215 results about "Problem resolution" patented technology

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.

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.

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 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.

Collecting task-specific knowledge for a classification task by AI in the industrial environment requires a high degree of expert knowledge about the respective area. For the purpose of formalisation and embedment in the machine learning model used, ML experts are however required. Close cooperation between data scientists and domain experts for each specific application is a prerequisite. This process is very costly and prone to errors in the event of incorrect communication, and as a result, these approaches are practical only for a limited number of applications where the benefit of solving the problem with an ML approach with a high degree of domain knowledge integration is sufficiently high to cover this cost. The proposed method overcomes the above-mentioned hurdles by providing a way of using knowledge contained in LLMs in small application-specific models. This is done by distilling expert knowledge, which is combined into rules for the direct classification and / or extraction of relevant data features in LLMs using domain-specific input requests and is then used in small task-specific models during inference or training.

The present disclosure relates to Wallow, a communication and collaboration platform for product management teams. It integrates real-time issue reporting, AI-enhanced analytics, and inclusive communication strategies. Team members can instantly report issues, ensuring immediate awareness and response. Wallow's AI module analyzes communication data to provide insights on issue frequency, recurring problems, and sentiment. The platform features a dedicated space for managing active issues, prioritizing unresolved problems. Additionally, Wallow supports the creation of functional teams and offers a centralized hub for all product-related information, enhancing team coordination and information sharing. This comprehensive approach improves efficiency and responsiveness, addressing the unique challenges of product development.

To output learning materials optimized for an individual.SOLUTION: An individual optimization device inputs problem information for each of a plurality of problems, having a first population correct answer rate for a certain problem, a predetermined thinking code domain representing characteristics of the problem among a plurality of thinking code domains, and a predetermined thinking skill required to solve the problem among a plurality of thinking skills, and inputs individual correct answer rate information showing the individual correct answer rate for each combination of a predetermined thinking code domain and a predetermined thinking skill, where the individual correct answer rate is the average correct answer rate for multiple past problems answered by an individual respondent who possesses that specific combination of domain and skill; calculates the difference between the first population correct answer rate and the individual correct answer rate for each of a plurality of problems; determines, among the problems, one or more problems where the difference is within a predetermined 1 or greater range to be matching problems belonging to the matching zone for the individual respondent; and outputs learning materials optimized for the individual.SELECTED DRAWING: Figure 7

The invention discloses a method, a device, a storage medium, a system and a computer program product for enhancing the reasoning capability of a multi-modal reasoning model. The method comprises the following steps: constructing a multi-source data set; determining a decision set for solving the problem; for each question and answer pair, executing the following processing: inputting the decision set and the current question in the current question and answer pair into a multi-modal reasoning model to obtain first state conversion data of the current question, the first state conversion data being logic data of a pre-estimated answer of the current question based on at least one decision in multiple decisions; based on a real answer in the current question and answer pair, a first data state of the first state conversion data is determined, and the first data state indicates that the first state conversion data is correct data or wrong data; and in response to all the question and answer pairs, executing the processing, and training the multi-modal reasoning model by using all the correct data and the question and answer pairs corresponding to the correct data to obtain a first multi-modal reasoning model with enhanced reasoning ability.

The invention provides a time sequence decomposition relaxation cutting method for a security constraint unit commitment problem, and relates to the field of data processing. In the method, a full-scheduling time domain is divided into a fixed window, an integer window, a relaxation window and a neglect window through a dynamic sliding time window, and a unit commitment problem is solved in stages. Firstly, sub-problems are established in an integer and a relaxation window, results are gradually solidified, a global safety feasible solution is obtained in combination with full-scene safety detection and a cut plane dynamic adding mechanism, then decision variables are released in an optimization window with the solution as an anchor point for local optimization, rolling optimization is carried out along a time axis to continuously reduce scheduling cost and improve safety margin, and finally, scheduling is completed. And finally, outputting an optimized full-time-domain scheduling scheme. By implementing the technical scheme provided by the invention, the timeliness of solving the SCUC problem by the server is improved.

The invention discloses an information processing method and device, a storage medium and computer equipment. The method comprises the steps that a main agent conducts intention recognition on user context information to obtain a to-be-executed target task, and the target task comprises at least one subtask; the main agent determines a sub-agent for executing the sub-task and a task execution permission of the sub-agent based on the task content of the sub-task and the description information of the sub-agent; the main agent sets a task identifier for the sub-task, and sends the task identifier, the task content and the task execution permission of the sub-task to the sub-agent; the sub-agent determines a task execution range based on the task content of the sub-task and the task execution permission, and executes the sub-task in the task execution range to obtain a task execution result; and the main agent generates reply information based on the task execution range and the task execution result of the sub-agent and sends the reply information to the client. The method can reduce the training cost of the model and improve the ability of the model to solve problems.

The present disclosure relates to a technique for addressing an issue to be resolved associated with an electronic document. The method discloses accessing an actionable portion associated with a particular knowledge domain of the electronic document and associated context. Further, retrieve data from data sources to provide additional information related to the particular knowledge domain and the associated context. Then structuring the retrieved data to produce a subset of organized data and determine the issue to be resolved related to the electronic document. Further, generate data elements associated with the issue to be resolved and map dependency relationships between data elements. Also, determine test goals associated with the issue to be resolved based on the dependency relationships. Thereafter, determine corresponding test cases associated with resolution and determines actionable test steps related to the issue to be resolved based on the corresponding test cases associated with the electronic document.

The invention relates to a defined domain event reasoning method based on a mixed graph, and belongs to the field of artificial intelligence, big data, knowledge graphs and natural language processing. In order to solve the problem that a traditional data processing method cannot effectively assist a decision maker to make a fast decision, event knowledge and logic relations contained in the multi-modal data characteristics of multiple sources are extracted, the multi-modal data are represented in a unified mode through a multi-modal networked synthesis representation method, and therefore the multi-modal data characteristics of multiple sources are obtained. Multi-modal data fusion is realized, and a mixed knowledge graph network capable of dynamically storing event nodes and relationships is constructed; and on the basis of the mixed knowledge graph, predicting different future development trends by adopting a trend-simulation reasoning prediction method. According to the method, the event development trend can be predicted and reasoned, and a decision maker is assisted to solve problems and formulate coping strategies through deduction and research.

A method includes: receiving, by a computing device, an issue definition of an issue with software; generating, by the computing device and based on the issue definition, an urgency score for the issue, the urgency score representing an urgency of resolving the issue; generating, by the computing device and based on the issue definition, a complexity score for the issue, the complexity score representing a complexity of the issue; identifying, by the computing device using natural language processing and based on the urgency score and the complexity score, an assignee to address the issue, the assignee being a team member of a plurality of team members; recommending, by the computing device, to a user the assignee for assignment to address the issue; and tracking, by the computing device, progress of resolving the issue.

The invention relates to the technical field of enterprise management and artificial intelligence, discloses a management method for cooperatively solving problems based on multiple roles, and aims to improve enterprise problem processing efficiency and cooperative capability. The management method for solving the problem based on multi-role cooperation comprises the following steps: collecting and cleaning front-line work details through an employee client of a scenarized template; the AI processing module calls a fusion model of NLP semantic analysis, computer vision identification and time series data comparison, identifies problems from work details and generates a report; the role matching engine screens matched experts and pushes related information to an expert client to input a solution; after receiving the scheme, the decision-making client synchronizes a decision-making instruction to the execution main body; and the server storage module associatively stores the full-process information through the unique identifier to form a reusable corpus packet. According to the method, multi-role efficient collaboration and knowledge precipitation are realized, and a management closed loop is optimized.

The present invention relates to a multi-hop reasoning method and device for a knowledge graph oriented to attribute fusion, belonging to the field of intelligent question answering, including: defining an attribute-oriented knowledge graph and multi-hop reasoning; the knowledge graph is used for structurally storing and representing entities of various objective facts and the attributes of the entities; multi-hop reasoning is used for exploring semantic paths in the knowledge graph by multi-hop logical reasoning, spanning multiple entities and the relationships between entities to solve problems; building an AMR-KG architecture for realizing the organic combination of the attributes of entities and multi-hop reasoning, and integrating attributes into the knowledge graph and the multi-hop reasoning process. The present invention improves the accuracy and efficiency of multi-hop reasoning, enhances the model's semantic understanding ability of complex relationships, provides a new reasoning framework for knowledge graphs rich in attributes, and provides new ideas for subsequent research.