65 results about "Identifying problems" patented technology

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.

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.

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.

The invention provides a KPI comprehensive evaluation and test optimization method of an ADAS system. The method comprises the steps of obtaining a work log in an ADAS test process; based on the work log, calculating a KPI index used for evaluating the ADAS system function; evaluating the KPI index according to a preset judgment rule so as to identify a security attention event and a performance weakness event in the ADAS system; for each identified security attention event and performance weakness event, automatically intercepting data in a preset time window before and after occurrence of the events, and marking the data as attention events; and performing fault judgment on the concerned event, and generating a test case optimization suggestion. The technical problems that in the prior art, troubleshooting and test case optimization are disjointed, only single fault positioning can be carried out on recognized problems, and association logic among fault sources, scene elements and test cases is not established, so that quick tracing is difficult when sensing data exceptions occur in the test process are solved.

A computer-implemented system (100, fig. 1A) comprises an interface (120, fig. 1A) for an operator input and output; a memory (130, fig. 1A); and a processor (110, fig. 1A) configured to receive the input 150, and based on the input to execute an invention process comprising: searching prior art data 160 stored in the memory; and based on the prior art data, generating a high-level invention concept 180, with invention detail; wherein in a first mode, the system outputs the high-level invention concept and invention detail, and in a second mode outputs the high-level invention concept, such that the user must provide the detailed invention. The system may record the invention process and invention concept in an audit log 182, (140, fig. 1A) and find trigger documents and enabling documents which include new scientific findings, identified problems, societal or technological trends which are critical to development of the invention. A system comprising citing and referencing extracted data from a language-based AI system search is also provided.

This invention relates to the field of application development technology and provides a method for identifying duplicate change orders in engineering projects. First, engineering information is correlated and integrated to obtain an intermediate dataset with a logical order of project implementation. Text extraction and data cleaning are then performed on the intermediate dataset to form change order text data. Next, the change order text data are paired based on instruction keywords to form an instruction control group. Then, a dual-thread similarity framework is used to calculate the similarity between two change order text data in the instruction control group to obtain first and second similarity data. If the first and second similarity data meet the duplication condition, the instruction control group is considered as a duplicate change order. In this way, duplicate change orders can be automatically identified, allowing for timely detection of problems and mitigation of company losses.

The invention provides a modeling method, a medium and equipment for a formal demand model with hierarchical characteristics, which comprises the following steps of: according to a natural language demand to be processed, dividing according to a system hierarchy, dividing demand entries in a demand document into sets according to a logical relationship, and dividing each set into a module; according to the divided demands, mode conversion type demands are separated from each module to be summarized, and for the remaining demands, the remaining demands are classified into conditional demands and event type demands according to the modules; constructing a domain concept library according to the divided original requirements; modeling the condition / event type demand according to the demand standardization template and generating an equivalent formalized description process; and generating an HVRM model according to the traceability relationship among the modeling elements. According to the method, the model-driven technology is adopted, the problem can be effectively recognized from the software requirement level, the problem can be accurately positioned, and therefore the development quality and safety of an airborne software system are greatly improved.

The invention provides a real-time fault judgment method and system based on an ADAS hardware-in-the-loop simulation platform. The real-time fault judgment method comprises the steps that an anomaly detection unit is connected into a test bench; when the test bench is started, the abnormity detection unit is controlled to carry out real-time detection on the test bench according to a preset time sequence so as to acquire real-time detection data; comparing the real-time detection data with preset operation data in real time; if the real-time detection data is different from the preset operation data, determining that the test bench has a fault point, and giving a fault alarm; and carrying out troubleshooting on the test bench according to the fault alarm. The invention solves the technical problems that an HIL test bench is usually adopted in the traditional automatic test, the HIL test bench cannot immediately identify component or link faults in the test process, and the problem needs to be found through log analysis after the test is finished, so that the troubleshooting lags.

The invention belongs to the field of nuclear power, and particularly relates to a nuclear power plant key information infrastructure asset management method and system, and the method comprises the steps: 1, designing a nuclear power plant key information infrastructure asset data field list; 2, intelligently drawing a network topological graph according to the nuclear power plant key information infrastructure asset data; 3, off-line acquisition, processing and storage are carried out on nuclear power plant key information infrastructure asset data; and 4, performing preventive maintenance analysis on the nuclear power plant key information infrastructure asset data. According to the invention, comprehensive, efficient and safe management of nuclear power plant key information infrastructure assets can be realized, and emergency purchase and equipment replacement frequency can be reduced by identifying problems in advance and taking measures.

According to the big data operation supervision platform based on intelligent government affairs, a semantic vector set capable of expressing a policy goal is established, and a unified government affair behavior life cycle diagram is constructed, so that structured mapping between the policy goal and an operation behavior is realized; through fusion of semantic similarity and flow structure consistency calculation and in combination with a deviation punishment mechanism, a deviation path which does not conform to policy intention, flow specifications or compliance requirements is accurately identified, and structured labeling is carried out; for the identified deviation path, introducing a causal analysis method of laws and regulations priority, cross-department interaction strength and policy association constraint, constructing a causal chain of deviation propagation and positioning an initial responsibility node, and ensuring that the responsibility chain has interpretability and reproducibility; and finally, integrating the causal chain and responsibility node information into a unified abnormal event object, calculating a risk priority, and generating a supervision action which can be directly executed in combination with a compliance feasibility condition, thereby realizing closed-loop management from problem discovery and cause analysis to execution and disposal.