98 results about "Historical model" patented technology

The invention discloses a terrain-level real-scene three-dimensional entity and element dynamic updating method, which comprises the following steps: S1, data acquisition and preprocessing: acquiring data of a target area by an acquisition mechanism based on entity and element type priorities, and preprocessing the acquired data to obtain model data of the target area; s2, change detection: comparing newly collected model data with historical model data, and identifying a change region and type; s3, entities and elements are extracted and updated, wherein the entities and the elements of the change area are processed to obtain updated models; s4, model fusion and optimization: fusing the updated entity and element model with the original model, and realizing efficient acquisition and preprocessing of target area data by using the multi-source surveying and mapping equipment and an acquisition mechanism based on entity and element type priorities; by dividing the small regions according to the region characteristics and correcting the acquisition frequency, the resource allocation is optimized, and the pertinence and efficiency of data acquisition are improved.

The invention provides a multi-source information fused virtualized network fault rapid positioning method, and relates to the technical field of electric digital data processing, and the method comprises the steps: classifying and sorting historical fault events, constructing a historical dynamic evaluation model, correcting a calibration index, capturing equipment performance changes and abnormities, and enhancing the fault recognition capability. Similar faults are accurately classified and preferentially processed according to severity; in the real-time monitoring stage, real-time multi-source and abnormal data are combined, a real-time dynamic evaluation model and a correction calibration index are calculated, a ratio is applied to calculate a fine adjustment strategy, the real-time performance and accuracy of fault positioning are remarkably improved, and network dynamic changes are quickly responded. And carrying out difference analysis on the adjusted real-time model and the historical model, calibrating a preliminary fault positioning result, and improving the positioning precision and reliability.

The invention belongs to the field of model matching, and discloses a three-dimensional model matching method and device and electronic equipment, and the method comprises the steps: converting a to-be-matched three-dimensional model into first point cloud data, carrying out the preprocessing of the first point cloud data, setting a registration parameter according to a preprocessing result, extracting a first feature of the first point cloud data, and carrying out the matching of the first feature and the second feature; and registering the first feature with a second feature of the historical three-dimensional model according to the registration parameter to determine a matching result. Three-dimensional model matching is converted into feature matching, and the automation degree and efficiency of the matching process are improved. According to the method, feature extraction is performed on the first point cloud data to obtain the first feature, and then the first feature is registered with the second feature in the preset feature library, so that the similarity between the to-be-matched model and the historical model can be quickly and accurately evaluated. In the whole process, the dependence on professional modeling software is reduced, the consumption of manpower and computing resources is reduced, and meanwhile, the efficiency and accuracy of three-dimensional model matching are improved.

The invention belongs to the technical field of information, and relates to a multi-label incremental learning method and system based on clear known and unknown knowledge. The method comprises the following steps: extracting visual features from an input image; obtaining fine-grained class perception features by using the class embedding and extracted visual features of the historical class and the current class; synthesizing unknown class features by utilizing the class perception features; predicting the probability of an old class by using a stability classifier, predicting the probability of a new class and the probability of an unknown class by using a plasticity classifier, combining the probability of the old class with the probability of the new class and the probability of the unknown class, and performing image classification; and supplementing old-class pseudo labels for the current image according to the class predicted by the historical model. According to the method, known and unknown contents can be clarified to adapt to historical, current and future knowledge in multi-label class increments, cross-task feature aliasing can be prevented, the problem of large inter-class forgetting difference can be relieved, features related to future classes can be developed, and preparation is made for future learning.

The invention relates to a heterogeneous federated learning method and system based on historical model double knowledge distillation, and belongs to the technical field of federated learning and knowledge distillation. The method comprises the steps that a server initializes a global model and constructs a historical global model storage list, and then the server generates a historical fusion model through weighted aggregation of multiple rounds of historical global models and sends related models to a client; after the client receives the global model and the historical fusion model of the round, local model training is carried out through a double knowledge distillation strategy based on a local private data set, the updated local model of the round is stored, global knowledge distillation takes the historical fusion model as a teacher model, and local knowledge distillation takes the historical local model of the client as a teacher model; global and local knowledge migration is balanced through hyper-parameters; the server averagely aggregates the model parameters updated by the client to generate and store a new round of global model; and iteratively optimizing to a convergent or preset training round number. According to the method, the performance of the model in a heterogeneous data environment is remarkably improved.

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 chip security mechanism evaluation method and computer equipment. The method comprises the following steps: acquiring test data of chips of the same series and historical models; extracting characteristic variables of test data of chips of historical models, and establishing a prediction model based on machine learning for evaluating the validity of a chip security mechanism; acquiring test data of a novel chip with the same data structure as the sample data; and inputting the test data of the novel chip into the established prediction model for prediction, and outputting a prediction result as an index of the validity of the chip security mechanism. According to the invention, rapid function security detection and evaluation can be realized for the same series of novel chips, and the function security detection efficiency and stability of the novel chips in a small sample scene are remarkably improved.

The invention relates to the technical field of building curtain walls, and discloses an industrial implementation method of a spatial special-shaped unit curtain wall. The method comprises the following steps: a design end generates a first model by using a BIM modeling tool, a production end performs production feasibility verification based on historical models, determines a first number of historical models of which the similarity with a first type unit corresponding to an unproduced first model is greater than a first threshold value, and then analyzes whether production can be performed based on the historical models; if yes, pilot production is carried out, or else, a design end is fed back for redesign. Further, the customization degree of the first type of unit bodies is evaluated, the first number is dynamically adjusted based on the customization degree, and the customization degree is set based on scores of the first information of the unit bodies. According to the method, by fully utilizing historical production data and experience, the cooperation of design and production is enhanced, the accuracy and efficiency of production feasibility judgment are improved, and the production risk and cost are reduced.

The invention discloses a computing node selection method and device and a storage medium, relates to the technical field of communication, is used for improving the computing node selection efficiency, and comprises the following steps: obtaining historical information of each computing node in a plurality of computing nodes; the historical information comprises a historical contribution degree, a historical model verification passing rate and a penalty factor; the computing node selection device determines the reputation value of each computing node in the plurality of computing nodes according to the historical information of each computing node in the plurality of computing nodes; the computing node selection device selects a target computing node set meeting the reputation value requirement from the plurality of computing nodes; the target computing nodes in the target computing node set are used for participating in model training. The method and the device are applied to the process of computing node selection.

The invention provides an underground traffic construction pipeline relocation optimization method and system. According to the method, the spatial position, the cross section size, the joint position and the maintenance record of the existing cable are acquired, and a channel structure is combined to construct a spatial layout model; scanning the side wall and the bottom plate of the channel by using a non-contact detector, and generating a channel distribution diagram based on the identified obstacle position, size parameter and structure boundary dynamic updating model; dividing the distribution map into grids, calculating a safety distance by taking a section size as a constraint, traversing a grid connection relation, and determining an initial installation path by avoiding a high-risk area marked at a joint position; then extracting a fault position and a maintenance frequency, binding an initial path with a spatial position by means of a historical model, calculating a path risk, and outputting a maintenance parameter; and adjusting the path trend, generating a target scheme and outputting a cable arrangement diagram. According to the invention, accurate optimization of the cable path is realized, the construction collision risk is avoided, and the long-term operation and maintenance cost is reduced.

The invention discloses a wave numerical value processing method for a shielded wharf, which relates to the technical field of wave forecasting and comprises the following steps of: obtaining a specific relational expression between forecast values and measured values of model boundary wave conditions and wind condition parameters; setting a wave mathematical model by adopting swan wave numerical simulation software, and simulating to obtain predicted values of historical in-port wave conditions and wind condition parameters by combining the predicted values of the historical model boundary wave conditions and wind condition parameters; deducing to obtain a specific relational expression between the forecast value and the measured value of the wave condition and wind condition parameters in the harbor; obtaining forecast values of the latest wave condition and wind condition parameters of the model boundary, and correcting the forecast values; and performing simulation to obtain initial forecast values of the wave conditions and the wind condition parameters in the harbor, and performing correction to obtain final forecast values of the wave conditions and the wind condition parameters in the harbor. According to the method, the forecast value of the in-port wave of the shielded wharf can be obtained according to the forecast value of the out-port wave, so that the operation arrangement of ships in the port is guided, and basic data support is provided for the operation management of the port.

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 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.

The invention belongs to the technical field of federated learning security defense, and particularly relates to a backdoor attack defense method based on model parameter similarity detection, a program, equipment and a storage medium. According to the method, the change of the updating amplitude and the output of each model is analyzed to reflect whether client data sample distribution changes or not, so that abnormal model updating is found. According to the method, records of historical model updating amplitude and historical output classification results are maintained for each model, the average similarity between the historical model updating amplitude and the historical output classification results is calculated, whether data sample distribution of a client changes or not is comprehensively evaluated based on the average similarity, and a model parameter normalization technology is combined to determine whether the data sample distribution of the client changes or not. Back door poisoning updates may be filtered while preserving benign model updates.

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 invention discloses a blockchain federal learning poisoning attack defense method supporting privacy protection. The method relates to five links of initialization, model training and blinding, malicious detection, model aggregation and model updating. According to the method, abnormal fluctuation is accumulated in a historical model updating form in a malicious detection process to highlight malicious behavior characteristics and realize accurate identification of malicious behaviors, so that poisoning attacks are effectively defended, and the robustness of a global model is improved. In addition, the poisoning attack defense effect of the method is not affected by model update protection, malicious detection can be carried out by using original features, and data privacy of participants is prevented from being directly exposed. And the contradiction that the original features updated by the model are hidden after privacy protection is implemented and are not beneficial to accurate identification of malicious detection is relieved to a certain extent, so that the overall security of block chain federal learning is further enhanced.

The invention provides a CO emission prediction method based on robust kernel feature space semi-supervised drift detection. The method comprises the following steps: calculating a first-order difference component of a historical sample through a pre-constructed historical model to construct a historical data set; calculating feature space statistics of the real-time data, and performing drift detection through the feature space statistics to obtain a drift sample; union set taking operation is carried out on the historical samples and the drift samples, and a new training set is obtained; and performing data updating on the historical model through the new training set, and predicting the CO emission through the updated prediction model. According to the method, through semi-supervised drift detection, the problems of concept drift identification and model dynamic updating under truth value deficiency during CEMS fault are solved, and the CO emission prediction precision and the robustness in a complex industrial scene are improved.

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 present invention discloses a chip safety mechanism evaluation method and computer equipment, the method comprising: obtaining test data of historical model chips of the same series; extracting characteristic variables of the test data of historical model chips, establishing a prediction model based on machine learning, and evaluating the effectiveness of chip safety mechanisms; obtaining test data of new model chips with the same data structure as sample data; inputting the test data of new model chips into the established prediction model for prediction, and outputting the prediction result as an indicator of the effectiveness of chip safety mechanisms. The present invention can realize rapid functional safety detection and evaluation for new model chips of the same series, and significantly improve the functional safety detection efficiency and stability of new model chips in small sample scenarios.

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.

A computing platform may train, using historical model performance information, a time to maintenance (TTM) prediction model to output, for a given cluster of machine learning models, a corresponding TTM. The computing platform may obtain model performance information for a plurality of machine learning models. The computing platform may cluster, using model performance information, each of the plurality of machine learning models into one of a plurality of clusters of machine learning models. For each cluster, the computing platform may identify, by inputting information of the corresponding cluster into the TTM prediction model, a TTM. The computing platform may detect, for a first cluster of the plurality of clusters, expiration of a first TTM, corresponding to the first cluster. The computing platform may update, based on detection of the expiration of the first TTM, a first plurality of machine learning models included in the first cluster.

The present application provides a method and device for earthquake survivor identification based on knowledge anti-forgetting, the method comprising: inputting audio information and visual information of a target video into an earthquake survivor identification model, and obtaining an earthquake survivor identification result of the target video output by the earthquake survivor identification model; the earthquake survivor identification model is obtained by training a historical model based on multiple training tasks and loss functions, the loss function is determined based on elastic weight constraint loss, feature reuse loss and classification loss, the elastic weight constraint loss is used to constrain parameter updates between two adjacent training tasks, and the feature reuse loss is used to reuse the data of trained training tasks when training based on training tasks, thereby improving the accuracy of earthquake survivor identification.