401 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 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 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 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 discloses an AI recruitment candidate comprehensive ability evaluation method based on a multi-modal large model, and relates to the technical field of artificial intelligence recruitment and multi-modal data processing. The method comprises the following steps: S1, constructing a post digital twinborn environment; S2, collecting candidate multi-modal interaction data; S3, training and optimizing a multi-modal large model; and S4, evaluating the comprehensive ability of the candidate and outputting a result. According to the method, a high-simulation post digital twinborn environment is constructed, multi-modal interaction data acquisition is combined, and deep analysis and evaluation are performed by using a multi-modal large model, so that the method not only considers the traditional resume and interview performance of candidates, but also improves the evaluation efficiency by simulating a real working scene. The method comprehensively evaluates the actual operation capability, the communication cooperation capability, the problem solving capability and other multi-dimensional capabilities of the candidates, and compared with a traditional recruitment mode, the method can more accurately predict the scene adaptation speed and the comprehensive capability performance of the candidates after the candidates enter the job.

The invention relates to the field of power transmission and communication, and discloses an intelligent cable monitoring system for monitoring the state of a cable in real time, and the system comprises a data collection module, a physical modeling module, an inverse problem solving module, an optimization algorithm module, a fault positioning module and a precision evaluation module. The method comprises the following steps of: arranging various sensors at key positions of a cable, acquiring temperature, stress, vibration and electromagnetic field data in real time, accurately positioning a cable fault position by combining physical modeling and an optimization algorithm, and optimizing a fault positioning result through precision evaluation and regularization adjustment. According to the method, the cable fault positioning precision is improved through multi-physics field joint modeling, inverse problem solving and optimization algorithms, and the problems of insufficient information dimension and poor flexibility are solved. Precision evaluation and regularization dynamic adjustment are introduced, the adaptive capacity of the system is enhanced, visual fault output and automatic report generation are designed, and the information interaction efficiency and the operation and maintenance response speed are improved.

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 relates to the technical field of quality supervision and early warning management, in particular to an AI-based subway engineering construction quality supervision and early warning management system and method, and the system comprises a process data fusion module which obtains the sensor monitoring data deviation degree, the material detection qualified rate and the personnel operation specification conformity of a shield tunneling process. According to the method, a quality vulnerability index for expressing process state fluctuation is established by fusing a sensor monitoring data deviation degree, a material detection qualification rate and a personnel operation specification conformity, and an accumulated risk assessment mode based on a process chain conduction relation is formed by combining dynamic quantification of an upstream process defect on a downstream influence probability. And the capability of identifying the coupling influence between the working procedures is enhanced. And synchronously analyzing information request metadata, scheduling personnel hour data, access control behavior records and rectification cycle time, and constructing a multi-dimensional collaborative efficiency index sequence covering information circulation efficiency, personnel operation load and problem solving efficiency.

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.

The embodiment of the invention provides a multi-mode problem solving method, a multi-mode problem solving system, computing equipment and a medium, and the multi-mode problem solving method comprises the steps that an input image is acquired, and the input image comprises a problem text and an initial problem graph; based on the initial topic graph, analyzing the topic text to obtain a topic analysis result and a target graph element; reasoning the question analysis result based on the target graphic element to obtain an answer text; based on the answer text, performing vector graphic marking on the target graphic element to obtain a vector graphic marking result; and based on a vector graph marking result, rendering the target graph element on the initial topic graph to obtain a rendered topic graph. Students can visually see the answering process and the final result, and the learning effect and the question solving experience are 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 field of voice questions and answers, and particularly discloses a large model customer service system based on multi-modal double-layer shunt reasoning, which introduces semantic complexity evaluation on a text input by a user, and forms a three-dimensional decision matrix in combination with confidence of front-end voice recognition. By dynamically weighting the intention confidence, the semantic complexity score and the speech recognition confidence, the system can accurately recognize queries which seem to be simple and actually complex. In this way, the traditional confidence trap is solved, even if a complex query generates virtually high intention confidence due to the fact that the complex query contains keywords, the high complexity score of the complex query can correct the shunting decision, and the query is guided to a heavy weight model capable of conducting deep semantic understanding. Finally, it is ensured that all kinds of user requests can be properly processed by the system level, and the accuracy and the problem solving rate of a customer service system are remarkably 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.

In some aspects, a logic design undergoes functional verification, which includes generating a test stimulus to apply to the logic design. The test stimulus includes test values for variables representing signals in the logic design. Generating the test stimulus involves a first problem of solving for the test values of the variables subject to constraints on the test values. It is solved as follows. A specification of the logic design is accessed. An array implication graph is generated from the specification. The array implication graph represents the problem as a set of two or more single-array constraint problems. Each single-array constraint problem solves for the test values of a single array of the variables subject to the constraints within that single array. The array implication graph also represents dependencies between different single-array constraint problems. The problem is solved based on the dependencies represented in the array implication graph.

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 provides a large-model multi-agent interaction coordination system, method and server, and relates to the technical field of agent coordination control, the large-model multi-agent interaction coordination system provided by the invention comprises a user access layer, an agent service layer, a protocol layer, a capability layer and a data layer; request information can be obtained through a user access layer; the intelligent agent service layer comprises an intelligent agent dispatching center and a plurality of intelligent agents communicating with the intelligent agent dispatching center, and the plurality of intelligent agents exchange data through the protocol layer; the agent service layer carries out instantiation decomposition on the request information to obtain a subtask agent to execute a subtask and generate feedback task data; the task data fed back by each agent is summarized to obtain the feedback information corresponding to the request information, and the feedback information is displayed in the user access layer, so that the whole system can realize efficient collaboration and cooperation of multiple agents, the ability of the whole system to deal with complex tasks is improved, and the problem solving efficiency is greatly improved.

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.

This platform features an AI-based text analysis system centered around a BERT (Bidirectional Encoder Representations from Transformers) model, fine-tuned for analyzing academic and industry literature. It includes a user-friendly interface linked to academic databases, enabling efficient retrieval and detailed analysis of pertinent papers. The enhanced BERT model processes these documents, extracting essential information and generating concise summaries, displayed on an interactive dashboard that highlights literature trends and relationships. The system supports multilingual queries and searches conference presentations, enhancing its versatility. A named entity recognition module identifies critical elements like datasets and methodologies. A standout feature is the integration of a Student Information System (SIS), which incorporates data on students' extracurricular activities related to decision-making. This aids educational institutions and employers by providing insights into students' leadership and problem-solving skills. This comprehensive tool streamlines the literature review process and supports informed decision-making through advanced text analysis and integrated student data.

To reduce work load of drawing.SOLUTION: A generation device having a processor executing a program and a storage device for storing a program executes: first generation processing for generating a first inquiry about a first hierarchy of a hierarchical structure related to problem solution composed of a plurality of hierarchies; first acquisition processing for acquiring a first reply including one or more first reply sentences to the first inquiry generated by the first generation processing; first creation processing for creating a first element for each first reply sentence in the first reply acquired by the first acquisition processing in a drawing area of the first hierarchy; second generation processing for generating a second inquiry about the first hierarchy regarding the second hierarchy different from the first hierarchy in the hierarchical structure; second acquisition processing for acquiring a second reply including one or more second replies to the second inquiry generated by the second generation processing; and second output processing for creating a second element for each second reply sentence in the second reply acquired by the second acquisition processing in a drawing area of the second hierarchy and associating the second element with the first element.SELECTED DRAWING: Figure 7

The invention relates to the technical field of optical measurement, in particular to an optical scattering method based on an inverse problem solving algorithm and a mixed measurement method for batch samples thereof, and the method comprises the following steps: carrying out optical measurement on samples to obtain actually measured optical signals; obtaining prior structure information of the sample; constructing an initial model; analyzing the initial model by utilizing strict coupling wave analysis to generate a simulation optical signal; judging whether the difference between the generated simulation optical signal and the actually measured optical signal meets a set requirement or not; if yes, outputting a measurement result; and if not, gradient calculation is carried out on each parameter in the initial model, gradients of all the parameters are obtained by utilizing an automatic differential algorithm, then the initial model is optimized by utilizing the gradients of the parameters to obtain an optimized model, and the step of analyzing by utilizing the strict coupling waves is repeatedly executed until a set requirement is met. According to the method, more detailed contour optimization can be provided, and the calculation efficiency can be improved.