159 results about "Problem resolution" patented technology

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.

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.

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 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.

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 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.

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.

Described are techniques for identifying an optimal AIOps solution for resolving issues. An alert to resolve a software development and / or information technology problem is routed to a secondary responder to handle after a primary responder failed to resolve the alert. Key performance indicators in handling the alert for both the primary and secondary responders may then determined. A determination is then made as to how the secondary responder performed in handling the alert in comparison to the primary responder based on the key performance indicators. The results of such a determination are stored as metadata. Furthermore, the matching portions of the runbooks used by the primary and secondary responders are identified and stored as metadata. The machine learning model is then trained to select the best runbook to be used by the secondary responder to handle future alerts based on the saved metadata and the metadata of the corresponding alerts.

An information providing device (10) includes a scope generation unit (121), an information collection unit (123), and an answer determination unit (124). The scope generation unit (121) generates a plurality of scopes relating to a problem on the basis of an inquiry message related to the problem. The information collection unit (123): generates, for at least one of the plurality of scopes, a question sentence in which the situation of the problem is questioned, and a target user of the question sentence; transmits the question sentence to the user; and acquires an answer to the question sentence. The answer determination unit (124) determines an operation for solving the problem on the basis of the answer.

This invention relates to a multi-agent collaborative management method and system for water data governance, belonging to the field of water data governance technology. It includes: constructing a data asset management agent, a quality diagnosis agent, and a conversational analysis agent, and setting a standardized decision-making process; building a structured message queue; when water demand processing data is received, the data asset management agent analyzes the water demand processing data to generate water management data and sends it to the message queue; when the quality diagnosis agent listens for water management data in the message queue, it retrieves the water management data, analyzes it, generates governance strategy instructions, and sends them to the message queue; combining the conversational analysis agent and the governance strategy instructions, it determines governance adjustment instructions and executes the governance adjustment instructions. This invention improves the overall efficiency of water data governance and shortens the average problem resolution cycle.

A device may receive and process client data, with a first machine learning model, to determine current state data identifying a current state of a client. The device may process the current state data and prior client data, with a second machine learning model, to determine a problem statement for the client and future state data of the client. The device may utilize the second machine learning model to identify initiatives for the client, and costs of the initiatives, based on the problem statement, the current state data, and the future state data, and to assign benefits and priorities to the initiatives. The device may process the initiatives, the benefits and priorities of the initiatives, and the costs of the initiatives, with the second machine learning model, to generate an initiative plan for solving a problem of the problem statement, and may perform actions based on the initiative plan.

This invention relates to a multimodal large-scale model reliable reasoning method and system for medical image-assisted diagnosis, comprising: designing a multimodal thought chain data generation mechanism; using plain text thought chain data and generated multimodal thought chain data to supervise and fine-tune the large model to achieve a cold start effect; further stimulating the reasoning ability of the large model through reinforcement learning, wherein the reward function of reinforcement learning includes multi-dimensional considerations; the calculated reward function values ​​of each dimension are connected to a dynamic reward scheduling mechanism, which adaptively adjusts the weight coefficients between the reward functions to achieve a balanced optimization of the objective function with multiple reward objectives, and outputs a reliable reasoning medical diagnosis result. This invention eliminates redundant information in the answer while accurately retaining the key information needed to solve the problem, thereby ensuring the accuracy and reliability of the answer. Compared with the prior art, this invention has the advantages of high accuracy, strong robustness, excellent reliability, and high reasoning efficiency.

The present disclosure provides a knowledge recommendation method, device, system and storage medium, which relate to the fields of artificial intelligence technology, especially big data, natural language processing (NLP), and intelligent customer service technology. The specific implementation scheme is as follows: obtaining a problem to be solved submitted by a terminal device; obtaining first knowledge that solves the problem to be solved from a preset knowledge base; determining the correlation score between each other knowledge and the first knowledge based on the subject of the first knowledge and the subject of other knowledge in the preset knowledge base; determining second knowledge related to the first knowledge from other knowledge based on the correlation score; recommending the title of the second knowledge and the first knowledge to the terminal device, wherein the first knowledge is the answer to the problem to be solved and the title of the second knowledge is a related question to the problem to be solved.

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

Examples described herein provide a method of knowledge authentication for artificial intelligence aided decision making. The method includes receiving knowledge from a subject expert and authenticating the knowledge based on structured decision making information. The method also includes generating an efficiency index for separate use of knowledge, wherein the efficiency index measures the order and efficiency of the decisions and methods taken to solve the problem. The method also includes storing the authenticated knowledge and an efficiency index, wherein the authenticated knowledge represents a proprietary technology that can be distributed at a deterministic level with respect to its availability. The method also includes generating a response to the user query using the authenticated knowledge and efficiency index using the trained machine learning model.

The invention discloses an LLM-based learner agent simulation learning and forgetting method and a feedback interaction framework with a teacher agent. The method mainly comprises the following steps: S1, a memory mechanism of a learner: mainly obtaining knowledge from a teacher agent through dialogue interaction and summarizing question answering experience of the learner, and storing the knowledge and the experience in a specific vector database in a two-tuple form to simulate memory so as to simulate the learning ability of the learner; s2, according to a forgetting mechanism, after a teacher message is received, a related vector database is retrieved through a vector similarity method, unforgotten memory is obtained and serves as supplement of an original message, the forgotten memory is filled into a prompt template to form a prompt, and the prompt is transmitted into a Monte Carlo Tree Of Though (MCTOT) module. The feedback evaluation is to analyze the evaluation and score of the teacher on the answer, and the score replaces the evaluation and score of the self-evaluation of the learner. Through the steps, the ability of the learner to solve the problem can be improved along with experience accumulation, and staged forgetting can be carried out to better fit the performance of the real learner.

The present application belongs to the field of abnormal root cause analysis, and relates to a system performance bottleneck detection method based on reinforcement learning. The system performance bottleneck detection method is as follows: first, extract system performance index data; second, find the first abnormal time period and abnormal dimension using threshold method; third, perform causal inference on abnormal data and further root cause analysis. The present application can effectively solve the system performance bottleneck detection problem in a high-load environment, help system administrators identify and solve problems faster, reduce the risk of system crashes, and improve the stability and reliability of the system, so that the system can respond to user requests faster and improve user satisfaction. The present application can be applied to a wider range of root cause analysis problems, effectively helping operation and maintenance personnel solve system performance bottleneck detection problems using artificial intelligence methods, and has good applicability and robustness.

The present disclosure provides a multimodal reasoning method based on Monte Carlo tree and dynamic retrieval. It is applied to scenarios where artificial intelligence assistants assist teachers in grading papers. Specifically, this framework includes a unified retrieval module: the retrieval module includes a mixed modal retrieval corpus and a multimodal retrieval module; the multimodal retrieval module includes two retrieval modules: a text retrieval module and a cross-modal retrieval module, which uses knowledge concept filtering to select key problem-solving insights, and uses Monte Carlo tree search and active retrieval mechanisms to automatically obtain progressive multimodal reasoning annotations for multimodal reasoning for data annotation; then the annotated data is used to gradually align the process reward model through course process reward modeling in two stages to achieve fine-grained verification of multimodal large model reasoning. The MCTS algorithm and active retrieval strategy are combined to improve multimodal reasoning. The framework can automatically obtain high-quality step-by-step reasoning annotations, thereby gradually aligning the process reward model, and ultimately achieving reliable automated multimodal reasoning verification.

The invention provides an application code quality management method and device, equipment, a medium and a product. The method comprises the following steps: after an application code is submitted to a version control warehouse, reminding a reviewer to manually review the application code; after the manual auditing is passed, constructing a continuous deployment task; pulling the application code from the version control warehouse through the continuous deployment task; performing quality inspection on the pulled application code through a code inspection component; and after the quality inspection is passed, performing application deployment. Various quality problems can be found through automatic quality inspection of the code inspection component, the code quality of software products is remarkably improved, and errors and faults during operation are reduced. The automatic code quality detection process reduces the time and workload of manual review, so that developers can concentrate on implementation and innovation work of business logic. And meanwhile, the rapid feedback mechanism is beneficial to rapid positioning and problem solving, and the development process is accelerated.

One or more computing devices, systems, and / or methods for providing an artificial intelligence based communication system for network issue resolution are provided. Featured related to operation of a communication network are input into one or more models to generate a set of resolution recommendations related to a low signal detected with respect to the communication network. A resolution recommendation is selected and provided as solution content to users of the communication network and / or to agents troubleshooting issues for users. Post recommendation metrics are used to identify a user that has not implemented a resolution action described by the solution content. Accordingly, a follow-up notification is sent to the user as a reminder to perform the resolution action.

The application relates to a reinforcement learning partition pre-interaction method and system, wherein the method comprises the following steps: constructing a bias measurement function for evaluating model bias of a virtual environment relative to a real environment; dividing the real environment into multiple trust intervals according to the model bias; performing parameter coarse adjustment on an intelligent agent of the reinforcement learning based on the trust intervals; in the parameter coarse adjustment stage, sampling experience data generated by interaction of the intelligent agent with the virtual environment and the real environment respectively to update network parameters of the intelligent agent; monitoring a performance index of the intelligent agent in the parameter coarse adjustment stage, and when the performance index meets a preset interaction mode switching condition, the intelligent agent enters a parameter fine adjustment stage; in the parameter fine adjustment stage, the intelligent agent completely interacts with the real environment, and parameters of the intelligent agent are adjusted to convergence, and the parameter-converged intelligent agent is used for solving a complex sequential decision problem. The application effectively adjusts parameters of the intelligent agent of the reinforcement learning, and makes the problem-solving capability of the intelligent agent more reliable.

The system according to the embodiment aims to provide appropriate advice for the problem of the person seeking advice and quickly match the person with professional assistance as needed. [Solution] A system according to an embodiment includes an analysis unit, an advice unit, and a matching unit. The analysis unit analyzes the natural language input of the client. The advice unit provides appropriate advice based on the problem analyzed by the analysis unit. The matching unit matches professional assistance when the advice provided by the advice unit is insufficient.