152 results about "Model transformation" patented technology

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 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 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.

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 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.

Embodiments described herein systems and methods for dynamically guiding a generative artificial intelligence model through structured, goal-oriented interactions using machine- generated rubrics for instructing operations of the generative Al model. The system selects, for each conversational turn, a first rubric defining response structure and a second rubric defining evaluation criteria. A generative model formulates prompts based on these rubrics and receives participant responses. An assessment module evaluates the responses against the second rubric to update the interaction context. A rubric orchestration module uses the updated context to select new rubrics for subsequent turns. This iterative process transforms a stateless generative model into a persistent, adaptive agent capable of conducting multi-turn interactions aligned with defined objectives.

A method and a system for performing domain adaptations of social media text by using lexical data transformations are provided. The method includes: receiving a first data set that is usable for training a machine learning (ML) model that is designed to perform natural language processing tasks; training the ML model by using the first data set; receiving a second data set that relates to a social media platform; transforming a subset of the first data set into a third data set that is suitable for the social media platform; and retraining the ML model by using a combination of the first data set, the second data set, and the third data set. The transformations may include injecting emojis, emoticons, user mention indicators, hashtags, retransmission indicators, URLs, and / or inverse lexical normalizations that are often used in social media posts.

The invention relates to a large-model heterogeneous reasoning engine method, device and equipment and a storage medium. The method comprises the following steps: acquiring an end side model generated by performing model conversion on a neural network model by a host end; constructing a calculation graph at the equipment end according to the end side model; a model reasoning task request is received, a task type is obtained, the computational graph is analyzed, differentiated task distribution is conducted on all nodes of the analyzed computational graph based on the task type, and all the nodes are bound with heterogeneous computing units executing different tasks; and according to a task allocation result, executing a model reasoning task through the heterogeneous computing unit bound with the allocated node. According to the method, the nodes are bound with the heterogeneous computing units executing different tasks, differentiated task distribution is performed on the nodes in the computing graph according to the task types, the computing power utilization rate of the heterogeneous computing units can be maximized when the end-side large model reasoning task is executed, the high throughput and low time delay requirements during reasoning task execution are ensured, and the reasoning efficiency of the end-side large model reasoning task is improved. The execution efficiency is improved.

The invention discloses a virtual sensor modeling and deployment method and system based on spatio-temporal feature fusion and a gradient boosting strategy, and relates to the technical field of industrial process monitoring and control, embedded intelligent sensing and data-driven modeling. The method comprises the following steps: collecting multi-source sensor data under a multi-environment condition and preprocessing the multi-source sensor data; constructing a spatial-temporal characteristic system which simultaneously represents historical memory, dynamic change and a multivariable coupling relationship, and performing characteristic screening and weight reduction; under a gradient lifting framework, adopting automatic hyper-parameter optimization to obtain a virtual sensor model with compromise between performance and complexity; the trained model is converted into a unified reasoning format irrelevant to a platform, and real-time reasoning is achieved on a resource-limited embedded control unit. Compared with the prior art, the method has higher prediction precision and generalization ability under the complex dynamic working condition, the model size and the single reasoning delay are controllable, and the method is suitable for being deployed and applied in a vehicle-mounted ECU, an industrial controller and an edge node.

The invention discloses a video benchmark model reasoning framework deployed at an embedded terminal. The video benchmark model reasoning framework comprises a model lightweight module, a cross-format conversion module, a data preprocessing module, a reasoning acceleration module and a result verification module. The model lightweight module is used for performing pruning and quantification processing on the original visual language reference model, and reducing the model parameter scale and the calculation complexity; the cross-format conversion module is used for sequentially converting the lightweight PyTorch model (. Pth) into an ONNX format, and then converting the ONNX format into an OM format suitable for a domestic AI processor; the data preprocessing module is used for carrying out standardization processing on input visual data (images) and text data, wherein the standardization processing comprises size adjustment, channel conversion and normalization; the reasoning acceleration module is used for optimizing a model reasoning path and supporting multi-thread parallel computing; and the result verification module is used for comparing the reasoning results of the converted model and the original model to ensure that the precision loss is within a preset threshold value.

The application relates to an unmanned vehicle-mounted camera target detection method based on an improved FCOS algorithm, and relates to the field of computer vision. An image is collected by an unmanned vehicle, the image is pretreated, and then is put into an improved FCOS network model for training. In the training process, the model performs feature extraction, prediction, loss calculation and parameter updating on the image. After multiple iterations, a trained detection model file can be obtained. After model conversion, the model can be applied and deployed on terminal equipment such as an unmanned vehicle. The application has stronger feature extraction capability, and the two-stage model constructed has better detection effect on small targets, effectively improves the recognition accuracy of the model, and improves the missed detection and false detection of the model.

The invention relates to the technical field of computational analysis model conversion, and particularly discloses a pile-soil interaction simulation conversion method from SACS to ANSYS, which comprises the following steps: analyzing an SACS file to extract model data, and classifying and storing the model data in a relational database; processing the extracted database, and performing unit conversion and curve data conversion; generating an APDL command stream for constructing an ANSYS model based on the processed database, and parameterizing to automatically construct the ANSYS model; and verifying the precision of the ANSYS model and iteratively correcting the model parameters based on the equivalent displacement until the result is within an acceptable error range. By adopting the method, high-fidelity conversion is realized, the conversion efficiency is improved, and the human error risk is reduced; an original verification and iterative correction mechanism ensures that the model conversion precision is reliable; the method is wide in applicability, and can effectively meet the conversion requirements of ocean engineering structure models comprising various soil, layered soil and complex pile foundation structures.

A high-precision three-dimensional automatic modeling method for a substation, comprising: collecting laser point cloud data of a substation and multi-view images of the substation; performing dense point cloud data registration, camera parameter reconstruction for the multi-view images, projection of the multi-view images, and adaptive point cloud density control; using three-dimensional Gaussian distribution functions to approximate the point cloud data, summing all the three-dimensional Gaussian distribution functions to obtain a Gaussian distribution geometric field of a three-dimensional Gaussian distribution point cloud representation, and obtaining a three-dimensional mesh model by means of three-dimensional mesh reconstruction and rendering; setting constraint conditions of devices on the basis of the three-dimensional mesh model, and using a volume segmentation algorithm and a three-dimensional labeling algorithm to generate a three-dimensional device model, device coordinates, and device category names; and converting the device model into a GIM file, generating a PMS file from the device coordinates and the device category names, and synchronizing the files to a GIM and PMS3.0. The present invention can effectively solve the problems of low efficiency and inconsistent quality in conventional manual modeling processes.

The invention provides a three-dimensional model generation method and device, electronic equipment and a storage medium, and relates to the technical field of animation creation. The method comprises the steps of determining initial structure information of a target model in response to an input instruction; receiving an adjustment instruction input by the input device for the initial structure information, and updating the initial structure information according to the adjustment instruction to obtain target structure information; determining target structure information of the target model; according to the target parameter information of each component, generating three-dimensional grid data corresponding to each component; and according to the target structure information and the three-dimensional grid data corresponding to each component, performing aggregation to generate model data of the target model. By introducing the concept of the structure information, the model is converted into the intermediate structure representation which can be understood and edited by the user, so that the initial structure information is accurately adjusted according with the willingness of the user based on the user operation, the obtained target structure information lays a foundation for realizing the controllable model, and the accuracy of generating the model is improved.

The application provides a building design software and cloud rendering platform synchronization method and system, and relates to the technical field of building information modeling. The method comprises the following steps: format conversion, converting the model in the building design software; data uploading, uploading the model data after format conversion to the cloud rendering platform; data display, displaying the model rendering effect after format conversion; data synchronization, synchronizing the modification operation of the user to the engine of the cloud rendering platform. The method and system provided by the application do not require the user to export and re-import the model file. After the user starts the cloud rendering platform, the user can directly return to the building design software to modify the model. Only the command defined by the plug-in needs to be triggered, and the plug-in will synchronize the user's operation to the cloud rendering engine, so that the user can see the rendered model in the browser, the display is seamlessly connected, the work efficiency is improved, and the design output effect is better.

The invention relates to the field of artificial intelligence, in particular to a model deployment method and device, equipment and a program product. The method comprises: using an operator and / or a structure adapted to a neural network processor, training a to-be-deployed model through a predetermined first training framework to obtain a trained model in a first format, the first training framework being different from a second training framework adapted to the neural network processor, and the second training framework being different from the second training framework; the model in the first format is a model obtained after training of a first training framework; performing parameter conversion and / or operator combination reconstruction on the model in the first format, and converting the model in the first format into a model in a second format which is a format supported by a neural network processor; and compiling the model in the second format, and deploying the compiled model to the edge device. As excessive computational nodes or intermediate conversion layers are not introduced in the method, the operator redundancy can be reduced, and the reasoning speed and the quantization precision can be improved.

A control system according to the present disclosure is provided with: a conversion device having a model conversion unit that creates model information comprising one or more nodes and correlation information for the nodes on the basis of model node information comprising one or more nodes and the correlation information for the nodes, and a program conversion unit that converts the model information into a program information comprising one or more nodes and the correlation information for the nodes; a program conversion unit that creates program information including a process associated with the node by one or more pieces of association information and the association information on the basis of a source code including the process associated with the node by one or more pieces of association information and the association information; and a relay device having a data processing execution unit that executes a predetermined process on the basis of the model information and the program information.

The application provides a deep learning model deployment method and device, the method comprising: obtaining a deep learning original model file, a first relationship table and a second relationship table; the first relationship table containing each original operator in the deep learning original model file and a first intermediate representation operator set version of each original operator; the second relationship table including the first intermediate representation operator set version, a corresponding relationship between a first inference engine version corresponding to the first intermediate representation operator set version and a first intermediate representation version; determining a version parameter required for deep learning original model conversion based on the deep learning original model file, the first relationship table and the second relationship table; determining a deployable intermediate representation model of the deep learning original model based on the version parameter; and deploying the deployable intermediate representation model on a target device. Based on this, the problem that a deep learning model deployment process needs to rely on a large number of manual operations, is not convenient and is not automated is solved.

A combination of fastening points is obtained by repeating following steps as an optimum value when an evaluation method of a physical quantity acting on a substrate is specified. The steps include: obtaining the physical quantity according to the combination of fastening points; generating a regression model expressing one fastening point candidate by a binary variable; converting the regression model into an Ising model based on the evaluation method; and determining the combination of fastening points by selecting one of fastening points from the fastening point candidate positions using an Ising machine so as to minimize an evaluation value of the Ising model.

This invention provides a modeling method for the shear force of a crescent-shaped shear blade, comprising the following steps: 1) expanding and parameterizing the theoretical shear blade arc into a function based on its formation process; 2) calculating the slope of each point on the arc; 3) determining the shear angle at each point on the arc based on the slope in step 2); 4) calculating the Nosari shear force using the shear blade shear's Nosari formula; 5) calculating the shear force at each point on the arc based on step 4); and 6) identifying the point with the maximum shear force within the range of the arc's values ​​as the design basis for the crescent-shaped shear blade. The data used in this modeling process are readily available, thus avoiding the impact of measurement errors on the shear force data, resulting in high calculation accuracy and precision. This invention solves the modeling problem for calculating the shear force of a crescent-shaped shear blade, transforming a complex model into a simple one, saving time, and offering low cost and high flexibility.

The invention provides a model conversion method and device and a storage medium. The method comprises the steps of receiving a model conversion request sent by a client based on a designer canvas; the model conversion request comprises a domain model and a model type to be converted; loading a model conversion engine; calling a model conversion engine to convert the domain model into an application class model under the model type; and if the conversion is completed, storing the application class model into a model database. According to the embodiment of the invention, through a mechanism of converting the application model from the domain model and in cooperation with recording and storage functions of the conversion operation, tracing of the model modification history is realized, the user is guided to complete configuration selection and data input through the built-in and predefined model conversion engine, operations such as manual compiling of complex conversion scripts by the user are reduced, and the user experience is improved. The technical operation threshold is greatly reduced, omission or errors possibly caused by manual synchronous operation are effectively reduced through the mechanism, and the consistency maintenance level between different abstract levels is remarkably improved.

The invention provides a fault prediction model training method, a fault prediction method, equipment, a storage medium and a computer program product, and the method comprises the steps: converting obtained text data samples corresponding to one or more pieces of second equipment into text semantic vectors through a first model; a triple sample is constructed based on the text semantic vector through the first model, a second model is queried based on the triple sample, and the second model is deployed on other devices except the first device; receiving a text semantic evaluation result output by the second model for the triple sample, and adjusting model parameters of the first model based on the text semantic evaluation result to obtain a trained first model; and determining a fault prediction model based on the trained first model and a classification model obtained after training the initial classification model based on the text data sample.

The invention discloses a function and model conversion method and device, equipment, a storage medium and a program product. The method comprises at least one of the following steps: performing function conversion on a first function; performing model conversion on the first model; function conversion information is obtained, and model conversion information is obtained; wherein the first function comprises at least one first model; therefore, the terminal can autonomously decide whether to perform the AI / ML function and / or model conversion or not, or determine whether to perform the AI / ML function and / or model conversion or not according to the network decision and the conversion information indicated to the terminal, and the timeliness of the AI / ML function and / or model conversion is improved.

The invention provides a power system load frequency control method based on a high-order all-drive system theory, and relates to the technical field of power system load frequency control. Comprising the following steps: collecting physical parameters of a power system, and establishing a power system state space equation mathematical model according to the physical parameters of the power system; verifying the controllability of the power system according to the state-space equation model, and obtaining a system controllability matrix; based on the controllability matrix, converting a state-space equation model of the power system into a high-order all-drive system model; providing a power system state observer and a spoofing attack observer according to the high-order all-drive system model; and providing a power system load frequency controller based on a high-order all-drive system theory according to the high-order all-drive system model and the observer. According to the method, the linear dynamic model of the electric power system more conforming to actual working conditions is constructed. Based on the high-order all-drive method, the design complexity of the power system controller can be reduced, and the stability of the power system is ensured.

The application provides a bridge data conversion method and system of BIM and a finite element model, and relates to the technical field of bridge model optimization. Based on Revit API, component traversal extraction and attribute information extraction are performed on the BIM model to generate a structured data table; a first classification model is used to automatically identify the component type, attribute supplementation is performed in combination with a second rule mapping process, and an optimized structured data table is obtained; subsequently, mesh division and quality optimization processing are performed to obtain high-quality mesh division information; finally, a target finite element model is constructed through material attribute mapping and information addition. The application realizes seamless connection of the BIM model and finite element analysis, provides efficient and accurate technical support for bridge structure design and analysis, effectively solves the problems of low model conversion efficiency and modification difficulty in bridge structure analysis, and improves the informatization and intelligentization level of bridge engineering.

This invention presents a simulation integration method for external deployment of deep learning models based on the TorchScript and FMU interface, belonging to the field of vehicle dynamics simulation and artificial intelligence technology. The method includes the following steps: Step 1, converting the PyTorch model to a TorchScript model; Step 2, loading the TorchScript model encapsulation DLL using a C++ interface; Step 3, encapsulating the DLL into an FMU using the FMI standard; Step 4, the simulation software calls the FMU in real time for vehicle simulation. Under the FMI 2.0 standard interface framework, this invention deploys a Transformer-based tire longitudinal force sequence prediction model in FMU form to the whole vehicle simulation environment, ensuring stable and reliable predictions through training and deployment. The method described in this invention achieves efficient and stable direct integration between the PyTorch model and vehicle simulation software, avoiding compatibility issues that may occur when using ONNX as an intermediate conversion step. The model deployment steps are simpler, and the deployment difficulty is significantly reduced. The combination of TorchScript and C++ interfaces ensures the efficiency and stability of real-time model calls, effectively improving the overall efficiency and reliability of the simulation.

The application provides a method and device for converting a Revit model into a SketchUp model, electronic equipment and a readable medium, and the method comprises the following steps: identifying components meeting entity conditions in a Revit model view and generating an entity component set to complete the screening of entity components in the Revit model, and further extracting the layer, face set, material, color and vertex set of the entity components to form a component data set and complete the extraction of component data required for model conversion. A Ruby script file is generated according to the component data set, and the script is loaded and executed in a SketchUp modeling environment to automatically generate a SketchUp model corresponding to the Revit model. The method can maximize the retention of key data such as color, material and layer of the model, guarantee the integrity and accuracy of the model, and thus does not require additional manual intervention and modification, thereby improving the efficiency of model conversion.

The application discloses a kind of vector pipeline three-dimensional instantiation coherent rendering method and system, it is related to pipeline network three-dimensional visualization technical field;The method comprises the following steps: obtaining vector pipeline data, and pipeline data is obtained by parsing;Build basic three-dimensional model set;Iterate pipeline data, obtain pipeline connection point information, build the first model matrix and first model data of each basic three-dimensional model, establish coherent rendering pipeline system;Iterate basic three-dimensional model set, according to basic three-dimensional model and corresponding first model matrix, render complete pipeline system by instantiation;According to the transformation rule of first model data, model transformation is carried out in the stage of graphic accelerator rendering, and coherent rendering is carried out to pipeline and pipeline connection place.The application constructs coherent pipeline model by analyzing vector pipeline data, and constructs coherent pipeline model by real-time instantiation, provides a feasible low-cost path for creating and rendering coherent three-dimensional pipeline based on real-time and present situation of vector pipeline data.

The application relates to the technical field of dynamic modeling, in particular to a physical simulation model generation method and system for unstructured environments, which specifically comprises the following steps: converting a CAD model of a physical prototype into a point cloud and voxelizing; constructing first characteristic values of each voxel according to local geometric features of the point cloud in each voxel and overall geometric features of the voxel; adjusting initial grid sizes of each voxel based on differences in distribution positions of the first characteristic values of each voxel in the overall and local neighborhoods, combining the first characteristic values, determining adaptive grid sizes of each voxel, and obtaining a final voxel division result of the CAD model; and performing physical simulation by using the final CAD model voxel division mode; the method solves the problem that a traditional physical simulation model generation method cannot distinguish local areas with obvious unstructured feature differences, thereby reducing the modeling effect; while ensuring the calculation accuracy of key areas, the overall calculation cost is significantly reduced.