42 results about "Method of support" patented technology

The invention discloses an unplanned secondary operation decision support method and system based on EMR analysis. According to the method, firstly, multi-source EMR data are collected in real time through an HL7 FHIR standardized interface, differential analysis is conducted through a time sequence analysis algorithm and a BioBERT model fused with a medical knowledge graph, and standardized clinical indexes are generated; secondly, the indexes are matched with an unplanned secondary surgery thematic knowledge graph in real time, multi-path probabilistic reasoning is carried out based on a Bayesian network, and a risk conclusion with confidence and a reasoning chain are output; and generating a structured report including risk early warning, core basis, possible reasons and processing suggestions, and pushing the structured report in multiple channels. And finally, collecting clinical feedback, optimizing the knowledge graph and the analytical model, and forming a closed loop. According to the invention, dependence on experience of doctors is reduced, and accuracy and timeliness of risk research and judgment are improved.

The invention relates to a federated learning method supporting heterogeneous model architecture search and zero sample knowledge fusion. The method comprises the following steps: providing a federated learning system to be subjected to federated learning, and when the federated learning system is configured to perform federated learning and any client executes teacher model generation processing, searching and generating a neural network local teacher model optimally matched with the client based on local private data in the client, and sending the generated neural network local teacher model to a connected server, after the server executes student model generation processing, at least generating a global shared student model, when the global shared student model is generated, training the constructed basic student model by using the pseudo-supervised training data set, and generating the global shared student model by using the pseudo-supervised training data set. And after the basic student model is subjected to distillation training, a global shared student model is generated. According to the method, the heterogeneous model of the client can be effectively supported, the personalized capability and privacy protection are improved, the communication cost is reduced, and the model generalization is excellent.

A method of supporting artificial intelligence and machine learning functionality is provided. The method includes receiving an RRC reconfiguration message including an inference configuration associated with a UE-side functionality. The method includes determining an applicability status of the inference configuration of the UE-side functionality. The method includes transmitting a first RRC reconfiguration complete message including the applicability status of the inference configuration of the UE-side functionality. In a case that a report configuration of applicability included in the RRC reconfiguration message is enabled, and a current applicability status of the inference configuration of the UE-side functionality is different from a previous applicability status reported in an RRC message, transmitting the current applicability status of the configuration of the UE-side functionality in a second UE assistance information message.

To provide a product presentation support system capable of supporting product presentation according to values.SOLUTION: The product presentation support system includes: an ideal behavior search unit that searches for ideal behavior from values using the relationship between values and ideal behavior; an action requirement associating unit that associates the elemental behaviors that constitute the searched ideal behavior with functional requirements using the relationship between element behavior and functional requirements; a state requirement associating unit that associates the element behaviors constituting the searched ideal behavior and functional requirements resulting from the state at the time of the element behavior using the relationship between the state during the element behavior and the functional requirements resulting from that state; a functional group generation unit that groups functional requirements that can be associated with a behavior element using the association of the element behavior with functional requirements and the distance between functional requirements; a product function connection unit that associates functional requirements grouped by the functional group generation unit and product functions; and a presentation policy generation unit that generates product information related to the entered values using the functionality information in the results of the associated product features.SELECTED DRAWING: Figure 1

The invention relates to the technical field of intelligent talent evaluation, and discloses a multi-dimension-based talent team evaluation method and system, and the method comprises the steps: collecting original talent data, and obtaining a standardized multi-source fusion talent data set through a deep semantic fusion method of a domain ontology; adopting a five-dimensional evaluation framework design and a dynamic weight learning method to obtain a multi-dimensional evaluation index system with a comprehensive initial weight; adopting a multi-factor adaptive weight adjustment method to obtain personalized dynamic weight configuration; a progressive mixed evaluation calculation method is adopted to obtain a comprehensive evaluation score; a multi-level time sequence evolution analysis and development potential prediction method is adopted to obtain a talent comprehensive value evaluation result; and an intelligent visual analysis and multi-dimensional decision support method is adopted to obtain a complete talent evaluation output result. According to the method, multi-dimensional dynamic evaluation and development potential prediction of talent teams can be realized, and scientific support is provided for talent management decision.

To support the configuration tasks of databases created using no-code or low-code.SOLUTION: A configuration operation identification unit (202) of a configuration task support system (1) identifies a configuration operation performed by a user in order to set up a database created using no-code or low-code. A machine learning model storage unit (200) stores a machine learning model which has been trained using training data generated on the basis of the configuration for training. A configuration task support unit (203) supports a database configuration task by a user on the basis of configuration operations and the machine learning model.SELECTED DRAWING: Figure 5

[Problem] The objective is to realize a technical due diligence support method that makes it easier to understand the results of software analysis. [Solution] A due diligence support method for evaluating the technical capabilities and software quality of a prospective acquisition company of a business operator, which is performed by a management terminal according to one embodiment of the present invention, wherein the control unit of the management terminal refers to information on the source code disclosed by the developer of the prospective acquisition company, performs an analysis to evaluate the technical capabilities and quality based on the information on the source code, and generates a report evaluating the technical capabilities and quality based on the analysis results. [Selected Figure] Figure 8

The invention discloses an inclined roadway supporting method, which relates to the technical field of coal mines, and comprises the following steps of: 1, collecting related data of a coal mine to be supported, and determining a supporting model parameter X; 2, searching according to the support model parameter X to obtain a support data group, and making an orthogonal table; 3, calculating to obtain a model parameter X '; the method comprises the steps of (1) obtaining model parameters X ', (2) obtaining model parameters X', (3) obtaining model parameters X ', (4) obtaining simulation material data by combining support data retrieval according to the model parameters X', (5) manufacturing a simulation material according to the simulation material data, (6) manufacturing a roadway support model and carrying out a pressurization experiment, and (7) recording data, repeating the step (6) until an orthogonal table is completed, and analyzing the orthogonal table to obtain an optimal support scheme.

The application discloses a kind of propelling judgment support method and device for AI assisted CAD modification, belong to computer aided design and intelligent interaction technical field.The method includes: first, local editing context is constructed and area constraint is divided, then matching operator is generated Modification direction and cluster screening, selected direction is constructed patch script and generates candidate scheme, preliminary and in-depth stage verification is carried out after gathering detection data, and integration context, direction and verification data, recommended total score is calculated using weighting algorithm, and structured result is output and encapsulated can be submitted modification object.The application constructs local editing context, divides target, protection and influence area, combines editing operator and cluster screening modification direction, improves targeting, generates candidate scheme by parameterized search, carries out multi-dimensional verification by geometry, assembly, manufacturing, integrates decision using weighting algorithm, outputs structured recommendation scheme, realizes scientific screening and standard execution of CAD modification, improves efficiency and controllability.

To support easy extraction of appropriate similar cases.SOLUTION: A similar case extraction support system 1 comprises: storage means for storing sentences representing the contents of items having correlations and content sentence vectors where the sentences have been converted to vectors; vector generation means for converting a sentence into a vector using a transformer-based model; calculation means for calculating a degree of similarity between an input sentence vector representing a vector of the input sentence converted by the vector generation means and a content sentence vector; order determination means for determining an order of the sentences representing the contents in accordance with the degree of similarity; and display means for displaying results including the sentences representing the contents, the degrees of similarity, and the orders.SELECTED DRAWING: Figure 1

The invention discloses a multi-model universal operation support method, system and device for universal verification of mechanical and electronic product fault detection and diagnosis, and relates to the technical field of electronic product health diagnose.The method comprises the steps that a multi-mode algorithm used for fault detection and diagnosis is subjected to unified expression packaging, and a plurality of algorithm component models are obtained; constructing an information interaction rule among the plurality of algorithm component models; according to the plurality of algorithm component models and the interaction rule, constructing a graph flow operation mechanism of the plurality of algorithm component models; performing operation environment simulation on the plurality of algorithm component models; and based on the constructed graph flow operation mechanism simulated by the plurality of algorithm components and the plurality of algorithm component models simulated by the operation environment, establishing a fault detection and diagnosis algorithm general host machine, and based on the fault detection and diagnosis algorithm general host machine, carrying out fault detection and diagnosis verification for mechanical and electronic products. Fault detection and diagnosis general verification can be carried out on typical machinery and electronic products in various different fields and different scenes, and the verification result is accurate and reliable.

An input information analysis unit that analyzes input information of a user regarding a system model, a model candidate specifying unit that specifies a system model to be generated from system model information in which a plurality of types of templates of the system models is registered, based on an analysis result of the input information, an information extraction unit that extracts necessary information used to generate the system model from a database that stores information regarding design, based on the analysis result of the input information, and a system model generation unit that generates a system model according to the input information, using the extracted necessary information and the template of the system model to be generated are included.

The invention discloses a data-driven load model key parameter analysis method supporting trusted simulation analysis. On the basis of extracting multi-type load time-frequency domain features, a mechanism type dynamic feature knowledge base is constructed, a data driving model is converted into an interpretable equivalent load model through approximate Bayesian estimation and an alternating direction multiplier method, and the precision loss of dynamic feature response curves of the two models is compared in the same simulation scene, so that the accuracy of the equivalent load model is improved. Therefore, the simulation credibility of the data-driven load model is improved. According to the invention, the mechanism reconstruction of the data-driven load model is realized. According to the equivalent load model, a load physical mechanism structure is explicitly introduced while the data fitting precision is kept, so that a model result has clear physical meaning and engineering interpretability, and the model is more reasonable in load modeling expression.

This invention provides an analysis support system, analysis support method, and program that can assist in the analysis of support member models, thereby reducing the effort and errors of analysts. [Solution] The analysis support system 1 has a selection means 6 that, when it selects one of the four support points corresponding to the support portion 105 of the support member 102 in which the pipe 101 is supported, selects a node located opposite the selected node from among the nodes excluding the selected node according to a predetermined procedure.

To provide an apparatus, a method, and a program that assist users in using AI. [Solution] The AI ​​usage assistance device of the present invention comprises a morphological analysis unit that performs morphological analysis on text information representing commands to be executed by the AI; keyword master data that stores pre-registered words, processing attribute codes corresponding to processing attributes predetermined for each word, and scores predetermined for the processing attribute codes; a keyword extraction unit that extracts words obtained by morphological analysis from the words stored in the keyword master data as keywords; a score calculation unit that calculates a total score by summing up the scores for each processing attribute code; and a first determination unit that decides to proceed to execute the processing attribute corresponding to the processing attribute code with the highest total score calculated by the score calculation unit.

To reduce the burden of manual work in life planning. [Solution] The information processing device comprises a classification unit that classifies multiple numerical data related to a subject's life plan into data items for generating life planning support information using content information and a language model that indicate the content of each numerical data, and a support information generation unit that generates support information using the numerical data as data for the data items classified by the classification unit. Furthermore, this information processing device can also support the subject's decision-making.

The application discloses a kind of index verification methods of supporting model attribute quantification evaluation in the field of system engineering technology, comprising the following steps: using model-based system engineering method, construct the system engineering model of problem solving;According to the requirement and constraint of model, the verification script of the constraint condition not changing with time in system model is contained by using KARMA language index verification part syntax definition;Index verification compiler compiles KARMA language index verification text, calls solver based on satisfiability module theory;The method is developed based on GOPPRR modeling theory, can support the establishment of multi-architecture model, not limited to the model of a certain field, the expression of model relationship, constraint condition and verification object is realized by using the mode of combining satisfiability module theory and GOPPRR modeling theory, the unified modeling language is extended by using satisfiability module theory, supports the interaction of modeling data and solving data, and reduces the learning cost of engineering personnel.

To provide an evaluation support system, an evaluation support method and an evaluation support program for generating a logic model in a measure and evaluating an impact.SOLUTION: The support device 20 includes a control unit 21 connected to the user device 10. The control unit 21 includes a logic model generation unit 212, an index generation unit 213, and an evaluation unit 214. The logic model generation unit 212 generates elements constituting each hierarchy of the logic model from the evaluation target designation information acquired from the user device 10, and estimates a causal relationship between the elements. The index generation unit 213 generates an index for evaluating the element from the evaluation target designation information. The evaluation unit 214 predicts an index value of an index of each layer of the logic model and outputs the index value to the user device 10.SELECTED DRAWING: Figure 1

This explanation support device includes a specification unit and an explanation basis acquisition unit. The specification unit receives, with respect to a first image, a first identification result and a first certainty factor output by a black-box type first inference engine, and, with respect to the first image, a second identification result and a second certainty factor output by a white-box type second inference engine. Furthermore, the specification unit receives at least one identification result and certainty factor from among the following: a third identification result and a third certainty factor output by a white-box type third inference engine different from the second inference engine, with respect to the first image; a fourth identification result and a fourth certainty factor output by the second inference engine, with respect to a second image separated by a predetermined number of frames from the first image; and a fifth identification result and a fifth certainty factor output by the second inference engine, with respect to a third image obtained by processing the first image. The specification unit identifies, from the certainty factors other than the received first certainty factor, a certainty factor closest to the first certainty factor, among the certainty factors associated with the same identification result as the first identification result. The explanation basis acquisition unit acquires identification basis information associated with the identified certainty factor and outputs the identification basis information to a monitor.

To visualize a relationship between design and manufacturing standards and product manufacturing performances without specifying parameters or keywords.SOLUTION: A design and manufacturing support device includes: a language extraction unit configured to extract product language information from product design and manufacturing performance information; a standard application range DB that stores application range language information indicating an application range of each of multiple accumulated design and manufacturing standards; an applicability calculation unit configured to refer to the standard application range DB and calculate, for each of the design and manufacturing standards, a similarity between the application range language information and the product language information extracted from the design and manufacturing performance information corresponding to each of multiple products designed or manufactured during a specified period, to obtain a calculated statistical value of the similarity as applicability; and an applicability history DB that stores a history of the calculated applicability for each design and manufacturing standard.SELECTED DRAWING: Figure 1

The items to be measured simultaneously and the sample size should be determined appropriately. [Solution] The processing unit 12 obtains first information 13 that shows the term-specific variance and the pairwise covariance of multiple terms when simultaneous measurement is performed for multiple terms included in the Hamiltonian corresponding to the problem to be solved by the variational quantum eigenvalue method, and for multiple terms that can be simultaneously measured by a quantum computer. Based on the constraint that the change in the mean of the sum of the multiple terms is less than or equal to a predetermined value, the processing unit 12 generates an optimization problem based on the first information 13 that minimizes the sum of the term-specific variance and pairwise covariance of the multiple terms. By solving the optimization problem, the processing unit 12 determines multiple first terms from among the multiple terms that will be subject to simultaneous measurement by a quantum computer. The processing unit 12 calculates the number of samples for simultaneous measurement based on the minimized sum.

This reduces the amount of work required to associate facility entities with equipment data models. [Solution] The association support method includes a first step, a second step, a third step, a fourth step, a fifth step, a sixth step, and a seventh step. In the first step, a plurality of equipment entities E3 are acquired, each of which associates the name of a device 10 with area information about the area where the device 10 is installed and tag information that defines the device's characteristics. In the second step, a plurality of equipment data models are acquired, each of which defines data points and attribute information for each model number of the device 10. In the third step, an association rule is acquired that associates area information or tag information with attribute information. In the fourth step, a selection of an equipment entity E3 is accepted, and in the fifth step, the area information or tag information is identified. In the sixth step, the attribute information is identified based on the association rule. In the seventh step, an equipment data model having the identified attribute information is identified from the plurality of equipment data models.

A test case generation support system 30 comprising an arithmetic unit 31 that executes: open-ended response generation processing for generating an open-ended response corresponding to an open-ended question for a predetermined topic which requires a response by inputting the open-ended question to a trained model 47; first closed-ended question generation processing for generating a first closed-ended question by inputting the open-ended question and the open-ended response to the trained model 47, the first closed-ended question asking whether the open-ended response is correct or incorrect and requiring that the open-ended response is correct as a correct answer; and second closed-ended question generation processing for changing a topic portion of the first closed-ended question by a predetermined algorithm and generating a second closed-ended question asking whether the changed topic portion is correct or incorrect.

To provide a system that can reduce the user's burden in system model creation and generate an appropriate system model that meets a user's intent.SOLUTION: A design support system includes: an input information analysis unit configured to analyze user input information related to a system model; a model candidate identification unit configured to identify, based on analysis results of the input information, a system model to be generated from system model information containing multiple types of system model templates registered therein; an information extraction unit configured to extract, based on the analysis results of the input information, necessary information to be used in generating the system model from a database storing design-related information; and a system model generation unit configured to use the extracted necessary information and a template of the system model to be generated to generate the system model based on the input information.SELECTED DRAWING: Figure 1

The present invention relates to a method and a system for supporting process optimization using generative AI, and more particularly, to a method and a system for supporting process optimization using generative AI, which can enable a user to more conveniently use a causal relationship between a plurality of steps and variables accompanying an arbitrary process by detecting or estimating the causal relationship by an artificial intelligence algorithm. And a hypothesis is derived from this, and simulation and verification are performed.

To provide a consensus-building support system and method that analyzes the context of opinions from stakeholders and automatically addresses emotional opposition and distrust. [Solution] The system is characterized by comprising: an information processing device; an input means for inputting the opinions of relevant parties to the information processing device; an analysis unit that analyzes the opinions input by the input means using an emotion recognition engine and calculates the emotional state as an emotion score; an estimation unit that estimates the consensus formation stage from the emotion score analyzed by the analysis unit; a dynamic dialogue generation unit that automatically generates dialogue content based on the consensus formation stage estimated by the estimation unit for the opinions and transmits the dialogue content to the person who expressed the opinion; a storage device that stores the history of the emotional state for each person who expressed the opinion, the consensus formation stage, and the dialogue content automatically generated by the dynamic dialogue generation unit; and a management unit that manages the history of the emotional state for each person who expressed the opinion, the consensus formation stage, and the dialogue content automatically generated by the dynamic dialogue generation unit stored in the storage device.

The invention belongs to the field of artificial intelligence aided decision making, and particularly relates to a complex system decision supporting method and device based on hybrid retrieval enhancement generation. According to the method, a knowledge graph is constructed for documents in the complex system field, a semantic embedding vector of each text block is generated, double retrieval channels of a vector retrieval channel and a graph retrieval channel are designed, and parallel retrieval is performed on user query; wherein the vector retrieval channel calculates semantic similarity between user query and all text blocks according to semantic embedding vectors of all the text blocks, and the graph retrieval channel performs graph traversal on the knowledge graph and calculates matching degree scores of all the text blocks based on traversal results; sorting results of the retrieval results in the two retrieval channels are obtained according to the similarity or scores, fusion scores of all text blocks in the retrieval results are calculated through a reciprocal rank fusion algorithm, and a fusion result is obtained; and decision suggestions are generated based on the fusion result, so that the accuracy and reliability of emergency decision support of the complex system are improved.

To support in verifying whether a function of an existing system is correctly moved to a new system in moving the existing system to the new system.SOLUTION: A support device for supporting in verifying the operation of a second system obtained by moving a first system described in a prescribed programming language to another programming language makes a natural language model emulate the first system to acquire a first execution log including a variable value of a program of the first system as its execution result by inputting a prompt including a first program of the first system to the natural language model, acquires a second execution log including a variable value of a second program and the second system by executing the second program of the second system, and specifies and outputs a mismatching portion at which the variable values do not match between the first system and the second system by comparing a first execution log with a second execution log.SELECTED DRAWING: Figure 1

This invention belongs to the field of industrial fault diagnosis and intelligent operation and maintenance technology. It discloses a human-computer collaborative fault diagnosis reasoning and maintenance decision support method and system enhanced by Large Language Model (LLM). The steps are as follows: (1) Construct a multi-dimensional industrial knowledge graph integrating alarms, phenomena, causes, and solutions. Through the character-level and semantic-level similarity fusion mechanism driven by the LLM, high-fidelity entity fusion and full causal chain modeling are achieved; (2) The LLM is used to complete entity extraction, knowledge purification, and path refinement. The accuracy and consistency of the knowledge graph are improved through redundancy pruning, semantic merging, and logical sorting; (3) The framework is deployed on a portable edge intelligent system. Local real-time reasoning is achieved based on heterogeneous hardware architecture, and a knowledge closed loop is formed by combining human-computer interaction. This invention effectively improves the accuracy, interpretability, and security of fault diagnosis, realizes real-time operation and maintenance decision support, and is suitable for complex and dynamic industrial fault diagnosis scenarios.