52 results about "Historical model" patented technology

The invention provides an undercarriage system modeling method based on a natural language demand text, and relates to the technical field of model-based computer aided design, and the method comprises the steps: S1, constructing a SysML meta-model knowledge base and an undercarriage system model knowledge base; s2, analyzing the natural language demand text, and generating a structured demand text of the undercarriage system; s3, mapping a structured demand text entity of the undercarriage system into a node type of a semantic graph; and S4, in combination with a historical model template and real-time retrieval, generating an undercarriage system SysML model according to the semantic map obtained in the step S3, and performing undercarriage system engineering analysis through the undercarriage system SysML model, thereby improving the efficiency and quality of aviation equipment development. On the basis of a retrieval enhancement generation technology and an intention routing mechanism, the modeling efficiency and accuracy of the undercarriage system are remarkably improved; a graph neural network and a semantic vector space model are adopted to realize deep fusion and conflict resolution of cross-domain knowledge; and through an incremental learning mechanism, model continuous evolution and design experience closed-loop iteration are supported.

The invention discloses a denture three-dimensional model feature construction and matching method based on curvature adaptive sampling, which comprises the following steps: S1, acquiring denture surface point cloud data through three-dimensional scanning, converting the data into an STL model, and comparing a historical STL model pre-stored in a folder; s2, constructing a self-adaptive sampling weight, selecting a triangular patch through replacement random sampling, and generating a sampling point; s3, carrying out normalization processing on the sampling points; s4, calculating the distribution of D2 and A3 by using the normalized sampling points, and obtaining corresponding shape features; s5, splicing the features to form a joint geometric feature vector; and S6, performing S1-S5 on the to-be-matched false tooth to obtain a matching vector, performing S2-S5 on the historical models one by one to obtain comparison vectors, calculating cosine similarity between the matching vector and each comparison vector, and outputting a Top-K similarity model. According to the method, key geometric structure feature expression is enhanced, robustness to point cloud density change, local defects and the like is high, and matching is ensured to be reliable and consistent.

A system, method, and a computer program product for determining anomalies in a dataset are provided. The dataset, which stores data structures of parameters, is received at an anomaly detection framework. The anomaly detection framework selects multiple models, including time series models, historical models, artificial intelligence models and isolation forest models to analyze the parameters in the dataset and determine anomalies in the data structures.

The application provides a data lake-based data processing method, device, equipment and medium. The method comprises the following steps: determining a preset data lake table; obtaining time series data and model data of a target device based on a flink framework and the data lake table; comparing the time series data with historical time series data stored in a target state backend of the flink framework to obtain a first comparison result; comparing the model data with historical model data stored in the target state backend to obtain a second comparison result; updating the historical time series data and the historical model data in the target state backend based on the first comparison result and the second comparison result to obtain an update result; and storing the time series data and the model data in a target database according to the update result. Through the method, unified real-time access to a data lake can be realized based on a data lake storage technology, stream batch integration at a storage level is realized, and stream batch integration at a computing level is realized based on a flink computing engine.

The application discloses a method for defending against poisoning attacks in blockchain federated learning supporting privacy protection, and relates to five links of initialization, model training and blinding, malicious detection, model aggregation and model updating. In the process of malicious detection, the method accumulates abnormal fluctuations in the form of historical model updates to highlight the characteristics of malicious behavior and realize accurate identification of malicious behavior, thereby effectively defending against poisoning attacks and improving the robustness of the global model. In addition, the poisoning attack defense effect of the method will not be affected by the protection of model updates, which can realize malicious detection using original features and avoid direct exposure of participant data privacy, to a certain extent, alleviate the contradiction that the original features of model updates are hidden after the implementation of privacy protection, which is not conducive to the accurate identification of malicious detection, thereby further enhancing the overall security of blockchain federated learning.

The application discloses a model training method and device, a computer readable storage medium and an electronic device. The method comprises the following steps: obtaining historical model training information of a plurality of nodes in a preset time period before the current time of each node in a joint learning task; when there is an interrupt node in the plurality of nodes, determining a current model training scheme of the plurality of nodes according to the historical model training information of each node. Through the technical scheme of the application, the current model training scheme can be determined in real time when an interrupt node appears in the process of executing the joint learning task, and the normal progress of the joint learning task is ensured.

Media plan generation systems and methods for TV advertising are disclosed. Embodiments of these systems and methods are adapted to generate a media plan comprising a core plan and a test plan. The core plan may be produced based on historical models regarding historical performance of an entity's media while the test plan may be produced based on a predictive model of media performance.

The invention relates to a method for accelerating model training recovery, electronic equipment and a storage medium. The method comprises the steps of executing a first task for constructing a training component based on an instruction of recovery model training, and independently executing a second task through a concurrent execution unit corresponding to a main execution unit; wherein the second task is used for loading the check point file corresponding to the training component to the memory from the external storage system; wherein the training component comprises a model object and an optimizer object, the check point file comprises state data, stored in historical model training, of the training component, and the first task and the second task are at least partially overlapped in execution time; and in response to an event of completing the first task and the second task, loading the state data in the memory into the corresponding training component to complete state recovery of the training component. According to the method, the parallel support of each task in the model training recovery process can be realized, and the time consumption of model training recovery is effectively reduced.

The invention relates to the technical field of operation efficiency evaluation, in particular to a civil aviation autonomous operation efficiency evaluation method, which comprises the following steps: acquiring operation information historical data which comprises operation information accumulated in the civil aviation operation process; multiple historical models are established based on the operation information historical data, each historical model is used for representing different autonomous operation scenes of the civil aviation, and each autonomous operation scene comprises a corresponding reference efficiency index; obtaining to-be-evaluated operation information, matching the to-be-evaluated operation information with a plurality of historical models, and obtaining a current autonomous operation scene; calculating an actual efficiency index corresponding to the current autonomous operation scene according to the to-be-evaluated operation information; performing visual comparison display on the actual efficiency index corresponding to the current autonomous operation scene and the reference efficiency index; the civil aviation operation efficiency can be comprehensively evaluated.

A system and method for sampling a flight envelope of an air vehicle under test. The method includes obtaining engineering models of the vehicle to identify how the vehicle behaves, obtaining empirical models through test data, and obtaining historical models of similar legacy aircraft. The method further includes ensembling the models into an integrated model and assessing the ensemble uncertainty at any particular test point, for a variety of aircraft conditions, to determine model reliability. The method includes determining the optimal order of test points based on local model uncertainty, testing the air vehicle at a point, revising the integrated model with that data and reassessing the uncertainty, and determining if a stop testing condition has been met, in which case the testing series is closed. The last calibrated integrated model could then be used to generate trusted data for any remaining required test using a certification by analysis method.

The invention belongs to the field of power system risk early warning, and particularly relates to a power grid node connectivity modeling and operation risk accurate early warning method and device based on meteorological disturbance driving, computer equipment and a storage medium. According to the method, real meteorological data of a plurality of preset time points are acquired and predicted meteorological data of a plurality of reference time points are obtained through prediction, so that continuous tracking of a meteorological change trend is realized; meanwhile, reference meteorological data are determined by using historical meteorological data, association between predicted meteorology and historical modes is established, risk level assessment is more fit with an actual meteorological disturbance rule, and the risk level assessment accuracy is improved by identifying a change time point and a corresponding level change amount in a basic risk level sequence, calculating a level adjustment coefficient and adjusting the sequence. The risk level is adjusted by effectively utilizing the time domain information, so that the influence of time domain accumulation can be introduced during risk early warning, the accuracy of risk early warning is improved, and the reliability of risk early warning of the power system is further improved.

The embodiment of the invention provides a model configuration parameter generation method and device, equipment and a computer storage medium. The method comprises the steps of obtaining preset target model task data; and based on the target model task data, according to a corresponding relationship between preset historical model task data and a historical model predicted calculation power value, determining a target model predicted calculation power value corresponding to the preset target model task data. And under the condition that the target model predicted computing power value is equal to the historical model predicted computing power value, historical model configuration parameters corresponding to the historical model predicted computing power value are used as target model configuration parameters, and the target model configuration parameters are used for constructing a target model. According to the invention, under the condition that the predicted calculation power value of the target model is equal to the predicted calculation power value of the historical model, the historical model configuration parameter corresponding to the predicted calculation power value of the historical model is used as the target model configuration parameter, so that the time for determining the target model configuration parameter can be shortened; therefore, the time for constructing the target model based on the target model configuration parameters is shortened.

The invention discloses a federated learning-oriented data-free hidden model poisoning attack method. The method does not need to access local training data of any client, only depends on historical global model parameters issued by a server, constructs simulated benign parameters based on an improved Shampoo optimization algorithm by estimating a global gradient, and generates reference attack parameters on the basis; then fusing historical attack directions and superposing multi-dimensional composite disturbances such as projection disturbance, low-rank disturbance, adversarial disturbance and statistical disturbance; and finally, dynamically limiting the disturbance amplitude by adopting a self-adaptive norm cutting mechanism based on historical model statistics and direction information to ensure that malicious parameters are difficult to distinguish from benign updating. Experiments prove that the method can reduce the model accuracy from 70% to about 10% under various mainstream aggregation strategies, and has high concealment and universality. The method reveals that the federal learning system still faces serious model security threats under the condition of no local data.

The present application relates to an incremental detection method based on elastic weight interference isolation and prototype drift calibration, comprising: in the elastic weight interference isolation stage, detecting the interference area of the current data through the historical model and generating an interference area set (new class target mistaken as background), combining the historical and current interference information importance parameters to construct a perception score, constructing a loss function to train the model and updating the old knowledge importance parameter; in the prototype drift calibration stage, establishing the old stage class prototype based on the historical features, performing drift compensation through the projection layer and generating drift compensation features, splicing the drift compensation features with the model output features to retrain the classification head, so as to calibrate the feature distribution and complete the two-stage technical closed loop. The incremental detection method can effectively solve the problem of catastrophic forgetting.

The invention provides an intention recognition method and device based on virtual adversarial training, and the method comprises the steps: obtaining an original text, and determining initial disturbance information based on the similarity between vocabularies in the original text and historical disturbance vocabularies; performing iterative updating on the initial disturbance information, determining an average value of a plurality of gradients obtained in an iteration process, and determining final disturbance information; and constructing a confrontation sample based on the final disturbance information and the original text, updating model parameters of the pre-trained intention recognition model based on the confrontation sample, and performing intention recognition based on the trained intention recognition model. The initial disturbance information of the original text is determined by introducing the disturbance vocabularies in the historical model training process, and is continuously updated and used in the adversarial training process, so that the noise of random initialization is reduced, a large amount of invalid disturbance is avoided, the adversarial training efficiency is improved, and the period of the model from development to deployment is shortened; and the service demand can be responded more quickly.

The invention discloses a geological exploration data three-dimensional modeling analysis G3DVelocity system based on IDL and an application method thereof, a storage medium and a program product. The geological exploration data three-dimensional modeling analysis G3DVelocity system comprises a database management subsystem, a model establishment analysis subsystem, a graph editing subsystem and a data management subsystem. The application method comprises the following steps: S1, performing format conversion on seismic exploration original data provided by a user in the database management subsystem, and storing the seismic exploration original data in a database; s2, according to user needs, the data can be edited and processed through the database management subsystem; s3, selecting data through a model establishment and analysis subsystem to perform spatial interpolation processing, establishing a three-dimensional model by using the data subjected to interpolation, and editing and analyzing the three-dimensional model according to the needs of a user; and S4, finally, the result model is stored in a graph editing subsystem, so that the historical model can be conveniently looked up and edited.

The invention discloses an atmospheric numerical forecasting method and system based on machine learning, and a storage medium, and relates to the technical field of meteorological information, and the method comprises the steps: generating a preliminary prediction field of a plurality of atmospheric physical variables in a specified time period in the future based on a deep learning model integrated with a multi-scale space-time attention mechanism; correcting the preliminary prediction field according to physical constraints; and comparing the corrected atmospheric state field data with atmospheric observation data in a historical model error library, adjusting a physical constraint weight of a corresponding region for an extreme weather scene according to a comparison result, and using a correction result for model training. Through deep fusion data driving prediction and a physical constraint correction mechanism, the reliability and precision of a prediction result are remarkably improved, a deep learning model integrated with a multi-scale space-time attention mechanism is utilized, complex space-time features in atmosphere state evolution are effectively captured, and the simulation capability of a multi-scale atmosphere process is enhanced.

This invention relates to the field of data processing technology and discloses a personalized federated learning method, system, and medium for edge scenarios. The method calculates a personalized model set composed of multiple client models, calculates the model combination weights using historical model combination weights and mutual information bias terms, and performs model interpolation training using the personalized model set and model combination weights. This can improve the personalization capability of client models and meet the client's expected training objectives.

The invention provides an intelligent knowledge question-answering method, device and equipment, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring a plurality of sample sequences and a general knowledge vector set of power vertical domain knowledge, wherein the sample sequences comprise a plurality of word units and time steps; performing word unit prediction analysis on the global context and the local context corresponding to each time step to obtain an alignment loss item; the alignment loss item represents the prediction difference between the global context and the local context; based on the general knowledge vector set, the historical model parameters and the currently updated model parameters, performing calculation to obtain knowledge loss items; the knowledge loss item represents the disturbance of the model parameter change on the general knowledge; based on the sample sequence, constructing a loss function by using a prediction loss item, an alignment loss item and a knowledge loss item, and training to obtain a question and answer model; and predicting the question of the user through the question and answer model to obtain the answer of the question. The answer prediction accuracy can be improved.

This invention discloses an intelligent layout system for the installation of exterior wall composite panels, relating to the field of intelligent construction technology. It collects building facade information to generate a structured digital twin model of the building site; matches the site model with historical construction digital twin models based on feature fingerprint similarity, extracting an initial intelligent layout model from the graph neural network architecture of the matching cases; analyzes the differences between the site model and the historical models and encodes them as adjustment instructions for the model input graph structure; performs transfer learning on the initial model, focusing on optimizing the difference areas, to obtain an optimized intelligent layout model; acquires site environmental data, combines it with a built-in physical and process knowledge base, drives the optimized model to generate multiple candidate schemes and perform construction feasibility simulations, outputting multi-dimensional construction scores for each scheme; compares the multi-dimensional construction scores and outputs the optimal layout scheme, solving the problems of traditional layout methods being unable to handle complex structures, ignoring environmental factors, and lacking reusable experience.

The invention discloses a federated learning-based risk assessment method, device and equipment and a storage medium, and is applied to the technical field of artificial intelligence, and the method comprises the steps: obtaining current training sample data, and carrying out the model updating of a local risk assessment model deployed under the own equipment based on the current training sample data, obtaining the current model parameter update quantity in the current model update time period; obtaining a historical model parameter update quantity, and determining a data quality score according to the current model parameter update quantity, the historical model parameter update quantity and the current training sample data; encrypting and transmitting the current model parameter update quantity, the data quality score and the node performance data of the equipment to a central server, performing model updating on a global risk assessment model deployed in the server, and encrypting and transmitting the updated global model parameter update quantity to each client; and updating the local risk assessment model under the own equipment based on the global model parameter update quantity.

A system and method for sampling a flight envelope of an air vehicle under test. The method includes obtaining engineering models of the vehicle to identify how the vehicle behaves, obtaining empirical models through test data, and obtaining historical models of similar legacy aircraft. The method further includes ensembling the models into an integrated model and assessing the ensemble uncertainty at any particular test point, for a variety of aircraft conditions, to determine model reliability. The method includes determining the optimal order of test points based on local model uncertainty, testing the air vehicle at a point, revising the integrated model with that data and reassessing the uncertainty, and determining if a stop testing condition has been met, in which case the testing series is closed. The last calibrated integrated model could then be used to generate trusted data for any remaining required test using a certification by analysis method.

Technologies are described herein for classifying personalized interactions at an application. A method can include receiving and storing user inputs associated with interactions between users of the application, building historical models based on the user inputs to classify the interactions, wherein building a historical model for a particular user comprises aggregating particular user inputs associated with a set of previous interactions between a particular user and other users, in association with a pending interaction for the particular user and during the pending transaction associating a current user input with the historical model for the particular user and based on analyzing the historical model for the particular user, determining that the pending interaction satisfies a condition, and interrupting the pending interaction based on determining that the pending interaction satisfies the condition.

The invention provides a method and system for determining machining time and a computer program product, and relates to the technical field of intelligent manufacturing. The method for determining the machining time comprises the steps that a three-dimensional model of a to-be-machined part is obtained, and the to-be-machined part is a machined part; based on the three-dimensional model, matching a first historical model from a historical database, and taking related data of the first historical model as template data; identifying to-be-processed features of the three-dimensional model through a part identification model; based on the template data, processing requirements of the to-be-processed features are determined; identifying feature parameters of the to-be-processed features by using a CAD module; and determining the machining time of the to-be-machined part based on the machining requirements, the feature parameters and the machining process knowledge base of the to-be-machined features. According to the method, the defects of inconsistent man-hour estimation and the like caused by disjunction of the process and calculation, lack of intelligent decision and incapability of self-optimization of the system in the traditional method are overcome.

The invention discloses a state monitoring device and method for a bolt connection plate, and relates to the field of structure connection monitoring, and the device comprises an acquisition module which is used for collecting the bolt distribution state information of the surface of a detection target; the modeling module is used for receiving the bolt distribution state information acquired by the acquisition module and constructing a bolt distribution model based on the bolt distribution state information; the relative position coordinates of the bolts are collected through the sensor, the distribution model is constructed, the connection state change degree can be analyzed based on a historical model, detection is triggered in a self-adaptive mode in combination with vibration, wind power and other environmental factors, omissions of manual inspection are avoided, subtle changes can be dynamically captured, the abnormal risk of the connection state is recognized by comparing model differences, and the detection accuracy is improved. And a to-be-checked bolt can be positioned.

The invention discloses a data distributed encryption sharing and multi-party collaborative learning method. The method comprises the following steps: initializing a fault detection model and issuing the fault detection model to a client; utilizing the equipment data to obtain a fault detection model after the current round of training; uploading the corresponding first model parameters to a server; calculating sensitivity according to historical model parameters; cutting a second model parameter obtained by aggregating the first model parameters, and adding differential privacy noise to obtain a third model parameter; and issuing the third model parameter to the client, and performing next round of federated learning training on the fault detection model by using the third model parameter until a preset termination condition is reached. According to the method, the sensitivity is obtained according to the importance and the privacy degree, the intensity of differential privacy noise is dynamically adjusted, the privacy protection and model accuracy are effectively balanced, a double-cloud server architecture is adopted and combined with a secret sharing mechanism, and it is ensured that privacy protection is achieved in the whole process of model training.

This specification discloses a model training method, a task execution time prediction method, and an apparatus, specifically including: filtering similar historical model training tasks based on historical resource usage data of historical model training tasks; inputting the historical resource usage data of historical model training tasks and similar historical model training tasks into a prediction model to determine the resource usage characteristic data corresponding to the historical model training tasks, thereby determining the predicted execution time corresponding to the historical model training tasks; training based on the predicted execution time and the execution time of historical tasks; and determining the predicted execution time of the target model training task based on the resource usage data of the target model training task. The method in this specification has higher prediction efficiency and greater accuracy. This effectively improves the utilization rate of training resources during subsequent resource allocation, avoiding resource waste and idleness, and greatly improving the overall training efficiency of the training process.

The invention discloses a model preferential method, device and equipment based on feature weight and a storage medium. The method comprises the following steps: acquiring historical predicted power data and historical model input data corresponding to at least two candidate power prediction models, and historical real power data corresponding to the historical predicted power data; on the basis of the historical predicted power data, the historical model input data and the historical real power data, generating a performance feature vector corresponding to each candidate power prediction model; and determining a target power prediction model from at least two candidate power prediction models based on the performance feature vector and the to-be-optimized performance feature weight corresponding to each performance feature. According to the technical scheme, the prediction precision of power prediction is improved.

The invention provides a method and system for determining sheet metal working hours and a computer program product, and relates to the technical field of intelligent manufacturing. The method for determining the sheet metal working time comprises the steps that a three-dimensional model of a to-be-machined part is obtained, wherein the to-be-machined part is a sheet metal machining part; based on the three-dimensional model, matching a first historical model from a historical database, and taking related data of the first historical model as template data; metal plate to-be-machined features of the three-dimensional model are recognized through a part recognition model; based on the template data, processing requirements of to-be-processed characteristics of the metal plate are determined; identifying characteristic parameters of the to-be-processed characteristics of the metal plate by using a CAD module; and determining the sheet metal processing time of the to-be-processed part based on the processing requirements, the characteristic parameters and the sheet metal processing technology knowledge base of the to-be-processed characteristics of the sheet metal. According to the method, the defects of inconsistent man-hour estimation and the like caused by disjunction of a process and calculation and lack of intelligent decision in a traditional method are overcome.

The invention relates to a process pushing method based on model feature recognition, which comprises the following steps: acquiring a historical part MBD model and process table data from a database through a data interface, and constructing an MBD historical model, an MBD process library and a historical MBD model information table; acquiring an open source three-dimensional model data set, training to obtain a processing feature recognition model, and performing feature recognition on the to-be-retrieved MBD model to obtain a processing feature recognition result; performing rough retrieval on the similar name MBD historical model by utilizing a historical MBD model information table and a to-be-retrieved model name, converting the similar name MBD historical model into an attribute adjacency graph so as to obtain a topological information vector, and fusing a processing feature recognition result to evaluate the similarity between MBD models so as to obtain a similar part model list with the similarity ranked from high to low; and positioning process data of each part in the MBD process library through the similar part model list, and pushing the process data to a process planning system to complete a process pushing process.