323 results about "Problem resolution" patented technology

The invention relates to the technical field of knowledge question answering, in particular to an intelligent question answering method and system based on multi-module collaborative optimization, and the method comprises the steps: inputting a question to be answered into a knowledge question answering model, and outputting a knowledge question answering result by the knowledge question answering model; a knowledge range judgment module in the model judges whether the problem can be solved by relying on own knowledge, and if not, a dynamic retrieval module is entered; the dynamic retrieval module performs similarity retrieval on the content of the memory knowledge base according to the question to be answered, and if the retrieval result does not meet the requirement, the multi-level question rewriting module is entered; the multi-level question rewriting module rewrites questions to be answered and inputs the rewritten questions into the knowledge screening module; the knowledge screening module outputs the screened documents according to the rewritten questions, the auto-reflection optimization module generates preliminary answers according to the documents and the questions and judges whether the preliminary answers are reasonable or not, auto-reflection optimization is carried out if the preliminary answers are not reasonable, and a new solution is provided for development of the intelligent question and answer technology.

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 text robot application system based on a large model, and the system comprises the following modules: a knowledge base and analysis module which receives and processes a business document, decomposes the content into semantic units, and marks context features; the question-answer generation module automatically generates question-answer pairs through deep semantic analysis and stores the question-answer pairs in a vector database; the question extension module is used for dynamically extending similar questions by using a natural language generation technology; the semantic intention analysis module is used for accurately extracting a problem core intention through context semantic analysis and mapping the problem core intention to a service path; the text workflow management module is used for realizing dynamic routing of multiple service scenes by adopting a logic tree model; the context semantic association module is used for analyzing and optimizing problem expression through multiple rounds of dialogues; the bottom response module is used for providing multi-level answers in combination with a question and answer database, document recall and dynamic generation; and the intelligent learning module records unsolved problems and optimizes the question and answer library and semantic rules through a learning algorithm.

The invention discloses a large-model multi-agent collaborative machine manufacturing knowledge question-answering method, which belongs to the technical field of computers, and comprises the following steps of: acquiring and inputting a problem in the field of machine manufacturing and a preset strategy configuration cue word into a central agent to judge whether to adopt a parallel strategy to solve the problem; the central agent determines that a problem decomposition mode is divided into parallel decomposition and serial decomposition according to whether a parallel strategy is adopted or not; for parallel decomposition, the central agent splits the question into a plurality of independent sub-questions and inputs the sub-questions into a retrieval agent multi-step reasoning module, answers corresponding to the sub-questions are obtained respectively, and finally the answers of the sub-questions are input into the central agent to obtain a final answer; and for serial decomposition, the central agent splits the question into a series of ordered solution steps according to a logic sequence, inputs the question which is firstly split into a multi-step reasoning module of the retrieval agent, then splits a new question according to an obtained answer and an original question, and the rest are done in the same manner until a final answer is obtained.

The invention relates to the technical field of AI computing, in particular to a knowledge reinforcement learning method and system based on multiple agents. Modifying parameters of the large language model by using a supervised fine-tuning training method for the pre-trained large language model; constructing a knowledge base of a large language model, and vectorizing, storing and retrieving texts in various file formats; a multi-agent workflow optimization information processing and decision-making process is developed, a real-time problem is decomposed, a logic chain for solving the problem is constructed, and internet texts are obtained, filtered and summarized. Compared with the prior art, the method has the advantages that the knowledge quantity of the pre-trained large language model in the specific field is increased, the knowledge base is high in flexibility, the obtained text is high in real-time performance, certain interpretability is achieved and the like, and then the ability of the large language model to solve problems in the specific field is improved.

The invention provides a question and answer model training method and device, electronic equipment, a storage medium and a program product. The method comprises the steps that multiple candidate reasoning paths are determined based on a first question sample carrying an answer label, and the candidate reasoning paths are used for indicating reasoning steps needing to be executed for solving a question corresponding to the first question sample; classifying the plurality of candidate reasoning paths to obtain a first reasoning path belonging to a first category and a second reasoning path belonging to a second category, the reasoning accuracy of the first reasoning path being greater than the reasoning accuracy of the second reasoning path; determining a first loss value of the question and answer model based on the first reasoning path and the answer label, and determining a second loss value of the question and answer model based on the first reasoning path and the second reasoning path; and training the question and answer model based on the first loss value and the second loss value to obtain a target question and answer model. Through the method, the question solving performance of the question and answer model can be effectively improved.

The invention discloses a hierarchical dynamic scheduling method based on a strategy optimization DDQN algorithm, which is used for solving a dynamic scheduling problem of a reentrant hybrid flow shop with a batch processor, and comprises the following steps: firstly, determining an objective function and a constraint condition of the scheduling problem; secondly, by introducing a self-attention mechanism, providing a layered structure based on the self-attention mechanism DDQN, and constructing two agents, namely a batching agent and a scheduling agent, to respectively solve batching sub-problems and scheduling sub-problems in the problem; besides, in order to cope with the multi-stage batch processing and reentry scheduling characteristics in the problem, a Markov decision process considering the characteristics of the two agents is designed, and the Markov decision process comprises states, actions and rewards. Furthermore, an action selection strategy and a soft start target network updating strategy based on a mask combined with an epsilon-greedy strategy are provided, so that the efficiency and generalization ability are improved. The method has a remarkable effect on solving the dynamic scheduling problem of the reentrant hybrid flow shop with the batch processor.

The invention discloses a path planning method based on an improved DQN algorithm. The path planning method aims at selecting a collision-free path to reach an ideal position. Path planning needs to solve the problems of target reaching, obstacle bypassing and the like, and an environment map needs to be preset in a traditional method. The deep reinforcement learning is used for solving a dynamic path planning problem, and a common path planning method is separated from online. DRL learns an optimal strategy through trial and error and environment interaction, and a DQN algorithm is applied to path planning but has the problems of slow convergence and the like. According to the improved DQN path planning algorithm and system provided by the invention, the problem is replayed and solved by improving a reward function and an action selection strategy and introducing prior knowledge and prior experience. Comprising the steps of constructing a two-dimensional grid network environment map, constructing, improving and optimizing a DQN path planning model based on the two-dimensional grid network environment map and a Markov decision process, improving strategy covering reward modeling, designing an attenuated epsilon-greedy strategy, and introducing prior knowledge and prior experience for replaying.

The invention provides a task scheduling method applied to a hybrid cloud environment. The method solves a plurality of problems in the prior art. Firstly, through comprehensive demand analysis and resource evaluation, the target and constraint conditions of task scheduling can be clarified, and blindness and randomness in the scheduling process are avoided. And secondly, through task classification and scheduling strategy formulation, optimal configuration of resources and efficient execution of tasks can be realized, and the stability and performance of the system are improved. In addition, by establishing a task scheduling model and designing a mathematical formula, the task scheduling process can be described more accurately, and the scheduling accuracy and efficiency are improved. And finally, through performance evaluation and optimization steps, problems can be found and solved in time, and the performance and effect of task scheduling are continuously improved.

Various systems and methods are presented herein regarding automatically identifying one or more entities to for assignment to resolving an issue and / or removing an entity from a group of entities assigned to resolve the issue. The issue can relate to an information technology / computer-based environment. The entities can be subject matter experts, having expertise identified to be related to the issue. A summary of the issue and activity relating to resolving the issue can be automatically generated and supplied to an entity who is being onboarded after the project is underway to resolve the issue. By implementing automated assignment / removal of entities, efficiency of the resolution is improved.

Systems and methods of the present invention provide for receiving, from a client, a request for a collaborative environment; displaying the collaborative environment; recording a plurality of attempt steps including events input by a learner to solve a problem, and solution steps input into the collaborative environment by an instructor, which identifies a timestamp within the plurality of events and generates alternative events solving the problem. The system then receives a request to reproduce the collaborative environment; and generates a reproduction of the collaborative environment.

The invention discloses an intelligent recommendation system and algorithm based on a knowledge graph, and relates to the technical field of information recommendation. The intelligent recommendation system based on the knowledge graph and the dynamic updating method thereof are achieved mainly by taking dynamic updating of the knowledge graph, multi-modal information fusion, cold start problem solving and recommendation algorithm optimization as the core, and the purpose is to establish and maintain the association relation graph between the user and the article and combine real-time data analysis to achieve the intelligent recommendation system based on the knowledge graph and the dynamic updating method of the intelligent recommendation system based on the knowledge graph. And the recommendation algorithm is continuously optimized to improve the accuracy and timeliness of recommendation.

A dynamic, infrastructure-aware incident response and automated problem resolution system (100) for cloud environments, comprising: (a) an infrastructure and topology mapping module configured to continuously scan, discover, and map virtualized and containerized cloud resources and their interdependencies in real time; (b) an incident detection and correlation module configured to ingest telemetry data from distributed monitoring sources and apply rule-based and machine learning models to detect, correlate and classify system incidents; (c) a context analysis and impact assessment module configured to analyse the scope, severity and business impact of incidents based on real-time infrastructure context and service dependency diagrams; (d) a policy-driven decision engine configured to dynamically select response actions based on predefined rules, compliance policies, service level agreements and historical incident data; e) an Automated Remediation Orchestrator configured to execute predefined or dynamic remediation playbooks through integrations with cloud orchestration and configuration tools; and (f) a feedback loop and learning module configured to collect post-incident data, evaluate the effectiveness of remedial actions, and continuously refine the detection and response logic using machine learning algorithms.

The invention provides a mathematical application question solving method and system based on a few-sample thinking chain prompt, and the method comprises the steps: coding a question sample and an inference chain thereof into feature vectors based on a pre-training encoder; classifying the feature vectors, selecting the first N feature vectors of each class, constructing cue words, and guiding a large language model to construct examples for each class of problems; an example most similar to the to-be-solved problem is extracted from the example set, and an initial prompt word is constructed based on the example and the to-be-solved problem; adjusting and optimizing the initial cue word based on a programmable declarative cue word optimization framework; and using the optimized cue word to guide the large language model to solve the problem to be solved. An optimal example similar to a to-be-solved problem is constructed by processing the problem in training data and a reasoning chain, so that the illusion risk of a large language model due to inconsistent example types is reduced; by utilizing a programmable cue word template optimization method, the generalization ability of the cue word template is improved, the large language model is effectively guided to solve, and the problem solving accuracy is improved.

A ML-based method and system for automatically generating responses to inputs using a multi-tier technique, is disclosed. Initially, first inputs are obtained from electronic devices of first users. An information is retrieved from external sources based on first inputs, using RAG engine. The responses are generated based on optimized information, using ML model. The ML-based method determines whether queries of first users within first inputs are resolved through the optimized responses. Issues are categorized using ML-based issue classification engine when the queries are not resolved. Dynamic forms are generated with fields based on categorization of issues, using a dynamic workflow engine. Second inputs are obtained and processed to determine whether queries are resolved using a rule-based logic engine. The first users are adapted to interact with second users to resolve the issues through communication channels. The responses are provided as output upon resolving the issues, to first users.

The invention relates to the technical field of product service maintenance, in particular to a product service maintenance method and system based on a problem classification model, and the method comprises the steps: collecting the problem description data of a steward in real time, carrying out semantic analysis and classified storage, generating a problem classification model, judging the emergency degree and complexity of a problem, and selecting a response strategy. The system integrates a natural language processing module, a voice recognition module and a problem classification module, accurate problem positioning and automatic solution generation are achieved through multi-modal data fusion, and meanwhile a manual intervention mechanism is triggered to ensure that complex problems are processed in time. According to the method, the problem feedback efficiency and the solving speed can be remarkably improved, the average response time is shortened, the labor cost is reduced, the initiative and safety of operation and maintenance of the rail transit equipment are improved, and remarkable economic benefits are brought to the industry.

The invention discloses a reward model training method, a question evaluation method, an electronic device and a storage medium, and relates to the technical field of large models.The reward model training method comprises the steps that a sample data set and a target cue word are obtained, and the sample data set comprises one or more text samples; inputting the target prompt word and the sample data set into a first question generation model, and generating a first question corresponding to each text sample; inputting the target prompt word and the sample data set into a second question generation model, and generating a second question corresponding to each text sample; wherein the problem generation capability of the first problem generation model is superior to the problem generation capability of the second problem generation model; constructing comparison data based on the text sample, the target prompt word, the first question and the second question; performing comparative learning on a preset reward model based on the comparative data to obtain a target reward model; wherein the target reward model is used for evaluating the problem quality. According to the invention, the quality evaluation problem can be solved.

The invention relates to the technical field of artificial intelligence, and particularly provides a data processing method of a large language model based on an improved hybrid expert architecture. The method comprises the following steps: processing input query information through a semantic understanding module to obtain an understanding result; based on the understanding result and a pre-stored knowledge graph, the query information is sent to a plurality of corresponding expert models, corresponding output results are obtained by the expert models, the plurality of expert models are arranged in multiple layers based on an improved hybrid expert architecture, and the expert models of different layers are obtained through division according to different expert dimensions; and carrying out fusion processing on the output results of the plurality of expert models to obtain an answer result corresponding to the query information. The problems that a large language model of a hybrid expert architecture in the related technology is low in accuracy and poor in problem solving capacity in the professional subdivision field are solved.

Provided are techniques for resolving issues by building a re-playable simulated customer environment. Environment data of an original customer environment is stored in a data hub. The environment data from the data hub us used to create a simulated customer environment. The simulated customer environment is played. In response to the playing, one or more issues in the original customer environment are identified using the simulated customer environment and one or more solutions for the one or more issues are identified. A recommendation is provided with a solution of the one or more solutions for solving at least one of the issues in the original customer environment. Application of the recommendation to the original customer environment to resolve the at least one of the issues is automatically initiated.

The invention discloses a digital twinborn-oriented power grid dynamic ontology modeling method and system, relates to the technical field of power grid dynamic ontology, and aims to solve the problem of inaccurate power grid dynamic modeling. According to the invention, through the power grid dynamic model after visualization processing, a worker can see the operation data of the power grid more visually, so that potential problems and risks can be found more easily. Therefore, the staff can make decisions in time and take corresponding measures to guarantee safe and stable operation of the power grid, after the interaction technology is added, the staff can perform interaction operation with the visual model through input equipment such as a mouse and a keyboard, the test result is analyzed by using a data analysis tool, and the test result is analyzed by using the data analysis tool. And error data in the model can be efficiently identified and diagnosed. According to the intelligent method, the accuracy and efficiency of error recognition are improved, and rapid positioning and problem solving are facilitated.

Provided is a problem solving support device that makes it possible to propose a pertinent solution to a problem for a client. A problem solving support device 1 includes: a storage unit for storing at least a first tree structure and a second tree structure, the first tree structure being composed of a plurality of first nodes that store data related to a problem, and the second tree structure being composed of a plurality of second nodes that store data related to a solution; a first assignment unit for individually comparing each of the first nodes included in the first tree structure with subject data to calculate, for each of the first nodes, a first weighting coefficient based on the degree of similarity with the subject data and for assigning the calculated first weighting coefficient to the first node; and a second assignment unit for assigning a second weighting coefficient based on the first weighting coefficient to each of the second nodes included in the second tree structure on the basis of the degree of relevance representing the strength of relevance defined between same-layer nodes arranged in the same layer of the first tree structure and the second tree structure.

A system, computer program product, and a computer implemented method are provided for interfacing with a virtual dialog environment to dynamically and optimally collected context for problem diagnosis and resolution. A context model is leveraged to identify context entities, and one or more corresponding context collection mechanisms. The context model is implemented in real-time to facilitate dynamic selection of one or more of the identified context collection mechanisms, which are selectively subject to execution to resolve the problem diagnosis.

To provide an invention support system which enables efficient achievement of superior inventions without requiring a lot of human resources.SOLUTION: An invention support system 100 is provided, comprising a user interface configured to transmit a text document to an artificial intelligence service provision site that responds in an interactive manner, requesting the site to apply problem solving theory, solve a problem, and establish an invention.SELECTED DRAWING: Figure 2

Various embodiments leverage artificial intelligence in identifying and potentially resolving compliance issues (e.g., with regulatory requirements, client-specified requirements, certification conditions, etc.), or preventing violations of law, rules and regulations. The AI can be configured to automatically generate requests for information. For example, a system analysis component can be configured to identify a specific compliance target (e.g., a branch location) and select or automatically generate questions to collect responsive information to ensure compliance, identify potential violations, and define any evidence required to identify or resolve issues (e.g., prove compliance, support potential violations, flagged issues, etc.). According to one example, the system can use trained AI models to analyze a set of rules and / or requirements to efficiently build questionnaires to address or demonstrate compliance.

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 invention provides a universal data management system and method based on a multi-modal agent, electronic equipment, a storage medium and a program product. The universal data management method based on the multi-modal intelligent agent comprises the following steps: S1, defining and realizing data acquisition, data cleaning, data labeling and a data storage tool set; s2, designing a task Prompt and inputting the task Prompt into the multi-mode intelligent agent; and S3, the multi-modal agent generates a treatment plan according to the treatment task and requirements and comprehensively uses the tool chain to complete the data treatment task. Through the data governance system and the data governance method, the working process is simplified; each team only needs to pay attention to development and maintenance of own tools and does not need to pay attention to the complexity of the whole process, so that task decoupling is realized; the multi-modal intelligent agent can flexibly select and switch different tools, and the more the tools are, the stronger the problem solving capability of the system is; the method is suitable for various data governance scenes, is not limited by specific industries and applications, and has high universality.

The invention belongs to the technical field of airport business and link tracking, and discloses a business-level link tracking method and system based on airport business. According to the method, a service-level multi-language adaptive probe based on a dynamic bytecode enhancement technology is utilized, semantic analysis is carried out on collected data through a multi-dimensional label identification engine, flight numbers and event coding service labels are extracted, association mapping of the service labels and technology calling links is established, a service link monitoring system is constructed in an airport multi-manufacturer environment, and the service link monitoring system is used for monitoring the airport multi-manufacturer environment. Service-level fault positioning is realized; performing cross-system link integration based on the service label association information, and constructing a service-driven full-link tracking view; and intelligent positioning and visual display of service faults are realized through multi-dimensional service topology analysis and a dynamic early warning mechanism. According to the invention, sampling can be carried out on demand, only key requests and long-time-consuming interfaces are recorded, and the system can be integrated with other monitoring systems so as to quickly respond and solve problems.

An ideation platform device and method using a diagram are disclosed. An ideation platform device using a diagram, according to one embodiment of the present invention, can comprise: a C-K canvas module for providing a C-K canvas divided into a concept space and a knowledge space and connecting a concept and knowledge to each other on the C-K canvas through a chaining process so as to help a solution search for resolving a problem; and an instance management module for storing and managing, as one instance, the C-K canvas, for which a solution search is completed, including the concept, the knowledge, and information about an interconnection relationship.

The embodiment of the invention relates to a problem processing method and device, equipment and a medium, and the method comprises the steps: obtaining a problem video corresponding to a problem feedback operation, the problem video is obtained by carrying out the screen recording of an interface of a target platform through a recording analysis tool before the problem feedback operation, and analyzing the problem video by using a video analysis model in the recording analysis tool to obtain a problem summary, and displaying the problem video and the problem summary on a problem detail page. By adopting the technical scheme, the problem processing tool can directly obtain the problem video reproduction problem, the communication cost of problem understanding is reduced, the problem processing tool can analyze the problem video by using the video analysis model in the recording analysis tool, the problem summary is directly obtained, and the problem processing efficiency is improved. The technical personnel can directly check the problem in the problem video based on the problem summary and quickly solve the problem, so that the problem processing efficiency is effectively improved, and the operation and maintenance cost is reduced.

The invention provides a question and answer model training method and device, electronic equipment and a storage medium. The method comprises the steps that a sample question is answered through an initial question and answer model, an initial answer of the sample question is obtained, and the difficulty of the initial question and answer model for solving the sample question is determined based on the initial answer and a label answer of the sample question; based on the difficulty, determining a target difficulty interval where the difficulty is located from a plurality of preset difficulty intervals; a target loss determination mode corresponding to the target difficulty interval is determined from the multiple loss determination modes, and the loss determination modes corresponding to different preset difficulty intervals are different; and determining a loss value of the initial question-answering model according to the target loss determination mode, and training the initial question-answering model based on the loss value to obtain a question-answering model for answering the question. Through the method, the question solving performance of the question and answer model can be effectively improved.