of each row comparator on feedback loop as the output of corresponding logic of state variable to be solved in QUBO problem, and relies on the natural evolution process of voltage in the circuit to realize the solution of QUBO problem in one step.The application does not depend on external digital circuit to provide clock, but updates in continuous time, has the advantages of fast speed, less peripheral circuit and low power consumption, and is suitable for the application scene of QUBO problem which is sensitive to power consumption and requires high solving speed.

An object of a system according to an embodiment is to find a problem specific to each region and present an appropriate solution and a process for realizing the solution.SOLUTION: A system according to an embodiment includes a problem finding unit, a solution presentation unit, and a process presentation unit. The problem finding unit finds and extracts a problem in an area. The solution presenting section presents an idea of a solution to the problem found by the problem finding section. The process presenting section presents a specific process for realizing the solution presented by the solution presenting section.SELECTED DRAWING: Figure 1

The application provides a robot customer service problem intelligent processing method and system, the method comprises the following steps: obtaining a user problem and performing feature extraction and integration on the user problem; constructing a user dynamic causal reasoning model to generate a multi-level problem solving strategy according to the user problem; constructing an 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 suitable for the current corresponding context; executing the response strategy and judging whether to trigger learning according to the user feedback; performing real-time adaptive learning and optimization on the adaptive response strategy generation model through user feedback and historical user feedback; analyzing the long-term trend of the model parameters of the adaptive response strategy generation model after multiple optimizations and historical user feedback, performing global optimization combined with the long-term trend, and generating a final response strategy. The application can perform well in a complex user service scene.

The present specification provides a question answering method and device, wherein the question answering method comprises: obtaining a question to be answered, the question to be answered comprising at least two blank positions; inputting the question to be answered into an encoder of a question answering model to obtain a semantic vector corresponding to the question to be answered; and decoding the semantic vector through a decoder of the question answering model to obtain answers corresponding to the at least two blank positions of the question to be answered. In this way, the question answering model better understands the context semantics of the question, and obtains the answers of all sub-questions in the question at one time, thereby realizing high-precision automatic problem solving and greatly improving the problem solving efficiency and correctness.

The application discloses a kind of based on real scene can cross software's software operation mixed interactive help method and system, including extracting electronic equipment software information, button information, generate corresponding training data;And pre-constructing using deep learning based on real scene semantic understanding can cross software's software operation mixed interactive help model, according to question information, input software operation mixed interactive help model, output each semantic analysis result's ranking score, select one or more semantic analysis results as user problem understanding result, provide interactive operation help for user, including step-by-step prompt or automatic completion button click operation.The application greatly widens the problem solving range of software operation interactive help system based on real scene semantic understanding, improves the convenience and problem solving efficiency, solves the problem that user uses general help instruction is not intuitive, enhances the good experience of user using product.

The present invention describes how a workgroup expert system can be established to mimic a real world task-expert and possess the equal four expert-Human-Intelligent (expert-HI) Problem-Solving (PS) competencies, including: 1) real-time concurrent workgroup-AI PS-processing, 2) real-time semantic workgroup-AI PS-transactions, 3) real-time task-domain workgroup-AI PS-collaborations and 4) real-time fine-grained adaptive workgroup-AI PS-services, based on multi-node workgroup architectures with derived workgroup-software methods and developed workgroup-system disciplines. Therefore, according to the Turing Test, the workgroup expert-task system should be deemed "Strong-AI-PS competent" for solving any task that is handled by one task expert with the help of functional processors, while all the current nodes-service-infrastructures with four node-AI-PS competencies can only mimic a group of real world functional processors with pre-developed logic-modelled processor-Human-Intelligent (processor-HI) PS-competencies for solving a pre-defined / specific multi-function-modelled task-oriented problem and should be deemed "Weak-AI-PS competent".

The application discloses a PCB intelligent manufacturing quality prevention and diagnosis method, comprising the following steps: collecting and structuring defect data of key processes in the whole PCB manufacturing process to form a knowledge base; collecting real-time production data corresponding to the associated parameters in the knowledge base; based on the collected real-time production data, using a pre-trained defect prediction model or / and a rule engine to perform risk prediction and early warning; when defects are actually detected by online detection equipment or the system triggers a risk warning, intelligent diagnosis of defect root causes and improvement measures are recommended; a feedback mechanism is established to record and evaluate the diagnosis results and improvement measures each time, and feedback to the knowledge base, defect prediction model and rule engine for data optimization and updating. The PCB intelligent manufacturing quality prevention and diagnosis method can realize intelligent prediction, rapid diagnosis and measure recommendation of defects, and significantly improve the quality stability and problem solving efficiency. The application also provides a PCB intelligent manufacturing quality prevention and diagnosis system.

The invention discloses an operator automatic optimization method, system and equipment based on multi-stage search and local optimization and a medium, belongs to the technical field of operator optimization, and aims to solve the technical problem of how to realize adaptive optimization of operator performance considering efficient search, performance modeling and universality. According to the technical scheme, after multiple sets of candidate scheduling schemes are automatically generated, the problem solving space is rapidly reduced through wide area search, the optimization precision is improved through local optimization, and self-adaptive optimization of operator performance is achieved; the method specifically comprises the following steps: abstractly representing operators: abstractly representing a target operator as operator feature information and hardware feature representation; generating operator optimization templates: converting the abstract representation of the target operator into parameterization rules, and automatically generating a plurality of candidate operator optimization templates by combining different levels of parameterization rules; sample evaluation and adaptive cost prediction model learning; local refined search is carried out; and outputting and integrating.

The disclosure provides a mistake question tutoring method and system based on generative artificial intelligence, and an electronic device. The method comprises obtaining mistake question data and determining a target knowledge point corresponding to the mistake question data; determining a corresponding teaching chain structure according to the target knowledge point, and adjusting the thinking prompt information of the guide question in the teaching chain structure based on the question type information in the mistake question data to generate a guide question chain corresponding to the target knowledge point; verifying the guide question chain and sending the guide question chain that passes the verification to the client to collect corresponding answer behavior data; determining a mistake cause result corresponding to the answer behavior data; and generating a corresponding mistake question training scheme based on the mistake cause result. By analyzing the mistake question data of the student, the knowledge point corresponding to the mistake question is identified to provide accurate direction for subsequent guidance and consolidation. The generated guide question chain guides the student to think independently, gradually builds the correct problem solving method, avoids directly giving the answer, and guides the student to establish the correct answer thinking.

The invention discloses a code repairing method and device, electronic equipment and a computer storage medium. The method comprises the steps of obtaining target problem information from target detection information corresponding to a target code; the target detection information is obtained by detecting the target code by an editor; then, obtaining a target related code corresponding to the target problem information; and finally, inputting the target problem information and the target related code into the target large model to obtain target repair information corresponding to the target problem information. According to the method, a more specific and feasible error modification suggestion (target repair information) is provided for target problem information in a mode of combining a large model with editor detection, and a developer does not need to query a webpage to find a problem solution any more, so that the developer can be directly assisted in code problem repair in a programming process; and the code writing efficiency and the code writing quality of developers are improved.

The invention discloses a clinical test quality control system which comprises a test process and specification management end, a quality index and index monitoring end, an auditing and checking management end, a file management and version control end, a risk management and problem solving end, a training and education management end and an approval and authorization management end. The test flow and specification management end, the quality index and index monitoring end, the auditing and checking management end, the file management and version control end, the risk management and problem solving end, the training and education management end and the approval and authorization management end are in data communication through a private network and a private line network. According to the method, the quality of the test can be effectively monitored, potential problems can be effectively recognized and corrected, it is ensured that the test meets the requirements of laws and regulations and standards, the credibility and reliability of the clinical test are improved, the rights and interests of subjects are protected, and a reliable basis is provided for explanation and popularization of test results.

The embodiment of the invention provides a power station customer service system construction method and device and a power station customer service system, and the method comprises the steps: building a fault data set according to an obtained energy storage power station technical file; performing fine tuning training on a preset large model based on the fault data set, so that the preset large model learns a corresponding relation among an operation scene, a fault phenomenon and a fault reason of the energy storage power station through fine tuning training; vectorizing the obtained energy storage power station technical file to obtain a vector database in which text information in the energy storage power station technical file corresponds to numerical vectors; and setting the vector database and the preset large model subjected to fine tuning training in a workflow of the energy storage power station customer service system as a reasoning layer, so as to perform reasoning on an input consultation statement to obtain a fault reason. According to the power station customer service system construction method provided by the invention, the customer service system capable of automatically determining the fault reason of the energy storage power station according to the description of the customer can be constructed, manual intervention is reduced, and the problem solving speed is improved.