300 results about "Model transformation" patented technology

The embodiment of the invention provides a multi-source heterogeneous data integration method and device fusing a large model conversion operator. The method comprises the steps that heterogeneous data are collected from a multi-source heterogeneous data source, preprocessing operation of data cleaning is carried out, and preprocessed data features are obtained; inputting the preprocessed data features and the target format into a large language model, and generating a conversion operator including data structure analysis, field mapping and type adaptation; and based on the conversion operator, performing distributed parallel data conversion and integration in a distributed computing framework, and integrating and verifying the integrated data. According to the scheme, by introducing the intelligent reasoning ability of a large language model, the efficiency optimization of distributed calculation and the quality verification mechanism of the whole process, the core problems of the traditional data integration technology in the aspects of rule stiffness, high manual dependency and quality control deficiency are systematically solved.

The invention relates to an NPU model optimization method and device based on TensorFlow and a storage medium. The method comprises the steps that a model analysis module reads and extracts the structure and parameters of a TensorFlow model; the model conversion module is used for converting the TensorFlow model into an intermediate representation supported by the NPU; through operator fusion, memory optimization and quantization, the model optimization module optimizes the converted TensorFlow model, and deploys the optimized TensorFlow model to the NPU execution module; and the performance monitoring module monitors the operation performance of the TensorFlow model on the NPU and outputs performance data. According to the method, an efficient model conversion tool is provided, the TensorFlow model is quickly converted into the format supported by the NPU, and the model conversion efficiency is improved; the hardware characteristics of low-precision calculation, parallel calculation and the like of the NPU are fully utilized, the calculation efficiency is improved, and the conversion time and the development cost are reduced by means of operator fusion, memory optimization, quantization and the like; manual operation of developers is reduced, the model deployment process is simplified, and the deployment difficulty is reduced; large-scale model deployment is supported, and actual application requirements are met.

An adapter to a base model of an artificial intelligence (Al) system is disclosed. The adapter includes a connector to connect the adapter to the base model such that during an operation of the Al system at least some portion of data transformed by the base model is propagated from the base model to the adapter and back from the adapter to the base model. The adapter includes a non-linear modifier to modify the data received from the base model non-linearly before returning the modified portion of the data back to the base model, and an Al trainer to tune the non-linear modifier of the adapter by propagating training data through the base model and the adapter and updating weights of the non-linear modifier of the adapter for given weights of the base model to optimize a loss function. Further, weight matrices for the base model and the adapter are jointly constructed by an additional module, which efficiently uses a pool of parameters to allocate to save memory requirement for adaptation of the Al system.

The invention relates to the technical field of artificial intelligence chips, and discloses a model performance estimation method and device and computer equipment, and the method comprises the steps: determining model configuration data and candidate distributed strategies of a target model; converting the original calculation graph corresponding to the single-card deployment state based on the model configuration data and the strategy configuration data of the candidate distributed strategies to obtain a distributed overhead calculation graph corresponding to the multi-card deployment state; and performing performance estimation on the candidate distributed strategy according to the basic overhead and the additional overhead in the distributed overhead calculation graph to obtain strategy performance data of the target model under the candidate distributed strategy, thereby realizing conversion of a single-card model into a multi-card model. And based on the multi-card model, simulation calculation of the multi-card interconnection mode is realized on the premise of limited hardware resources, so that the influence of a communication operator and a topological structure corresponding to the multi-card interconnection mode in the overall operation of the model is reflected, and the upper limit of the model performance can be accurately evaluated in a simulator verification stage before silicon is applied.

The invention discloses a rotor reliability constrained rolling bearing assembly parameter robust design method. The method comprises the following steps: constructing a dynamic model for an actual rotor-bearing system; constructing an uncertainty parameter vector and a design variable vector; a target function based on robustness and a constraint function based on reliability are constructed, so that an uncertainty optimization model is obtained; constructing an augmented input variable, and establishing a candidate orthogonal polynomial basis function set; on the basis, constructing and evaluating polynomial chaos-Kriging models for the target function and the constraint function respectively, and screening out an optimal polynomial chaos-Kriging model; calculating the expectation and the standard deviation of the target function and the failure probability of the constraint function under each design variable vector; and converting the uncertainty optimization model into an unconstrained single-target optimization model, randomly generating population individuals of a heuristic optimization algorithm in a feasible region of design variables, and iteratively searching an optimal solution of the unconstrained single-target optimization model as a rolling bearing assembly scheme.

The invention relates to the technical field of deep learning, in particular to a PyTorch framework-based compatibility expansion system, which comprises a cross-framework model converter, a heterogeneous hardware abstraction layer, a hybrid computational graph execution engine, an intelligent distributed trainer and a self-adaptive optimizer, the cross-frame model converter converts input model formats of different frames into PyTorch executable formats, the heterogeneous hardware abstraction layer supports rear ends of various hardware and automatically selects an optimal calculation path, and the hybrid calculation graph execution engine fuses dynamic graph flexibility and static subgraph optimization and supports dynamic control function execution flow. An intelligent distributed trainer automatically selects a parallel scheme and is compatible with multi-protocol communication, a self-adaptive optimizer performs dynamic optimization based on hardware characteristics and a PyTorch tensor, a cross-frame model converter supports model conversion of multiple deep learning frames, the cost of migration among different frames by a user is reduced, and the universality of the PyTorch frame is improved.

The invention discloses a source-network-load-storage coordinated optimization scheduling method and system based on multiple time scales, and the method comprises the steps: building a multi-time-scale optimization scheduling frame through defining scheduling stages of different time resolutions; on the basis of a multi-time-scale optimization scheduling framework, a mathematical model is established to describe an objective function and an optimization variable of each stage, and a multi-time-scale optimization scheduling model is established in combination with general constraint conditions; converting the model into a mixed integer second-order cone programming problem by utilizing a linear processing technology of convex relaxation and segmentation; solving the mixed integer second-order cone programming problem by adopting an optimization solver to obtain an optimization scheduling strategy adapted to different time scale demand changes; the method is not only suitable for a traditional power system, but also provides powerful support for development of a modern intelligent power grid, and ensures efficient, reliable and sustainable operation of the power system.

A processor-implemented method for converting a trained language model into a language model in an architecture of mixture of experts (MoE), and a computing device using the same is provided. The method for converting a trained language model into a language model in an architecture of mixture of experts using a computing device according to an embodiment of the disclosure may include dividing a plurality of layers included in a target language model and extracting a feed-forward network (FFN) included in each of the plurality of layers, generating an MoE block of the MoE language model, which corresponds to the feed-forward network, generating an input tensor, comparing output tensors between the feed-forward network and the MoE block for the input tensor to obtain a first loss, and updating a weight of the MoE block, based on the first loss.

Systems and methods are provided for adapting a pretrained language model to perform cybersecurity-specific named entity recognition and relation extraction. The method includes introducing a pretrained language model and a corpus of security text to a model adaptor, and generating a fine-tuned language model through unsupervised training utilizing the security text corpus. The method further includes combining a joint extraction model from a head for joint extraction with the fine-tuned language model to form an adapted joint extraction model that can perform entity and relation label prediction. The method further includes applying distant labels to security text in the corpus of security text to produce security text with distant labels, and performing Distant Supervision Training for joint extraction on the adapted joint extraction model using the security text to transform the adapted joint extraction model into a Security Language Model for name-entity recognition (NER) and relation extraction (RE).

A privacy protection heterogeneous federal learning method with Byzantine robustness includes training a local model and calculating a sketch, removing an abnormal local model, selecting a client with quick response capability, encrypting local update and submitting ciphertext, executing weighted aggregation and distributing an updated global model. The method has the beneficial effects that a high-dimensional local model is converted into a low-dimensional sketch by adopting locality sensitive hashing, and the quality of the local model is effectively evaluated on the premise of protecting data privacy. Then, the Byzantine clients are identified and removed based on the hierarchical clustering technology, aggregation weights are distributed to the remaining clients according to the local model quality, and the global model convergence speed is increased; in addition, by selecting the clients which are high in response speed and have representative data sets to participate in model training, the problem of outdated persons caused by system isomerism is solved under the condition that the global model accuracy is not affected.

The invention discloses a vehicle collision accident severity prediction method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting the multi-modal data of a vehicle collision accident, and carrying out the preprocessing of the multi-modal data; converting the pre-processed structured numerical data into a first feature map through a Grubrum angle field method; converting the preprocessed unstructured text data into a text semantic vector through a pre-training language model, and converting the text semantic vector into a second feature map; performing size alignment on the first feature map and the second feature map, and performing splicing on a channel dimension to obtain a multi-channel fusion image; and inputting the multi-channel fusion image into a first deep learning model, and outputting an accident severity prediction result. According to the method, the recognition sensitivity and the prediction recall rate of serious injury accidents are effectively improved, meanwhile, complex artificial feature engineering is avoided, and the generalization ability and the interpretability of the model are enhanced.

The invention discloses an application migration method and device, equipment, a readable storage medium and a program product, and relates to the technical field of artificial intelligence, and the application migration method comprises the steps that self-defined resources corresponding to reasoning applications are managed through a self-defined controller, so that automatic execution of a driving migration task is achieved. Specifically, on the basis of file conversion resources, an original model file is transformed; transforming an application mirror image based on the mirror image conversion resource; and migrating the reasoning application to a target resource pool corresponding to the target environment based on the application program migration resource. Namely, the migration process of the whole reasoning application is driven by a user-defined controller, and model transformation, mirror image adaptation, data migration and application deployment are automatically completed, so that efficient migration of the reasoning application among different environments is ensured. Therefore, the technical problem of cross-heterogeneous resource pool migration of the inference application can be solved, and the technical effects of getting rid of the dependence of cross-heterogeneous resource pool migration on manpower and guaranteeing the continuity and stability of services are achieved.

Described are a system, method, and computer program product for distributed execution of a machine-learning model on a server cluster. The method includes initiating retrieval of a machine-learning model from a data repository and converting the machine-learning model to an executable format. The method includes transmitting the converted machine-learning model to each node of the server cluster and executing the converted machine-learning model on each node. The method includes generating an initial performance metric based on execution of the converted machine-learning model on each node. The method includes transmitting the plurality of initial performance metrics from each node to an external processor and combining the plurality of initial performance metrics to produce a combined performance metric. The method includes modifying a model hyperparameter of the machine-learning model based on the combined performance metric and executing the modified machine-learning model in a computer system to evaluate real-time event data.

The invention discloses an end-to-end zero code AI model automatic training and deployment system and method, and belongs to the technical field of artificial intelligence and machine learning engineering, and the system comprises a user interaction layer which is used for providing a graphical user interface and receiving a model training instruction configured by a user; the business logic layer responds to the instruction, encapsulates the instruction into a structured work order, automatically executes data management, model training and model conversion tasks based on the work order, and generates a model deployment package capable of directly running on at least one target hardware platform; and the resource management layer is used for monitoring and scheduling bottom computing resources to support task execution of the business logic layer. According to the method, a Web-based graphical interface is provided, all operations are completed through clicking, dragging and pull-down selection, dependence on programming and command line operations is thoroughly eliminated, and complete AI workflow zero-code operation from data to deployment in a single system is achieved.

The invention belongs to the technical field of electric power system dispatching optimization and electric power and electric quantity balance analysis, and relates to a time sequence random production simulation method considering a flexibility demand in an electric power market environment, which comprises the following steps: 1, constructing an electric power system time sequence-random production simulation framework considering the flexibility demand in the market environment; 2, the upper layer model obtains a start-stop scheduling scheme by taking the minimization of the total operation cost of the system as a target, calculates the time sequence flexibility supply capability of each type of unit, and provides an input basis for the lower layer; 3, constructing a multi-state flexibility supply model of the unit under different time scales in a lower layer model, carrying out multi-time scale system flexibility supply and demand probabilistic matching by adopting an equivalent electric quantity function method, and realizing flexibility index evaluation under a short time scale; 4, converting and solving the model; according to the invention, support is provided for coordinating the operation economy and flexibility of the system under security constraints.

The invention relates to the technical field of three-dimensional modeling, particularly provides a three-dimensional geometric model conversion method, a controller and a storage medium, and aims to solve the problem of how to improve the model conversion efficiency of a three-dimensional geometric model. In order to achieve the purpose, the method comprises the steps that an original model file, containing model data in an active format, of the three-dimensional geometric model is obtained, a target application scene suitable for the three-dimensional geometric model obtained after model conversion is obtained, data mapping and data optimization are conducted on the original model file on the basis of the target application scene, and the three-dimensional geometric model is obtained. And obtaining a target model file containing the model data in the target format. According to the method, the target application scene of the three-dimensional geometric model is obtained in advance, invalid data and incomplete data in the original model file can be filtered, the model data in multiple source formats are converted into the model data adaptive to the target application scene, the data mapping and data optimization speed is increased, and the data mapping efficiency is improved. And the model conversion efficiency and the scene application degree of the three-dimensional geometric model are improved.

The invention discloses a method for cooperative scheduling of an automatic assembly station and an AGV (Automatic Guided Vehicle). The method comprises the following steps: constructing a disjunction graph, and defining nodes, directed edges and undirected edges in the disjunction graph; an island type assembly workshop scheduling model is constructed, an objective function and constraint conditions are set, and the objective function is used for minimizing the maximum completion time; converting the island type assembly workshop scheduling model into a Markov decision process, setting a state space and an action space, and obtaining a reward function; an ORA-PPO algorithm network model is constructed, an environment state feature vector is input, the ORA-PPO algorithm network model is optimized according to the target function, parameters are updated, an optimal solution of the target function is obtained, and the environment state feature vector comprises elements in a state space and an action space. According to the invention, the problem of cooperative scheduling of multi-product and multi-process equipment and the AGV in the prior art is solved.

The invention discloses a model discretization method and device, electronic equipment and a computer readable storage medium. Comprising the steps that a to-be-discretized CAD model is obtained, the to-be-discretized CAD model is analyzed, geometric features of the to-be-discretized CAD model are obtained, and the geometric features at least comprise faces and edges; for any adjacent edge A and edge B, discretizing the edge A and the edge B according to the relationship between the edge A and the edge B, and discretizing the edge A and the edge B respectively to obtain a discrete point column of the edge A and a discrete point column of the edge B; and for any adjacent surface C and surface D, discretizing the surface C and the surface D according to the relationship between the surface C and the surface D, and discretizing the surface C and the surface D respectively to obtain a discrete grid of the surface C and a discrete grid of the surface D. According to the method, when the model is converted into the net-shaped structure, it is guaranteed that selfing of the grids does not occur.

The invention relates to the technical field of automatic control of an under-actuated system, and provides a mast crane online trajectory planning method and system capable of guaranteeing state constraint, the method comprises the following steps: constructing a linear kinematics model of a mast crane and carrying out model transformation to obtain a state space model for trajectory planning; solving analytic solutions of matrix indexes related to the sub-model with the driving state and the sub-model without the driving state; discretization is carried out through a zero-order retention method, a discrete model is obtained, and a parameter matrix of the discrete model is calculated through an analytical solution of a matrix index; constructing a prediction model and an error model; converting a state constraint into an input constraint, and selecting a cost function to construct quadratic programming; and solving the optimal solution of the quadratic programming, and constructing the online track of the mast crane according to the optimal solution. According to the mast type crane, the state constraint during operation of the crane is guaranteed while accurate hoisting of cargoes is achieved, and the safety of the mast type crane in the hoisting and transporting process is greatly improved.

The invention provides an AI model cross-platform deployment system and method based on a unified interface and an intermediate presentation layer, and is used for solving the technical problem that the deployment adaptation of an AI model among different cloud platforms is difficult. The system adopts a hierarchical architecture design and comprises a unified interface layer, a middle presentation layer and a platform adaptation layer. Wherein the unified interface layer provides a standardized model deployment interface specification; the middle presentation layer converts the AI model into a platform-independent middle format and manages model dependence and configuration information; and the platform adaptation layer is responsible for converting the intermediate format into a deployment format of a target platform and calling a corresponding platform API to complete deployment. Through the innovative design of the unified interface and the middle presentation layer, the development cost of cross-platform deployment is remarkably reduced, the portability of the model is improved, and the technical problem in the cross-platform deployment process of the model is effectively solved.

According to the model conversion method provided by the invention, the height map of the terrain and the weight distribution maps of the plurality of material layers are acquired, the target three-dimensional grid model is generated according to the height map, the weight distribution maps of the plurality of material layers are combined to generate the weight map, and based on the position of the target three-dimensional grid model in the world coordinate system, the target three-dimensional grid model is converted into the target three-dimensional grid model. Calculating texture mapping coordinates; and generating a material instance corresponding to the target three-dimensional grid model according to a preset material template, the weight map and the texture mapping coordinate. Through effective integration and conversion processing of terrain height information and multilayer material weight information, automatic conversion from two-dimensional terrain data to a three-dimensional grid model with complete material information is realized, and it is ensured that the finally generated three-dimensional model can accurately express a complex material mixing effect. And the visual authenticity and rendering efficiency of the terrain model are obviously improved.

The invention relates to the technical field of spiking neural networks, in particular to a spiking neural network conversion method and system based on differentiable adaptive optimization, and the method comprises the steps: constructing and pre-training a target ANN model; in a pre-trained ANN model, performing simulation processing on the activation value of each layer by using a global differential value representation operator; on the basis of a loss function, an original weight parameter in the target ANN model and a trainable excitation threshold in a global differentiable value representation operator are placed in the same optimization framework for joint fine tuning, and an optimal weight and an optimal threshold learned by each layer are obtained; and an equivalent SNN model is constructed. According to the method, precise correspondence between continuous activation and pulse sequences can be optimized, the method is suitable for multiple pulse coding modes, quantization errors are reduced to the maximum extent, the deployment requirements of neuromorphic hardware and an edge intelligent computing platform can be deeply adapted, and an efficient and universal model conversion solution is provided for the neuromorphic computing industry.

The invention relates to a three-dimensional model storage, exchange and retrieval method. The method comprises the following steps: executing a neutral format conversion process of a three-dimensional model; extracting feature information from the three-dimensional model and storing the feature information; analyzing the stored feature information to construct a retrieval index; consistency verification before and after conversion of the three-dimensional model is executed; respectively executing visual format conversion for the three-dimensional model and the neutral format; and executing data exchange, online browsing and retrieval according to an instruction of the user.

The invention discloses a building structure explosion multistage early warning and damage assessment integrated analysis method, which relates to the field of building structure safety, and comprises a BIM-FEM model conversion module, an FEM explosion response and damage assessment module, an Internet of Things gas sensor acquisition module, an early warning analysis and grading early warning module and a data storage module. And an efficient automatic model conversion interface between the BIM and the FEM is developed. The interface realizes bidirectional data conversion between the BIM model and the finite element analysis model; a gas explosion load is applied to finite element analysis software, dynamic response and damage of each component under the gas explosion load are studied through numerical simulation, and anti-explosion damage evaluation attributes of each component are formed. The problem of low modeling efficiency of a complex structure in finite element analysis software is effectively solved, and automatic data extraction, model mapping and result write-back between the BIM model and the FEM model are realized.

The invention discloses a vibration detection method for constructing an edge calculation model based on Simulink physical modeling, and the method comprises the steps: constructing a multi-degree-of-freedom vibration model through Simulink, dynamically adjusting the quality, rigidity, a damping matrix, temperature and load parameters, simulating the vibration characteristics of a complex working condition, and fusing simulation and actual measurement data to generate a training set; the model is converted into an edge available format and compressed to be within 100 KB, and edge hardware is adapted; carrying out joint training and online updating on edge nodes by utilizing transfer learning and the like; and the vibration signals are classified in real time through a lightweight model. According to the method, the problems of fixed model parameters, insufficient data fusion and poor edge adaptability in the traditional technology are solved, accurate simulation of the vibration characteristics of the industrial equipment, efficient data utilization and low-delay detection of the edge end are realized, the vibration detection precision and real-time performance are improved, and the method is suitable for fault diagnosis and predictive maintenance of the industrial equipment.

The invention discloses a predefined space-time pitch angle control method of a variable-speed wind generating set, and belongs to the technical field of variable-speed wind generating set control. The method is characterized by comprising the following steps of 1, obtaining a dynamic model of the variable-speed wind turbine according to the Betz theory; step 2, obtaining a predefined space-time reaching law with a buffer area according to the improved barrier function; 3, constructing a sliding mode variable, and converting a non-affine model of the variable speed wind turbine into an affine model based on an unknown smooth nonlinear function; and 4, compensating the unknown smooth nonlinear function by using a neural network, and obtaining a pitch angle controller according to the neural network. According to the predefined space-time pitch angle control method of the variable-speed wind generating set, the state space is divided into the multiple parts including the buffer areas, dependence on model parameters is reduced, the buffeting phenomenon is weakened through the improved obstacle function, and then the power generation efficiency is improved, and the operation cost is reduced.

The invention relates to the technical field of artificial intelligence and robot control, in particular to an imitation learning mechanical arm control method based on edge TPU deployment, which comprises the following steps: (1) constructing a multi-mode sensing system which comprises a three-path camera module, a master-slave mechanical arm module and a TPU hardware development board; (2) constructing a training data set through demonstration action execution and synchronous data acquisition; (3) training an imitation learning strategy model based on a Transform architecture, and converting the model into a BMODEL format adapted to the TPU; (4) deploying an optimized BMODEL model on a TPU hardware platform, generating an action sequence through real-time reasoning, and driving a mechanical arm to execute a task in combination with inverse kinematics solution and dynamic control scheduling; and (5) realizing action error real-time calibration and system closed-loop control through a multi-view visual tracking and state feedback mechanism. According to the method, the compatibility and integration problems of the TPU chip and an existing mechanical arm control system can be effectively solved, and the response performance and the intelligent level of the mechanical arm autonomous control system are remarkably improved.

A computer-implemented method (100) for controlling a vehicle (400), comprising providing (110) an environment module (220) configured to determine an environment model based on environment information characterizing an environment of the vehicle (400); determining (120), by means of the environment module (220), an actual environment model of the vehicle (400) based on actual environment information of the vehicle (400); converting (130) the actual environment model into an actual language model, wherein the actual language model describes the environment of the actual environment model in a semantic language; providing (140) a language module (230) and inputting (150) the actual language model into the large language module (230); Using (160) the language module (230) to plan and / or explain a movement trajectory of the vehicle (400) based on the actual language model.

The invention relates to a segmented fusion SPICE to IBIS model conversion method and system, and the method comprises the steps: obtaining data, precisely extracting voltage / current curve data from a simulation result of SPICE simulation, carrying out the transient analysis, guaranteeing that a finally generated IBIS model can accurately reflect the electrical behavior of the SPICE model, increasing the voltage / current sampling point density of DC scanning in the SPICE simulation, and obtaining the electrical behavior of the SPICE model. Particularly, in a non-linear severe change region, a segmentation interval is adaptively adjusted according to curvature change, so that the accuracy of curve data extraction is ensured; a compensation item is added in a [Model] section of the IBIS model to compensate the packaging parasitic effect, the accuracy of model conversion is improved, meanwhile, simulation data under the multi-PVT condition is compressed through PCA, redundant information is reduced, the model generation efficiency is improved, and high-precision and high-efficiency conversion from the SPICE model to the IBIS model is achieved.

The invention belongs to the technical field of man-machine cooperative interaction, and relates to a man-machine cooperative compliant interaction method based on fuzzy variable admittance and human body motion prediction, which comprises the following steps: step 1, establishing a man-machine cooperative interaction admittance model, and converting the interaction force applied to an end effector of a mechanical arm by a collaborator into the end speed of the mechanical arm; 2, designing an admittance model parameter adaptive law based on a fuzzy theory, dynamically adjusting a virtual damping parameter, and designing a virtual mass adaptive law based on a virtual damping change condition, so that an admittance model dynamically adapts to a movement mode of a collaborator; and step 3, predicting a human body motion intention based on the LSTM network, generating an auxiliary speed item, and jointly generating an optimal man-machine compliant interaction speed in combination with an admittance reference speed obtained based on admittance model transformation. According to the method, the robot body motion information and the human body motion intention in a man-machine system are fully utilized, the self-adaptive admittance interaction device fused with the motion intention of the human operator is designed, and the naturalness and smoothness of man-machine interaction are improved.