82 results about "Identifying problems" patented technology

The invention provides a knowledge graph enhanced RAG intelligent question-answering method based on a complex process, which comprises the following steps: uploading a heterogeneous data file, creating an embedded vector and a vector index, and obtaining a private database; performing entity relationship extraction on the private database to obtain a knowledge graph, and combining the knowledge graph with the large model to obtain a query model; using the query model to decompose user questions, identify question types, question intentions and entity relationships, then judging whether related entities exist in the knowledge graph, if yes, outputting answers by using the query model, and if not, calling the large language model to perform question and answer reasoning; and constructing a historical question and answer abstract, and optimizing answers. According to the method, the accuracy and the credibility of answering are improved, meanwhile, the visual representation of the knowledge graph also enables the user to more intuitively understand how the system obtains a specific answer, and the interactivity and the credibility between the user and the system are enhanced.

The invention provides a water conservancy industry electronic dark bidding document enterprise internal examination method and system based on artificial intelligence, and belongs to the field of water conservancy industry bidding. Performing automatic pre-auditing on the bidding file: performing compliance inspection and integrity inspection, identifying problems existing in the bidding file, and providing improvement suggestions to ensure that the problems conform to the basic requirements of electronic dark label review; carrying out data standardization and cleaning on the bidding file: carrying out semantic extension and ambiguity elimination, identifying images, tables and handwritten contents in the bidding file, identifying a main body structure of the water conservancy design drawing, extracting engineering quantity list data, and carrying out compliance verification; identifying an abnormal behavior in the bidding file through a machine learning model; and an internal examination decision tree is constructed according to preset key indexes and score weights of the water conservancy project, an interpretable artificial intelligence algorithm is utilized to carry out internal examination decision making on the bidding document, an internal examination score result is fed back, and specific score deduction reasons are explained. And the standardization and the accuracy of internal examination of the bidding document are improved.

The invention discloses a fault monitoring method and system using AI intelligent image recognition, and the method achieves the continuous monitoring of the subtle change of equipment through collecting the time-space information image data of the operation state of the equipment and extracting the dynamic characteristics of motion trail, deformation rate, spectrum change and the like. The dynamic features are associated with the historical state of the device, and a dynamic incidence matrix is constructed to analyze potential trend deviation points in a time sequence, so that possible faults are predicted, and not only occurred problems are identified. An abnormal mode matching rule is generated by calculating a prediction window and combining the prediction window with equipment operation environment parameters, meanwhile, a response threshold value is adjusted to adapt to equipment performance attenuation characteristics, and finally an early warning signal is generated through cross validation of the abnormal mode matching rule and the prediction window. According to the method, the abnormal development trend can be identified before the equipment has an obvious fault, early warning is given out in advance, the transformation from post-event identification to pre-event early warning is realized, and the fault prevention capability is remarkably enhanced.

The invention provides an automatic budget auditing method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: receiving a to-be-audited budget file and an auditing rule; based on the auditing rule and the file format and the professional attribute of the budget file, determining an analysis agent; performing structured analysis on the budget file based on the analysis agent to generate to-be-audited data; and calling the vertical domain large language model, and performing budget auditing on the to-be-audited data based on the auditing rule. According to the method and the system provided by the invention, structured analysis is performed on the budget file by using the analysis agent, so that the automation degree and the accuracy of auditing are improved; according to the budget auditing method, budget auditing is carried out by calling the vertical domain large language model subjected to vertical domain parameter fine adjustment, so that problems and abnormal conditions in the budget file can be identified more accurately, the budget auditing quality and efficiency are improved, and a more reliable auditing result is provided for a user; through automatic budget auditing, the budget auditing efficiency is remarkably improved.

To provide a system for contributing to the improvement of corporate management and business performance by efficiently identifying problems and defining management issues for each layer of a company.SOLUTION: In a data processing system equipped with a data processing apparatus and smart devices, a specific processing unit of the data processing apparatus collects information directly or indirectly from members of each layer, such as the president, general manager, department manager, and person in charge, as specific processing, and analyzes this information to extract problems and issues for each layer. The extracted problems are then used to derive definitions of management issues using a generative AI system.SELECTED DRAWING: Figure 2

The invention provides a knowledge question answering method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a target question and identifying question content corresponding to the target question; determining a question model type matched with the question content by analyzing the question content; searching a corresponding target knowledge document with the highest association degree according to the question content; and determining a preset response strategy corresponding to the question model type, and converting the target knowledge document into a target question response by adopting the preset response strategy. Therefore, the question content is obtained by identifying the target question, and the question model type is matched according to the question content, so that the associated knowledge document can be efficiently retrieved, the high-quality question response can be generated according to the response strategy, and the accuracy and efficiency of the response can be improved.

The invention discloses an IT work order risk management and control method based on combination of large and small models, and belongs to the technical field of IT work order systems. According to the method, the problem that IT work order risks cannot be effectively identified and managed in the prior art is solved, factors such as occurrence probabilities, influence degrees and solutions of different types of problems can be analyzed by adopting a mode of combining large and small models, targeted risk early warning and coping strategies are provided for IT teams, and the risk management efficiency is improved. According to the method and the system, multiple data mining technologies are combined, deep mining and understanding of the work order data are realized, and problems can be found in time and corresponding measures can be taken by monitoring whether the abnormal value in the abnormal value processing process causes that the data quality does not meet the data quality requirement or not in real time, so that the accuracy and reliability of the data are ensured.

The invention relates to the technical field of steel structure damage identification, and discloses a steel structure damage identification system and method based on deep learning, and the system comprises a crack obtaining module, a number threshold value presetting module and a comparison response module. According to the method, when the number of the cracks is larger than or equal to the approximate threshold value, the degree of approaching the crack number threshold value is high, at the moment, the crack number is approached, and the probability of potential safety hazards becomes high, so that the number of the cracks is graded firstly; and then shortening the time interval of damage detection and identification according to the incremental equal proportion of levels, so as to improve the frequency of damage detection and identification when the number of cracks is close to the threshold value of the number of cracks, so that problems can be found in time, and safety accidents are avoided.

To provide a system capable of creating an individual learning plan, identifying the problems and weak points of a learner, and taking a proper measure.SOLUTION: A system according to an embodiment comprises a plan creation unit, an identification unit, a tracking unit, and a motivation unit. The plan creation unit analyzes the learning history and goal of a user, and creates a learning plan according to the user. The identification unit identifies the problems and weak points of the user in a learning process of the learning plan created by the plan creation unit. The provision unit provides the user with descriptions and a simulation test on the basis of the problems and weak points identified by the identification unit. The tracking unit tracks the progress of the user in the learning plan created by the plan creation unit. The motivation unit improves motivation on the basis of the progress state tracked by the tracking unit.SELECTED DRAWING: Figure 1

The invention discloses a post-processing method and device for medicine management data, and relates to the field of medicine data processing, and the method comprises the steps: constructing a medicine knowledge graph; identifying the problems of dosage abnormity, contraindication conflict or inconsistency between the medicine batch and the curative effect in the input data through a pre-training abnormity detection model; inputting the abnormal data into an adversarial sample generator, generating a repair candidate data set, and embedding vectorization constraints of a knowledge graph into a generator network; performing logic verification on the candidate data set, traversing graph related entities and relation paths, and calculating conflict scores; if the score is lower than a threshold value, outputting repair data, otherwise, optimizing the generator or requesting manual intervention; generating a minimization confirmation interface according to a priority rule, and only displaying an abnormal field and a correction suggestion; and feeding back a confirmation result to the generator and the atlas, and updating parameters and weights. According to the invention, the problems of low efficiency and poor accuracy of medicine data abnormity identification and restoration are solved.

Systems as described herein may include document scanning to identify problems with the documents. A document scanning system may determine a scope of a document scan. The system may initiate a silent mode of a browser application and send a request to access an enterprise document repository. The system may be granted read-only access and determine page elements associated with a document within the scope. Based on the page elements, the system may use a machine learning model to determine an indication of one or more problems with a document. Accordingly, the system may generate a health report for each document within the scope of the document scan.

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 discloses a product performance management method and system based on automation technology. By utilizing computer system integration technology and integrating gitlab, jenkins, and jira tools, the original data of indicators required for product performance are automatically obtained, and the original data of indicators are cleaned by custom cleaning rules, and then the cleaned data is written into a database library cluster. The present invention has formulated a set of performance indicators for measuring products, including product dimension indicators and personal dimension indicators, which are convenient for observing the current performance of each product line. The present invention is based on a data visualization platform for performance indicators, which can display the aggregated data of various indicators in real time. On this basis, any role can timely view the current ranking of their own product performance data, promptly identify problems and solve them in a timely manner.

A system is provided.SOLUTION: A system comprising: means for collecting an operation log of a user in real time; means for transmitting the collected operation log to a server; means for analyzing the received operation log and identifying a problem that the user is facing; means for retrieving appropriate support information for the identified problem from a database; means for generating a solution statement using a generative AI model based on the retrieved support information; and means for displaying the generated solution statement on a user device.SELECTED DRAWING: Figure 1

The present invention disclosure provides a system for performing root cause analysis, comprising a processor and a memory, the processor is configured to at least one problem associated with running of an application by at least one user, wherein identifying problem includes determining a priority level of one or more services being impacted based on service level agreements, identify at least one root cause associated with the problem, identify one or more root cause corrective actions (RCCA) based on root cause and implement at least one RCCA for troubleshooting the identified problem. Further, the system generates a RCCA form for use by a user group for identifying and validating an additional RCCA and update the database to include the additional RCCA based on a quality score for the identified root cause corrective action. The system is configured to create a RCCA form, auto-fill the RCCA form, identify one or more RCCA implemented for the identified problem, and determine a quality score of the RCCA.

We provide the system. [Solution] Means of obtaining business data from users, A means of analyzing acquired business data and identifying problems, A means for generating specific instructions for artificial intelligence based on identified tasks, A means of presenting generated instructions to the user and executing them if selected, A means of monitoring the progress of work and preparing regular reports, A system that includes this.

This invention provides a control method and apparatus based on a lean manufacturing system, comprising: calculating the operational maturity value of a target lean manufacturing system to identify problems to be improved; constructing manufacturing strategic goals by comparing the target factory with business objectives based on pre-built quantitative indicators; streamlining the organizational structure based on pre-built lean processes, lean organizational structure, and lean departmental functions and responsibilities; constructing a multi-level key performance indicator system and a facilitating management meeting mechanism; and finally, constructing an implementation plan and controlling the target factory according to the implementation plan to achieve the optimization goals. This invention solves the problems of high deployment costs and poor application effects in existing technologies.

The invention belongs to the technical field of artificial intelligence and data management, and particularly relates to a method for discovering and generating problems through AI, which comprises the following steps: S1, receiving a natural language text of a service system as a work content, and distributing a global unique request identifier; s2, performing context analysis, standard comparative analysis and historical comparative analysis by adopting an AI problem discovery model; s3, fusing and sorting the content of the problem clue list based on the global unique request identifier, and generating a structured problem clue list; s4, querying a solution library according to clue attributes in the problem clue list, generating corresponding solution suggestions, and outputting the solution suggestions; and S5, according to metadata in the question clue list, a rule engine of the business system pushes the natural language question list and the solving suggestions to a corresponding terminal or system. The problem that problem grabbing is low in real-time performance and accuracy is solved.

A method and system for course assessment. The method including: receiving assessments from a plurality of assessors for at least one learner; reviewing the assessments for any issues by, for example, an AI agent; if issues are found, returning the assessments for additional review by, for example, an AI agent, if not, continuing; aggregating the assessments by, for example, an AI agent; reviewing the aggregated assessment for any aggregated issues by, for example, an AI agent; if aggregated issues are found, returning the aggregated assessments for additional review by, for example, an AI agent, if not, continuing; and providing the aggregated assessment to the learner. The system makes use of processor and memory to execute instructions to implement the method.

A method performed by an intent manager that controls an environment based on an intent for the environment includes receiving information about the environment. Based on the information, the method identifies a problem in the environment that needs to be solved to implement the intent, and obtains an efficiency map associated with the problem. The efficiency map indicates an efficiency score of the suggested agent when generating a suggested action for the environment to solve the problem. Based on the efficiency score, a suggestion agent is invoked to generate a suggested action for the environment to solve the problem. The method determines whether a suggested action generated by a suggested agent is selected for implementation on the environment; and adjusting an efficiency score of the suggested agent based on whether the suggested action is selected for implementation on the environment.

An industrial work order analysis system applies statistical and machine learning analytics to both open and closed work orders to identify problems and abnormalities that could impact manufacturing and maintenance operations. The analysis system applies algorithms to learn normal maintenance behaviors or characteristics for different types of maintenance tasks and to flag abnormal maintenance behaviors that deviate significantly from normal maintenance procedures. Based on this analysis, embodiments of the work order analysis system can identify unnecessarily costly maintenance procedures or practices, as well as predict asset failures and offer enterprise-specific recommendations intended to reduce machine downtime and optimize the maintenance process.

The present invention disclosure provides a system for performing root cause analysis, comprising a processor and a memory, the processor is configured to at least one problem associated with running of an application by at least one user, wherein identifying problem includes determining a priority level of one or more services being impacted based on service level agreements, identify at least one root cause associated with the problem, identify one or more root cause corrective actions (RCCA) based on root cause and implement at least one RCCA for troubleshooting the identified problem. Further, the system generates a RCCA form for use by a user group for identifying and validating an additional RCCA and update the database to include the additional RCCA based on a quality score for the identified root cause corrective action. The system is configured to create a RCCA form, auto-fill the RCCA form, identify one or more RCCA implemented for the identified problem, and determine a quality score of the RCCA.

The method includes receiving input describing marine equipment and at least one issue associated with the marine equipment. The method includes determining an identification of the marine equipment. Additionally, the method includes applying the input and the identification of the marine equipment to one or more machine learning algorithms. The one or more machine learning algorithms implement one or more rules that classify and identify problems in the marine equipment. The one or more machine learning algorithms are trained based on metadata for the marine equipment and components of the marine equipment. The method includes outputting at least one solution to the at least one issue.

The present invention provides a robot control method and system based on a large language model. This system employs a dual large language model evaluation mechanism. The large language model of the code generation and modification module uses Claude 3.5sonnet to generate control code, which is then evaluated and optimized using OpenAI's GPT-4O evaluation model. This reduces the risk of difficulty identifying problems during evaluation due to the homogeneity of the same module, thereby improving the quality, safety, and security of the generated code. In addition to the dual large language model evaluation mechanism, the system incorporates safety prompts into the prompts of the generated model and provides generation template examples. This restricts operation within a specific framework, reduces the likelihood of errors, and further ensures the standardization and security of the generated code. Furthermore, a control verification module constructs a simple simulation environment after the control code is generated, enabling early screening of the generated code for syntax errors and logical vulnerabilities, further reducing potential safety hazards.

The invention discloses a multi-dimensional education data analysis and decision support system, relates to the technical field of education decision support, and solves the technical problems that courses and even problems existing in a teaching method are difficult to identify and effective decision suggestions cannot be provided for an education platform in an existing scheme. The system comprises an analysis decision module and a data processing module connected with the analysis decision module. A learning state label is set by analyzing the performance of a target object in course learning, and a data basis is provided for analyzing whether personal problems exist or not; establishing learning state sequences according to the learning state labels, judging whether personal problems exist or not by comparing the learning state sequences of the target objects of the same class, judging whether class problems exist or course problems by comparing the learning state sequences of the target objects of different classes, and identifying the problems through multi-dimensional education data; and the reliability of related decisions can be improved.

To detect problems by specifying keywords having a possibility of going viral in a negative manner and offensive expressions by analyzing post contents.SOLUTION: A system includes text data input means for analyzing contribution contents, specification means for specifying a keyword, writing style, tone, or the like having a possibility of going viral in a negative manner, and detection means for detecting expressions having a problem and offensive words.SELECTED DRAWING: Figure 1

The invention relates to the field of education, and discloses an online composition correction platform for teaching, which comprises a composition uploading and saving module, a language processing module, a multi-angle evaluation module, a personalized guidance and suggestion module, a content and thought feedback module, a teacher auxiliary participation module, a report and statistics module and a technical support and user management module. The online composition correcting platform not only pays attention to grammar and spelling, but also pays attention to feedback of content and thinking. By pointing out the problems of logic errors, unclear viewpoints and the like and providing corresponding suggestions, the platform helps students to develop thinking ability and improve continuity and persuasion of discussion. In addition, the teacher participates in the correction process, and more professional and personalized guidance can be provided. The participation and feedback of teachers can more accurately guide the writing of students, identify questions and provide deeper explanations and suggestions.

The invention provides a building node real-time monitoring method and system based on artificial intelligence. The monitoring method comprises the following steps: mapping data monitored by monitoring terminals configured on building nodes into a three-dimensional model of a building; performing feature extraction on the three-dimensional model of the building to obtain building parameters; inputting the building parameters into a pre-configured neural network model to obtain an analysis result; and when the analysis result is abnormal, outputting early warning information. According to the building node real-time monitoring method and system based on artificial intelligence, efficient and accurate building node monitoring is achieved, the health state of a building is analyzed, problems are found in time, and related personnel are notified to solve the problems.