97 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 relates to the technical field of data processing, and discloses a data quality verification and restoration method based on big data, comprising the following steps: S1, setting a data quality assessment rule; s2, defining related rules, indexes, quality templates and quality tasks, and evaluating the accuracy, integrity, consistency and credibility of the big data set; s3, recovering data integrity and accuracy by adopting a proper recovery technology; and S4, generating a data quality report and visual content, presenting a data quality condition for the user, assisting the user in discovering problems and formulating improvement measures, and supporting subsequent data analysis and decision. According to the method, the data quality can be improved, the data credibility can be enhanced, data analysis and decision can be optimized, automatic and intelligent processing can be realized, flexible rule customization and management can be provided, the effects are beneficial to improving the data quality and availability, and more efficient, accurate and reliable data processing and decision support can be brought to users.

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 is applicable to the field of intelligent customer service, and discloses an intelligent customer service reply method, which comprises the following steps: collecting user data; analyzing the emotional state of the user based on the user data to obtain an analysis result; generating a response reply according to the analysis result; sending the questionnaire to the user; acquiring behavior data of the user in a questionnaire processing process; and optimizing the questionnaire based on the behavior data. The method aims at comprehensively collecting and analyzing the user data, accurately analyzing the emotional state of the user, and generating personalized response reply according to the emotional state. On the basis, the system sends a questionnaire to the user to deeply understand user preferences and demands, and in the investigation process, the system captures and records behavior data of the user in real time for evaluating rationality of questionnaire design and user participation degree. By deeply mining and analyzing the behavior data, the system can accurately identify problems existing in the questionnaire and automatically implement optimization measures so as to comprehensively improve the user experience and data quality of the questionnaire.

The invention discloses a dangerous cargo risk early warning method and system and a computer program. The dangerous cargo risk early warning method comprises the steps of obtaining multi-source monitoring data; analyzing the multi-source monitoring data through a preset rule engine and a trained dangerous cargo risk prediction model to obtain a risk prediction result; and performing early warning according to a risk prediction result. According to the technical scheme provided by the invention, the historical and real-time multi-source monitoring data is acquired, and the rule engine and the machine learning algorithm are used for predicting dangerous cargo risk factors, so that dangerous cargo risk early warning is more real-time and accurate; compared with manual inspection and video monitoring, data can be acquired more frequently, and key information is synchronized in a data center; multiple data sources are covered, and the influence of various factors on a monitoring station can be considered more comprehensively; by means of automatic data collection and intelligent data analysis, manpower resources can be saved, problems and risks can be found more timely, and therefore potential loss and cost are reduced.

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.

The invention discloses a large model refinement-based product defect information crawling method, which comprises the following steps of: firstly, collecting original text data related to product defects from various network channels through a crawler method; secondly, performing fine adjustment on the pre-trained large language model on the collected data set, so that the semantic content related to the defect can be better understood and analyzed; and finally, carrying out deep analysis on original defect data by utilizing the fine-tuned model, and accurately extracting products, defective parts, damage events and various valuable defect feature information. The method gives full play to the advantages of a large language model in semantic comprehension and text analysis, core content related to product defects can be accurately recognized, irrelevant noise information can be effectively filtered out, the accuracy of information extraction is greatly improved, potential defect information hidden in user feedback is mined, and the method is suitable for popularization and application. And an enterprise is helped to find problems in time and carry out targeted improvement.

Systems, methods, and computer-readable storage media for issue spotting in legal cases, and more specifically to training and using an Artificial Intelligence (AI) model to identify possible causes of action in a legal dispute. A system can receive a plurality of cause of action models, and tune an existing AI model using the plurality of action models, resulting in a tuned AI model. The system can then convert the tuned AI model into an AI algorithm, where inputs to the AI algorithm include a jurisdiction and at least one piece of dispute information associated with the dispute, and outputs of the AI algorithm include a cause of action for the dispute and elements of the cause of action.

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 discloses an evaluation system and method based on an artificial intelligence large model, the system comprises a data set management module, a my model module and a model evaluation module, and the method comprises the following steps: S1, model management; s2, performing model evaluation; s3, performing online evaluation; and S4, evaluating the report. Compared with a static index, the method is more flexible, covers more complex scenes, aims at comprehensive evaluation of the professional field, and comprehensively evaluates a large model in the aspects of safety, functionality, accuracy and confusion; more edge scenes are covered, a large model often encounters various extreme or unseen inputs in practical application, automatic rules can effectively identify and perform necessary intervention, and referees can perform evaluation from a more humanized perspective, so that the defects of traditional evaluation are overcome; according to the method, the evaluation efficiency and expansibility can be improved, large-scale evaluation tasks are automatically processed according to rules, the evaluation process after model deployment is accelerated, and problems can be quickly executed and found in mass data.

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

Methods and systems for evaluating individual semiconductor metrology tool productivity based on both individual tool productivity metrics and group productivity metrics are described herein. The productivity metrics associated with each individual tool are combined with productivity metrics associated with a group of tools to quickly and less mispositively identify problematic tools. In particular, in situations where productivity is driven by low-frequency events, tool productivity results are obtained significantly faster. Values of one or more accuracy metrics indicative of a confidence of measuring a ranking of individual tools in the population of tools are estimated. In addition, a probability of a fault event is predicted based on a difference between a predicted probability distribution of future fault events associated with individual tools of the group of measurement tools and an actual observed distribution of fault events.

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.

The invention provides a teaching quality continuous improvement system and method, and belongs to the technical field of data driving, and the system comprises a teaching data access module which is used for achieving the breaking of a data island, and achieving the unified integration of multi-source heterogeneous data; the data development treatment module is used for constructing a data warehouse, constructing a comprehensive, scientific and systematic teaching evaluation system and comprehensively evaluating the teaching effect; the teaching data sharing module is used for supporting unified management and publishing of data services; the teaching replay evaluation module is used for identifying problems and improvement points through a four-force model; and the enabling teaching improvement module formulates targeted improvement measures and continuously optimizes teaching strategies. According to the invention, the learning output is intelligently and repeatedly evaluated through a four-force model, the teaching quality is enabled to be continuously improved, and the learning efficiency and effect are improved; by correctly understanding and applying data, it can be ensured that teaching reform proceeds along a correct track, and a practical and effective method is adopted to systematically plan courses and execute teaching management.

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.